From 0b5b0e80f93309a7e017010fcccfef045cdec47b Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Mon, 7 Apr 2025 20:46:33 -0700 Subject: [PATCH 001/244] WIP: add initial support for dq 2D conv --- .../xnnpack/partition/config/gemm_configs.py | 6 ++ .../xnnpack/quantizer/xnnpack_quantizer.py | 1 + .../quantizer/xnnpack_quantizer_utils.py | 11 +++ backends/xnnpack/test/ops/test_conv2d.py | 86 ++++++++++++++++++- 4 files changed, 103 insertions(+), 1 deletion(-) diff --git a/backends/xnnpack/partition/config/gemm_configs.py b/backends/xnnpack/partition/config/gemm_configs.py index 8712c2709ac..a05bf623e05 100644 --- a/backends/xnnpack/partition/config/gemm_configs.py +++ b/backends/xnnpack/partition/config/gemm_configs.py @@ -358,6 +358,11 @@ def check_constraints(self, node: torch.fx.Node, ep: ExportedProgram) -> bool: why(node, "Only support 1D + 2D Conv") return False # Only support 1D + 2D Conv + precision = self._detect_precision(node) + if precision == ConfigPrecisionType.DYNAMIC_QUANT and len(conv_stride) != 2: + why(node, "Only support 2D Conv for dynamic quantization") + return False + kernel_node = get_input_node(node, 1) weight_quant_params = QuantParams.from_weights(kernel_node, ep) @@ -394,6 +399,7 @@ def supported_precision_types(self): return [ ConfigPrecisionType.FP32, ConfigPrecisionType.STATIC_QUANT, + ConfigPrecisionType.DYNAMIC_QUANT, ] diff --git a/backends/xnnpack/quantizer/xnnpack_quantizer.py b/backends/xnnpack/quantizer/xnnpack_quantizer.py index 0ddee53a41a..fdabd0383e6 100644 --- a/backends/xnnpack/quantizer/xnnpack_quantizer.py +++ b/backends/xnnpack/quantizer/xnnpack_quantizer.py @@ -265,6 +265,7 @@ class XNNPACKQuantizer(Quantizer): DYNAMIC_OPS = [ "linear", + "conv", ] def __init__(self) -> None: diff --git a/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py b/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py index ce459806c6e..4763e39fa2f 100644 --- a/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py +++ b/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py @@ -304,6 +304,17 @@ def _do_annotate_conv( for n in gm.graph.nodes: if not is_conv_node(n): continue + + # TODO: Check for dynamically quantized convs and check if nn.Conv2d is always lowered + # Only dynamically quantize 2D convolutions + # Handle both nn.Conv2d and aten.conv2d.default + if n.op == "call_module": + mod = gm.get_submodule(n.target) + if not hasattr(mod, "padding") or len(mod.padding) != 2: + continue + elif n.op == "call_function" and n.target != torch.ops.aten.conv2d.default: + continue + conv_node = n # This is hacky! diff --git a/backends/xnnpack/test/ops/test_conv2d.py b/backends/xnnpack/test/ops/test_conv2d.py index 80b731bd18e..5001d2b6e88 100644 --- a/backends/xnnpack/test/ops/test_conv2d.py +++ b/backends/xnnpack/test/ops/test_conv2d.py @@ -18,6 +18,10 @@ except: has_quantized_ops = False +from executorch.backends.xnnpack.partition.config.xnnpack_config import ( + ConfigPrecisionType, +) +from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner from executorch.backends.xnnpack.quantizer.xnnpack_quantizer import ( get_symmetric_quantization_config, ) @@ -26,7 +30,10 @@ ) from executorch.backends.xnnpack.test.test_xnnpack_utils import randomize_bn from executorch.backends.xnnpack.test.tester import Quantize, Tester - +from executorch.backends.xnnpack.test.tester.tester import ( + Partition, + ToEdgeTransformAndLower, +) from executorch.exir.dialects._ops import ops as exir_ops @@ -223,6 +230,61 @@ def _test( .run_method_and_compare_outputs(qtol=1) ) + def _test_dq_conv2d( + self, + m: torch.nn.Module, + inputs, + dynamic_shapes, + atol=5e-02, + ): + quant_config = get_symmetric_quantization_config( + is_per_channel=True, + is_dynamic=True, + act_qmin=-128, + act_qmax=127, + weight_qmin=-128, + weight_qmax=127, + ) + + DynamicallyQuantizedPartitioner = XnnpackPartitioner( + config_precisions=ConfigPrecisionType.DYNAMIC_QUANT, + per_op_mode=False, + ) + + tester = Tester(m, inputs, dynamic_shapes=dynamic_shapes) + tester = tester.quantize(Quantize(quantization_config=quant_config)) + + # Print after quantization + tester.stages["quantize"] = tester.stages[tester.cur] + print("\n----------Annotated Graph:") + print(tester.stages["quantize"].graph_module.code) + + exported = tester.export() + + # Print after exporting + tester.stages["export"] = exported.stages[exported.cur] + print("\n----------Exported Graph:") + print(tester.stages["export"].graph_module.code) + + # Check for choose_qparams + tester.check(["torch.ops.quantized_decomposed.choose_qparams"]) + + tester.to_edge_transform_and_lower( + ToEdgeTransformAndLower([DynamicallyQuantizedPartitioner]) + ) + + # Print after lower and partition + print("\n----------Lowered Graph:") + print(tester.stages[tester.cur].graph_module.code) + + tester.check(["executorch_exir_dialects_edge__ops_aten_convolution_default"]) + tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1}) + tester.check_not(["executorch_exir_dialects_edge__ops_aten_conv2d_default"]) + + tester.to_executorch() + tester.serialize() + tester.run_method_and_compare_outputs(atol=atol) + def test_fp16_conv2d(self) -> None: for transpose in (True, False): for has_bias in (True, False): @@ -699,3 +761,25 @@ def forward(self, x): .serialize() .run_method_and_compare_outputs(qtol=1) ) + + def test_dq_conv2d(self) -> None: + class SimpleConv2d(torch.nn.Module): + def __init__(self): + super().__init__() + self.conv = torch.nn.Conv2d(1, 2, 3) + self.conv.weight.requires_grad = False + self.conv.bias.requires_grad = False + + def forward(self, x): + return self.conv(x) + + def get_inputs(self): + return (torch.randn(1, 1, 8, 8),) + + model = SimpleConv2d() + self._test_dq_conv2d( + model, + model.get_inputs(), + dynamic_shapes=None, + atol=5e-2, + ) From 8fcb1170dcddb39e70a2072227ac9e990773b477 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Fri, 11 Apr 2025 17:25:31 -0700 Subject: [PATCH 002/244] Permute before quant --- .../channels_last_tagged_reshape_pass.py | 28 +++++++++++++++++-- .../xnnpack/quantizer/xnnpack_quantizer.py | 2 ++ .../quantizer/xnnpack_quantizer_utils.py | 10 ------- backends/xnnpack/runtime/XNNCompiler.cpp | 2 +- backends/xnnpack/test/ops/test_conv2d.py | 25 ++++------------- 5 files changed, 34 insertions(+), 33 deletions(-) diff --git a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py index 89a44f303df..ca8de0c32df 100644 --- a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py +++ b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py @@ -282,16 +282,38 @@ def input_to_nhwc( ChannelsLastTaggedReshapePass.PARTNER_NODE ] else: - # Need to create NHWC node - with graph_module.graph.inserting_after(input_node): + # trace back to permute + origin = input_node + while hasattr(origin, "args") and isinstance(origin.args, tuple) and len(origin.args) > 0: + origin = origin.args[0] + + # at x choose_qparams and quantize insert permute + with graph_module.graph.inserting_after(origin): input_node_nhwc = self.create_call_function_node( graph_module=graph_module, target=exir_ops.edge.aten._to_copy.default, - args=(input_node,), + args=(origin,), memory_format=torch.channels_last, ) + + for user in list(origin.users): + if user != input_node_nhwc: + user.replace_input_with(origin, input_node_nhwc) + + graph_module.recompile() self.mark_as_nhwc_node(input_node_nhwc) + # TODO: uncomment, use case when permute not needed + # # Need to create NHWC node ----------------------------- CONVERSION HAPPENING ----->> + # with graph_module.graph.inserting_after(input_node): + # input_node_nhwc = self.create_call_function_node( + # graph_module=graph_module, + # target=exir_ops.edge.aten._to_copy.default, + # args=(input_node,), + # memory_format=torch.channels_last, + # ) + # self.mark_as_nhwc_node(input_node_nhwc) + self.insert_copy_and_assign_partner_nodes_quantization_sensitive( graph_module=graph_module, original_input=input_node, diff --git a/backends/xnnpack/quantizer/xnnpack_quantizer.py b/backends/xnnpack/quantizer/xnnpack_quantizer.py index fdabd0383e6..9e24d7b0030 100644 --- a/backends/xnnpack/quantizer/xnnpack_quantizer.py +++ b/backends/xnnpack/quantizer/xnnpack_quantizer.py @@ -71,8 +71,10 @@ def _supported_symmetric_quantized_operators() -> dict[str, list[OperatorPattern "conv2d": [ [torch.nn.Conv2d, torch.nn.ReLU], [torch.nn.Conv2d, F.relu], + [torch.nn.Conv2d], [F.conv2d, torch.nn.ReLU], [F.conv2d, F.relu], + [F.conv2d], ], "linear": [[torch.nn.Linear], [F.linear]], "add": [[torch.add]], diff --git a/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py b/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py index 4763e39fa2f..91babc26cc9 100644 --- a/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py +++ b/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py @@ -305,16 +305,6 @@ def _do_annotate_conv( if not is_conv_node(n): continue - # TODO: Check for dynamically quantized convs and check if nn.Conv2d is always lowered - # Only dynamically quantize 2D convolutions - # Handle both nn.Conv2d and aten.conv2d.default - if n.op == "call_module": - mod = gm.get_submodule(n.target) - if not hasattr(mod, "padding") or len(mod.padding) != 2: - continue - elif n.op == "call_function" and n.target != torch.ops.aten.conv2d.default: - continue - conv_node = n # This is hacky! diff --git a/backends/xnnpack/runtime/XNNCompiler.cpp b/backends/xnnpack/runtime/XNNCompiler.cpp index c0204831c07..988dab86ab7 100644 --- a/backends/xnnpack/runtime/XNNCompiler.cpp +++ b/backends/xnnpack/runtime/XNNCompiler.cpp @@ -1172,7 +1172,7 @@ Error defineStaticTransposeNode( ET_CHECK_OR_RETURN_ERROR( status == xnn_status_success, Internal, - "Failed to create sigmoid node %i with code: %s", + "Failed to create static transpose node %i with code: %s", node->debug_handle(), xnn_status_to_string(status)); diff --git a/backends/xnnpack/test/ops/test_conv2d.py b/backends/xnnpack/test/ops/test_conv2d.py index 5001d2b6e88..dbd9f75ecaf 100644 --- a/backends/xnnpack/test/ops/test_conv2d.py +++ b/backends/xnnpack/test/ops/test_conv2d.py @@ -240,10 +240,6 @@ def _test_dq_conv2d( quant_config = get_symmetric_quantization_config( is_per_channel=True, is_dynamic=True, - act_qmin=-128, - act_qmax=127, - weight_qmin=-128, - weight_qmax=127, ) DynamicallyQuantizedPartitioner = XnnpackPartitioner( @@ -254,35 +250,26 @@ def _test_dq_conv2d( tester = Tester(m, inputs, dynamic_shapes=dynamic_shapes) tester = tester.quantize(Quantize(quantization_config=quant_config)) - # Print after quantization tester.stages["quantize"] = tester.stages[tester.cur] - print("\n----------Annotated Graph:") - print(tester.stages["quantize"].graph_module.code) exported = tester.export() - # Print after exporting tester.stages["export"] = exported.stages[exported.cur] - print("\n----------Exported Graph:") - print(tester.stages["export"].graph_module.code) - # Check for choose_qparams tester.check(["torch.ops.quantized_decomposed.choose_qparams"]) tester.to_edge_transform_and_lower( ToEdgeTransformAndLower([DynamicallyQuantizedPartitioner]) ) - # Print after lower and partition - print("\n----------Lowered Graph:") - print(tester.stages[tester.cur].graph_module.code) - - tester.check(["executorch_exir_dialects_edge__ops_aten_convolution_default"]) tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1}) tester.check_not(["executorch_exir_dialects_edge__ops_aten_conv2d_default"]) tester.to_executorch() - tester.serialize() + + #tester.serialize() + tester.serialize().dump_artifact("conv2d.pte") + tester.run_method_and_compare_outputs(atol=atol) def test_fp16_conv2d(self) -> None: @@ -766,7 +753,7 @@ def test_dq_conv2d(self) -> None: class SimpleConv2d(torch.nn.Module): def __init__(self): super().__init__() - self.conv = torch.nn.Conv2d(1, 2, 3) + self.conv = torch.nn.Conv2d(3, 10, 3, ) self.conv.weight.requires_grad = False self.conv.bias.requires_grad = False @@ -774,7 +761,7 @@ def forward(self, x): return self.conv(x) def get_inputs(self): - return (torch.randn(1, 1, 8, 8),) + return (torch.randn(1, 3, 8, 8),) model = SimpleConv2d() self._test_dq_conv2d( From 4d064da53ab0f3c39cb124b8f5f0865eb826e92d Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Fri, 11 Apr 2025 20:20:33 -0700 Subject: [PATCH 003/244] Refactor permute code --- .../channels_last_tagged_reshape_pass.py | 28 ++++++++----------- backends/xnnpack/test/ops/test_conv2d.py | 10 +++---- backends/xnnpack/xnnpack_preprocess.py | 2 +- 3 files changed, 16 insertions(+), 24 deletions(-) diff --git a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py index ca8de0c32df..2bbc62fa588 100644 --- a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py +++ b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py @@ -282,12 +282,13 @@ def input_to_nhwc( ChannelsLastTaggedReshapePass.PARTNER_NODE ] else: - # trace back to permute + # Need to create NHWC node origin = input_node + # TODO: safe/correct to always trace back? + # Trace back to source node while hasattr(origin, "args") and isinstance(origin.args, tuple) and len(origin.args) > 0: origin = origin.args[0] - # at x choose_qparams and quantize insert permute with graph_module.graph.inserting_after(origin): input_node_nhwc = self.create_call_function_node( graph_module=graph_module, @@ -296,24 +297,17 @@ def input_to_nhwc( memory_format=torch.channels_last, ) - for user in list(origin.users): - if user != input_node_nhwc: - user.replace_input_with(origin, input_node_nhwc) + # If input_node was not source + if origin != input_node: + print("Permuted\n\n") + # Replace downstream source node with NHWC node + for user in list(origin.users): + if user != input_node_nhwc: + user.replace_input_with(origin, input_node_nhwc) + graph_module.recompile() - graph_module.recompile() self.mark_as_nhwc_node(input_node_nhwc) - # TODO: uncomment, use case when permute not needed - # # Need to create NHWC node ----------------------------- CONVERSION HAPPENING ----->> - # with graph_module.graph.inserting_after(input_node): - # input_node_nhwc = self.create_call_function_node( - # graph_module=graph_module, - # target=exir_ops.edge.aten._to_copy.default, - # args=(input_node,), - # memory_format=torch.channels_last, - # ) - # self.mark_as_nhwc_node(input_node_nhwc) - self.insert_copy_and_assign_partner_nodes_quantization_sensitive( graph_module=graph_module, original_input=input_node, diff --git a/backends/xnnpack/test/ops/test_conv2d.py b/backends/xnnpack/test/ops/test_conv2d.py index dbd9f75ecaf..902bdc5b50b 100644 --- a/backends/xnnpack/test/ops/test_conv2d.py +++ b/backends/xnnpack/test/ops/test_conv2d.py @@ -249,15 +249,14 @@ def _test_dq_conv2d( tester = Tester(m, inputs, dynamic_shapes=dynamic_shapes) tester = tester.quantize(Quantize(quantization_config=quant_config)) - - tester.stages["quantize"] = tester.stages[tester.cur] - exported = tester.export() - tester.stages["export"] = exported.stages[exported.cur] - tester.check(["torch.ops.quantized_decomposed.choose_qparams"]) + tester.stages["export"] = exported.stages[exported.cur] + print("\n----------Exported Graph:") + print(tester.stages["export"].graph_module.code) + tester.to_edge_transform_and_lower( ToEdgeTransformAndLower([DynamicallyQuantizedPartitioner]) ) @@ -266,7 +265,6 @@ def _test_dq_conv2d( tester.check_not(["executorch_exir_dialects_edge__ops_aten_conv2d_default"]) tester.to_executorch() - #tester.serialize() tester.serialize().dump_artifact("conv2d.pte") diff --git a/backends/xnnpack/xnnpack_preprocess.py b/backends/xnnpack/xnnpack_preprocess.py index 84cdfd69a48..086eeccbd58 100644 --- a/backends/xnnpack/xnnpack_preprocess.py +++ b/backends/xnnpack/xnnpack_preprocess.py @@ -144,7 +144,7 @@ def preprocess( graph_module = ep.graph_module node_to_external_map = generate_node_to_external_map(ep, graph_module) - + print("\n----------XNNPack Preprocess Graph:", graph_module) # Make sure all inputs are contiguous_format or NCHW or default dim order assert_default_dim_order(graph_module) From 2905b984015574ba0dd459a7a32ff92441ae6bce Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Fri, 11 Apr 2025 21:30:55 -0700 Subject: [PATCH 004/244] Corrects input to conv --- .../channels_last_tagged_reshape_pass.py | 29 ++++++++++--------- backends/xnnpack/test/ops/test_conv2d.py | 14 ++++----- backends/xnnpack/xnnpack_preprocess.py | 2 +- 3 files changed, 24 insertions(+), 21 deletions(-) diff --git a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py index 2bbc62fa588..29de407defd 100644 --- a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py +++ b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py @@ -283,31 +283,34 @@ def input_to_nhwc( ] else: # Need to create NHWC node - origin = input_node + source_node = input_node + # TODO: safe/correct to always trace back? - # Trace back to source node - while hasattr(origin, "args") and isinstance(origin.args, tuple) and len(origin.args) > 0: - origin = origin.args[0] + # Trace back to find original source node + while ( + hasattr(source_node, "args") + and isinstance(source_node.args, tuple) + and len(source_node.args) > 0 + ): + source_node = source_node.args[0] - with graph_module.graph.inserting_after(origin): + with graph_module.graph.inserting_after(source_node): input_node_nhwc = self.create_call_function_node( graph_module=graph_module, target=exir_ops.edge.aten._to_copy.default, - args=(origin,), + args=(source_node,), memory_format=torch.channels_last, ) - # If input_node was not source - if origin != input_node: - print("Permuted\n\n") + # If input_node was not the original source node + if source_node != input_node: + input_node = source_node # Replace downstream source node with NHWC node - for user in list(origin.users): + for user in list(input_node.users): if user != input_node_nhwc: - user.replace_input_with(origin, input_node_nhwc) + user.replace_input_with(input_node, input_node_nhwc) graph_module.recompile() - self.mark_as_nhwc_node(input_node_nhwc) - self.insert_copy_and_assign_partner_nodes_quantization_sensitive( graph_module=graph_module, original_input=input_node, diff --git a/backends/xnnpack/test/ops/test_conv2d.py b/backends/xnnpack/test/ops/test_conv2d.py index 902bdc5b50b..64e3d5e53ad 100644 --- a/backends/xnnpack/test/ops/test_conv2d.py +++ b/backends/xnnpack/test/ops/test_conv2d.py @@ -249,14 +249,10 @@ def _test_dq_conv2d( tester = Tester(m, inputs, dynamic_shapes=dynamic_shapes) tester = tester.quantize(Quantize(quantization_config=quant_config)) - exported = tester.export() + tester.export() tester.check(["torch.ops.quantized_decomposed.choose_qparams"]) - tester.stages["export"] = exported.stages[exported.cur] - print("\n----------Exported Graph:") - print(tester.stages["export"].graph_module.code) - tester.to_edge_transform_and_lower( ToEdgeTransformAndLower([DynamicallyQuantizedPartitioner]) ) @@ -265,7 +261,7 @@ def _test_dq_conv2d( tester.check_not(["executorch_exir_dialects_edge__ops_aten_conv2d_default"]) tester.to_executorch() - #tester.serialize() + # tester.serialize() tester.serialize().dump_artifact("conv2d.pte") tester.run_method_and_compare_outputs(atol=atol) @@ -751,7 +747,11 @@ def test_dq_conv2d(self) -> None: class SimpleConv2d(torch.nn.Module): def __init__(self): super().__init__() - self.conv = torch.nn.Conv2d(3, 10, 3, ) + self.conv = torch.nn.Conv2d( + 3, + 10, + 3, + ) self.conv.weight.requires_grad = False self.conv.bias.requires_grad = False diff --git a/backends/xnnpack/xnnpack_preprocess.py b/backends/xnnpack/xnnpack_preprocess.py index 086eeccbd58..84cdfd69a48 100644 --- a/backends/xnnpack/xnnpack_preprocess.py +++ b/backends/xnnpack/xnnpack_preprocess.py @@ -144,7 +144,7 @@ def preprocess( graph_module = ep.graph_module node_to_external_map = generate_node_to_external_map(ep, graph_module) - print("\n----------XNNPack Preprocess Graph:", graph_module) + # Make sure all inputs are contiguous_format or NCHW or default dim order assert_default_dim_order(graph_module) From 0fef04a2ac4e9d350a8449cab2d9792d64305497 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Sat, 12 Apr 2025 01:03:59 -0700 Subject: [PATCH 005/244] Add is_dequant check for trace back when inserting permute --- .../channels_last_tagged_reshape_pass.py | 35 ++++++++++--------- backends/xnnpack/test/ops/test_conv2d.py | 2 +- 2 files changed, 20 insertions(+), 17 deletions(-) diff --git a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py index 29de407defd..f02a5f816a1 100644 --- a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py +++ b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py @@ -283,29 +283,32 @@ def input_to_nhwc( ] else: # Need to create NHWC node - source_node = input_node - - # TODO: safe/correct to always trace back? - # Trace back to find original source node - while ( - hasattr(source_node, "args") - and isinstance(source_node.args, tuple) - and len(source_node.args) > 0 - ): - source_node = source_node.args[0] + # TODO: Best way to determine if trace back required? + is_dequant = ( + input_node.op == "call_function" + and getattr(input_node.target, "__name__", "") + == "quantized_decomposed.dequantize_per_tensor.tensor" + ) + + if is_dequant: + # Trace back to find original source node + while ( + hasattr(input_node, "args") + and isinstance(input_node.args, tuple) + and len(input_node.args) > 0 + ): + input_node = input_node.args[0] - with graph_module.graph.inserting_after(source_node): + with graph_module.graph.inserting_after(input_node): input_node_nhwc = self.create_call_function_node( graph_module=graph_module, target=exir_ops.edge.aten._to_copy.default, - args=(source_node,), + args=(input_node,), memory_format=torch.channels_last, ) - # If input_node was not the original source node - if source_node != input_node: - input_node = source_node - # Replace downstream source node with NHWC node + if is_dequant: + # Replace downstream input_nodes with NHWC node for user in list(input_node.users): if user != input_node_nhwc: user.replace_input_with(input_node, input_node_nhwc) diff --git a/backends/xnnpack/test/ops/test_conv2d.py b/backends/xnnpack/test/ops/test_conv2d.py index 64e3d5e53ad..b03108dbb48 100644 --- a/backends/xnnpack/test/ops/test_conv2d.py +++ b/backends/xnnpack/test/ops/test_conv2d.py @@ -248,7 +248,7 @@ def _test_dq_conv2d( ) tester = Tester(m, inputs, dynamic_shapes=dynamic_shapes) - tester = tester.quantize(Quantize(quantization_config=quant_config)) + tester.quantize(Quantize(quantization_config=quant_config)) tester.export() tester.check(["torch.ops.quantized_decomposed.choose_qparams"]) From f8f998c4f41c745a499b1171bcd4834de273e6c4 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Sat, 12 Apr 2025 01:09:29 -0700 Subject: [PATCH 006/244] Fix node identity check --- backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py index f02a5f816a1..dd8eea754a0 100644 --- a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py +++ b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py @@ -310,7 +310,7 @@ def input_to_nhwc( if is_dequant: # Replace downstream input_nodes with NHWC node for user in list(input_node.users): - if user != input_node_nhwc: + if user is not input_node_nhwc: user.replace_input_with(input_node, input_node_nhwc) graph_module.recompile() From 2efe9bbf50daa2eeeb0a8ef1bd8b89b7b5e65e04 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Sun, 13 Apr 2025 14:32:55 -0700 Subject: [PATCH 007/244] Use existing is_dequant check and update atol --- .../channels_last_tagged_reshape_pass.py | 20 ++++++------------- backends/xnnpack/test/ops/test_conv2d.py | 2 +- 2 files changed, 7 insertions(+), 15 deletions(-) diff --git a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py index dd8eea754a0..cbd16f7de08 100644 --- a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py +++ b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py @@ -9,10 +9,10 @@ import torch from executorch.backends.xnnpack._passes.xnnpack_pass import XNNPACKPass from executorch.backends.xnnpack.utils.utils import is_param_node +from executorch.backends.xnnpack.utils.quant_utils import is_dequant from executorch.exir.dialects._ops import ops as exir_ops from executorch.exir.pass_base import PassResult - # TODO(T151254305) use subgraph_rewriter class ChannelsLastTaggedReshapePass(XNNPACKPass): """ @@ -283,20 +283,12 @@ def input_to_nhwc( ] else: # Need to create NHWC node - # TODO: Best way to determine if trace back required? - is_dequant = ( - input_node.op == "call_function" - and getattr(input_node.target, "__name__", "") - == "quantized_decomposed.dequantize_per_tensor.tensor" - ) + # TODO: If input is dequant does that it's from dynamic quantization? + input_is_dequant = is_dequant(input_node) - if is_dequant: + if input_is_dequant: # Trace back to find original source node - while ( - hasattr(input_node, "args") - and isinstance(input_node.args, tuple) - and len(input_node.args) > 0 - ): + while getattr(input_node, "args", None): input_node = input_node.args[0] with graph_module.graph.inserting_after(input_node): @@ -307,7 +299,7 @@ def input_to_nhwc( memory_format=torch.channels_last, ) - if is_dequant: + if input_is_dequant: # Replace downstream input_nodes with NHWC node for user in list(input_node.users): if user is not input_node_nhwc: diff --git a/backends/xnnpack/test/ops/test_conv2d.py b/backends/xnnpack/test/ops/test_conv2d.py index b03108dbb48..77e1f0b3f89 100644 --- a/backends/xnnpack/test/ops/test_conv2d.py +++ b/backends/xnnpack/test/ops/test_conv2d.py @@ -766,5 +766,5 @@ def get_inputs(self): model, model.get_inputs(), dynamic_shapes=None, - atol=5e-2, + atol=3.0, ) From 3762e0d7bf70e22a7d0157053cf4a8e2adc8a6c0 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Mon, 14 Apr 2025 19:35:25 -0700 Subject: [PATCH 008/244] Implement replace_all_uses_with function --- .../channels_last_tagged_reshape_pass.py | 8 +- backends/xnnpack/test/ops/test_conv2d.py | 6 +- .../spinquant/third-party/FFHT/Makefile | 236 +++++++++++++++++- 3 files changed, 230 insertions(+), 20 deletions(-) diff --git a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py index cbd16f7de08..80d47b4630e 100644 --- a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py +++ b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py @@ -283,7 +283,7 @@ def input_to_nhwc( ] else: # Need to create NHWC node - # TODO: If input is dequant does that it's from dynamic quantization? + # TODO: Replace with check to determine if dynamic quant input_is_dequant = is_dequant(input_node) if input_is_dequant: @@ -301,10 +301,8 @@ def input_to_nhwc( if input_is_dequant: # Replace downstream input_nodes with NHWC node - for user in list(input_node.users): - if user is not input_node_nhwc: - user.replace_input_with(input_node, input_node_nhwc) - graph_module.recompile() + input_node.replace_all_uses_with(input_node_nhwc) + input_node_nhwc.args = (input_node,) self.insert_copy_and_assign_partner_nodes_quantization_sensitive( graph_module=graph_module, diff --git a/backends/xnnpack/test/ops/test_conv2d.py b/backends/xnnpack/test/ops/test_conv2d.py index 77e1f0b3f89..a33c9989770 100644 --- a/backends/xnnpack/test/ops/test_conv2d.py +++ b/backends/xnnpack/test/ops/test_conv2d.py @@ -244,7 +244,7 @@ def _test_dq_conv2d( DynamicallyQuantizedPartitioner = XnnpackPartitioner( config_precisions=ConfigPrecisionType.DYNAMIC_QUANT, - per_op_mode=False, + per_op_mode=True, ) tester = Tester(m, inputs, dynamic_shapes=dynamic_shapes) @@ -762,9 +762,11 @@ def get_inputs(self): return (torch.randn(1, 3, 8, 8),) model = SimpleConv2d() + inputs = model.get_inputs() + self._test_dq_conv2d( model, - model.get_inputs(), + inputs, dynamic_shapes=None, atol=3.0, ) diff --git a/extension/llm/custom_ops/spinquant/third-party/FFHT/Makefile b/extension/llm/custom_ops/spinquant/third-party/FFHT/Makefile index 7cbeb3ddae9..b06ca7909ae 100644 --- 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@echo "... dumb_fht.i" + @echo "... dumb_fht.s" +.PHONY : help + + + +#============================================================================= +# Special targets to cleanup operation of make. + +# Special rule to run CMake to check the build system integrity. +# No rule that depends on this can have commands that come from listfiles +# because they might be regenerated. +cmake_check_build_system: + cd /Users/zuby/PycharmProjects/src/executorch && $(CMAKE_COMMAND) -S$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR) --check-build-system CMakeFiles/Makefile.cmake 0 +.PHONY : cmake_check_build_system + From 4112c6a11fe2e2f9a7040072434732afc435c3d9 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Mon, 14 Apr 2025 19:38:22 -0700 Subject: [PATCH 009/244] Remove cmake file --- .../spinquant/third-party/FFHT/Makefile | 236 +----------------- 1 file changed, 13 insertions(+), 223 deletions(-) diff --git a/extension/llm/custom_ops/spinquant/third-party/FFHT/Makefile 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-.SUFFIXES: +test_%: test_%.c $(OBJ) + $(CC) $< $(OBJ) -o $@ $(CFLAGS) -# Disable VCS-based implicit rules. -% : %,v +test_double_header_only: test_double_header_only.c + $(CC) $< -o $@ $(CFLAGS) -# Disable VCS-based implicit rules. -% : RCS/% +test_float_header_only: test_double_header_only.c + $(CC) $< -o $@ $(CFLAGS) -# Disable VCS-based implicit rules. -% : RCS/%,v - -# Disable VCS-based implicit rules. -% : SCCS/s.% - -# Disable VCS-based implicit rules. -% : s.% - -.SUFFIXES: .hpux_make_needs_suffix_list - -# Command-line flag to silence nested $(MAKE). -$(VERBOSE)MAKESILENT = -s - -#Suppress display of executed commands. -$(VERBOSE).SILENT: - -# A target that is always out of date. -cmake_force: -.PHONY : cmake_force - -#============================================================================= -# Set environment variables for the build. - -# The shell in which to execute make rules. -SHELL = /bin/sh - -# The CMake executable. -CMAKE_COMMAND = 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-#============================================================================= -# Special targets to cleanup operation of make. - -# Special rule to run CMake to check the build system integrity. -# No rule that depends on this can have commands that come from listfiles -# because they might be regenerated. -cmake_check_build_system: - cd /Users/zuby/PycharmProjects/src/executorch && $(CMAKE_COMMAND) -S$(CMAKE_SOURCE_DIR) -B$(CMAKE_BINARY_DIR) --check-build-system CMakeFiles/Makefile.cmake 0 -.PHONY : cmake_check_build_system - + rm -f test_float test_double test_float_header_only test_double_header_only $(OBJ) From cdd6f2df68908350fca0f0eabdfcdaefa02bb227 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Tue, 15 Apr 2025 18:31:05 -0700 Subject: [PATCH 010/244] Restore original supported conv2d operators --- backends/xnnpack/quantizer/xnnpack_quantizer.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/backends/xnnpack/quantizer/xnnpack_quantizer.py b/backends/xnnpack/quantizer/xnnpack_quantizer.py index 9e24d7b0030..fdabd0383e6 100644 --- a/backends/xnnpack/quantizer/xnnpack_quantizer.py +++ b/backends/xnnpack/quantizer/xnnpack_quantizer.py @@ -71,10 +71,8 @@ def _supported_symmetric_quantized_operators() -> dict[str, list[OperatorPattern "conv2d": [ [torch.nn.Conv2d, torch.nn.ReLU], [torch.nn.Conv2d, F.relu], - [torch.nn.Conv2d], [F.conv2d, torch.nn.ReLU], [F.conv2d, F.relu], - [F.conv2d], ], "linear": [[torch.nn.Linear], [F.linear]], "add": [[torch.add]], From 7150872bdec7102257366e246359f7610ccdefe1 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Tue, 15 Apr 2025 18:34:05 -0700 Subject: [PATCH 011/244] Add dynamic quant check before NHWC permute --- .../_passes/channels_last_tagged_reshape_pass.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py index 80d47b4630e..1a52827418d 100644 --- a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py +++ b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py @@ -9,7 +9,7 @@ import torch from executorch.backends.xnnpack._passes.xnnpack_pass import XNNPACKPass from executorch.backends.xnnpack.utils.utils import is_param_node -from executorch.backends.xnnpack.utils.quant_utils import is_dequant +from executorch.backends.xnnpack.utils.quant_utils import is_dynamic_qdq from executorch.exir.dialects._ops import ops as exir_ops from executorch.exir.pass_base import PassResult @@ -283,11 +283,11 @@ def input_to_nhwc( ] else: # Need to create NHWC node - # TODO: Replace with check to determine if dynamic quant - input_is_dequant = is_dequant(input_node) + # Check if input uses dynamic quantization + is_dynamic_input = is_dynamic_qdq(input_node) - if input_is_dequant: - # Trace back to find original source node + if is_dynamic_input: + # Trace back to original source node while getattr(input_node, "args", None): input_node = input_node.args[0] @@ -299,7 +299,7 @@ def input_to_nhwc( memory_format=torch.channels_last, ) - if input_is_dequant: + if is_dynamic_input: # Replace downstream input_nodes with NHWC node input_node.replace_all_uses_with(input_node_nhwc) input_node_nhwc.args = (input_node,) From 6b44c4bbec825cd4a1626e8c5c8336dcd00407d9 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Tue, 15 Apr 2025 19:24:51 -0700 Subject: [PATCH 012/244] Refactor dq conv2d test --- backends/xnnpack/test/ops/test_conv2d.py | 55 +++++++++--------------- 1 file changed, 21 insertions(+), 34 deletions(-) diff --git a/backends/xnnpack/test/ops/test_conv2d.py b/backends/xnnpack/test/ops/test_conv2d.py index a33c9989770..20a668ed623 100644 --- a/backends/xnnpack/test/ops/test_conv2d.py +++ b/backends/xnnpack/test/ops/test_conv2d.py @@ -176,6 +176,20 @@ def get_inputs(self): return (torch.randn(2, 2, 4, 4),) +class DQConv2d(torch.nn.Module): + def __init__(self): + super().__init__() + self.conv = torch.nn.Conv2d(3, 10, 3) + self.conv.weight.requires_grad = False + self.conv.bias.requires_grad = False + + def forward(self, x): + return self.conv(x) + + def get_inputs(self): + return (torch.randn(1, 3, 8, 8),) + + class TestConv2d(unittest.TestCase): def setUp(self): torch._dynamo.reset() @@ -230,12 +244,11 @@ def _test( .run_method_and_compare_outputs(qtol=1) ) - def _test_dq_conv2d( + def _test_dq( self, m: torch.nn.Module, inputs, dynamic_shapes, - atol=5e-02, ): quant_config = get_symmetric_quantization_config( is_per_channel=True, @@ -250,21 +263,15 @@ def _test_dq_conv2d( tester = Tester(m, inputs, dynamic_shapes=dynamic_shapes) tester.quantize(Quantize(quantization_config=quant_config)) tester.export() - tester.check(["torch.ops.quantized_decomposed.choose_qparams"]) - tester.to_edge_transform_and_lower( ToEdgeTransformAndLower([DynamicallyQuantizedPartitioner]) ) - tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1}) tester.check_not(["executorch_exir_dialects_edge__ops_aten_conv2d_default"]) - tester.to_executorch() - # tester.serialize() - tester.serialize().dump_artifact("conv2d.pte") - - tester.run_method_and_compare_outputs(atol=atol) + tester.serialize() + tester.run_method_and_compare_outputs(qtol=1) def test_fp16_conv2d(self) -> None: for transpose in (True, False): @@ -743,30 +750,10 @@ def forward(self, x): .run_method_and_compare_outputs(qtol=1) ) - def test_dq_conv2d(self) -> None: - class SimpleConv2d(torch.nn.Module): - def __init__(self): - super().__init__() - self.conv = torch.nn.Conv2d( - 3, - 10, - 3, - ) - self.conv.weight.requires_grad = False - self.conv.bias.requires_grad = False - - def forward(self, x): - return self.conv(x) - - def get_inputs(self): - return (torch.randn(1, 3, 8, 8),) - - model = SimpleConv2d() - inputs = model.get_inputs() - - self._test_dq_conv2d( + def test_qs8_dq_conv2d(self) -> None: + model = DQConv2d() + self._test_dq( model, - inputs, + model.get_inputs(), dynamic_shapes=None, - atol=3.0, ) From 7054f2ef59375878a4edbbfb4cbfbf808f11c570 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Tue, 15 Apr 2025 19:31:54 -0700 Subject: [PATCH 013/244] Revert formatting --- backends/xnnpack/quantizer/xnnpack_quantizer_utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py b/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py index 91babc26cc9..ce459806c6e 100644 --- a/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py +++ b/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py @@ -304,7 +304,6 @@ def _do_annotate_conv( for n in gm.graph.nodes: if not is_conv_node(n): continue - conv_node = n # This is hacky! From fc48e03b088399e7e80f58847f8ff2990e8b3b84 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Tue, 15 Apr 2025 20:21:51 -0700 Subject: [PATCH 014/244] Add check to only annotate dq conv2d --- .../xnnpack/quantizer/xnnpack_quantizer_utils.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py b/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py index ce459806c6e..2c2c074815e 100644 --- a/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py +++ b/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py @@ -42,6 +42,8 @@ "propagate_annotation", ] +from pytorch.ao.test.dtypes.test_bitpacking import dimensions + # In the absence of better name, just winging it with QuantizationConfig @dataclass(eq=True, frozen=True) @@ -323,6 +325,17 @@ def _do_annotate_conv( assert isinstance(weight, Node) input_qspec_map[weight] = get_weight_qspec(quantization_config) + # Only annotate dynamically quantized conv if it's 2D + if ( + quantization_config + and quantization_config.input_activation + and quantization_config.input_activation.is_dynamic + ): + weight_val = weight.meta.get("val", None) + weight_shape = getattr(weight_val, "shape", None) + if weight_shape is not None and len(weight_shape) != 4: + continue + # adding weight node to the partition as well partition = [conv_node, conv_node.args[1]] From 84b3634cfaab3859463e067e7f0b8b902b8db153 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Tue, 15 Apr 2025 20:24:35 -0700 Subject: [PATCH 015/244] Remove unused import --- backends/xnnpack/quantizer/xnnpack_quantizer_utils.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py b/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py index 2c2c074815e..f400f51a464 100644 --- a/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py +++ b/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py @@ -42,8 +42,6 @@ "propagate_annotation", ] -from pytorch.ao.test.dtypes.test_bitpacking import dimensions - # In the absence of better name, just winging it with QuantizationConfig @dataclass(eq=True, frozen=True) From 62e30e574cff026a7ac043c89a697f74af809242 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Wed, 16 Apr 2025 15:15:24 -0700 Subject: [PATCH 016/244] Add computation for non-batch dims; remove non-batch dims check --- backends/xnnpack/operators/quant_params.py | 7 ++++++- backends/xnnpack/runtime/XNNCompiler.cpp | 5 ----- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/backends/xnnpack/operators/quant_params.py b/backends/xnnpack/operators/quant_params.py index e695b151560..ddb9db865f3 100644 --- a/backends/xnnpack/operators/quant_params.py +++ b/backends/xnnpack/operators/quant_params.py @@ -145,9 +145,14 @@ def quantize_tensor(self, tensor: torch.Tensor) -> torch.Tensor: def _from_dynamic_input_node(cls, quant_node: torch.fx.Node) -> QuantParams: q_input = quant_node.args[0] # fp32 input assert isinstance(q_input, torch.fx.Node) - # TODO - materialize this from the quant_node scale count and val shape num_nonbatch_dims = 1 + # Compute non-batch dimensions (shape length - 1), defaulting to 1 + q_input_val = q_input.meta.get("val", None) + q_input_shape = getattr(q_input_val, "shape", None) + if q_input_shape is not None: + num_nonbatch_dims = max(len(q_input_shape) - 1, 1) + return cls( per_channel=False, # True is not valid q_input=q_input, diff --git a/backends/xnnpack/runtime/XNNCompiler.cpp b/backends/xnnpack/runtime/XNNCompiler.cpp index 988dab86ab7..0b187d05df0 100644 --- a/backends/xnnpack/runtime/XNNCompiler.cpp +++ b/backends/xnnpack/runtime/XNNCompiler.cpp @@ -512,11 +512,6 @@ Error defineTensor( buffer_ptr == nullptr, Internal, "Dynamically quantized tensor should not have constant data but found non-nullptr"); - // TODO(T179441835): Dynamic Quantization with num_nonbatch_dims > 1 - ET_CHECK_OR_RETURN_ERROR( - qparams->num_nonbatch_dims() == 1, - Internal, - "Dynamically Quantized Tensors currently only support per token quantization"); status = xnn_define_dynamically_quantized_tensor_value( /*subgraph=*/subgraph_ptr, /*datatype=*/getDataType(tensor_value->datatype()), From 3c7fe328af6c28c3af70c5c41784f098bf7f4706 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Wed, 16 Apr 2025 15:21:21 -0700 Subject: [PATCH 017/244] Refactor test and imports --- .../_passes/channels_last_tagged_reshape_pass.py | 3 ++- backends/xnnpack/test/ops/test_conv2d.py | 11 ++++------- 2 files changed, 6 insertions(+), 8 deletions(-) diff --git a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py index 1a52827418d..768df1f4f04 100644 --- a/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py +++ b/backends/xnnpack/_passes/channels_last_tagged_reshape_pass.py @@ -8,11 +8,12 @@ import torch from executorch.backends.xnnpack._passes.xnnpack_pass import XNNPACKPass -from executorch.backends.xnnpack.utils.utils import is_param_node from executorch.backends.xnnpack.utils.quant_utils import is_dynamic_qdq +from executorch.backends.xnnpack.utils.utils import is_param_node from executorch.exir.dialects._ops import ops as exir_ops from executorch.exir.pass_base import PassResult + # TODO(T151254305) use subgraph_rewriter class ChannelsLastTaggedReshapePass(XNNPACKPass): """ diff --git a/backends/xnnpack/test/ops/test_conv2d.py b/backends/xnnpack/test/ops/test_conv2d.py index 20a668ed623..0c456ebc21a 100644 --- a/backends/xnnpack/test/ops/test_conv2d.py +++ b/backends/xnnpack/test/ops/test_conv2d.py @@ -30,10 +30,7 @@ ) from executorch.backends.xnnpack.test.test_xnnpack_utils import randomize_bn from executorch.backends.xnnpack.test.tester import Quantize, Tester -from executorch.backends.xnnpack.test.tester.tester import ( - Partition, - ToEdgeTransformAndLower, -) +from executorch.backends.xnnpack.test.tester.tester import ToEdgeTransformAndLower from executorch.exir.dialects._ops import ops as exir_ops @@ -176,7 +173,7 @@ def get_inputs(self): return (torch.randn(2, 2, 4, 4),) -class DQConv2d(torch.nn.Module): +class Conv2dDynamicQuant(torch.nn.Module): def __init__(self): super().__init__() self.conv = torch.nn.Conv2d(3, 10, 3) @@ -750,8 +747,8 @@ def forward(self, x): .run_method_and_compare_outputs(qtol=1) ) - def test_qs8_dq_conv2d(self) -> None: - model = DQConv2d() + def test_dq_conv2d(self) -> None: + model = Conv2dDynamicQuant() self._test_dq( model, model.get_inputs(), From 064671b2c5c5b97804b3042d3eb629e181291d2a Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Wed, 16 Apr 2025 15:35:32 -0700 Subject: [PATCH 018/244] Update comments --- backends/xnnpack/quantizer/xnnpack_quantizer_utils.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py b/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py index f400f51a464..92eff3d0d68 100644 --- a/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py +++ b/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py @@ -331,6 +331,8 @@ def _do_annotate_conv( ): weight_val = weight.meta.get("val", None) weight_shape = getattr(weight_val, "shape", None) + + # Skip if not a 4D weight tensor (i.e. not conv2d) if weight_shape is not None and len(weight_shape) != 4: continue From d5c4970d0d7c53692d805c5c6c2526c026b58710 Mon Sep 17 00:00:00 2001 From: pssrawat <34485295+pssrawat@users.noreply.github.com> Date: Sun, 6 Apr 2025 17:31:54 -0400 Subject: [PATCH 019/244] Support slice ops with default start Differential Revision: D72503552 Pull Request resolved: https://github.com/pytorch/executorch/pull/9923 --- backends/xnnpack/operators/op_slice_copy.py | 4 +++- backends/xnnpack/test/ops/test_slice_copy.py | 12 ++++++++++++ 2 files changed, 15 insertions(+), 1 deletion(-) diff --git a/backends/xnnpack/operators/op_slice_copy.py b/backends/xnnpack/operators/op_slice_copy.py index 40d8e5f04eb..d9056afa832 100644 --- a/backends/xnnpack/operators/op_slice_copy.py +++ b/backends/xnnpack/operators/op_slice_copy.py @@ -69,7 +69,9 @@ def define_node( output_shape = [output_shape[i] for i in PERM_NCHW_TO_NHWC] dim_of_slice = PERM_NHWC_TO_NCHW[dim_of_slice] - slice_begin_index = cast(int, node.args[2]) + slice_begin_index = 0 + if len(node.args) > 2 and node.args[2]: + slice_begin_index = cast(int, node.args[2]) if slice_begin_index < 0: slice_begin_index = input_shape[dim_of_slice] + slice_begin_index diff --git a/backends/xnnpack/test/ops/test_slice_copy.py b/backends/xnnpack/test/ops/test_slice_copy.py index ea65571b1e8..857c78480ad 100644 --- a/backends/xnnpack/test/ops/test_slice_copy.py +++ b/backends/xnnpack/test/ops/test_slice_copy.py @@ -69,6 +69,18 @@ def forward(self, x): # Note that two of the slices are optimized away as they are identity. self._test_slice_copy(ConvSlice(), inputs, 4, 2) + def test_fp32_slice_copy_default_start(self): + """ + XNNPACK supports default start in slice op. + """ + + class Slice(torch.nn.Module): + def forward(self, x): + return torch.ops.aten.slice.Tensor(x, 0, None, 2) + + inputs = (torch.randn(5, 5),) + self._test_slice_copy(Slice(), inputs, 1, 1) + def test_fp32_slice_copy_stride_non_1(self): """ XNNPACK does not support strided slicing. From c573b6f4113a0b5fb83466fbc9c3fafdd29384a4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Martin=20Lindstr=C3=B6m?= <33344797+martinlsm@users.noreply.github.com> Date: Mon, 7 Apr 2025 06:35:55 +0200 Subject: [PATCH 020/244] Arm backend: Add pytest.mark.flaky on U85 tests in test_mm.py (#9926) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The output verification sometimes fails for the mm tests on U85. Add pytest.mark.flaky decorators to the tests to prevent sporadic failures. Co-authored-by: Martin Lindström --- backends/arm/test/ops/test_mm.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/backends/arm/test/ops/test_mm.py b/backends/arm/test/ops/test_mm.py index 6b906067f7b..a4503280db9 100644 --- a/backends/arm/test/ops/test_mm.py +++ b/backends/arm/test/ops/test_mm.py @@ -6,6 +6,7 @@ from typing import Callable +import pytest import torch from executorch.backends.arm.test import common from executorch.backends.arm.test.tester.test_pipeline import ( @@ -53,6 +54,7 @@ def test_mm_tosa_u55(test_data_generator: Callable[[], tuple]): @parameterized.expand(MM.test_data_generators) +@pytest.mark.flaky # Investigate flakiness (MLETORCH-870) def test_mm_tosa_u85(test_data_generator: Callable[[], tuple]): test_data = test_data_generator() EthosU85PipelineBI[test_t](MM(), test_data, MM.aten_op, MM.exir_op).run() @@ -67,6 +69,7 @@ def test_mm_tosa_u55_on_fvp(test_data_generator: Callable[[], tuple]): @parameterized.expand(MM.test_data_generators) @common.SkipIfNoCorstone320 +@pytest.mark.flaky # Investigate flakiness (MLETORCH-870) def test_mm_tosa_u85_on_fvp(test_data_generator: Callable[[], tuple]): test_data = test_data_generator() EthosU85PipelineBI[test_t]( From 644e55d8a95cfcc67261b32c3150867788e9ac0d Mon Sep 17 00:00:00 2001 From: Sebastian Larsson <38941629+Sebastian-Larsson@users.noreply.github.com> Date: Mon, 7 Apr 2025 14:05:06 +0200 Subject: [PATCH 021/244] Arm backend: Convert assert to throw ValueError in op_exp (#9929) Asserts are converted to proper raises to ensure graph integrity. It should not be possible for exp to have more than 1 input for a correctly formatted graph, but in the node visitor we cannot know for sure that the graph is formatted correctly. torch.exp supports more data types than fp32, which is why it should be checked. Signed-off-by: Sebastian Larsson --- backends/arm/operators/op_exp.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/backends/arm/operators/op_exp.py b/backends/arm/operators/op_exp.py index 4b8232ef6e7..d9f0e4f5197 100644 --- a/backends/arm/operators/op_exp.py +++ b/backends/arm/operators/op_exp.py @@ -36,7 +36,14 @@ def define_node( output: TosaArg, ) -> None: - assert len(node.all_input_nodes) == 1 - assert inputs[0].dtype == output.dtype == ts.DType.FP32 + if len(node.all_input_nodes) != 1: + raise ValueError( + f"Expected 1 input for {self.target}, got {len(node.all_input_nodes)}" + ) + if inputs[0].dtype != ts.DType.FP32 or output.dtype != ts.DType.FP32: + raise ValueError( + f"Input and output for {self.target} need to be FP32, got input dtype: " + f"{inputs[0].dtype} and output dtype: {output.dtype}" + ) tosa_graph.addOperator(TosaOp.Op().EXP, [inputs[0].name], [output.name]) From 9451faa5c0d3bc923b8c01a526db15636bd1ca6e Mon Sep 17 00:00:00 2001 From: Sebastian Larsson <38941629+Sebastian-Larsson@users.noreply.github.com> Date: Mon, 7 Apr 2025 15:52:46 +0200 Subject: [PATCH 022/244] Arm backend: Convert assert to raise ValueError for comparison operators (#9931) Asserts are converted to proper raises to ensure graph integrity. Signed-off-by: Sebastian Larsson --- backends/arm/operators/op_eq.py | 8 +++++--- backends/arm/operators/op_ge.py | 8 +++++--- backends/arm/operators/op_gt.py | 8 +++++--- backends/arm/operators/op_le.py | 8 +++++--- backends/arm/operators/op_lt.py | 8 +++++--- 5 files changed, 25 insertions(+), 15 deletions(-) diff --git a/backends/arm/operators/op_eq.py b/backends/arm/operators/op_eq.py index 02fc89099e0..de49b267641 100644 --- a/backends/arm/operators/op_eq.py +++ b/backends/arm/operators/op_eq.py @@ -34,9 +34,11 @@ def define_node( inputs: List[TosaArg], output: TosaArg, ) -> None: - assert ( - inputs[0].dtype == inputs[1].dtype - ), "EQ must have the same dtypes as input" + if inputs[0].dtype != inputs[1].dtype: + raise TypeError( + "All inputs need to have the same data type for operator EQ but got " + f"{inputs[0].dtype=}, {inputs[1].dtype=}" + ) input_nodes = inputs # Handle quantization diff --git a/backends/arm/operators/op_ge.py b/backends/arm/operators/op_ge.py index e4de12f3327..d18156e1a50 100644 --- a/backends/arm/operators/op_ge.py +++ b/backends/arm/operators/op_ge.py @@ -34,9 +34,11 @@ def define_node( inputs: List[TosaArg], output: TosaArg, ) -> None: - assert ( - inputs[0].dtype == inputs[1].dtype - ), "GE must have the same dtypes as input" + if inputs[0].dtype != inputs[1].dtype: + raise TypeError( + "All inputs need to have the same data type for operator GE but got " + f"{inputs[0].dtype=}, {inputs[1].dtype=}" + ) input_nodes = inputs # Handle quantization diff --git a/backends/arm/operators/op_gt.py b/backends/arm/operators/op_gt.py index 65cf8197bdc..25ff2463c5c 100644 --- a/backends/arm/operators/op_gt.py +++ b/backends/arm/operators/op_gt.py @@ -34,9 +34,11 @@ def define_node( inputs: List[TosaArg], output: TosaArg, ) -> None: - assert ( - inputs[0].dtype == inputs[1].dtype - ), "GT must have the same dtypes as input" + if inputs[0].dtype != inputs[1].dtype: + raise TypeError( + "All inputs need to have the same data type for operator GT but got " + f"{inputs[0].dtype=}, {inputs[1].dtype=}" + ) input_nodes = inputs # Handle quantization diff --git a/backends/arm/operators/op_le.py b/backends/arm/operators/op_le.py index 8fea2b92088..8ed5539034b 100644 --- a/backends/arm/operators/op_le.py +++ b/backends/arm/operators/op_le.py @@ -34,9 +34,11 @@ def define_node( inputs: List[TosaArg], output: TosaArg, ) -> None: - assert ( - inputs[0].dtype == inputs[1].dtype - ), "LE must have the same dtypes as input" + if inputs[0].dtype != inputs[1].dtype: + raise TypeError( + "All inputs need to have the same data type for operator LE but got " + f"{inputs[0].dtype=}, {inputs[1].dtype=}" + ) input_nodes = inputs # Handle quantization diff --git a/backends/arm/operators/op_lt.py b/backends/arm/operators/op_lt.py index da93ab41799..3bb71f611a5 100644 --- a/backends/arm/operators/op_lt.py +++ b/backends/arm/operators/op_lt.py @@ -34,9 +34,11 @@ def define_node( inputs: List[TosaArg], output: TosaArg, ) -> None: - assert ( - inputs[0].dtype == inputs[1].dtype - ), "LT must have the same dtypes as input" + if inputs[0].dtype != inputs[1].dtype: + raise TypeError( + "All inputs need to have the same data type for operator LT but got " + f"{inputs[0].dtype=}, {inputs[1].dtype=}" + ) input_nodes = inputs # Handle quantization From 968dec9693eddc8eb96b0300d09abb9e4d0f8e54 Mon Sep 17 00:00:00 2001 From: shewu-quic <138087975+shewu-quic@users.noreply.github.com> Date: Tue, 8 Apr 2025 00:48:09 +0800 Subject: [PATCH 023/244] Qualcomm AI Engine Direct - Fix mobilebert finetune script (#9927) Summary: - As title, it's broken in https://github.com/pytorch/executorch/pull/9643 --- examples/qualcomm/scripts/mobilebert_fine_tune.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/examples/qualcomm/scripts/mobilebert_fine_tune.py b/examples/qualcomm/scripts/mobilebert_fine_tune.py index 47a489f6d52..864c78afe53 100755 --- a/examples/qualcomm/scripts/mobilebert_fine_tune.py +++ b/examples/qualcomm/scripts/mobilebert_fine_tune.py @@ -23,7 +23,6 @@ make_output_dir, make_quantizer, parse_skip_delegation_node, - QnnPartitioner, setup_common_args_and_variables, SimpleADB, ) @@ -273,19 +272,15 @@ def calibrator(gm): quantizer = make_quantizer(quant_dtype=quant_dtype) backend_options = generate_htp_compiler_spec(quant_dtype is not None) - partitioner = QnnPartitioner( - generate_qnn_executorch_compiler_spec( - soc_model=getattr(QcomChipset, args.model), - backend_options=backend_options, - ), - skip_node_id_set=skip_node_id_set, - skip_node_op_set=skip_node_op_set, + compiler_specs = generate_qnn_executorch_compiler_spec( + soc_model=getattr(QcomChipset, args.model), + backend_options=backend_options, ) # skip embedding layer cause it's quantization sensitive graph_module, _ = skip_annotation( nn_module=model, quantizer=quantizer, - partitioner=partitioner, + compiler_specs=compiler_specs, sample_input=inputs[0], calibration_cb=calibrator, fp_node_op_set={torch.ops.aten.embedding.default}, From 412b1c05fae73a20333c69854ac2b1e8b952bbb3 Mon Sep 17 00:00:00 2001 From: Hossein Askari Date: Mon, 7 Apr 2025 13:20:11 -0400 Subject: [PATCH 024/244] aten.leakyrelu.default Op registery Differential Revision: D69199644 Pull Request resolved: https://github.com/pytorch/executorch/pull/8227 --- backends/vulkan/op_registry.py | 1 + 1 file changed, 1 insertion(+) diff --git a/backends/vulkan/op_registry.py b/backends/vulkan/op_registry.py index b33430a6bca..97a428c77aa 100644 --- a/backends/vulkan/op_registry.py +++ b/backends/vulkan/op_registry.py @@ -277,6 +277,7 @@ def register_binary_op(features: OpFeatures): exir_ops.edge.aten.rsqrt.default, exir_ops.edge.aten.tanh.default, exir_ops.edge.aten.round.default, + exir_ops.edge.aten.leaky_relu.default, ] ) def register_unary_op(features: OpFeatures): From 24825d9dccdb2a217f472fa006441c9d69dada8b Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Mon, 7 Apr 2025 11:50:46 -0700 Subject: [PATCH 025/244] Update release.yaml (#9891) This is used to categorize PRs between releases: https://docs.github.com/en/repositories/releasing-projects-on-github/automatically-generated-release-notes --- .github/release.yml | 61 ++++++++++++++++++++++++++++++++------------- 1 file changed, 43 insertions(+), 18 deletions(-) diff --git a/.github/release.yml b/.github/release.yml index 8caa4ede084..fc4accd252a 100644 --- a/.github/release.yml +++ b/.github/release.yml @@ -15,57 +15,82 @@ changelog: - title: ARM labels: - "release notes: arm" + - "module: arm" + - "partner: arm" - title: NXP - labels: + labels: - "release notes: nxp" + - "module: nxp" - title: Exir - labels: + labels: - "release notes: exir" + - "module: exir" - title: Misc - labels: + labels: - "release notes: misc" - title: Apple - labels: + labels: - "release notes: apple" + - "module: coreml" + - "module: mps" + - title: Android + labels: + - "module: android" + - title: IOS + labels: + - "module: ios" - title: Build - labels: + labels: - "release notes: build" - title: Vulkan - labels: + labels: - "release notes: vulkan" + - "module: vulkan" - title: Cadence - labels: + labels: - "release notes: cadence" + - "module: cadence" - title: Runtime - labels: + labels: - "release notes: runtime" + - "module: runtime" - title: XNNPACK - labels: + labels: - "release notes: xnnpack" + - "module: xnnpack" - title: Devtools - labels: + labels: - "release notes: devtools" + - "module: devtools" - title: Examples - labels: + labels: - "release notes: examples" + - title: LLM + labels: + - "module: llm" - title: Mediatek - labels: + labels: - "release notes: mediatek" + - "partner: mediatek" - title: Openvino - labels: + labels: - "release notes: openvino" - title: Qualcomm - labels: + labels: - "release notes: qualcomm" + - "partner: qualcomm" + - "module: qnn" - title: Training - labels: + labels: - "release notes: training" + - "module: training" - title: Quantization - labels: + labels: - "release notes: quantization" - title: Ops & kernels - labels: - - "release notes: ops & kernels" + labels: + - "release notes: ops & kernels" + - "module: kernels" - title: Other Changes labels: - "*" From 78f152bfdba31bc97c43083ca9a4e19d9f44494f Mon Sep 17 00:00:00 2001 From: Mengwei Liu Date: Mon, 7 Apr 2025 11:57:57 -0700 Subject: [PATCH 026/244] Add code structure and a few other links to CONTRIBUTING.md (#9793) As titled. This PR mainly adds the mapping between our code structure to the documentations/tutorials. Also a few other touch-ups. cc @mergennachin @byjlw --------- Co-authored-by: Mengwei Liu --- CONTRIBUTING.md | 94 ++++++++++++++++++- README.md | 57 +---------- .../using-executorch-building-from-source.md | 11 +++ 3 files changed, 104 insertions(+), 58 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index ed1e2b30323..82076c2727d 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -9,6 +9,95 @@ Set up your environment by following the instructions at https://pytorch.org/executorch/stable/getting-started-setup.html to clone the repo and install the necessary requirements. +Refer to this [document](https://pytorch.org/executorch/main/using-executorch-building-from-source.html) to build ExecuTorch from source. + +### Dev Setup for Android +For Android, please refer to the [Android documentation](docs/source/using-executorch-android.md). + +### Dev Setup for Apple +For Apple, please refer to the [iOS documentation](docs/source/using-executorch-ios.md). +  + +## Codebase structure + +
+
+executorch
+├── backends - Backend delegate implementations for various hardware targets. Each backend uses partitioner to split the graph into subgraphs that can be executed on specific hardware, quantizer to optimize model precision, and runtime components to execute the graph on target hardware. For details refer to the backend documentation and the Export and Lowering tutorial for more information.
+│   ├── apple - Apple-specific backends.
+│   │   ├── coreml - CoreML backend for Apple devices. See doc.
+│   │   └── mps - Metal Performance Shaders backend for Apple devices. See doc.
+│   ├── arm - ARM architecture backends. See doc.
+│   ├── cadence - Cadence-specific backends. See doc.
+│   ├── example - Example backend implementations.
+│   ├── mediatek - MediaTek-specific backends. See doc.
+│   ├── openvino - OpenVINO backend for Intel hardware.
+│   ├── qualcomm - Qualcomm-specific backends. See doc.
+│   ├── transforms - Transformations for backend optimization.
+│   ├── vulkan - Vulkan backend for cross-platform GPU support. See doc.
+│   └── xnnpack - XNNPACK backend for optimized neural network operations. See doc.
+├── codegen - Tooling to autogenerate bindings between kernels and the runtime.
+├── configurations - Configuration files.
+├── devtools - Model profiling, debugging, and inspection. Please refer to the tools documentation for more information.
+│   ├── bundled_program - a tool for validating ExecuTorch model. See doc.
+│   ├── etdump - ETDump - a format for saving profiling and debugging data from runtime. See doc.
+│   ├── etrecord - ETRecord - AOT debug artifact for ExecuTorch. See doc.
+│   ├── inspector - Python API to inspect ETDump and ETRecord. See doc.
+│   └── visualization - Visualization tools for representing model structure and performance metrics.
+├── docs - Static docs tooling and documentation source files.
+├── examples - Examples of various user flows, such as model export, delegates, and runtime execution.
+├── exir - Ahead-of-time library: model capture and lowering APIs. EXport Intermediate Representation (EXIR) is a format for representing the result of torch.export. This directory contains utilities and passes for lowering the EXIR graphs into different dialects and eventually suitable to run on target hardware.
+│   ├── _serialize - Serialize final export artifact.
+│   ├── backend - Backend delegate ahead of time APIs.
+│   ├── capture - Program capture.
+│   ├── dialects - Op sets for various dialects in the export process. Please refer to the EXIR spec and the backend dialect doc for more details.
+│   ├── emit - Conversion from ExportedProgram to ExecuTorch execution instructions.
+│   ├── operator - Operator node manipulation utilities.
+│   ├── passes - Built-in compiler passes.
+│   ├── program - Export artifacts.
+│   ├── serde - Graph module serialization/deserialization.
+│   ├── verification - IR verification.
+├── extension - Extensions built on top of the runtime.
+│   ├── android - ExecuTorch wrappers for Android apps. Please refer to the Android documentation and Javadoc for more information.
+│   ├── apple - ExecuTorch wrappers for iOS apps. Please refer to the iOS documentation and how to integrate into Apple platform for more information.
+│   ├── aten_util - Converts to and from PyTorch ATen types.
+│   ├── data_loader - 1st party data loader implementations.
+│   ├── evalue_util - Helpers for working with EValue objects.
+│   ├── gguf_util - Tools to convert from the GGUF format.
+│   ├── kernel_util - Helpers for registering kernels.
+│   ├── llm - Library to run LLM on ExecuTorch including common optimization passes, runtime C++ components. Please refer to the LLM documentation for more information.
+│   ├── memory_allocator - 1st party memory allocator implementations.
+│   ├── module - A simplified C++ wrapper for the runtime. An abstraction that deserializes and executes an ExecuTorch artifact (.pte file). Refer to the module documentation for more information.
+│   ├── parallel - C++ threadpool integration.
+│   ├── pybindings - Python API for executorch runtime. This is powering up the runtime Python API for ExecuTorch.
+│   ├── pytree - C++ and Python flattening and unflattening lib for pytrees.
+│   ├── runner_util - Helpers for writing C++ PTE-execution tools.
+│   ├── tensor - Tensor maker and TensorPtr, details in this documentation. For how to use TensorPtr and Module, please refer to the "Using ExecuTorch with C++" doc.
+│   ├── testing_util - Helpers for writing C++ tests.
+│   ├── threadpool - Threadpool.
+│   └── training - Experimental libraries for on-device training.
+├── kernels - 1st party kernel implementations.
+│   ├── aten - ATen kernel implementations.
+│   ├── optimized - Optimized kernel implementations.
+│   ├── portable - Reference implementations of ATen operators.
+│   ├── prim_ops - Special ops used in executorch runtime for control flow and symbolic primitives.
+│   └── quantized - Quantized kernel implementations.
+├── profiler - Utilities for profiling runtime execution.
+├── runtime - Core C++ runtime. These components are used to execute the ExecuTorch program. Please refer to the runtime documentation for more information.
+│   ├── backend - Backend delegate runtime APIs.
+│   ├── core - Core structures used across all levels of the runtime. Basic components such as Tensor, EValue, Error and Result etc.
+│   ├── executor - Model loading, initialization, and execution. Runtime components that execute the ExecuTorch program, such as Program, Method. Refer to the runtime API documentation for more information.
+│   ├── kernel - Kernel registration and management.
+│   └── platform - Layer between architecture specific code and portable C++.
+├── schema - ExecuTorch PTE file format flatbuffer schemas.
+├── scripts - Utility scripts for building libs, size management, dependency management, etc.
+├── shim - Compatibility layer between OSS and Internal builds.
+├── test - Broad scoped end-to-end tests.
+├── third-party - Third-party dependencies.
+├── tools - Tools for building ExecuTorch from source, for different built tools (CMake, Buck).
+└── util - Various helpers and scripts.
+
+   ## Contributing workflow @@ -221,7 +310,7 @@ CI is run automatically on all pull requests. However, if you want to run tests - The `sh test/build_size_test.sh` script will compile the C++runtime along with portable kernels. - The `test/run_oss_cpp_tests.sh` script will build and run C++ tests locally -- Running `pytest` from the root directory will run Python tests locally. +- Running `pytest` from the root directory will run Python tests locally. Make sure to run this after finishing [Dev Install](#dev-install). ### Writing Tests To help keep code quality high, ExecuTorch uses a combination of unit tests and @@ -279,7 +368,8 @@ for basics. - Good title: "Add XYZ method to ABC" - Give the PR a clear and thorough description. Don't just describe what the PR does: the diff will do that. Explain *why* you are making this change, in a - way that will make sense to someone years from now. + way that will make sense to someone years from now. If the PR is a bug fix, + include the issue number at the beginning of the description: "Fixes #1234" - Explain how you have tested your changes by including repeatable instructions for testing the PR. - If you added tests, this can be as simple as the command you used to run the diff --git a/README.md b/README.md index dd1fafe715b..d60220bae85 100644 --- a/README.md +++ b/README.md @@ -65,62 +65,7 @@ We welcome contributions. To get started review the [guidelines](CONTRIBUTING.md ## Directory Structure -``` -executorch -├── backends # Backend delegate implementations. -├── codegen # Tooling to autogenerate bindings between kernels and the runtime. -├── configurations -├── docs # Static docs tooling. -├── examples # Examples of various user flows, such as model export, delegates, and runtime execution. -├── exir # Ahead-of-time library: model capture and lowering APIs. -| ├── _serialize # Serialize final export artifact. -| ├── backend # Backend delegate ahead of time APIs -| ├── capture # Program capture. -| ├── dialects # Op sets for various dialects in the export process. -| ├── emit # Conversion from ExportedProgram to ExecuTorch execution instructions. -| ├── operator # Operator node manipulation utilities. -| ├── passes # Built-in compiler passes. -| ├── program # Export artifacts. -| ├── serde # Graph module serialization/deserialization. -| ├── verification # IR verification. -├── extension # Extensions built on top of the runtime. -| ├── android # ExecuTorch wrappers for Android apps. -| ├── apple # ExecuTorch wrappers for iOS apps. -| ├── aten_util # Converts to and from PyTorch ATen types. -| ├── data_loader # 1st party data loader implementations. -| ├── evalue_util # Helpers for working with EValue objects. -| ├── gguf_util # Tools to convert from the GGUF format. -| ├── kernel_util # Helpers for registering kernels. -| ├── memory_allocator # 1st party memory allocator implementations. -| ├── module # A simplified C++ wrapper for the runtime. -| ├── parallel # C++ threadpool integration. -| ├── pybindings # Python API for executorch runtime. -| ├── pytree # C++ and Python flattening and unflattening lib for pytrees. -| ├── runner_util # Helpers for writing C++ PTE-execution tools. -| ├── testing_util # Helpers for writing C++ tests. -| ├── training # Experimental libraries for on-device training -├── kernels # 1st party kernel implementations. -| ├── aten -| ├── optimized -| ├── portable # Reference implementations of ATen operators. -| ├── prim_ops # Special ops used in executorch runtime for control flow and symbolic primitives. -| ├── quantized -├── profiler # Utilities for profiling runtime execution. -├── runtime # Core C++ runtime. -| ├── backend # Backend delegate runtime APIs. -| ├── core # Core structures used across all levels of the runtime. -| ├── executor # Model loading, initialization, and execution. -| ├── kernel # Kernel registration and management. -| ├── platform # Layer between architecture specific code and portable C++. -├── schema # ExecuTorch PTE file format flatbuffer schemas. -├── scripts # Utility scripts for building libs, size management, dependency management, etc. -├── tools # Development tool management. -├── devtools # Model profiling, debugging, and introspection. -├── shim # Compatibility layer between OSS and Internal builds -├── test # Broad scoped end-to-end tests. -├── third-party # Third-party dependencies. -├── util # Various helpers and scripts. -``` +Please refer to the [Codebase structure](CONTRIBUTING.md#codebase-structure) section of the [Contributing Guidelines](CONTRIBUTING.md) for more details. ## License ExecuTorch is BSD licensed, as found in the LICENSE file. diff --git a/docs/source/using-executorch-building-from-source.md b/docs/source/using-executorch-building-from-source.md index 668f696f040..582cd3285bf 100644 --- a/docs/source/using-executorch-building-from-source.md +++ b/docs/source/using-executorch-building-from-source.md @@ -88,6 +88,17 @@ Or alternatively, [install conda on your machine](https://conda.io/projects/cond pip install -e . ``` + If C++ files are being modified, you will still have to reinstall ExecuTorch from source. + +> **_WARNING:_** +> Some modules can't be imported directly in editable mode. This is a known [issue](https://github.com/pytorch/executorch/issues/9558) and we are actively working on a fix for this. To workaround this: +> ```bash +> # This will fail +> python -c "from executorch.exir import CaptureConfig" +> # But this will succeed +> python -c "from executorch.exir.capture import CaptureConfig" +> ``` + > **_NOTE:_** Cleaning the build system > > When fetching a new version of the upstream repo (via `git fetch` or `git From d1e4aa84fdc45ed181dd5a40733fe7e5dd6fae06 Mon Sep 17 00:00:00 2001 From: Kimish Patel Date: Mon, 7 Apr 2025 12:14:16 -0700 Subject: [PATCH 027/244] Add a path to use quantized gemm from torchao in sdpa Differential Revision: D71370593 Pull Request resolved: https://github.com/pytorch/executorch/pull/9933 --- extension/llm/custom_ops/TARGETS | 14 + extension/llm/custom_ops/op_sdpa.cpp | 94 +++- extension/llm/custom_ops/op_sdpa.h | 20 + extension/llm/custom_ops/op_sdpa_aot.cpp | 143 ++++++ extension/llm/custom_ops/op_sdpa_impl.h | 135 ++++- extension/llm/custom_ops/targets.bzl | 23 +- .../llm/custom_ops/test_quantized_sdpa.py | 470 ++++++++++++++++++ .../llm/custom_ops/test_sdpa_with_kv_cache.py | 6 +- 8 files changed, 860 insertions(+), 45 deletions(-) create mode 100644 extension/llm/custom_ops/test_quantized_sdpa.py diff --git a/extension/llm/custom_ops/TARGETS b/extension/llm/custom_ops/TARGETS index 5d0c0490506..61be3d191a7 100644 --- a/extension/llm/custom_ops/TARGETS +++ b/extension/llm/custom_ops/TARGETS @@ -47,3 +47,17 @@ runtime.python_test( "//caffe2:torch", ], ) + +runtime.python_test( + name = "test_quantized_sdpa", + srcs = [ + "test_quantized_sdpa.py", + ], + preload_deps = [ + ":custom_ops_aot_lib_mkl_noomp", + ":custom_ops_aot_py", + ], + deps = [ + "//caffe2:torch", + ], +) diff --git a/extension/llm/custom_ops/op_sdpa.cpp b/extension/llm/custom_ops/op_sdpa.cpp index 202ff17188d..391d2ab0646 100644 --- a/extension/llm/custom_ops/op_sdpa.cpp +++ b/extension/llm/custom_ops/op_sdpa.cpp @@ -44,7 +44,9 @@ bool validate_flash_attention_args( "scaled_dot_product_attention_flash_attention: Q/K/V should have the same head size"); ET_CHECK_OR_RETURN_FALSE( - (query.scalar_type() == ScalarType::Float), "Query must be Float type"); + (query.scalar_type() == ScalarType::Float) || + (query.scalar_type() == ScalarType::Char), + "Query must be Float type"); ET_CHECK_OR_RETURN_FALSE( (query.scalar_type() == key.scalar_type()) && @@ -354,9 +356,14 @@ Tensor& custom_sdpa_out_impl( output, "Invalid arguments"); + int64_t seq_len = q.size(1); + auto q_seq_len = q.size(1); + bool is_seq_at_dim_1{true}; if (q.scalar_type() == ScalarType::Char) { is_seq_at_dim_1 = false; + seq_len = q.size(2); + q_seq_len = q.size(2); ET_KERNEL_CHECK_MSG( ctx, q_scales.has_value() && q_zero_points.has_value() && @@ -390,9 +397,6 @@ Tensor& custom_sdpa_out_impl( ET_CHECK_MSG(q.dim() == 4, "query must be a 4D tensor"); - const int64_t seq_len = q.size(1); - auto q_seq_len = q.size(1); - const int64_t num_keys_for_causal_attention = start_pos + seq_len; ET_KERNEL_CHECK( @@ -418,12 +422,12 @@ Tensor& custom_sdpa_out_impl( is_causal, attn_mask, scale, - nullopt, // q_zero_points - nullopt, // q_scales - nullopt, // k_zero_points - nullopt, // k_scales - nullopt, // v_zero_points - nullopt, // v_scales + q_zero_points, // q_zero_points + q_scales, // q_scales + k_zero_points, // k_zero_points + k_scales, // k_scales + v_zero_points, // v_zero_points + v_scales, // v_scales is_seq_at_dim_1, /* is_seq_at_dim_1 */ start_pos, num_keys_for_causal_attention); @@ -437,12 +441,12 @@ Tensor& custom_sdpa_out_impl( is_causal, attn_mask, scale, - nullopt, // q_zero_points - nullopt, // q_scales - nullopt, // k_zero_points - nullopt, // k_scales - nullopt, // v_zero_points - nullopt, // v_scales + q_zero_points, // q_zero_points + q_scales, // q_scales + k_zero_points, // k_zero_points + k_scales, // k_scales + v_zero_points, // v_zero_points + v_scales, // v_scales is_seq_at_dim_1, /* is_seq_at_dim_1 */ start_pos, num_keys_for_causal_attention); @@ -456,12 +460,12 @@ Tensor& custom_sdpa_out_impl( is_causal, attn_mask, scale, - nullopt, // q_zero_points - nullopt, // q_scales - nullopt, // k_zero_points - nullopt, // k_scales - nullopt, // v_zero_points - nullopt, // v_scales + q_zero_points, // q_zero_points + q_scales, // q_scales + k_zero_points, // k_zero_points + k_scales, // k_scales + v_zero_points, // v_zero_points + v_scales, // v_scales is_seq_at_dim_1, /* is_seq_at_dim_1 */ start_pos, num_keys_for_causal_attention); @@ -470,6 +474,45 @@ Tensor& custom_sdpa_out_impl( return output; } +#ifdef ENABLE_CUSTOM_QUANTIZED_SDPA +Tensor& custom_quantized_sdpa_out( + RuntimeContext& ctx, + const Tensor& q, + const Tensor& k, + const Tensor& v, + const int64_t start_pos, + const optional& attn_mask, + const double dropout_p, + const bool is_causal, + // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy + const optional scale, + const optional& q_zero_points, + const optional& q_scales, + const optional& k_zero_points, + const optional& k_scales, + const optional& v_zero_points, + const optional& v_scales, + Tensor& output) { + return custom_sdpa_out_impl( + ctx, + q, + k, + v, + start_pos, + attn_mask, + dropout_p, + is_causal, + scale, + output, + q_zero_points, + q_scales, + k_zero_points, + k_scales, + v_zero_points, + v_scales); +} +#endif // ENABLE_CUSTOM_QUANTIZED_SDPA + /* Input params @param[in] q_projected Projected query with query weights. @@ -570,3 +613,10 @@ EXECUTORCH_LIBRARY( llama, "custom_sdpa.out", torch::executor::native::custom_sdpa_out); + +#ifdef ENABLE_CUSTOM_QUANTIZED_SDPA +EXECUTORCH_LIBRARY( + llama, + "custom_quantized_sdpa.out", + torch::executor::native::custom_quantized_sdpa_out); +#endif // ENABLE_CUSTOM_QUANTIZED_SDPA diff --git a/extension/llm/custom_ops/op_sdpa.h b/extension/llm/custom_ops/op_sdpa.h index bc2202b9bd8..92b8a41b706 100644 --- a/extension/llm/custom_ops/op_sdpa.h +++ b/extension/llm/custom_ops/op_sdpa.h @@ -56,6 +56,26 @@ Tensor& flash_attention_kernel_out( const optional scale, Tensor& output); +#ifdef ENABLE_CUSTOM_QUANTIZED_SDPA +Tensor& custom_quantized_sdpa_out( + RuntimeContext& ctx, + const Tensor& q, + const Tensor& k, + const Tensor& v, + const int64_t start_pos, + const optional& attn_mask, + const double dropout_p, + const bool is_causal, + // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy + const optional scale, + const optional& q_zero_points, + const optional& q_scales, + const optional& k_zero_points, + const optional& k_scales, + const optional& v_zero_points, + const optional& v_scales, + Tensor& output); +#endif // ENABLE_CUSTOM_QUANTIZED_SDPA } // namespace native } // namespace executor } // namespace torch diff --git a/extension/llm/custom_ops/op_sdpa_aot.cpp b/extension/llm/custom_ops/op_sdpa_aot.cpp index 213adf1c8ab..a3adcbbf866 100644 --- a/extension/llm/custom_ops/op_sdpa_aot.cpp +++ b/extension/llm/custom_ops/op_sdpa_aot.cpp @@ -77,6 +77,47 @@ at::Tensor custom_sdpa_aten( // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy const std::optional scale); +#ifdef ENABLE_CUSTOM_QUANTIZED_SDPA +Tensor& custom_quantized_sdpa_out_no_context( + const Tensor& q, + const Tensor& k, + const Tensor& v, + const int64_t start_pos, + // @lint-ignore CLANGTIDY facebook-hte-ConstantArgumentPassByValue + // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy + const optional attn_mask, + const double dropout_p, + const bool is_causal, + // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy + const optional scale, + const optional q_zero_points, + const optional q_scales, + const optional k_zero_points, + const optional k_scales, + const optional v_zero_points, + const optional v_scales, + Tensor& output); + +at::Tensor custom_quantized_sdpa_aten( + const at::Tensor& q, + const at::Tensor& k, + const at::Tensor& v, + const int64_t start_pos, + // @lint-ignore CLANGTIDY facebook-hte-ConstantArgumentPassByValue + // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy + const std::optional attn_mask, + const double dropout_p, + const bool is_causal, + // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy + const std::optional scale, + const std::optional& q_zero_points, + const std::optional& q_scales, + const std::optional& k_zero_points, + const std::optional& k_scales, + const std::optional& v_zero_points, + const std::optional& v_scales); +#endif // ENABLE_CUSTOM_QUANTIZED_SDPA + Tensor& update_cache_out_no_context( const Tensor& value, Tensor& cache, @@ -198,6 +239,85 @@ at::Tensor custom_sdpa_aten( return output; } +#ifdef ENABLE_CUSTOM_QUANTIZED_SDPA +Tensor& custom_quantized_sdpa_out_no_context( + const Tensor& q, + const Tensor& k, + const Tensor& v, + const int64_t start_pos, + // @lint-ignore CLANGTIDY facebook-hte-ConstantArgumentPassByValue + // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy + const optional attn_mask, + const double dropout_p, + const bool is_causal, + // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy + const optional scale, + const optional q_zero_points, + const optional q_scales, + const optional k_zero_points, + const optional k_scales, + const optional v_zero_points, + const optional v_scales, + Tensor& output) { + executorch::aten::RuntimeContext context{}; + return torch::executor::native::custom_quantized_sdpa_out( + context, + q, + k, + v, + start_pos, + attn_mask, + dropout_p, + is_causal, + scale, + q_zero_points, + q_scales, + k_zero_points, + k_scales, + v_zero_points, + v_scales, + output); +} + +at::Tensor custom_quantized_sdpa_aten( + const at::Tensor& q, + const at::Tensor& k, + const at::Tensor& v, + const int64_t start_pos, + // @lint-ignore CLANGTIDY facebook-hte-ConstantArgumentPassByValue + // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy + const std::optional attn_mask, + const double dropout_p, + const bool is_causal, + // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy + const std::optional scale, + const std::optional& q_zero_points, + const std::optional& q_scales, + const std::optional& k_zero_points, + const std::optional& k_scales, + const std::optional& v_zero_points, + const std::optional& v_scales) { + auto output = at::empty(q.sizes()); + WRAP_TO_ATEN(custom_quantized_sdpa_out_no_context, 14) + (q, + k, + v, + start_pos, + attn_mask, + dropout_p, + is_causal, + scale, + q_zero_points, + q_scales, + k_zero_points, + k_scales, + v_zero_points, + v_scales, + output); + return output; +} +#endif // ENABLE_CUSTOM_QUANTIZED_SDPA + Tensor& update_cache_out_no_context( const Tensor& value, Tensor& cache, @@ -245,6 +365,20 @@ TORCH_LIBRARY_FRAGMENT(llama, m) { m.def( "update_cache.out(Tensor value, Tensor(a!) cache, " "SymInt start_pos, *, Tensor(b!) out) -> Tensor(b!)"); +#ifdef ENABLE_CUSTOM_QUANTIZED_SDPA + m.def( + "custom_quantized_sdpa(Tensor query, Tensor key, Tensor value, SymInt start_pos, " + "Tensor? attn_mask=None, float drpout_p=0.0, bool is_causal=False, " + "float? scale=None, Tensor? q_zero_points=None, Tensor? q_scales=None, " + "Tensor? k_zero_points=None, Tensor? k_scales=None, Tensor? v_zero_points=None, " + "Tensor? v_scales=None) -> Tensor"); + m.def( + "custom_quantized_sdpa.out(Tensor query, Tensor key, Tensor value, SymInt start_pos, " + "Tensor? attn_mask=None, float drpout_p=0.0, bool is_causal=False, " + "float? scale=None, Tensor? q_zero_points=None, Tensor? q_scales=None, " + "Tensor? k_zero_points=None, Tensor? k_scales=None, Tensor? v_zero_points=None, " + "Tensor? v_scales=None, *, Tensor(a!) out) -> Tensor(a!)"); +#endif // ENABLE_CUSTOM_QUANTIZED_SDPA } // TODO: Rename this file to op_custom_ops_aot.cpp @@ -263,4 +397,13 @@ TORCH_LIBRARY_IMPL(llama, CompositeExplicitAutograd, m) { m.impl( "update_cache.out", WRAP_TO_ATEN(torch::executor::native::update_cache_out_no_context, 3)); +#ifdef ENABLE_CUSTOM_QUANTIZED_SDPA + m.impl( + "custom_quantized_sdpa", + torch::executor::native::custom_quantized_sdpa_aten); + m.impl( + "custom_quantized_sdpa.out", + WRAP_TO_ATEN( + torch::executor::native::custom_quantized_sdpa_out_no_context, 14)); +#endif // ENABLE_CUSTOM_QUANTIZED_SDPA } diff --git a/extension/llm/custom_ops/op_sdpa_impl.h b/extension/llm/custom_ops/op_sdpa_impl.h index 0639c539ed1..5a0fb708220 100644 --- a/extension/llm/custom_ops/op_sdpa_impl.h +++ b/extension/llm/custom_ops/op_sdpa_impl.h @@ -23,6 +23,10 @@ #endif #include +#if defined(ENABLE_CUSTOM_QUANTIZED_SDPA) +#include +#endif + namespace torch { namespace executor { @@ -67,7 +71,37 @@ void _q_at_k_gemm( q_data.dtype == ScalarType::Char || q_data.dtype == ScalarType::Float, "q and k must be either int8 or float"); if (q_data.dtype == ScalarType::Char) { - ET_CHECK_MSG(false, "int8 not supported yet"); +#if defined(ENABLE_CUSTOM_QUANTIZED_SDPA) + if constexpr (std::is_same::value) { + int a_stride_m_tmp, b_stride_n_tmp; + auto kernel = torchao::kernels::cpu::quantized_matmul:: + get_int8_a_int8_b_channelwise_qmatmul( + q_m, k_n, qk_k, false, true, a_stride_m_tmp, b_stride_n_tmp); + kernel( + q_m, + k_n, + qk_k, + static_cast(q_data.data), + q_stride_m, + static_cast(k_data.data), + k_stride_n, + qk_data, + k_n, + static_cast(q_data.zero_points), + static_cast(k_data.zero_points), + static_cast(q_data.scales), + static_cast(k_data.scales), + 1, + 1); + } else { + ET_CHECK_MSG( + false, "Accumulation in dtype other than float not supported yet"); + } +#else + ET_CHECK_MSG( + false, + "Quantized SDPA is not enabled. Check ENABLE_CUSTOM_QUANTIZED_SDPA compile flag"); +#endif } else { ::executorch::cpublas::gemm( ::executorch::cpublas::TransposeType::Transpose, @@ -99,7 +133,35 @@ void _qk_at_v_gemm( const int64_t o_stride_m, const accum_t beta) { if (v_data.dtype == ScalarType::Char) { - ET_CHECK_MSG(false, "int8 not supported yet"); +#if defined(ENABLE_CUSTOM_QUANTIZED_SDPA) + if constexpr (std::is_same::value) { + int a_stride_m_tmp, b_stride_n_tmp; + auto kernel = torchao::kernels::cpu::quantized_matmul:: + get_fp32_a_input_channelwise_8bit_b_f32_c_matmul( + m, n, k, false, false, a_stride_m_tmp, b_stride_n_tmp); + kernel( + m, + n, + k, + qk_data, + qk_stride_m /*lhs_stride_m*/, + static_cast(v_data.data), + v_stride_n /*rhs_stride_n*/, + o_data, + o_stride_m /*out_stride_n*/, + static_cast(v_data.zero_points), + static_cast(v_data.scales), + beta, + 1); + } else { + ET_CHECK_MSG( + false, "Accumulation in dtype other than float not supported yet"); + } +#else + ET_CHECK_MSG( + false, + "Quantized SDPA is not enabled. Check ENABLE_CUSTOM_QUANTIZED_SDPA compile flag"); +#endif } else { ::executorch::cpublas::gemm( ::executorch::cpublas::TransposeType::NoTranspose, @@ -394,7 +456,10 @@ void cpu_flash_attention( kvSize); } - bool is_quantized_sdpa = query.scalar_type() == ScalarType::Char; + bool is_quantized_sdpa = false; +#if defined(ENABLE_CUSTOM_QUANTIZED_SDPA) + is_quantized_sdpa = query.scalar_type() == ScalarType::Char; +#endif auto strides = query.strides(); int64_t qStrideB = strides[0]; @@ -426,6 +491,33 @@ void cpu_flash_attention( vStrideN = strides[1]; } + int64_t q_quant_params_StrideB = 0; + int64_t q_quant_params_StrideH = 0; + int64_t q_quant_params_StrideM = 0; + int64_t k_quant_params_StrideB = 0; + int64_t k_quant_params_StrideH = 0; + int64_t k_quant_params_StrideN = 0; + int64_t v_quant_params_StrideB = 0; + int64_t v_quant_params_StrideH = 0; + int64_t v_quant_params_StrideN = 0; + + if (is_quantized_sdpa) { + strides = q_zero_points.value().strides(); + q_quant_params_StrideB = strides[0]; + q_quant_params_StrideH = strides[1]; + q_quant_params_StrideM = strides[2]; + + strides = k_zero_points.value().strides(); + k_quant_params_StrideB = strides[0]; + k_quant_params_StrideH = strides[1]; + k_quant_params_StrideN = strides[2]; + + strides = v_zero_points.value().strides(); + v_quant_params_StrideB = strides[0]; + v_quant_params_StrideH = strides[1]; + v_quant_params_StrideN = strides[2]; + } + strides = output.strides(); int64_t oStrideB = strides[0]; int64_t oStrideH = strides[1]; @@ -473,7 +565,11 @@ void cpu_flash_attention( /* qk_sum */ qSplitSize + /* dst */ qSplitSize * headSize; - int64_t size_bytes = size_per_thread * num_thread * query.element_size(); + // Since all intermediate compute is accum_t, we need to + // allocate a buffer accordingly. + int64_t size_of_intermediate_precision = sizeof(accum_t); + int64_t size_bytes = size_per_thread * num_thread * query.element_size() * + size_of_intermediate_precision; std::vector buf_vec(size_bytes); void* buf = reinterpret_cast(buf_vec.data()); // Need to double check the following @@ -559,14 +655,18 @@ void cpu_flash_attention( int64_t q_offset = i * qStrideB + j * qStrideH + m * qStrideM; int64_t k_offset = i * kStrideB + j_kv * kStrideH + n * kStrideN; if (is_quantized_sdpa) { - ET_CHECK_MSG( - !is_seq_at_dim_1, "For quantized SDPA, seq_len must be at dim 2"); - q_scales_ptr = q_scales.value().const_data_ptr() + q_offset; - k_scales_ptr = k_scales.value().const_data_ptr() + k_offset; - q_zero_points_ptr = - q_zero_points.value().const_data_ptr() + q_offset; - k_zero_points_ptr = - k_zero_points.value().const_data_ptr() + k_offset; + int64_t q_quant_params_offset = i * q_quant_params_StrideB + + j * q_quant_params_StrideH + m * q_quant_params_StrideM; + int64_t k_quant_params_offset = i * k_quant_params_StrideB + + j_kv * k_quant_params_StrideH + n * k_quant_params_StrideN; + q_scales_ptr = + q_scales.value().const_data_ptr() + q_quant_params_offset; + k_scales_ptr = + k_scales.value().const_data_ptr() + k_quant_params_offset; + q_zero_points_ptr = q_zero_points.value().const_data_ptr() + + q_quant_params_offset; + k_zero_points_ptr = k_zero_points.value().const_data_ptr() + + k_quant_params_offset; q_sub_matrix_data_ptr = (const int8_t*)(q_data) + q_offset; k_sub_matrix_data_ptr = (const int8_t*)(k_data) + k_offset; } else { @@ -719,11 +819,12 @@ void cpu_flash_attention( const int8_t* v_zero_points_ptr = nullptr; int64_t v_offset = i * vStrideB + j_kv * vStrideH + n * vStrideN; if (is_quantized_sdpa) { - ET_CHECK_MSG( - !is_seq_at_dim_1, "For quantized SDPA, seq_len must be at dim 2"); - v_scales_ptr = v_scales.value().const_data_ptr() + v_offset; - v_zero_points_ptr = - v_zero_points.value().const_data_ptr() + v_offset; + int64_t v_quant_params_offset = i * v_quant_params_StrideB + + j_kv * v_quant_params_StrideH + n * v_quant_params_StrideN; + v_scales_ptr = + v_scales.value().const_data_ptr() + v_quant_params_offset; + v_zero_points_ptr = v_zero_points.value().const_data_ptr() + + v_quant_params_offset; v_sub_matrix_data_ptr = (const int8_t*)(v_data) + v_offset; } else { v_sub_matrix_data_ptr = (const scalar_t*)(v_data) + v_offset; diff --git a/extension/llm/custom_ops/targets.bzl b/extension/llm/custom_ops/targets.bzl index 5b68715e401..545f6516bb7 100644 --- a/extension/llm/custom_ops/targets.bzl +++ b/extension/llm/custom_ops/targets.bzl @@ -9,6 +9,18 @@ load( "get_compiler_optimization_flags", ) +def _get_quantized_sdpa_deps(): + if runtime.is_oss: + return [] + else: + return ["//pytorch/ao/torchao/experimental/kernels/cpu/interface:interface"] + +def _get_quantized_preproc_flags(): + if runtime.is_oss: + return [] + else: + return ["-DENABLE_CUSTOM_QUANTIZED_SDPA"] + def define_common_targets(): """Defines targets that should be shared between fbcode and xplat. @@ -33,7 +45,8 @@ def define_common_targets(): headers = [ "op_sdpa_impl.h", ], - preprocessor_flags = get_vec_preprocessor_flags(), + exported_preprocessor_flags = get_vec_preprocessor_flags() + + _get_quantized_preproc_flags(), exported_deps = [ "//executorch/runtime/kernel:kernel_includes", "//executorch/kernels/portable/cpu:scalar_utils", @@ -45,8 +58,12 @@ def define_common_targets(): deps = [ "//executorch/kernels/portable/cpu/util:reduce_util", "//executorch/extension/llm/custom_ops/spinquant:fast_hadamard_transform", - ] + get_vec_deps(), - compiler_flags = ["-Wno-missing-prototypes", "-Wno-global-constructors"] + get_compiler_optimization_flags(), + ] + get_vec_deps() + _get_quantized_sdpa_deps(), + compiler_flags = ["-Wno-missing-prototypes", "-Wno-global-constructors"] + get_compiler_optimization_flags() + + select({ + "DEFAULT": [], + "ovr_config//cpu:arm64": ["-march=armv8.2-a+dotprod"], + }), visibility = [ "//executorch/...", "//executorch/extension/llm/custom_ops/...", diff --git a/extension/llm/custom_ops/test_quantized_sdpa.py b/extension/llm/custom_ops/test_quantized_sdpa.py new file mode 100644 index 00000000000..f5540a4e614 --- /dev/null +++ b/extension/llm/custom_ops/test_quantized_sdpa.py @@ -0,0 +1,470 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +# pyre-unsafe + +import unittest + +import torch +import torch.nn.functional as F + +from .custom_ops import custom_ops_lib # noqa + + +class SDPATestForCustomQuantizedSDPA(unittest.TestCase): + """ + This test is to test the custom quantized SDPA op + Tensors are in [B, H, S, D] format + """ + + def setUp(self): + from torch.ao.quantization.fx._decomposed import ( # noqa: F401 + quantized_decomposed_lib, + ) + + torch.manual_seed(42) + self.n_batch = 1 + self.n_heads_kv = 32 + self.n_heads_q = 32 + self.head_dim = 128 + self.max_seq_len = 2048 + self.quantized_dtype = torch.int8 + self.float_dtype = torch.float32 + self.q_shape = None + self.kv_shape = None + + def _scale_tensor(self, tensor, min_value, max_value, scale=True): + normalized_tensor = (tensor - tensor.min()) / (tensor.max() - tensor.min()) + + scaled_tensor = normalized_tensor * (max_value - min_value) + min_value + + return scaled_tensor if scale else tensor + + def setup_caches_and_mask(self, tensor_scale_max, tensor_scale_min, scale_tensors): + self.mask = torch.full( + (self.max_seq_len, self.max_seq_len), + float("-inf"), + ) + self.mask = torch.triu(self.mask, diagonal=1) + + self.k = self._scale_tensor( + torch.rand(self.kv_shape), + tensor_scale_max, + tensor_scale_min, + scale_tensors, + ) + self.v = self._scale_tensor( + torch.rand(self.kv_shape), + tensor_scale_max, + tensor_scale_min, + scale_tensors, + ) + + def _sdpa_ref( + self, + q_quantized, + k_quantized, + v_quantized, + start_pos, + q_zero_point, + q_scale, + k_zero_point, + k_scale, + v_zero_point, + v_scale, + attn_mask, + ): + q = torch.ops.quantized_decomposed.dequantize_per_token( + q_quantized, + q_scale, + q_zero_point, + torch.iinfo(self.quantized_dtype).min, + torch.iinfo(self.quantized_dtype).max, + self.quantized_dtype, + self.float_dtype, + ) + k = torch.ops.quantized_decomposed.dequantize_per_token( + k_quantized, + k_scale, + k_zero_point, + torch.iinfo(self.quantized_dtype).min, + torch.iinfo(self.quantized_dtype).max, + self.quantized_dtype, + self.float_dtype, + ) + v = torch.ops.quantized_decomposed.dequantize_per_token( + v_quantized, + v_scale, + v_zero_point, + torch.iinfo(self.quantized_dtype).min, + torch.iinfo(self.quantized_dtype).max, + self.quantized_dtype, + self.float_dtype, + ) + + num_heads_q = q.size(1) + num_heads_kv = k.size(1) + seq_len = q.size(2) + k = torch.narrow(k, 2, 0, start_pos + seq_len) + v = torch.narrow(v, 2, 0, start_pos + seq_len) + if num_heads_q != num_heads_kv: + assert ( + num_heads_q % num_heads_kv == 0 + ), f"{num_heads_q} not divisible by {num_heads_kv}" + n_reps = num_heads_q // num_heads_kv + if n_reps > 1: + k = k.repeat_interleave(n_reps, dim=1) + v = v.repeat_interleave(n_reps, dim=1) + out = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + return out + + def _int_matmul( + self, quantized_q, quantized_k, q_zero_points, q_scale, k_zero_point, k_scale + ): + row_sum_q = torch.sum(quantized_q, dim=-1, keepdim=True) + row_sum_k = torch.sum(quantized_k, dim=-1, keepdim=True) + q_at_k = torch.matmul(quantized_q, quantized_k.transpose(-2, -1)) + row_sum_q_scaled = row_sum_q * k_zero_point.squeeze(-1).unsqueeze(0) + row_sum_k_scaled = q_zero_points * row_sum_k.squeeze(-1).unsqueeze(0) + zero_points_product = ( + quantized_q.size(-1) * q_zero_points * k_zero_point.squeeze(-1).unsqueeze(0) + ) + res = q_at_k - row_sum_q_scaled - row_sum_k_scaled + zero_points_product + q_scale_mul_k_scale = q_scale * k_scale.squeeze(-1).unsqueeze(0) + res = res.to(torch.float32) * q_scale_mul_k_scale + return res + + def _quantized_sdpa_ref( + self, + quantized_q, + quantized_k, + quantized_v, + q_zero_points, + q_scale, + k_scale, + k_zero_point, + v_scale, + v_zero_point, + attn_mask, + ): + import math + + quantized_q = quantized_q.to(torch.int32) + quantized_k = quantized_k.to(torch.int32) + quantized_v = quantized_v.to(torch.int32) + batch_size = quantized_q.size(0) + num_heads_q = quantized_q.size(1) + num_heads_kv = quantized_k.size(1) + q_scale = q_scale.to(torch.float32) + k_scale = k_scale.to(torch.float32) + q_zero_points = q_zero_points.to(torch.int32) + k_zero_point = k_zero_point.to(torch.int32) + if num_heads_q != num_heads_kv: + assert ( + num_heads_q % num_heads_kv == 0 + ), f"{num_heads_q} not divisible by {num_heads_kv}" + n_reps = num_heads_q // num_heads_kv + if n_reps > 1: + quantized_k = quantized_k.repeat_interleave(n_reps, dim=1) + quantized_v = quantized_v.repeat_interleave(n_reps, dim=1) + res_b = [] + scale_factor = 1 / math.sqrt(quantized_k.size(-1)) + dequantized_v = torch.ops.quantized_decomposed.dequantize_per_token( + quantized_v, + v_scale, + v_zero_point, + torch.iinfo(torch.int8).min, + torch.iinfo(torch.int8).max, + torch.int8, + torch.float32, + ) + for b in range(batch_size): + res_h = [] + for h in range(num_heads_q): + q_at_k = self._int_matmul( + quantized_q[b][h], + quantized_k[b][h], + q_zero_points[b][h], + q_scale[b][h], + k_zero_point[b][h], + k_scale[b][h], + ) + q_at_k = q_at_k * scale_factor + q_at_k += attn_mask + attn_weight = torch.softmax(q_at_k, dim=-1) + y = torch.matmul(attn_weight, dequantized_v[b][h]) + res_h.append(y) + res = torch.stack(res_h, dim=0) + res_b.append(res.unsqueeze(0)) + res = torch.cat(res_b, dim=0) + return res + + def _test_sdpa_common( + self, + n_heads_kv, + n_heads_q, + head_dim, + max_seq_len, + start_pos, + seq_len, + scale_tensors=False, + atol=1e-5, + is_seq_at_dim_2=True, + ): + # Range arbitrarily chosen to reproduce a numerical error on x86 in some of the long context tests + tensor_scale_max = 15 + tensor_scale_min = -15 + self.n_heads_kv = n_heads_kv + self.n_heads_q = n_heads_q + self.head_dim = head_dim + self.max_seq_len = max_seq_len + seq_dim = 2 + self.q_shape = (self.n_batch, self.n_heads_q, seq_len, self.head_dim) + self.kv_shape = (self.n_batch, self.n_heads_q, self.max_seq_len, self.head_dim) + if not is_seq_at_dim_2: + seq_dim = 1 + self.q_shape = (self.n_batch, seq_len, self.n_heads_q, self.head_dim) + self.kv_shape = ( + self.n_batch, + self.max_seq_len, + self.n_heads_kv, + self.head_dim, + ) + + q = self._scale_tensor( + torch.rand(self.q_shape), + tensor_scale_max, + tensor_scale_min, + scale_tensors, + ) + self.setup_caches_and_mask(tensor_scale_max, tensor_scale_min, scale_tensors) + k = self.k + v = self.v + + quantized_dtype = self.quantized_dtype + q_scale, q_zero_point = ( + torch.ops.quantized_decomposed.choose_qparams_per_token_asymmetric.default( + q, quantized_dtype + ) + ) + k_scale, k_zero_point = ( + torch.ops.quantized_decomposed.choose_qparams_per_token_asymmetric.default( + k, quantized_dtype + ) + ) + v_scale, v_zero_point = ( + torch.ops.quantized_decomposed.choose_qparams_per_token_asymmetric.default( + v, quantized_dtype + ) + ) + + q_quantized = torch.ops.quantized_decomposed.quantize_per_token( + q, + q_scale, + q_zero_point, + torch.iinfo(quantized_dtype).min, + torch.iinfo(quantized_dtype).max, + quantized_dtype, + ) + k_quantized = torch.ops.quantized_decomposed.quantize_per_token( + k, + k_scale, + k_zero_point, + torch.iinfo(quantized_dtype).min, + torch.iinfo(quantized_dtype).max, + quantized_dtype, + ) + v_quantized = torch.ops.quantized_decomposed.quantize_per_token( + v, + v_scale, + v_zero_point, + torch.iinfo(quantized_dtype).min, + torch.iinfo(quantized_dtype).max, + quantized_dtype, + ) + + start_pos = 0 + seq_len = q.size(seq_dim) + attn_mask = self.mask[start_pos : start_pos + seq_len, :] + attn_mask = attn_mask[:, : start_pos + seq_len] + + # quantized_sdpa_ref_output = self._quantized_sdpa_ref(q_quantized, k_quantized, v_quantized, q_zero_point, q_scale, k_scale, k_zero_point, v_scale, v_zero_point, attn_mask) + + from torch.nn.attention import SDPBackend + + with torch.nn.attention.sdpa_kernel( + [SDPBackend.FLASH_ATTENTION] + ), torch.no_grad(): + ref_output = self._sdpa_ref( + q_quantized, + k_quantized, + v_quantized, + start_pos, + q_zero_point, + q_scale, + k_zero_point, + k_scale, + v_zero_point, + v_scale, + attn_mask, + ) + + q_zero_point_int8 = q_zero_point.to(dtype=torch.int8) + k_zero_point_int8 = k_zero_point.to(dtype=torch.int8) + v_zero_point_int8 = v_zero_point.to(dtype=torch.int8) + q_scale_fp32 = q_scale.to(dtype=torch.float32) + k_scale_fp32 = k_scale.to(dtype=torch.float32) + v_scale_fp32 = v_scale.to(dtype=torch.float32) + + op_output = torch.ops.llama.custom_quantized_sdpa( + q_quantized, + k_quantized, + v_quantized, + start_pos, + None, + 0, + True, + None, + q_zero_point_int8, + q_scale_fp32, + k_zero_point_int8, + k_scale_fp32, + v_zero_point_int8, + v_scale_fp32, + ) + self.assertTrue(torch.allclose(ref_output, op_output, atol=atol)) + # Following line crashes due to some weird issues in mkldnn with crash in mkl_sgemm with `wild jump` + # self.assertTrue(torch.allclose(ref_output, quantized_sdpa_ref_output, atol=1e-3)) + + start_pos = seq_len + seq_len = q.size(seq_dim) + attn_mask = self.mask[start_pos : start_pos + seq_len, :] + attn_mask = attn_mask[:, : start_pos + seq_len] + with torch.nn.attention.sdpa_kernel( + [SDPBackend.FLASH_ATTENTION] + ), torch.no_grad(): + ref_output = self._sdpa_ref( + q_quantized, + k_quantized, + v_quantized, + start_pos, + q_zero_point, + q_scale, + k_zero_point, + k_scale, + v_zero_point, + v_scale, + attn_mask, + ) + op_output = torch.ops.llama.custom_quantized_sdpa( + q_quantized, + k_quantized, + v_quantized, + start_pos, + None, + 0, + True, + None, + q_zero_point_int8, + q_scale_fp32, + k_zero_point_int8, + k_scale_fp32, + v_zero_point_int8, + v_scale_fp32, + ) + self.assertTrue(torch.allclose(ref_output, op_output, atol=atol)) + + def test_sdpa_with_custom_quantized(self): + n_heads_kv = 8 + n_heads_q = 8 + head_dim = 128 + max_seq_len = 2048 + seq_len = 24 + start_pos = 0 + self._test_sdpa_common( + n_heads_kv, + n_heads_q, + head_dim, + max_seq_len, + start_pos, + seq_len, + True, + atol=1e-4, + ) + + def test_sdpa_with_custom_quantized_seq_len_1(self): + n_heads_kv = 4 + n_heads_q = 4 + head_dim = 4 + max_seq_len = 8 + seq_len = 1 + start_pos = 0 + self._test_sdpa_common( + n_heads_kv, n_heads_q, head_dim, max_seq_len, start_pos, seq_len + ) + + def test_sdpa_with_custom_quantized_seq_len_small(self): + n_heads_kv = 4 + n_heads_q = 4 + head_dim = 4 + max_seq_len = 8 + seq_len = 4 + start_pos = 0 + self._test_sdpa_common( + n_heads_kv, n_heads_q, head_dim, max_seq_len, start_pos, seq_len + ) + + def test_sdpa_with_custom_quantized_seq_len_llava_example(self): + n_heads_kv = 32 + n_heads_q = 32 + head_dim = 128 + max_seq_len = 2048 + seq_len = 634 + start_pos = 0 + self._test_sdpa_common( + n_heads_kv, n_heads_q, head_dim, max_seq_len, start_pos, seq_len + ) + + def test_sdpa_with_custom_quantized_seq_len_130_gqa(self): + n_heads_kv = 8 + n_heads_q = 32 + head_dim = 128 + max_seq_len = 2048 + seq_len = 130 + start_pos = 0 + # For some reason when scaling tensors, the test fails with smaller atol + self._test_sdpa_common( + n_heads_kv, + n_heads_q, + head_dim, + max_seq_len, + start_pos, + seq_len, + True, + atol=1e-3, + ) + + def test_sdpa_with_custom_quantized_seq_len_llava_example_gqa(self): + n_heads_kv = 16 + n_heads_q = 32 + head_dim = 128 + max_seq_len = 2048 + seq_len = 634 + start_pos = 0 + self._test_sdpa_common( + n_heads_kv, n_heads_q, head_dim, max_seq_len, start_pos, seq_len + ) + + def test_sdpa_with_cache_mqa(self): + n_heads_kv = 1 + n_heads_q = 8 + head_dim = 128 + max_seq_len = 2048 + seq_len = 24 + start_pos = 0 + self._test_sdpa_common( + n_heads_kv, n_heads_q, head_dim, max_seq_len, start_pos, seq_len + ) diff --git a/extension/llm/custom_ops/test_sdpa_with_kv_cache.py b/extension/llm/custom_ops/test_sdpa_with_kv_cache.py index a1f054a153e..41497b17a66 100644 --- a/extension/llm/custom_ops/test_sdpa_with_kv_cache.py +++ b/extension/llm/custom_ops/test_sdpa_with_kv_cache.py @@ -489,11 +489,11 @@ def _test_sdpa_common( class SDPATestForLargeSeqLength(SDPATestCommon): def test_sdpa_with_cache_seq_len_130(self): - n_heads_kv = 32 - n_heads_q = 32 + n_heads_kv = 8 + n_heads_q = 8 head_dim = 128 max_seq_len = 2048 - seq_len = 130 + seq_len = 24 self._test_sdpa_common( n_heads_kv, n_heads_q, head_dim, max_seq_len, seq_len, True ) From d2af44bf6efa3c030d347564fd6c3efa9700aea0 Mon Sep 17 00:00:00 2001 From: Max Ren <40742183+mcr229@users.noreply.github.com> Date: Mon, 7 Apr 2025 14:23:32 -0700 Subject: [PATCH 028/244] per_channel_group can't be dynamic Differential Revision: D72488540 Pull Request resolved: https://github.com/pytorch/executorch/pull/9934 --- backends/xnnpack/utils/quant_utils.py | 26 +++++++++++++++++++++++--- 1 file changed, 23 insertions(+), 3 deletions(-) diff --git a/backends/xnnpack/utils/quant_utils.py b/backends/xnnpack/utils/quant_utils.py index db1914e3910..cb91b78c123 100644 --- a/backends/xnnpack/utils/quant_utils.py +++ b/backends/xnnpack/utils/quant_utils.py @@ -12,6 +12,7 @@ from executorch.exir.backend.canonical_partitioners.config_partitioner import ( format_target_name, ) +from torch.fx.experimental.symbolic_shapes import free_symbols, has_free_symbols _Q_OPS = { "quantize_per_tensor.tensor", @@ -126,8 +127,8 @@ def is_affine_qdq(node: torch.fx.Node) -> bool: def _get_block_size_input_scale(node: torch.fx.Node): assert is_affine_qdq(node) block_size = node.args[1] - input_val = node.all_input_nodes[0].meta["val"] - scale_val = node.all_input_nodes[1].meta["val"] + input_val = cast(torch.fx.Node, node.args[0]).meta["val"] + scale_val = cast(torch.fx.Node, node.args[2]).meta["val"] return block_size, input_val, scale_val @@ -145,7 +146,21 @@ def is_per_token(node: torch.fx.Node): flag &= block_size[i] == 1 scale_numel_expected *= input_val.shape[i] - flag &= block_size[-1] == input_val.shape[-1] + ic_block_size = block_size[-1] + if isinstance(ic_block_size, torch.fx.Node): + ic_block_size = ic_block_size.meta["val"] + assert free_symbols( + ic_block_size + ), f"block_size: {block_size} given, but {block_size[-1]} is not a dynamic symint" + + ic_dim = input_val.shape[-1] + if isinstance(ic_dim, torch.fx.Node): + ic_dim = ic_dim.meta["val"] + assert free_symbols( + ic_dim + ), f"input_shape: {input_val.shape} given, but {input_val.shape[-1]} is not a dynamic symint" + + flag &= ic_dim == ic_block_size flag &= scale_val.numel() == scale_numel_expected return flag @@ -160,6 +175,11 @@ def is_per_channel_group(node: torch.fx.Node): return True elif is_affine_qdq(node): block_size, input_val, scale_val = _get_block_size_input_scale(node) + # per channel group is only valid on static weights + # so scales and weights can't have dynamic shape + if has_free_symbols(input_val.shape) or has_free_symbols(scale_val.shape): + return False + flag = True flag &= len(block_size) == 2 flag &= block_size[0] == 1 From 3cb3316c1b390400caed3b5b4d718c052ee26e2e Mon Sep 17 00:00:00 2001 From: Val Tarasyuk Date: Mon, 7 Apr 2025 14:40:23 -0700 Subject: [PATCH 029/244] Update Executorch ops registration for rms_norm (#9920) Summary: Allows use of `aten::rms_norm` in place of `cadence::rms_norm` custom op, which is no longer needed. Differential Revision: D72485973 --- backends/cadence/aot/ops_registrations.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/backends/cadence/aot/ops_registrations.py b/backends/cadence/aot/ops_registrations.py index dec6feb1b8d..368f499aa85 100644 --- a/backends/cadence/aot/ops_registrations.py +++ b/backends/cadence/aot/ops_registrations.py @@ -293,6 +293,15 @@ "attention_mask.out(Tensor input, Tensor start, Tensor stop, *, Tensor(a!) out) -> Tensor(a!)" ) +# Custom ops in aten namespace. RMSNorm is usually decomposed, so having +# an out-variant is non-standard + +lib_aten = Library("aten", "FRAGMENT") + +lib_aten.define( + "rms_norm.out(Tensor input, SymInt[] normalized_shape, Tensor? weight=None, float? eps=None, *, Tensor(a!) out) -> Tensor(a!)" +) + @register_fake("cadence::quantize_per_tensor") def quantize_per_tensor_meta( @@ -619,15 +628,6 @@ def linalg_vector_norm_meta( return X.new_empty([], dtype=X.dtype) -@register_fake("cadence::rms_norm") -def rms_norm_meta( - X: torch.Tensor, - eps: float, - weight: torch.Tensor, -) -> torch.Tensor: - return X.new_empty(X.shape, dtype=X.dtype) - - @register_fake("cadence::requantize") def requantize_meta( input: torch.Tensor, From 15dc927c8fb185906a3ef3025a376af63132fba9 Mon Sep 17 00:00:00 2001 From: Jack-Khuu Date: Mon, 7 Apr 2025 15:40:25 -0700 Subject: [PATCH 030/244] [Easy] Fix numbering typo in Llama README docs (#9936) ### Summary Numbering scheme jumps from 4-> 6 ![image](https://github.com/user-attachments/assets/3d1876f2-b72f-400d-85bf-16679c21077b) ![image](https://github.com/user-attachments/assets/8fca230b-c989-484d-9411-40418c55721d) ### Test plan Markdown cc @mergennachin @byjlw --- examples/models/llama/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/models/llama/README.md b/examples/models/llama/README.md index 0bef45ea3ae..39e4c79873b 100644 --- a/examples/models/llama/README.md +++ b/examples/models/llama/README.md @@ -371,7 +371,7 @@ adb push cmake-out-android/examples/models/llama/llama_main /data/local/tmp/llam ``` adb shell "cd /data/local/tmp/llama && ./llama_main --model_path --tokenizer_path --prompt \"What is the capital of France?\" --seq_len 120" --warmup=1 ``` -## Step 6: Build Mobile apps +## Step 5: Build Mobile apps ### iOS From 7238cda4a1c2a59cd3688a60c5895cff6e60f8c6 Mon Sep 17 00:00:00 2001 From: Sicheng Stephen Jia Date: Mon, 7 Apr 2025 18:54:20 -0400 Subject: [PATCH 031/244] [ET-VK] Improve packing format for int4 linear operator + misc improvements (#9949) ## Context Improve performance of the quantized int4 linear shader by packing the scales and zeros tensor, as well as the weight tensor in a more optimal way. See the comments in the `pack_int4_linear_weight_transposed_interleave` shader for more details about how the new packing works. ## Changes * Split int8 quantized linear and int4 quantized linear into separate C++ files for better code organization * Introduce packing shader for int4 weights * Update int4 linear shader to account for packed weights ## Impact This change massively improves the performance of the weight int4 quantized linear operator. With this change, running LLaMa 3.2 1B can now achieve 10 tok/s, from 0.9 tok/s on an Adreno 740. This is a 10x improvement! With this change: ``` /home/ssjia/scratch/bin/app_bin: 1 file pushed, 0 skipped. 332.3 MB/s (74692800 bytes in 0.214s) I 00:00:00.003353 executorch:cpuinfo_utils.cpp:62] Reading file /sys/devices/soc0/image_version I 00:00:00.003533 executorch:cpuinfo_utils.cpp:78] Failed to open midr file /sys/devices/soc0/image_version I 00:00:00.003563 executorch:cpuinfo_utils.cpp:91] Reading file /sys/devices/system/cpu/cpu0/regs/identification/midr_el1 I 00:00:00.003685 executorch:cpuinfo_utils.cpp:91] Reading file /sys/devices/system/cpu/cpu1/regs/identification/midr_el1 I 00:00:00.003747 executorch:cpuinfo_utils.cpp:91] Reading file /sys/devices/system/cpu/cpu2/regs/identification/midr_el1 I 00:00:00.003799 executorch:cpuinfo_utils.cpp:91] Reading file /sys/devices/system/cpu/cpu3/regs/identification/midr_el1 I 00:00:00.003852 executorch:cpuinfo_utils.cpp:91] Reading file /sys/devices/system/cpu/cpu4/regs/identification/midr_el1 I 00:00:00.003902 executorch:cpuinfo_utils.cpp:91] Reading file /sys/devices/system/cpu/cpu5/regs/identification/midr_el1 I 00:00:00.003976 executorch:main.cpp:69] Resetting threadpool with num threads = 6 I 00:00:00.004289 executorch:runner.cpp:68] Creating LLaMa runner: model_path=/data/local/tmp/llama3-1b/vk/llama3.pte, tokenizer_path=/data/local/tmp/tokenizer.model I 00:00:04.841690 executorch:runner.cpp:101] Reading metadata from model I 00:00:04.841808 executorch:runner.cpp:126] Metadata: get_vocab_size = 128256 I 00:00:04.841830 executorch:runner.cpp:126] Metadata: get_bos_id = 128000 I 00:00:04.841851 executorch:runner.cpp:126] Metadata: use_sdpa_with_kv_cache = 1 I 00:00:04.841874 executorch:runner.cpp:126] Metadata: use_kv_cache = 1 I 00:00:04.841893 executorch:runner.cpp:126] Metadata: get_max_context_len = 128 I 00:00:04.841909 executorch:runner.cpp:126] Metadata: get_max_seq_len = 128 I 00:00:04.841927 executorch:runner.cpp:126] Metadata: enable_dynamic_shape = 0 I 00:00:04.841945 executorch:runner.cpp:133] eos_id = 128009 I 00:00:04.841951 executorch:runner.cpp:133] eos_id = 128001 I 00:00:04.841963 executorch:runner.cpp:188] RSS after loading model: 2229.828125 MiB (0 if unsupported) <|begin_of_text|><|start_header_id|>system<|end_header_id|>Tell me a short story.<|eot_id|><|start_header_id|>assistant<|end_header_id|> I 00:00:06.239633 executorch:runner.cpp:258] RSS after prompt prefill: 2229.828125 MiB (0 if unsupported) Here's a short story for you: **The Library of Lost Memories** In a small, dusty town nestled between two great rivers, there was a library that held the secrets of the past. It was a place where memories were stored, not retrieved, and the librarians were the guardians of the past. The library was called the Library of Lost Memories, and it was said that anyone who entered its doors would be given a glimpse into the memories of those who had come before. The librarians were wise and kind, and they would only allow those who wereI 00:00:17.699086 executorch:runner.cpp:272] RSS after finishing text generation: 2229.828125 MiB (0 if unsupported) I 00:00:17.699155 executorch:stats.h:108] Prompt Tokens: 14 Generated Tokens: 113 I 00:00:17.699161 executorch:stats.h:114] Model Load Time: 4.837000 (seconds) I 00:00:17.699165 executorch:stats.h:124] Total inference time: 12.857000 (seconds) Rate: 8.788987 (tokens/second) I 00:00:17.699168 executorch:stats.h:132] Prompt evaluation: 1.398000 (seconds) Rate: 10.014306 (tokens/second) I 00:00:17.699171 executorch:stats.h:143] Generated 113 tokens: 11.459000 (seconds) Rate: 9.861244 (tokens/second) I 00:00:17.699174 executorch:stats.h:151] Time to first generated token: 1.398000 (seconds) I 00:00:17.699177 executorch:stats.h:158] Sampling time over 127 tokens: 549246500.843000 (seconds) ``` Before this change: ``` /home/ssjia/scratch/bin/app_bin: 1 file pushed, 0 skipped. 302.0 MB/s (74637464 bytes in 0.236s) I 00:00:00.003050 executorch:cpuinfo_utils.cpp:62] Reading file /sys/devices/soc0/image_version I 00:00:00.003200 executorch:cpuinfo_utils.cpp:78] Failed to open midr file /sys/devices/soc0/image_version I 00:00:00.003226 executorch:cpuinfo_utils.cpp:91] Reading file /sys/devices/system/cpu/cpu0/regs/identification/midr_el1 I 00:00:00.003337 executorch:cpuinfo_utils.cpp:91] Reading file /sys/devices/system/cpu/cpu1/regs/identification/midr_el1 I 00:00:00.003396 executorch:cpuinfo_utils.cpp:91] Reading file /sys/devices/system/cpu/cpu2/regs/identification/midr_el1 I 00:00:00.003449 executorch:cpuinfo_utils.cpp:91] Reading file /sys/devices/system/cpu/cpu3/regs/identification/midr_el1 I 00:00:00.003502 executorch:cpuinfo_utils.cpp:91] Reading file /sys/devices/system/cpu/cpu4/regs/identification/midr_el1 I 00:00:00.003553 executorch:cpuinfo_utils.cpp:91] Reading file /sys/devices/system/cpu/cpu5/regs/identification/midr_el1 I 00:00:00.003629 executorch:main.cpp:69] Resetting threadpool with num threads = 6 I 00:00:00.004075 executorch:runner.cpp:68] Creating LLaMa runner: model_path=/data/local/tmp/llama3-1b/vk/llama3.pte, tokenizer_path=/data/local/tmp/tokenizer.model I 00:00:05.417531 executorch:runner.cpp:101] Reading metadata from model I 00:00:05.417647 executorch:runner.cpp:126] Metadata: get_vocab_size = 128256 I 00:00:05.417669 executorch:runner.cpp:126] Metadata: get_bos_id = 128000 I 00:00:05.417698 executorch:runner.cpp:126] Metadata: use_sdpa_with_kv_cache = 1 I 00:00:05.417716 executorch:runner.cpp:126] Metadata: use_kv_cache = 1 I 00:00:05.417735 executorch:runner.cpp:126] Metadata: get_max_context_len = 128 I 00:00:05.417751 executorch:runner.cpp:126] Metadata: get_max_seq_len = 128 I 00:00:05.417768 executorch:runner.cpp:126] Metadata: enable_dynamic_shape = 0 I 00:00:05.417787 executorch:runner.cpp:133] eos_id = 128009 I 00:00:05.417793 executorch:runner.cpp:133] eos_id = 128001 I 00:00:05.417808 executorch:runner.cpp:188] RSS after loading model: 2230.812500 MiB (0 if unsupported) <|begin_of_text|><|start_header_id|>system<|end_header_id|>Tell me a short story.<|eot_id|><|start_header_id|>assistant<|end_header_id|> I 00:00:19.689616 executorch:runner.cpp:258] RSS after prompt prefill: 2230.812500 MiB (0 if unsupported) Here's a short story for you: **The Library of Lost Memories** In a small, dusty town nestled between two great rivers, there was a library that held the secrets of the past. It was a place where memories were stored, not retrieved, and the librarians were the guardians of the past. The library was called the Library of Lost Memories, and it was said that anyone who entered its doors would be given a glimpse into the memories of those who had come before. The librarians were wise and kind, and they would only allow those who wereI 00:02:15.269693 executorch:runner.cpp:272] RSS after finishing text generation: 2230.812500 MiB (0 if unsupported) I 00:02:15.269810 executorch:stats.h:108] Prompt Tokens: 14 Generated Tokens: 113 I 00:02:15.269825 executorch:stats.h:114] Model Load Time: 5.414000 (seconds) I 00:02:15.269832 executorch:stats.h:124] Total inference time: 129.852000 (seconds) Rate: 0.870221 (tokens/second) I 00:02:15.269837 executorch:stats.h:132] Prompt evaluation: 14.271000 (seconds) Rate: 0.981010 (tokens/second) I 00:02:15.269841 executorch:stats.h:143] Generated 113 tokens: 115.581000 (seconds) Rate: 0.977669 (tokens/second) I 00:02:15.269844 executorch:stats.h:151] Time to first generated token: 14.271000 (seconds) I 00:02:15.269847 executorch:stats.h:158] Sampling time over 127 tokens: 549711269.115000 (seconds) PyTorchObserver {"prompt_tokens":14,"generated_tokens":113,"model_load_start_ms":1743712527974,"model_load_end_ms":1743712533388,"inference_start_ms":1743712533388,"inference_end_ms":1743712663240,"prompt_eval_end_ms":1743712547659,"first_token_ms":1743712547659,"aggregate_sampling_time_ms":549711269115,"SCALING_FACTOR_UNITS_PER_SECOND":1000} ``` Differential Revision: [D72412950](https://our.internmc.facebook.com/intern/diff/D72412950/) --- .../vulkan/runtime/api/containers/Tensor.cpp | 4 +- ..._linear_weight_transposed_interleaved.glsl | 136 +++++++++++++ ..._linear_weight_transposed_interleaved.yaml | 13 ++ .../runtime/graph/ops/glsl/q_4w_linear.glsl | 134 ++++++------ .../runtime/graph/ops/glsl/q_4w_linear.yaml | 19 +- .../ops/impl/QuantizedLinearGroupwiseInt4.cpp | 191 ++++++++++++++++++ ...izedLinear.cpp => QuantizedLinearInt8.cpp} | 149 -------------- backends/vulkan/runtime/vk_api/Adapter.h | 7 + .../test/op_tests/linear_weight_int4_test.cpp | 56 ++++- 9 files changed, 479 insertions(+), 230 deletions(-) create mode 100644 backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.glsl create mode 100644 backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml create mode 100644 backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp rename backends/vulkan/runtime/graph/ops/impl/{QuantizedLinear.cpp => QuantizedLinearInt8.cpp} (64%) diff --git a/backends/vulkan/runtime/api/containers/Tensor.cpp b/backends/vulkan/runtime/api/containers/Tensor.cpp index 4cbd1290401..0712062a37e 100644 --- a/backends/vulkan/runtime/api/containers/Tensor.cpp +++ b/backends/vulkan/runtime/api/containers/Tensor.cpp @@ -497,9 +497,7 @@ vTensor::vTensor( VK_CHECK_COND( dim_order_is_valid(dim_order_), "computed dim order is invalid"); - if (storage_type != utils::kBuffer) { - set_logical_limits(storage_.image_extents_); - } + set_logical_limits(storage_.image_extents_); } // NOLINTNEXTLINE diff --git a/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.glsl b/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.glsl new file mode 100644 index 00000000000..c7fcdcc775a --- /dev/null +++ b/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.glsl @@ -0,0 +1,136 @@ +/* + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + * + * This source code is licensed under the BSD-style license found in the + * LICENSE file in the root directory of this source tree. + */ + +#version 450 core + +#define PRECISION ${PRECISION} + +${define_required_extensions("uint8")} +${define_required_extensions("int8")} + +layout(std430) buffer; + +${layout_declare_tensor(B, "w", "t_qmat2", "uint8", STORAGE, is_scalar_array=False)} +${layout_declare_tensor(B, "r", "nchw_4x2", "uint8", "buffer")} + +layout(push_constant) uniform restrict Block { + ivec4 qmat2_sizes; +}; + +layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in; + +uint8_t get_first(const uint8_t packed) { + return uint8_t((packed & 0xF0) >> 4); +} + +uint8_t get_second(const uint8_t packed) { + return uint8_t(packed & 0x0F); +} + +uint8_t combine(const uint8_t first, const uint8_t second) { + return uint8_t(first << 4 | second); +} + +/* + * This shader packs the weight tensor into a texture. + * + * The original tensor has a (W, H) shape of (K / 2, N) and each scalar element + * is a uint8_t, which contains 2 packed 4 bit uint values. + * + * The transform performed by this shader is to first transpose the tensor, so + * the shape of the packed tensor becomes (N / 2, K). Then, the 4 bit integers + * are re-packed in groups of 8. For each 4 uint8_t values, the "left" 4-bits + * of each value contain the 0, 1, 2, 3 4-bit values, and the "right" 4-bits of + * each value contain the 4, 5, 6, 7 4-bit values. + * + * As a concrete example, consider the following weight tensor. The | demarks + * the packing boundary, so 1| 2 represents a single uint8_t value with 1 in the + * leftmost 4 bits and 2 in the rightmost 4 bits. + * + * 1| 2, 3| 4, 5| 6, 7| 8, + * 9|10, 11|12, 13|14, 15|16, + * 17|18, 19|20, 21|22, 23|24, + * 25|26, 27|28, 29|30, 31|32, + * 33|34, 35|36, 37|38, 39|40, + * 41|42, 43|44, 45|46, 47|48, + * 49|50, 51|52, 53|54, 55|56, + * 57|58, 59|60, 61|62, 63|64, + * + * After packing, the packed tensor would contain + * + * 1|33, 9|41, 17|49, 25|57, + * 2|34, 10|42, 18|50, 26|58, + * 3|35, 11|43, 19|51, 27|59, + * 4|36, 12|44, 20|52, 28|60, + * 5|37, 13|45, 21|53, 29|61, + * 6|38, 14|46, 22|54, 30|62, + * 7|39, 15|47, 23|55, 31|63, + * 8|40, 16|48, 24|56, 32|64, + * + * The purpose of interleaving is to make it easier to extract the unpacked + * values in order using the u8vec4 vectorized type. With the packing in place, + * The 4-bit values can be extracted via + * + * u8vec4 packed; + * u8vec4 vals_0123 = (packed & 0xF0) >> 4; + * u8vec4 vals_4567 = (packed | 0x0F); + */ +void main() { + // Each thread writes 2 output texels along the height axis + ivec2 packed_pos = ivec2( + gl_GlobalInvocationID.x, + gl_GlobalInvocationID.y << 1); + + // The packed tensor is width packed + if ((packed_pos.x << 2) >= qmat2_sizes.x || packed_pos.y >= qmat2_sizes.y) { + return; + } + + int out_col = packed_pos.x << 3; + int out_row = packed_pos.y; + + int in_col = out_row; + int in_int8_col = in_col >> 1; + int in_row = out_col; + + int in_numrows = qmat2_sizes.x << 1; + int in_numcols = qmat2_sizes.y; + int in_num_int8_cols = qmat2_sizes.y >> 1; + + uint8_t in_vals[8][2]; + for (int r = 0; r < 8; ++r) { + if (in_row + r < in_numrows) { + uint8_t in_val_packed = nchw_4x2[(in_row + r) * in_num_int8_cols + in_int8_col]; + in_vals[r][0] = get_first(in_val_packed); + in_vals[r][1] = get_second(in_val_packed); + } else { + in_vals[r][0] = uint8_t(254); + in_vals[r][1] = uint8_t(254); + } + } + + u8vec4 out_tex_1 = u8vec4( + combine(in_vals[0][0], in_vals[4][0]), + combine(in_vals[1][0], in_vals[5][0]), + combine(in_vals[2][0], in_vals[6][0]), + combine(in_vals[3][0], in_vals[7][0])); + + u8vec4 out_tex_2 = u8vec4( + combine(in_vals[0][1], in_vals[4][1]), + combine(in_vals[1][1], in_vals[5][1]), + combine(in_vals[2][1], in_vals[6][1]), + combine(in_vals[3][1], in_vals[7][1])); + + $if STORAGE == "buffer": + int stride = qmat2_sizes.x >> 2; + t_qmat2[packed_pos.y * stride + packed_pos.x] = out_tex_1; + t_qmat2[(packed_pos.y + 1) * stride + packed_pos.x] = out_tex_2; + $else: + imageStore(t_qmat2, ivec3(packed_pos.xy, 0), out_tex_1); + imageStore(t_qmat2, ivec3(packed_pos.x, packed_pos.y + 1, 0), out_tex_2); +} diff --git a/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml b/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml new file mode 100644 index 00000000000..168b18fffe4 --- /dev/null +++ b/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml @@ -0,0 +1,13 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +pack_int4_linear_weight_transposed_interleaved: + parameter_names_with_default_values: + STORAGE: texture3d + shader_variants: + - NAME: pack_int4_linear_weight_transposed_interleaved_texture3d + - NAME: pack_int4_linear_weight_transposed_interleaved_buffer + STORAGE: buffer diff --git a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl index b702a110a65..29f2934f957 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl +++ b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl @@ -8,34 +8,30 @@ #version 450 core -#include "indexing_utils.h" - #define PRECISION ${PRECISION} -#define FOUR 4 - -#define VEC4_T ${texel_load_type(DTYPE, STORAGE)} -#define FLOAT_T ${buffer_scalar_type(DTYPE)} +#define T ${buffer_scalar_type(DTYPE)} +#define VEC4_T ${buffer_gvec_type(DTYPE, 4)} -${define_active_storage_type(STORAGE)} - -${define_required_extensions([DTYPE, "uint8", "uint16"])} -#extension GL_EXT_control_flow_attributes : require +${define_required_extensions(DTYPE)} +${define_required_extensions("int8")} layout(std430) buffer; -${layout_declare_tensor(B, "w", "ret", DTYPE, STORAGE)} -${layout_declare_tensor(B, "r", "x", DTYPE, STORAGE)} -${layout_declare_tensor(B, "r", "weights", "uint8", "buffer")} -${layout_declare_tensor(B, "r", "qparams", DTYPE, STORAGE)} -${layout_declare_ubo(B, "ivec3", "ret_limits")} -${layout_declare_ubo(B, "ivec4", "x_sizes")} -${layout_declare_ubo(B, "ivec4", "weights_strides")} -${layout_declare_ubo(B, "ivec4", "qparams_strides")} +${layout_declare_tensor(B, "w", "t_out", DTYPE, OUT_STORAGE, is_scalar_array=False)} +${layout_declare_tensor(B, "r", "t_mat1", DTYPE, IN_STORAGE, is_scalar_array=False)} +${layout_declare_tensor(B, "r", "t_qmat2", "uint8", WEIGHT_STORAGE, is_scalar_array=False)} +${layout_declare_tensor(B, "r", "t_qparams", DTYPE, "texture3D")} + +layout(push_constant) uniform restrict Block { + ivec4 out_sizes; + ivec4 mat1_sizes; + ivec4 qmat2_sizes; +}; layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in; -layout(constant_id = 3) const int group_size = 1; +layout(constant_id = 3) const int group_size = 64; /* * This shader computes a linear operator between a floating point input matrix @@ -43,9 +39,11 @@ layout(constant_id = 3) const int group_size = 1; * * The (W, H, C) shape of each tensor is: * - x: (K, M) - * - weights: (K / 2, N) + * - weights: (N / 2, K) * - The weights tensor has a data type of `uint8`. Each element in the tensor * contains 2 4-bit values packed into a uint8. + * - See the pack_int4_linear_weight_transposed_interleave shader to see more + * details on how the weight tensor is stored. * - qparams: (2, N, number_of_groups) * - This tensor contains the scales and zeros quantization parameters for the * weights tensor. The weight tensor is quantized group-wise, which means @@ -57,56 +55,68 @@ layout(constant_id = 3) const int group_size = 1; * Note that this shader assumes that all tensors are width packed. */ void main() { - // output positions being calculated are (n, m), (n + 1, m), ... - // This means multiplying the m-th row of x with the n-th, (n+1)-th, ... rows - // of the weights tensor. - const u16vec3 ret_pos = u16vec3(gl_GlobalInvocationID); - if (any(greaterThanEqual(ret_pos, ret_limits))) { + const uint out_row = gl_GlobalInvocationID.y; + // Each thread writes out 2 texels along the width axis, equivalent to 8 + // scalar elements. Therefore multiply the thread_idx.x by 8. + const uint out_col = gl_GlobalInvocationID.x << 3; + // Similar reasoning to the above, each thread works on 2 texels along the + // width axis so multiply thread_idx.x by 2. + const int out_col_texel_idx = int(gl_GlobalInvocationID.x) << 1; + + if (out_col >= out_sizes.x || out_row >= out_sizes.y) { return; } - // Since ret is width packed, need to multiply by 4 - const uint16_t n = uint16_t(ret_pos.x * 4); + const int num_blocks = mat1_sizes.x / group_size; - // K is guaranteed to be a multiple of group size - const uint16_t num_blocks = uint16_t(x_sizes.x / group_size); + VEC4_T sums[2]; - uint16_t k_texel_i = uint16_t(0); - vec4 sums = vec4(0.0); - for (uint16_t block_idx = uint16_t(0); block_idx < num_blocks; block_idx++) { - vec4 scales; - vec4 zeros; + sums[0] = VEC4_T(0); + sums[1] = VEC4_T(0); - [[unroll]] for (int comp = 0; comp < 4; comp++) { - const vec4 scale_and_zero = load_texel( - qparams, u16vec3(0, n + comp, block_idx)); - scales[comp] = scale_and_zero.x; - zeros[comp] = scale_and_zero.y; - } + VEC4_T scales[2]; + VEC4_T zeros[2]; + + $if WEIGHT_STORAGE == "buffer": + const int qmat2_stride = qmat2_sizes.x >> 2; + + for (int block_idx = 0; block_idx < num_blocks; ++block_idx) { + scales[0] = texelFetch(t_qparams, ivec3(out_col_texel_idx, 0, block_idx), 0); + zeros[0] = texelFetch(t_qparams, ivec3(out_col_texel_idx, 1, block_idx), 0); - for (uint16_t i = uint16_t(0); i < group_size; i += uint16_t(4), k_texel_i++) { - const VEC4_T x_texel = load_texel( - x, u16vec3(k_texel_i, ret_pos.y, ret_pos.z)); - - [[unroll]] for (int comp = 0; comp < 4; comp++) { - const int weights_bufi = (n + comp) * weights_strides.y + (k_texel_i * 2); - // Need to read 4 unpacked values, which corresponds to 2 packed values - const uint8_t weights_val_1 = weights[weights_bufi]; - const uint8_t weights_val_2 = weights[weights_bufi + 1]; - - const u8vec4 weights_texel = u8vec4( - (weights_val_1 & 0xF0) >> 4, - weights_val_1 & 0x0F, - (weights_val_2 & 0xF0) >> 4, - weights_val_2 & 0x0F); - - // Note that the unpacked 4-bit values are unsigned, therefore they must - // first be "centered" around 0 by subtracting 8 before applying the - // scale and zero point. - sums[comp] += dot( - x_texel, (vec4(weights_texel) - 8.0) * scales[comp] + zeros[comp]); + scales[1] = texelFetch(t_qparams, ivec3(out_col_texel_idx + 1, 0, block_idx), 0); + zeros[1] = texelFetch(t_qparams, ivec3(out_col_texel_idx + 1, 1, block_idx), 0); + + for (int g_idx = 0; g_idx < group_size; g_idx += 4) { + const int k = block_idx * group_size + g_idx; + + $if IN_STORAGE == "buffer": + const VEC4_T mat1_tex = t_mat1[(out_row * mat1_sizes.x + k) >> 2]; + $else: + const VEC4_T mat1_tex = texelFetch(t_mat1, ivec3(k >> 2, out_row, 0), 0); + + for (int comp = 0; comp < 4; ++comp) { + $if WEIGHT_STORAGE == "buffer": + const u8vec4 packed_weight_tex = t_qmat2[(k + comp) * qmat2_stride + gl_GlobalInvocationID.x]; + $else: + const uvec4 packed_weight_tex = texelFetch( + t_qmat2, + ivec3(gl_GlobalInvocationID.x, k + comp, 0), + 0); + + const uvec4 weight_tex_1 = (packed_weight_tex & 0xF0) >> 4; + const uvec4 weight_tex_2 = packed_weight_tex & 0x0F; + + sums[0] += mat1_tex[comp] * ((vec4(weight_tex_1) - 8.0) * scales[0] + zeros[0]); + sums[1] += mat1_tex[comp] * ((vec4(weight_tex_2) - 8.0) * scales[1] + zeros[1]); } } } - write_texel(ret, ret_pos, sums); + + $if OUT_STORAGE == "buffer": + t_out[(out_row * out_sizes.x + out_col) >> 2] = sums[0]; + t_out[(out_row * out_sizes.x + out_col + 4) >> 2] = sums[1]; + $else: + imageStore(t_out, ivec3(out_col_texel_idx, out_row, 0), sums[0]); + imageStore(t_out, ivec3(out_col_texel_idx + 1, out_row, 0), sums[1]); } diff --git a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.yaml b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.yaml index 40d95d4a05f..fac9c25c220 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.yaml +++ b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.yaml @@ -7,10 +7,17 @@ q_4w_linear: parameter_names_with_default_values: DTYPE: float - STORAGE: texture3d - generate_variant_forall: - DTYPE: - - VALUE: float - - VALUE: half + OUT_STORAGE: texture3d + IN_STORAGE: texture3d + WEIGHT_STORAGE: texture3d shader_variants: - - NAME: q_4w_linear_texture3d + - NAME: q_4w_linear_texture3d_texture3d_texture3d_float + - NAME: q_4w_linear_texture3d_buffer_texture3d_float + IN_STORAGE: buffer + - NAME: q_4w_linear_buffer_buffer_texture3d_float + OUT_STORAGE: buffer + IN_STORAGE: buffer + - NAME: q_4w_linear_buffer_buffer_buffer_float + OUT_STORAGE: buffer + IN_STORAGE: buffer + WEIGHT_STORAGE: buffer diff --git a/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp b/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp new file mode 100644 index 00000000000..b795e574291 --- /dev/null +++ b/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp @@ -0,0 +1,191 @@ +/* + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + * + * This source code is licensed under the BSD-style license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include + +#include + +#include +#include + +namespace vkcompute { + +void check_q_4w_linear_args( + ComputeGraph& graph, + const ValueRef mat1, + const ValueRef mat2_data, + const ValueRef group_size, + const ValueRef scales_and_zeros, + const ValueRef out) { + VK_CHECK_COND(graph.int16_shader_types_enabled()); + VK_CHECK_COND(graph.int8_buffers_enabled()); + + VK_CHECK_COND(graph.val_is_tensor(mat1)); + VK_CHECK_COND(graph.val_is_tref(mat2_data)); + VK_CHECK_COND(graph.val_is_tref(scales_and_zeros)); + + VK_CHECK_COND(graph.dim_of(mat1) <= 3); + VK_CHECK_COND(graph.dim_of(mat2_data) == 2); + VK_CHECK_COND(graph.dim_of(scales_and_zeros) == 3); + + VK_CHECK_COND(graph.size_at(-3, mat1) == 1); + const int K = graph.size_at(-1, mat1); + VK_CHECK_COND(graph.size_at(-1, mat2_data) * 2 == K); + + const int group_size_val = graph.extract_scalar(group_size); + VK_CHECK_COND(K % group_size_val == 0); + // Due to the way weight packing works, group size needs to be a multiple of 8 + VK_CHECK_COND(group_size_val % 8 == 0); + + VK_CHECK_COND(graph.has_standard_axis_map(mat1)); + VK_CHECK_COND(graph.has_standard_axis_map(out)); +} + +void resize_q_4w_linear_node( + ComputeGraph* graph, + const std::vector& args, + const std::vector& extra_args) { + (void)extra_args; + + vTensorPtr out = graph->get_tensor(args[0].refs[0]); + vTensorPtr mat1 = graph->get_tensor(args[1].refs[0]); + vTensorPtr mat2 = graph->get_tensor(args[1].refs[1]); + + const int out_cols = utils::val_at(-2, mat1->sizes()); + const int out_rows = utils::val_at(-1, mat2->sizes()) * 2; + + std::vector new_out_sizes(3); + if (mat1->sizes().size() == 2) { + new_out_sizes.resize(2); + new_out_sizes.at(0) = out_cols; + new_out_sizes.at(1) = out_rows; + } else { + new_out_sizes.at(0) = mat1->sizes().at(0); + new_out_sizes.at(1) = out_cols; + new_out_sizes.at(2) = out_rows; + } + + out->virtual_resize(new_out_sizes); +} + +ValueRef prepack_int4_linear_weight_transposed_interleaved( + ComputeGraph& graph, + const ValueRef qmat2_data) { + std::vector qmat2_orig_sizes = graph.sizes_of(qmat2_data); + const int64_t ndim = graph.dim_of(qmat2_data); + + const int64_t K = qmat2_orig_sizes.at(ndim - 1) * 2; + const int64_t N = qmat2_orig_sizes.at(ndim - 2); + const int64_t N_div2 = N / int64_t(2); + + utils::StorageType storage_type = utils::kTexture3D; + utils::uvec3 max_extents = + graph.context()->adapter_ptr()->max_texture_extents(); + if (N_div2 > max_extents[0] * 4 || K > max_extents[1]) { + storage_type = utils::kBuffer; + } + + std::vector qmat2_sizes{K, N_div2}; + ValueRef qmat2 = graph.add_tensor( + qmat2_sizes, vkcompute::vkapi::kByte, storage_type, utils::kWidthPacked); + + utils::uvec3 global_wg_size; + global_wg_size = graph.logical_limits_of(qmat2); + global_wg_size[1] = utils::div_up(global_wg_size[1], uint32_t(2)); + + std::string kernel_name = "pack_int4_linear_weight_transposed_interleaved"; + add_storage_type_suffix(kernel_name, storage_type); + + graph.prepack_nodes().emplace_back(new PrepackNode( + graph, + VK_KERNEL_FROM_STR(kernel_name), + global_wg_size, + graph.create_local_wg_size(global_wg_size), + // Inputs and Outputs + qmat2_data, + qmat2, + // UBOs + {}, + // Specialization Constants + {}, + // Push Constants + {graph.sizes_pc_of(qmat2)})); + + return qmat2; +} + +void add_q_4w_linear_node( + ComputeGraph& graph, + const ValueRef mat1, + const ValueRef mat2_data, + const ValueRef group_size, + const ValueRef scales_and_zeros_data, + const ValueRef out) { + check_q_4w_linear_args( + graph, mat1, mat2_data, group_size, scales_and_zeros_data, out); + + ValueRef mat2 = + prepack_int4_linear_weight_transposed_interleaved(graph, mat2_data); + + ValueRef scales_and_zeros = prepack_standard_hw_transposed( + graph, scales_and_zeros_data, utils::kTexture3D, utils::kWidthPacked); + + std::string kernel_name = "q_4w_linear"; + add_storage_type_suffix(kernel_name, graph.storage_type_of(out)); + add_storage_type_suffix(kernel_name, graph.storage_type_of(mat1)); + add_storage_type_suffix(kernel_name, graph.storage_type_of(mat2)); + add_dtype_suffix(kernel_name, graph.dtype_of(out)); + + const uint32_t group_size_val = graph.extract_scalar(group_size); + + utils::uvec3 global_wg_size = graph.logical_limits_of(out); + global_wg_size[0] = utils::div_up(global_wg_size[0], uint32_t(2)); + + utils::uvec3 local_wg_size = graph.create_local_wg_size(global_wg_size); + + graph.execute_nodes().emplace_back(new DispatchNode( + graph, + VK_KERNEL_FROM_STR(kernel_name), + global_wg_size, + local_wg_size, + // Inputs and Outputs + {{out, vkapi::kWrite}, {{mat1, mat2, scales_and_zeros}, vkapi::kRead}}, + // Shader params buffers + {}, + // Specialization Constants + {SV(group_size_val)}, + // Resizing Logic + resize_q_4w_linear_node, + {}, + // Push Constants + {graph.sizes_pc_of(out), + graph.sizes_pc_of(mat1), + graph.sizes_pc_of(mat2)})); +} + +void linear_weight_int4( + ComputeGraph& graph, + const std::vector& args) { + return add_q_4w_linear_node( + graph, + args[0], // mat1 + args[1], // mat2 + args[2], // group_size + args[3], // scales_and_zeros + // There is an unused variable inner_k_tiles which is used to call + // _convert_weight_to_int4pack in the AOT custom op, which is why the 4th + // argument is skipped. + args[5] // out + ); +} + +REGISTER_OPERATORS { + VK_REGISTER_OP(et_vk.linear_weight_int4.default, linear_weight_int4); +} + +} // namespace vkcompute diff --git a/backends/vulkan/runtime/graph/ops/impl/QuantizedLinear.cpp b/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearInt8.cpp similarity index 64% rename from backends/vulkan/runtime/graph/ops/impl/QuantizedLinear.cpp rename to backends/vulkan/runtime/graph/ops/impl/QuantizedLinearInt8.cpp index f4f5c853ddd..49085ff4e06 100644 --- a/backends/vulkan/runtime/graph/ops/impl/QuantizedLinear.cpp +++ b/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearInt8.cpp @@ -268,157 +268,8 @@ void weight_int8pack_mm( return add_q_8w_linear_node(graph, args[0], args[1], args[2], args[3]); } -void check_q_4w_linear_args( - ComputeGraph& graph, - const ValueRef mat1, - const ValueRef mat2_data, - const ValueRef group_size, - const ValueRef scales_and_zeros, - const ValueRef out) { - VK_CHECK_COND(graph.int16_shader_types_enabled()); - VK_CHECK_COND(graph.int8_buffers_enabled()); - - VK_CHECK_COND(graph.val_is_tensor(mat1)); - VK_CHECK_COND(graph.val_is_tref(mat2_data)); - VK_CHECK_COND(graph.val_is_tref(scales_and_zeros)); - - VK_CHECK_COND(graph.dim_of(mat1) <= 3); - VK_CHECK_COND(graph.dim_of(mat2_data) == 2); - VK_CHECK_COND(graph.dim_of(scales_and_zeros) == 3); - - VK_CHECK_COND(graph.size_at(-3, mat1) == 1); - const int K = graph.size_at(-1, mat1); - VK_CHECK_COND(graph.size_at(-1, mat2_data) * 2 == K); - - const int group_size_val = graph.extract_scalar(group_size); - VK_CHECK_COND(K % group_size_val == 0); - - VK_CHECK_COND(graph.has_standard_axis_map(mat1)); - VK_CHECK_COND(graph.has_standard_axis_map(out)); -} - -void resize_q_4w_linear_node( - ComputeGraph* graph, - const std::vector& args, - const std::vector& extra_args) { - (void)extra_args; - - vTensorPtr out = graph->get_tensor(args[0].refs[0]); - vTensorPtr mat1 = graph->get_tensor(args[1].refs[0]); - vTensorPtr mat2 = graph->get_tensor(args[1].refs[1]); - - const int out_cols = utils::val_at(-2, mat1->sizes()); - const int out_rows = utils::val_at(-2, mat2->sizes()); - - std::vector new_out_sizes(3); - if (mat1->sizes().size() == 2) { - new_out_sizes.resize(2); - new_out_sizes.at(0) = out_cols; - new_out_sizes.at(1) = out_rows; - } else { - new_out_sizes.at(0) = mat1->sizes().at(0); - new_out_sizes.at(1) = out_cols; - new_out_sizes.at(2) = out_rows; - } - - out->virtual_resize(new_out_sizes); -} - -void add_q_4w_linear_node( - ComputeGraph& graph, - const ValueRef mat1, - const ValueRef mat2_data, - const ValueRef group_size, - const ValueRef scales_and_zeros_data, - const ValueRef out) { - check_q_4w_linear_args( - graph, mat1, mat2_data, group_size, scales_and_zeros_data, out); - - utils::StorageType storage_type = graph.storage_type_of(out); - - ValueRef mat2 = prepack_direct_copy_buffer(graph, mat2_data); - - ValueRef scales_and_zeros = prepack_standard( - graph, - scales_and_zeros_data, - graph.storage_type_of(out), - utils::kWidthPacked); - - std::string kernel_name = "q_4w_linear"; - add_storage_type_suffix(kernel_name, storage_type); - add_dtype_suffix(kernel_name, graph.dtype_of(out)); - - const uint32_t group_size_val = graph.extract_scalar(group_size); - - ValueRef mat1_W_packed = mat1; - ValueRef out_W_packed = out; - auto viewFn = VK_GET_OP_FN("aten.view_copy.default"); - // Create temporary tensors to store the width packed versions of mat1 and out - TmpTensor mat1_tmp( - &graph, graph.sizes_of(mat1), graph.dtype_of(mat1), utils::kWidthPacked); - TmpTensor out_tmp( - &graph, graph.sizes_of(out), graph.dtype_of(out), utils::kWidthPacked); - if (storage_type == utils::kTexture3D) { - if (!graph.is_buffer_storage(out) && - graph.packed_dim_of(mat1) != WHCN::kWidthDim) { - // Ensure mat1 is width packed - mat1_W_packed = mat1_tmp; - viewFn(graph, {mat1, graph.add_none(), mat1_W_packed}); - // Ensure out is packed correctly - out_W_packed = out_tmp; - } - } - - vkapi::ParamsBindList ubos({}); - ubos.append(graph.logical_limits_ubo(out_W_packed)); - ubos.append(graph.sizes_ubo(mat1_W_packed)); - ubos.append(graph.strides_ubo(mat2)); - ubos.append(graph.strides_ubo(scales_and_zeros)); - - utils::uvec3 global_wg_size = graph.logical_limits_of(out_W_packed); - utils::uvec3 local_wg_size = graph.create_local_wg_size(global_wg_size); - - graph.execute_nodes().emplace_back(new DispatchNode( - graph, - VK_KERNEL_FROM_STR(kernel_name), - global_wg_size, - local_wg_size, - // Inputs and Outputs - {{out_W_packed, vkapi::MemoryAccessType::WRITE}, - {{mat1_W_packed, mat2, scales_and_zeros}, - vkapi::MemoryAccessType::READ}}, - // Shader params buffers - ubos, - // Specialization Constants - {SV(group_size_val)}, - // Resizing Logic - resize_q_4w_linear_node, - {})); - if (!graph.is_buffer_storage(out) && - graph.packed_dim_of(out) != WHCN::kWidthDim) { - viewFn(graph, {out_W_packed, graph.add_none(), out}); - } -} - -void linear_weight_int4( - ComputeGraph& graph, - const std::vector& args) { - return add_q_4w_linear_node( - graph, - args[0], // mat1 - args[1], // mat2 - args[2], // group_size - args[3], // scales_and_zeros - // There is an unused variable inner_k_tiles which is used to call - // _convert_weight_to_int4pack in the AOT custom op, which is why the 4th - // argument is skipped. - args[5] // out - ); -} - REGISTER_OPERATORS { VK_REGISTER_OP(aten._weight_int8pack_mm.default, weight_int8pack_mm); - VK_REGISTER_OP(et_vk.linear_weight_int4.default, linear_weight_int4); } } // namespace vkcompute diff --git a/backends/vulkan/runtime/vk_api/Adapter.h b/backends/vulkan/runtime/vk_api/Adapter.h index be0554161d3..68371d3eebf 100644 --- a/backends/vulkan/runtime/vk_api/Adapter.h +++ b/backends/vulkan/runtime/vk_api/Adapter.h @@ -211,6 +211,13 @@ class Adapter final { return physical_device_.min_ubo_alignment; } + inline utils::uvec3 max_texture_extents() const { + return { + physical_device_.properties.limits.maxImageDimension1D, + physical_device_.properties.limits.maxImageDimension2D, + physical_device_.properties.limits.maxImageDimension3D}; + } + // Command Buffer Submission void diff --git a/backends/vulkan/test/op_tests/linear_weight_int4_test.cpp b/backends/vulkan/test/op_tests/linear_weight_int4_test.cpp index 66a585844cf..884b068bc24 100644 --- a/backends/vulkan/test/op_tests/linear_weight_int4_test.cpp +++ b/backends/vulkan/test/op_tests/linear_weight_int4_test.cpp @@ -152,13 +152,17 @@ vkcompute::vkapi::ScalarType from_at_scalartype(c10::ScalarType at_scalartype) { } } -void test_vulkan_linear_int4( +void test_vulkan_linear_int4_impl( const int B, const int M, const int K, const int N, const int group_size = 32, - const int inner_k_tiles = 8) { + const int inner_k_tiles = 8, + const vkcompute::utils::StorageType in_storage = + vkcompute::utils::kTexture3D, + const vkcompute::utils::StorageType out_storage = + vkcompute::utils::kTexture3D) { assert(K % group_size == 0); at::Tensor x = at::rand({B, M, K}, at::device(at::kCPU).dtype(at::kFloat)); @@ -169,8 +173,13 @@ void test_vulkan_linear_int4( at::Tensor scales_and_zeros = at::rand({k_groups, N, 2}, at::device(at::kCPU).dtype(at::kFloat)); - at::Tensor out_ref = dequantize_and_linear( - x, weights_4x2, group_size, scales_and_zeros, inner_k_tiles); + at::Tensor weights_int = unpack_weights_4x2(weights_4x2); + at::Tensor out_ref = linear_weight_int4_reference_impl( + x, + at::_convert_weight_to_int4pack_for_cpu(weights_int, group_size), + group_size, + scales_and_zeros, + inner_k_tiles); // Build Vulkan graph using namespace vkcompute; @@ -188,14 +197,13 @@ void test_vulkan_linear_int4( MAKE_TENSORREF_FOR(weights_4x2); MAKE_TENSORREF_FOR(scales_and_zeros); -#define MAKE_INPUT_FOR(x) \ - IOValueRef r_##x = graph.add_input_tensor( \ - x.sizes().vec(), from_at_scalartype(x.scalar_type())); - - MAKE_INPUT_FOR(x); + IOValueRef r_x = graph.add_input_tensor( + x.sizes().vec(), from_at_scalartype(x.scalar_type()), in_storage); const ValueRef r_out = graph.add_tensor( - out_ref.sizes().vec(), from_at_scalartype(out_ref.scalar_type())); + out_ref.sizes().vec(), + from_at_scalartype(out_ref.scalar_type()), + out_storage); VK_GET_OP_FN("et_vk.linear_weight_int4.default") (graph, @@ -229,6 +237,34 @@ void test_vulkan_linear_int4( ASSERT_TRUE(at::allclose(vk_out, out_ref, 1e-4, 1e-4)); } +void test_vulkan_linear_int4( + const int B, + const int M, + const int K, + const int N, + const int group_size = 32, + const int inner_k_tiles = 8) { + test_vulkan_linear_int4_impl( + B, + M, + K, + N, + group_size, + inner_k_tiles, + vkcompute::utils::kBuffer, + vkcompute::utils::kBuffer); + + test_vulkan_linear_int4_impl( + B, + M, + K, + N, + group_size, + inner_k_tiles, + vkcompute::utils::kTexture3D, + vkcompute::utils::kTexture3D); +} + TEST(VulkanInt4LinearTest, test_reference_impl) { test_reference_linear_int4( /*B = */ 1, From 9846436338ed654320a8e6e69d598134dcbf28ee Mon Sep 17 00:00:00 2001 From: Sicheng Stephen Jia Date: Mon, 7 Apr 2025 18:54:55 -0400 Subject: [PATCH 032/244] [ET-VK][ez] Make squeeze insertion requirements more strict (#9950) ## Context Refactor the `SqueezeUnsqueezeInputs` pass to be more clear about its intention. For Llama models, input shapes to 4 bit linear will oftentimes have the shape `[1, seq_len, dim]`; under the current implementation of the pass, the input would be squeezed to `[seq_len, dim]` even though the squeeze is not necessary. The original intention of thispass was to squeeze inputs with shape `[batch_size, 1, dim]` to `[batch_size, dim]` before calling the 4-bit linear operator. ## Changes To avoid inserting unnecessary squeeze/unsqueezes, be more specific about when squeeze/unsqueeze should be added. I would like to consider refactoring this pass in the future, since the logic is currently a bit uninttuitive. Squeeze/unsqueeze is also inserted for gelu and relu, but this is to create a chain of unsqueeze/squeeze that will be eliminated by a later pass (see https://github.com/pytorch/executorch/pull/8601 / D69673068). I think eventually it will be good to rewrite the pass to make shape management more explicit and self contained within the pass rather than inserting ops which are expected to be removed later on. Differential Revision: [D72480178](https://our.internmc.facebook.com/intern/diff/D72480178/) --- .../_passes/squeeze_unsqueeze_inputs.py | 23 +++++++++++++++---- 1 file changed, 18 insertions(+), 5 deletions(-) diff --git a/backends/vulkan/_passes/squeeze_unsqueeze_inputs.py b/backends/vulkan/_passes/squeeze_unsqueeze_inputs.py index a0160efa90f..b4337829d7f 100644 --- a/backends/vulkan/_passes/squeeze_unsqueeze_inputs.py +++ b/backends/vulkan/_passes/squeeze_unsqueeze_inputs.py @@ -27,6 +27,19 @@ class SqueezeUnsqueezeInputs(ExportPass): exir_ops.edge.aten.gelu.default, } + def should_squeeze(self, op, shape: List[int]) -> bool: # pyre-ignore + if len(shape) == 3: + return shape[1] == 1 and shape[0] > 1 + if len(shape) == 4: + # No need to squeeze if all dims are 1 except the width dim + if all(dim == 1 for dim in shape[:-1]): + return False + # Otherwise, check for squeezable dim + return 1 in shape[:-1] + + # Prefer not to introduce additional orchestration ops by default + return False + def call_operator( self, op, # pyre-ignore @@ -34,18 +47,18 @@ def call_operator( kwargs: Dict[str, Argument], meta: NodeMetadata, ) -> ProxyValue: - def _squeezable(shape: List[int]) -> bool: - return len(shape) > 2 and 1 in shape - if op not in self._squeezable_ops: return super().call_operator(op, args, kwargs, meta) - # pyre-ignore[16]: `None` has no attribute `node` input_shape = args[0].node.meta["val"].shape output_shape = meta["val"].shape - if not _squeezable(input_shape): + + if not self.should_squeeze(op, input_shape): return super().call_operator(op, args, kwargs, meta) + def _squeezable(shape: List[int]) -> bool: + return len(shape) > 2 and 1 in shape + # squeeze input tensor squeeze_shape = list(input_shape) while _squeezable(squeeze_shape): From 84acf8d9b9b8bd8a38439429275b5b05c3de7e9b Mon Sep 17 00:00:00 2001 From: Sicheng Stephen Jia Date: Mon, 7 Apr 2025 18:55:29 -0400 Subject: [PATCH 033/244] [ET-VK][ez] Allow logit linear layer to be lowered to Vulkan (#9951) ## Context Due to poor performance of Vulkan's int4 linear operator, the final logit layer of the transformer model was not being delegated to vulkan, and was instead quantized and executed with the XNNPACK delegate. However, with D72412950 / https://github.com/pytorch/executorch/pull/9883 decent performance can now be achieved with Vulkan/s int4 linear op. Therefore, the final logit layer can be lowered to Vulkan. ## Changes * Remove limit from `VkInt4WeightOnlyQuantizer` that was causing it to ignore the logit layer of the transformer * Do not apply XNNPACK partitioner and quantizer when lowering with Vulkan Differential Revision: [D72480177](https://our.internmc.facebook.com/intern/diff/D72480177/) cc @manuelcandales @cbilgin --- .../vulkan/_passes/int4_weight_only_quantizer.py | 14 +------------- backends/vulkan/op_registry.py | 2 ++ examples/models/llama/export_llama_lib.py | 4 ---- .../models/llama/source_transformation/quantize.py | 11 ----------- 4 files changed, 3 insertions(+), 28 deletions(-) diff --git a/backends/vulkan/_passes/int4_weight_only_quantizer.py b/backends/vulkan/_passes/int4_weight_only_quantizer.py index 409cbb4b755..d0b73b8af0e 100644 --- a/backends/vulkan/_passes/int4_weight_only_quantizer.py +++ b/backends/vulkan/_passes/int4_weight_only_quantizer.py @@ -118,9 +118,6 @@ def _vk_replace_linear_int4( # Use custom vulkan linear layer as default linear_class: Type[torch.nn.Module] = VkWeightOnlyInt4Linear, copy_weights: bool = False, - # Serves the same purpose as `tensor_dim_limit` in - # executorch.backends.vulkan.partitioner.VulkanSupportedOperators - feature_limit: int = 16384, ): for name, child in module.named_children(): if isinstance(child, torch.nn.Linear) and ( @@ -131,8 +128,6 @@ def _vk_replace_linear_int4( if ( _check_linear_int4_k(child.in_features, groupsize, inner_k_tiles) or padding_allowed - ) and ( - child.out_features < feature_limit and child.in_features < feature_limit ): new_linear = linear_class( child.in_features, @@ -175,7 +170,6 @@ def __init__( inner_k_tiles: Optional[int] = 8, device: torch.device = torch.device("cpu"), # noqa precision: torch.dtype = torch.float32, - feature_limit: int = 16384, ) -> None: super().__init__() assert inner_k_tiles in [2, 4, 8] @@ -186,9 +180,6 @@ def __init__( self.padding_allowed: bool = padding_allowed self.device: torch.device = device self.precision: torch.dtype = precision - # Serves the same purpose as `tensor_dim_limit` in - # executorch.backends.vulkan.partitioner.VulkanSupportedOperators - self.feature_limit = feature_limit @torch.no_grad() def _create_quantized_state_dict( @@ -197,10 +188,7 @@ def _create_quantized_state_dict( cur_state_dict = model.state_dict() for fqn, mod in model.named_modules(): # Add additional check to make sure features do not exceed feature limit - if isinstance(mod, torch.nn.Linear) and ( - mod.out_features < self.feature_limit - and mod.in_features < self.feature_limit - ): + if isinstance(mod, torch.nn.Linear): out_features = mod.out_features in_features = mod.in_features logging.info(f"linear: {fqn}, in={in_features}, out={out_features}") diff --git a/backends/vulkan/op_registry.py b/backends/vulkan/op_registry.py index 97a428c77aa..026f1db9273 100644 --- a/backends/vulkan/op_registry.py +++ b/backends/vulkan/op_registry.py @@ -393,6 +393,7 @@ def register_int8_mm_op(features: OpFeatures): @update_features(exir_ops.edge.et_vk.linear_weight_int4.default) def register_int4_mm_op(features: OpFeatures): + features.buffer_impl = True features.texture_impl = TextureImplFeatures( uses_axis_map=False, valid_packed_dims={PackedDim.WIDTH}, @@ -401,6 +402,7 @@ def register_int4_mm_op(features: OpFeatures): features.optimal_storage = VkStorageType.TEXTURE_3D features.optimal_layout = VkMemoryLayout.TENSOR_WIDTH_PACKED features.handles_own_prepacking = True + features.skip_limits_check = {1} return features diff --git a/examples/models/llama/export_llama_lib.py b/examples/models/llama/export_llama_lib.py index 8e6d4fefb0e..249a25f23c4 100644 --- a/examples/models/llama/export_llama_lib.py +++ b/examples/models/llama/export_llama_lib.py @@ -793,10 +793,6 @@ def _to_edge_and_lower_llama( # noqa: C901 args.enable_dynamic_shape, ) ) - # Apply XNNPACK after Vulkan so that undelegated ops can be accelerated by XNNPACK - partitioners.append( - get_xnnpack_partitioner(dynamic_quant_only_partitioner=False) - ) modelname = f"vulkan_{modelname}" # Need to remove asserts from the graph to prevent graph breaks diff --git a/examples/models/llama/source_transformation/quantize.py b/examples/models/llama/source_transformation/quantize.py index 2ef016de097..d51d4378705 100644 --- a/examples/models/llama/source_transformation/quantize.py +++ b/examples/models/llama/source_transformation/quantize.py @@ -206,17 +206,6 @@ def quantize( # noqa C901 q_group_size = 256 if group_size is None else group_size model = VkInt4WeightOnlyQuantizer(groupsize=q_group_size).quantize(model) - # Apply additional quantizer for linear layers that aren't lowered to Vulkan - # at the moment - from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer - - # 1. Quantize in checkpoint dtype. - model = Int8DynActInt4WeightQuantizer( - precision=checkpoint_torch_dtype, groupsize=q_group_size - ).quantize(model) - # 2. Set the computation dtype (what weights/acts dequantize to). - model = set_8da4w_computation_dtype(model, computation_torch_dtype) - return model else: raise Exception(f"Unrecognized quantize mode: {qmode}") From ec114d9404a7a4901e88f1362da7483581203c25 Mon Sep 17 00:00:00 2001 From: derekxu Date: Mon, 7 Apr 2025 19:17:08 -0700 Subject: [PATCH 034/244] Expose L4 ops to ExecuTorch Client and add MWA to ExecuTorch Client Differential Revision: D72598211 Pull Request resolved: https://github.com/pytorch/executorch/pull/9946 --- kernels/quantized/targets.bzl | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/kernels/quantized/targets.bzl b/kernels/quantized/targets.bzl index a2533cb003a..7bd8f6852a7 100644 --- a/kernels/quantized/targets.bzl +++ b/kernels/quantized/targets.bzl @@ -61,6 +61,10 @@ def define_common_targets(): name = "all_quantized_ops", ops_schema_yaml_target = ":quantized.yaml", define_static_targets = True, + visibility = [ + "//executorch/...", + "@EXECUTORCH_CLIENTS", + ], ) # On Windows we can only compile these two ops currently, so adding a From 8166803aff58c1b6fab15c40ab14c827c0fae248 Mon Sep 17 00:00:00 2001 From: Gasoonjia Date: Mon, 7 Apr 2025 21:07:20 -0700 Subject: [PATCH 035/244] introducing filter to etdumpgen Differential Revision: D72151631 Pull Request resolved: https://github.com/pytorch/executorch/pull/9937 --- devtools/etdump/etdump_filter.h | 5 +- devtools/etdump/etdump_flatcc.cpp | 18 ++++ devtools/etdump/etdump_flatcc.h | 10 ++ devtools/etdump/tests/etdump_test.cpp | 129 +++++++++++++++++++++++- devtools/etdump/tests/targets.bzl | 1 + runtime/core/event_tracer.h | 12 +-- runtime/core/test/event_tracer_test.cpp | 6 ++ 7 files changed, 172 insertions(+), 9 deletions(-) diff --git a/devtools/etdump/etdump_filter.h b/devtools/etdump/etdump_filter.h index 545823a5556..29db43be8b9 100644 --- a/devtools/etdump/etdump_filter.h +++ b/devtools/etdump/etdump_filter.h @@ -77,8 +77,9 @@ class ETDumpFilter : public ::executorch::runtime::EventTracerFilterBase { * * @return A Result indicating whether the event matches the filter * criteria. - * - True if the event matches the filter, or filter is unset. - * - False if the event does not match or is unknown. + * - True if the event matches the filter. + * - False if the event does not match, or is unknown, or filter is + * unset. * - An error code if an error occurs during filtering. */ Result filter( diff --git a/devtools/etdump/etdump_flatcc.cpp b/devtools/etdump/etdump_flatcc.cpp index ea464f2f5ce..f6e18bf49d2 100644 --- a/devtools/etdump/etdump_flatcc.cpp +++ b/devtools/etdump/etdump_flatcc.cpp @@ -369,6 +369,19 @@ Result ETDumpGen::log_intermediate_output_delegate_helper( InvalidArgument, "Only name or delegate_debug_index can be valid. Check DelegateMappingBuilder documentation for more details."); + if (filter_) { + Result result = filter_->filter(name, delegate_debug_index); + if (!result.ok()) { + return result; + } + + // If the filter returns true, meaning this event should be filtered out and + // we should not log it. + if (result.get()) { + return false; + } + } + check_ready_to_add_events(); int64_t string_id = name != nullptr ? create_string_entry(name) : -1; @@ -654,6 +667,11 @@ DataSinkBase* ETDumpGen::get_data_sink() { return data_sink_; } +void ETDumpGen::set_delegation_intermediate_output_filter( + EventTracerFilterBase* filter) { + filter_ = filter; +} + long ETDumpGen::write_tensor_or_raise_error(Tensor tensor) { // Previously, the function copy_tensor_to_debug_buffer returned 0xFF..F when // given an empty tensor, which is an invalid offset for most buffers. In our diff --git a/devtools/etdump/etdump_flatcc.h b/devtools/etdump/etdump_flatcc.h index 6b51745eee3..dc0f822f922 100644 --- a/devtools/etdump/etdump_flatcc.h +++ b/devtools/etdump/etdump_flatcc.h @@ -25,6 +25,7 @@ namespace executorch { namespace etdump { using ::executorch::runtime::DelegateDebugIntId; +using ::executorch::runtime::EventTracerFilterBase; using ::executorch::runtime::Result; namespace internal { @@ -146,6 +147,13 @@ class ETDumpGen : public ::executorch::runtime::EventTracer { const char* name, DelegateDebugIntId delegate_debug_index, const double& output) override; + + /** + * Set the filter of event tracer for delegation intermediate outputs. + */ + virtual void set_delegation_intermediate_output_filter( + EventTracerFilterBase* event_tracer_filter) override; + void set_debug_buffer(::executorch::runtime::Span buffer); void set_data_sink(DataSinkBase* data_sink); ETDumpResult get_etdump_data(); @@ -188,6 +196,8 @@ class ETDumpGen : public ::executorch::runtime::EventTracer { int bundled_input_index_ = -1; State state_ = State::Init; struct internal::ETDumpStaticAllocator alloc_; + + EventTracerFilterBase* filter_ = nullptr; }; } // namespace etdump diff --git a/devtools/etdump/tests/etdump_test.cpp b/devtools/etdump/tests/etdump_test.cpp index c64bab0448c..9d39a8bbde1 100644 --- a/devtools/etdump/tests/etdump_test.cpp +++ b/devtools/etdump/tests/etdump_test.cpp @@ -12,6 +12,7 @@ #include #include +#include #include #include #include @@ -33,6 +34,7 @@ using ::executorch::runtime::AllocatorID; using ::executorch::runtime::ArrayRef; using ::executorch::runtime::BoxedEvalueList; using ::executorch::runtime::DelegateDebugIdType; +using ::executorch::runtime::DelegateDebugIntId; using ::executorch::runtime::Error; using ::executorch::runtime::EValue; using ::executorch::runtime::EventTracerEntry; @@ -45,6 +47,8 @@ using ::executorch::runtime::testing::TensorFactory; using ::executorch::etdump::BufferDataSink; using ::executorch::etdump::FileDataSink; +using ::executorch::etdump::ETDumpFilter; + class ProfilerETDumpTest : public ::testing::Test { protected: void SetUp() override { @@ -75,6 +79,70 @@ class ProfilerETDumpTest : public ::testing::Test { "Must set data sink before writing tensor-like data"); } + void check_log_with_filter( + const char* name, + DelegateDebugIntId delegate_debug_index, + bool use_tensor_input, + bool expected_log, + bool expected_ok) { + TensorFactory tf; + for (size_t i = 0; i < 2; i++) { + const size_t buffer_size = 2048; + + void* ptr = malloc(buffer_size); + auto buffer_data_sink = BufferDataSink::create(ptr, buffer_size); + auto filter = ETDumpFilter(); + filter.add_regex("filtered.*"); + filter.set_debug_handle_range(1, 10); + etdump_gen[i]->set_delegation_intermediate_output_filter(&filter); + + etdump_gen[i]->create_event_block("test_block"); + etdump_gen[i]->set_data_sink(&buffer_data_sink.get()); + + // size of empty etdump + size_t initial_size = 68; + + // Perform logging + + if (use_tensor_input) { + auto tensor = tf.ones({3, 2}); + auto result = etdump_gen[i]->log_intermediate_output_delegate( + name, delegate_debug_index, tensor); + ASSERT_EQ(result.ok(), expected_ok); + if (expected_ok) { + ASSERT_EQ(result.get(), expected_log); + } + } else { // use tensor_list instead + std::vector tensors = {tf.ones({5, 4}), tf.ones({7, 6})}; + Result result = etdump_gen[i]->log_intermediate_output_delegate( + name, + delegate_debug_index, + ArrayRef(tensors.data(), tensors.size())); + ASSERT_EQ(result.ok(), expected_ok); + if (expected_ok) { + ASSERT_EQ(result.get(), expected_log); + } + } + + // Get final size of etdump + ETDumpResult final_result = etdump_gen[i]->get_etdump_data(); + size_t final_size = final_result.size; + // Check if the size of etdump has changed based on logging success + if (expected_log) { + ASSERT_NE(initial_size, final_size); // Expect size change if logged + } else { + ASSERT_EQ( + initial_size, final_size); // Expect no size change if not logged + } + + if (!etdump_gen[i]->is_static_etdump()) { + free(final_result.buf); + } + + free(ptr); + } + } + ETDumpGen* etdump_gen[2]; uint8_t* buf = nullptr; std::unique_ptr temp_file; @@ -652,7 +720,7 @@ TEST_F(ProfilerETDumpTest, VerifyDelegateIntermediateLogging) { void* ptr = malloc(2048); Span buffer((uint8_t*)ptr, 2048); - ; + auto buffer_data_sink = BufferDataSink::create(ptr, 2048); auto file_data_sink = FileDataSink::create(dump_file_path.c_str()); @@ -892,3 +960,62 @@ TEST_F(ProfilerETDumpTest, WriteAfterGetETDumpData) { } } } + +TEST_F(ProfilerETDumpTest, LogWithRegexAndUnsetDelegateDebugIdOnTensor) { + check_log_with_filter( + "filtered_event", + kUnsetDelegateDebugIntId, + /*use_tensor_input=*/true, + /*expected_log=*/false, + /*expected_ok=*/true); +} + +TEST_F(ProfilerETDumpTest, LogWithRegexAndUnsetDelegateDebugIdOnTensorList) { + check_log_with_filter( + "filtered_event", + kUnsetDelegateDebugIntId, + /*use_tensor_input=*/true, + /*expected_log=*/false, + /*expected_ok=*/true); +} + +TEST_F(ProfilerETDumpTest, LogWithNullptrAndInRange) { + check_log_with_filter( + nullptr, + 5, + /*use_tensor_input=*/true, + /*expected_log=*/false, + /*expected_ok=*/true); +} +TEST_F(ProfilerETDumpTest, LogWithNonMatchingRegexAndOutOfRange) { + check_log_with_filter( + "unfiltered_event", + kUnsetDelegateDebugIntId, + /*use_tensor_input=*/true, + /*expected_log=*/true, + /*expected_ok=*/true); +} +TEST_F(ProfilerETDumpTest, LogWithNullptrAndOutOfRange) { + check_log_with_filter( + nullptr, + 20, + /*use_tensor_input=*/true, + /*expected_log=*/true, + /*expected_ok=*/true); +} +TEST_F(ProfilerETDumpTest, LogWithRegexAndInRange) { + check_log_with_filter( + "filtered_event", + 5, + /*use_tensor_input=*/true, + /*expected_log=*/false, + /*expected_ok=*/false); +} +TEST_F(ProfilerETDumpTest, LogWithNullptrAndUnsetDebugHandle) { + check_log_with_filter( + nullptr, + kUnsetDelegateDebugIntId, + /*use_tensor_input=*/true, + /*expected_log=*/false, + /*expected_ok=*/false); +} diff --git a/devtools/etdump/tests/targets.bzl b/devtools/etdump/tests/targets.bzl index 7f266eed5a7..10eb8608362 100644 --- a/devtools/etdump/tests/targets.bzl +++ b/devtools/etdump/tests/targets.bzl @@ -19,6 +19,7 @@ def define_common_targets(): "//executorch/extension/testing_util:temp_file", "//executorch/runtime/platform:platform", "//executorch/runtime/core/exec_aten/testing_util:tensor_util", + "//executorch/devtools/etdump:etdump_filter", ], ) diff --git a/runtime/core/event_tracer.h b/runtime/core/event_tracer.h index 77d7fc64102..e810437f7d2 100644 --- a/runtime/core/event_tracer.h +++ b/runtime/core/event_tracer.h @@ -439,6 +439,12 @@ class EventTracer { DelegateDebugIntId delegate_debug_index, const double& output) = 0; + /** + * Set the filter of event tracer for delegation intermediate outputs. + */ + virtual void set_delegation_intermediate_output_filter( + EventTracerFilterBase* event_tracer_filter) = 0; + /** * Helper function to set the chain id ands debug handle. Users have two * options, the first is that they can directly pass in the chain id and debug @@ -513,12 +519,6 @@ class EventTracer { event_tracer_profiling_level_ = profiling_level; } - /** - * Set the filter of event tracer for delegation intermediate outputs. - */ - void set_delegation_intermediate_output_filter( - EventTracerFilterBase* event_tracer_filter); - /** * Return the current level of event tracer profiling. */ diff --git a/runtime/core/test/event_tracer_test.cpp b/runtime/core/test/event_tracer_test.cpp index 224e87cc2b1..ee16aa924a6 100644 --- a/runtime/core/test/event_tracer_test.cpp +++ b/runtime/core/test/event_tracer_test.cpp @@ -28,6 +28,7 @@ using executorch::runtime::EValue; using executorch::runtime::EventTracer; using executorch::runtime::EventTracerDebugLogLevel; using executorch::runtime::EventTracerEntry; +using executorch::runtime::EventTracerFilterBase; using executorch::runtime::kUnsetChainId; using executorch::runtime::kUnsetDebugHandle; using executorch::runtime::kUnsetDelegateDebugIntId; @@ -90,6 +91,11 @@ class DummyEventTracer : public EventTracer { (void)metadata_len; } + void set_delegation_intermediate_output_filter( + EventTracerFilterBase* event_tracer_filter) override { + (void)event_tracer_filter; + } + void log_profiling_delegate( const char* name, DelegateDebugIntId delegate_debug_id, From 80191a579c43d1d0a5dba00a01392067453cc6d1 Mon Sep 17 00:00:00 2001 From: Adrian Lundell <36153706+AdrianLundell@users.noreply.github.com> Date: Tue, 8 Apr 2025 13:32:25 +0200 Subject: [PATCH 036/244] Arm backend: Add pre-push checks for op tests (#9899) - Check that @SkipIfNoCorstone is not used - Check that @expectedFailureOnFVP is not used - Check that unittest.TestCase is not used - Check that on_fvp suffix is not used - Check that op and target is parsed from the test name Signed-off-by: Adrian Lundell --- backends/arm/scripts/parse_test_names.py | 102 +++++++++++++++++++++++ backends/arm/scripts/pre-push | 38 +++++++++ 2 files changed, 140 insertions(+) create mode 100644 backends/arm/scripts/parse_test_names.py diff --git a/backends/arm/scripts/parse_test_names.py b/backends/arm/scripts/parse_test_names.py new file mode 100644 index 00000000000..8aabf7c2c59 --- /dev/null +++ b/backends/arm/scripts/parse_test_names.py @@ -0,0 +1,102 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +from executorch.exir.dialects.edge.spec.utils import SAMPLE_INPUT + +# Add edge ops which we lower but which are not included in exir/dialects/edge/edge.yaml here. +CUSTOM_EDGE_OPS = ["linspace.default", "eye.default"] +ALL_EDGE_OPS = SAMPLE_INPUT.keys() | CUSTOM_EDGE_OPS + +# Add all targets and TOSA profiles we support here. +TARGETS = {"tosa_BI", "tosa_MI", "u55_BI", "u85_BI"} + + +def get_edge_ops(): + """ + Returns a set with edge_ops with names on the form to be used in unittests: + 1. Names are in lowercase. + 2. Overload is ignored if it is 'default', otherwise its appended with an underscore. + 3. Overly verbose name are shortened by removing certain prefixes/suffixes. + + Examples: + abs.default -> abs + split_copy.Tensor -> split_tensor + """ + edge_ops = set() + for edge_name in ALL_EDGE_OPS: + op, overload = edge_name.split(".") + + # Normalize names + op = op.lower() + op = op.removeprefix("_") + op = op.removesuffix("_copy") + op = op.removesuffix("_with_indices") + op = op.removesuffix("_no_training") + overload = overload.lower() + + if overload == "default": + edge_ops.add(op) + else: + edge_ops.add(f"{op}_{overload}") + + return edge_ops + + +def parse_test_name(test_name: str, edge_ops: set[str]) -> tuple[str, str, bool]: + """ + Parses a test name on the form + test_OP_TARGET__ + where OP must match a string in edge_ops and TARGET must match one string in TARGETS. + The "not_delegated" suffix indicates that the test tests that the op is not delegated. + + Examples of valid names: "test_mm_u55_BI_not_delegated" or "test_add_scalar_tosa_MI_two_inputs". + + Returns a tuple (OP, TARGET, IS_DELEGATED) if valid. + """ + test_name = test_name.removeprefix("test_") + is_delegated = "not_delegated" not in test_name + assert ( + "reject" not in test_name + ), f"Use 'not_delegated' instead of 'reject' in {test_name}" + + op = "None" + target = "None" + for potential_target in TARGETS: + index = test_name.find(potential_target) + if index != -1: + op = test_name[: index - 1] + target = potential_target + break + # Special case for convolution + op = op.removesuffix("_1d") + op = op.removesuffix("_2d") + + assert target != "None", f"{test_name} does not contain one of {TARGETS}" + assert ( + op in edge_ops + ), f"Parsed unvalid OP from {test_name}, {op} does not exist in edge.yaml or CUSTOM_EDGE_OPS" + + return op, target, is_delegated + + +if __name__ == "__main__": + """Parses a list of test names given on the commandline.""" + import sys + + sys.tracebacklimit = 0 # Do not print stack trace + + edge_ops = get_edge_ops() + exit_code = 0 + + for test_name in sys.argv[1:]: + try: + assert test_name[:5] == "test_", f"Unexpected input: {test_name}" + parse_test_name(test_name, edge_ops) + except AssertionError as e: + print(e) + exit_code = 1 + else: + print(f"{test_name} OK") + + sys.exit(exit_code) diff --git a/backends/arm/scripts/pre-push b/backends/arm/scripts/pre-push index ac0584c6f73..4eeb0f50d71 100755 --- a/backends/arm/scripts/pre-push +++ b/backends/arm/scripts/pre-push @@ -166,6 +166,44 @@ for COMMIT in ${COMMITS}; do fi fi + # Op test checks + op_test_files=$(echo $commit_files | grep -oE 'backends/arm/test/ops/\S+') + if [ "$op_test_files" ]; then + + # TODO: These checks can be removed when all unittests are refactored. + if grep -icq "SkipIfNoCorstone" $op_test_files; then + echo -e "${ERROR} @SkipIfNoCorstone300/320 is deprecated;"\ + "please use XfailIfNoCorstone300/320 instead." >&2 + FAILED=1 + fi + + if grep -icq "conftest.expectedFailureOnFVP" $op_test_files; then + echo -e "${ERROR} @conftest.expectedFailureOnFVP is deprecated;"\ + "please use XfailIfCorstone300/320 instead." >&2 + FAILED=1 + fi + + if grep -icq "unittest.TestCase" $op_test_files; then + echo -e "${ERROR} Use of the Unittest test framework is deprecated;"\ + "please use Pytest instead." >&2 + FAILED=1 + fi + + if grep -icq "on_fvp(" $op_test_files; then + echo -e "${ERROR} All unittests should run on FVP if relevant,"\ + "on_fvp suffix can be excluded." >&2 + FAILED=1 + fi + + # Check that the tested op and target is parsed correctly from the test name + test_names=$(grep -h "def test_" $op_test_files | cut -d"(" -f1 | cut -d" " -f2) + python ./backends/arm/scripts/parse_test_names.py $test_names + if [ $? -ne 0 ]; then + echo -e "${ERROR} Failed op test name check." >&2 + FAILED=1 + fi + fi + echo "" # Newline to visually separate commit processing done From 265d5fe22ae528df7a805c20f1193f72381329ef Mon Sep 17 00:00:00 2001 From: mcremon-meta <134334895+mcremon-meta@users.noreply.github.com> Date: Tue, 8 Apr 2025 09:34:53 -0700 Subject: [PATCH 037/244] Fix naming convention in quantizer Differential Revision: D72594670 Pull Request resolved: https://github.com/pytorch/executorch/pull/9941 --- backends/cadence/aot/quantizer/quantizer.py | 44 ++++++++++----------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/backends/cadence/aot/quantizer/quantizer.py b/backends/cadence/aot/quantizer/quantizer.py index 62727985452..69c9518166b 100644 --- a/backends/cadence/aot/quantizer/quantizer.py +++ b/backends/cadence/aot/quantizer/quantizer.py @@ -43,7 +43,7 @@ from torch.ao.quantization.quantizer.composable_quantizer import ComposableQuantizer -act_qspec_asym8u = QuantizationSpec( +act_qspec_asym8s = QuantizationSpec( dtype=torch.int8, quant_min=-128, quant_max=127, @@ -52,7 +52,7 @@ observer_or_fake_quant_ctr=HistogramObserver.with_args(eps=2**-12), ) -wgt_qspec_asym8u = QuantizationSpec( +wgt_qspec_asym8s = QuantizationSpec( dtype=torch.int8, quant_min=-128, quant_max=127, @@ -61,7 +61,7 @@ observer_or_fake_quant_ctr=MinMaxObserver, ) -wgt_qspec_asym8s = QuantizationSpec( +wgt_qspec_sym8s = QuantizationSpec( dtype=torch.int8, quant_min=-128, quant_max=127, @@ -72,17 +72,17 @@ bias_qspec: Optional[QuantizationSpec] = None -qconfig_A8uW8u = QuantizationConfig( - act_qspec_asym8u, - act_qspec_asym8u, - wgt_qspec_asym8u, +qconfig_A8W8 = QuantizationConfig( + act_qspec_asym8s, + act_qspec_asym8s, + wgt_qspec_asym8s, None, ) -qconfig_A8uW8s = QuantizationConfig( - act_qspec_asym8u, - act_qspec_asym8u, - wgt_qspec_asym8s, +qconfig_A8W8sym = QuantizationConfig( + act_qspec_asym8s, + act_qspec_asym8s, + wgt_qspec_sym8s, None, ) @@ -189,15 +189,15 @@ def get_supported_operators(cls) -> List[OperatorConfig]: def get_cadence_default_quantizers() -> List[Quantizer]: return [ - CadenceAtenQuantizer(AddmmPattern(), qconfig_A8uW8u), - CadenceAtenQuantizer(BmmPattern(), qconfig_A8uW8u), - CadenceAtenQuantizer(Conv1dPattern(), qconfig_A8uW8s), - CadenceAtenQuantizer(Conv2dPattern(), qconfig_A8uW8s), - CadenceAtenQuantizer(LayerNormPattern(), qconfig_A8uW8u), - CadenceAtenQuantizer(LinearPattern(), qconfig_A8uW8u), - CadenceAtenQuantizer(MatmulPattern(), qconfig_A8uW8u), - CadenceAtenQuantizer(ReluPattern0(), qconfig_A8uW8u), - CadenceAtenQuantizer(ReluPattern1(), qconfig_A8uW8u), + CadenceAtenQuantizer(AddmmPattern(), qconfig_A8W8), + CadenceAtenQuantizer(BmmPattern(), qconfig_A8W8), + CadenceAtenQuantizer(Conv1dPattern(), qconfig_A8W8sym), + CadenceAtenQuantizer(Conv2dPattern(), qconfig_A8W8sym), + CadenceAtenQuantizer(LayerNormPattern(), qconfig_A8W8), + CadenceAtenQuantizer(LinearPattern(), qconfig_A8W8), + CadenceAtenQuantizer(MatmulPattern(), qconfig_A8W8), + CadenceAtenQuantizer(ReluPattern0(), qconfig_A8W8), + CadenceAtenQuantizer(ReluPattern1(), qconfig_A8W8), ] @@ -244,6 +244,6 @@ class CadenceWakeWordQuantizer(CadenceQuantizer): def __init__(self, quantizers: Optional[list[Quantizer]] = None) -> None: if quantizers is None: quantizers = get_cadence_default_quantizers() - quantizers.append(CadenceAtenQuantizer(AddPattern(), qconfig_A8uW8u)) - quantizers.append(CadenceAtenQuantizer(CatPattern(), qconfig_A8uW8u)) + quantizers.append(CadenceAtenQuantizer(AddPattern(), qconfig_A8W8)) + quantizers.append(CadenceAtenQuantizer(CatPattern(), qconfig_A8W8)) super().__init__(quantizers) From 4f6b6e05f450d4b796d1201991d900de1ab12e54 Mon Sep 17 00:00:00 2001 From: Mergen Nachin Date: Tue, 8 Apr 2025 12:56:53 -0400 Subject: [PATCH 038/244] Minor fixes on Intro How It Works (#9939) - Replace SDK with Developer Tools - Added a few links. Co-authored-by: Scott Roy <161522778+metascroy@users.noreply.github.com> --- docs/source/intro-how-it-works.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/intro-how-it-works.md b/docs/source/intro-how-it-works.md index 9b8ee87f78f..83d2381c4c4 100644 --- a/docs/source/intro-how-it-works.md +++ b/docs/source/intro-how-it-works.md @@ -17,10 +17,10 @@ ExecuTorch provides the following benefits to engineers who need to deploy machi * **Export that is robust and powerful.** Export uses [`torch.export()`](https://pytorch.org/docs/main/export.html), which uses the same technology used in PyTorch 2.x to capture PyTorch programs for fast execution. While eager mode is flexible and allows experimentation in Python, it may not work well if Python isn't available or cannot deliver efficient execution. The _Export Intermediate Representation (Export IR)_ that export flow generates can describe a wide range of dynamism in PyTorch models, including control flow and dynamic shapes, which makes it a powerful tool for fully capturing existing PyTorch models with little effort. * **Operator standardization.** During the graph export process, the nodes in the graph represent operators such as addition, multiplication, or convolution. These operators are part of a small standardized list called the [Core ATen Op set](https://pytorch.org/docs/main/torch.compiler_ir.html#core-aten-ir). Most PyTorch programs can be decomposed into a graph using this small set of operators during export. Small list of standardized operators reduces the surface, needed to be covered, by third-party operator libraries as well as accelerator backends, in order to run models exported for ExecuTorch. ExecuTorch runtime ships with one such library, called portable operator library, that implements core ATen opset. -* **Standardization for compiler interfaces (aka delegates) and the OSS ecosystem.** In addition to the _Operator standardization_ above, ExecuTorch has a standardized interface for delegation to compilers. This allows third-party vendors and compilers to implement interfaces and API entry points for compilation and execution of (either partial or full) graphs targeting their specialized hardware. This provides greater flexibility in terms of hardware support and performance optimization, as well as easier integration with the PyTorch open source ecosystem for on-device AI. -* **First-party SDK and toolchain.** Due to the above standardization efforts, it was possible to build a unified first-party SDK for ExecuTorch, where developers can export, compile, and deploy to a wide range of target devices--such as iOS, Android, and microcontrollers--using the same SDK, streamlining the process and gaining productivity. Additionally, the SDK provides profiling and debugging functionality to easily inspect intermediate states, which are core parts of most developer workflows. +* **Standardization for compiler interfaces (aka delegates) and the OSS ecosystem.** In addition to the _Operator standardization_ above, ExecuTorch has a [standardized interface](./compiler-delegate-and-partitioner.md) for delegation to compilers. This allows third-party vendors and compilers to implement interfaces and API entry points for compilation and execution of (either partial or full) graphs targeting their specialized hardware. This provides greater flexibility in terms of hardware support and performance optimization, as well as easier integration with the PyTorch open source ecosystem for on-device AI. +* **First-party Developer Tools** Due to the above standardization efforts, it was possible to build unified first-party [developer tools](./devtools-overview.md) for ExecuTorch, where developers can export, compile, and deploy to a wide range of target devices—such as iOS, Android, and microcontrollers—using the same APIs, streamlining the process and increasing productivity. Additionally, ExecuTorch provides profiling and debugging functionality to easily inspect intermediate states, which are core parts of most developer workflows. * **No intermediate conversions necessary.** ExecuTorch's main design principle is to allow developers to run their models on target devices without the need for converting to third-party intermediate representations. This eliminates a number of problems that on-device developers typically face when working with these conversion steps, such as lack of debuggability and profiling, the need to familiarize themselves with hardware-specific tools, and models not being able to run due to conversion steps failing. -* **Ease of customization.** Developers can optimize their deployment for even better performance gains on the target architecture by applying custom techniques, such as linking with high-performance operator implementations or customizing memory planning based on storage and latency trade-offs. This level of customization is made possible through the standardization of the compiler pass interface and registration APIs on exported graphs. +* **Ease of customization.** Developers can optimize their deployment for even better performance gains on the target architecture by applying custom techniques, such as [linking with high-performance operator implementations](./kernel-library-custom-aten-kernel.md) or [customizing memory planning](./compiler-memory-planning.md) based on storage and latency trade-offs. This level of customization is made possible through the standardization of the [compiler pass interface](./compiler-custom-compiler-passes.md) and registration APIs on exported graphs. * **Low overhead runtime and execution.** The ExecuTorch runtime, written in C++, is highly efficient and can run on a wide range of architectures, including Linux, iOS, Android, embedded systems, and bare metal hardware, with little additional setup or configuration. It is capable of linking in only those operators needed for the model, resulting in a minimal runtime binary size. It is also able to run at low latency because of ahead-of-time compilation and memory planning stages, with the runtime responsible only for execution (e.g., call operator `conv` and save the result in memory location X). The above highlights the key advantages of ExecuTorch across three main categories: portability, productivity, and performance. We consider it to be an ideal choice for enabling on-device AI across mobile and edge computing platforms. From 75190a973ed870ef4241c308cb0807afa8b8e2d6 Mon Sep 17 00:00:00 2001 From: gmagogsfm Date: Tue, 8 Apr 2025 10:16:49 -0700 Subject: [PATCH 039/244] executorch/backends/xnnpack/test/ops Differential Revision: D72436120 Pull Request resolved: https://github.com/pytorch/executorch/pull/9956 --- .../test/ops/test_check_quant_params.py | 3 +-- examples/llm_manual/export_nanogpt.py | 2 +- .../mediatek/aot_utils/oss_utils/utils.py | 4 ++- .../mediatek/model_export_scripts/llama.py | 2 +- .../models/phi-3-mini/export_phi-3-mini.py | 2 +- examples/models/test/test_export.py | 4 ++- .../portable/scripts/export_and_delegate.py | 8 +++--- examples/xnnpack/aot_compiler.py | 4 +-- examples/xnnpack/quantization/example.py | 8 ++++-- exir/backend/test/test_partitioner.py | 26 +++++++++++++------ exir/backend/test/test_passes.py | 4 ++- exir/emit/test/test_emit.py | 4 +-- exir/tests/test_passes.py | 7 ++--- exir/tests/test_quantization.py | 2 +- exir/tests/test_quantize_io_pass.py | 6 +++-- extension/export_util/utils.py | 2 +- extension/llm/export/builder.py | 1 + extension/llm/export/test_export_passes.py | 5 +--- 18 files changed, 57 insertions(+), 37 deletions(-) diff --git a/backends/xnnpack/test/ops/test_check_quant_params.py b/backends/xnnpack/test/ops/test_check_quant_params.py index b76935a9f72..a04751ca5f1 100644 --- a/backends/xnnpack/test/ops/test_check_quant_params.py +++ b/backends/xnnpack/test/ops/test_check_quant_params.py @@ -52,7 +52,7 @@ def _test_check_quant_message(self, ep_modifier, expected_message): torch._dynamo.reset() mod = torch.nn.Linear(10, 10) quantizer = XNNPACKQuantizer() - captured = export_for_training(mod, (torch.randn(1, 10),)).module() + captured = export_for_training(mod, (torch.randn(1, 10),), strict=True).module() quantizer.set_global(get_symmetric_quantization_config(is_per_channel=True)) prepared = prepare_pt2e(captured, quantizer) @@ -68,7 +68,6 @@ def _test_check_quant_message(self, ep_modifier, expected_message): self.assertEquals(str(context.exception), expected_message) def test_in_per_tensor_quant(self): - for invalid_scale in [ float("nan"), float("inf"), diff --git a/examples/llm_manual/export_nanogpt.py b/examples/llm_manual/export_nanogpt.py index 9de2e831e25..8c948479f2a 100644 --- a/examples/llm_manual/export_nanogpt.py +++ b/examples/llm_manual/export_nanogpt.py @@ -28,7 +28,7 @@ # The torch.no_grad() call tells PyTorch to exclude training-specific logic. with sdpa_kernel([SDPBackend.MATH]), torch.no_grad(): m = export_for_training( - model, example_inputs, dynamic_shapes=dynamic_shape + model, example_inputs, dynamic_shapes=dynamic_shape, strict=True ).module() traced_model = export(m, example_inputs, dynamic_shapes=dynamic_shape, strict=True) diff --git a/examples/mediatek/aot_utils/oss_utils/utils.py b/examples/mediatek/aot_utils/oss_utils/utils.py index 2246b8eeb15..25362788e31 100755 --- a/examples/mediatek/aot_utils/oss_utils/utils.py +++ b/examples/mediatek/aot_utils/oss_utils/utils.py @@ -30,7 +30,9 @@ def build_executorch_binary( if quant_dtype not in Precision: raise AssertionError(f"No support for Precision {quant_dtype}.") - captured_model = torch.export.export_for_training(model, inputs).module() + captured_model = torch.export.export_for_training( + model, inputs, strict=True + ).module() annotated_model = prepare_pt2e(captured_model, quantizer) print("Quantizing the model...") # calibration diff --git a/examples/mediatek/model_export_scripts/llama.py b/examples/mediatek/model_export_scripts/llama.py index 5da17727075..413df21d5cc 100644 --- a/examples/mediatek/model_export_scripts/llama.py +++ b/examples/mediatek/model_export_scripts/llama.py @@ -319,7 +319,7 @@ def export_to_et_ir( ) print("Getting pre autograd ATen Dialect Graph") pre_autograd_aten_dialect = torch.export.export_for_training( - model, example_inputs, dynamic_shapes=dynamic_shapes + model, example_inputs, dynamic_shapes=dynamic_shapes, strict=True ).module() # NOTE: Will be replaced with export quantizer = NeuropilotQuantizer() quantizer.setup_precision(getattr(Precision, precision)) diff --git a/examples/models/phi-3-mini/export_phi-3-mini.py b/examples/models/phi-3-mini/export_phi-3-mini.py index 8fa948e7dc7..11c2f3834eb 100644 --- a/examples/models/phi-3-mini/export_phi-3-mini.py +++ b/examples/models/phi-3-mini/export_phi-3-mini.py @@ -65,7 +65,7 @@ def export(args) -> None: xnnpack_quantizer.set_global(xnnpack_quant_config) model = export_for_training( - model, example_inputs, dynamic_shapes=dynamic_shapes + model, example_inputs, dynamic_shapes=dynamic_shapes, strict=True ).module() model = prepare_pt2e(model, xnnpack_quantizer) # pyre-fixme[6] model(*example_inputs) diff --git a/examples/models/test/test_export.py b/examples/models/test/test_export.py index 9a4ff7a35ed..306f54c0e89 100644 --- a/examples/models/test/test_export.py +++ b/examples/models/test/test_export.py @@ -29,7 +29,9 @@ def collect_executorch_and_eager_outputs( Returns a tuple containing the outputs of the eager mode model and the executorch mode model. """ eager_model = eager_model.eval() - model = torch.export.export_for_training(eager_model, example_inputs).module() + model = torch.export.export_for_training( + eager_model, example_inputs, strict=True + ).module() edge_model = export_to_edge(model, example_inputs) executorch_prog = edge_model.to_executorch() diff --git a/examples/portable/scripts/export_and_delegate.py b/examples/portable/scripts/export_and_delegate.py index 6a8a28d5338..1c2adf67688 100644 --- a/examples/portable/scripts/export_and_delegate.py +++ b/examples/portable/scripts/export_and_delegate.py @@ -61,7 +61,7 @@ def export_composite_module_with_lower_graph(): m_compile_spec = m.get_compile_spec() # pre-autograd export. eventually this will become torch.export - m = torch.export.export_for_training(m, m_inputs).module() + m = torch.export.export_for_training(m, m_inputs, strict=True).module() edge = export_to_edge(m, m_inputs) logging.info(f"Exported graph:\n{edge.exported_program().graph}") @@ -84,7 +84,7 @@ def forward(self, *args): m = CompositeModule() m = m.eval() # pre-autograd export. eventually this will become torch.export - m = torch.export.export_for_training(m, m_inputs).module() + m = torch.export.export_for_training(m, m_inputs, strict=True).module() composited_edge = export_to_edge(m, m_inputs) # The graph module is still runnerable @@ -134,7 +134,7 @@ def get_example_inputs(self): m = Model() m_inputs = m.get_example_inputs() # pre-autograd export. eventually this will become torch.export - m = torch.export.export_for_training(m, m_inputs).module() + m = torch.export.export_for_training(m, m_inputs, strict=True).module() edge = export_to_edge(m, m_inputs) logging.info(f"Exported graph:\n{edge.exported_program().graph}") @@ -171,7 +171,7 @@ def export_and_lower_the_whole_graph(): m_inputs = m.get_example_inputs() # pre-autograd export. eventually this will become torch.export - m = torch.export.export_for_training(m, m_inputs).module() + m = torch.export.export_for_training(m, m_inputs, strict=True).module() edge = export_to_edge(m, m_inputs) logging.info(f"Exported graph:\n{edge.exported_program().graph}") diff --git a/examples/xnnpack/aot_compiler.py b/examples/xnnpack/aot_compiler.py index 6db0d82a274..f67150169dc 100644 --- a/examples/xnnpack/aot_compiler.py +++ b/examples/xnnpack/aot_compiler.py @@ -87,14 +87,14 @@ model = model.eval() # pre-autograd export. eventually this will become torch.export - ep = torch.export.export_for_training(model, example_inputs) + ep = torch.export.export_for_training(model, example_inputs, strict=True) model = ep.module() if args.quantize: logging.info("Quantizing Model...") # TODO(T165162973): This pass shall eventually be folded into quantizer model = quantize(model, example_inputs, quant_type) - ep = torch.export.export_for_training(model, example_inputs) + ep = torch.export.export_for_training(model, example_inputs, strict=True) edge = to_edge_transform_and_lower( ep, diff --git a/examples/xnnpack/quantization/example.py b/examples/xnnpack/quantization/example.py index 3e30c239215..90a6b94d02b 100644 --- a/examples/xnnpack/quantization/example.py +++ b/examples/xnnpack/quantization/example.py @@ -60,7 +60,9 @@ def verify_xnnpack_quantizer_matching_fx_quant_model(model_name, model, example_ m = model # 1. pytorch 2.0 export quantization flow (recommended/default flow) - m = torch.export.export_for_training(m, copy.deepcopy(example_inputs)).module() + m = torch.export.export_for_training( + m, copy.deepcopy(example_inputs), strict=True + ).module() quantizer = XNNPACKQuantizer() quantization_config = get_symmetric_quantization_config(is_per_channel=True) quantizer.set_global(quantization_config) @@ -177,7 +179,9 @@ def main() -> None: model = model.eval() # pre-autograd export. eventually this will become torch.export - model = torch.export.export_for_training(model, example_inputs).module() + model = torch.export.export_for_training( + model, example_inputs, strict=True + ).module() start = time.perf_counter() quantized_model = quantize(model, example_inputs) end = time.perf_counter() diff --git a/exir/backend/test/test_partitioner.py b/exir/backend/test/test_partitioner.py index 917dae32d74..e9320cf415d 100644 --- a/exir/backend/test/test_partitioner.py +++ b/exir/backend/test/test_partitioner.py @@ -76,7 +76,7 @@ def partition( mlp = MLP() example_inputs = mlp.get_random_inputs() - model = export_for_training(mlp, example_inputs).module() + model = export_for_training(mlp, example_inputs, strict=True).module() aten = export(model, example_inputs, strict=True) spec_key = "path" spec_value = "/a/b/c/d" @@ -137,7 +137,7 @@ def partition( mlp = MLP() example_inputs = mlp.get_random_inputs() - model = export_for_training(mlp, example_inputs).module() + model = export_for_training(mlp, example_inputs, strict=True).module() aten = export(model, example_inputs, strict=True) edge = exir.to_edge(aten) @@ -177,7 +177,7 @@ def partition( mlp = MLP() example_inputs = mlp.get_random_inputs() - model = export_for_training(mlp, example_inputs).module() + model = export_for_training(mlp, example_inputs, strict=True).module() edge = exir.to_edge(export(model, example_inputs, strict=True)) with self.assertRaisesRegex( @@ -229,7 +229,9 @@ def partition( partition_tags=partition_tags, ) - model = export_for_training(self.AddConst(), (torch.ones(2, 2),)).module() + model = export_for_training( + self.AddConst(), (torch.ones(2, 2),), strict=True + ).module() edge = exir.to_edge(export(model, (torch.ones(2, 2),), strict=True)) delegated = edge.to_backend(PartitionerNoTagData()) @@ -308,7 +310,9 @@ def partition( partition_tags=partition_tags, ) - model = export_for_training(self.AddConst(), (torch.ones(2, 2),)).module() + model = export_for_training( + self.AddConst(), (torch.ones(2, 2),), strict=True + ).module() edge = exir.to_edge(export(model, (torch.ones(2, 2),), strict=True)) delegated = edge.to_backend(PartitionerTagData()) @@ -383,7 +387,9 @@ def partition( partition_tags=partition_tags, ) - model = export_for_training(self.AddConst(), (torch.ones(2, 2),)).module() + model = export_for_training( + self.AddConst(), (torch.ones(2, 2),), strict=True + ).module() edge = exir.to_edge(export(model, (torch.ones(2, 2),), strict=True)) delegated = edge.to_backend(PartitionerTagData()) @@ -471,7 +477,9 @@ def partition( ) inputs = (torch.ones(2, 2),) - model = export_for_training(ReuseConstData(), (torch.ones(2, 2),)).module() + model = export_for_training( + ReuseConstData(), (torch.ones(2, 2),), strict=True + ).module() edge = exir.to_edge(export(model, (torch.ones(2, 2),), strict=True)) exec_prog = edge.to_backend(PartitionerTagData()).to_executorch() executorch_module = _load_for_executorch_from_buffer(exec_prog.buffer) @@ -531,7 +539,9 @@ def partition( partition_tags=partition_tags, ) - model = export_for_training(ReuseConstData(), (torch.ones(2, 2),)).module() + model = export_for_training( + ReuseConstData(), (torch.ones(2, 2),), strict=True + ).module() edge = exir.to_edge(export(model, (torch.ones(2, 2),), strict=True)) with self.assertRaises(RuntimeError) as error: _ = edge.to_backend(PartitionerTagData()) diff --git a/exir/backend/test/test_passes.py b/exir/backend/test/test_passes.py index bc18f090238..1cdf494fa01 100644 --- a/exir/backend/test/test_passes.py +++ b/exir/backend/test/test_passes.py @@ -28,7 +28,9 @@ def forward(self, x): z = x - self.const return y, z - model = export_for_training(ReuseConstData(), (torch.ones(2, 2),)).module() + model = export_for_training( + ReuseConstData(), (torch.ones(2, 2),), strict=True + ).module() edge = exir.to_edge( torch.export.export(model, (torch.ones(2, 2),), strict=True) ) diff --git a/exir/emit/test/test_emit.py b/exir/emit/test/test_emit.py index 362796146ee..d5ebf1ffae9 100644 --- a/exir/emit/test/test_emit.py +++ b/exir/emit/test/test_emit.py @@ -1751,8 +1751,8 @@ def forward(self, x): module_1(*example_inputs) module_2(*example_inputs) - ep1 = export_for_training(module_1, example_inputs) - ep2 = export_for_training(module_2, example_inputs) + ep1 = export_for_training(module_1, example_inputs, strict=True) + ep2 = export_for_training(module_2, example_inputs, strict=True) edge_program_manager = exir.to_edge( {"forward1": ep1, "forward2": ep2}, diff --git a/exir/tests/test_passes.py b/exir/tests/test_passes.py index 39dbd3f51d3..887ca39864a 100644 --- a/exir/tests/test_passes.py +++ b/exir/tests/test_passes.py @@ -1164,7 +1164,9 @@ def forward(self, query, key, value): value = torch.randn(32, 32, 32, 32) # Capture the model - m = torch.export.export_for_training(M(32), (query, key, value)).module() + m = torch.export.export_for_training( + M(32), (query, key, value), strict=True + ).module() # 8w16a quantization from torch.ao.quantization.observer import ( @@ -1405,8 +1407,7 @@ def quantize_model( ) -> Tuple[EdgeProgramManager, int, int]: # program capture m = torch.export.export_for_training( - m_eager, - example_inputs, + m_eager, example_inputs, strict=True ).module() quantizer = XNNPACKQuantizer() diff --git a/exir/tests/test_quantization.py b/exir/tests/test_quantization.py index 61e3410186e..0a0a85077bb 100644 --- a/exir/tests/test_quantization.py +++ b/exir/tests/test_quantization.py @@ -52,7 +52,7 @@ def test_resnet(self) -> None: m_copy = copy.deepcopy(m) # program capture m = torch.export.export_for_training( - m, copy.deepcopy(example_inputs) + m, copy.deepcopy(example_inputs), strict=True ).module() quantizer = XNNPACKQuantizer() diff --git a/exir/tests/test_quantize_io_pass.py b/exir/tests/test_quantize_io_pass.py index aab941b538c..ddc0294ba68 100644 --- a/exir/tests/test_quantize_io_pass.py +++ b/exir/tests/test_quantize_io_pass.py @@ -39,12 +39,14 @@ def _quantize(self, mod, example_inputs): operator_config = get_symmetric_quantization_config() quantizer.set_global(operator_config) m = torch.export.export_for_training( - mod, copy.deepcopy(example_inputs) + mod, copy.deepcopy(example_inputs), strict=True ).module() m = prepare_pt2e(m, quantizer) _ = m(*example_inputs) m = convert_pt2e(m) - exported_program = torch.export.export_for_training(m, example_inputs) + exported_program = torch.export.export_for_training( + m, example_inputs, strict=True + ) return exported_program def _check_count(self, op, count, epm): diff --git a/extension/export_util/utils.py b/extension/export_util/utils.py index 2679930178a..aa3a736af3c 100644 --- a/extension/export_util/utils.py +++ b/extension/export_util/utils.py @@ -108,7 +108,7 @@ def export_to_exec_prog( ) -> ExecutorchProgramManager: m = model.eval() # pre-autograd export. eventually this will become torch.export - m = export_for_training(m, example_inputs).module() + m = export_for_training(m, example_inputs, strict=True).module() core_aten_ep = _to_core_aten( m, diff --git a/extension/llm/export/builder.py b/extension/llm/export/builder.py index cf3a1087cfb..8fcb5798327 100644 --- a/extension/llm/export/builder.py +++ b/extension/llm/export/builder.py @@ -234,6 +234,7 @@ def _export(self, module: Optional[torch.nn.Module] = None) -> ExportedProgram: self.example_inputs, kwargs=self.example_kwarg_inputs, dynamic_shapes=dynamic_shape, + strict=True, ) return exported_module diff --git a/extension/llm/export/test_export_passes.py b/extension/llm/export/test_export_passes.py index 12ce18ebb79..b0c5af7e65f 100644 --- a/extension/llm/export/test_export_passes.py +++ b/extension/llm/export/test_export_passes.py @@ -10,10 +10,7 @@ class RemoveRedundantTransposesPassTest(unittest.TestCase): def _export(self, model, example_inputs): - exported_module = export_for_training( - model, - example_inputs, - ) + exported_module = export_for_training(model, example_inputs, strict=True) return exported_module.module() def _check(self, model, example_inputs, key, before_count, after_count): From edbd2e4f02dfdeb70d44352a198a7fb9ea382bf5 Mon Sep 17 00:00:00 2001 From: Jacob Szwejbka Date: Tue, 8 Apr 2025 10:24:57 -0700 Subject: [PATCH 040/244] Allow emitting mutable buffer names in schema Differential Revision: D72579501 Pull Request resolved: https://github.com/pytorch/executorch/pull/9935 --- exir/capture/_config.py | 5 +++++ exir/emit/_emit_program.py | 2 ++ exir/emit/_emitter.py | 10 +++++++++- exir/emit/test/test_emit.py | 24 ++++++++++++++++++++++++ exir/program/_program.py | 1 + 5 files changed, 41 insertions(+), 1 deletion(-) diff --git a/exir/capture/_config.py b/exir/capture/_config.py index abb7aa74b93..9267af4f2dc 100644 --- a/exir/capture/_config.py +++ b/exir/capture/_config.py @@ -97,3 +97,8 @@ class ExecutorchBackendConfig: # If set to true, all trainable weights will be stored in a separate file, # external to the PTE file. external_mutable_weights: bool = False + + # If set to true, all mutable buffers will have their fully qualified names + # serialized in the PTE file. Its value is ignored if mutable buffers are not + # memory planned as the names must be serialized in that case. + emit_mutable_buffer_names: bool = False diff --git a/exir/emit/_emit_program.py b/exir/emit/_emit_program.py index f9571143a1b..f456626feed 100644 --- a/exir/emit/_emit_program.py +++ b/exir/emit/_emit_program.py @@ -118,6 +118,7 @@ def emit_program( methods: Union[ExportedProgram, Dict[str, ExportedProgram]], emit_stacktrace: bool = False, prim_getters: Optional[Dict[str, Any]] = None, + emit_mutable_buffer_names: bool = False, ) -> EmitterOutput: """ Given a exported program, it returns the program in the format @@ -163,6 +164,7 @@ def emit_program( operator_cache={}, delegate_cache={}, emit_stacktrace=emit_stacktrace, + emit_mutable_buffer_names=emit_mutable_buffer_names, ) gm = _remove_non_user_outputs(exported_program) diff --git a/exir/emit/_emitter.py b/exir/emit/_emitter.py index 0cbc63bde21..9cc8a1e809c 100644 --- a/exir/emit/_emitter.py +++ b/exir/emit/_emitter.py @@ -149,6 +149,7 @@ class _EmitterState: # delegate_cache: the key is hash(delegated_payload) and the value is the index in delegates delegate_cache: Dict[str, int] emit_stacktrace: bool + emit_mutable_buffer_names: bool spec2id_dict: Dict[TensorSpec, int] = field(default_factory=dict) @@ -1610,7 +1611,7 @@ def _find_fqn_for_placeholder( ) return fqn, is_mutable_buffer - def placeholder( + def placeholder( # noqa: C901 self, target: _Target, args: Tuple[_Argument, ...], kwargs: Dict[str, _Argument] ) -> _AbstractValue: """Emits the value within the placeholder node. @@ -1639,6 +1640,13 @@ def placeholder( else: spec.extra_tensor_info.fully_qualified_name = fqn spec.extra_tensor_info.location = TensorDataLocation.EXTERNAL + if self.emitter_state.emit_mutable_buffer_names and is_mutable_buffer: + if spec.extra_tensor_info is None: + spec.extra_tensor_info = ExtraTensorInfo( + fully_qualified_name=fqn, location=TensorDataLocation.SEGMENT + ) + else: + spec.extra_tensor_info.fully_qualified_name = fqn # From the fqn find the corresponding tensor real_tensor = None diff --git a/exir/emit/test/test_emit.py b/exir/emit/test/test_emit.py index d5ebf1ffae9..a2df53e8cf3 100644 --- a/exir/emit/test/test_emit.py +++ b/exir/emit/test/test_emit.py @@ -1819,3 +1819,27 @@ def forward(self, input, label): ] self.assertEqual(external_map["net.linear.weight"], 0) self.assertEqual(external_map["net.linear.bias"], 1) + + def test_emit_mutable_buffer_names(self) -> None: + class Net(nn.Module): + def __init__(self): + super().__init__() + self.linear = nn.Linear(2, 2) + self.register_buffer("buffer", torch.zeros(1, 2)) + + def forward(self, x): + self.buffer.add_(1) + return self.linear(x) + self.buffer + + net = Net() + + ep = export(net, (torch.randn(1, 2),), strict=True) + # Lower the graph to edge dialect. + ep = to_edge(ep) + # Lower the graph to executorch. + ep = ep.to_executorch( + config=ExecutorchBackendConfig(emit_mutable_buffer_names=True) + ) + for val in ep.executorch_program.execution_plan[0].values: + if isinstance(val, Tensor) and val.extra_tensor_info: + self.assertEqual(val.extra_tensor_info.fully_qualified_name, "buffer") diff --git a/exir/program/_program.py b/exir/program/_program.py index 7a2120f9e9b..ef857ffd011 100644 --- a/exir/program/_program.py +++ b/exir/program/_program.py @@ -1612,6 +1612,7 @@ def __init__( self._execution_programs, backend_config.emit_stacktrace, self._config_methods, + backend_config.emit_mutable_buffer_names, ) # Serialize emitter output, ready to be written to a file. From 409916ed9908b42f7934f2f65effa21dc6ec5454 Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Tue, 8 Apr 2025 11:29:24 -0700 Subject: [PATCH 041/244] Update test_ios_ci.sh to use the app from executorch-examples repo (#9596) Co-authored-by: Huy Do --- .ci/scripts/test_ios_ci.sh | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/.ci/scripts/test_ios_ci.sh b/.ci/scripts/test_ios_ci.sh index 50c6448d4b2..e87dbec8444 100755 --- a/.ci/scripts/test_ios_ci.sh +++ b/.ci/scripts/test_ios_ci.sh @@ -7,7 +7,7 @@ set -e -APP_PATH="examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo" +APP_PATH="executorch-examples/apple/ExecuTorchDemo/ExecuTorchDemo" MODEL_NAME="mv3" SIMULATOR_NAME="executorch" @@ -34,6 +34,10 @@ say() { echo -e "\033[1m\n\t** $1 **\n\033[0m" } +say "Cloning the Demo App" + +git clone --depth 1 https://github.com/pytorch-labs/executorch-examples.git + say "Installing CoreML Backend Requirements" ./backends/apple/coreml/scripts/install_requirements.sh From ff38d9f8c533a61b324c55a3e5b2b3b0c2b808db Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Tue, 8 Apr 2025 12:01:55 -0700 Subject: [PATCH 042/244] android-release-artifacts.yml allow upload AAR to maven (#9954) Add an option to upload the AAR to maven staging repository. Validated it's working both with and without the AAR upload option. --- .../workflows/android-release-artifacts.yml | 25 ++++++++++++++++++- .../android/executorch_android/build.gradle | 2 +- 2 files changed, 25 insertions(+), 2 deletions(-) diff --git a/.github/workflows/android-release-artifacts.yml b/.github/workflows/android-release-artifacts.yml index 24aa6c1ad27..2503bf2214b 100644 --- a/.github/workflows/android-release-artifacts.yml +++ b/.github/workflows/android-release-artifacts.yml @@ -7,6 +7,10 @@ on: description: Version name to be uploaded for AAR release required: false type: string + upload_to_maven: + description: Upload the AAR to maven staging repository + required: false + type: boolean concurrency: group: ${{ github.workflow }}-${{ github.ref }} @@ -31,11 +35,14 @@ jobs: build-aar: name: build-aar needs: check-if-aar-exists - uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@main + if: ${{ !github.event.pull_request.head.repo.fork }} + uses: pytorch/test-infra/.github/workflows/linux_job_v2.yml@release/2.7 + secrets: inherit permissions: id-token: write contents: read with: + secrets-env: EXECUTORCH_MAVEN_SIGNING_KEYID EXECUTORCH_MAVEN_SIGNING_PASSWORD EXECUTORCH_MAVEN_CENTRAL_PASSWORD EXECUTORCH_MAVEN_CENTRAL_USERNAME EXECUTORCH_MAVEN_SIGNING_GPG_KEY_CONTENTS runner: linux.2xlarge docker-image: executorch-ubuntu-22.04-clang12-android submodules: 'true' @@ -52,6 +59,16 @@ jobs: PYTHON_EXECUTABLE=python bash .ci/scripts/setup-linux.sh --build-tool buck2 export ARTIFACTS_DIR_NAME=artifacts-to-be-uploaded + mkdir -p ~/.gradle + touch ~/.gradle/gradle.properties + echo "signing.keyId=${SECRET_EXECUTORCH_MAVEN_SIGNING_KEYID}" >> ~/.gradle/gradle.properties + echo "signing.password=${SECRET_EXECUTORCH_MAVEN_SIGNING_PASSWORD}" >> ~/.gradle/gradle.properties + echo "mavenCentralUsername=${SECRET_EXECUTORCH_MAVEN_CENTRAL_USERNAME}" >> ~/.gradle/gradle.properties + echo "mavenCentralPassword=${SECRET_EXECUTORCH_MAVEN_CENTRAL_PASSWORD}" >> ~/.gradle/gradle.properties + echo "signing.secretKeyRingFile=/tmp/secring.gpg" >> ~/.gradle/gradle.properties + + echo -n "$SECRET_EXECUTORCH_MAVEN_SIGNING_GPG_KEY_CONTENTS" | base64 -d > /tmp/secring.gpg + # Build AAR Package mkdir aar-out export BUILD_AAR_DIR=aar-out @@ -61,6 +78,12 @@ jobs: shasum -a 256 "${ARTIFACTS_DIR_NAME}/executorch.aar" + # Publish to maven staging + UPLOAD_TO_MAVEN="${{ inputs.upload_to_maven }}" + if [[ "$UPLOAD_TO_MAVEN" == "true" ]]; then + (cd aar-out; ANDROID_HOME="${ANDROID_SDK:-/opt/android/sdk}" ./gradlew :executorch_android:publishToMavenCentral) + fi + upload-release-aar: name: upload-release-aar needs: build-aar diff --git a/extension/android/executorch_android/build.gradle b/extension/android/executorch_android/build.gradle index b284ce3896e..913a011406d 100644 --- a/extension/android/executorch_android/build.gradle +++ b/extension/android/executorch_android/build.gradle @@ -48,7 +48,7 @@ mavenPublishing { publishToMavenCentral(SonatypeHost.DEFAULT) signAllPublications() - coordinates("org.pytorch", "executorch-android", "0.5.0-SNAPSHOT") + coordinates("org.pytorch", "executorch-android", "0.7.0") pom { name = "ExecuTorch Android" From 64a21c892d2440015b058d7af6b094086f3e66d2 Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Tue, 8 Apr 2025 12:04:09 -0700 Subject: [PATCH 043/244] [Release 0.6] update torchao pin (#9947) This PR is the first step to addressing https://github.com/pytorch/executorch/issues/9940 and making torchao installable on ET/release/0.6. 1. This PR bumps torchao pin to torchao/release/0.10.0 branch. 2. We will follow-up with a PR in release/0.6 to bump the pin and add torchao==0.10.0 to the pyproject.toml file. --- third-party/ao | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/third-party/ao b/third-party/ao index 923242e22b5..8b264ce1e15 160000 --- a/third-party/ao +++ b/third-party/ao @@ -1 +1 @@ -Subproject commit 923242e22b5fb67646473605ab959b90cc450abc +Subproject commit 8b264ce1e1597f4c3f6476609334be05d294f092 From fb2e9d8969324f614fd1ee3cbefab48dcf55f446 Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Tue, 8 Apr 2025 12:07:13 -0700 Subject: [PATCH 044/244] Update test_ios.sh (#9968) --- scripts/test_ios.sh | 26 ++++++-------------------- 1 file changed, 6 insertions(+), 20 deletions(-) diff --git a/scripts/test_ios.sh b/scripts/test_ios.sh index 385c85f3dfe..245f7b06f7a 100755 --- a/scripts/test_ios.sh +++ b/scripts/test_ios.sh @@ -15,7 +15,7 @@ set -e OUTPUT="${1:-executorch}" EXIT_STATUS=0 -APP_PATH="examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo" +APP_PATH="executorch-examples/apple/ExecuTorchDemo/ExecuTorchDemo" MODEL_NAME="mv3" SIMULATOR_NAME="executorch" @@ -44,26 +44,17 @@ say() { echo -e "\033[1m\n\t** $1 **\n\033[0m" } -say "Cloning the Code" - -pushd . > /dev/null -git clone -b viable/strict https://github.com/pytorch/executorch.git "$OUTPUT" -cd "$OUTPUT" - -say "Updating the Submodules" - -git submodule update --init - say "Activating a Virtual Environment" -python3 -m venv .venv -source .venv/bin/activate +python3 -m venv .venv && source .venv/bin/activate && pip install --upgrade pip say "Installing Requirements" -pip install --upgrade cmake pip setuptools wheel zstd +./install_executorch.sh -./install_executorch.sh --pybind coreml mps xnnpack +say "Cloning the Demo App" + +git clone --depth 1 https://github.com/pytorch-labs/executorch-examples.git say "Installing CoreML Backend Requirements" @@ -88,11 +79,6 @@ say "Downloading Labels" curl https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt \ -o "$APP_PATH/Resources/Models/MobileNet/imagenet_classes.txt" -say "Building Frameworks" - -./scripts/build_apple_frameworks.sh --coreml --custom --mps --optimized --portable --quantized --xnnpack -mv cmake-out "$APP_PATH/Frameworks" - say "Creating Simulator" xcrun simctl create "$SIMULATOR_NAME" "iPhone 15" From faad34441f53aa7a3685d08be005a84cd72066cc Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Tue, 8 Apr 2025 12:47:49 -0700 Subject: [PATCH 045/244] Update demo-apps-ios.md (#9970) cc @mergennachin @byjlw --- docs/source/demo-apps-ios.md | 142 ++++++++++++++++++++++++++++++++++- 1 file changed, 141 insertions(+), 1 deletion(-) diff --git a/docs/source/demo-apps-ios.md b/docs/source/demo-apps-ios.md index d68b1309e2b..09371e32918 100644 --- a/docs/source/demo-apps-ios.md +++ b/docs/source/demo-apps-ios.md @@ -1 +1,141 @@ -```{include} ../../examples/demo-apps/apple_ios/ExecuTorchDemo/README.md +# Building an ExecuTorch iOS Demo App + +Welcome to the tutorial on setting up the ExecuTorch iOS Demo App! + +This app uses the +[MobileNet v3](https://pytorch.org/vision/main/models/mobilenetv3.html) model to +process live camera images leveraging three different backends: +[XNNPACK](https://github.com/google/XNNPACK), +[Core ML](https://developer.apple.com/documentation/coreml) and +[Metal Performance Shaders (MPS)](https://developer.apple.com/documentation/metalperformanceshaders) +(Xcode 15+ and iOS 17+ only). + +![](_static/img/demo_ios_app.jpg) + +## Prerequisites + +Before we start, make sure you have the following tools installed: + +### 1. Xcode 15 and Command Line Tools + +Install Xcode 15 from the +[Mac App Store](https://apps.apple.com/app/xcode/id497799835) and then install +the Command Line Tools using the terminal: + +```bash +xcode-select --install +``` + +### 2. Python 3.10+ + +Python 3.10 or above, along with `pip`, should be pre-installed on MacOS 13.5+. +If needed, [download Python](https://www.python.org/downloads/macos/) and +install it. Verify the Python and pip versions using these commands: + +```bash +which python3 pip +python3 --version +pip --version +``` + +### 3. Getting Started Tutorial + +Follow the [Setting Up ExecuTorch](https://pytorch.org/executorch/stable/getting-started-setup) +tutorial to configure the basic environment: + +```bash +git clone -b viable/strict https://github.com/pytorch/executorch.git && cd executorch + +python3 -m venv .venv && source .venv/bin/activate && pip install --upgrade pip + +./install_executorch.sh +``` + +### 4. Clone the Demo App + +```bash +git clone --depth 1 https://github.com/pytorch-labs/executorch-examples.git +``` + +### 5. Backend Dependencies + +Also, follow the corresponding sections from [Core ML](https://pytorch.org/executorch/stable/build-run-coreml) and +[MPS](https://pytorch.org/executorch/stable/build-run-mps) tutorials to install additional dependencies for those +backends: + +```bash +./backends/apple/coreml/scripts/install_requirements.sh + +./backends/apple/mps/install_requirements.sh +``` + +## Models and Labels + +Now, let's move on to exporting and bundling the MobileNet v3 model. + +### 1. Export Model + +Export the MobileNet v3 model with Core ML, MPS and XNNPACK backends, and move +the exported model to a specific location where the Demo App will pick them up: + +```bash +MODEL_NAME="mv3" + +python3 -m examples.portable.scripts.export --model_name="$MODEL_NAME" +python3 -m examples.apple.coreml.scripts.export --model_name="$MODEL_NAME" +python3 -m examples.apple.mps.scripts.mps_example --model_name="$MODEL_NAME" +python3 -m examples.xnnpack.aot_compiler --model_name="$MODEL_NAME" --delegate + +APP_PATH="executorch-examples/apple/ExecuTorchDemo/ExecuTorchDemo" + +mkdir -p "$APP_PATH/Resources/Models/MobileNet/" +mv $MODEL_NAME*.pte "$APP_PATH/Resources/Models/MobileNet/" +``` + +### 2. Download Labels + +Download the MobileNet model labels required for image classification: + +```bash +curl https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt \ + -o "$APP_PATH/Resources/Models/MobileNet/imagenet_classes.txt" +``` + +## Final Steps + +We're almost done! Now, we just need to open the project in Xcode, run the +tests, and finally run the app. + +### 1. Open Project in Xcode + +Double-click on the project file under `executorch-examples/apple/ExecuTorchDemo` or run the command: + +```bash +open $APP_PATH.xcodeproj +``` + +### 2. Run Tests + +You can run tests on Simulaltor directly in Xcode with `Cmd + U` or use the +command line: + +```bash +xcrun simctl create executorch "iPhone 15" +xcodebuild clean test \ + -project $APP_PATH.xcodeproj \ + -scheme App \ + -destination name=executorch +xcrun simctl delete executorch +``` + +### 3. Run App + +Finally, connect the device, set up Code Signing in Xcode, and then run the app +using `Cmd + R`. Try installing a Release build for better performance. + +Congratulations! You've successfully set up the ExecuTorch iOS Demo App. Now, +you can explore and enjoy the power of ExecuTorch on your iOS device! + +Learn more about integrating and running [ExecuTorch on Apple](https://pytorch.org/executorch/stable/apple-runtime) platforms. + +![](_static/img/demo_ios_xcode.jpg) From 6771afe4b2d43f7e7ac48c0fdef1047306c9a5c0 Mon Sep 17 00:00:00 2001 From: Mergen Nachin Date: Tue, 8 Apr 2025 17:06:28 -0400 Subject: [PATCH 046/244] Getting Started, compare against reference eager model for verification in pybind (#9978) --- docs/source/getting-started.md | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/docs/source/getting-started.md b/docs/source/getting-started.md index fbca80cf23b..a3eb056fb32 100644 --- a/docs/source/getting-started.md +++ b/docs/source/getting-started.md @@ -86,7 +86,17 @@ runtime = Runtime.get() input_tensor: torch.Tensor = torch.randn(1, 3, 224, 224) program = runtime.load_program("model.pte") method = program.load_method("forward") -outputs: List[torch.Tensor] = method.execute([input_tensor]) +output: List[torch.Tensor] = method.execute([input_tensor]) +print("Run succesfully via executorch") + +from torchvision.models.mobilenetv2 import MobileNet_V2_Weights +import torchvision.models as models + +eager_reference_model = models.mobilenetv2.mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT).eval() +eager_reference_output = eager_reference_model(input_tensor) + +print("Comparing against original PyTorch module") +print(torch.allclose(output[0], eager_reference_output, rtol=1e-3, atol=1e-5)) ``` From 4235eca2c7984165f4e415c1e6324830fdd62025 Mon Sep 17 00:00:00 2001 From: Mergen Nachin Date: Tue, 8 Apr 2025 17:08:25 -0400 Subject: [PATCH 047/244] Add python version in building from source page (#9975) --- docs/source/using-executorch-building-from-source.md | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/using-executorch-building-from-source.md b/docs/source/using-executorch-building-from-source.md index 582cd3285bf..8fe67984445 100644 --- a/docs/source/using-executorch-building-from-source.md +++ b/docs/source/using-executorch-building-from-source.md @@ -29,6 +29,7 @@ Windows (x86_64) - Otherwise, Python's built-in virtual environment manager `python venv` is a good alternative. * `g++` version 7 or higher, `clang++` version 5 or higher, or another C++17-compatible toolchain. +* `python` version 3.10-3.12 Note that the cross-compilable core runtime code supports a wider range of toolchains, down to C++17. See the [Runtime Overview](./runtime-overview.md) for From 313405080c008774256f24f89c62b51577fd3e42 Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Tue, 8 Apr 2025 15:41:58 -0700 Subject: [PATCH 048/244] Delete examples/demo-apps/apple_ios/ExecuTorchDemo directory (#9976) --- .../ExecuTorchDemo.xcodeproj/project.pbxproj | 864 ------------------ .../xcshareddata/xcschemes/App.xcscheme | 90 -- .../MobileNetClassifierTest.xcscheme | 54 -- .../Resources/Examples/Arctic fox.jpg | Bin 211531 -> 0 bytes .../Resources/Examples/Samoyed.jpg | Bin 661378 -> 0 bytes .../Resources/Examples/hot pot.jpg | Bin 29676 -> 0 bytes .../ExecuTorchDemo/Sources/App/App.swift | 18 - .../Sources/App/CameraController.swift | 130 --- .../Sources/App/CameraPreview.swift | 36 - .../App/ClassificationController.swift | 76 -- .../Sources/App/ContentView.swift | 67 -- .../Sources/App/CustomViews.swift | 92 -- .../ImageClassification.swift | 23 - .../MobileNet/MobileNet-Bridging-Header.h | 9 - .../Sources/MobileNet/MobileNetClassifier.h | 25 - .../Sources/MobileNet/MobileNetClassifier.mm | 60 -- .../MobileNet/MobileNetClassifier.swift | 169 ---- .../Test/MobileNetClassifierTest.swift | 72 -- .../apple_ios/ExecuTorchDemo/README.md | 136 --- 19 files changed, 1921 deletions(-) delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo.xcodeproj/project.pbxproj delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo.xcodeproj/xcshareddata/xcschemes/App.xcscheme delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo.xcodeproj/xcshareddata/xcschemes/MobileNetClassifierTest.xcscheme delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Resources/Examples/Arctic fox.jpg delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Resources/Examples/Samoyed.jpg delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Resources/Examples/hot pot.jpg delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/App.swift delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/CameraController.swift delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/CameraPreview.swift delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/ClassificationController.swift delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/ContentView.swift delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/CustomViews.swift delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/ImageClassification/ImageClassification.swift delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNet-Bridging-Header.h delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNetClassifier.h delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNetClassifier.mm delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNetClassifier.swift delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/Test/MobileNetClassifierTest.swift delete mode 100644 examples/demo-apps/apple_ios/ExecuTorchDemo/README.md diff --git a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo.xcodeproj/project.pbxproj b/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo.xcodeproj/project.pbxproj deleted file mode 100644 index 3a8f1ba8d25..00000000000 --- a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo.xcodeproj/project.pbxproj +++ /dev/null @@ -1,864 +0,0 @@ -// !$*UTF8*$! -{ - archiveVersion = 1; - classes = { - }; - objectVersion = 56; - objects = 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zPwMkPl)XN1ccQJbSM$0RGmn{&`SD=-2_>bZ!fdt@T?E&;docX?LOOYKSvNAv!G>8_ zD=&7FS&r2&P=1#W&T&1M+sZqFSm}3mjILQE(+SkQUYQN4><8WB`501K&B5T^iI+G% zD}=3|YLmZ3bh-sH7dr{(ygU2`gSs0C%D}_c%Z!oib+5G+BGB43Z1SW_oe(J((uf94 zDhPlnLE>BH5DJEL?g*k`ZT|qVJf}ywRfpb+N zYLRjr2QdIKZ-9?*hCne){*kzRSi^$RBwebiNRw{4)lmM>oN7K<)_@sj!YL?@M}^S+ zloT5TsGc-rn}P{iB(GYo5~M$4^ZcM@aVdz%CLK{z72ocoQ;C66E~-0U&$<&akZAh^kWsA1 z{9j|E6Y@fsm2<;&RhMkNR5oKFGd(8aIX9N+Z4z|TttVIgVh*Maix%OPc?|Nhr7>xu z%n`y@^SUOz8m>#)6@2@GYnPj`SUcLa^=FJ&>aOS(`08YF$MQvX4OR54U8(i1c1hpyhDYrZ>GBe71n2ucs$aD1dsP0hVpb)e;w{PS?XtC; mp-R=4%3WGHqov4ugimIA94H1kr~q83H4q9#R#>P`fB)GP{~`) -> Void)? - - func startCapturing(withTimeInterval interval: TimeInterval, - callback: @escaping (Result) -> Void) { - authorize { error in - if let error { - DispatchQueue.main.async { - callback(.failure(error)) - } - return - } - self.setup { error in - if let error { - DispatchQueue.main.async { - callback(.failure(error)) - } - return - } - self.captureSession.startRunning() - DispatchQueue.main.async { - self.callback = callback - self.timer = Timer.scheduledTimer(withTimeInterval: interval, repeats: true) { _ in - self.photoOutput.capturePhoto(with: AVCapturePhotoSettings(), delegate: self) - } - } - } - } - } - - private func authorize(_ completion: @escaping (Error?) -> Void) { - switch AVCaptureDevice.authorizationStatus(for: .video) { - case .authorized: - DispatchQueue.global(qos: .userInitiated).async { - completion(nil) - } - case .notDetermined: - AVCaptureDevice.requestAccess(for: .video) { granted in - DispatchQueue.global(qos: .userInitiated).async { - if granted { - completion(nil) - } else { - completion(CameraControllerError.authorization("Camera access denied")) - } - } - } - default: - DispatchQueue.global(qos: .userInitiated).async { - completion(CameraControllerError.authorization("Camera access denied")) - } - } - } - - private func setup(_ callback: (Error?) -> Void) { - guard let videoCaptureDevice = AVCaptureDevice.default(for: .video) - else { - callback(CameraControllerError.setup("Cannot get video capture device")) - return - } - let videoInput: AVCaptureDeviceInput - do { - videoInput = try AVCaptureDeviceInput(device: videoCaptureDevice) - } catch { - callback(CameraControllerError.setup("Cannot set up video input: \(error)")) - return - } - if captureSession.canAddInput(videoInput) { - captureSession.addInput(videoInput) - } else { - callback(CameraControllerError.setup("Cannot add video input")) - return - } - if captureSession.canAddOutput(photoOutput) { - captureSession.addOutput(photoOutput) - } else { - callback(CameraControllerError.setup("Cannot add photo output")) - return - } - callback(nil) - } - - func photoOutput(_ output: AVCapturePhotoOutput, didFinishProcessingPhoto photo: AVCapturePhoto, error: Error?) { - guard let callback = self.callback else { - print("No image capturing callback set") - return - } - if let error { - callback(.failure(CameraControllerError.capture("Image capture error: \(error)"))) - } - guard let imageData = photo.fileDataRepresentation(), - let image = UIImage(data: imageData), - let cgImage = image.cgImage - else { - callback(.failure(CameraControllerError.capture("Couldn't get image data"))) - return - } - var orientation = UIImage.Orientation.up - switch UIDevice.current.orientation { - case .portrait: - orientation = .right - case .portraitUpsideDown: - orientation = .left - case .landscapeRight: - orientation = .down - default: - break - } - callback(.success(UIImage(cgImage: cgImage, scale: image.scale, orientation: orientation))) - } -} diff --git a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/CameraPreview.swift b/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/CameraPreview.swift deleted file mode 100644 index 0a7d9f8b5af..00000000000 --- a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/CameraPreview.swift +++ /dev/null @@ -1,36 +0,0 @@ -/* - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -import AVFoundation -import SwiftUI - -struct CameraPreview: UIViewRepresentable { - let captureSession: AVCaptureSession - - func makeUIView(context: Context) -> UIView { - let view = CameraView(frame: UIScreen.main.bounds) - view.videoPreviewLayer?.session = captureSession - return view - } - - func updateUIView(_ uiView: UIView, context: Context) { - if let view = uiView as? CameraView { - view.videoPreviewLayer?.frame = uiView.bounds - } - } -} - -final class CameraView: UIView { - override class var layerClass: AnyClass { - return AVCaptureVideoPreviewLayer.self - } - - var videoPreviewLayer: AVCaptureVideoPreviewLayer? { - return layer as? AVCaptureVideoPreviewLayer - } -} diff --git a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/ClassificationController.swift b/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/ClassificationController.swift deleted file mode 100644 index 5a50da9c22d..00000000000 --- a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/ClassificationController.swift +++ /dev/null @@ -1,76 +0,0 @@ -/* - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -import ImageClassification -import MobileNetClassifier -import SwiftUI - -enum Mode: String, CaseIterable { - case xnnpack = "XNNPACK" - case coreML = "Core ML" - case mps = "MPS" -} - -class ClassificationController: ObservableObject { - @AppStorage("mode") var mode: Mode = .xnnpack - @Published var classifications: [Classification] = [] - @Published var elapsedTime: TimeInterval = 0.0 - @Published var isRunning = false - - private let queue = DispatchQueue(label: "org.pytorch.executorch.demo", qos: .userInitiated) - private var classifier: ImageClassification? - private var currentMode: Mode = .xnnpack - - func classify(_ image: UIImage) { - guard !isRunning else { - print("Dropping frame") - return - } - isRunning = true - - if currentMode != mode { - currentMode = mode - classifier = nil - } - queue.async { - var classifications: [Classification] = [] - var elapsedTime: TimeInterval = -1 - do { - if self.classifier == nil { - self.classifier = try self.createClassifier(for: self.currentMode) - } - let startTime = CFAbsoluteTimeGetCurrent() - classifications = try self.classifier?.classify(image: image) ?? [] - elapsedTime = (CFAbsoluteTimeGetCurrent() - startTime) * 1000 - } catch { - print("Error classifying image: \(error)") - } - DispatchQueue.main.async { - self.classifications = classifications - self.elapsedTime = elapsedTime - self.isRunning = false - } - } - } - - private func createClassifier(for mode: Mode) throws -> ImageClassification? { - let modelFileName: String - switch mode { - case .coreML: - modelFileName = "mv3_coreml_all" - case .mps: - modelFileName = "mv3_mps_float16" - case .xnnpack: - modelFileName = "mv3_xnnpack_fp32" - } - guard let modelFilePath = Bundle.main.path(forResource: modelFileName, ofType: "pte"), - let labelsFilePath = Bundle.main.path(forResource: "imagenet_classes", ofType: "txt") - else { return nil } - return try MobileNetClassifier(modelFilePath: modelFilePath, labelsFilePath: labelsFilePath) - } -} diff --git a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/ContentView.swift b/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/ContentView.swift deleted file mode 100644 index 9005a20d1af..00000000000 --- a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/ContentView.swift +++ /dev/null @@ -1,67 +0,0 @@ -/* - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -import AVFoundation -import SwiftUI - -struct ContentView: View { - @StateObject private var cameraController = CameraController() - @StateObject private var classificationController = ClassificationController() - - var body: some View { - ZStack { - cameraPreview - controlPanel - } - } - - private var cameraPreview: some View { - CameraPreview(captureSession: cameraController.captureSession) - .aspectRatio(contentMode: .fill) - .edgesIgnoringSafeArea(.all) - .onAppear(perform: startCapturing) - .onDisappear(perform: stopCapturing) - } - - private var controlPanel: some View { - VStack(spacing: 0) { - TopBar(title: "ExecuTorch Demo") - ClassificationLabelView(controller: classificationController) - Spacer() - ClassificationTimeView(controller: classificationController) - ModeSelector(controller: classificationController) - } - } - - private func startCapturing() { - UIApplication.shared.isIdleTimerDisabled = true - cameraController.startCapturing(withTimeInterval: 1.0) { result in - switch result { - case .success(let image): - self.classificationController.classify(image) - case .failure(let error): - self.handleError(error) - } - } - } - - private func stopCapturing() { - UIApplication.shared.isIdleTimerDisabled = false - } - - private func handleError(_ error: Error) { - stopCapturing() - print(error) - } -} - -struct ContentView_Previews: PreviewProvider { - static var previews: some View { - ContentView() - } -} diff --git a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/CustomViews.swift b/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/CustomViews.swift deleted file mode 100644 index d00fefdb514..00000000000 --- a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/App/CustomViews.swift +++ /dev/null @@ -1,92 +0,0 @@ -/* - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -import AVFoundation -import ImageClassification -import SwiftUI - -struct TopBar: View { - let title: String - - var body: some View { - Text(title) - .font(.title) - .foregroundColor(.white) - .frame(maxWidth: .infinity) - .background(Color.black.opacity(0.5)) - } -} - -struct ClassificationLabelView: View { - @ObservedObject var controller: ClassificationController - - var body: some View { - VStack(alignment: .leading) { - ForEach(controller.classifications.prefix(3), id: \.label) { classification in - Text("\(classification.label) \(Int(classification.confidence * 100))%") - .font(.footnote) - .foregroundColor(.white) - } - } - .padding() - .frame(maxWidth: .infinity) - .background(Color.black.opacity(0.5)) - } -} - -struct ClassificationTimeView: View { - @ObservedObject var controller: ClassificationController - - var body: some View { - VStack { - if controller.isRunning { - ProgressView() - .progressViewStyle(CircularProgressViewStyle(tint: .white)) - .frame(width: nil, height: 34, alignment: .center) - } else { - Text("\n\(controller.elapsedTime, specifier: "%.2f") ms") - .font(.footnote) - .foregroundColor(.white) - } - } - .frame(maxWidth: .infinity) - .background(Color.black.opacity(0.5)) - } -} - -struct ModeSelector: View { - @ObservedObject var controller: ClassificationController - - var body: some View { - HStack { - ForEach(Mode.allCases, id: \.self) { mode in - ModeButton(mode: mode, controller: controller) - } - } - .padding() - .frame(maxWidth: .infinity) - .background(Color.black.opacity(0.5)) - } -} - -struct ModeButton: View { - var mode: Mode - @ObservedObject var controller: ClassificationController - - var body: some View { - Button(action: { controller.mode = mode }) { - Text(mode.rawValue) - .fontWeight(.semibold) - .foregroundColor(.white) - .padding(.horizontal, 10) - .padding(.vertical, 5) - .background(controller.mode == mode ? Color.red : Color.clear) - .cornerRadius(15) - } - } -} diff --git a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/ImageClassification/ImageClassification.swift b/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/ImageClassification/ImageClassification.swift deleted file mode 100644 index 1c515e98a62..00000000000 --- a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/ImageClassification/ImageClassification.swift +++ /dev/null @@ -1,23 +0,0 @@ -/* - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -import UIKit - -public struct Classification { - public let label: String - public let confidence: Float - - public init(label: String, confidence: Float) { - self.label = label - self.confidence = confidence - } -} - -public protocol ImageClassification { - func classify(image: UIImage) throws -> [Classification] -} diff --git a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNet-Bridging-Header.h b/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNet-Bridging-Header.h deleted file mode 100644 index 6d1eb27932b..00000000000 --- a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNet-Bridging-Header.h +++ /dev/null @@ -1,9 +0,0 @@ -/* - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -#import "MobileNetClassifier.h" diff --git a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNetClassifier.h b/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNetClassifier.h deleted file mode 100644 index 74051909a36..00000000000 --- a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNetClassifier.h +++ /dev/null @@ -1,25 +0,0 @@ -/* - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -#import - -NS_ASSUME_NONNULL_BEGIN - -FOUNDATION_EXPORT NSErrorDomain const ETMobileNetClassifierErrorDomain; - -@interface ETMobileNetClassifier : NSObject - -- (instancetype)initWithFilePath:(NSString*)filePath; -- (BOOL)classifyWithInput:(float*)input - output:(float*)output - outputSize:(NSInteger)predictionBufferSize - error:(NSError**)error; - -@end - -NS_ASSUME_NONNULL_END diff --git a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNetClassifier.mm b/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNetClassifier.mm deleted file mode 100644 index 59b66e510bd..00000000000 --- a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNetClassifier.mm +++ /dev/null @@ -1,60 +0,0 @@ -/* - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -#import "MobileNetClassifier.h" - -#import -#import - -using namespace ::executorch::extension; - -NSErrorDomain const ETMobileNetClassifierErrorDomain = - @"MobileNetClassifierErrorDomain"; -const int32_t kSize = 224; -const int32_t kChannels = 3; - -@implementation ETMobileNetClassifier { - std::unique_ptr _module; -} - -- (instancetype)initWithFilePath:(NSString*)filePath { - self = [super init]; - if (self) { - _module = std::make_unique(filePath.UTF8String); - } - return self; -} - -- (BOOL)classifyWithInput:(float*)input - output:(float*)output - outputSize:(NSInteger)outputSize - error:(NSError**)error { - const auto result = - _module->forward(from_blob(input, {1, kChannels, kSize, kSize})); - - if (!result.ok()) { - if (error) { - *error = [NSError - errorWithDomain:ETMobileNetClassifierErrorDomain - code:NSInteger(result.error()) - userInfo:@{ - NSLocalizedDescriptionKey : [NSString - stringWithFormat: - @"Failed to run forward on the torch module, error code: %i", - result.error()] - }]; - } - return NO; - } - const auto outputData = result->at(0).toTensor().const_data_ptr(); - std::copy(outputData, outputData + outputSize, output); - - return YES; -} - -@end diff --git a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNetClassifier.swift b/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNetClassifier.swift deleted file mode 100644 index b7a6e30a285..00000000000 --- a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/MobileNetClassifier.swift +++ /dev/null @@ -1,169 +0,0 @@ -/* - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -import ExecuTorch -import ImageClassification -import UIKit - -import os.log - -public enum MobileNetClassifierError: Error { - case inputPointer - case rawData - case transform - - var localizedDescription: String { - switch self { - case .inputPointer: - return "Cannot get the input pointer base address" - case .rawData: - return "Cannot get the pixel data from the image" - case .transform: - return "Cannot transform the image" - } - } -} - -// See https://pytorch.org/vision/main/models/generated/torchvision.models.mobilenet_v3_small.html -// on model input/output spec. -public class MobileNetClassifier: ImageClassification { - private static let resizeSize: CGFloat = 256 - private static let cropSize: CGFloat = 224 - - private var mobileNetClassifier: ETMobileNetClassifier - private var labels: [String] = [] - private var rawDataBuffer: [UInt8] - private var normalizedBuffer: [Float] - - public init?(modelFilePath: String, labelsFilePath: String) throws { - labels = try String(contentsOfFile: labelsFilePath, encoding: .utf8) - .components(separatedBy: .newlines) - mobileNetClassifier = ETMobileNetClassifier(filePath: modelFilePath) - rawDataBuffer = [UInt8](repeating: 0, count: Int(Self.cropSize * Self.cropSize) * 4) - normalizedBuffer = [Float](repeating: 0, count: rawDataBuffer.count / 4 * 3) - - #if DEBUG - Log.shared.add(sink: self) - #endif - } - - deinit { - #if DEBUG - Log.shared.remove(sink: self) - #endif - } - - public func classify(image: UIImage) throws -> [Classification] { - var input = try normalize(rawData(from: transformed(image))) - var output = [Float](repeating: 0, count: labels.count) - - try input.withUnsafeMutableBufferPointer { inputPointer in - guard let inputPointerBaseAddress = inputPointer.baseAddress else { - throw MobileNetClassifierError.inputPointer - } - try mobileNetClassifier.classify( - withInput: inputPointerBaseAddress, - output: &output, - outputSize: labels.count) - } - return softmax(output).enumerated().sorted(by: { $0.element > $1.element }) - .compactMap { (index, probability) -> Classification? in - guard index < labels.count else { return nil } - return Classification(label: labels[index], confidence: probability) - } - } - - private func transformed(_ image: UIImage) throws -> UIImage { - let aspectRatio = image.size.width / image.size.height - let targetSize = - aspectRatio > 1 - ? CGSize(width: Self.resizeSize * aspectRatio, height: Self.resizeSize) - : CGSize(width: Self.resizeSize, height: Self.resizeSize / aspectRatio) - let cropRect = CGRect( - x: (targetSize.width - Self.cropSize) / 2, - y: (targetSize.height - Self.cropSize) / 2, - width: Self.cropSize, - height: Self.cropSize) - - UIGraphicsBeginImageContextWithOptions(cropRect.size, false, 1) - defer { UIGraphicsEndImageContext() } - image.draw( - in: CGRect( - x: -cropRect.origin.x, - y: -cropRect.origin.y, - width: targetSize.width, - height: targetSize.height)) - guard let resizedAndCroppedImage = UIGraphicsGetImageFromCurrentImageContext() - else { - throw MobileNetClassifierError.transform - } - return resizedAndCroppedImage - } - - private func rawData(from image: UIImage) throws -> [UInt8] { - guard let cgImage = image.cgImage else { - throw MobileNetClassifierError.rawData - } - let context = CGContext( - data: &rawDataBuffer, - width: cgImage.width, - height: cgImage.height, - bitsPerComponent: 8, - bytesPerRow: cgImage.width * 4, - space: CGColorSpaceCreateDeviceRGB(), - bitmapInfo: CGImageAlphaInfo.premultipliedLast.rawValue | CGBitmapInfo.byteOrder32Big.rawValue - ) - context?.draw( - cgImage, - in: CGRect( - origin: CGPoint.zero, - size: CGSize(width: cgImage.width, height: cgImage.height))) - return rawDataBuffer - } - - private func normalize(_ rawData: [UInt8]) -> [Float] { - let mean: [Float] = [0.485, 0.456, 0.406] - let std: [Float] = [0.229, 0.224, 0.225] - let pixelCount = rawData.count / 4 - - for i in 0.. [Float] { - let maxInput = input.max() ?? 0 - let expInput = input.map { exp($0 - maxInput) } - let sumExpInput = expInput.reduce(0, +) - return expInput.map { $0 / sumExpInput } - } -} - -#if DEBUG -extension MobileNetClassifier: LogSink { - public func log(level: LogLevel, timestamp: TimeInterval, filename: String, line: UInt, message: String) { - let logMessage = "executorch:\(filename):\(line) \(message)" - - switch level { - case .debug: - os_log(.debug, "%{public}@", logMessage) - case .info: - os_log(.info, "%{public}@", logMessage) - case .error: - os_log(.error, "%{public}@", logMessage) - case .fatal: - os_log(.fault, "%{public}@", logMessage) - default: - os_log("%{public}@", logMessage) - } - } -} -#endif diff --git a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/Test/MobileNetClassifierTest.swift b/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/Test/MobileNetClassifierTest.swift deleted file mode 100644 index 7936699bb9b..00000000000 --- a/examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Sources/MobileNet/Test/MobileNetClassifierTest.swift +++ /dev/null @@ -1,72 +0,0 @@ -/* - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -import ImageClassification -import XCTest - -@testable import MobileNetClassifier - -final class MobileNetClassifierTest: XCTestCase { - - func testV3WithPortableBackend() throws { - try run(model: "mv3") - } - - func testV3WithCoreMLBackend() throws { - try run(model: "mv3_coreml_all") - } - - func testV3WithMPSBackend() throws { - try run(model: "mv3_mps_float16") - } - - func testV3WithXNNPACKBackend() throws { - try run(model: "mv3_xnnpack_fp32") - } - - private func run(model modelName: String) throws { - guard - let modelFilePath = Bundle(for: type(of: self)) - .path(forResource: modelName, ofType: "pte") - else { - XCTFail("Failed to get model path") - return - } - guard - let labelsFilePath = Bundle(for: type(of: self)) - .path(forResource: "imagenet_classes", ofType: "txt") - else { - XCTFail("Failed to get labels path") - return - } - let classifier = try MobileNetClassifier( - modelFilePath: modelFilePath, - labelsFilePath: labelsFilePath) - for expectedClassification in [ - Classification(label: "Arctic fox", confidence: 0.9), - Classification(label: "Samoyed", confidence: 0.7), - Classification(label: "hot pot", confidence: 0.8), - ] { - guard - let imagePath = Bundle(for: type(of: self)) - .path(forResource: expectedClassification.label, ofType: "jpg"), - let image = UIImage(contentsOfFile: imagePath) - else { - XCTFail("Failed to get image path or image") - return - } - guard let classification = try classifier?.classify(image: image).first - else { - XCTFail("Failed to run the model") - return - } - XCTAssertEqual(classification.label, expectedClassification.label) - XCTAssertGreaterThan(classification.confidence, expectedClassification.confidence) - } - } -} diff --git a/examples/demo-apps/apple_ios/ExecuTorchDemo/README.md b/examples/demo-apps/apple_ios/ExecuTorchDemo/README.md deleted file mode 100644 index a66a1f75954..00000000000 --- a/examples/demo-apps/apple_ios/ExecuTorchDemo/README.md +++ /dev/null @@ -1,136 +0,0 @@ -# Building an ExecuTorch iOS Demo App - -Welcome to the tutorial on setting up the ExecuTorch iOS Demo App! - -This app uses the -[MobileNet v3](https://pytorch.org/vision/main/models/mobilenetv3.html) model to -process live camera images leveraging three different backends: -[XNNPACK](https://github.com/google/XNNPACK), -[Core ML](https://developer.apple.com/documentation/coreml) and -[Metal Performance Shaders (MPS)](https://developer.apple.com/documentation/metalperformanceshaders) -(Xcode 15+ and iOS 17+ only). - -![](_static/img/demo_ios_app.jpg) - -## Prerequisites - -Before we start, make sure you have the following tools installed: - -### 1. Xcode 15 and Command Line Tools - -Install Xcode 15 from the -[Mac App Store](https://apps.apple.com/app/xcode/id497799835) and then install -the Command Line Tools using the terminal: - -```bash -xcode-select --install -``` - -### 2. Python 3.10+ - -Python 3.10 or above, along with `pip`, should be pre-installed on MacOS 13.5+. -If needed, [download Python](https://www.python.org/downloads/macos/) and -install it. Verify the Python and pip versions using these commands: - -```bash -which python3 pip -python3 --version -pip --version -``` - -### 3. Getting Started Tutorial - -Follow the [Setting Up ExecuTorch](https://pytorch.org/executorch/stable/getting-started-setup) -tutorial to configure the basic environment: - -```bash -git clone -b viable/strict https://github.com/pytorch/executorch.git && cd executorch - -python3 -m venv .venv && source .venv/bin/activate - -pip install --upgrade cmake pip setuptools wheel - -./install_executorch.sh --pybind coreml mps xnnpack -``` - -### 4. Backend Dependencies - -Also, follow the corresponding sections from [Core ML](https://pytorch.org/executorch/stable/build-run-coreml) and -[MPS](https://pytorch.org/executorch/stable/build-run-mps) tutorials to install additional dependencies for those -backends: - -```bash -./backends/apple/coreml/scripts/install_requirements.sh - -./backends/apple/mps/install_requirements.sh -``` - -## Models and Labels - -Now, let's move on to exporting and bundling the MobileNet v3 model. - -### 1. Export Model - -Export the MobileNet v3 model with Core ML, MPS and XNNPACK backends, and move -the exported model to a specific location where the Demo App will pick them up: - -```bash -MODEL_NAME="mv3" - -python3 -m examples.portable.scripts.export --model_name="$MODEL_NAME" -python3 -m examples.apple.coreml.scripts.export --model_name="$MODEL_NAME" -python3 -m examples.apple.mps.scripts.mps_example --model_name="$MODEL_NAME" -python3 -m examples.xnnpack.aot_compiler --model_name="$MODEL_NAME" --delegate - -mkdir -p examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Resources/Models/MobileNet/ -mv "$MODEL_NAME*.pte" examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Resources/Models/MobileNet/ -``` - -### 2. Download Labels - -Download the MobileNet model labels required for image classification: - -```bash -curl https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt \ - -o examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo/Resources/Models/MobileNet/imagenet_classes.txt -``` - -## Final Steps - -We're almost done! Now, we just need to open the project in Xcode, run the -tests, and finally run the app. - -### 1. Open Project in Xcode - -Double-click on the project file under -`examples/demo-apps/apple_ios/ExecuTorchDemo` or run the command: - -```bash -open examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo.xcodeproj -``` - -### 2. Run Tests - -You can run tests on Simulaltor directly in Xcode with `Cmd + U` or use the -command line: - -```bash -xcrun simctl create executorch "iPhone 15" -xcodebuild clean test \ - -project examples/demo-apps/apple_ios/ExecuTorchDemo/ExecuTorchDemo.xcodeproj \ - -scheme App \ - -destination name=executorch -xcrun simctl delete executorch -``` - -### 3. Run App - -Finally, connect the device, set up Code Signing in Xcode, and then run the app -using `Cmd + R`. Try installing a Release build for better performance. - -Congratulations! You've successfully set up the ExecuTorch iOS Demo App. Now, -you can explore and enjoy the power of ExecuTorch on your iOS device! - -Learn more about integrating and running [ExecuTorch on Apple](https://pytorch.org/executorch/stable/apple-runtime) platforms. - -![](_static/img/demo_ios_xcode.jpg) From a0a9a2a12399dec0f95a472c7c7265b396cb397f Mon Sep 17 00:00:00 2001 From: cccclai Date: Tue, 8 Apr 2025 17:22:48 -0700 Subject: [PATCH 049/244] Add op_amax support (#9955) Summary: As title, add op_amax to support an internal model, add unit test in test_qnn_delegate.py Differential Revision: D72613814 --- backends/qualcomm/_passes/layout_transform.py | 1 + backends/qualcomm/builders/__init__.py | 2 + backends/qualcomm/builders/op_amax.py | 84 +++++++++++++++++++ backends/qualcomm/builders/qnn_constants.py | 7 ++ backends/qualcomm/quantizer/annotators.py | 5 ++ backends/qualcomm/tests/models.py | 10 +++ backends/qualcomm/tests/test_qnn_delegate.py | 15 ++++ 7 files changed, 124 insertions(+) create mode 100644 backends/qualcomm/builders/op_amax.py diff --git a/backends/qualcomm/_passes/layout_transform.py b/backends/qualcomm/_passes/layout_transform.py index 17960a6029b..4d47c38bc03 100644 --- a/backends/qualcomm/_passes/layout_transform.py +++ b/backends/qualcomm/_passes/layout_transform.py @@ -47,6 +47,7 @@ class LayoutTransform(ExportPass): layout_agnostic_ops = { exir_ops.edge.aten.abs.default, exir_ops.edge.aten.add.Tensor, + exir_ops.edge.aten.amax.default, exir_ops.edge.aten.bitwise_or.Tensor, exir_ops.edge.aten.bmm.default, exir_ops.edge.aten.bitwise_and.Tensor, diff --git a/backends/qualcomm/builders/__init__.py b/backends/qualcomm/builders/__init__.py index cc85333f26b..645b823d0e5 100644 --- a/backends/qualcomm/builders/__init__.py +++ b/backends/qualcomm/builders/__init__.py @@ -9,6 +9,7 @@ op_abs, op_adaptive_avg_pool2d, op_add, + op_amax, op_and, op_arange, op_argmin, @@ -95,6 +96,7 @@ op_abs, op_adaptive_avg_pool2d, op_add, + op_amax, op_and, op_arange, op_argmin, diff --git a/backends/qualcomm/builders/op_amax.py b/backends/qualcomm/builders/op_amax.py new file mode 100644 index 00000000000..06f7c998502 --- /dev/null +++ b/backends/qualcomm/builders/op_amax.py @@ -0,0 +1,84 @@ +# Copyright (c) Qualcomm Innovation Center, Inc. +# All rights reserved +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +from typing import cast, Dict, List + +import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper + +import numpy as np + +import torch +from executorch.backends.qualcomm.utils.constants import QCOM_AXIS_ORDER, QCOM_DATA + +from .node_visitor import NodeVisitor, register_node_visitor +from .qnn_constants import OpAmax, QNN_OP_PACKAGE_NAME_QTI_AISW + + +@register_node_visitor +class AMax(NodeVisitor): + target = ["aten.amax.default"] + + def __init__(self, *args) -> None: + super().__init__(*args) + + def define_node( + self, + node: torch.fx.Node, + nodes_to_wrappers: Dict[torch.fx.Node, PyQnnWrapper.TensorWrapper], + ) -> PyQnnWrapper.PyQnnOpWrapper: + input_node = node.args[0] + input_tensor = self.get_tensor(input_node, node) + input_tensor_wrapper = self.define_tensor( + input_node, + node, + input_tensor, + PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE, + nodes_to_wrappers, + ) + + # mean dims and keep dims + mean_dims = cast(List[int], node.args[1]) + mean_dims = [ + mean_dim % len(input_node.meta["val"].shape) for mean_dim in mean_dims + ] + if QCOM_AXIS_ORDER in node.meta: + mean_dims = [ + node.meta[QCOM_AXIS_ORDER].index(mean_dim) for mean_dim in mean_dims + ] + mean_dims_shape = [len(mean_dims)] + + output_tensor = self.get_tensor(node, node) + output_tensor_wrapper = self.define_tensor( + node, + node, + output_tensor, + PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE, + nodes_to_wrappers, + ) + + reduce_max_op = PyQnnWrapper.PyQnnOpWrapper( + node.name, + QNN_OP_PACKAGE_NAME_QTI_AISW, + OpAmax.op_name, + ) + reduce_max_op.AddInputTensors([input_tensor_wrapper]) + reduce_max_op.AddOutputTensors([output_tensor_wrapper]) + reduce_max_op.AddTensorParam( + OpAmax.param_axes, + PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_32, + len(mean_dims_shape), + mean_dims_shape, + np.array(mean_dims, dtype=np.uint32), + True, + ) + if len(node.args) > 2: + keep_dims = cast(bool, node.args[2]) + reduce_max_op.AddScalarParam( + OpAmax.param_keep_dims, + PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_BOOL_8, + {QCOM_DATA: keep_dims}, + ) + + return reduce_max_op diff --git a/backends/qualcomm/builders/qnn_constants.py b/backends/qualcomm/builders/qnn_constants.py index 9613c755c7c..31822a174b9 100644 --- a/backends/qualcomm/builders/qnn_constants.py +++ b/backends/qualcomm/builders/qnn_constants.py @@ -14,6 +14,13 @@ # instead of replicating them here. +@dataclass(init=False, frozen=True) +class OpAmax: + op_name: str = "ReduceMax" + param_axes: str = "axes" + param_keep_dims: str = "keep_dims" + + @dataclass(init=False, frozen=True) class OpBatchnorm: op_name: str = "Batchnorm" diff --git a/backends/qualcomm/quantizer/annotators.py b/backends/qualcomm/quantizer/annotators.py index 93af5e86c97..52662202795 100644 --- a/backends/qualcomm/quantizer/annotators.py +++ b/backends/qualcomm/quantizer/annotators.py @@ -182,6 +182,11 @@ def annotate_add(node: Node, quantization_config: QuantizationConfig) -> None: annotate_binary(node, quantization_config) +@register_annotator([torch.ops.aten.amax.default]) +def annotate_amax(node: Node, quantization_config: QuantizationConfig) -> None: + annotate_binary(node, quantization_config) + + @register_annotator([torch.ops.aten.argmin.default]) def annotate_argmin(node: Node, quantization_config: QuantizationConfig) -> None: if _is_annotated([node]): diff --git a/backends/qualcomm/tests/models.py b/backends/qualcomm/tests/models.py index 0857a597d88..f56aed6f76c 100644 --- a/backends/qualcomm/tests/models.py +++ b/backends/qualcomm/tests/models.py @@ -72,6 +72,16 @@ def forward(self, x): return torch.any(x, dim=self.dim, keepdim=self.keepdim) +class AMax(torch.nn.Module): + def __init__(self, dim=None, keepdim=False): + super().__init__() + self.dim = dim + self.keepdim = keepdim + + def forward(self, x): + return torch.amax(x, dim=self.dim, keepdim=self.keepdim) + + class Arange(torch.nn.Module): def __init__(self, start, end, step, dtype): super().__init__() diff --git a/backends/qualcomm/tests/test_qnn_delegate.py b/backends/qualcomm/tests/test_qnn_delegate.py index 795459a9f77..e33eb16d1e5 100644 --- a/backends/qualcomm/tests/test_qnn_delegate.py +++ b/backends/qualcomm/tests/test_qnn_delegate.py @@ -113,6 +113,13 @@ def test_qnn_backend_adaptive_avg_pool2d(self): sample_input = (torch.randn(1, 512, 7, 7),) self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_amax(self): + modules = [AMax(dim=1, keepdim=False), AMax(dim=1, keepdim=True)] # noqa: F405 + sample_input = (torch.randn(4, 4),) + for i, module in enumerate(modules): + with self.subTest(i=i): + self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_any(self): modules = [Any(), Any(dim=[0, 1]), Any(dim=1, keepdim=True)] # noqa: F405 sample_input = (torch.randn(3, 3, 3) > 0,) @@ -1111,6 +1118,14 @@ def test_qnn_backend_adaptive_avg_pool2d(self): module = self.get_qdq_module(module, sample_input) self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_amax(self): + modules = [AMax(dim=1, keepdim=False), AMax(dim=1, keepdim=True)] # noqa: F405 + sample_input = (torch.randn(4, 4),) + for i, module in enumerate(modules): + with self.subTest(i=i): + module = self.get_qdq_module(module, sample_input) + self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_any(self): modules = [Any(), Any(dim=[0, 1]), Any(dim=1, keepdim=True)] # noqa: F405 sample_input = (torch.randn(3, 3, 3) > 0,) From f61d71965cae968df96610052b0feef2d35fde79 Mon Sep 17 00:00:00 2001 From: zxc503 <554182714@163.com> Date: Wed, 9 Apr 2025 10:13:10 +0800 Subject: [PATCH 050/244] Modify executorch_module_static and executorch_tensor spelling problem in doc (#9957) ### Summary Modify executorch_module_static and executorch_tensor in doc to extension_module_static and extension_tensor cc @mergennachin @byjlw Co-authored-by: zxc503 <1@1> --- docs/source/backends-coreml.md | 4 ++-- docs/source/backends-xnnpack.md | 4 ++-- docs/source/getting-started.md | 4 ++-- docs/source/using-executorch-cpp.md | 6 +++--- 4 files changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/source/backends-coreml.md b/docs/source/backends-coreml.md index 126727735ae..2d5e4256bcc 100644 --- a/docs/source/backends-coreml.md +++ b/docs/source/backends-coreml.md @@ -172,8 +172,8 @@ add_subdirectory("executorch") target_link_libraries( my_target PRIVATE executorch - executorch_module_static - executorch_tensor + extension_module_static + extension_tensor optimized_native_cpu_ops_lib coremldelegate) ``` diff --git a/docs/source/backends-xnnpack.md b/docs/source/backends-xnnpack.md index 1fcc6d8c51b..45055ea4e13 100644 --- a/docs/source/backends-xnnpack.md +++ b/docs/source/backends-xnnpack.md @@ -128,8 +128,8 @@ add_subdirectory("executorch") target_link_libraries( my_target PRIVATE executorch - executorch_module_static - executorch_tensor + extension_module_static + extension_tensor optimized_native_cpu_ops_lib xnnpack_backend) ``` diff --git a/docs/source/getting-started.md b/docs/source/getting-started.md index a3eb056fb32..18d4541e843 100644 --- a/docs/source/getting-started.md +++ b/docs/source/getting-started.md @@ -170,8 +170,8 @@ add_subdirectory("executorch") target_link_libraries( my_target PRIVATE executorch - executorch_module_static - executorch_tensor + extension_module_static + extension_tensor optimized_native_cpu_ops_lib xnnpack_backend) ``` diff --git a/docs/source/using-executorch-cpp.md b/docs/source/using-executorch-cpp.md index 4f8a83830e0..aedd3ff914e 100644 --- a/docs/source/using-executorch-cpp.md +++ b/docs/source/using-executorch-cpp.md @@ -38,7 +38,7 @@ Running a model using the low-level runtime APIs allows for a high-degree of con ## Building with CMake -ExecuTorch uses CMake as the primary build system. Inclusion of the module and tensor APIs are controlled by the `EXECUTORCH_BUILD_EXTENSION_MODULE` and `EXECUTORCH_BUILD_EXTENSION_TENSOR` CMake options. As these APIs may not be supported on embedded systems, they are disabled by default when building from source. The low-level API surface is always included. To link, add the `executorch` target as a CMake dependency, along with `executorch_module_static` and `executorch_tensor`, if desired. +ExecuTorch uses CMake as the primary build system. Inclusion of the module and tensor APIs are controlled by the `EXECUTORCH_BUILD_EXTENSION_MODULE` and `EXECUTORCH_BUILD_EXTENSION_TENSOR` CMake options. As these APIs may not be supported on embedded systems, they are disabled by default when building from source. The low-level API surface is always included. To link, add the `executorch` target as a CMake dependency, along with `extension_module_static` and `extension_tensor`, if desired. ``` # CMakeLists.txt @@ -47,8 +47,8 @@ add_subdirectory("executorch") target_link_libraries( my_target PRIVATE executorch - executorch_module_static - executorch_tensor + extension_module_static + extension_tensor optimized_native_cpu_ops_lib xnnpack_backend) ``` From 65787ece060470589ddc61c4bd7ef695f51dd88a Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Tue, 8 Apr 2025 19:20:24 -0700 Subject: [PATCH 051/244] Just build AAR in place Differential Revision: D72679075 Pull Request resolved: https://github.com/pytorch/executorch/pull/9985 --- .ci/scripts/build_android_instrumentation.sh | 6 +-- .github/workflows/_android.yml | 2 +- .../android/executorch_android/build.gradle | 3 ++ scripts/build_android_library.sh | 46 ++++++++----------- 4 files changed, 27 insertions(+), 30 deletions(-) diff --git a/.ci/scripts/build_android_instrumentation.sh b/.ci/scripts/build_android_instrumentation.sh index 5e074d9e215..747dd7063a2 100644 --- a/.ci/scripts/build_android_instrumentation.sh +++ b/.ci/scripts/build_android_instrumentation.sh @@ -12,10 +12,10 @@ if [[ -z "${PYTHON_EXECUTABLE:-}" ]]; then fi which "${PYTHON_EXECUTABLE}" -mkdir -p "${BUILD_AAR_DIR}"/executorch_android/src/androidTest/resources -cp extension/module/test/resources/add.pte "${BUILD_AAR_DIR}"/executorch_android/src/androidTest/resources +mkdir -p extension/android/executorch_android/src/androidTest/resources +cp extension/module/test/resources/add.pte extension/android/executorch_android/src/androidTest/resources -pushd "${BUILD_AAR_DIR}" +pushd extension/android ANDROID_HOME="${ANDROID_SDK:-/opt/android/sdk}" ./gradlew :executorch_android:testDebugUnitTest ANDROID_HOME="${ANDROID_SDK:-/opt/android/sdk}" ./gradlew :executorch_android:assembleAndroidTest popd diff --git a/.github/workflows/_android.yml b/.github/workflows/_android.yml index e29833015d3..6598b6f3b42 100644 --- a/.github/workflows/_android.yml +++ b/.github/workflows/_android.yml @@ -37,7 +37,7 @@ jobs: mkdir -p ${ARTIFACTS_DIR_NAME}/library_test_dir bash .ci/scripts/build_android_instrumentation.sh - cp ${BUILD_AAR_DIR}/executorch_android/build/outputs/apk/androidTest/debug/executorch_android-debug-androidTest.apk "${ARTIFACTS_DIR_NAME}/library_test_dir" + cp extension/android/executorch_android/build/outputs/apk/androidTest/debug/executorch_android-debug-androidTest.apk "${ARTIFACTS_DIR_NAME}/library_test_dir" mkdir -p ${ARTIFACTS_DIR_NAME}/fp32-xnnpack-custom bash examples/models/llama/install_requirements.sh diff --git a/extension/android/executorch_android/build.gradle b/extension/android/executorch_android/build.gradle index 913a011406d..70e18289442 100644 --- a/extension/android/executorch_android/build.gradle +++ b/extension/android/executorch_android/build.gradle @@ -27,6 +27,9 @@ android { } sourceSets { + main { + jniLibs.srcDirs = ['../../../cmake-out-android-so/'] + } androidTest { resources.srcDirs += [ 'src/androidTest/resources' ] } diff --git a/scripts/build_android_library.sh b/scripts/build_android_library.sh index 8a385ad6876..20450218de6 100755 --- a/scripts/build_android_library.sh +++ b/scripts/build_android_library.sh @@ -12,11 +12,6 @@ if [[ -z "${PYTHON_EXECUTABLE:-}" ]]; then fi which "${PYTHON_EXECUTABLE}" -copy_src() { - cp -r extension/android/build.gradle extension/android/settings.gradle extension/android/gradlew extension/android/gradle extension/android/gradlew.bat extension/android/gradle.properties "${BUILD_AAR_DIR}" - cp -r extension/android/executorch_android "${BUILD_AAR_DIR}/executorch_android" -} - build_android_native_library() { ANDROID_ABI="$1" ANDROID_NDK="${ANDROID_NDK:-/opt/ndk}" @@ -93,41 +88,39 @@ build_android_native_library() { cmake --build "${CMAKE_OUT}"/extension/android -j "${CMAKE_JOBS}" --config "${EXECUTORCH_CMAKE_BUILD_TYPE}" # Copy artifacts to ABI specific directory - mkdir -p "${BUILD_AAR_DIR}/executorch_android/src/main/jniLibs/${ANDROID_ABI}" - cp "${CMAKE_OUT}"/extension/android/*.so "${BUILD_AAR_DIR}/executorch_android/src/main/jniLibs/${ANDROID_ABI}/" + local SO_STAGE_DIR="cmake-out-android-so/${ANDROID_ABI}" + mkdir -p ${SO_STAGE_DIR} + cp "${CMAKE_OUT}"/extension/android/*.so "${SO_STAGE_DIR}/libexecutorch.so" # Copy QNN related so library if [ -n "$QNN_SDK_ROOT" ] && [ "$ANDROID_ABI" == "arm64-v8a" ]; then - cp "${CMAKE_OUT}"/lib/libqnn_executorch_backend.so "${BUILD_AAR_DIR}/executorch_android/src/main/jniLibs/${ANDROID_ABI}/" - cp "${QNN_SDK_ROOT}"/lib/aarch64-android/libQnnHtp.so "${BUILD_AAR_DIR}/executorch_android/src/main/jniLibs/${ANDROID_ABI}/" - cp "${QNN_SDK_ROOT}"/lib/aarch64-android/libQnnSystem.so "${BUILD_AAR_DIR}/executorch_android/src/main/jniLibs/${ANDROID_ABI}/" - cp "${QNN_SDK_ROOT}"/lib/aarch64-android/libQnnHtpV69Stub.so "${BUILD_AAR_DIR}/executorch_android/src/main/jniLibs/${ANDROID_ABI}/" - cp "${QNN_SDK_ROOT}"/lib/aarch64-android/libQnnHtpV73Stub.so "${BUILD_AAR_DIR}/executorch_android/src/main/jniLibs/${ANDROID_ABI}/" - cp "${QNN_SDK_ROOT}"/lib/aarch64-android/libQnnHtpV75Stub.so "${BUILD_AAR_DIR}/executorch_android/src/main/jniLibs/${ANDROID_ABI}/" - cp "${QNN_SDK_ROOT}"/lib/hexagon-v69/unsigned/libQnnHtpV69Skel.so "${BUILD_AAR_DIR}/executorch_android/src/main/jniLibs/${ANDROID_ABI}/" - cp "${QNN_SDK_ROOT}"/lib/hexagon-v73/unsigned/libQnnHtpV73Skel.so "${BUILD_AAR_DIR}/executorch_android/src/main/jniLibs/${ANDROID_ABI}/" - cp "${QNN_SDK_ROOT}"/lib/hexagon-v75/unsigned/libQnnHtpV75Skel.so "${BUILD_AAR_DIR}/executorch_android/src/main/jniLibs/${ANDROID_ABI}/" + cp "${CMAKE_OUT}"/lib/libqnn_executorch_backend.so ${SO_STAGE_DIR} + cp "${QNN_SDK_ROOT}"/lib/aarch64-android/libQnnHtp.so ${SO_STAGE_DIR} + cp "${QNN_SDK_ROOT}"/lib/aarch64-android/libQnnSystem.so ${SO_STAGE_DIR} + cp "${QNN_SDK_ROOT}"/lib/aarch64-android/libQnnHtpV69Stub.so ${SO_STAGE_DIR} + cp "${QNN_SDK_ROOT}"/lib/aarch64-android/libQnnHtpV73Stub.so ${SO_STAGE_DIR} + cp "${QNN_SDK_ROOT}"/lib/aarch64-android/libQnnHtpV75Stub.so ${SO_STAGE_DIR} + cp "${QNN_SDK_ROOT}"/lib/hexagon-v69/unsigned/libQnnHtpV69Skel.so ${SO_STAGE_DIR} + cp "${QNN_SDK_ROOT}"/lib/hexagon-v73/unsigned/libQnnHtpV73Skel.so ${SO_STAGE_DIR} + cp "${QNN_SDK_ROOT}"/lib/hexagon-v75/unsigned/libQnnHtpV75Skel.so ${SO_STAGE_DIR} fi # Copy MTK related so library if [ -n "$NEURON_BUFFER_ALLOCATOR_LIB" ] && [ -n "$NEURON_USDK_ADAPTER_LIB" ] && [ "$ANDROID_ABI" == "arm64-v8a" ]; then - cp "${CMAKE_OUT}"/backends/mediatek/libneuron_backend.so ${BUILD_AAR_DIR}/executorch_android/src/main/jniLibs/${ANDROID_ABI}/ - cp "${NEURON_BUFFER_ALLOCATOR_LIB}" ${BUILD_AAR_DIR}/executorch_android/src/main/jniLibs/${ANDROID_ABI}/ - cp "${NEURON_USDK_ADAPTER_LIB}" ${BUILD_AAR_DIR}/executorch_android/src/main/jniLibs/${ANDROID_ABI}/ + cp "${CMAKE_OUT}"/backends/mediatek/libneuron_backend.so ${SO_STAGE_DIR} + cp "${NEURON_BUFFER_ALLOCATOR_LIB}" ${SO_STAGE_DIR} + cp "${NEURON_USDK_ADAPTER_LIB}" ${SO_STAGE_DIR} fi } build_aar() { - pushd "${BUILD_AAR_DIR}" - # Rename libexecutorch_jni.so to libexecutorch.so for soname consistency - # between Java and JNI - find . -type f -name "libexecutorch_jni.so" -exec bash -c 'mv "$1" "${1/_jni/}"' bash {} \; if [ "$EXECUTORCH_CMAKE_BUILD_TYPE" == "Release" ]; then - find . -type f -name "*.so" -exec "$ANDROID_NDK"/toolchains/llvm/prebuilt/*/bin/llvm-strip {} \; + find cmake-out-android-so -type f -name "*.so" -exec "$ANDROID_NDK"/toolchains/llvm/prebuilt/*/bin/llvm-strip {} \; fi + pushd extension/android/ ANDROID_HOME="${ANDROID_SDK:-/opt/android/sdk}" ./gradlew build - cp executorch_android/build/outputs/aar/executorch_android-debug.aar executorch.aar popd + cp extension/android/executorch_android/build/outputs/aar/executorch_android-debug.aar "${BUILD_AAR_DIR}/executorch.aar" } main() { @@ -135,12 +128,13 @@ main() { BUILD_AAR_DIR="$(mktemp -d)" fi export BUILD_AAR_DIR + mkdir -p $BUILD_AAR_DIR if [ -z "$ANDROID_ABIS" ]; then ANDROID_ABIS=("arm64-v8a" "x86_64") fi export ANDROID_ABIS - copy_src + mkdir -p cmake-out-android-so/ for ANDROID_ABI in "${ANDROID_ABIS[@]}"; do build_android_native_library ${ANDROID_ABI} done From df576053c0466ccdd9f1f04218d6f6400163f37e Mon Sep 17 00:00:00 2001 From: Sicheng Stephen Jia Date: Wed, 9 Apr 2025 13:48:40 -0400 Subject: [PATCH 052/244] [ez][release blocker fix] Insert `linalg_vector_norm` into decomp table used for Edge export (#9938) Summary: ## Context Addresses this [release blocker](https://github.com/orgs/pytorch/projects/99/views/1?pane=issue&itemId=104088363&issue=pytorch%7Cpytorch%7C150207) issue. Some models cannot export because they use `linalg_vector_norm` which is not currently an ATen operator. I initially tried adding the op to the core decomp table, but the decomp is not passing pytorch correctness tests. Please see https://github.com/pytorch/pytorch/pull/150241 for more details. ## Changes Since we currently cannot include the op in PyTorch's decomp table, instead we can insert the op into the edge decomp table directly. This PR is a simple change to add `linalg_vector_norm` directly to the edge decomp table. Test Plan: Tested exporting and running a model with the `linalg_vector_norm` op via the following script. ``` import torch from executorch.exir import to_edge_transform_and_lower, EdgeCompileConfig from torch.export import Dim, export from executorch.extension.pybindings.portable_lib import ( # @manual _load_for_executorch_from_buffer, ) class Model(torch.nn.Module): def __init__(self): super().__init__() def forward(self, x): return torch.linalg.vector_norm(x, 2) model = Model() inputs = (torch.randn(1,1,16,16),) dynamic_shapes = { "x": { 2: Dim("h", min=16, max=1024), 3: Dim("w", min=16, max=1024), } } exported_program = export(model, inputs, dynamic_shapes=dynamic_shapes) executorch_program = to_edge_transform_and_lower( exported_program, compile_config=EdgeCompileConfig(_check_ir_validity=False), ).to_executorch() executorch_module = _load_for_executorch_from_buffer( executorch_program.buffer ) model_output = executorch_module.run_method( "forward", tuple(inputs) ) print(model_output) ``` --- exir/program/test/test_program.py | 12 +++++------- exir/tracer.py | 12 +++++++++++- 2 files changed, 16 insertions(+), 8 deletions(-) diff --git a/exir/program/test/test_program.py b/exir/program/test/test_program.py index fca8bd2212f..fd7c51fdccb 100644 --- a/exir/program/test/test_program.py +++ b/exir/program/test/test_program.py @@ -725,17 +725,17 @@ def count_nodes(graph_module, target): ) def test_edge_dialect_non_core_aten_ops(self): - class LinalgNorm(torch.nn.Module): + class LinalgRank(torch.nn.Module): def __init__(self): super().__init__() def forward(self, x: torch.Tensor) -> torch.Tensor: - return torch.linalg.norm(x) + return torch.linalg.matrix_rank(x) from torch._export.verifier import SpecViolationError - input = torch.arange(9, dtype=torch.float) - 4 - ep = torch.export.export(LinalgNorm(), (input,), strict=True) + input = torch.ones((9, 9, 9), dtype=torch.float) + ep = torch.export.export(LinalgRank(), (input,), strict=True) # aten::linalg_norm is not a core op, so it should error out with self.assertRaises(SpecViolationError): @@ -748,9 +748,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: ep, compile_config=EdgeCompileConfig( _check_ir_validity=True, - _core_aten_ops_exception_list=[ - torch.ops.aten.linalg_vector_norm.default - ], + _core_aten_ops_exception_list=[torch.ops.aten._linalg_svd.default], ), ) except SpecViolationError: diff --git a/exir/tracer.py b/exir/tracer.py index 82f93424a14..c749df510ad 100644 --- a/exir/tracer.py +++ b/exir/tracer.py @@ -631,8 +631,18 @@ def _default_decomposition_table( ] # pyre-fixme[7]: Expected `Dict[OpOverload, typing.Callable[..., executorch.e... return get_decompositions(decomp_opset) + + decomps = default_decompositions() + # Add edge specific decompositions + additional_decomp_ops = [ + # TODO: Eventually this op should be added to the core decompo table, and will not + # need to be added here. + torch.ops.aten.linalg_vector_norm.default, + ] + additional_decomps = get_decompositions(additional_decomp_ops) + decomps.update(additional_decomps) # pyre-fixme[7]: Expected `Dict[OpOverload, typing.Callable[..., executorch.exir.... - return default_decompositions() + return decomps def dynamo_trace( From 7e34471966f5730c82060a65be3aef6fee5fb4f0 Mon Sep 17 00:00:00 2001 From: Mergen Nachin Date: Wed, 9 Apr 2025 13:58:58 -0400 Subject: [PATCH 053/244] Add maven version in main in Getting Started page (#9980) --- docs/source/getting-started.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/source/getting-started.md b/docs/source/getting-started.md index 18d4541e843..e69d72c1477 100644 --- a/docs/source/getting-started.md +++ b/docs/source/getting-started.md @@ -121,6 +121,8 @@ To add the library to your app, add the following dependency to gradle build rul dependencies { implementation("org.pytorch:executorch-android:0.5.1") } + +# See latest available versions in https://mvnrepository.com/artifact/org.pytorch/executorch-android ``` #### Runtime APIs From f51623b0b25f2bd33344415f2aaf2905f481dfc5 Mon Sep 17 00:00:00 2001 From: Mengwei Liu Date: Wed, 9 Apr 2025 12:14:47 -0700 Subject: [PATCH 054/244] [doc] Fix tokenizer related documentation (#10000) `extension.llm.tokenizer.tokenizer` -> `pytorch_tokenizers.tools.llama2c.convert` --- .../android/LlamaDemo/docs/delegates/qualcomm_README.md | 2 +- examples/models/llama2/README.md | 2 +- examples/models/phi-3-mini/README.md | 2 +- examples/qualcomm/oss_scripts/llama/README.md | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md b/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md index f6952df97ad..e8bc1dc1d26 100644 --- a/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md +++ b/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md @@ -135,7 +135,7 @@ You may also wonder what the "--metadata" flag is doing. This flag helps export Convert tokenizer for Llama 2 ``` -python -m extension.llm.tokenizer.tokenizer -t tokenizer.model -o tokenizer.bin +python -m pytorch_tokenizers.tools.llama2c.convert -t tokenizer.model -o tokenizer.bin ``` Rename tokenizer for Llama 3 with command: `mv tokenizer.model tokenizer.bin`. We are updating the demo app to support tokenizer in original format directly. diff --git a/examples/models/llama2/README.md b/examples/models/llama2/README.md index 92ddbf74d94..615ad3948fc 100644 --- a/examples/models/llama2/README.md +++ b/examples/models/llama2/README.md @@ -41,7 +41,7 @@ You can export and run the original Llama 2 7B model. ``` 4. Create tokenizer.bin. ``` - python -m extension.llm.tokenizer.tokenizer -t -o tokenizer.bin + python -m pytorch_tokenizers.tools.llama2c.convert -t -o tokenizer.bin ``` Pass the converted `tokenizer.bin` file instead of `tokenizer.model` for subsequent steps. diff --git a/examples/models/phi-3-mini/README.md b/examples/models/phi-3-mini/README.md index ba878d42a3f..f52f2a3a06d 100644 --- a/examples/models/phi-3-mini/README.md +++ b/examples/models/phi-3-mini/README.md @@ -13,7 +13,7 @@ pip uninstall -y transformers ; pip install transformers==4.44.2 ``` cd executorch wget -O tokenizer.model "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/tokenizer.model?download=true" -python -m extension.llm.tokenizer.tokenizer -t tokenizer.model -o tokenizer.bin +python -m pytorch_tokenizers.tools.llama2c.convert -t tokenizer.model -o tokenizer.bin ``` 2. Export the model. This step will take a few minutes to finish. ``` diff --git a/examples/qualcomm/oss_scripts/llama/README.md b/examples/qualcomm/oss_scripts/llama/README.md index cd468eebb26..9b6ec9574eb 100644 --- a/examples/qualcomm/oss_scripts/llama/README.md +++ b/examples/qualcomm/oss_scripts/llama/README.md @@ -41,7 +41,7 @@ wget "https://huggingface.co/karpathy/tinyllamas/resolve/main/stories110M.pt" wget "https://raw.githubusercontent.com/karpathy/llama2.c/master/tokenizer.model" # tokenizer.bin: -python -m extension.llm.tokenizer.tokenizer -t tokenizer.model -o tokenizer.bin +python -m pytorch_tokenizers.tools.llama2c.convert -t tokenizer.model -o tokenizer.bin # params.json: echo '{"dim": 768, "multiple_of": 32, "n_heads": 12, "n_layers": 12, "norm_eps": 1e-05, "vocab_size": 32000}' > params.json From c42a6b9a342af1b867e95933ed005519f3c8ba7f Mon Sep 17 00:00:00 2001 From: Mengwei Liu Date: Wed, 9 Apr 2025 13:24:24 -0700 Subject: [PATCH 055/244] Fix tokenizer convert in xnnpack_README.md (#10003) As titled. --- .../android/LlamaDemo/docs/delegates/xnnpack_README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md b/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md index 59b74a3c1ac..c00ecb93154 100644 --- a/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md +++ b/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md @@ -103,7 +103,7 @@ You may wonder what the ‘--metadata’ flag is doing. This flag helps export t * Convert tokenizer for Llama 2 and Llava (skip this for Llama 3.x) ``` -python -m extension.llm.tokenizer.tokenizer -t tokenizer.model -o tokenizer.bin +python -m pytorch_tokenizers.tools.llama2c.convert -t tokenizer.model -o tokenizer.bin ``` ### For LLaVA model From 7cfc14818319ced6fa6c51c4047617c011328430 Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Wed, 9 Apr 2025 14:15:34 -0700 Subject: [PATCH 056/244] using-executorch-android.md: Use an easier example (#10008) And it's working without additional requirements! --- docs/source/using-executorch-android.md | 22 +++++++++++++--------- 1 file changed, 13 insertions(+), 9 deletions(-) diff --git a/docs/source/using-executorch-android.md b/docs/source/using-executorch-android.md index f1a18ba4ae4..4528de55219 100644 --- a/docs/source/using-executorch-android.md +++ b/docs/source/using-executorch-android.md @@ -172,18 +172,22 @@ public class MainActivity extends Activity { protected void onCreate(Bundle savedInstanceState) { super.onCreate(savedInstanceState); // Load the ExecuTorch module - module = Module.load("/path/to/module.pte"); - } - public void runInference(View view) { - // Prepare input data - Tensor input = Tensor.fromBlob(getInputData()); - // Run inference - Tensor output = module.forward(EValue.from(input))[0].toTensor(); - // Process output data - processOutput(output); + Module module = Module.load("/data/local/tmp/add.pte"); + Tensor tensor1 = Tensor.fromBlob(new float[] {1.0f}, new long[] {1}); + Tensor tensor2 = Tensor.fromBlob(new float[] {20.0f}, new long[] {1}); + + EValue eValue1 = EValue.from(tensor1); + EValue eValue2 = EValue.from(tensor2); + float result = module.forward(eValue1, eValue2)[0].toTensor().getDataAsFloatArray()[0]; } } ``` + +Push the corresponding pte file to the phone: +```sh +adb push extension/module/test/resources/add.pte /data/local/tmp/ +``` + This example loads an ExecuTorch module, prepares input data, runs inference, and processes the output data. Please use [DeepLabV3AndroidDemo](https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo) From 2b071be42a94d399dbd5c33d04e69b2f5300c655 Mon Sep 17 00:00:00 2001 From: "Mengtao (Martin) Yuan" Date: Wed, 9 Apr 2025 15:38:59 -0700 Subject: [PATCH 057/244] [Doc] Update getting-started-architecture.md (#10027) Fix a broken link --- docs/source/getting-started-architecture.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/getting-started-architecture.md b/docs/source/getting-started-architecture.md index 2472b3547fe..f395bbacea0 100644 --- a/docs/source/getting-started-architecture.md +++ b/docs/source/getting-started-architecture.md @@ -18,7 +18,7 @@ There are three phases to deploy a PyTorch model to on-device: program preparati ExecuTorch extends the flexibility and usability of PyTorch to edge devices. It leverages PyTorch 2 compiler and export functionality -([TorchDynamo](https://pytorch.org/docs/stable/dynamo/index.html), +([TorchDynamo](https://pytorch.org/docs/stable/torch.compiler_dynamo_overview.html), [AOTAutograd](https://pytorch.org/functorch/stable/notebooks/aot_autograd_optimizations.html), [Quantization](https://pytorch.org/docs/main/quantization.html), [dynamic shapes](https://pytorch.org/get-started/pytorch-2.0/#pytorch-2x-faster-more-pythonic-and-as-dynamic-as-ever), From f061ae83d235edb7aab41baace73f91ce4cd05cf Mon Sep 17 00:00:00 2001 From: Jack-Khuu Date: Wed, 9 Apr 2025 16:38:33 -0700 Subject: [PATCH 058/244] Typo updates for README and Contributions (#10025) ### Summary Pulling in the non 0.6 changes from: https://github.com/pytorch/executorch/pull/10006 https://github.com/pytorch/executorch/pull/10016 ### Test plan md --- CONTRIBUTING.md | 16 ++++++---------- README.md | 4 ++-- 2 files changed, 8 insertions(+), 12 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 82076c2727d..801375b589b 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,7 +1,6 @@ Thank you for your interest in contributing to ExecuTorch! We want to make it easy to contribute to this project. -  ## Dev Install @@ -91,7 +90,7 @@ executorch │ └── platform - Layer between architecture specific code and portable C++. ├── schema - ExecuTorch PTE file format flatbuffer schemas. ├── scripts - Utility scripts for building libs, size management, dependency management, etc. -├── shim - Compatibility layer between OSS and Internal builds. +├── shim_et - Compatibility layer between OSS and Internal builds. ├── test - Broad scoped end-to-end tests. ├── third-party - Third-party dependencies. ├── tools - Tools for building ExecuTorch from source, for different built tools (CMake, Buck). @@ -192,9 +191,6 @@ in the Github repo. ## Coding Style -Goal: Encourage standards that make it easier to read, edit, maintain, and debug -the ExecuTorch code. - ### lintrunner We use [`lintrunner`](https://pypi.org/project/lintrunner/) to help make sure the @@ -259,7 +255,7 @@ toolchains, and having access to relatively modern C++ features. #### C/C++ standard library usage -**Restricted usage of the C++ standard library.** +**Restricted usage of the C++ standard library** Rationale: ExecuTorch is intended to be portable to bare-metal systems that lack certain features, like dynamic memory, threading, and locking, required by parts @@ -280,7 +276,7 @@ careful to also manually destroy objects initialized in this way. #### C++ language features -**Exceptions: Do not use.** +**Exceptions: Do not use** - Rationale: Exceptions are not widely supported on some classes of microcontrollers and DSPs, and they can significantly increase binary size. @@ -289,12 +285,12 @@ must work with threading** - Rationale: The core runtime must work on systems that do not have threading support. -**RTTI, dynamic_cast, and ``: Do not use.** +**RTTI, dynamic_cast, and ``: Do not use** - Rationale: RTTI adds extra data to every virtual class. ExecuTorch doesn't have a strong need for `dynamic_cast` and friends, so it's better to reduce the binary size. -**Templates and template metaprogramming: Be careful and avoid if possible.** +**Templates and template metaprogramming: Be careful and avoid if possible** - Rationale: Most templating results in code generation, and is one of the most common sources of binary bloat. Some use of templates is fine (e.g. an `ArrayRef`, or code that handles multiple `ScalarType` types), but for the @@ -359,7 +355,7 @@ docs](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/ for basics. 1. Push your branch to your fork of `pytorch/executorch`. Most people do not - have permission to push a branch directoy to the upstream repo. + have permission to push a branch directory to the upstream repo. 1. Create your PR - Use the `main` branch as the base. - Give the PR a clear and descriptive title. It will become the title of the diff --git a/README.md b/README.md index d60220bae85..025a8780739 100644 --- a/README.md +++ b/README.md @@ -49,9 +49,9 @@ Key value propositions of ExecuTorch are: ## Getting Started To get started you can: -- Visit the [Step by Step Tutorial](https://pytorch.org/executorch/main/index.html) on getting things running locally and deploy a model to a device +- Visit the [Step by Step Tutorial](https://pytorch.org/executorch/main/index.html) to get things running locally and deploy a model to a device - Use this [Colab Notebook](https://pytorch.org/executorch/stable/getting-started-setup.html#quick-setup-colab-jupyter-notebook-prototype) to start playing around right away -- Jump straight into LLMs use cases by following specific instructions for [Llama](./examples/models/llama/README.md) and [Llava](./examples/models/llava/README.md) +- Jump straight into LLM use cases by following specific instructions for [Llama](./examples/models/llama/README.md) and [Llava](./examples/models/llava/README.md) ## Feedback and Engagement From da65dcbbd3bdc2e3d6e750e310199c1c4ecb20e5 Mon Sep 17 00:00:00 2001 From: Chun-I Tsai Date: Thu, 10 Apr 2025 08:18:24 +0800 Subject: [PATCH 059/244] Qualcomm AI Engine Direct - Add submodule quant config setting (#9355) - Add API to qnn quantizer for setting submodule quant config - Refine QnnQuantizer setting functions --------- Co-authored-by: Chun-I Tsai --- backends/qualcomm/_passes/decompose_einsum.py | 3 + .../_passes/decompose_linalg_vector_norm.py | 3 + backends/qualcomm/_passes/utils.py | 8 + backends/qualcomm/quantizer/quantizer.py | 219 ++++++++++++------ backends/qualcomm/tests/models.py | 12 + backends/qualcomm/tests/test_qnn_delegate.py | 44 +++- backends/qualcomm/tests/utils.py | 16 +- backends/transforms/decompose_sdpa.py | 3 + examples/qualcomm/utils.py | 23 +- extension/llm/export/quantizer_lib.py | 33 ++- 10 files changed, 253 insertions(+), 111 deletions(-) diff --git a/backends/qualcomm/_passes/decompose_einsum.py b/backends/qualcomm/_passes/decompose_einsum.py index cbf8cbf1249..046c1598311 100644 --- a/backends/qualcomm/_passes/decompose_einsum.py +++ b/backends/qualcomm/_passes/decompose_einsum.py @@ -8,6 +8,8 @@ from executorch.exir.pass_base import ExportPass, PassResult from torch.fx.experimental.proxy_tensor import make_fx +from .utils import copy_nn_module_stack + class DecomposeEinsum(ExportPass): """ @@ -36,6 +38,7 @@ def call(self, graph_module: torch.fx.GraphModule) -> PassResult: remap[f"arg1_{i+1}"] = arg for decomposed_node in decomposed_module.graph.nodes: + copy_nn_module_stack(node, decomposed_node) # This is the arg[0] equation string, which is not required anymore after decomposition if "arg0" in decomposed_node.name: continue diff --git a/backends/qualcomm/_passes/decompose_linalg_vector_norm.py b/backends/qualcomm/_passes/decompose_linalg_vector_norm.py index 7d70f5c9342..993f088da12 100644 --- a/backends/qualcomm/_passes/decompose_linalg_vector_norm.py +++ b/backends/qualcomm/_passes/decompose_linalg_vector_norm.py @@ -8,6 +8,8 @@ from executorch.exir import to_edge from executorch.exir.pass_base import ExportPass, PassResult +from .utils import copy_nn_module_stack + class LinalgVectorNorm(torch.nn.Module): def __init__(self, exp, dim, keepdim): @@ -62,6 +64,7 @@ def call(self, graph_module: torch.fx.GraphModule) -> PassResult: remap = {"x": node.args[0]} for decomposed_node in decomposed_module.graph.nodes: + copy_nn_module_stack(node, decomposed_node) # no need to copy existent 'output' if decomposed_node.op == "output": for user in node.users.copy(): diff --git a/backends/qualcomm/_passes/utils.py b/backends/qualcomm/_passes/utils.py index d538fe0d34f..a8eb6b192ee 100755 --- a/backends/qualcomm/_passes/utils.py +++ b/backends/qualcomm/_passes/utils.py @@ -121,6 +121,14 @@ def get_passes_dependency_for_capture_program(): } +def copy_nn_module_stack(src, target): + """ + Copy meta["nn_module_stack"] from src node to target node if existing. + """ + if value := src.meta.get("nn_module_stack"): + target.meta["nn_module_stack"] = value + + def is_float_tensor(node: torch.fx.Node) -> bool: if "val" not in node.meta or not isinstance(node.meta["val"], FakeTensor): return False diff --git a/backends/qualcomm/quantizer/quantizer.py b/backends/qualcomm/quantizer/quantizer.py index 3620841aff9..8e65607dd84 100644 --- a/backends/qualcomm/quantizer/quantizer.py +++ b/backends/qualcomm/quantizer/quantizer.py @@ -3,9 +3,10 @@ # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. +from dataclasses import dataclass from enum import IntEnum, unique from functools import partial -from typing import Callable, Dict, Optional, Sequence, Set, Tuple +from typing import Callable, Dict, List, Optional, Sequence, Set, Tuple import torch from executorch.backends.qualcomm._passes.qnn_pass_manager import QnnPassManager @@ -58,7 +59,7 @@ class QuantDtype(IntEnum): use_8a8w = 4 -quant_config_dict = { +QUANT_CONFIG_DICT = { # PTQ (QuantDtype.use_16a16w, False): ( get_16a16w_qnn_ptq_config, @@ -123,6 +124,59 @@ class QuantDtype(IntEnum): } +@dataclass +class ModuleQConfig: + quant_dtype: QuantDtype = QuantDtype.use_8a8w + is_qat: bool = False + is_conv_per_channel: bool = False + is_linear_per_channel: bool = False + act_observer: Optional[ + torch.ao.quantization.observer.UniformQuantizationObserverBase + ] = None + + def __post_init__(self): + if (self.quant_dtype, self.is_qat) not in QUANT_CONFIG_DICT: + raise RuntimeError( + f"the quant config, (quant_dtype: {self.quant_dtype}, is_qat: {self.is_qat}) is not support" + ) + ( + quant_config_func, + per_channel_quant_config_func, + per_block_quant_config_func, + ) = QUANT_CONFIG_DICT[(self.quant_dtype, self.is_qat)] + self.quant_config = ( + quant_config_func(act_observer=self.act_observer) + if self.act_observer + else quant_config_func() + ) + self.per_channel_quant_config = ( + per_channel_quant_config_func(act_observer=self.act_observer) + if self.act_observer + else per_channel_quant_config_func() + ) + self.use_per_channel_weight_quant_ops = set() + if self.is_conv_per_channel: + self.use_per_channel_weight_quant_ops.update( + { + torch.ops.aten.conv1d.default, + torch.ops.aten.conv2d.default, + torch.ops.aten.conv_transpose2d.input, + } + ) + if self.is_linear_per_channel: + self.use_per_channel_weight_quant_ops.update( + { + torch.ops.aten.linear.default, + } + ) + if per_block_quant_config_func: + self.per_block_quant_config = ( + per_block_quant_config_func(act_observer=self.act_observer) + if self.act_observer + else per_block_quant_config_func() + ) + + class QnnQuantizer(Quantizer): SUPPORTED_OPS: Set = set(OP_ANNOTATOR.keys()) @@ -130,14 +184,11 @@ def __init__(self): super().__init__() self.quant_ops: Set[OpOverload] = self.SUPPORTED_OPS.copy() - self.is_qat = False - self.quant_dtype = QuantDtype.use_8a8w - self.quant_config: QuantizationConfig = get_8a8w_qnn_ptq_config() - self.per_channel_quant_config = get_ptq_per_channel_quant_config() - self.per_block_quant_config = get_ptq_per_block_quant_config() + self.default_quant_config = ModuleQConfig() + self.submodule_qconfig_list: List[ + Tuple[Callable[[torch.fx.Node], bool], ModuleQConfig] + ] = [] self.block_size_map = {} - self.use_per_channel_weight_quant_ops: Set[OpOverload] = set() - self.use_per_block_weight_quant_ops: Set[OpOverload] = set() self.custom_quant_annotations: Sequence[Callable] = [] self.discard_nodes: Set[str] = set() @@ -155,41 +206,38 @@ def _annotate_custom_annotation(self, gm: GraphModule) -> None: for annotation_func in self.custom_quant_annotations: annotation_func(gm) - def _get_quant_config(self, op: torch.fx.Node) -> Optional[QuantizationConfig]: + def _get_submodule_qconfig(self, node: torch.fx.Node): + for func, qconfig in self.submodule_qconfig_list: + if func(node): + return qconfig + return self.default_quant_config + + def _get_quant_config(self, node: torch.fx.Node) -> Optional[QuantizationConfig]: """ - Priority: - 1. is one of use_per_block_weight_quant_ops - 2. is one of use_per_channel_weight_quant_ops - 3. quant config + How to pick: + 1. is one of per_block_quant_config + 2. Pick specific submodule config if given. + 3. Pick one if op belongs to use_per_channel_weight_quant_ops + 4. If not 3, pick normal quant config """ - target = op.target - if isinstance(target, str): + op = node.target + if isinstance(op, str): return - if target in self.use_per_block_weight_quant_ops: - if block_size := self.block_size_map.get(op.name): - self.per_block_quant_config.block_size = block_size - return self.per_block_quant_config + if block_size := self.block_size_map.get(node.name): + config = self.default_quant_config.per_block_quant_config + config.block_size = block_size + return config - if target in self.use_per_channel_weight_quant_ops: - return self.per_channel_quant_config + config = self._get_submodule_qconfig(node) - if target in self.quant_ops: - return self.quant_config + if op in config.use_per_channel_weight_quant_ops: + return config.per_channel_quant_config - print(f"No quant config is implemented for op, {op}") - - def _update_per_block_weight_quant_ops(self, ops: Set[OpOverload], enable: bool): - if enable: - self.use_per_block_weight_quant_ops.update(ops) - else: - self.use_per_block_weight_quant_ops.difference_update(ops) + if op in self.quant_ops: + return config.quant_config - def _update_per_channel_weight_quant_ops(self, ops: Set[OpOverload], enable: bool): - if enable: - self.use_per_channel_weight_quant_ops.update(ops) - else: - self.use_per_channel_weight_quant_ops.difference_update(ops) + print(f"No quant config is implemented for op, {op}") def add_custom_quant_annotations( self, custom_quant_annotations: Sequence[Callable] @@ -212,55 +260,74 @@ def annotate(self, model: GraphModule) -> GraphModule: def get_supported_ops(self) -> Set[OpOverload]: return self.SUPPORTED_OPS - def set_quant_config( - self, quant_dtype: QuantDtype, is_qat=False, act_observer=None + def set_default_quant_config( + self, + quant_dtype: QuantDtype, + is_qat=False, + is_conv_per_channel=False, + is_linear_per_channel=False, + act_observer=None, ) -> None: - self.quant_dtype = quant_dtype - self.is_qat = is_qat - if (quant_dtype, is_qat) not in quant_config_dict: - raise RuntimeError( - f"the quant config, (quant_dtype: {quant_dtype}, is_qat: {is_qat}) is not support" - ) - - quant_config_fuc, per_channel_quant_config_fuc, per_block_quant_config_fuc = ( - quant_config_dict[(quant_dtype, is_qat)] - ) - self.quant_config = ( - quant_config_fuc(act_observer=act_observer) - if act_observer - else quant_config_fuc() + self.default_quant_config = ModuleQConfig( + quant_dtype, + is_qat, + is_conv_per_channel, + is_linear_per_channel, + act_observer, ) - self.per_channel_quant_config = ( - per_channel_quant_config_fuc(act_observer=act_observer) - if act_observer - else per_channel_quant_config_fuc() - ) - if per_block_quant_config_fuc is not None: - self.per_block_quant_config = ( - per_block_quant_config_fuc(act_observer=act_observer) - if act_observer - else per_block_quant_config_fuc() - ) def set_block_size_map(self, block_size_map: Dict[str, Tuple]) -> None: self.block_size_map = block_size_map - def set_per_block_conv_quant(self, enable: bool) -> None: - conv_ops = {torch.ops.aten.conv2d.default} - self._update_per_block_weight_quant_ops(conv_ops, enable) - - def set_per_channel_conv_quant(self, enable: bool) -> None: - conv_ops = {torch.ops.aten.conv1d.default, torch.ops.aten.conv2d.default} - self._update_per_channel_weight_quant_ops(conv_ops, enable) - - def set_per_channel_linear_quant(self, enable: bool) -> None: - linear_ops = { - torch.ops.aten.linear.default, - } - self._update_per_channel_weight_quant_ops(linear_ops, enable) + def set_submodule_qconfig_list( + self, submodule_qconfig_list: List[Tuple[Callable, ModuleQConfig]] + ) -> None: + """ + Set specific quant config from a callback function. + If a node fits more than one callback, only apply the first one. + """ + self.submodule_qconfig_list = submodule_qconfig_list def transform_for_annotation(self, model: GraphModule) -> GraphModule: return QnnPassManager().transform_for_annotation_pipeline(model) def validate(self, model: GraphModule) -> None: pass + + +def get_submodule_type_predicate(module_type_str): + """ + An example of nn_module_stack + { + 'L__self__': ('', 'executorch.backends.qualcomm.tests.models.SubModules'), + 'L__self___add': ('add', 'executorch.backends.qualcomm.tests.models.Add') + } + """ + + def predicate(node): + if nn_module_stack := node.meta.get("nn_module_stack"): + for _, type_name in nn_module_stack.values(): + if module_type_str in type_name: + return True + return False + + return predicate + + +def get_submodule_name_predicate(module_name_str): + """ + An example of nn_module_stack + { + 'L__self__': ('', 'executorch.backends.qualcomm.tests.models.SubModules'), + 'L__self___add': ('add', 'executorch.backends.qualcomm.tests.models.Add') + } + """ + + def predicate(node): + if nn_module_stack := node.meta.get("nn_module_stack"): + for name in nn_module_stack.keys(): + if module_name_str in name: + return True + return False + + return predicate diff --git a/backends/qualcomm/tests/models.py b/backends/qualcomm/tests/models.py index f56aed6f76c..f1171b129e6 100644 --- a/backends/qualcomm/tests/models.py +++ b/backends/qualcomm/tests/models.py @@ -1460,6 +1460,18 @@ def forward(self, x): return 10 - x +class SimpleSubModules(torch.nn.Module): + def __init__(self): + super().__init__() + self.add = Add() + self.sub = Sub() + + def forward(self, a, b, c, d): + lhs = self.add(a, b) + rhs = self.sub(c, d) + return torch.mul(lhs, rhs) + + class SumIntList(torch.nn.Module): def __init__(self): super().__init__() diff --git a/backends/qualcomm/tests/test_qnn_delegate.py b/backends/qualcomm/tests/test_qnn_delegate.py index e33eb16d1e5..9aba5a059e0 100644 --- a/backends/qualcomm/tests/test_qnn_delegate.py +++ b/backends/qualcomm/tests/test_qnn_delegate.py @@ -24,6 +24,7 @@ from executorch.backends.qualcomm.tests.utils import ( generate_context_binary, + ModuleQConfig, QuantDtype, TestQNN, validate_context_binary, @@ -1252,7 +1253,6 @@ def test_qnn_backend_conv2d_block(self): module = self.get_qdq_module( module, sample_input, - is_conv_per_block=True, quant_dtype=QuantDtype.use_16a4w_block, block_size_map={"conv2d": (1, 128, 1, 1)}, ) @@ -1341,8 +1341,8 @@ def test_qnn_backend_element_wise_add(self): for module in comb[QCOM_MODULE]: for sample_input in comb[QCOM_SAMPLE_INPUTS]: with self.subTest(i=index): - module = self.get_qdq_module(module, sample_input) - self.lower_module_and_test_output(module, sample_input) + gm = self.get_qdq_module(module, sample_input) + self.lower_module_and_test_output(gm, sample_input) index += 1 def test_qnn_backend_element_wise_and(self): @@ -1382,8 +1382,8 @@ def test_qnn_backend_element_wise_div(self): for module in comb[QCOM_MODULE]: for sample_input in comb[QCOM_SAMPLE_INPUTS]: with self.subTest(i=index): - module = self.get_qdq_module(module, sample_input) - self.lower_module_and_test_output(module, sample_input) + gm = self.get_qdq_module(module, sample_input) + self.lower_module_and_test_output(gm, sample_input) index += 1 def test_qnn_backend_element_wise_mul(self): @@ -1410,8 +1410,8 @@ def test_qnn_backend_element_wise_mul(self): for module in comb[QCOM_MODULE]: for sample_input in comb[QCOM_SAMPLE_INPUTS]: with self.subTest(i=index): - module = self.get_qdq_module(module, sample_input) - self.lower_module_and_test_output(module, sample_input) + gm = self.get_qdq_module(module, sample_input) + self.lower_module_and_test_output(gm, sample_input) index += 1 def test_qnn_backend_element_wise_or(self): @@ -1470,8 +1470,8 @@ def test_qnn_backend_element_wise_sub(self): for module in comb[QCOM_MODULE]: for sample_input in comb[QCOM_SAMPLE_INPUTS]: with self.subTest(i=index): - module = self.get_qdq_module(module, sample_input) - self.lower_module_and_test_output(module, sample_input) + gm = self.get_qdq_module(module, sample_input) + self.lower_module_and_test_output(gm, sample_input) index += 1 def test_qnn_backend_elu(self): @@ -2137,6 +2137,32 @@ def test_qnn_backend_simple_model(self): module = self.get_qdq_module(module, sample_input) self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_submodules(self): + module = SimpleSubModules() # noqa: F405 + sample_input = ( + torch.rand(1, 3, 8, 8), + torch.rand(1, 3, 8, 8), + torch.rand(1, 3, 8, 8), + torch.rand(1, 3, 8, 8), + ) + + from executorch.backends.qualcomm.quantizer.quantizer import ( + get_submodule_type_predicate, + ) + + submodule_qconfig_list = [ + ( + get_submodule_type_predicate("Add"), + ModuleQConfig(QuantDtype.use_16a16w), + ) # noqa: F405 + ] + module = self.get_qdq_module( + module, + sample_input, + submodule_qconfig_list=submodule_qconfig_list, + ) + self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_topk_and_index(self): module = TopKandIndex() # noqa: F405 sample_input = (torch.randn(3, 10),) diff --git a/backends/qualcomm/tests/utils.py b/backends/qualcomm/tests/utils.py index 41c56c08a85..42eec15891c 100644 --- a/backends/qualcomm/tests/utils.py +++ b/backends/qualcomm/tests/utils.py @@ -16,7 +16,7 @@ from executorch import exir from executorch.backends.qualcomm.qnn_preprocess import QnnBackend -from executorch.backends.qualcomm.quantizer.quantizer import QuantDtype +from executorch.backends.qualcomm.quantizer.quantizer import ModuleQConfig, QuantDtype from executorch.backends.qualcomm.serialization.qc_schema import QcomChipset from executorch.backends.qualcomm.utils.constants import ( QCOM_DTYPE, @@ -497,7 +497,6 @@ def get_qdq_module( self, module: torch.nn.Module, inputs: Tuple[torch.Tensor], - is_conv_per_block: Optional[bool] = False, is_conv_per_channel: Optional[bool] = True, is_linear_per_channel: Optional[bool] = False, custom_quant_annotations: Tuple[Callable] = (), @@ -505,7 +504,9 @@ def get_qdq_module( dynamic_shapes: Dict = None, bypass_check: bool = False, block_size_map: Dict[str, Tuple] = None, + submodule_qconfig_list: Optional[List[Tuple[Callable, ModuleQConfig]]] = None, ) -> torch.fx.GraphModule: + module = module.eval() m = torch.export.export( module, inputs, dynamic_shapes=dynamic_shapes, strict=True ).module() @@ -513,9 +514,9 @@ def get_qdq_module( quantizer = make_quantizer( quant_dtype=quant_dtype, custom_annotations=custom_quant_annotations, - per_block_conv=is_conv_per_block, per_channel_conv=is_conv_per_channel, per_channel_linear=is_linear_per_channel, + submodule_qconfig_list=submodule_qconfig_list, ) if block_size_map is not None: quantizer.set_block_size_map(block_size_map) @@ -543,6 +544,7 @@ def get_prepared_qat_module( is_linear_per_channel: Optional[bool] = False, custom_quant_annotations: Tuple[Callable] = (), quant_dtype: QuantDtype = QuantDtype.use_8a8w, + submodule_qconfig_list: Optional[List[Tuple[Callable, ModuleQConfig]]] = None, ) -> torch.fx.GraphModule: m = torch.export.export_for_training(module, inputs, strict=True).module() @@ -551,12 +553,12 @@ def get_prepared_qat_module( custom_annotations=custom_quant_annotations, per_channel_conv=is_conv_per_channel, per_channel_linear=is_linear_per_channel, + is_qat=True, + submodule_qconfig_list=submodule_qconfig_list, ) - if quant_dtype == QuantDtype.use_8a8w: - quantizer.set_quant_config(quant_dtype, is_qat=True) - else: - raise RuntimeError("Shuld not be here") + submodule_qconfig_list = submodule_qconfig_list or [] + quantizer.set_submodule_qconfig_list(submodule_qconfig_list) prepared = prepare_qat_pt2e(m, quantizer) return torch.ao.quantization.move_exported_model_to_train(prepared) diff --git a/backends/transforms/decompose_sdpa.py b/backends/transforms/decompose_sdpa.py index 329dab96df2..73e9d986c3d 100644 --- a/backends/transforms/decompose_sdpa.py +++ b/backends/transforms/decompose_sdpa.py @@ -62,6 +62,9 @@ def call( # Copy node from decompose graph module for decomposed_node in decomposed_module.graph.nodes: + node.meta["nn_module_stack"] = decomposed_node.meta.get( + "nn_module_stack" + ) if decomposed_node.op == "placeholder": continue diff --git a/examples/qualcomm/utils.py b/examples/qualcomm/utils.py index 2b2f32b037b..b17bc8f98bd 100755 --- a/examples/qualcomm/utils.py +++ b/examples/qualcomm/utils.py @@ -14,12 +14,16 @@ import tempfile from pathlib import Path -from typing import Callable, List, Optional +from typing import Callable, List, Optional, Tuple import numpy as np import torch -from executorch.backends.qualcomm.quantizer.quantizer import QnnQuantizer, QuantDtype +from executorch.backends.qualcomm.quantizer.quantizer import ( + ModuleQConfig, + QnnQuantizer, + QuantDtype, +) from executorch.backends.qualcomm.serialization.qc_schema import QcomChipset from executorch.backends.qualcomm.utils.utils import ( generate_htp_compiler_spec, @@ -254,18 +258,23 @@ def qat_train(ori_model, captured_model, quantizer, dataset): def make_quantizer( quant_dtype: Optional[QuantDtype] = QuantDtype.use_8a8w, custom_annotations=(), - per_block_conv=False, per_channel_conv=True, per_channel_linear=False, act_observer=MovingAverageMinMaxObserver, is_qat=False, + callback_qconfig_list: Optional[List[Tuple[Callable, ModuleQConfig]]] = None, ): quantizer = QnnQuantizer() quantizer.add_custom_quant_annotations(custom_annotations) - quantizer.set_per_block_conv_quant(per_block_conv) - quantizer.set_per_channel_conv_quant(per_channel_conv) - quantizer.set_per_channel_linear_quant(per_channel_linear) - quantizer.set_quant_config(quant_dtype, is_qat, act_observer) + quantizer.set_default_quant_config( + quant_dtype, + is_qat=is_qat, + is_conv_per_channel=per_channel_conv, + is_linear_per_channel=per_channel_linear, + act_observer=act_observer, + ) + callback_qconfig_list = callback_qconfig_list or [] + quantizer.set_submodule_qconfig_list(callback_qconfig_list) return quantizer diff --git a/extension/llm/export/quantizer_lib.py b/extension/llm/export/quantizer_lib.py index 40d81075d9f..e516ae4c0a9 100644 --- a/extension/llm/export/quantizer_lib.py +++ b/extension/llm/export/quantizer_lib.py @@ -166,30 +166,39 @@ def get_qnn_quantizer( backend == "qnn" ), f"The quantization config is for backend {backend} instead of qnn." qnn_quantizer = QnnQuantizer() # pyre-fixme[16] - qnn_quantizer.set_per_channel_conv_quant(enable=True) - qnn_quantizer.set_per_channel_linear_quant(enable=True) + # more custom quantization are supported including 16a4w etc. default to 8bit quantized custom_annotations = () if quant_config == "8a8w": quant_dtype = QuantDtype.use_8a8w # pyre-fixme[16] - qnn_quantizer.set_quant_config(quant_dtype, is_qat=is_qat) + qnn_quantizer.set_default_quant_config( + quant_dtype, + is_qat=is_qat, + is_conv_per_channel=True, + is_linear_per_channel=True, + ) elif quant_config == "16a16w": - quant_dtype = QuantDtype.use_16a16w # pyre-fixme[16] # Due to the error with 16a16w in Qnn Htp, we need to disable per channel linear quantization when use 16a16w # TODO: enable it after the issue is fixed logging.warning( "Disable per channel quantization for linear and conv due to the error with QNN HTP 16a16w." ) - qnn_quantizer.set_per_channel_conv_quant(enable=False) - qnn_quantizer.set_per_channel_linear_quant(enable=False) - qnn_quantizer.set_quant_config( - quant_dtype, is_qat=is_qat, act_observer=MinMaxObserver + quant_dtype = QuantDtype.use_16a16w # pyre-fixme[16] + qnn_quantizer.set_default_quant_config( + quant_dtype, + is_qat=is_qat, + is_conv_per_channel=False, + is_linear_per_channel=False, + act_observer=MinMaxObserver, ) elif quant_config == "16a4w": - # pyre-ignore: Undefined attribute [16]: Module `executorch.backends` has no attribute `qualcomm`. - quant_dtype = QuantDtype.use_16a4w - qnn_quantizer.set_quant_config( - quant_dtype, is_qat=is_qat, act_observer=MinMaxObserver + quant_dtype = QuantDtype.use_16a16w # pyre-fixme[16] + qnn_quantizer.set_default_quant_config( + quant_dtype, + is_qat=is_qat, + is_conv_per_channel=True, + is_linear_per_channel=True, + act_observer=MinMaxObserver, ) # pyre-ignore: Undefined attribute [16]: Module `executorch.backends` has no attribute `qualcomm`. custom_annotations = (custom_annotate_llama_matmul_16a8w,) From a7dddb4422957ea04795268b2f809281b7be5427 Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Wed, 9 Apr 2025 19:08:31 -0700 Subject: [PATCH 060/244] Fix LLM getting-started.md (#10028) Remove unnecessary line Fix ETDump part --- docs/source/llm/getting-started.md | 24 +++++++++++------------- 1 file changed, 11 insertions(+), 13 deletions(-) diff --git a/docs/source/llm/getting-started.md b/docs/source/llm/getting-started.md index 035da31f119..f19982660ae 100644 --- a/docs/source/llm/getting-started.md +++ b/docs/source/llm/getting-started.md @@ -395,7 +395,6 @@ At this point, the working directory should contain the following files: If all of these are present, you can now build and run: ```bash -./install_executorch.sh --clean (mkdir cmake-out && cd cmake-out && cmake ..) cmake --build cmake-out -j10 ./cmake-out/nanogpt_runner @@ -661,19 +660,15 @@ edge_config = get_xnnpack_edge_compile_config() # Convert to edge dialect and lower to XNNPack. edge_manager = to_edge_transform_and_lower(traced_model, partitioner = [XnnpackPartitioner()], compile_config = edge_config) et_program = edge_manager.to_executorch() -``` - -Finally, ensure that the runner links against the `xnnpack_backend` target in CMakeLists.txt. +with open("nanogpt.pte", "wb") as file: + file.write(et_program.buffer) ``` -add_executable(nanogpt_runner main.cpp) -target_link_libraries( - nanogpt_runner - PRIVATE - executorch - extension_module_static # Provides the Module class - optimized_native_cpu_ops_lib # Provides baseline cross-platform kernels - xnnpack_backend) # Provides the XNNPACK CPU acceleration backend + +Then run: +```bash +python export_nanogpt.py +./cmake-out/nanogpt_runner ``` For more information, see [Quantization in ExecuTorch](../quantization-overview.md). @@ -782,11 +777,14 @@ Run the export script and the ETRecord will be generated as `etrecord.bin`. An ETDump is an artifact generated at runtime containing a trace of the model execution. For more information, see [the ETDump docs](../etdump.md). -Include the ETDump header in your code. +Include the ETDump header and namespace in your code. ```cpp // main.cpp #include + +using executorch::etdump::ETDumpGen; +using torch::executor::etdump_result; ``` Create an Instance of the ETDumpGen class and pass it to the Module constructor. From 799d526419129296c51fdcf51c601635dc596917 Mon Sep 17 00:00:00 2001 From: Itamar Oren Date: Wed, 9 Apr 2025 19:16:25 -0700 Subject: [PATCH 061/244] Fix deprecated unittest asserts in executorch Differential Revision: D72616610 Pull Request resolved: https://github.com/pytorch/executorch/pull/9960 --- backends/xnnpack/test/ops/test_check_quant_params.py | 2 +- examples/models/llava/test/test_llava.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/backends/xnnpack/test/ops/test_check_quant_params.py b/backends/xnnpack/test/ops/test_check_quant_params.py index a04751ca5f1..d05b1fce540 100644 --- a/backends/xnnpack/test/ops/test_check_quant_params.py +++ b/backends/xnnpack/test/ops/test_check_quant_params.py @@ -65,7 +65,7 @@ def _test_check_quant_message(self, ep_modifier, expected_message): with self.assertRaises(ValueError) as context: to_edge_transform_and_lower(aten, partitioner=[XnnpackPartitioner()]) - self.assertEquals(str(context.exception), expected_message) + self.assertEqual(str(context.exception), expected_message) def test_in_per_tensor_quant(self): for invalid_scale in [ diff --git a/examples/models/llava/test/test_llava.py b/examples/models/llava/test/test_llava.py index 5fd60399415..36381b27124 100644 --- a/examples/models/llava/test/test_llava.py +++ b/examples/models/llava/test/test_llava.py @@ -131,7 +131,7 @@ def test_llava_export(self): # being tested, using llama_transformer new_tokens = [torch.argmax(pte_prefill_after_img).item()] # TODO: uncomment this line - # self.assertEquals(new_tokens[0], 1932) # When + # self.assertEqual(new_tokens[0], 1932) # When for i in range(4): print(i, llava_model.tokenizer.decode(new_tokens[i])) token_embeds = llava_module.run_method( From 7567b7ec8b289e6a938fa02c1f95f51021e28bd5 Mon Sep 17 00:00:00 2001 From: Mengwei Liu Date: Wed, 9 Apr 2025 20:16:17 -0700 Subject: [PATCH 062/244] Add a namespace for ATen mode Differential Revision: D72440313 Pull Request resolved: https://github.com/pytorch/executorch/pull/9894 --- .../RegisterCodegenUnboxedKernels.cpp | 4 +- devtools/bundled_program/bundled_program.cpp | 7 +- devtools/bundled_program/bundled_program.h | 10 +- .../portable/custom_ops/custom_ops_2_out.cpp | 2 +- .../flat_tensor/flat_tensor_data_map.cpp | 2 +- extension/flat_tensor/flat_tensor_data_map.h | 6 +- extension/flat_tensor/targets.bzl | 44 ++--- extension/module/module.cpp | 17 +- extension/module/module.h | 22 ++- extension/module/targets.bzl | 2 +- extension/pybindings/pybindings.cpp | 16 +- extension/runner_util/inputs.cpp | 6 +- extension/runner_util/inputs.h | 9 +- extension/runner_util/inputs_aten.cpp | 4 +- extension/tensor/tensor_ptr.cpp | 2 +- kernels/aten/cpu/op__empty_dim_order.cpp | 2 +- kernels/aten/cpu/op__to_dim_order_copy.cpp | 2 +- .../portable/cpu/op__to_dim_order_copy.cpp | 2 +- kernels/portable/test/op_gelu_test.cpp | 2 +- kernels/prim_ops/register_prim_ops.cpp | 7 +- kernels/prim_ops/targets.bzl | 2 +- kernels/quantized/cpu/op_quantize.cpp | 3 +- kernels/quantized/test/op_add_test.cpp | 2 +- .../quantized/test/op_embedding2b_test.cpp | 2 +- .../quantized/test/op_embedding4b_test.cpp | 2 +- kernels/quantized/test/op_embedding_test.cpp | 2 +- .../quantized/test/op_mixed_linear_test.cpp | 2 +- kernels/quantized/test/op_mixed_mm_test.cpp | 2 +- kernels/test/TestUtil.h | 2 +- .../test/custom_kernel_example/op_relu.cpp | 2 +- kernels/test/op_atan2_test.cpp | 2 +- kernels/test/op_cdist_forward_test.cpp | 3 +- kernels/test/op_clamp_test.cpp | 2 +- kernels/test/op_diagonal_copy_test.cpp | 2 +- kernels/test/op_flip_test.cpp | 2 +- kernels/test/op_ge_test.cpp | 2 +- kernels/test/op_gt_test.cpp | 2 +- kernels/test/op_le_test.cpp | 2 +- kernels/test/op_lt_test.cpp | 2 +- kernels/test/op_maximum_test.cpp | 2 +- kernels/test/op_native_batch_norm_test.cpp | 2 +- kernels/test/op_native_group_norm_test.cpp | 2 +- kernels/test/op_ne_test.cpp | 2 +- kernels/test/op_pdist_forward_test.cpp | 2 +- kernels/test/op_prod_test.cpp | 4 +- kernels/test/op_reflection_pad1d_test.cpp | 2 +- kernels/test/op_reflection_pad2d_test.cpp | 2 +- kernels/test/op_reflection_pad3d_test.cpp | 2 +- kernels/test/op_replication_pad1d_test.cpp | 2 +- kernels/test/op_replication_pad2d_test.cpp | 2 +- kernels/test/op_replication_pad3d_test.cpp | 2 +- kernels/test/op_roll_test.cpp | 2 +- kernels/test/op_topk_test.cpp | 3 +- runtime/backend/backend_execution_context.h | 6 +- runtime/backend/backend_init_context.h | 8 +- runtime/backend/interface.cpp | 4 +- runtime/backend/interface.h | 19 +- runtime/core/event_tracer_hooks.h | 37 ++-- runtime/core/exec_aten/exec_aten.h | 15 ++ .../testing_util/test/tensor_factory_test.cpp | 2 +- runtime/core/exec_aten/util/tensor_util.h | 164 +++++++++--------- .../core/exec_aten/util/tensor_util_aten.cpp | 5 +- .../exec_aten/util/test/tensor_util_test.cpp | 44 ++--- runtime/core/named_data_map.h | 9 +- runtime/core/targets.bzl | 63 +++---- runtime/core/tensor_layout.cpp | 4 +- runtime/core/tensor_layout.h | 4 +- runtime/executor/method.cpp | 5 +- runtime/executor/method.h | 6 +- runtime/executor/method_meta.cpp | 5 +- runtime/executor/method_meta.h | 8 +- runtime/executor/platform_memory_allocator.h | 4 +- runtime/executor/program.cpp | 5 +- runtime/executor/program.h | 7 +- runtime/executor/pte_data_map.cpp | 4 +- runtime/executor/pte_data_map.h | 4 +- runtime/executor/targets.bzl | 53 +++--- runtime/executor/tensor_parser.h | 13 +- runtime/executor/tensor_parser_aten.cpp | 3 + runtime/executor/tensor_parser_exec_aten.cpp | 5 +- runtime/executor/tensor_parser_portable.cpp | 10 +- runtime/executor/test/executor_test.cpp | 12 +- runtime/executor/test/targets.bzl | 2 +- .../test/test_backend_compiler_lib.cpp | 12 +- .../test_backend_with_delegate_mapping.cpp | 12 +- runtime/kernel/kernel_runtime_context.h | 10 +- runtime/kernel/operator_registry.cpp | 4 +- runtime/kernel/operator_registry.h | 29 ++-- runtime/kernel/targets.bzl | 30 ++-- .../test/kernel_runtime_context_test.cpp | 2 +- runtime/kernel/test/targets.bzl | 13 ++ .../test/test_generated_lib_and_aten.cpp | 45 +++++ shim_et/xplat/executorch/codegen/codegen.bzl | 2 +- 93 files changed, 517 insertions(+), 411 deletions(-) create mode 100644 runtime/kernel/test/test_generated_lib_and_aten.cpp diff --git a/codegen/templates/RegisterCodegenUnboxedKernels.cpp b/codegen/templates/RegisterCodegenUnboxedKernels.cpp index 3076cde1a99..180baf9b2a9 100644 --- a/codegen/templates/RegisterCodegenUnboxedKernels.cpp +++ b/codegen/templates/RegisterCodegenUnboxedKernels.cpp @@ -22,8 +22,8 @@ // JIT op registry instead of c10 dispatcher. JIT op registry only takes boxed // kernels, so we are calling unboxing functions in UnboxingFunctions.h to cast // arguments into C++ types (instead of IValue) and delegate to unboxed kernels. -using KernelSpan = - ::executorch::runtime::Span; +using KernelSpan = ::executorch::runtime::Span< + const ::executorch::ET_RUNTIME_NAMESPACE::Kernel>; namespace torch { namespace executor { namespace function { diff --git a/devtools/bundled_program/bundled_program.cpp b/devtools/bundled_program/bundled_program.cpp index f12262f7dd0..4e5beb84615 100644 --- a/devtools/bundled_program/bundled_program.cpp +++ b/devtools/bundled_program/bundled_program.cpp @@ -27,9 +27,9 @@ using executorch::aten::ArrayRef; using executorch::aten::Half; using executorch::aten::ScalarType; using executorch::aten::Tensor; +using ::executorch::ET_RUNTIME_NAMESPACE::Method; using ::executorch::runtime::Error; using ::executorch::runtime::EValue; -using ::executorch::runtime::Method; using ::executorch::runtime::Result; namespace executorch { @@ -332,8 +332,9 @@ ET_NODISCARD Error load_bundled_input( static_cast(status)); } - ::executorch::runtime::internal::event_tracer_set_bundled_input_index( - method.get_event_tracer(), testset_idx); + ::executorch::ET_RUNTIME_NAMESPACE::internal:: + event_tracer_set_bundled_input_index( + method.get_event_tracer(), testset_idx); return Error::Ok; } diff --git a/devtools/bundled_program/bundled_program.h b/devtools/bundled_program/bundled_program.h index 884ca6f21bc..cedb2448eca 100644 --- a/devtools/bundled_program/bundled_program.h +++ b/devtools/bundled_program/bundled_program.h @@ -18,7 +18,7 @@ namespace bundled_program { * An opaque pointer to a serialized bundled program. */ using SerializedBundledProgram = const void; - +using ::executorch::ET_RUNTIME_NAMESPACE::Method; /** * Load testset_idx-th bundled input of method_idx-th Method test in * bundled_program_ptr to given Method. @@ -31,7 +31,7 @@ using SerializedBundledProgram = const void; * execution. */ ET_NODISCARD ::executorch::runtime::Error load_bundled_input( - ::executorch::runtime::Method& method, + Method& method, SerializedBundledProgram* bundled_program_ptr, size_t testset_idx); @@ -49,7 +49,7 @@ ET_NODISCARD ::executorch::runtime::Error load_bundled_input( * execution. */ ET_NODISCARD ::executorch::runtime::Error verify_method_outputs( - ::executorch::runtime::Method& method, + Method& method, SerializedBundledProgram* bundled_program_ptr, size_t testset_idx, double rtol = 1e-5, @@ -106,7 +106,7 @@ using serialized_bundled_program = ::executorch::bundled_program::SerializedBundledProgram; ET_NODISCARD inline ::executorch::runtime::Error LoadBundledInput( - ::executorch::runtime::Method& method, + Method& method, serialized_bundled_program* bundled_program_ptr, size_t testset_idx) { return ::executorch::bundled_program::load_bundled_input( @@ -115,7 +115,7 @@ ET_NODISCARD inline ::executorch::runtime::Error LoadBundledInput( ET_NODISCARD inline ::executorch::runtime::Error VerifyResultWithBundledExpectedOutput( - ::executorch::runtime::Method& method, + Method& method, serialized_bundled_program* bundled_program_ptr, size_t testset_idx, double rtol = 1e-5, diff --git a/examples/portable/custom_ops/custom_ops_2_out.cpp b/examples/portable/custom_ops/custom_ops_2_out.cpp index 138a8eeed89..2fb50e521c1 100644 --- a/examples/portable/custom_ops/custom_ops_2_out.cpp +++ b/examples/portable/custom_ops/custom_ops_2_out.cpp @@ -13,7 +13,7 @@ namespace native { using executorch::aten::ScalarType; using executorch::aten::Tensor; -using executorch::runtime::KernelRuntimeContext; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; namespace { void check_preconditions(const Tensor& in, Tensor& out) { diff --git a/extension/flat_tensor/flat_tensor_data_map.cpp b/extension/flat_tensor/flat_tensor_data_map.cpp index bf54ae014b5..8aa0af13928 100644 --- a/extension/flat_tensor/flat_tensor_data_map.cpp +++ b/extension/flat_tensor/flat_tensor_data_map.cpp @@ -25,8 +25,8 @@ using executorch::runtime::Result; using executorch::runtime::Span; using executorch::aten::ScalarType; +using executorch::ET_RUNTIME_NAMESPACE::TensorLayout; using executorch::runtime::DataLoader; -using executorch::runtime::TensorLayout; namespace executorch { namespace extension { diff --git a/extension/flat_tensor/flat_tensor_data_map.h b/extension/flat_tensor/flat_tensor_data_map.h index 972a5fa9c55..0e7aee8ffc8 100644 --- a/extension/flat_tensor/flat_tensor_data_map.h +++ b/extension/flat_tensor/flat_tensor_data_map.h @@ -32,7 +32,8 @@ namespace extension { /** * A NamedDataMap implementation for FlatTensor-serialized data. */ -class FlatTensorDataMap final : public executorch::runtime::NamedDataMap { +class FlatTensorDataMap final + : public executorch::ET_RUNTIME_NAMESPACE::NamedDataMap { public: /** * Creates a new DataMap that wraps FlatTensor data. @@ -51,7 +52,8 @@ class FlatTensorDataMap final : public executorch::runtime::NamedDataMap { * @return Error::NotFound if the key is not present. */ ET_NODISCARD - executorch::runtime::Result + executorch::runtime::Result< + const executorch::ET_RUNTIME_NAMESPACE::TensorLayout> get_metadata(const char* key) const override; /** diff --git a/extension/flat_tensor/targets.bzl b/extension/flat_tensor/targets.bzl index 0d49995aa6e..4ac515b7bf0 100644 --- a/extension/flat_tensor/targets.bzl +++ b/extension/flat_tensor/targets.bzl @@ -1,24 +1,26 @@ load("@fbsource//xplat/executorch/build:runtime_wrapper.bzl", "runtime") def define_common_targets(): - runtime.cxx_library( - name = "flat_tensor_data_map", - srcs = [ - "flat_tensor_data_map.cpp", - ], - exported_headers = ["flat_tensor_data_map.h"], - deps = [ - "//executorch/runtime/core:core", - "//executorch/runtime/core:evalue", - "//executorch/runtime/core:named_data_map", - "//executorch/runtime/core/exec_aten:lib", - "//executorch/runtime/core/exec_aten/util:tensor_util", - ], - exported_deps = [ - "//executorch/extension/flat_tensor/serialize:flat_tensor_header", - "//executorch/extension/flat_tensor/serialize:generated_headers", - ], - visibility = [ - "//executorch/...", - ], - ) + for aten_mode in [True, False]: + aten_suffix = "_aten" if aten_mode else "" + runtime.cxx_library( + name = "flat_tensor_data_map" + aten_suffix, + srcs = [ + "flat_tensor_data_map.cpp", + ], + exported_headers = ["flat_tensor_data_map.h"], + deps = [ + "//executorch/runtime/core:core", + "//executorch/runtime/core:evalue", + "//executorch/runtime/core:named_data_map" + aten_suffix, + "//executorch/runtime/core/exec_aten:lib" + aten_suffix, + "//executorch/runtime/core/exec_aten/util:tensor_util", + ], + exported_deps = [ + "//executorch/extension/flat_tensor/serialize:flat_tensor_header", + "//executorch/extension/flat_tensor/serialize:generated_headers", + ], + visibility = [ + "//executorch/...", + ], + ) diff --git a/extension/module/module.cpp b/extension/module/module.cpp index 400a2c45049..6c534b8d560 100644 --- a/extension/module/module.cpp +++ b/extension/module/module.cpp @@ -37,6 +37,9 @@ namespace executorch { namespace extension { +using ET_RUNTIME_NAMESPACE::MethodMeta; +using ET_RUNTIME_NAMESPACE::Program; + namespace { runtime::Result> load_file( const std::string& file_path, @@ -113,7 +116,7 @@ Module::Module( } Module::Module( - std::shared_ptr program, + std::shared_ptr program, std::unique_ptr memory_allocator, std::unique_ptr temp_allocator, std::unique_ptr event_tracer, @@ -131,7 +134,7 @@ Module::Module( runtime::runtime_init(); } -runtime::Error Module::load(const runtime::Program::Verification verification) { +runtime::Error Module::load(const Program::Verification verification) { if (!is_loaded()) { // Load the program if (!data_loader_) { @@ -156,10 +159,10 @@ runtime::Error Module::load(const runtime::Program::Verification verification) { } // else: either the map itself was provided or we have no data map, either // way no work to do. - auto program = ET_UNWRAP_UNIQUE( - runtime::Program::load(data_loader_.get(), verification)); - program_ = std::shared_ptr( - program.release(), [](runtime::Program* pointer) { delete pointer; }); + auto program = + ET_UNWRAP_UNIQUE(Program::load(data_loader_.get(), verification)); + program_ = std::shared_ptr( + program.release(), [](Program* pointer) { delete pointer; }); } return runtime::Error::Ok; } @@ -224,7 +227,7 @@ runtime::Error Module::load_method( return runtime::Error::Ok; } -runtime::Result Module::method_meta( +runtime::Result Module::method_meta( const std::string& method_name) { ET_CHECK_OK_OR_RETURN_ERROR(load_method(method_name)); return methods_.at(method_name).method->method_meta(); diff --git a/extension/module/module.h b/extension/module/module.h index 45d2cc1d14b..73c7328ee0a 100644 --- a/extension/module/module.h +++ b/extension/module/module.h @@ -19,6 +19,11 @@ namespace executorch { namespace extension { +using ET_RUNTIME_NAMESPACE::Method; +using ET_RUNTIME_NAMESPACE::MethodMeta; +using ET_RUNTIME_NAMESPACE::NamedDataMap; +using ET_RUNTIME_NAMESPACE::Program; + /** * A facade class for loading programs and executing methods within them. */ @@ -95,7 +100,7 @@ class Module { * @param[in] data_map_loader A DataLoader used for loading external weights. */ explicit Module( - std::shared_ptr program, + std::shared_ptr program, std::unique_ptr memory_allocator = nullptr, std::unique_ptr temp_allocator = nullptr, std::unique_ptr event_tracer = nullptr, @@ -116,8 +121,8 @@ class Module { */ ET_NODISCARD runtime::Error load( - const runtime::Program::Verification verification = - runtime::Program::Verification::Minimal); + const Program::Verification verification = + Program::Verification::Minimal); /** * Checks if the program is loaded. @@ -134,7 +139,7 @@ class Module { * * @returns Shared pointer to the program or nullptr if it's not yet loaded. */ - inline std::shared_ptr program() const { + inline std::shared_ptr program() const { return program_; } @@ -224,8 +229,7 @@ class Module { * @returns A method metadata, or an error if the program or method failed to * load. */ - runtime::Result method_meta( - const std::string& method_name); + runtime::Result method_meta(const std::string& method_name); /** * Execute a specific method with the given input values and retrieve the @@ -473,20 +477,20 @@ class Module { std::vector> planned_spans; std::unique_ptr planned_memory; std::unique_ptr memory_manager; - std::unique_ptr method; + std::unique_ptr method; std::vector inputs; }; std::string file_path_; std::string data_map_path_; LoadMode load_mode_{LoadMode::MmapUseMlock}; - std::shared_ptr program_; + std::shared_ptr program_; std::unique_ptr data_loader_; std::unique_ptr memory_allocator_; std::unique_ptr temp_allocator_; std::unique_ptr event_tracer_; std::unique_ptr data_map_loader_; - std::unique_ptr data_map_; + std::unique_ptr data_map_; protected: std::unordered_map methods_; diff --git a/extension/module/targets.bzl b/extension/module/targets.bzl index 09a610a1fca..d8019ce9c4e 100644 --- a/extension/module/targets.bzl +++ b/extension/module/targets.bzl @@ -25,7 +25,7 @@ def define_common_targets(): "//executorch/extension/memory_allocator:malloc_memory_allocator", "//executorch/extension/data_loader:file_data_loader", "//executorch/extension/data_loader:mmap_data_loader", - "//executorch/extension/flat_tensor:flat_tensor_data_map", + "//executorch/extension/flat_tensor:flat_tensor_data_map" + aten_suffix, ], exported_deps = [ "//executorch/runtime/executor:program" + aten_suffix, diff --git a/extension/pybindings/pybindings.cpp b/extension/pybindings/pybindings.cpp index a998e591f30..766288d7e47 100644 --- a/extension/pybindings/pybindings.cpp +++ b/extension/pybindings/pybindings.cpp @@ -85,26 +85,26 @@ void et_pal_emit_log_message( namespace py = pybind11; using executorch::bundled_program::verify_method_outputs; +using ::executorch::ET_RUNTIME_NAMESPACE::BackendInterface; +using ::executorch::ET_RUNTIME_NAMESPACE::get_backend_class; +using ::executorch::ET_RUNTIME_NAMESPACE::get_backend_name; +using ::executorch::ET_RUNTIME_NAMESPACE::get_num_registered_backends; +using ::executorch::ET_RUNTIME_NAMESPACE::get_registered_kernels; +using ::executorch::ET_RUNTIME_NAMESPACE::Kernel; +using ::executorch::ET_RUNTIME_NAMESPACE::Method; +using ::executorch::ET_RUNTIME_NAMESPACE::Program; using ::executorch::extension::BufferDataLoader; using ::executorch::extension::MallocMemoryAllocator; using ::executorch::extension::MmapDataLoader; using ::executorch::runtime::ArrayRef; -using ::executorch::runtime::BackendInterface; using ::executorch::runtime::DataLoader; using ::executorch::runtime::Error; using ::executorch::runtime::EValue; using ::executorch::runtime::EventTracerDebugLogLevel; -using ::executorch::runtime::get_backend_class; -using ::executorch::runtime::get_backend_name; -using ::executorch::runtime::get_num_registered_backends; -using ::executorch::runtime::get_registered_kernels; using ::executorch::runtime::HierarchicalAllocator; -using ::executorch::runtime::Kernel; using ::executorch::runtime::MemoryAllocator; using ::executorch::runtime::MemoryManager; -using ::executorch::runtime::Method; using ::executorch::runtime::prof_result_t; -using ::executorch::runtime::Program; using ::executorch::runtime::Result; using ::executorch::runtime::Span; using ::executorch::runtime::Tag; diff --git a/extension/runner_util/inputs.cpp b/extension/runner_util/inputs.cpp index 11cd176b5d1..842ba25532f 100644 --- a/extension/runner_util/inputs.cpp +++ b/extension/runner_util/inputs.cpp @@ -12,12 +12,12 @@ #include #include +using executorch::ET_RUNTIME_NAMESPACE::Method; +using executorch::ET_RUNTIME_NAMESPACE::MethodMeta; +using executorch::ET_RUNTIME_NAMESPACE::TensorInfo; using executorch::runtime::Error; -using executorch::runtime::Method; -using executorch::runtime::MethodMeta; using executorch::runtime::Result; using executorch::runtime::Tag; -using executorch::runtime::TensorInfo; namespace executorch { namespace extension { diff --git a/extension/runner_util/inputs.h b/extension/runner_util/inputs.h index 73722c0d7bf..214b76d67e3 100644 --- a/extension/runner_util/inputs.h +++ b/extension/runner_util/inputs.h @@ -15,6 +15,9 @@ namespace executorch { namespace extension { +using ::executorch::ET_RUNTIME_NAMESPACE::Method; +using ::executorch::ET_RUNTIME_NAMESPACE::TensorInfo; + /** * RAII helper that frees a set of buffers when destroyed. Movable. */ @@ -80,7 +83,7 @@ struct PrepareInputTensorsOptions { * @returns An error on failure. */ executorch::runtime::Result prepare_input_tensors( - executorch::runtime::Method& method, + Method& method, PrepareInputTensorsOptions options = {}); namespace internal { @@ -89,8 +92,8 @@ namespace internal { * fills it with ones, and sets the input at `input_index`. */ executorch::runtime::Error fill_and_set_input( - executorch::runtime::Method& method, - executorch::runtime::TensorInfo& tensor_meta, + Method& method, + TensorInfo& tensor_meta, size_t input_index, void* data_ptr); } // namespace internal diff --git a/extension/runner_util/inputs_aten.cpp b/extension/runner_util/inputs_aten.cpp index 83d12dac42d..b89562a2f69 100644 --- a/extension/runner_util/inputs_aten.cpp +++ b/extension/runner_util/inputs_aten.cpp @@ -15,8 +15,8 @@ #include using executorch::runtime::Error; -using executorch::runtime::Method; -using executorch::runtime::TensorInfo; +using executorch::runtime::aten::Method; +using executorch::runtime::aten::TensorInfo; namespace executorch { namespace extension { diff --git a/extension/tensor/tensor_ptr.cpp b/extension/tensor/tensor_ptr.cpp index c1742fc599b..8a35e83a526 100644 --- a/extension/tensor/tensor_ptr.cpp +++ b/extension/tensor/tensor_ptr.cpp @@ -188,7 +188,7 @@ TensorPtr clone_tensor_ptr(const executorch::aten::Tensor& tensor) { runtime::Error resize_tensor_ptr( TensorPtr& tensor, const std::vector& sizes) { - return runtime::resize_tensor( + return ET_RUNTIME_NAMESPACE::resize_tensor( *tensor, executorch::aten::ArrayRef( sizes.data(), sizes.size())); diff --git a/kernels/aten/cpu/op__empty_dim_order.cpp b/kernels/aten/cpu/op__empty_dim_order.cpp index e75963a9c4e..654b29c778d 100644 --- a/kernels/aten/cpu/op__empty_dim_order.cpp +++ b/kernels/aten/cpu/op__empty_dim_order.cpp @@ -102,7 +102,7 @@ Tensor& _empty_dim_order_out( IntArrayRef size, OptionalIntArrayRef dim_order, Tensor& out) { - executorch::runtime::KernelRuntimeContext ctx{}; + KernelRuntimeContext ctx{}; return _empty_dim_order_out(ctx, size, dim_order, out); } diff --git a/kernels/aten/cpu/op__to_dim_order_copy.cpp b/kernels/aten/cpu/op__to_dim_order_copy.cpp index 10793d24db5..a8216c9a8e9 100644 --- a/kernels/aten/cpu/op__to_dim_order_copy.cpp +++ b/kernels/aten/cpu/op__to_dim_order_copy.cpp @@ -116,7 +116,7 @@ Tensor& _to_dim_order_copy_out( bool non_blocking, OptionalArrayRef dim_order, Tensor& out) { - executorch::runtime::KernelRuntimeContext ctx{}; + KernelRuntimeContext ctx{}; return _to_dim_order_copy_out(ctx, self, non_blocking, dim_order, out); } diff --git a/kernels/portable/cpu/op__to_dim_order_copy.cpp b/kernels/portable/cpu/op__to_dim_order_copy.cpp index 40ce86e8fdc..70fc3507f05 100644 --- a/kernels/portable/cpu/op__to_dim_order_copy.cpp +++ b/kernels/portable/cpu/op__to_dim_order_copy.cpp @@ -125,7 +125,7 @@ Tensor& _to_dim_order_copy_out( bool non_blocking, OptionalArrayRef dim_order, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return _to_dim_order_copy_out(context, self, non_blocking, dim_order, out); } diff --git a/kernels/portable/test/op_gelu_test.cpp b/kernels/portable/test/op_gelu_test.cpp index 19e757b4bd0..2e5cad55c35 100644 --- a/kernels/portable/test/op_gelu_test.cpp +++ b/kernels/portable/test/op_gelu_test.cpp @@ -25,7 +25,7 @@ using torch::executor::testing::TensorFactory; // executorch/kernels/test/op_gelu_test.cpp instead. Tensor& op_gelu_out(const Tensor& self, string_view approximate, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::native::gelu_out(context, self, approximate, out); } diff --git a/kernels/prim_ops/register_prim_ops.cpp b/kernels/prim_ops/register_prim_ops.cpp index 1d197b63584..62aead8978f 100644 --- a/kernels/prim_ops/register_prim_ops.cpp +++ b/kernels/prim_ops/register_prim_ops.cpp @@ -381,14 +381,13 @@ static Kernel prim_ops[] = { }; -executorch::runtime::Span kernel_span( - prim_ops, - prim_ops + sizeof(prim_ops) / sizeof(Kernel)); +executorch::runtime::Span + kernel_span(prim_ops, prim_ops + sizeof(prim_ops) / sizeof(Kernel)); // Return value not used. Keep the static variable assignment to register // operators in static initialization time. auto success_with_kernel_reg = - executorch::runtime::register_kernels(kernel_span); + executorch::ET_RUNTIME_NAMESPACE::register_kernels(kernel_span); } // namespace } // namespace function diff --git a/kernels/prim_ops/targets.bzl b/kernels/prim_ops/targets.bzl index c1af21a7e73..d2cff10a194 100644 --- a/kernels/prim_ops/targets.bzl +++ b/kernels/prim_ops/targets.bzl @@ -56,7 +56,7 @@ def define_common_targets(): ":et_copy_index" + aten_suffix, ":et_view" + aten_suffix, "//executorch/runtime/core:evalue" + aten_suffix, - "//executorch/runtime/kernel:operator_registry", + "//executorch/runtime/kernel:operator_registry" + aten_suffix, "//executorch/runtime/kernel:kernel_includes" + aten_suffix, ], ) diff --git a/kernels/quantized/cpu/op_quantize.cpp b/kernels/quantized/cpu/op_quantize.cpp index 5079109683f..632bddd58c4 100644 --- a/kernels/quantized/cpu/op_quantize.cpp +++ b/kernels/quantized/cpu/op_quantize.cpp @@ -22,6 +22,7 @@ namespace native { using Tensor = executorch::aten::Tensor; using Scalar = executorch::aten::Scalar; using ScalarType = executorch::aten::ScalarType; +using KernelRuntimeContext = torch::executor::KernelRuntimeContext; namespace { @@ -214,7 +215,7 @@ Tensor& quantize_per_tensor_tensor_args_out( int64_t quant_max, ScalarType dtype, Tensor& out) { - auto context = executorch::runtime::KernelRuntimeContext(); + auto context = KernelRuntimeContext(); auto& res = quantize_per_tensor_tensor_args_out( context, input, scale, zero_point, quant_min, quant_max, dtype, out); ET_CHECK(context.failure_state() == Error::Ok); diff --git a/kernels/quantized/test/op_add_test.cpp b/kernels/quantized/test/op_add_test.cpp index 17dd1cfb3fc..3f258827973 100644 --- a/kernels/quantized/test/op_add_test.cpp +++ b/kernels/quantized/test/op_add_test.cpp @@ -24,7 +24,7 @@ using executorch::aten::optional; using executorch::aten::Scalar; using executorch::aten::ScalarType; using executorch::aten::Tensor; -using executorch::runtime::KernelRuntimeContext; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; using torch::executor::native::add_out; using torch::executor::native::dequantize_per_tensor_out; using torch::executor::native::quantize_per_tensor_out; diff --git a/kernels/quantized/test/op_embedding2b_test.cpp b/kernels/quantized/test/op_embedding2b_test.cpp index bf48fa4227b..a350b77ec0d 100644 --- a/kernels/quantized/test/op_embedding2b_test.cpp +++ b/kernels/quantized/test/op_embedding2b_test.cpp @@ -21,7 +21,7 @@ using executorch::aten::ArrayRef; using executorch::aten::optional; using executorch::aten::ScalarType; using executorch::aten::Tensor; -using executorch::runtime::KernelRuntimeContext; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; using torch::executor::native::quantized_embedding_2bit_out; using torch::executor::testing::TensorFactory; diff --git a/kernels/quantized/test/op_embedding4b_test.cpp b/kernels/quantized/test/op_embedding4b_test.cpp index 9f205be80e3..6ab10376b88 100644 --- a/kernels/quantized/test/op_embedding4b_test.cpp +++ b/kernels/quantized/test/op_embedding4b_test.cpp @@ -21,7 +21,7 @@ using executorch::aten::ArrayRef; using executorch::aten::optional; using executorch::aten::ScalarType; using executorch::aten::Tensor; -using executorch::runtime::KernelRuntimeContext; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; using torch::executor::native::quantized_embedding_4bit_out; using torch::executor::testing::TensorFactory; diff --git a/kernels/quantized/test/op_embedding_test.cpp b/kernels/quantized/test/op_embedding_test.cpp index 252aca41314..6c949bd6e69 100644 --- a/kernels/quantized/test/op_embedding_test.cpp +++ b/kernels/quantized/test/op_embedding_test.cpp @@ -24,7 +24,7 @@ using executorch::aten::optional; using executorch::aten::Scalar; using executorch::aten::ScalarType; using executorch::aten::Tensor; -using executorch::runtime::KernelRuntimeContext; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; using torch::executor::native::dequantize_per_tensor_out; using torch::executor::native::embedding_out; using torch::executor::native::quantize_per_tensor_out; diff --git a/kernels/quantized/test/op_mixed_linear_test.cpp b/kernels/quantized/test/op_mixed_linear_test.cpp index 6b86b199f60..833fc766ffd 100644 --- a/kernels/quantized/test/op_mixed_linear_test.cpp +++ b/kernels/quantized/test/op_mixed_linear_test.cpp @@ -20,7 +20,7 @@ using namespace ::testing; using executorch::aten::optional; using executorch::aten::ScalarType; using executorch::aten::Tensor; -using executorch::runtime::KernelRuntimeContext; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; using torch::executor::native::quantized_mixed_linear_out; using torch::executor::testing::TensorFactory; diff --git a/kernels/quantized/test/op_mixed_mm_test.cpp b/kernels/quantized/test/op_mixed_mm_test.cpp index e20ac96d610..4d81089fa91 100644 --- a/kernels/quantized/test/op_mixed_mm_test.cpp +++ b/kernels/quantized/test/op_mixed_mm_test.cpp @@ -20,7 +20,7 @@ using namespace ::testing; using executorch::aten::optional; using executorch::aten::ScalarType; using executorch::aten::Tensor; -using executorch::runtime::KernelRuntimeContext; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; using torch::executor::native::quantized_mixed_mm_out; using torch::executor::testing::TensorFactory; diff --git a/kernels/test/TestUtil.h b/kernels/test/TestUtil.h index aa220f5bfd5..7ec20c11bef 100644 --- a/kernels/test/TestUtil.h +++ b/kernels/test/TestUtil.h @@ -116,6 +116,6 @@ class OperatorTest : public ::testing::Test { } protected: - executorch::runtime::KernelRuntimeContext context_; + ::torch::executor::KernelRuntimeContext context_; bool expect_failure_; }; diff --git a/kernels/test/custom_kernel_example/op_relu.cpp b/kernels/test/custom_kernel_example/op_relu.cpp index 2cc3eefe0a8..074ebe6b900 100644 --- a/kernels/test/custom_kernel_example/op_relu.cpp +++ b/kernels/test/custom_kernel_example/op_relu.cpp @@ -17,8 +17,8 @@ namespace native { using executorch::aten::ScalarType; using executorch::aten::Tensor; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; using executorch::runtime::Error; -using executorch::runtime::KernelRuntimeContext; using executorch::runtime::resize_tensor; using executorch::runtime::tensors_have_same_shape_and_dtype; diff --git a/kernels/test/op_atan2_test.cpp b/kernels/test/op_atan2_test.cpp index e69ea0e90c8..436826e2b6d 100644 --- a/kernels/test/op_atan2_test.cpp +++ b/kernels/test/op_atan2_test.cpp @@ -23,7 +23,7 @@ using torch::executor::testing::SupportedFeatures; using torch::executor::testing::TensorFactory; Tensor& op_atan2_out(const Tensor& self, const Tensor& other, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::atan2_outf(context, self, other, out); } diff --git a/kernels/test/op_cdist_forward_test.cpp b/kernels/test/op_cdist_forward_test.cpp index 32465ca439b..9ddab4c3c49 100644 --- a/kernels/test/op_cdist_forward_test.cpp +++ b/kernels/test/op_cdist_forward_test.cpp @@ -21,6 +21,7 @@ using executorch::aten::ArrayRef; using executorch::aten::optional; using executorch::aten::ScalarType; using executorch::aten::Tensor; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; using torch::executor::testing::TensorFactory; Tensor& op_cdist_forward_out( @@ -29,7 +30,7 @@ Tensor& op_cdist_forward_out( double p, optional compute_mode, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + KernelRuntimeContext context{}; return torch::executor::aten::_cdist_forward_outf( context, x1, x2, p, compute_mode, out); } diff --git a/kernels/test/op_clamp_test.cpp b/kernels/test/op_clamp_test.cpp index a1003e892e0..8a021c70303 100644 --- a/kernels/test/op_clamp_test.cpp +++ b/kernels/test/op_clamp_test.cpp @@ -260,7 +260,7 @@ class OpClampTensorOutTest : public OperatorTest { const optional& min, const optional& max, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::clamp_outf(context, self, min, max, out); } }; diff --git a/kernels/test/op_diagonal_copy_test.cpp b/kernels/test/op_diagonal_copy_test.cpp index cc0bd02e1a5..35d19bf661e 100644 --- a/kernels/test/op_diagonal_copy_test.cpp +++ b/kernels/test/op_diagonal_copy_test.cpp @@ -27,7 +27,7 @@ Tensor& op_diagonal_copy_out( int64_t dim1, int64_t dim2, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::diagonal_copy_outf( context, input, offset, dim1, dim2, out); } diff --git a/kernels/test/op_flip_test.cpp b/kernels/test/op_flip_test.cpp index f240dfd4ad3..be06e397be2 100644 --- a/kernels/test/op_flip_test.cpp +++ b/kernels/test/op_flip_test.cpp @@ -22,7 +22,7 @@ using executorch::aten::Tensor; using torch::executor::testing::TensorFactory; Tensor& op_flip_out(const Tensor& input, IntArrayRef dims, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::flip_outf(context, input, dims, out); } diff --git a/kernels/test/op_ge_test.cpp b/kernels/test/op_ge_test.cpp index 4b21644a5c5..a79502b266e 100644 --- a/kernels/test/op_ge_test.cpp +++ b/kernels/test/op_ge_test.cpp @@ -18,7 +18,7 @@ using namespace ::testing; using executorch::aten::Scalar; using executorch::aten::ScalarType; using executorch::aten::Tensor; -using executorch::runtime::KernelRuntimeContext; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; using torch::executor::testing::TensorFactory; class OpGeTensorOutTest : public OperatorTest { diff --git a/kernels/test/op_gt_test.cpp b/kernels/test/op_gt_test.cpp index 29a2fb0e8b8..96c0e95f950 100644 --- a/kernels/test/op_gt_test.cpp +++ b/kernels/test/op_gt_test.cpp @@ -18,7 +18,7 @@ using namespace ::testing; using executorch::aten::Scalar; using executorch::aten::ScalarType; using executorch::aten::Tensor; -using executorch::runtime::KernelRuntimeContext; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; using torch::executor::testing::TensorFactory; class OpGtScalarOutTest : public OperatorTest { diff --git a/kernels/test/op_le_test.cpp b/kernels/test/op_le_test.cpp index 49ef5235d0f..bcd40d24d89 100644 --- a/kernels/test/op_le_test.cpp +++ b/kernels/test/op_le_test.cpp @@ -18,7 +18,7 @@ using namespace ::testing; using executorch::aten::Scalar; using executorch::aten::ScalarType; using executorch::aten::Tensor; -using executorch::runtime::KernelRuntimeContext; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; using torch::executor::testing::TensorFactory; class OpLeScalarOutTest : public OperatorTest { diff --git a/kernels/test/op_lt_test.cpp b/kernels/test/op_lt_test.cpp index 51ccb310e4a..eee12c50521 100644 --- a/kernels/test/op_lt_test.cpp +++ b/kernels/test/op_lt_test.cpp @@ -18,7 +18,7 @@ using namespace ::testing; using executorch::aten::Scalar; using executorch::aten::ScalarType; using executorch::aten::Tensor; -using executorch::runtime::KernelRuntimeContext; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; using torch::executor::testing::TensorFactory; class OpLtScalarOutTest : public OperatorTest { diff --git a/kernels/test/op_maximum_test.cpp b/kernels/test/op_maximum_test.cpp index 9c701e208eb..faa18fa56cd 100644 --- a/kernels/test/op_maximum_test.cpp +++ b/kernels/test/op_maximum_test.cpp @@ -21,7 +21,7 @@ using executorch::aten::Tensor; using torch::executor::testing::TensorFactory; Tensor& op_maximum_out(const Tensor& self, const Tensor& other, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::maximum_outf(context, self, other, out); } diff --git a/kernels/test/op_native_batch_norm_test.cpp b/kernels/test/op_native_batch_norm_test.cpp index 67e46b27508..bf05a87312d 100644 --- a/kernels/test/op_native_batch_norm_test.cpp +++ b/kernels/test/op_native_batch_norm_test.cpp @@ -173,7 +173,7 @@ class OpNativeBatchNormLegitOutTest : public OperatorTest { executorch::aten::Tensor& out0, executorch::aten::Tensor& out1, executorch::aten::Tensor& out2) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::_native_batch_norm_legit_outf( context, input, diff --git a/kernels/test/op_native_group_norm_test.cpp b/kernels/test/op_native_group_norm_test.cpp index ea742e97231..7452350ad29 100644 --- a/kernels/test/op_native_group_norm_test.cpp +++ b/kernels/test/op_native_group_norm_test.cpp @@ -32,7 +32,7 @@ ::std::tuple op_native_group_norm_out( Tensor& out0, Tensor& out1, Tensor& out2) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::native_group_norm_outf( context, input, weight, bias, N, C, HxW, group, eps, out0, out1, out2); } diff --git a/kernels/test/op_ne_test.cpp b/kernels/test/op_ne_test.cpp index 6cb0217ec0f..fe4e6c3621c 100644 --- a/kernels/test/op_ne_test.cpp +++ b/kernels/test/op_ne_test.cpp @@ -18,7 +18,7 @@ using namespace ::testing; using executorch::aten::Scalar; using executorch::aten::ScalarType; using executorch::aten::Tensor; -using executorch::runtime::KernelRuntimeContext; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; using torch::executor::testing::TensorFactory; class OpNeTest : public OperatorTest { diff --git a/kernels/test/op_pdist_forward_test.cpp b/kernels/test/op_pdist_forward_test.cpp index e6c0d472517..2b28591f7fc 100644 --- a/kernels/test/op_pdist_forward_test.cpp +++ b/kernels/test/op_pdist_forward_test.cpp @@ -23,7 +23,7 @@ using executorch::aten::Tensor; using torch::executor::testing::TensorFactory; Tensor& op_pdist_forward_out(const Tensor& input, double p, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::_pdist_forward_outf(context, input, p, out); } diff --git a/kernels/test/op_prod_test.cpp b/kernels/test/op_prod_test.cpp index f9cf53ded57..11a7e3fae4f 100644 --- a/kernels/test/op_prod_test.cpp +++ b/kernels/test/op_prod_test.cpp @@ -23,7 +23,7 @@ using torch::executor::testing::TensorFactory; Tensor& op_prod_out(const Tensor& self, optional dtype, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::prod_outf(context, self, dtype, out); } @@ -33,7 +33,7 @@ Tensor& op_prod_int_out( bool keepdim, optional dtype, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::prod_outf( context, self, dim, keepdim, dtype, out); } diff --git a/kernels/test/op_reflection_pad1d_test.cpp b/kernels/test/op_reflection_pad1d_test.cpp index 5f3b2a1c273..aebf057326a 100644 --- a/kernels/test/op_reflection_pad1d_test.cpp +++ b/kernels/test/op_reflection_pad1d_test.cpp @@ -25,7 +25,7 @@ Tensor& op_reflection_pad1d_out( const Tensor& input, ArrayRef padding, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::reflection_pad1d_outf( context, input, padding, out); } diff --git a/kernels/test/op_reflection_pad2d_test.cpp b/kernels/test/op_reflection_pad2d_test.cpp index 8696b5dff7b..01e0619b9f1 100644 --- a/kernels/test/op_reflection_pad2d_test.cpp +++ b/kernels/test/op_reflection_pad2d_test.cpp @@ -25,7 +25,7 @@ Tensor& op_reflection_pad2d_out( const Tensor& input, ArrayRef padding, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::reflection_pad2d_outf( context, input, padding, out); } diff --git a/kernels/test/op_reflection_pad3d_test.cpp b/kernels/test/op_reflection_pad3d_test.cpp index 7d5cc84c6bc..55ed906a958 100644 --- a/kernels/test/op_reflection_pad3d_test.cpp +++ b/kernels/test/op_reflection_pad3d_test.cpp @@ -25,7 +25,7 @@ Tensor& op_reflection_pad3d_out( const Tensor& input, ArrayRef padding, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::reflection_pad3d_outf( context, input, padding, out); } diff --git a/kernels/test/op_replication_pad1d_test.cpp b/kernels/test/op_replication_pad1d_test.cpp index 9a6d3b2285e..f8a3fc0a48b 100644 --- a/kernels/test/op_replication_pad1d_test.cpp +++ b/kernels/test/op_replication_pad1d_test.cpp @@ -25,7 +25,7 @@ Tensor& op_replication_pad1d_out( const Tensor& input, ArrayRef padding, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::replication_pad1d_outf( context, input, padding, out); } diff --git a/kernels/test/op_replication_pad2d_test.cpp b/kernels/test/op_replication_pad2d_test.cpp index 00bc76ac093..7f62f5c9b6e 100644 --- a/kernels/test/op_replication_pad2d_test.cpp +++ b/kernels/test/op_replication_pad2d_test.cpp @@ -25,7 +25,7 @@ Tensor& op_replication_pad2d_out( const Tensor& input, ArrayRef padding, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::replication_pad2d_outf( context, input, padding, out); } diff --git a/kernels/test/op_replication_pad3d_test.cpp b/kernels/test/op_replication_pad3d_test.cpp index 010870298d9..5b931fee3f9 100644 --- a/kernels/test/op_replication_pad3d_test.cpp +++ b/kernels/test/op_replication_pad3d_test.cpp @@ -25,7 +25,7 @@ Tensor& op_replication_pad3d_out( const Tensor& input, ArrayRef padding, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::replication_pad3d_outf( context, input, padding, out); } diff --git a/kernels/test/op_roll_test.cpp b/kernels/test/op_roll_test.cpp index fc5baaad4a7..4407e395db6 100644 --- a/kernels/test/op_roll_test.cpp +++ b/kernels/test/op_roll_test.cpp @@ -26,7 +26,7 @@ Tensor& op_roll_out( ArrayRef shifts, ArrayRef dims, Tensor& out) { - executorch::runtime::KernelRuntimeContext context{}; + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context{}; return torch::executor::aten::roll_outf(context, input, shifts, dims, out); } diff --git a/kernels/test/op_topk_test.cpp b/kernels/test/op_topk_test.cpp index 46098a81b68..bdd185daaae 100644 --- a/kernels/test/op_topk_test.cpp +++ b/kernels/test/op_topk_test.cpp @@ -106,7 +106,8 @@ std::tuple op_topk_values( Tensor& values, Tensor& indices) { TempMemoryAllocator allocator = TempMemoryAllocator(); - executorch::runtime::KernelRuntimeContext context(nullptr, &allocator); + executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext context( + nullptr, &allocator); return torch::executor::aten::topk_outf( context, input, k, dim, largest, sorted, values, indices); } diff --git a/runtime/backend/backend_execution_context.h b/runtime/backend/backend_execution_context.h index d2790b158ef..7ee41d8e5b1 100644 --- a/runtime/backend/backend_execution_context.h +++ b/runtime/backend/backend_execution_context.h @@ -12,7 +12,7 @@ #include namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { /** * BackendExecutionContext will be used to inject run time context. @@ -68,13 +68,13 @@ class BackendExecutionContext final { const char* method_name_ = nullptr; }; -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch namespace torch { namespace executor { // TODO(T197294990): Remove these deprecated aliases once all users have moved // to the new `::executorch` namespaces. -using ::executorch::runtime::BackendExecutionContext; +using ::executorch::ET_RUNTIME_NAMESPACE::BackendExecutionContext; } // namespace executor } // namespace torch diff --git a/runtime/backend/backend_init_context.h b/runtime/backend/backend_init_context.h index de1661c3af0..71c5182f401 100644 --- a/runtime/backend/backend_init_context.h +++ b/runtime/backend/backend_init_context.h @@ -7,12 +7,12 @@ */ #pragma once +#include #include #include namespace executorch { -namespace runtime { - +namespace ET_RUNTIME_NAMESPACE { /** * BackendInitContext will be used to inject runtime info for to initialize * delegate. @@ -70,13 +70,13 @@ class BackendInitContext final { const NamedDataMap* named_data_map_ = nullptr; }; -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch namespace torch { namespace executor { // TODO(T197294990): Remove these deprecated aliases once all users have moved // to the new `::executorch` namespaces. -using ::executorch::runtime::BackendInitContext; +using ::executorch::ET_RUNTIME_NAMESPACE::BackendInitContext; } // namespace executor } // namespace torch diff --git a/runtime/backend/interface.cpp b/runtime/backend/interface.cpp index 4fb1eadfa87..ffeb133fbf2 100644 --- a/runtime/backend/interface.cpp +++ b/runtime/backend/interface.cpp @@ -9,7 +9,7 @@ #include namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { // Pure-virtual dtors still need an implementation. BackendInterface::~BackendInterface() {} @@ -66,5 +66,5 @@ Result get_backend_name(size_t index) { return registered_backends[index].name; } -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch diff --git a/runtime/backend/interface.h b/runtime/backend/interface.h index 0a3c069a201..95705d48f92 100644 --- a/runtime/backend/interface.h +++ b/runtime/backend/interface.h @@ -22,7 +22,7 @@ #include namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { struct SizedBuffer { void* buffer; @@ -150,19 +150,20 @@ size_t get_num_registered_backends(); */ Result get_backend_name(size_t index); -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch namespace torch { namespace executor { // TODO(T197294990): Remove these deprecated aliases once all users have moved // to the new `::executorch` namespaces. -using ::executorch::runtime::Backend; -using ::executorch::runtime::CompileSpec; -using ::executorch::runtime::DelegateHandle; -using ::executorch::runtime::get_backend_class; -using ::executorch::runtime::register_backend; -using ::executorch::runtime::SizedBuffer; -using PyTorchBackendInterface = ::executorch::runtime::BackendInterface; +using ::executorch::ET_RUNTIME_NAMESPACE::Backend; +using ::executorch::ET_RUNTIME_NAMESPACE::CompileSpec; +using ::executorch::ET_RUNTIME_NAMESPACE::DelegateHandle; +using ::executorch::ET_RUNTIME_NAMESPACE::get_backend_class; +using ::executorch::ET_RUNTIME_NAMESPACE::register_backend; +using ::executorch::ET_RUNTIME_NAMESPACE::SizedBuffer; +using PyTorchBackendInterface = + ::executorch::ET_RUNTIME_NAMESPACE::BackendInterface; } // namespace executor } // namespace torch diff --git a/runtime/core/event_tracer_hooks.h b/runtime/core/event_tracer_hooks.h index 40754160c41..cd74b447ca8 100644 --- a/runtime/core/event_tracer_hooks.h +++ b/runtime/core/event_tracer_hooks.h @@ -30,7 +30,7 @@ */ namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { namespace internal { /** @@ -305,7 +305,7 @@ inline void event_tracer_set_bundled_input_index( } } // namespace internal -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch namespace torch { @@ -313,18 +313,27 @@ namespace executor { namespace internal { // TODO(T197294990): Remove these deprecated aliases once all users have moved // to the new `::executorch` namespaces. -using ::executorch::runtime::internal::event_tracer_begin_profiling_event; -using ::executorch::runtime::internal::event_tracer_create_event_block; -using ::executorch::runtime::internal::event_tracer_end_profiling_event; -using ::executorch::runtime::internal::event_tracer_log_evalue; -using ::executorch::runtime::internal::event_tracer_log_evalue_output; -using ::executorch::runtime::internal::event_tracer_set_bundled_input_index; -using ::executorch::runtime::internal::event_tracer_track_allocation; -using ::executorch::runtime::internal::event_tracer_track_allocator; -using ::executorch::runtime::internal::EventTracerProfileInstructionScope; -using ::executorch::runtime::internal::EventTracerProfileMethodScope; -using ::executorch::runtime::internal::EventTracerProfileOpScope; -using ::executorch::runtime::internal::EventTracerProfileScope; +using ::executorch::ET_RUNTIME_NAMESPACE::internal:: + event_tracer_begin_profiling_event; +using ::executorch::ET_RUNTIME_NAMESPACE::internal:: + event_tracer_create_event_block; +using ::executorch::ET_RUNTIME_NAMESPACE::internal:: + event_tracer_end_profiling_event; +using ::executorch::ET_RUNTIME_NAMESPACE::internal::event_tracer_log_evalue; +using ::executorch::ET_RUNTIME_NAMESPACE::internal:: + event_tracer_log_evalue_output; +using ::executorch::ET_RUNTIME_NAMESPACE::internal:: + event_tracer_set_bundled_input_index; +using ::executorch::ET_RUNTIME_NAMESPACE::internal:: + event_tracer_track_allocation; +using ::executorch::ET_RUNTIME_NAMESPACE::internal:: + event_tracer_track_allocator; +using ::executorch::ET_RUNTIME_NAMESPACE::internal:: + EventTracerProfileInstructionScope; +using ::executorch::ET_RUNTIME_NAMESPACE::internal:: + EventTracerProfileMethodScope; +using ::executorch::ET_RUNTIME_NAMESPACE::internal::EventTracerProfileOpScope; +using ::executorch::ET_RUNTIME_NAMESPACE::internal::EventTracerProfileScope; } // namespace internal } // namespace executor diff --git a/runtime/core/exec_aten/exec_aten.h b/runtime/core/exec_aten/exec_aten.h index 704bb868abd..10075ab5920 100644 --- a/runtime/core/exec_aten/exec_aten.h +++ b/runtime/core/exec_aten/exec_aten.h @@ -47,6 +47,21 @@ #endif +/** + * This hack is for separating out ATen mode vs non-ATen mode. In ATen mode, + * we use the ATen types directly. In non-ATen mode, we use the portable types. + * To avoid duplicate symbols and/or duplicate operator registration, when a + * user depends on both the ATen mode and non-ATen mode versions of the + * ExecuTorch library. + */ +#ifndef ET_RUNTIME_NAMESPACE +#if defined(USE_ATEN_LIB) +#define ET_RUNTIME_NAMESPACE runtime::aten +#else +#define ET_RUNTIME_NAMESPACE runtime +#endif +#endif + namespace executorch { namespace aten { diff --git a/runtime/core/exec_aten/testing_util/test/tensor_factory_test.cpp b/runtime/core/exec_aten/testing_util/test/tensor_factory_test.cpp index ed8cc00f4ef..feb00f79b8f 100644 --- a/runtime/core/exec_aten/testing_util/test/tensor_factory_test.cpp +++ b/runtime/core/exec_aten/testing_util/test/tensor_factory_test.cpp @@ -26,8 +26,8 @@ using executorch::aten::SizesType; using executorch::aten::StridesType; using executorch::aten::Tensor; using executorch::aten::TensorList; +using executorch::ET_RUNTIME_NAMESPACE::resize_tensor; using executorch::runtime::Error; -using executorch::runtime::resize_tensor; using executorch::runtime::TensorShapeDynamism; using executorch::runtime::testing::TensorFactory; using executorch::runtime::testing::TensorListFactory; diff --git a/runtime/core/exec_aten/util/tensor_util.h b/runtime/core/exec_aten/util/tensor_util.h index 4e5a0cebb07..b0b79882361 100644 --- a/runtime/core/exec_aten/util/tensor_util.h +++ b/runtime/core/exec_aten/util/tensor_util.h @@ -396,8 +396,7 @@ #scalar_tensor " could not be extracted: wrong type or out of range"); namespace executorch { -namespace runtime { - +namespace ET_RUNTIME_NAMESPACE { // // Utility functions for checking tensor attributes // @@ -446,10 +445,10 @@ inline bool tensor_can_cast_to( executorch::aten::Tensor a, executorch::aten::ScalarType dtype) { ET_CHECK_OR_RETURN_FALSE( - torch::executor::canCast(a.scalar_type(), dtype), + ::torch::executor::canCast(a.scalar_type(), dtype), "Tensor of dtype %s cannot cast to dtype %s", - torch::executor::toString(a.scalar_type()), - torch::executor::toString(dtype)); + ::torch::executor::toString(a.scalar_type()), + ::torch::executor::toString(dtype)); return true; } @@ -458,7 +457,7 @@ inline bool tensor_is_bool_type(executorch::aten::Tensor t) { ET_CHECK_OR_RETURN_FALSE( t.scalar_type() == executorch::aten::ScalarType::Bool, "Expected to find bool type, but tensor has type %s", - torch::executor::toString(t.scalar_type())); + ::torch::executor::toString(t.scalar_type())); return true; } @@ -469,8 +468,8 @@ inline bool tensor_is_type( ET_CHECK_OR_RETURN_FALSE( t.scalar_type() == dtype, "Expected to find %s type, but tensor has type %s", - torch::executor::toString(dtype), - torch::executor::toString(t.scalar_type())); + ::torch::executor::toString(dtype), + ::torch::executor::toString(t.scalar_type())); return true; } @@ -482,9 +481,9 @@ inline bool tensor_is_type( ET_LOG_MSG_AND_RETURN_IF_FALSE( t.scalar_type() == dtype || t.scalar_type() == dtype2, "Expected to find %s or %s type, but tensor has type %s", - torch::executor::toString(dtype), - torch::executor::toString(dtype2), - torch::executor::toString(t.scalar_type())); + ::torch::executor::toString(dtype), + ::torch::executor::toString(dtype2), + ::torch::executor::toString(t.scalar_type())); return true; } @@ -498,10 +497,10 @@ inline bool tensor_is_type( t.scalar_type() == dtype || t.scalar_type() == dtype2 || t.scalar_type() == dtype3, "Expected to find %s, %s, or %s type, but tensor has type %s", - torch::executor::toString(dtype), - torch::executor::toString(dtype2), - torch::executor::toString(dtype3), - torch::executor::toString(t.scalar_type())); + ::torch::executor::toString(dtype), + ::torch::executor::toString(dtype2), + ::torch::executor::toString(dtype3), + ::torch::executor::toString(t.scalar_type())); return true; } @@ -510,36 +509,36 @@ inline bool tensor_is_integral_type( executorch::aten::Tensor t, bool includeBool = false) { ET_CHECK_OR_RETURN_FALSE( - torch::executor::isIntegralType(t.scalar_type(), includeBool), + ::torch::executor::isIntegralType(t.scalar_type(), includeBool), "Expected to find a integral type, but tensor has type %s", - torch::executor::toString(t.scalar_type())); + ::torch::executor::toString(t.scalar_type())); return true; } inline bool tensor_is_floating_type(executorch::aten::Tensor t) { ET_CHECK_OR_RETURN_FALSE( - torch::executor::isFloatingType(t.scalar_type()), + ::torch::executor::isFloatingType(t.scalar_type()), "Expected to find a floating type, but tensor has type %s", - torch::executor::toString(t.scalar_type())); + ::torch::executor::toString(t.scalar_type())); return true; } inline bool tensor_is_real_type(executorch::aten::Tensor t) { ET_CHECK_OR_RETURN_FALSE( - torch::executor::isRealType(t.scalar_type()), + ::torch::executor::isRealType(t.scalar_type()), "Expected to find a real type, but tensor has type %s", - torch::executor::toString(t.scalar_type())); + ::torch::executor::toString(t.scalar_type())); return true; } inline bool tensor_is_realh_type(executorch::aten::Tensor t) { ET_CHECK_OR_RETURN_FALSE( - torch::executor::isRealHType(t.scalar_type()), + ::torch::executor::isRealHType(t.scalar_type()), "Expected to find a real type, but tensor has type %s", - torch::executor::toString(t.scalar_type())); + ::torch::executor::toString(t.scalar_type())); return true; } @@ -548,16 +547,16 @@ inline bool tensor_is_realhbf16_type(executorch::aten::Tensor t) { ET_CHECK_OR_RETURN_FALSE( executorch::runtime::isRealHBF16Type(t.scalar_type()), "Expected to find a real type, but tensor has type %s", - torch::executor::toString(t.scalar_type())); + ::torch::executor::toString(t.scalar_type())); return true; } inline bool tensor_is_realhb_type(executorch::aten::Tensor t) { ET_CHECK_OR_RETURN_FALSE( - torch::executor::isRealHBType(t.scalar_type()), + ::torch::executor::isRealHBType(t.scalar_type()), "Expected to find a real type, but tensor has type %s", - torch::executor::toString(t.scalar_type())); + ::torch::executor::toString(t.scalar_type())); return true; } @@ -566,25 +565,25 @@ inline bool tensor_is_realhbbf16_type(executorch::aten::Tensor t) { ET_CHECK_OR_RETURN_FALSE( executorch::runtime::isRealHBBF16Type(t.scalar_type()), "Expected to find a real type, but tensor has type %s", - torch::executor::toString(t.scalar_type())); + ::torch::executor::toString(t.scalar_type())); return true; } inline bool tensor_is_complex_type(executorch::aten::Tensor t) { ET_CHECK_OR_RETURN_FALSE( - torch::executor::isComplexType(t.scalar_type()), + ::torch::executor::isComplexType(t.scalar_type()), "Expected to find a complex type, but tensor has type %s", - torch::executor::toString(t.scalar_type())); + ::torch::executor::toString(t.scalar_type())); return true; } inline bool tensor_is_bits_type(executorch::aten::Tensor t) { ET_CHECK_OR_RETURN_FALSE( - torch::executor::isBitsType(t.scalar_type()), + ::torch::executor::isBitsType(t.scalar_type()), "Expected to find a bits type, but tensor has type %s", - torch::executor::toString(t.scalar_type())); + ::torch::executor::toString(t.scalar_type())); return true; } @@ -595,8 +594,8 @@ inline bool tensors_have_same_dtype( ET_CHECK_OR_RETURN_FALSE( a.scalar_type() == b.scalar_type(), ET_TENSOR_CHECK_PREFIX__ ": dtype={%s, %s}", - torch::executor::toString(a.scalar_type()), - torch::executor::toString(b.scalar_type())); + ::torch::executor::toString(a.scalar_type()), + ::torch::executor::toString(b.scalar_type())); return true; } @@ -607,9 +606,9 @@ inline bool tensors_have_same_dtype( ET_CHECK_OR_RETURN_FALSE( a.scalar_type() == b.scalar_type() && b.scalar_type() == c.scalar_type(), ET_TENSOR_CHECK_PREFIX__ ": dtype={%s, %s, %s}", - torch::executor::toString(a.scalar_type()), - torch::executor::toString(b.scalar_type()), - torch::executor::toString(c.scalar_type())); + ::torch::executor::toString(a.scalar_type()), + ::torch::executor::toString(b.scalar_type()), + ::torch::executor::toString(c.scalar_type())); return true; } @@ -1349,60 +1348,61 @@ inline size_t calculate_linear_index( return index; } -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch namespace torch { namespace executor { // TODO(T197294990): Remove these deprecated aliases once all users have moved // to the new `::executorch` namespaces. -using ::executorch::runtime::calculate_linear_index; -using ::executorch::runtime::coordinateToIndex; -using ::executorch::runtime::dim_is_valid; -using ::executorch::runtime::extract_scalar_tensor; -using ::executorch::runtime::get_dim_order; -using ::executorch::runtime::getLeadingDims; -using ::executorch::runtime::getTrailingDims; -using ::executorch::runtime::indexToCoordinate; +using ::executorch::ET_RUNTIME_NAMESPACE::calculate_linear_index; +using ::executorch::ET_RUNTIME_NAMESPACE::coordinateToIndex; +using ::executorch::ET_RUNTIME_NAMESPACE::dim_is_valid; +using ::executorch::ET_RUNTIME_NAMESPACE::extract_scalar_tensor; +using ::executorch::ET_RUNTIME_NAMESPACE::get_dim_order; +using ::executorch::ET_RUNTIME_NAMESPACE::getLeadingDims; +using ::executorch::ET_RUNTIME_NAMESPACE::getTrailingDims; +using ::executorch::ET_RUNTIME_NAMESPACE::indexToCoordinate; +using ::executorch::ET_RUNTIME_NAMESPACE::nonempty_size; +using ::executorch::ET_RUNTIME_NAMESPACE::nonzero_dim; +using ::executorch::ET_RUNTIME_NAMESPACE::resize; +using ::executorch::ET_RUNTIME_NAMESPACE::resize_tensor; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_can_cast_to; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_dim_has_index; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_has_dim; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_has_expected_size; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_has_non_empty_dim; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_has_rank_greater_or_equal_to; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_has_rank_smaller_or_equal_to; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_has_valid_dim_order; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_is_bits_type; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_is_bool_type; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_is_complex_type; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_is_contiguous; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_is_default_dim_order; +using ::executorch::ET_RUNTIME_NAMESPACE:: + tensor_is_default_or_channels_last_dim_order; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_is_floating_type; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_is_integral_type; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_is_rank; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_is_real_type; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_is_realh_type; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_is_realhb_type; +using ::executorch::ET_RUNTIME_NAMESPACE::tensor_is_scalar; +using ::executorch::ET_RUNTIME_NAMESPACE::tensors_have_same_dim_order; +using ::executorch::ET_RUNTIME_NAMESPACE::tensors_have_same_dtype; +using ::executorch::ET_RUNTIME_NAMESPACE::tensors_have_same_rank; +using ::executorch::ET_RUNTIME_NAMESPACE::tensors_have_same_shape; +using ::executorch::ET_RUNTIME_NAMESPACE::tensors_have_same_shape_and_dtype; +using ::executorch::ET_RUNTIME_NAMESPACE::tensors_have_same_size_at_dims; +using ::executorch::ET_RUNTIME_NAMESPACE::tensors_have_same_strides; using ::executorch::runtime::kTensorDimensionLimit; -using ::executorch::runtime::nonempty_size; -using ::executorch::runtime::nonzero_dim; -using ::executorch::runtime::resize; -using ::executorch::runtime::resize_tensor; -using ::executorch::runtime::tensor_can_cast_to; -using ::executorch::runtime::tensor_dim_has_index; -using ::executorch::runtime::tensor_has_dim; -using ::executorch::runtime::tensor_has_expected_size; -using ::executorch::runtime::tensor_has_non_empty_dim; -using ::executorch::runtime::tensor_has_rank_greater_or_equal_to; -using ::executorch::runtime::tensor_has_rank_smaller_or_equal_to; -using ::executorch::runtime::tensor_has_valid_dim_order; -using ::executorch::runtime::tensor_is_bits_type; -using ::executorch::runtime::tensor_is_bool_type; -using ::executorch::runtime::tensor_is_complex_type; -using ::executorch::runtime::tensor_is_contiguous; -using ::executorch::runtime::tensor_is_default_dim_order; -using ::executorch::runtime::tensor_is_default_or_channels_last_dim_order; -using ::executorch::runtime::tensor_is_floating_type; -using ::executorch::runtime::tensor_is_integral_type; -using ::executorch::runtime::tensor_is_rank; -using ::executorch::runtime::tensor_is_real_type; -using ::executorch::runtime::tensor_is_realh_type; -using ::executorch::runtime::tensor_is_realhb_type; -using ::executorch::runtime::tensor_is_scalar; -using ::executorch::runtime::tensors_have_same_dim_order; -using ::executorch::runtime::tensors_have_same_dtype; -using ::executorch::runtime::tensors_have_same_rank; -using ::executorch::runtime::tensors_have_same_shape; -using ::executorch::runtime::tensors_have_same_shape_and_dtype; -using ::executorch::runtime::tensors_have_same_size_at_dims; -using ::executorch::runtime::tensors_have_same_strides; namespace internal { -using ::executorch::runtime::internal::copy_tensor_data; -using ::executorch::runtime::internal::reset_data_ptr; -using ::executorch::runtime::internal::resize_tensor_impl; -using ::executorch::runtime::internal::set_tensor_data; -using ::executorch::runtime::internal::share_tensor_data; +using ::executorch::ET_RUNTIME_NAMESPACE::internal::copy_tensor_data; +using ::executorch::ET_RUNTIME_NAMESPACE::internal::reset_data_ptr; +using ::executorch::ET_RUNTIME_NAMESPACE::internal::resize_tensor_impl; +using ::executorch::ET_RUNTIME_NAMESPACE::internal::set_tensor_data; +using ::executorch::ET_RUNTIME_NAMESPACE::internal::share_tensor_data; } // namespace internal } // namespace executor } // namespace torch diff --git a/runtime/core/exec_aten/util/tensor_util_aten.cpp b/runtime/core/exec_aten/util/tensor_util_aten.cpp index 4df273d4dbb..ddfd0560a69 100644 --- a/runtime/core/exec_aten/util/tensor_util_aten.cpp +++ b/runtime/core/exec_aten/util/tensor_util_aten.cpp @@ -12,7 +12,7 @@ #include namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { /** * Implementation for ATen tensor util, should only be included in * `_aten` target and only be used in ATen mode. Explicitly taking @@ -214,6 +214,5 @@ Error resize_tensor_impl( } } // namespace internal - -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch diff --git a/runtime/core/exec_aten/util/test/tensor_util_test.cpp b/runtime/core/exec_aten/util/test/tensor_util_test.cpp index 7d30b0bbdbe..cdc391adf20 100644 --- a/runtime/core/exec_aten/util/test/tensor_util_test.cpp +++ b/runtime/core/exec_aten/util/test/tensor_util_test.cpp @@ -17,7 +17,7 @@ using namespace ::testing; using executorch::aten::ScalarType; using executorch::aten::Tensor; -using executorch::runtime::extract_scalar_tensor; +using executorch::ET_RUNTIME_NAMESPACE::extract_scalar_tensor; using executorch::runtime::testing::TensorFactory; class TensorUtilTest : public ::testing::Test { @@ -148,13 +148,13 @@ TEST_F(TensorUtilTest, GetLeadingDimsSmokeTest) { Tensor t = tf_int_.ones({2, 3, 4}); // getLeadingDims(t, 1) => t.size(0) - EXPECT_EQ(executorch::runtime::getLeadingDims(t, 1), 2); + EXPECT_EQ(executorch::ET_RUNTIME_NAMESPACE::getLeadingDims(t, 1), 2); // getLeadingDims(t, 2) => t.size(0) * t.size(1) - EXPECT_EQ(executorch::runtime::getLeadingDims(t, 2), 6); + EXPECT_EQ(executorch::ET_RUNTIME_NAMESPACE::getLeadingDims(t, 2), 6); // getLeadingDims(t, 3) => t.size(0) * t.size(1) * t.size(2) - EXPECT_EQ(executorch::runtime::getLeadingDims(t, 3), 24); + EXPECT_EQ(executorch::ET_RUNTIME_NAMESPACE::getLeadingDims(t, 3), 24); } TEST_F(TensorUtilTest, GetLeadingDimsInputOutOfBoundDies) { @@ -162,9 +162,9 @@ TEST_F(TensorUtilTest, GetLeadingDimsInputOutOfBoundDies) { Tensor t = tf_int_.ones({2, 3, 4}); // dim needs to be in the range [0, t.dim()] - ET_EXPECT_DEATH(executorch::runtime::getLeadingDims(t, -2), ""); - ET_EXPECT_DEATH(executorch::runtime::getLeadingDims(t, -1), ""); - ET_EXPECT_DEATH(executorch::runtime::getLeadingDims(t, 4), ""); + ET_EXPECT_DEATH(executorch::ET_RUNTIME_NAMESPACE::getLeadingDims(t, -2), ""); + ET_EXPECT_DEATH(executorch::ET_RUNTIME_NAMESPACE::getLeadingDims(t, -1), ""); + ET_EXPECT_DEATH(executorch::ET_RUNTIME_NAMESPACE::getLeadingDims(t, 4), ""); } TEST_F(TensorUtilTest, GetTrailingDimsSmokeTest) { @@ -172,13 +172,13 @@ TEST_F(TensorUtilTest, GetTrailingDimsSmokeTest) { Tensor t = tf_int_.ones({2, 3, 4}); // getTrailingDims(t, 1) => t.size(2) - EXPECT_EQ(executorch::runtime::getTrailingDims(t, 1), 4); + EXPECT_EQ(executorch::ET_RUNTIME_NAMESPACE::getTrailingDims(t, 1), 4); // getTrailingDims(t, 0) => t.size(1) * t.size(2) - EXPECT_EQ(executorch::runtime::getTrailingDims(t, 0), 12); + EXPECT_EQ(executorch::ET_RUNTIME_NAMESPACE::getTrailingDims(t, 0), 12); // getTrailingDims(t, -1) => t.size(0) * t.size(1) * t.size(2) - EXPECT_EQ(executorch::runtime::getTrailingDims(t, -1), 24); + EXPECT_EQ(executorch::ET_RUNTIME_NAMESPACE::getTrailingDims(t, -1), 24); } TEST_F(TensorUtilTest, GetTrailingDimsInputOutOfBoundDies) { @@ -186,9 +186,9 @@ TEST_F(TensorUtilTest, GetTrailingDimsInputOutOfBoundDies) { Tensor t = tf_int_.ones({2, 3, 4}); // dim needs to be in the range [-1, t.dim() - 1) - ET_EXPECT_DEATH(executorch::runtime::getTrailingDims(t, -2), ""); - ET_EXPECT_DEATH(executorch::runtime::getTrailingDims(t, 3), ""); - ET_EXPECT_DEATH(executorch::runtime::getTrailingDims(t, 4), ""); + ET_EXPECT_DEATH(executorch::ET_RUNTIME_NAMESPACE::getTrailingDims(t, -2), ""); + ET_EXPECT_DEATH(executorch::ET_RUNTIME_NAMESPACE::getTrailingDims(t, 3), ""); + ET_EXPECT_DEATH(executorch::ET_RUNTIME_NAMESPACE::getTrailingDims(t, 4), ""); } TEST_F(TensorUtilTest, ContiguousCheckSupported) { @@ -421,7 +421,7 @@ TEST_F(TensorUtilTest, BoolTensorNotScalarFails) { // TEST_F(TensorUtilTest, TensorIsRankTest) { - using executorch::runtime::tensor_is_rank; + using executorch::ET_RUNTIME_NAMESPACE::tensor_is_rank; Tensor a = tf_float_.ones({2, 3, 5}); EXPECT_TRUE(tensor_is_rank(a, 3)); @@ -430,7 +430,7 @@ TEST_F(TensorUtilTest, TensorIsRankTest) { } TEST_F(TensorUtilTest, TensorHasDimTest) { - using executorch::runtime::tensor_has_dim; + using executorch::ET_RUNTIME_NAMESPACE::tensor_has_dim; Tensor a = tf_float_.ones({2, 3, 5}); EXPECT_TRUE(tensor_has_dim(a, 2)); @@ -445,7 +445,7 @@ TEST_F(TensorUtilTest, TensorHasDimTest) { } TEST_F(TensorUtilTest, TensorsHaveSameDtypeTest) { - using executorch::runtime::tensors_have_same_dtype; + using executorch::ET_RUNTIME_NAMESPACE::tensors_have_same_dtype; Tensor a = tf_float_.ones({2, 3}); Tensor b = tf_float_.ones({2, 3}); Tensor c = tf_float_.ones({3, 3}); @@ -458,7 +458,7 @@ TEST_F(TensorUtilTest, TensorsHaveSameDtypeTest) { } TEST_F(TensorUtilTest, TensorsHaveSameSizeAtDimTest) { - using executorch::runtime::tensors_have_same_size_at_dims; + using executorch::ET_RUNTIME_NAMESPACE::tensors_have_same_size_at_dims; Tensor a = tf_float_.ones({2, 3, 4, 5}); Tensor b = tf_float_.ones({5, 4, 3, 2}); @@ -470,7 +470,7 @@ TEST_F(TensorUtilTest, TensorsHaveSameSizeAtDimTest) { } TEST_F(TensorUtilTest, TensorsHaveSameShapeTest) { - using executorch::runtime::tensors_have_same_shape; + using executorch::ET_RUNTIME_NAMESPACE::tensors_have_same_shape; Tensor a = tf_float_.ones({2, 3}); Tensor b = tf_int_.ones({2, 3}); Tensor c = tf_byte_.ones({2, 3}); @@ -493,7 +493,7 @@ TEST_F(TensorUtilTest, TensorsHaveSameShapeTest) { } TEST_F(TensorUtilTest, TensorsHaveSameShapeAndDtypeTest) { - using executorch::runtime::tensors_have_same_shape_and_dtype; + using executorch::ET_RUNTIME_NAMESPACE::tensors_have_same_shape_and_dtype; Tensor a = tf_float_.ones({2, 3}); Tensor b = tf_float_.ones({2, 3}); Tensor c = tf_float_.ones({2, 3}); @@ -515,7 +515,7 @@ TEST_F(TensorUtilTest, TensorsHaveSameShapeAndDtypeTest) { } TEST_F(TensorUtilTest, TensorsHaveSameStridesTest) { - using executorch::runtime::tensors_have_same_strides; + using executorch::ET_RUNTIME_NAMESPACE::tensors_have_same_strides; Tensor a = tf_float_.full_channels_last({4, 5, 2, 3}, 1); Tensor b = tf_float_.full_channels_last({4, 5, 2, 3}, 2); Tensor c = tf_float_.full_channels_last({4, 5, 2, 3}, 3); @@ -530,7 +530,7 @@ TEST_F(TensorUtilTest, TensorsHaveSameStridesTest) { } TEST_F(TensorUtilTest, TensorIsContiguous) { - using executorch::runtime::tensor_is_contiguous; + using executorch::ET_RUNTIME_NAMESPACE::tensor_is_contiguous; // Note that the strides.size() == 0 case is not tested, since Tensor a = tf_float_.full_channels_last({4, 5, 2, 3}, 1); Tensor b = tf_float_.ones({4, 5, 2, 3}); @@ -547,7 +547,7 @@ TEST_F(TensorUtilTest, ResizeZeroDimTensor) { Tensor a = tf_float_.ones({}); EXPECT_EQ( - executorch::runtime::resize_tensor(a, {}), + executorch::ET_RUNTIME_NAMESPACE::resize_tensor(a, {}), executorch::runtime::Error::Ok); EXPECT_EQ(a.dim(), 0); } diff --git a/runtime/core/named_data_map.h b/runtime/core/named_data_map.h index e79c7035989..14179d22795 100644 --- a/runtime/core/named_data_map.h +++ b/runtime/core/named_data_map.h @@ -22,8 +22,7 @@ #include namespace executorch { -namespace runtime { - +namespace ET_RUNTIME_NAMESPACE { /** * Interface to access and retrieve data via name. * See executorch/extension/flat_tensor/ for an example. @@ -37,8 +36,8 @@ class ET_EXPERIMENTAL NamedDataMap { * @param key The name of the tensor. * @return Result containing TensorLayout with tensor metadata. */ - ET_NODISCARD virtual Result - get_metadata(const char* key) const = 0; + ET_NODISCARD virtual Result get_metadata( + const char* key) const = 0; /** * Get data by key. * @@ -78,7 +77,7 @@ class ET_EXPERIMENTAL NamedDataMap { ET_NODISCARD virtual Result get_key(size_t index) const = 0; }; -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch #ifdef __GNUC__ diff --git a/runtime/core/targets.bzl b/runtime/core/targets.bzl index 3195e727d96..d3e02b1afb5 100644 --- a/runtime/core/targets.bzl +++ b/runtime/core/targets.bzl @@ -95,9 +95,9 @@ def define_common_targets(): "@EXECUTORCH_CLIENTS", ], exported_deps = [ - "//executorch/runtime/core:core", - "//executorch/runtime/core/exec_aten:lib" + aten_suffix, + ":core", ":tag", + "//executorch/runtime/core/exec_aten:lib" + aten_suffix, ], ) @@ -119,6 +119,37 @@ def define_common_targets(): ], ) + runtime.cxx_library( + name = "named_data_map" + aten_suffix, + exported_headers = [ + "named_data_map.h", + ], + visibility = [ + "//executorch/...", + "@EXECUTORCH_CLIENTS", + ], + exported_deps = [ + ":tensor_layout" + aten_suffix, + "//executorch/runtime/core/exec_aten:lib" + aten_suffix, + ], + ) + + + runtime.cxx_library( + name = "tensor_layout" + aten_suffix, + srcs = ["tensor_layout.cpp"], + exported_headers = ["tensor_layout.h"], + deps = [ + "//executorch/runtime/core/portable_type/c10/c10:c10", + ], + exported_deps = [ + ":core", + "//executorch/runtime/core/exec_aten:lib" + aten_suffix, + "//executorch/runtime/core/exec_aten/util:scalar_type_util" + aten_suffix, + ], + visibility = ["//executorch/..."], + ) + runtime.cxx_library( name = "tag", srcs = ["tag.cpp"], @@ -133,31 +164,3 @@ def define_common_targets(): "//executorch/...", ], ) - - runtime.cxx_library( - name = "named_data_map", - exported_headers = [ - "named_data_map.h", - ], - visibility = [ - "//executorch/...", - "@EXECUTORCH_CLIENTS", - ], - exported_deps = [ - ":tensor_layout", - ], - ) - - runtime.cxx_library( - name = "tensor_layout", - srcs = ["tensor_layout.cpp"], - exported_headers = ["tensor_layout.h"], - deps = [ - "//executorch/runtime/core/portable_type/c10/c10:c10", - ], - exported_deps = [ - ":core", - "//executorch/runtime/core/exec_aten:lib", - ], - visibility = ["//executorch/..."], - ) diff --git a/runtime/core/tensor_layout.cpp b/runtime/core/tensor_layout.cpp index 2b862e6dc14..d33f79f27c4 100644 --- a/runtime/core/tensor_layout.cpp +++ b/runtime/core/tensor_layout.cpp @@ -13,7 +13,7 @@ #include namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { namespace { Result calculate_nbytes( @@ -51,5 +51,5 @@ Result TensorLayout::create( } return TensorLayout(sizes, dim_order, scalar_type, nbytes.get()); } -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch diff --git a/runtime/core/tensor_layout.h b/runtime/core/tensor_layout.h index c2c3833f528..42131e6506e 100644 --- a/runtime/core/tensor_layout.h +++ b/runtime/core/tensor_layout.h @@ -14,7 +14,7 @@ #include namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { /** * Describes the layout of a tensor. @@ -89,5 +89,5 @@ class ET_EXPERIMENTAL TensorLayout final { const size_t nbytes_; }; -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch diff --git a/runtime/executor/method.cpp b/runtime/executor/method.cpp index 41d44522a22..63a3166bfdd 100644 --- a/runtime/executor/method.cpp +++ b/runtime/executor/method.cpp @@ -32,9 +32,8 @@ #include namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { -using deserialization::NamedData; using internal::PlatformMemoryAllocator; /** @@ -1640,5 +1639,5 @@ Method::~Method() { } // All other fields are trivially destructible. } -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch diff --git a/runtime/executor/method.h b/runtime/executor/method.h index 0ca2df440ad..c730af5778b 100644 --- a/runtime/executor/method.h +++ b/runtime/executor/method.h @@ -32,7 +32,7 @@ struct EValue; } // namespace executorch_flatbuffer namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { // Forward declare NamedData. This is a public header and must not include // internal data types. @@ -394,14 +394,14 @@ class Method final { void log_outputs(); }; -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch namespace torch { namespace executor { // TODO(T197294990): Remove these deprecated aliases once all users have moved // to the new `::executorch` namespaces. -using ::executorch::runtime::Method; +using ::executorch::ET_RUNTIME_NAMESPACE::Method; } // namespace executor } // namespace torch diff --git a/runtime/executor/method_meta.cpp b/runtime/executor/method_meta.cpp index 8f84fea940f..687b9c30471 100644 --- a/runtime/executor/method_meta.cpp +++ b/runtime/executor/method_meta.cpp @@ -16,7 +16,7 @@ #include namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { namespace { Result get_tag( @@ -279,6 +279,5 @@ size_t MethodMeta::num_instructions() const { } return num_instructions; } - -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch diff --git a/runtime/executor/method_meta.h b/runtime/executor/method_meta.h index d9bb64d68a7..a74cb090523 100644 --- a/runtime/executor/method_meta.h +++ b/runtime/executor/method_meta.h @@ -20,7 +20,7 @@ struct ExecutionPlan; } // namespace executorch_flatbuffer namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { /** * Metadata about a specific tensor of an ExecuTorch Program. @@ -240,14 +240,14 @@ class MethodMeta final { const executorch_flatbuffer::ExecutionPlan* s_plan_; }; -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch namespace torch { namespace executor { // TODO(T197294990): Remove these deprecated aliases once all users have moved // to the new `::executorch` namespaces. -using ::executorch::runtime::MethodMeta; -using ::executorch::runtime::TensorInfo; +using ::executorch::ET_RUNTIME_NAMESPACE::MethodMeta; +using ::executorch::ET_RUNTIME_NAMESPACE::TensorInfo; } // namespace executor } // namespace torch diff --git a/runtime/executor/platform_memory_allocator.h b/runtime/executor/platform_memory_allocator.h index 09195a460ac..7ab58bf0f3d 100644 --- a/runtime/executor/platform_memory_allocator.h +++ b/runtime/executor/platform_memory_allocator.h @@ -17,7 +17,7 @@ #include namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { namespace internal { /** @@ -107,5 +107,5 @@ class PlatformMemoryAllocator final : public MemoryAllocator { }; } // namespace internal -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch diff --git a/runtime/executor/program.cpp b/runtime/executor/program.cpp index 14e0b83d8aa..238c806b1d6 100644 --- a/runtime/executor/program.cpp +++ b/runtime/executor/program.cpp @@ -28,8 +28,7 @@ #endif namespace executorch { -namespace runtime { - +namespace ET_RUNTIME_NAMESPACE { namespace { /** @@ -535,5 +534,5 @@ Error Program::load_mutable_subsegment_into( segment_base_offset_ + segment->offset() + offset, size, info, buffer); } -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch diff --git a/runtime/executor/program.h b/runtime/executor/program.h index 0932e51619f..9670fd7c79f 100644 --- a/runtime/executor/program.h +++ b/runtime/executor/program.h @@ -36,8 +36,7 @@ struct Program; } // namespace executorch_flatbuffer namespace executorch { -namespace runtime { - +namespace ET_RUNTIME_NAMESPACE { namespace testing { // Provides test access to private Program methods. class ProgramTestFriend; @@ -313,14 +312,14 @@ class Program final { std::optional pte_data_map_; }; -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch namespace torch { namespace executor { // TODO(T197294990): Remove these deprecated aliases once all users have moved // to the new `::executorch` namespaces. -using ::executorch::runtime::Program; +using ::executorch::ET_RUNTIME_NAMESPACE::Program; } // namespace executor } // namespace torch diff --git a/runtime/executor/pte_data_map.cpp b/runtime/executor/pte_data_map.cpp index 5829395028a..fd064cb8256 100644 --- a/runtime/executor/pte_data_map.cpp +++ b/runtime/executor/pte_data_map.cpp @@ -10,7 +10,7 @@ #include namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { namespace internal { /* static */ executorch::runtime::Result PteDataMap::create( @@ -83,5 +83,5 @@ ET_NODISCARD executorch::runtime::Result PteDataMap::get_key( } } // namespace internal -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch diff --git a/runtime/executor/pte_data_map.h b/runtime/executor/pte_data_map.h index 01c15555786..b26c0ac42f9 100644 --- a/runtime/executor/pte_data_map.h +++ b/runtime/executor/pte_data_map.h @@ -46,7 +46,7 @@ using FlatbufferDataSegment = flatbuffers:: #endif namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { namespace internal { /** @@ -147,5 +147,5 @@ class PteDataMap final : public NamedDataMap { }; } // namespace internal -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch diff --git a/runtime/executor/targets.bzl b/runtime/executor/targets.bzl index cfb6c607359..649b2c13cc1 100644 --- a/runtime/executor/targets.bzl +++ b/runtime/executor/targets.bzl @@ -42,30 +42,33 @@ def define_common_targets(): ], ) - runtime.cxx_library( - name = "pte_data_map", - srcs = [ - "pte_data_map.cpp", - ], - exported_headers = [ - "pte_data_map.h", - ], - visibility = [ - "//executorch/runtime/executor/...", - "@EXECUTORCH_CLIENTS", - ], - exported_deps = [ - "//executorch/runtime/core:core", - "//executorch/runtime/core:named_data_map", - ], - deps = [ - "//executorch/schema:program", - ], - exported_preprocessor_flags = [] if runtime.is_oss else ["-DEXECUTORCH_INTERNAL_FLATBUFFERS=1"], - ) for aten_mode in get_aten_mode_options(): aten_suffix = "_aten" if aten_mode else "" + + runtime.cxx_library( + name = "pte_data_map" + aten_suffix, + srcs = [ + "pte_data_map.cpp", + ], + exported_headers = [ + "pte_data_map.h", + ], + visibility = [ + "//executorch/runtime/executor/...", + "@EXECUTORCH_CLIENTS", + ], + exported_deps = [ + "//executorch/runtime/core:core", + "//executorch/runtime/core:named_data_map" + aten_suffix, + "//executorch/runtime/core/exec_aten/util:scalar_type_util" + aten_suffix, + ], + deps = [ + "//executorch/schema:program", + ], + exported_preprocessor_flags = [] if runtime.is_oss else ["-DEXECUTORCH_INTERNAL_FLATBUFFERS=1"], + ) + runtime.cxx_library( name = "program" + aten_suffix, exported_deps = [ @@ -103,17 +106,17 @@ def define_common_targets(): preprocessor_flags = _program_preprocessor_flags(), exported_deps = [ ":memory_manager", - ":pte_data_map", - "//executorch/runtime/backend:interface", + ":pte_data_map" + aten_suffix, + "//executorch/runtime/backend:interface" + aten_suffix, "//executorch/runtime/core:core", - "//executorch/runtime/core:named_data_map", + "//executorch/runtime/core:named_data_map" + aten_suffix, "//executorch/runtime/core:evalue" + aten_suffix, "//executorch/runtime/core:event_tracer" + aten_suffix, "//executorch/runtime/core/exec_aten:lib" + aten_suffix, "//executorch/runtime/core/exec_aten/util:scalar_type_util" + aten_suffix, "//executorch/runtime/core/exec_aten/util:tensor_util" + aten_suffix, "//executorch/runtime/kernel:kernel_runtime_context" + aten_suffix, - "//executorch/runtime/kernel:operator_registry", + "//executorch/runtime/kernel:operator_registry" + aten_suffix, "//executorch/runtime/platform:platform", "//executorch/schema:extended_header", ], diff --git a/runtime/executor/tensor_parser.h b/runtime/executor/tensor_parser.h index 1fae84cfb05..e2b5ff8d6ea 100644 --- a/runtime/executor/tensor_parser.h +++ b/runtime/executor/tensor_parser.h @@ -21,7 +21,7 @@ #include namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { namespace deserialization { /// Data structure to hold key and data buffer for external data used @@ -142,7 +142,7 @@ ET_NODISCARD Result getTensorDataPtr( Span external_constants = {}); } // namespace deserialization -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch namespace torch { @@ -150,10 +150,11 @@ namespace executor { namespace deserialization { // TODO(T197294990): Remove these deprecated aliases once all users have moved // to the new `::executorch` namespaces. -using ::executorch::runtime::deserialization::getTensorDataPtr; -using ::executorch::runtime::deserialization::parseListOptionalType; -using ::executorch::runtime::deserialization::parseTensor; -using ::executorch::runtime::deserialization::parseTensorList; +using ::executorch::ET_RUNTIME_NAMESPACE::deserialization::getTensorDataPtr; +using ::executorch::ET_RUNTIME_NAMESPACE::deserialization:: + parseListOptionalType; +using ::executorch::ET_RUNTIME_NAMESPACE::deserialization::parseTensor; +using ::executorch::ET_RUNTIME_NAMESPACE::deserialization::parseTensorList; } // namespace deserialization } // namespace executor } // namespace torch diff --git a/runtime/executor/tensor_parser_aten.cpp b/runtime/executor/tensor_parser_aten.cpp index d1a2f712853..2d454d15be5 100644 --- a/runtime/executor/tensor_parser_aten.cpp +++ b/runtime/executor/tensor_parser_aten.cpp @@ -19,7 +19,9 @@ #include // @donotremove @manual=//caffe2/aten:ATen-core namespace executorch { +// This file is only used in ATen mode, so we use the runtime_aten namespace. namespace runtime { +namespace aten { namespace deserialization { namespace { @@ -126,5 +128,6 @@ Result parseTensor( } } // namespace deserialization +} // namespace aten } // namespace runtime } // namespace executorch diff --git a/runtime/executor/tensor_parser_exec_aten.cpp b/runtime/executor/tensor_parser_exec_aten.cpp index 14ba5e0d42c..aa27bbf929d 100644 --- a/runtime/executor/tensor_parser_exec_aten.cpp +++ b/runtime/executor/tensor_parser_exec_aten.cpp @@ -16,11 +16,10 @@ #include namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { namespace deserialization { using executorch::aten::ScalarType; -using executorch::runtime::TensorLayout; // Provides access to private Program methods. class TensorParser final { public: @@ -256,5 +255,5 @@ ET_NODISCARD Result getTensorDataPtr( } } // namespace deserialization -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch diff --git a/runtime/executor/tensor_parser_portable.cpp b/runtime/executor/tensor_parser_portable.cpp index 787af8b506b..e1f09d557ac 100644 --- a/runtime/executor/tensor_parser_portable.cpp +++ b/runtime/executor/tensor_parser_portable.cpp @@ -18,13 +18,13 @@ #include namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { namespace deserialization { using executorch::runtime::Span; -using torch::executor::ScalarType; -using torch::executor::Tensor; -using torch::executor::TensorImpl; +using ::torch::executor::ScalarType; +using ::torch::executor::Tensor; +using ::torch::executor::TensorImpl; Result parseTensor( const Program* program, @@ -176,5 +176,5 @@ Result parseTensor( } } // namespace deserialization -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch diff --git a/runtime/executor/test/executor_test.cpp b/runtime/executor/test/executor_test.cpp index 328b23a8df3..e2a44429941 100644 --- a/runtime/executor/test/executor_test.cpp +++ b/runtime/executor/test/executor_test.cpp @@ -22,14 +22,14 @@ using executorch::aten::Scalar; using executorch::aten::ScalarType; using executorch::aten::SizesType; using executorch::aten::Tensor; +using executorch::ET_RUNTIME_NAMESPACE::get_op_function_from_registry; +using executorch::ET_RUNTIME_NAMESPACE::Kernel; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; +using executorch::ET_RUNTIME_NAMESPACE::OpFunction; +using executorch::ET_RUNTIME_NAMESPACE::register_kernel; +using executorch::ET_RUNTIME_NAMESPACE::registry_has_op_function; using executorch::runtime::Error; using executorch::runtime::EValue; -using executorch::runtime::get_op_function_from_registry; -using executorch::runtime::Kernel; -using executorch::runtime::KernelRuntimeContext; -using executorch::runtime::OpFunction; -using executorch::runtime::register_kernel; -using executorch::runtime::registry_has_op_function; using executorch::runtime::Result; using executorch::runtime::testing::TensorFactory; diff --git a/runtime/executor/test/targets.bzl b/runtime/executor/test/targets.bzl index dd5262b5ac6..940601779cb 100644 --- a/runtime/executor/test/targets.bzl +++ b/runtime/executor/test/targets.bzl @@ -70,7 +70,7 @@ def define_common_targets(is_fbcode = False): "//executorch/runtime/core/exec_aten:lib" + aten_suffix, "//executorch/runtime/core:evalue" + aten_suffix, "//executorch/runtime/kernel:kernel_runtime_context" + aten_suffix, - "//executorch/runtime/kernel:operator_registry", + "//executorch/runtime/kernel:operator_registry" + aten_suffix, "//executorch/runtime/platform:platform", ], ) diff --git a/runtime/executor/test/test_backend_compiler_lib.cpp b/runtime/executor/test/test_backend_compiler_lib.cpp index 9eea6384d6f..ce631eb4f57 100644 --- a/runtime/executor/test/test_backend_compiler_lib.cpp +++ b/runtime/executor/test/test_backend_compiler_lib.cpp @@ -13,13 +13,13 @@ #include #include /* strtol */ +using executorch::ET_RUNTIME_NAMESPACE::Backend; +using executorch::ET_RUNTIME_NAMESPACE::BackendExecutionContext; +using executorch::ET_RUNTIME_NAMESPACE::BackendInitContext; +using executorch::ET_RUNTIME_NAMESPACE::BackendInterface; +using executorch::ET_RUNTIME_NAMESPACE::CompileSpec; +using executorch::ET_RUNTIME_NAMESPACE::DelegateHandle; using executorch::runtime::ArrayRef; -using executorch::runtime::Backend; -using executorch::runtime::BackendExecutionContext; -using executorch::runtime::BackendInitContext; -using executorch::runtime::BackendInterface; -using executorch::runtime::CompileSpec; -using executorch::runtime::DelegateHandle; using executorch::runtime::Error; using executorch::runtime::EValue; using executorch::runtime::FreeableBuffer; diff --git a/runtime/executor/test/test_backend_with_delegate_mapping.cpp b/runtime/executor/test/test_backend_with_delegate_mapping.cpp index e6d84aca189..a0b79b09c6d 100644 --- a/runtime/executor/test/test_backend_with_delegate_mapping.cpp +++ b/runtime/executor/test/test_backend_with_delegate_mapping.cpp @@ -14,13 +14,13 @@ #include /* strtol */ #include +using executorch::ET_RUNTIME_NAMESPACE::Backend; +using executorch::ET_RUNTIME_NAMESPACE::BackendExecutionContext; +using executorch::ET_RUNTIME_NAMESPACE::BackendInitContext; +using executorch::ET_RUNTIME_NAMESPACE::BackendInterface; +using executorch::ET_RUNTIME_NAMESPACE::CompileSpec; +using executorch::ET_RUNTIME_NAMESPACE::DelegateHandle; using executorch::runtime::ArrayRef; -using executorch::runtime::Backend; -using executorch::runtime::BackendExecutionContext; -using executorch::runtime::BackendInitContext; -using executorch::runtime::BackendInterface; -using executorch::runtime::CompileSpec; -using executorch::runtime::DelegateHandle; using executorch::runtime::Error; using executorch::runtime::EValue; using executorch::runtime::FreeableBuffer; diff --git a/runtime/kernel/kernel_runtime_context.h b/runtime/kernel/kernel_runtime_context.h index ad269f5dd4b..6facecc7632 100644 --- a/runtime/kernel/kernel_runtime_context.h +++ b/runtime/kernel/kernel_runtime_context.h @@ -15,7 +15,7 @@ #include namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { /** * Runtime state and functionality for kernel implementations. @@ -107,7 +107,7 @@ class KernelRuntimeContext { Error failure_state_ = Error::Ok; }; -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch // TODO(T197294990): Remove these deprecated aliases once all users have moved @@ -115,15 +115,15 @@ class KernelRuntimeContext { namespace torch { namespace executor { /// DEPRECATED: Use ::executorch::runtime::KernelRuntimeContext instead. -using ::executorch::runtime::KernelRuntimeContext; +using ::executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; /// DEPRECATED: Use ::executorch::runtime::KernelRuntimeContext instead. -using RuntimeContext = ::executorch::runtime::KernelRuntimeContext; +using RuntimeContext = ::executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; } // namespace executor } // namespace torch namespace executorch { namespace aten { /// DEPRECATED: Use ::executorch::runtime::KernelRuntimeContext instead. -using RuntimeContext = ::executorch::runtime::KernelRuntimeContext; +using RuntimeContext = ::executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; } // namespace aten } // namespace executorch // DEPRECATED: The executorch::aten:: namespace is deprecated. Use diff --git a/runtime/kernel/operator_registry.cpp b/runtime/kernel/operator_registry.cpp index 85705e5b3fd..d7e7b298c10 100644 --- a/runtime/kernel/operator_registry.cpp +++ b/runtime/kernel/operator_registry.cpp @@ -15,7 +15,7 @@ #include namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { namespace { @@ -258,5 +258,5 @@ Span get_registered_kernels() { return {registered_kernels, num_registered_kernels}; } -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch diff --git a/runtime/kernel/operator_registry.h b/runtime/kernel/operator_registry.h index a3cdcd66cee..f7a62208dd8 100644 --- a/runtime/kernel/operator_registry.h +++ b/runtime/kernel/operator_registry.h @@ -40,7 +40,7 @@ } namespace executorch { -namespace runtime { +namespace ET_RUNTIME_NAMESPACE { class KernelRuntimeContext; // Forward declaration using OpFunction = void (*)(KernelRuntimeContext&, EValue**); @@ -258,38 +258,41 @@ ET_NODISCARD inline Error register_kernel(const Kernel& kernel) { return register_kernels({&kernel, 1}); }; -} // namespace runtime +} // namespace ET_RUNTIME_NAMESPACE } // namespace executorch namespace torch { namespace executor { // TODO(T197294990): Remove these deprecated aliases once all users have moved // to the new `::executorch` namespaces. -using ::executorch::runtime::Kernel; -using ::executorch::runtime::KernelKey; -using ::executorch::runtime::KernelRuntimeContext; -using ::executorch::runtime::OpFunction; -using ::executorch::runtime::TensorMeta; -using KernelRuntimeContext = ::executorch::runtime::KernelRuntimeContext; +using ::executorch::ET_RUNTIME_NAMESPACE::Kernel; +using ::executorch::ET_RUNTIME_NAMESPACE::KernelKey; +using ::executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; +using ::executorch::ET_RUNTIME_NAMESPACE::OpFunction; +using ::executorch::ET_RUNTIME_NAMESPACE::TensorMeta; +using KernelRuntimeContext = + ::executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; inline ::executorch::runtime::Error register_kernels(ArrayRef kernels) { - return ::executorch::runtime::register_kernels( + return ::executorch::ET_RUNTIME_NAMESPACE::register_kernels( {kernels.data(), kernels.size()}); } inline OpFunction getOpsFn( const char* name, ArrayRef meta_list = {}) { - auto result = ::executorch::runtime::get_op_function_from_registry( - name, {meta_list.data(), meta_list.size()}); + auto result = + ::executorch::ET_RUNTIME_NAMESPACE::get_op_function_from_registry( + name, {meta_list.data(), meta_list.size()}); ET_CHECK(result.ok()); // get_op_function_from_registry() logs details. return *result; } inline bool hasOpsFn(const char* name, ArrayRef meta_list = {}) { - return ::executorch::runtime::registry_has_op_function( + return ::executorch::ET_RUNTIME_NAMESPACE::registry_has_op_function( name, {meta_list.data(), meta_list.size()}); } inline ArrayRef get_kernels() { - Span kernels = ::executorch::runtime::get_registered_kernels(); + Span kernels = + ::executorch::ET_RUNTIME_NAMESPACE::get_registered_kernels(); return ArrayRef(kernels.data(), kernels.size()); } } // namespace executor diff --git a/runtime/kernel/targets.bzl b/runtime/kernel/targets.bzl index b6aa9d7a95e..8a945f19881 100644 --- a/runtime/kernel/targets.bzl +++ b/runtime/kernel/targets.bzl @@ -21,21 +21,6 @@ def define_common_targets(): TARGETS and BUCK files that call this function. """ - runtime.cxx_library( - name = "operator_registry", - srcs = ["operator_registry.cpp"], - exported_headers = ["operator_registry.h"], - visibility = [ - "//executorch/...", - "@EXECUTORCH_CLIENTS", - ], - exported_deps = [ - "//executorch/runtime/core:core", - "//executorch/runtime/core:evalue", - ], - preprocessor_flags = _operator_registry_preprocessor_flags(), - ) - runtime.cxx_library( name = "operator_registry_MAX_NUM_KERNELS_TEST_ONLY", srcs = ["operator_registry.cpp"], @@ -68,6 +53,21 @@ def define_common_targets(): for aten_mode in get_aten_mode_options(): aten_suffix = "_aten" if aten_mode else "" + runtime.cxx_library( + name = "operator_registry" + aten_suffix, + srcs = ["operator_registry.cpp"], + exported_headers = ["operator_registry.h"], + visibility = [ + "//executorch/...", + "@EXECUTORCH_CLIENTS", + ], + exported_deps = [ + "//executorch/runtime/core:core", + "//executorch/runtime/core:evalue" + aten_suffix, + ], + preprocessor_flags = _operator_registry_preprocessor_flags(), + ) + runtime.cxx_library( name = "kernel_runtime_context" + aten_suffix, exported_headers = [ diff --git a/runtime/kernel/test/kernel_runtime_context_test.cpp b/runtime/kernel/test/kernel_runtime_context_test.cpp index 50bd860fb9c..2c3b536b0d4 100644 --- a/runtime/kernel/test/kernel_runtime_context_test.cpp +++ b/runtime/kernel/test/kernel_runtime_context_test.cpp @@ -13,8 +13,8 @@ #include using namespace ::testing; +using executorch::ET_RUNTIME_NAMESPACE::KernelRuntimeContext; using executorch::runtime::Error; -using executorch::runtime::KernelRuntimeContext; using executorch::runtime::MemoryAllocator; using executorch::runtime::Result; diff --git a/runtime/kernel/test/targets.bzl b/runtime/kernel/test/targets.bzl index bd66fc05b6f..4b3ed0f3139 100644 --- a/runtime/kernel/test/targets.bzl +++ b/runtime/kernel/test/targets.bzl @@ -101,3 +101,16 @@ def define_common_targets(): ":specialized_kernel_generated_lib", ], ) + + if aten_mode: + # Make sure we can depend on both generated_lib and generated_lib_aten + # in the same binary. + runtime.cxx_test( + name = "test_generated_lib_and_aten", + srcs = ["test_generated_lib_and_aten.cpp"], + deps = [ + "//executorch/kernels/portable:generated_lib", + "//executorch/kernels/portable:generated_lib_aten", + "//executorch/runtime/kernel:operator_registry_aten", + ], + ) diff --git a/runtime/kernel/test/test_generated_lib_and_aten.cpp b/runtime/kernel/test/test_generated_lib_and_aten.cpp new file mode 100644 index 00000000000..f9bfebc4a80 --- /dev/null +++ b/runtime/kernel/test/test_generated_lib_and_aten.cpp @@ -0,0 +1,45 @@ +/* + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + * + * This source code is licensed under the BSD-style license found in the + * LICENSE file in the root directory of this source tree. + */ + +#include +#include + +#include +#include +#include +#include +#include +#include +#include + +using namespace ::testing; +using executorch::aten::ScalarType; +using executorch::runtime::Error; +using executorch::runtime::EValue; + +class GeneratedLibAndAtenTest : public ::testing::Test { + public: + void SetUp() override { + executorch::runtime::runtime_init(); + } +}; + +TEST_F(GeneratedLibAndAtenTest, GetKernelsFromATenRegistry) { + // Check if the kernel exists in the ATen registry + bool has_kernel = + executorch::runtime::aten::registry_has_op_function("aten::add.out"); + EXPECT_TRUE(has_kernel) + << "Kernel 'aten::add.out' not found in ATen registry"; + + // Get the kernel from the ATen registry + auto result = + executorch::runtime::aten::get_op_function_from_registry("aten::add.out"); + EXPECT_EQ(result.error(), Error::Ok) + << "Failed to get kernel from ATen registry"; + EXPECT_NE(*result, nullptr) << "Kernel function from ATen registry is null"; +} diff --git a/shim_et/xplat/executorch/codegen/codegen.bzl b/shim_et/xplat/executorch/codegen/codegen.bzl index a6d6d59e0c2..e1cebaa1140 100644 --- a/shim_et/xplat/executorch/codegen/codegen.bzl +++ b/shim_et/xplat/executorch/codegen/codegen.bzl @@ -688,7 +688,7 @@ def executorch_generated_lib( "ovr_config//os:windows": [], }) + compiler_flags, deps = [ - "//executorch/runtime/kernel:operator_registry", + "//executorch/runtime/kernel:operator_registry" + aten_suffix, "//executorch/kernels/prim_ops:prim_ops_registry" + aten_suffix, "//executorch/runtime/core:evalue" + aten_suffix, "//executorch/codegen:macros", From d797d414017fad8bd39a8c04400be602ececd8ba Mon Sep 17 00:00:00 2001 From: Gasoonjia Date: Wed, 9 Apr 2025 20:50:39 -0700 Subject: [PATCH 063/244] remove reduntant log_delegate_intermediate_logging_helper call Differential Revision: D72754398 Pull Request resolved: https://github.com/pytorch/executorch/pull/10032 --- devtools/etdump/etdump_flatcc.cpp | 16 ++++------------ 1 file changed, 4 insertions(+), 12 deletions(-) diff --git a/devtools/etdump/etdump_flatcc.cpp b/devtools/etdump/etdump_flatcc.cpp index f6e18bf49d2..4e300ee4065 100644 --- a/devtools/etdump/etdump_flatcc.cpp +++ b/devtools/etdump/etdump_flatcc.cpp @@ -323,40 +323,32 @@ Result ETDumpGen::log_intermediate_output_delegate( const char* name, DelegateDebugIntId delegate_debug_index, const ArrayRef output) { - log_intermediate_output_delegate_helper(name, delegate_debug_index, output); - Result result = log_intermediate_output_delegate_helper( + return log_intermediate_output_delegate_helper( name, delegate_debug_index, output); - return result; } Result ETDumpGen::log_intermediate_output_delegate( const char* name, DelegateDebugIntId delegate_debug_index, const int& output) { - log_intermediate_output_delegate_helper(name, delegate_debug_index, output); - Result result = log_intermediate_output_delegate_helper( + return log_intermediate_output_delegate_helper( name, delegate_debug_index, output); - return result; } Result ETDumpGen::log_intermediate_output_delegate( const char* name, DelegateDebugIntId delegate_debug_index, const bool& output) { - log_intermediate_output_delegate_helper(name, delegate_debug_index, output); - Result result = log_intermediate_output_delegate_helper( + return log_intermediate_output_delegate_helper( name, delegate_debug_index, output); - return result; } Result ETDumpGen::log_intermediate_output_delegate( const char* name, DelegateDebugIntId delegate_debug_index, const double& output) { - log_intermediate_output_delegate_helper(name, delegate_debug_index, output); - Result result = log_intermediate_output_delegate_helper( + return log_intermediate_output_delegate_helper( name, delegate_debug_index, output); - return result; } template From a805c775d7d264ddbe84aaa89cb5712644e513a1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Martin=20Lindstr=C3=B6m?= <33344797+martinlsm@users.noreply.github.com> Date: Thu, 10 Apr 2025 13:53:05 +0200 Subject: [PATCH 064/244] Arm backend: Set all 16/32 bit sigmoid tests to flaky (#9993) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Tests in test_sigmoid_16bit.py and test_sigmoid_32bit.py randomly fails due to a Vela bug regarding handling of the table op sigmoid is converted to. Set all affected tests to flaky until the bug resolved. Co-authored-by: Martin Lindström --- backends/arm/test/ops/test_sigmoid_16bit.py | 10 ++++++---- backends/arm/test/ops/test_sigmoid_32bit.py | 10 ++++++---- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/backends/arm/test/ops/test_sigmoid_16bit.py b/backends/arm/test/ops/test_sigmoid_16bit.py index c3907887ac9..240000e6973 100644 --- a/backends/arm/test/ops/test_sigmoid_16bit.py +++ b/backends/arm/test/ops/test_sigmoid_16bit.py @@ -81,7 +81,7 @@ def forward(self, x): @common.parametrize("test_data", test_data_suite) -@pytest.mark.flaky(reruns=5) +@pytest.mark.flaky(reruns=32) # Flaky due to Vela bug: MLBEDSW-10642 def test_sigmoid_tosa_BI(test_data): pipeline = TosaPipelineBI( Sigmoid(), (test_data(),), Sigmoid.aten_op, Sigmoid.exir_op @@ -97,7 +97,7 @@ def test_sigmoid_tosa_BI(test_data): "ramp": "AssertionError: Output 0 does not match reference output. MLETORCH-787" }, ) -@pytest.mark.flaky(reruns=5) +@pytest.mark.flaky(reruns=32) # Flaky due to Vela bug: MLBEDSW-10642 def test_sigmoid_add_sigmoid_tosa_BI(test_data): pipeline = TosaPipelineBI( SigmoidAddSigmoid(), (test_data(),), Sigmoid.aten_op, Sigmoid.exir_op @@ -110,6 +110,7 @@ def test_sigmoid_add_sigmoid_tosa_BI(test_data): "test_data", test_data_suite, ) +@pytest.mark.flaky(reruns=32) # Flaky due to Vela bug: MLBEDSW-10642 def test_sigmoid_tosa_u55(test_data): pipeline = OpNotSupportedPipeline( Sigmoid(), (test_data(),), "TOSA-0.80+BI+u55", {Sigmoid.exir_op: 1} @@ -122,6 +123,7 @@ def test_sigmoid_tosa_u55(test_data): "test_data", test_data_suite, ) +@pytest.mark.flaky(reruns=32) # Flaky due to Vela bug: MLBEDSW-10642 def test_sigmoid_add_sigmoid_tosa_u55(test_data): pipeline = OpNotSupportedPipeline( SigmoidAddSigmoid(), @@ -135,7 +137,7 @@ def test_sigmoid_add_sigmoid_tosa_u55(test_data): @common.parametrize("test_data", test_data_suite) -@pytest.mark.flaky(reruns=5) +@pytest.mark.flaky(reruns=32) # Flaky due to Vela bug: MLBEDSW-10642 @common.XfailIfNoCorstone320 def test_sigmoid_tosa_u85(test_data): pipeline = EthosU85PipelineBI( @@ -152,7 +154,7 @@ def test_sigmoid_tosa_u85(test_data): "ramp": "AssertionError: Output 0 does not match reference output.", }, ) -@pytest.mark.flaky(reruns=5) +@pytest.mark.flaky(reruns=32) # Flaky due to Vela bug: MLBEDSW-10642 @common.XfailIfNoCorstone320 def test_sigmoid_add_sigmoid_tosa_u85(test_data): pipeline = EthosU85PipelineBI( diff --git a/backends/arm/test/ops/test_sigmoid_32bit.py b/backends/arm/test/ops/test_sigmoid_32bit.py index 5388eae83c3..14808eedaf9 100644 --- a/backends/arm/test/ops/test_sigmoid_32bit.py +++ b/backends/arm/test/ops/test_sigmoid_32bit.py @@ -97,7 +97,7 @@ def forward(self, x): @common.parametrize("test_data", test_data_suite) -@pytest.mark.flaky(reruns=5) +@pytest.mark.flaky(reruns=32) # Flaky due to Vela bug: MLBEDSW-10642 def test_sigmoid_tosa_BI(test_data): pipeline = TosaPipelineBI( Sigmoid(), @@ -110,7 +110,7 @@ def test_sigmoid_tosa_BI(test_data): @common.parametrize("test_data", test_data_suite) -@pytest.mark.flaky(reruns=5) +@pytest.mark.flaky(reruns=32) # Flaky due to Vela bug: MLBEDSW-10642 def test_sigmoid_add_sigmoid_tosa_BI(test_data): pipeline = TosaPipelineBI( SigmoidAddSigmoid(), @@ -123,6 +123,7 @@ def test_sigmoid_add_sigmoid_tosa_BI(test_data): @common.parametrize("test_data", test_data_suite) +@pytest.mark.flaky(reruns=32) # Flaky due to Vela bug: MLBEDSW-10642 def test_sigmoid_tosa_u55(test_data): pipeline = OpNotSupportedPipeline( Sigmoid(), (test_data(),), "TOSA-0.80+BI+u55", {Sigmoid.exir_op: 1} @@ -132,6 +133,7 @@ def test_sigmoid_tosa_u55(test_data): @common.parametrize("test_data", test_data_suite) +@pytest.mark.flaky(reruns=32) # Flaky due to Vela bug: MLBEDSW-10642 def test_sigmoid_add_sigmoid_tosa_u55(test_data): pipeline = OpNotSupportedPipeline( SigmoidAddSigmoid(), @@ -145,7 +147,7 @@ def test_sigmoid_add_sigmoid_tosa_u55(test_data): @common.parametrize("test_data", test_data_suite) -@pytest.mark.flaky(reruns=5) +@pytest.mark.flaky(reruns=32) # Flaky due to Vela bug: MLBEDSW-10642 @common.XfailIfNoCorstone320 def test_sigmoid_tosa_u85(test_data): pipeline = EthosU85PipelineBI( @@ -162,7 +164,7 @@ def test_sigmoid_tosa_u85(test_data): "ramp": "AssertionError: Output 0 does not match reference output.", }, ) -@pytest.mark.flaky(reruns=5) +@pytest.mark.flaky(reruns=32) # Flaky due to Vela bug: MLBEDSW-10642 @common.XfailIfNoCorstone320 def test_sigmoid_add_sigmoid_tosa_u85(test_data): pipeline = EthosU85PipelineBI( From fca5f85d65003ba55c0d169338bb5af733e41cde Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Martin=20Lindstr=C3=B6m?= <33344797+martinlsm@users.noreply.github.com> Date: Thu, 10 Apr 2025 13:54:03 +0200 Subject: [PATCH 065/244] Arm backend: Raise atol in MobileNetV3 unit tests (#9959) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit MobileNetV3 was sporadically failing with the previously set absolute difference threshold. Raise it to prevent flaky test status. Co-authored-by: Martin Lindström --- backends/arm/test/models/test_mobilenet_v3_arm.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/backends/arm/test/models/test_mobilenet_v3_arm.py b/backends/arm/test/models/test_mobilenet_v3_arm.py index 7e0afe4a54f..f6f8f8f3e0c 100644 --- a/backends/arm/test/models/test_mobilenet_v3_arm.py +++ b/backends/arm/test/models/test_mobilenet_v3_arm.py @@ -46,7 +46,7 @@ def test_mv3_tosa_BI(): aten_op=[], exir_op=[], use_to_edge_transform_and_lower=True, - atol=0.3, + atol=0.5, qtol=1, ) pipeline.run() @@ -63,7 +63,7 @@ def test_mv3_u55_BI(): exir_ops=[], run_on_fvp=True, use_to_edge_transform_and_lower=True, - atol=0.3, + atol=0.5, qtol=1, ) pipeline.run() @@ -80,7 +80,7 @@ def test_mv3_u85_BI(): exir_ops=[], run_on_fvp=True, use_to_edge_transform_and_lower=True, - atol=0.3, + atol=0.5, qtol=1, ) pipeline.run() From 9e497849bdea821ecc46d2e2e11d6905d345d057 Mon Sep 17 00:00:00 2001 From: Mergen Nachin Date: Thu, 10 Apr 2025 09:00:47 -0400 Subject: [PATCH 066/244] Add examples for CPP demo app (#10022) --- docs/source/getting-started.md | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/docs/source/getting-started.md b/docs/source/getting-started.md index e69d72c1477..e0d87d7d87c 100644 --- a/docs/source/getting-started.md +++ b/docs/source/getting-started.md @@ -99,6 +99,7 @@ print("Comparing against original PyTorch module") print(torch.allclose(output[0], eager_reference_output, rtol=1e-3, atol=1e-5)) ``` +For complete examples of exporting and running the model, please refer to our [examples GitHub repository](https://github.com/pytorch-labs/executorch-examples/tree/main/mv2/python).

@@ -178,6 +179,7 @@ target_link_libraries( xnnpack_backend) ``` + #### Runtime APIs Both high-level and low-level C++ APIs are provided. The low-level APIs are platform independent, do not dynamically allocate memory, and are most suitable for resource-constrained embedded systems. The high-level APIs are provided as a convenience wrapper around the lower-level APIs, and make use of dynamic memory allocation and standard library constructs to reduce verbosity. @@ -194,8 +196,8 @@ using namespace ::executorch::extension; Module module("/path/to/model.pte"); // Create an input tensor. -float input[1 * 3 * 256 * 256]; -auto tensor = from_blob(input, {1, 3, 256, 256}); +float input[1 * 3 * 224 * 224]; +auto tensor = from_blob(input, {1, 3, 224, 224}); // Perform an inference. const auto result = module.forward(tensor); @@ -208,6 +210,8 @@ if (result.ok()) { For more information on the C++ APIs, see [Running an ExecuTorch Model Using the Module Extension in C++](extension-module.md) and [Managing Tensor Memory in C++](extension-tensor.md). +For complete examples of building and running C++ application, please refer to our [examples GitHub repository](https://github.com/pytorch-labs/executorch-examples/tree/main/mv2/cpp). +
## Next Steps From 1a1bb7a0acf23bede54c2d338e0c3a49a0f41d8f Mon Sep 17 00:00:00 2001 From: Sicheng Stephen Jia Date: Thu, 10 Apr 2025 09:42:06 -0400 Subject: [PATCH 067/244] [arm][ez] Add `xfail` for `norm` tests (#10043) Summary: ## Context https://github.com/pytorch/executorch/pull/9938 made it so that `linalg_vector_norm` is now decomposed when exporting to Edge. However, this broke some tests in the arm delegate because export passes cannot handle the decomposed operator sequence. To account for this, add `xfail` for the failing tests since `linalg_vector_norm` is not supported in TOSA yet. ## Changes Add `xfail` for `norm` tests in `test_torch_functions.py` Test Plan: ## Test Plan Check CI that failing test is recovered. --- backends/arm/test/models/test_torch_functions.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/backends/arm/test/models/test_torch_functions.py b/backends/arm/test/models/test_torch_functions.py index 19e2395adfe..5cd4bd3aaed 100644 --- a/backends/arm/test/models/test_torch_functions.py +++ b/backends/arm/test/models/test_torch_functions.py @@ -101,6 +101,7 @@ def forward(self, *args): "Requires dynamic output shape.", "topk": "NotImplementedError: No registered serialization name for found", "sort": "NotImplementedError: No registered serialization name for found", + "norm": "An error occurred when running the 'KeepDimsFalseToSqueezePass' pass after the following passes:", }, ) def test_torch_fns_MI(test_data): @@ -129,6 +130,7 @@ def test_torch_fns_MI(test_data): "topk": "NotImplementedError: No registered serialization name for found", "sort": "NotImplementedError: No registered serialization name for found", "t": "MLETORCH-855: Issue with Quantization folding.", + "norm": "An error occurred when running the 'KeepDimsFalseToSqueezePass' pass after the following passes:", }, strict=False, ) From c9624805c1e027c9ba3ca5763a1edb809940d203 Mon Sep 17 00:00:00 2001 From: lucylq Date: Thu, 10 Apr 2025 09:08:15 -0700 Subject: [PATCH 068/244] Add link to executorch-examples in module/c++ doc (#10026) https://github.com/pytorch-labs/executorch-examples/tree/main/mv2/cpp --- docs/source/using-executorch-cpp.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/source/using-executorch-cpp.md b/docs/source/using-executorch-cpp.md index aedd3ff914e..d64dad97da9 100644 --- a/docs/source/using-executorch-cpp.md +++ b/docs/source/using-executorch-cpp.md @@ -32,6 +32,8 @@ if (result.ok()) { For more information on the Module class, see [Running an ExecuTorch Model Using the Module Extension in C++](extension-module.md). For information on high-level tensor APIs, see [Managing Tensor Memory in C++](extension-tensor.md). +For complete examples of building and running a C++ application using the Module API, refer to our [examples GitHub repository](https://github.com/pytorch-labs/executorch-examples/tree/main/mv2/cpp). + ## Low-Level APIs Running a model using the low-level runtime APIs allows for a high-degree of control over memory allocation, placement, and loading. This allows for advanced use cases, such as placing allocations in specific memory banks or loading a model without a file system. For an end to end example using the low-level runtime APIs, see [Running an ExecuTorch Model in C++ Tutorial](running-a-model-cpp-tutorial.md). From 9fee71b071860e15ab7e1c5b6ddf3976ce257c94 Mon Sep 17 00:00:00 2001 From: Ryan OShea <86965113+ArmRyan@users.noreply.github.com> Date: Thu, 10 Apr 2025 19:02:10 +0200 Subject: [PATCH 069/244] Arm Backend: Add Tiny Stories llama test case for TOSA BI (#9996) Test currently xfails with key error relating to scalar_tensor Signed-off-by: Ryan O'Shea --- backends/arm/test/models/test_llama.py | 28 +++++++++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/backends/arm/test/models/test_llama.py b/backends/arm/test/models/test_llama.py index 89196674c48..378c86cb420 100644 --- a/backends/arm/test/models/test_llama.py +++ b/backends/arm/test/models/test_llama.py @@ -102,7 +102,7 @@ def prepare_model(self): def test_llama_tosa_MI(self): llama_model, llama_inputs, llama_meta = self.prepare_model() - if llama_model is None and llama_inputs is None and llama_meta is None: + if llama_model is None or llama_inputs is None: pytest.skip("Missing model and/or input files") with torch.no_grad(): @@ -123,3 +123,29 @@ def test_llama_tosa_MI(self): rtol=1.1, # TODO: MLETORCH-825 decrease tolerance ) ) + + @pytest.mark.xfail(reason="KeyError: scalar_tensor_1 (MLETORCH-907)") + def test_llama_tosa_BI(self): + llama_model, llama_inputs, llama_meta = self.prepare_model() + + if llama_model is None or llama_inputs is None: + pytest.skip("Missing model and/or input files") + + with torch.no_grad(): + ( + ArmTester( + llama_model, + example_inputs=llama_inputs, + compile_spec=common.get_tosa_compile_spec("TOSA-0.80+BI"), + constant_methods=llama_meta, + ) + .quantize() + .export() + .to_edge_transform_and_lower() + .to_executorch() + .run_method_and_compare_outputs( + inputs=llama_inputs, + atol=4.3, + rtol=1.1, # TODO: Tolerance needs to be updated after MLETORCH-907 + ) + ) From 09113d8a74235cbe4be556467dbc167a7960b147 Mon Sep 17 00:00:00 2001 From: pytorchbot Date: Thu, 10 Apr 2025 13:20:39 -0400 Subject: [PATCH 070/244] [Executorch][SDPA] Refactor + Make quantized sdpa handle sequence at dim 1 or 2 (#10060) For quantized SDPA we want to evaluate performance impact of having seq at dim 1 as well as dim 2. This diff refactors the code to enable this. The same should be done also for float SDPA but left for future. Differential Revision: [D71833060](https://our.internmc.facebook.com/intern/diff/D71833060/) --- extension/llm/custom_ops/op_sdpa.cpp | 35 +++--- extension/llm/custom_ops/op_sdpa.h | 1 + extension/llm/custom_ops/op_sdpa_aot.cpp | 18 ++- extension/llm/custom_ops/op_sdpa_impl.h | 119 ++++++++++++------ .../llm/custom_ops/test_quantized_sdpa.py | 78 +++++++++++- 5 files changed, 189 insertions(+), 62 deletions(-) diff --git a/extension/llm/custom_ops/op_sdpa.cpp b/extension/llm/custom_ops/op_sdpa.cpp index 391d2ab0646..c5c9b79b280 100644 --- a/extension/llm/custom_ops/op_sdpa.cpp +++ b/extension/llm/custom_ops/op_sdpa.cpp @@ -264,14 +264,14 @@ Tensor& flash_attention_kernel_out( InvalidArgument, output); - auto q_seq_len = query.size(2); + auto seq_len = query.size(2); ET_SWITCH_FLOAT_TYPES( query.scalar_type(), ctx, "flash_attention", CTYPE, [&] { // TODO we need to re-evaluate this for ARM CPUs // And there can be many so instead of templatizing // we might consider another appraoch - if (q_seq_len >= 768) { + if (seq_len >= 768) { sdpa::impl::cpu_flash_attention( output, query, @@ -287,7 +287,7 @@ Tensor& flash_attention_kernel_out( nullopt, nullopt, nullopt); - } else if (q_seq_len >= 192) { + } else if (seq_len >= 192) { sdpa::impl::cpu_flash_attention( output, query, @@ -341,7 +341,8 @@ Tensor& custom_sdpa_out_impl( const optional& k_zero_points = nullopt, const optional& k_scales = nullopt, const optional& v_zero_points = nullopt, - const optional& v_scales = nullopt) { + const optional& v_scales = nullopt, + bool is_seq_at_dim_2 = false) { ET_KERNEL_CHECK_MSG( ctx, !attn_mask.has_value() || !is_causal, @@ -357,13 +358,15 @@ Tensor& custom_sdpa_out_impl( "Invalid arguments"); int64_t seq_len = q.size(1); - auto q_seq_len = q.size(1); + SeqDim seq_dim{SeqDim::TWO}; + if (!is_seq_at_dim_2) { + seq_dim = SeqDim::ONE; + } - bool is_seq_at_dim_1{true}; if (q.scalar_type() == ScalarType::Char) { - is_seq_at_dim_1 = false; - seq_len = q.size(2); - q_seq_len = q.size(2); + if (seq_dim == SeqDim::TWO) { + seq_len = q.size(2); + } ET_KERNEL_CHECK_MSG( ctx, q_scales.has_value() && q_zero_points.has_value() && @@ -412,7 +415,7 @@ Tensor& custom_sdpa_out_impl( // TODO we need to re-evaluate this for ARM CPUs // And there can be many so instead of templatizing // we might consider another appraoch - if (q_seq_len >= 768) { + if (seq_len >= 768) { sdpa::impl::cpu_flash_attention( output, q, @@ -428,10 +431,10 @@ Tensor& custom_sdpa_out_impl( k_scales, // k_scales v_zero_points, // v_zero_points v_scales, // v_scales - is_seq_at_dim_1, /* is_seq_at_dim_1 */ + seq_dim, /* seq_dim */ start_pos, num_keys_for_causal_attention); - } else if (q_seq_len >= 192) { + } else if (seq_len >= 192) { sdpa::impl::cpu_flash_attention( output, q, @@ -447,7 +450,7 @@ Tensor& custom_sdpa_out_impl( k_scales, // k_scales v_zero_points, // v_zero_points v_scales, // v_scales - is_seq_at_dim_1, /* is_seq_at_dim_1 */ + seq_dim, /* seq_dim */ start_pos, num_keys_for_causal_attention); } else { @@ -466,7 +469,7 @@ Tensor& custom_sdpa_out_impl( k_scales, // k_scales v_zero_points, // v_zero_points v_scales, // v_scales - is_seq_at_dim_1, /* is_seq_at_dim_1 */ + seq_dim, /* seq_dim */ start_pos, num_keys_for_causal_attention); } @@ -492,6 +495,7 @@ Tensor& custom_quantized_sdpa_out( const optional& k_scales, const optional& v_zero_points, const optional& v_scales, + const bool is_seq_at_dim_2, Tensor& output) { return custom_sdpa_out_impl( ctx, @@ -509,7 +513,8 @@ Tensor& custom_quantized_sdpa_out( k_zero_points, k_scales, v_zero_points, - v_scales); + v_scales, + is_seq_at_dim_2); } #endif // ENABLE_CUSTOM_QUANTIZED_SDPA diff --git a/extension/llm/custom_ops/op_sdpa.h b/extension/llm/custom_ops/op_sdpa.h index 92b8a41b706..3deb27b3989 100644 --- a/extension/llm/custom_ops/op_sdpa.h +++ b/extension/llm/custom_ops/op_sdpa.h @@ -74,6 +74,7 @@ Tensor& custom_quantized_sdpa_out( const optional& k_scales, const optional& v_zero_points, const optional& v_scales, + const bool is_seq_at_dim_1, Tensor& output); #endif // ENABLE_CUSTOM_QUANTIZED_SDPA } // namespace native diff --git a/extension/llm/custom_ops/op_sdpa_aot.cpp b/extension/llm/custom_ops/op_sdpa_aot.cpp index a3adcbbf866..2da915a19b8 100644 --- a/extension/llm/custom_ops/op_sdpa_aot.cpp +++ b/extension/llm/custom_ops/op_sdpa_aot.cpp @@ -96,6 +96,7 @@ Tensor& custom_quantized_sdpa_out_no_context( const optional k_scales, const optional v_zero_points, const optional v_scales, + const bool is_seq_at_dim_2, Tensor& output); at::Tensor custom_quantized_sdpa_aten( @@ -115,7 +116,8 @@ at::Tensor custom_quantized_sdpa_aten( const std::optional& k_zero_points, const std::optional& k_scales, const std::optional& v_zero_points, - const std::optional& v_scales); + const std::optional& v_scales, + const bool is_seq_at_dim_2); #endif // ENABLE_CUSTOM_QUANTIZED_SDPA Tensor& update_cache_out_no_context( @@ -258,6 +260,7 @@ Tensor& custom_quantized_sdpa_out_no_context( const optional k_scales, const optional v_zero_points, const optional v_scales, + const bool is_seq_at_dim_2, Tensor& output) { executorch::aten::RuntimeContext context{}; return torch::executor::native::custom_quantized_sdpa_out( @@ -276,6 +279,7 @@ Tensor& custom_quantized_sdpa_out_no_context( k_scales, v_zero_points, v_scales, + is_seq_at_dim_2, output); } @@ -296,9 +300,10 @@ at::Tensor custom_quantized_sdpa_aten( const std::optional& k_zero_points, const std::optional& k_scales, const std::optional& v_zero_points, - const std::optional& v_scales) { + const std::optional& v_scales, + const bool is_seq_at_dim_2) { auto output = at::empty(q.sizes()); - WRAP_TO_ATEN(custom_quantized_sdpa_out_no_context, 14) + WRAP_TO_ATEN(custom_quantized_sdpa_out_no_context, 15) (q, k, v, @@ -313,6 +318,7 @@ at::Tensor custom_quantized_sdpa_aten( k_scales, v_zero_points, v_scales, + is_seq_at_dim_2, output); return output; } @@ -371,13 +377,13 @@ TORCH_LIBRARY_FRAGMENT(llama, m) { "Tensor? attn_mask=None, float drpout_p=0.0, bool is_causal=False, " "float? scale=None, Tensor? q_zero_points=None, Tensor? q_scales=None, " "Tensor? k_zero_points=None, Tensor? k_scales=None, Tensor? v_zero_points=None, " - "Tensor? v_scales=None) -> Tensor"); + "Tensor? v_scales=None, bool is_seq_at_dim_2=False) -> Tensor"); m.def( "custom_quantized_sdpa.out(Tensor query, Tensor key, Tensor value, SymInt start_pos, " "Tensor? attn_mask=None, float drpout_p=0.0, bool is_causal=False, " "float? scale=None, Tensor? q_zero_points=None, Tensor? q_scales=None, " "Tensor? k_zero_points=None, Tensor? k_scales=None, Tensor? v_zero_points=None, " - "Tensor? v_scales=None, *, Tensor(a!) out) -> Tensor(a!)"); + "Tensor? v_scales=None, bool is_seq_at_dim_2=False, *, Tensor(a!) out) -> Tensor(a!)"); #endif // ENABLE_CUSTOM_QUANTIZED_SDPA } @@ -404,6 +410,6 @@ TORCH_LIBRARY_IMPL(llama, CompositeExplicitAutograd, m) { m.impl( "custom_quantized_sdpa.out", WRAP_TO_ATEN( - torch::executor::native::custom_quantized_sdpa_out_no_context, 14)); + torch::executor::native::custom_quantized_sdpa_out_no_context, 15)); #endif // ENABLE_CUSTOM_QUANTIZED_SDPA } diff --git a/extension/llm/custom_ops/op_sdpa_impl.h b/extension/llm/custom_ops/op_sdpa_impl.h index 5a0fb708220..7607a1e283d 100644 --- a/extension/llm/custom_ops/op_sdpa_impl.h +++ b/extension/llm/custom_ops/op_sdpa_impl.h @@ -32,6 +32,8 @@ namespace executor { namespace native { +enum class SeqDim { ONE = 1, TWO }; + namespace sdpa::impl { struct MaybeQuantizedMatrixData { @@ -39,6 +41,8 @@ struct MaybeQuantizedMatrixData { const int8_t* zero_points{nullptr}; const float* scales{nullptr}; int64_t m = 0, n = 0; + const int64_t zero_points_stride{1}; + const int64_t scales_stride{1}; ScalarType dtype{ScalarType::Float}; MaybeQuantizedMatrixData() = default; MaybeQuantizedMatrixData( @@ -47,12 +51,15 @@ struct MaybeQuantizedMatrixData { const float* scales_, int64_t m_, int64_t n_, + int64_t qparams_stride, ScalarType dtype_) : data(data_), zero_points(zero_points_), scales(scales_), m(m_), n(n_), + zero_points_stride(qparams_stride), + scales_stride(qparams_stride), dtype(dtype_) {} }; @@ -91,8 +98,9 @@ void _q_at_k_gemm( static_cast(k_data.zero_points), static_cast(q_data.scales), static_cast(k_data.scales), - 1, - 1); + // LHS and RHS are assumed to have same stride for qparams + q_data.zero_points_stride, + k_data.zero_points_stride); } else { ET_CHECK_MSG( false, "Accumulation in dtype other than float not supported yet"); @@ -152,7 +160,7 @@ void _qk_at_v_gemm( static_cast(v_data.zero_points), static_cast(v_data.scales), beta, - 1); + v_data.zero_points_stride); } else { ET_CHECK_MSG( false, "Accumulation in dtype other than float not supported yet"); @@ -351,6 +359,40 @@ sdpa_with_kv_cache does not use attn_mask. TODO: Just handle conversion of bool mask to float */ +/** + * @brief Implements Flash Attention algorithm on CPU + * + * This function computes scaled dot-product attention with optimizations for + CPU. + * It supports both regular and quantized attention computation. + * + * @tparam scalar_t The data type for computation (e.g., float) + * @tparam q_split_size Block size for query matrix in tiling algorithm + * @tparam kv_split_size Block size for key/value matrices in tiling algorithm + * + * @param output Output tensor to store attention results + * @param query Query tensor [Batch x Num_heads x Q_seq_len x Dim_per_head] + * @param key Key tensor [Batch x Num_heads_kv x KV_seq_len x Dim_per_head] + * @param value Value tensor [Batch x Num_heads_kv x KV_seq_len x Dim_per_head] + * @param dropout_p Dropout probability (not used in current implementation) + * @param is_causal Whether to apply causal mask (lower triangular) + * @param attn_mask Optional explicit attention mask + * @param scale Optional custom scaling factor (default: 1/sqrt(head_dim)) + * @param q_zero_points Optional zero points for quantized query + * @param q_scales Optional scales for quantized query + * @param k_zero_points Optional zero points for quantized key + * @param k_scales Optional scales for quantized key + * @param v_zero_points Optional zero points for quantized value + * @param v_scales Optional scales for quantized value + * @param seq_dim Which dimension is sequence dimension. + If SeqDim::One, then query, key, value are + expected to be in shape [Batch x Q_seq_len x Dim_per_head x Num_heads] and + output is expected to be in shape [Batch x Q_seq_len x Dim_per_head x + Num_heads] + * @param start_pos Starting position for causal masking in generation + * @param num_keys_for_causal_attention Number of keys to consider for causal + attention (-1 for all) + */ template void cpu_flash_attention( Tensor& output, @@ -367,22 +409,10 @@ void cpu_flash_attention( const optional& k_scales, const optional& v_zero_points, const optional& v_scales, - bool is_seq_at_dim_1 = false, + const SeqDim seq_dim = SeqDim::TWO, const int64_t start_pos = 0, const int64_t num_keys_for_causal_attention = -1) { (void)dropout_p; - // Query (Batch x Num_heads x Q_seq_len x Dim_per_head) - // Key (Batch x Num_heads x KV_seq_len x Dim_per_head) - // Value (Batch x Num_heads x KV_seq_len x Dim_per_head) - - /* - // -> (Batch x Q_seq_len x Num_heads x Dim_per_head) - at::Tensor query = q.transpose(1, 2); - // -> (Batch x KV_seq_len x Num_heads x Dim_per_head) - at::Tensor key = k.transpose(1, 2); - // -> (Batch x KV_seq_len x Num_heads x Dim_per_head) - at::Tensor value = v.transpose(1, 2); - */ // Without this we have out-of-bounds writes for // causal masking @@ -408,7 +438,7 @@ void cpu_flash_attention( int64_t kvSize = value.size(2); int64_t num_heads_kv = key.size(1); - if (is_seq_at_dim_1) { + if (seq_dim == SeqDim::ONE) { num_head = query.size(2); num_heads_kv = key.size(2); qSize = query.size(1); @@ -466,7 +496,7 @@ void cpu_flash_attention( int64_t qStrideH = strides[1]; int64_t qStrideM = strides[2]; - if (is_seq_at_dim_1) { + if (seq_dim == SeqDim::ONE) { qStrideH = strides[2]; qStrideM = strides[1]; } @@ -476,7 +506,7 @@ void cpu_flash_attention( int64_t kStrideH = strides[1]; int64_t kStrideN = strides[2]; - if (is_seq_at_dim_1) { + if (seq_dim == SeqDim::ONE) { kStrideH = strides[2]; kStrideN = strides[1]; } @@ -486,7 +516,7 @@ void cpu_flash_attention( int64_t vStrideH = strides[1]; int64_t vStrideN = strides[2]; - if (is_seq_at_dim_1) { + if (seq_dim == SeqDim::ONE) { vStrideH = strides[2]; vStrideN = strides[1]; } @@ -502,20 +532,36 @@ void cpu_flash_attention( int64_t v_quant_params_StrideN = 0; if (is_quantized_sdpa) { - strides = q_zero_points.value().strides(); - q_quant_params_StrideB = strides[0]; - q_quant_params_StrideH = strides[1]; - q_quant_params_StrideM = strides[2]; - - strides = k_zero_points.value().strides(); - k_quant_params_StrideB = strides[0]; - k_quant_params_StrideH = strides[1]; - k_quant_params_StrideN = strides[2]; - - strides = v_zero_points.value().strides(); - v_quant_params_StrideB = strides[0]; - v_quant_params_StrideH = strides[1]; - v_quant_params_StrideN = strides[2]; + auto q_strides = q_zero_points.value().strides(); + q_quant_params_StrideB = q_strides[0]; + q_quant_params_StrideH = q_strides[1]; + q_quant_params_StrideM = q_strides[2]; + + auto k_strides = k_zero_points.value().strides(); + k_quant_params_StrideB = k_strides[0]; + k_quant_params_StrideH = k_strides[1]; + k_quant_params_StrideN = k_strides[2]; + + auto v_strides = v_zero_points.value().strides(); + v_quant_params_StrideB = v_strides[0]; + v_quant_params_StrideH = v_strides[1]; + v_quant_params_StrideN = v_strides[2]; + + ET_CHECK_MSG( + (v_quant_params_StrideN == k_quant_params_StrideN) && + (v_quant_params_StrideN == q_quant_params_StrideM), + "Quant params strides must be same for seq dim"); + + if (seq_dim == SeqDim::ONE) { + q_quant_params_StrideH = q_strides[2]; + q_quant_params_StrideM = q_strides[1]; + + k_quant_params_StrideH = k_strides[2]; + k_quant_params_StrideN = k_strides[1]; + + v_quant_params_StrideH = v_strides[2]; + v_quant_params_StrideN = v_strides[1]; + } } strides = output.strides(); @@ -523,7 +569,7 @@ void cpu_flash_attention( int64_t oStrideH = strides[1]; int64_t oStrideM = strides[2]; - if (is_seq_at_dim_1) { + if (seq_dim == SeqDim::ONE) { oStrideH = strides[2]; oStrideM = strides[1]; } @@ -679,6 +725,7 @@ void cpu_flash_attention( q_scales_ptr, qBlockSize, headSize, + q_quant_params_StrideM, query.scalar_type()); MaybeQuantizedMatrixData k_sub_matrix_data = MaybeQuantizedMatrixData( static_cast(k_sub_matrix_data_ptr), @@ -686,6 +733,7 @@ void cpu_flash_attention( k_scales_ptr, kvBlockSize, headSize, + k_quant_params_StrideN, key.scalar_type()); _q_at_k_gemm( qBlockSize, @@ -835,6 +883,7 @@ void cpu_flash_attention( v_scales_ptr, kvBlockSize, headSize, + v_quant_params_StrideN, value.scalar_type()); // Calculate Softmax(q @ k.T) @ v _qk_at_v_gemm( diff --git a/extension/llm/custom_ops/test_quantized_sdpa.py b/extension/llm/custom_ops/test_quantized_sdpa.py index f5540a4e614..f7b28e1508f 100644 --- a/extension/llm/custom_ops/test_quantized_sdpa.py +++ b/extension/llm/custom_ops/test_quantized_sdpa.py @@ -35,6 +35,7 @@ def setUp(self): self.float_dtype = torch.float32 self.q_shape = None self.kv_shape = None + self.is_seq_at_dim_2 = True def _scale_tensor(self, tensor, min_value, max_value, scale=True): normalized_tensor = (tensor - tensor.min()) / (tensor.max() - tensor.min()) @@ -105,6 +106,10 @@ def _sdpa_ref( self.float_dtype, ) + if not self.is_seq_at_dim_2: + q = q.transpose(1, 2).contiguous() + k = k.transpose(1, 2).contiguous() + v = v.transpose(1, 2).contiguous() num_heads_q = q.size(1) num_heads_kv = k.size(1) seq_len = q.size(2) @@ -119,6 +124,8 @@ def _sdpa_ref( k = k.repeat_interleave(n_reps, dim=1) v = v.repeat_interleave(n_reps, dim=1) out = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask) + if not self.is_seq_at_dim_2: + out = out.transpose(1, 2).contiguous() return out def _int_matmul( @@ -212,7 +219,7 @@ def _test_sdpa_common( seq_len, scale_tensors=False, atol=1e-5, - is_seq_at_dim_2=True, + is_seq_at_dim_2=False, ): # Range arbitrarily chosen to reproduce a numerical error on x86 in some of the long context tests tensor_scale_max = 15 @@ -221,9 +228,10 @@ def _test_sdpa_common( self.n_heads_q = n_heads_q self.head_dim = head_dim self.max_seq_len = max_seq_len + self.is_seq_at_dim_2 = is_seq_at_dim_2 seq_dim = 2 self.q_shape = (self.n_batch, self.n_heads_q, seq_len, self.head_dim) - self.kv_shape = (self.n_batch, self.n_heads_q, self.max_seq_len, self.head_dim) + self.kv_shape = (self.n_batch, self.n_heads_kv, self.max_seq_len, self.head_dim) if not is_seq_at_dim_2: seq_dim = 1 self.q_shape = (self.n_batch, seq_len, self.n_heads_q, self.head_dim) @@ -286,7 +294,6 @@ def _test_sdpa_common( quantized_dtype, ) - start_pos = 0 seq_len = q.size(seq_dim) attn_mask = self.mask[start_pos : start_pos + seq_len, :] attn_mask = attn_mask[:, : start_pos + seq_len] @@ -334,6 +341,7 @@ def _test_sdpa_common( k_scale_fp32, v_zero_point_int8, v_scale_fp32, + is_seq_at_dim_2, ) self.assertTrue(torch.allclose(ref_output, op_output, atol=atol)) # Following line crashes due to some weird issues in mkldnn with crash in mkl_sgemm with `wild jump` @@ -374,6 +382,7 @@ def _test_sdpa_common( k_scale_fp32, v_zero_point_int8, v_scale_fp32, + is_seq_at_dim_2, ) self.assertTrue(torch.allclose(ref_output, op_output, atol=atol)) @@ -393,6 +402,18 @@ def test_sdpa_with_custom_quantized(self): seq_len, True, atol=1e-4, + is_seq_at_dim_2=True, + ) + self._test_sdpa_common( + n_heads_kv, + n_heads_q, + head_dim, + max_seq_len, + start_pos, + seq_len, + True, + atol=1e-4, + is_seq_at_dim_2=False, ) def test_sdpa_with_custom_quantized_seq_len_1(self): @@ -403,7 +424,22 @@ def test_sdpa_with_custom_quantized_seq_len_1(self): seq_len = 1 start_pos = 0 self._test_sdpa_common( - n_heads_kv, n_heads_q, head_dim, max_seq_len, start_pos, seq_len + n_heads_kv, + n_heads_q, + head_dim, + max_seq_len, + start_pos, + seq_len, + is_seq_at_dim_2=True, + ) + self._test_sdpa_common( + n_heads_kv, + n_heads_q, + head_dim, + max_seq_len, + start_pos, + seq_len, + is_seq_at_dim_2=False, ) def test_sdpa_with_custom_quantized_seq_len_small(self): @@ -414,7 +450,22 @@ def test_sdpa_with_custom_quantized_seq_len_small(self): seq_len = 4 start_pos = 0 self._test_sdpa_common( - n_heads_kv, n_heads_q, head_dim, max_seq_len, start_pos, seq_len + n_heads_kv, + n_heads_q, + head_dim, + max_seq_len, + start_pos, + seq_len, + is_seq_at_dim_2=True, + ) + self._test_sdpa_common( + n_heads_kv, + n_heads_q, + head_dim, + max_seq_len, + start_pos, + seq_len, + is_seq_at_dim_2=False, ) def test_sdpa_with_custom_quantized_seq_len_llava_example(self): @@ -466,5 +517,20 @@ def test_sdpa_with_cache_mqa(self): seq_len = 24 start_pos = 0 self._test_sdpa_common( - n_heads_kv, n_heads_q, head_dim, max_seq_len, start_pos, seq_len + n_heads_kv, + n_heads_q, + head_dim, + max_seq_len, + start_pos, + seq_len, + is_seq_at_dim_2=True, + ) + self._test_sdpa_common( + n_heads_kv, + n_heads_q, + head_dim, + max_seq_len, + start_pos, + seq_len, + is_seq_at_dim_2=False, ) From 20198b0111410750e9b5c2e477307ef3d1a36f73 Mon Sep 17 00:00:00 2001 From: pytorchbot Date: Thu, 10 Apr 2025 13:29:56 -0400 Subject: [PATCH 071/244] [Executorch][llama] Renamed quantized_kv_cache to custom_kv_cache (#10061) Because old name was misnomer ghstack-source-id: 277233486 @exported-using-ghexport Differential Revision: [D71833067](https://our.internmc.facebook.com/intern/diff/D71833067/) --- examples/models/llama/TARGETS | 10 +++++----- examples/models/llama/export_llama_lib.py | 8 ++++---- .../{quantized_kv_cache.py => custom_kv_cache.py} | 0 .../source_transformation/test_quantized_kv_cache.py | 2 +- .../test_sdpa_with_quantized_kv_cache.py | 2 +- examples/models/llava/export_llava.py | 6 +++--- examples/models/llava/model.py | 2 +- 7 files changed, 15 insertions(+), 15 deletions(-) rename examples/models/llama/source_transformation/{quantized_kv_cache.py => custom_kv_cache.py} (100%) diff --git a/examples/models/llama/TARGETS b/examples/models/llama/TARGETS index 93ac18c993d..12eb5fd13dc 100644 --- a/examples/models/llama/TARGETS +++ b/examples/models/llama/TARGETS @@ -108,7 +108,7 @@ runtime.python_library( "source_transformation/pre_quantization.py", "source_transformation/prune_vocab.py", "source_transformation/quantize.py", - "source_transformation/quantized_kv_cache.py", + "source_transformation/custom_kv_cache.py", "source_transformation/rms_norm.py", "source_transformation/rope.py", "source_transformation/sdpa.py", @@ -208,9 +208,9 @@ runtime.python_library( ) runtime.python_library( - name = "quantized_kv_cache", + name = "custom_kv_cache", srcs = [ - "source_transformation/quantized_kv_cache.py", + "source_transformation/custom_kv_cache.py", ], _is_external_target = True, visibility = ["//executorch/..."], @@ -240,7 +240,7 @@ runtime.python_test( "//executorch/extension/llm/custom_ops:custom_ops_aot_lib", ], deps = [ - ":quantized_kv_cache", + ":custom_kv_cache", "//caffe2:torch", "//executorch/examples/models/llama:llama_transformer", ], @@ -255,7 +255,7 @@ runtime.python_test( "//executorch/extension/llm/custom_ops:custom_ops_aot_lib", ], deps = [ - ":quantized_kv_cache", + ":custom_kv_cache", ":sdpa", "//caffe2:torch", "//executorch/examples/models/llama:llama_transformer", diff --git a/examples/models/llama/export_llama_lib.py b/examples/models/llama/export_llama_lib.py index 249a25f23c4..01179e8ee56 100644 --- a/examples/models/llama/export_llama_lib.py +++ b/examples/models/llama/export_llama_lib.py @@ -59,14 +59,14 @@ ) from .source_transformation.attention import replace_attention_to_attention_sha +from .source_transformation.custom_kv_cache import ( + replace_kv_cache_with_custom_kv_cache, + replace_kv_cache_with_quantized_kv_cache, +) from .source_transformation.quantize import ( get_quant_embedding_transform, get_quant_weight_transform, ) -from .source_transformation.quantized_kv_cache import ( - replace_kv_cache_with_custom_kv_cache, - replace_kv_cache_with_quantized_kv_cache, -) from .source_transformation.rms_norm import replace_rms_norm_with_native_rms_norm from .source_transformation.rope import materialze_broadcast_of_rope_freq_cis diff --git a/examples/models/llama/source_transformation/quantized_kv_cache.py b/examples/models/llama/source_transformation/custom_kv_cache.py similarity index 100% rename from examples/models/llama/source_transformation/quantized_kv_cache.py rename to examples/models/llama/source_transformation/custom_kv_cache.py diff --git a/examples/models/llama/source_transformation/test_quantized_kv_cache.py b/examples/models/llama/source_transformation/test_quantized_kv_cache.py index 4252518a4ee..07c8e1bf9a0 100644 --- a/examples/models/llama/source_transformation/test_quantized_kv_cache.py +++ b/examples/models/llama/source_transformation/test_quantized_kv_cache.py @@ -10,7 +10,7 @@ from executorch.examples.models.llama.attention import KVCache -from executorch.examples.models.llama.source_transformation.quantized_kv_cache import ( +from executorch.examples.models.llama.source_transformation.custom_kv_cache import ( QuantizedCacheType, QuantizedKVCache, ) diff --git a/examples/models/llama/source_transformation/test_sdpa_with_quantized_kv_cache.py b/examples/models/llama/source_transformation/test_sdpa_with_quantized_kv_cache.py index 35c88e10b6b..b2c93d7d93d 100644 --- a/examples/models/llama/source_transformation/test_sdpa_with_quantized_kv_cache.py +++ b/examples/models/llama/source_transformation/test_sdpa_with_quantized_kv_cache.py @@ -10,7 +10,7 @@ from executorch.examples.models.llama.attention import KVCache -from executorch.examples.models.llama.source_transformation.quantized_kv_cache import ( +from executorch.examples.models.llama.source_transformation.custom_kv_cache import ( CustomKVCache, QuantizedCacheType, QuantizedKVCache, diff --git a/examples/models/llava/export_llava.py b/examples/models/llava/export_llava.py index 63ae0f4a118..5fcddb610b7 100644 --- a/examples/models/llava/export_llava.py +++ b/examples/models/llava/export_llava.py @@ -20,13 +20,13 @@ build_args_parser, get_quantizer_and_quant_params, ) +from executorch.examples.models.llama.source_transformation.custom_kv_cache import ( + replace_kv_cache_with_custom_kv_cache, +) from executorch.examples.models.llama.source_transformation.quantize import ( EmbeddingQuantHandler, get_quant_weight_transform, ) -from executorch.examples.models.llama.source_transformation.quantized_kv_cache import ( - replace_kv_cache_with_custom_kv_cache, -) from executorch.examples.models.llama.source_transformation.sdpa import ( replace_sdpa_with_custom_op, ) diff --git a/examples/models/llava/model.py b/examples/models/llava/model.py index 6ce4b701bbe..351356607c8 100644 --- a/examples/models/llava/model.py +++ b/examples/models/llava/model.py @@ -15,7 +15,7 @@ from executorch.examples.models.llama.llama_transformer import Transformer from executorch.examples.models.llama.model_args import ModelArgs -from executorch.examples.models.llama.source_transformation.quantized_kv_cache import ( +from executorch.examples.models.llama.source_transformation.custom_kv_cache import ( replace_kv_cache_with_custom_kv_cache, ) from executorch.examples.models.llama.source_transformation.sdpa import ( From 0174b6d2cdbfb9356aed1fcb466b9b0bf0cc0452 Mon Sep 17 00:00:00 2001 From: pytorchbot Date: Thu, 10 Apr 2025 13:33:41 -0400 Subject: [PATCH 072/244] [Executorch][llama] Enable quantized sdpa (#10062) Enable leveraging quantized sdpa op when quantized kv cache is used. Instead of adding yet another arg, at the moment I have chosen to leverage quantize_kv_cache option. Differential Revision: [D71833064](https://our.internmc.facebook.com/intern/diff/D71833064/) --- examples/models/llama/TARGETS | 17 ++ examples/models/llama/export_llama_lib.py | 5 + .../source_transformation/custom_kv_cache.py | 63 +++++-- .../llama/source_transformation/sdpa.py | 122 +++++++++++- .../test_quantized_sdpa.py | 173 ++++++++++++++++++ extension/llm/custom_ops/CMakeLists.txt | 4 + extension/llm/custom_ops/custom_ops.py | 124 +++++++++++++ extension/llm/custom_ops/op_sdpa.cpp | 4 - extension/llm/custom_ops/op_sdpa.h | 2 - extension/llm/custom_ops/op_sdpa_aot.cpp | 8 - extension/llm/custom_ops/op_sdpa_impl.h | 16 -- third-party/ao | 2 +- 12 files changed, 489 insertions(+), 51 deletions(-) create mode 100644 examples/models/llama/source_transformation/test_quantized_sdpa.py diff --git a/examples/models/llama/TARGETS b/examples/models/llama/TARGETS index 12eb5fd13dc..b892613c0f8 100644 --- a/examples/models/llama/TARGETS +++ b/examples/models/llama/TARGETS @@ -274,3 +274,20 @@ runtime.python_test( ":export_library", ], ) + +runtime.python_test( + name = "quantized_sdpa_source_transform_test", + srcs = [ + "source_transformation/test_quantized_sdpa.py", + ], + preload_deps = [ + "//executorch/extension/llm/custom_ops:custom_ops_aot_lib", + "//executorch/extension/llm/custom_ops:custom_ops_aot_py", + ], + deps = [ + ":custom_kv_cache", + ":sdpa", + "//caffe2:torch", + "//executorch/examples/models/llama:llama_transformer", + ], +) diff --git a/examples/models/llama/export_llama_lib.py b/examples/models/llama/export_llama_lib.py index 01179e8ee56..64cbc9e23af 100644 --- a/examples/models/llama/export_llama_lib.py +++ b/examples/models/llama/export_llama_lib.py @@ -63,6 +63,7 @@ replace_kv_cache_with_custom_kv_cache, replace_kv_cache_with_quantized_kv_cache, ) + from .source_transformation.quantize import ( get_quant_embedding_transform, get_quant_weight_transform, @@ -77,6 +78,7 @@ replace_sdpa_with_coreml_sdpa, replace_sdpa_with_custom_op, replace_sdpa_with_flex_sdpa, + replace_sdpa_with_quantized_sdpa, replace_sdpa_with_simple_sdpa, ) from .source_transformation.vulkan_rope import replace_with_vulkan_rotary_emb @@ -1222,11 +1224,14 @@ def _get_source_transforms( # noqa if args.use_sdpa_with_kv_cache: transforms.append(replace_kv_cache_with_custom_kv_cache) + # todo: do this optionally transforms.append(replace_sdpa_with_custom_op) if args.quantize_kv_cache: assert args.use_kv_cache, "quantize_kv_cache requires use_kv_cache=True" transforms.append(replace_kv_cache_with_quantized_kv_cache) + # Right now + transforms.append(replace_sdpa_with_quantized_sdpa) if args.use_kv_cache: if args.qnn: diff --git a/examples/models/llama/source_transformation/custom_kv_cache.py b/examples/models/llama/source_transformation/custom_kv_cache.py index e7138622ed9..1158a8ba7a6 100644 --- a/examples/models/llama/source_transformation/custom_kv_cache.py +++ b/examples/models/llama/source_transformation/custom_kv_cache.py @@ -52,6 +52,8 @@ def __init__( self.use_custom_update_cache_op = use_custom_update_cache_op self.quantized_cache_dtype = torch.int8 self.cache_fp_type = torch.float32 + self.return_float_values = True + self.max_context_length = max_context_length cache_shape = (max_batch_size, max_context_length, n_heads, head_dim) scale_shape = (max_batch_size, max_context_length, n_heads, 1) self.register_buffer( @@ -61,17 +63,17 @@ def __init__( "v_cache", torch.zeros(cache_shape, dtype=self.quantized_cache_dtype) ) self.register_buffer( - "k_cache_scales", torch.ones(scale_shape, dtype=torch.float64) + "k_cache_scales", torch.ones(scale_shape, dtype=torch.float32) ) self.register_buffer( - "v_cache_scales", torch.ones(scale_shape, dtype=torch.float64) + "v_cache_scales", torch.ones(scale_shape, dtype=torch.float32) ) if cache_type == QuantizedCacheType.AffineAsymmetric: self.register_buffer( - "k_cache_zero_points", torch.ones(scale_shape, dtype=torch.int64) + "k_cache_zero_points", torch.ones(scale_shape, dtype=torch.int8) ) self.register_buffer( - "v_cache_zero_points", torch.ones(scale_shape, dtype=torch.int64) + "v_cache_zero_points", torch.ones(scale_shape, dtype=torch.int8) ) def _quantize(self, value): @@ -91,20 +93,15 @@ def _quantize(self, value): ) return quantized_value, scales, zero_points - def update(self, input_pos, k_val, v_val): - """ - k_val, v_val: [B, H, S, D] - return: [B, H, S, D] - However the storage is [B, S, H, D] so we incur transpose in, transpose out - This shall be removed by subsequent post-export graph pass - """ - k_val = k_val.transpose(1, 2) - v_val = v_val.transpose(1, 2) - # quantize current k_val and store it in the cache + def _quantize_and_update(self, input_pos, k_val, v_val): quantized_k_val, k_scales, k_zero_points = self._quantize(k_val) - quantized_v_val, v_scales, v_zero_points = self._quantize(v_val) + k_scales = k_scales.to(torch.float32) + k_zero_points = k_zero_points.to(self.quantized_cache_dtype) + v_scales = v_scales.to(torch.float32) + v_zero_points = v_zero_points.to(self.quantized_cache_dtype) + if self.use_custom_update_cache_op: start_pos = input_pos[0].item() _ = torch.ops.llama.update_cache(quantized_k_val, self.k_cache, start_pos) @@ -125,10 +122,13 @@ def update(self, input_pos, k_val, v_val): self.v_cache_scales[:, input_pos] = v_scales self.v_cache_zero_points[:, input_pos] = v_zero_points + def _update_and_return_float_values(self, input_pos, k_val, v_val): + self._quantize_and_update(input_pos, k_val, v_val) + k_out = torch.ops.quantized_decomposed.dequantize_per_token( self.k_cache, - self.k_cache_scales, - self.k_cache_zero_points, + self.k_cache_scales.to(torch.float64), + self.k_cache_zero_points.to(torch.int64), torch.iinfo(self.quantized_cache_dtype).min, torch.iinfo(self.quantized_cache_dtype).max, self.quantized_cache_dtype, @@ -136,14 +136,16 @@ def update(self, input_pos, k_val, v_val): ) v_out = torch.ops.quantized_decomposed.dequantize_per_token( self.v_cache, - self.v_cache_scales, - self.v_cache_zero_points, + self.v_cache_scales.to(torch.float64), + self.v_cache_zero_points.to(torch.int64), torch.iinfo(self.quantized_cache_dtype).min, torch.iinfo(self.quantized_cache_dtype).max, self.quantized_cache_dtype, self.cache_fp_type, ) + # When returning float values we jsut use the last value + # instead of dequantized value. start_pos = input_pos[0].item() if self.use_custom_update_cache_op: _ = torch.ops.llama.update_cache(k_val, k_out, start_pos) @@ -152,6 +154,29 @@ def update(self, input_pos, k_val, v_val): k_out[:, input_pos] = k_val v_out[:, input_pos] = v_val + return k_out, v_out + + def _update_and_return_quantized_values(self, input_pos, k_val, v_val): + self._quantize_and_update(input_pos, k_val, v_val) + + return self.k_cache, self.v_cache + + def update(self, input_pos, k_val, v_val): + """ + k_val, v_val: [B, H, S, D] + return: [B, H, S, D] + However the storage is [B, S, H, D] so we incur transpose in, transpose out + This shall be removed by subsequent post-export graph pass + """ + k_val = k_val.transpose(1, 2) + v_val = v_val.transpose(1, 2) + + if self.return_float_values: + k_out, v_out = self._update_and_return_float_values(input_pos, k_val, v_val) + else: + k_out, v_out = self._update_and_return_quantized_values( + input_pos, k_val, v_val + ) return k_out.transpose(1, 2), v_out.transpose(1, 2) @classmethod diff --git a/examples/models/llama/source_transformation/sdpa.py b/examples/models/llama/source_transformation/sdpa.py index 1bb7d277545..a50c6aeea22 100644 --- a/examples/models/llama/source_transformation/sdpa.py +++ b/examples/models/llama/source_transformation/sdpa.py @@ -13,7 +13,9 @@ import torch -from executorch.examples.models.llama.attention import KVCache, SDPA +from executorch.examples.models.llama.attention import Attention, KVCache, SDPA + +from .custom_kv_cache import QuantizedKVCache class SDPACustom(torch.nn.Module): @@ -76,6 +78,124 @@ def replace_sdpa_with_custom_op(module: torch.nn.Module) -> torch.nn.Module: return module +class QuantizedSDPA(torch.nn.Module): + """ + A quantized version of the SDPA (Scaled Dot Product Attention) module. + + This module implements attention computation using quantized key-value pairs + to reduce memory footprint and potentially improve performance. It works with + a QuantizedKVCache to store and retrieve quantized key-value tensors. + + The quantization process converts floating point tensors to int8, which requires + maintaining scale and zero point values for proper dequantization during computation. + + Args: + dim (int): The dimension of the model + kv_cache (QuantizedKVCache): The cache for storing quantized key-value pairs + Note that it needs to own kv_cache to access scales and zero points, and since + SDPA forward signature only accepts q, k and v, to allow accessing scales and + zero points, we need to pass kv_cache to SDPA. + """ + + def __init__(self, dim: int, kv_cache: QuantizedKVCache): + super().__init__() + self.dim = dim + self.quantized_dtype = torch.int8 + self.float_dtype = torch.float32 + self.kv_cache = kv_cache + + def forward( + self, + input_pos: torch.Tensor, + q: torch.Tensor, + k_quantized: torch.Tensor, + v_quantized: torch.Tensor, + bsz, + seqlen, + mask, + ): + q = q.transpose(1, 2) # (bs, seqlen, n_local_heads, head_dim) + k_quantized = k_quantized.transpose(1, 2) + v_quantized = v_quantized.transpose(1, 2) + + q_scale, q_zero_point = ( + torch.ops.quantized_decomposed.choose_qparams_per_token_asymmetric.default( + q, self.quantized_dtype + ) + ) + q_quantized = torch.ops.quantized_decomposed.quantize_per_token( + q, + q_scale, + q_zero_point, + torch.iinfo(self.quantized_dtype).min, + torch.iinfo(self.quantized_dtype).max, + self.quantized_dtype, + ) + q_zero_point_int8 = q_zero_point.to(dtype=torch.int8) + q_scale_fp32 = q_scale.to(dtype=torch.float32) + + k_zero_point_int8 = self.kv_cache.k_cache_zero_points + k_scale_fp32 = self.kv_cache.k_cache_scales + v_zero_point_int8 = self.kv_cache.v_cache_zero_points + v_scale_fp32 = self.kv_cache.v_cache_scales + + start_pos = input_pos[0].item() + output = torch.ops.llama.custom_quantized_sdpa( + q_quantized, + k_quantized, + v_quantized, + start_pos, + None, + 0, + True, + None, + q_zero_point_int8, + q_scale_fp32, + k_zero_point_int8, + k_scale_fp32, + v_zero_point_int8, + v_scale_fp32, + ) + + return output.view(bsz, seqlen, self.dim) + + +def _update_attention_module_with_quantized_sdpa( + module: torch.nn.Module, kv_cache: QuantizedKVCache +): + sdpa = getattr(module, "SDPA", None) + assert sdpa is not None + # pyre-ignore + setattr(module, "SDPA", QuantizedSDPA(sdpa.dim, kv_cache)) # noqa: B010 + + +def _replace_sdpa_with_quantized_sdpa(module: torch.nn.Module): + for _, child in module.named_children(): + if isinstance(child, Attention): + kv_cache = getattr(child, "kv_cache", None) + if kv_cache is None: + continue + if not isinstance(kv_cache, QuantizedKVCache): + continue + # Only when kv_cache is QuantizedKVCache, we replace SDPA with QuantizedSDPA + sdpa = getattr(child, "SDPA", None) + if sdpa is None: + continue + if not isinstance(sdpa, SDPACustom): + continue + kv_cache.return_float_values = False + _update_attention_module_with_quantized_sdpa(child, kv_cache) + else: + _replace_sdpa_with_quantized_sdpa(child) + + +def replace_sdpa_with_quantized_sdpa(module: torch.nn.Module) -> torch.nn.Module: + from executorch.extension.llm.custom_ops import custom_ops # noqa + + _replace_sdpa_with_quantized_sdpa(module) + return module + + class SDPASimple(torch.nn.Module): def __init__( self, diff --git a/examples/models/llama/source_transformation/test_quantized_sdpa.py b/examples/models/llama/source_transformation/test_quantized_sdpa.py new file mode 100644 index 00000000000..242f3a0876d --- /dev/null +++ b/examples/models/llama/source_transformation/test_quantized_sdpa.py @@ -0,0 +1,173 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import unittest + +import torch + +from executorch.examples.models.llama.attention import Attention, KVCache, SDPA +from executorch.examples.models.llama.source_transformation.custom_kv_cache import ( + QuantizedCacheType, + QuantizedKVCache, +) +from executorch.examples.models.llama.source_transformation.sdpa import ( + QuantizedSDPA, + replace_sdpa_with_custom_op, + replace_sdpa_with_quantized_sdpa, + SDPACustom, +) + + +class MockAttention(Attention): + """Mock Attention class for testing purposes.""" + + def __init__( + self, dim, head_dim, n_rep, max_context_len=100, enable_dynamic_shape=False + ): + super().__init__() + self.dim = dim + self.head_dim = head_dim + self.n_rep = n_rep + self.SDPA = SDPA(dim, head_dim, n_rep, max_context_len, enable_dynamic_shape) + self.kv_cache = None + + def forward(self, x, freqs_cos, freqs_sin, **kwargs): + # Not used in tests + pass + + +class QuantizedSDPATest(unittest.TestCase): + def setUp(self): + torch.manual_seed(42) + self.max_batch_size = 1 + self.max_context_len = 5 + self.n_kv_heads = 4 + self.n_heads = 8 + self.head_dim = 16 + self.dim = self.n_heads * self.head_dim + self.enable_dynamic_shape = False + self.dtype = torch.float32 + + def _create_test_model(self): + """Create a simple model with SDPA modules for testing.""" + model = torch.nn.Module() + attention = MockAttention( + self.dim, self.head_dim, self.n_heads // self.n_kv_heads + ) + # Add KVCache to the attention module + attention.kv_cache = KVCache( + self.max_batch_size, + self.max_context_len, + self.n_kv_heads, + self.head_dim, + self.enable_dynamic_shape, + dtype=self.dtype, + ) + model.attention = attention + return model + + def test_replace_sdpa_with_quantized_sdpa(self): + """Test that replace_sdpa_with_quantized_sdpa correctly transforms SDPA to QuantizedSDPA.""" + # Create a model with SDPA + model = self._create_test_model() + + # First replace standard SDPA with SDPACustom (required before quantization) + model = replace_sdpa_with_custom_op(model) + self.assertIsInstance(model.attention.SDPA, SDPACustom) + + # Replace KVCache with QuantizedKVCache + model.attention.kv_cache = QuantizedKVCache.from_float( + model.attention.kv_cache, + QuantizedCacheType.AffineAsymmetric, + use_custom_update_cache_op=True, + ) + self.assertIsInstance(model.attention.kv_cache, QuantizedKVCache) + + # Set return_float_values to False to enable quantized operation + model.attention.kv_cache.return_float_values = False + + # Apply the transformation + model = replace_sdpa_with_quantized_sdpa(model) + + # Verify that SDPA has been replaced with QuantizedSDPA + self.assertIsInstance(model.attention.SDPA, QuantizedSDPA) + + # Verify that the QuantizedSDPA has the correct properties + self.assertEqual(model.attention.SDPA.dim, self.dim) + self.assertEqual(model.attention.SDPA.quantized_dtype, torch.int8) + self.assertEqual(model.attention.SDPA.float_dtype, torch.float32) + self.assertIs(model.attention.SDPA.kv_cache, model.attention.kv_cache) + + def test_no_replacement_when_no_quantized_kv_cache(self): + """Test that SDPA is not replaced when there's no QuantizedKVCache.""" + # Create a model with SDPA + model = self._create_test_model() + + # First replace standard SDPA with SDPACustom + model = replace_sdpa_with_custom_op(model) + self.assertIsInstance(model.attention.SDPA, SDPACustom) + + # Apply the transformation without replacing KVCache + model = replace_sdpa_with_quantized_sdpa(model) + + # Verify that SDPA has NOT been replaced with QuantizedSDPA + self.assertIsInstance(model.attention.SDPA, SDPACustom) + self.assertNotIsInstance(model.attention.SDPA, QuantizedSDPA) + + def test_forward_functionality(self): + """Test that the QuantizedSDPA forward function works correctly.""" + # This test requires the custom ops to be loaded, so we'll check if they're available + try: + from executorch.extension.llm.custom_ops import custom_ops # noqa + except ImportError: + self.skipTest( + "Custom ops not available, skipping forward functionality test" + ) + + # Create a model with SDPA + model = self._create_test_model() + + # First replace standard SDPA with SDPACustom + model = replace_sdpa_with_custom_op(model) + + # Replace KVCache with QuantizedKVCache + model.attention.kv_cache = QuantizedKVCache.from_float( + model.attention.kv_cache, + QuantizedCacheType.AffineAsymmetric, + use_custom_update_cache_op=True, + ) + + # Set return_float_values to False to enable quantized operation + model.attention.kv_cache.return_float_values = False + + # Save the original SDPACustom for comparison + # Apply the transformation + model = replace_sdpa_with_quantized_sdpa(model) + + # Create test inputs + input_pos = torch.tensor([0], dtype=torch.int64) + bsz = 1 + seqlen = 1 + q = torch.randn(bsz, self.n_heads, seqlen, self.head_dim, dtype=self.dtype) + k = torch.randn(bsz, self.n_kv_heads, seqlen, self.head_dim, dtype=self.dtype) + v = torch.randn(bsz, self.n_kv_heads, seqlen, self.head_dim, dtype=self.dtype) + + # Update the KV cache + k_quantized, v_quantized = model.attention.kv_cache.update(input_pos, k, v) + + # Run the forward pass with the quantized SDPA + try: + output = model.attention.SDPA( + input_pos, q, k_quantized, v_quantized, bsz, seqlen, None + ) + + # Verify the output shape + self.assertEqual(output.shape, (bsz, seqlen, self.dim)) + except Exception: + # If the forward pass fails, it might be due to missing custom ops + self.skipTest( + "Custom ops not available, skipping forward functionality test" + ) diff --git a/extension/llm/custom_ops/CMakeLists.txt b/extension/llm/custom_ops/CMakeLists.txt index fd2ead6c8b0..42e82dc360f 100644 --- a/extension/llm/custom_ops/CMakeLists.txt +++ b/extension/llm/custom_ops/CMakeLists.txt @@ -21,6 +21,9 @@ if(NOT EXECUTORCH_ROOT) endif() set(_common_compile_options -Wno-deprecated-declarations -fPIC) +if(CMAKE_SYSTEM_PROCESSOR MATCHES "arm64|aarch64") + list(APPEND _common_compile_options "-march=armv8.2-a+dotprod") +endif() include(${EXECUTORCH_ROOT}/tools/cmake/Utils.cmake) include(${EXECUTORCH_ROOT}/tools/cmake/Codegen.cmake) @@ -38,6 +41,7 @@ include(${EXECUTORCH_SRCS_FILE}) # Let files say "include ". set(_common_include_directories ${EXECUTORCH_ROOT}/..) +list(APPEND _common_include_directories ${EXECUTORCH_ROOT}/third-party/ao) # Custom op libraries set(custom_ops_libs pthreadpool) diff --git a/extension/llm/custom_ops/custom_ops.py b/extension/llm/custom_ops/custom_ops.py index d299b314816..6d96a926497 100644 --- a/extension/llm/custom_ops/custom_ops.py +++ b/extension/llm/custom_ops/custom_ops.py @@ -229,3 +229,127 @@ def update_cache_meta( # workaround. Should we just return cache instead? But I am afraid that # will result in extra memory allocation return torch.empty((1,), dtype=value.dtype, device="meta") + + +def _validate_quantized_sdpa_params( + query, + key, + value, + start_pos, + seq_len, + attn_mask, + drpout_p, + is_causal, + scale, + q_scale, + q_zero_point, + k_scale, + k_zero_point, + v_scale, + v_zero_point, + is_seq_at_dim_2, +): + assert ( + query.dim() == 4 + ), f"Expected query to be 4 dimensional but got {query.dim()} dimensions." + assert ( + key.dim() == 4 + ), f"Expected key to be 4 dimensional but got {key.dim()} dimensions." + assert ( + value.dim() == 4 + ), f"Expected value to be 4 dimensional but got {value.dim()} dimensions." + + assert (q_scale is not None) and ( + q_zero_point is not None + ), "q_scale and q_zero_point must be provided" + assert (k_scale is not None) and ( + k_zero_point is not None + ), "k_scale and k_zero_point must be provided" + assert (v_scale is not None) and ( + v_zero_point is not None + ), "v_scale and v_zero_point must be provided" + + assert query.dtype == torch.int8, f"Expected query to be int8 but got {query.dtype}" + assert key.dtype == torch.int8, f"Expected key to be int8 but got {key.dtype}" + assert value.dtype == torch.int8, f"Expected value to be int8 but got {value.dtype}" + + assert ( + q_scale.dtype == torch.float32 + ), f"Expected q_scale to be float32 but got {q_scale.dtype}" + assert ( + q_zero_point.dtype == torch.int8 + ), f"Expected q_zero_point to be int8 but got {q_zero_point.dtype}" + assert ( + k_scale.dtype == torch.float32 + ), f"Expected k_scale to be float32 but got {k_scale.dtype}" + assert ( + k_zero_point.dtype == torch.int8 + ), f"Expected k_zero_point to be int8 but got {k_zero_point.dtype}" + assert ( + v_scale.dtype == torch.float32 + ), f"Expected v_scale to be float32 but got {v_scale.dtype}" + assert ( + v_zero_point.dtype == torch.int8 + ), f"Expected v_zero_point to be int8 but got {v_zero_point.dtype}" + + assert ( + query.size()[:-1] == q_scale.size()[:-1] + ), f"Expected query and q_scale to have same size except last dimensions but got {query.size()} and {q_scale.size()}" + assert ( + query.size()[:-1] == q_zero_point.size()[:-1] + ), f"Expected query and q_zero_point to have same size except last dimensions but got {query.size()} and {q_zero_point.size()}" + + assert ( + key.size()[:-1] == k_scale.size()[:-1] + ), f"Expected key and k_scale to have same size except last dimensions but got {key.size()} and {k_scale.size()}" + assert ( + key.size()[:-1] == k_zero_point.size()[:-1] + ), f"Expected key and k_zero_point to have same size except last dimensions but got {key.size()} and {k_zero_point.size()}" + + assert ( + value.size()[:-1] == v_scale.size()[:-1] + ), f"Expected value and v_scale to have same size except last dimensions but got {value.size()} and {v_scale.size()}" + assert ( + value.size()[:-1] == v_zero_point.size()[:-1] + ), f"Expected value and v_zero_point to have same size except last dimensions but got {value.size()} and {v_zero_point.size()}" + + +@impl(custom_ops_lib, "custom_quantized_sdpa", "Meta") +def custom_quantized_sdpa_meta( + query, + key, + value, + start_pos, + attn_mask=None, + drpout_p=0.0, + is_causal=False, + scale=None, + q_zero_point=None, + q_scale=None, + k_zero_point=None, + k_scale=None, + v_zero_point=None, + v_scale=None, + is_seq_at_dim_2=False, +): + seq_len = query.size(1) + _validate_quantized_sdpa_params( + query, + key, + value, + start_pos, + seq_len, + attn_mask, + drpout_p, + is_causal, + scale, + q_scale, + q_zero_point, + k_scale, + k_zero_point, + v_scale, + v_zero_point, + is_seq_at_dim_2, + ) + + return torch.empty(query.size(), dtype=torch.float32, device="meta") diff --git a/extension/llm/custom_ops/op_sdpa.cpp b/extension/llm/custom_ops/op_sdpa.cpp index c5c9b79b280..a6f80a0d66d 100644 --- a/extension/llm/custom_ops/op_sdpa.cpp +++ b/extension/llm/custom_ops/op_sdpa.cpp @@ -477,7 +477,6 @@ Tensor& custom_sdpa_out_impl( return output; } -#ifdef ENABLE_CUSTOM_QUANTIZED_SDPA Tensor& custom_quantized_sdpa_out( RuntimeContext& ctx, const Tensor& q, @@ -516,7 +515,6 @@ Tensor& custom_quantized_sdpa_out( v_scales, is_seq_at_dim_2); } -#endif // ENABLE_CUSTOM_QUANTIZED_SDPA /* Input params @@ -619,9 +617,7 @@ EXECUTORCH_LIBRARY( "custom_sdpa.out", torch::executor::native::custom_sdpa_out); -#ifdef ENABLE_CUSTOM_QUANTIZED_SDPA EXECUTORCH_LIBRARY( llama, "custom_quantized_sdpa.out", torch::executor::native::custom_quantized_sdpa_out); -#endif // ENABLE_CUSTOM_QUANTIZED_SDPA diff --git a/extension/llm/custom_ops/op_sdpa.h b/extension/llm/custom_ops/op_sdpa.h index 3deb27b3989..9d357eb6ea1 100644 --- a/extension/llm/custom_ops/op_sdpa.h +++ b/extension/llm/custom_ops/op_sdpa.h @@ -56,7 +56,6 @@ Tensor& flash_attention_kernel_out( const optional scale, Tensor& output); -#ifdef ENABLE_CUSTOM_QUANTIZED_SDPA Tensor& custom_quantized_sdpa_out( RuntimeContext& ctx, const Tensor& q, @@ -76,7 +75,6 @@ Tensor& custom_quantized_sdpa_out( const optional& v_scales, const bool is_seq_at_dim_1, Tensor& output); -#endif // ENABLE_CUSTOM_QUANTIZED_SDPA } // namespace native } // namespace executor } // namespace torch diff --git a/extension/llm/custom_ops/op_sdpa_aot.cpp b/extension/llm/custom_ops/op_sdpa_aot.cpp index 2da915a19b8..ff367c85c8a 100644 --- a/extension/llm/custom_ops/op_sdpa_aot.cpp +++ b/extension/llm/custom_ops/op_sdpa_aot.cpp @@ -77,7 +77,6 @@ at::Tensor custom_sdpa_aten( // @lint-ignore CLANGTIDY facebook-hte-ParameterMightThrowOnCopy const std::optional scale); -#ifdef ENABLE_CUSTOM_QUANTIZED_SDPA Tensor& custom_quantized_sdpa_out_no_context( const Tensor& q, const Tensor& k, @@ -118,7 +117,6 @@ at::Tensor custom_quantized_sdpa_aten( const std::optional& v_zero_points, const std::optional& v_scales, const bool is_seq_at_dim_2); -#endif // ENABLE_CUSTOM_QUANTIZED_SDPA Tensor& update_cache_out_no_context( const Tensor& value, @@ -241,7 +239,6 @@ at::Tensor custom_sdpa_aten( return output; } -#ifdef ENABLE_CUSTOM_QUANTIZED_SDPA Tensor& custom_quantized_sdpa_out_no_context( const Tensor& q, const Tensor& k, @@ -322,7 +319,6 @@ at::Tensor custom_quantized_sdpa_aten( output); return output; } -#endif // ENABLE_CUSTOM_QUANTIZED_SDPA Tensor& update_cache_out_no_context( const Tensor& value, @@ -371,7 +367,6 @@ TORCH_LIBRARY_FRAGMENT(llama, m) { m.def( "update_cache.out(Tensor value, Tensor(a!) cache, " "SymInt start_pos, *, Tensor(b!) out) -> Tensor(b!)"); -#ifdef ENABLE_CUSTOM_QUANTIZED_SDPA m.def( "custom_quantized_sdpa(Tensor query, Tensor key, Tensor value, SymInt start_pos, " "Tensor? attn_mask=None, float drpout_p=0.0, bool is_causal=False, " @@ -384,7 +379,6 @@ TORCH_LIBRARY_FRAGMENT(llama, m) { "float? scale=None, Tensor? q_zero_points=None, Tensor? q_scales=None, " "Tensor? k_zero_points=None, Tensor? k_scales=None, Tensor? v_zero_points=None, " "Tensor? v_scales=None, bool is_seq_at_dim_2=False, *, Tensor(a!) out) -> Tensor(a!)"); -#endif // ENABLE_CUSTOM_QUANTIZED_SDPA } // TODO: Rename this file to op_custom_ops_aot.cpp @@ -403,7 +397,6 @@ TORCH_LIBRARY_IMPL(llama, CompositeExplicitAutograd, m) { m.impl( "update_cache.out", WRAP_TO_ATEN(torch::executor::native::update_cache_out_no_context, 3)); -#ifdef ENABLE_CUSTOM_QUANTIZED_SDPA m.impl( "custom_quantized_sdpa", torch::executor::native::custom_quantized_sdpa_aten); @@ -411,5 +404,4 @@ TORCH_LIBRARY_IMPL(llama, CompositeExplicitAutograd, m) { "custom_quantized_sdpa.out", WRAP_TO_ATEN( torch::executor::native::custom_quantized_sdpa_out_no_context, 15)); -#endif // ENABLE_CUSTOM_QUANTIZED_SDPA } diff --git a/extension/llm/custom_ops/op_sdpa_impl.h b/extension/llm/custom_ops/op_sdpa_impl.h index 7607a1e283d..1f19fa75de7 100644 --- a/extension/llm/custom_ops/op_sdpa_impl.h +++ b/extension/llm/custom_ops/op_sdpa_impl.h @@ -23,9 +23,7 @@ #endif #include -#if defined(ENABLE_CUSTOM_QUANTIZED_SDPA) #include -#endif namespace torch { namespace executor { @@ -78,7 +76,6 @@ void _q_at_k_gemm( q_data.dtype == ScalarType::Char || q_data.dtype == ScalarType::Float, "q and k must be either int8 or float"); if (q_data.dtype == ScalarType::Char) { -#if defined(ENABLE_CUSTOM_QUANTIZED_SDPA) if constexpr (std::is_same::value) { int a_stride_m_tmp, b_stride_n_tmp; auto kernel = torchao::kernels::cpu::quantized_matmul:: @@ -105,11 +102,6 @@ void _q_at_k_gemm( ET_CHECK_MSG( false, "Accumulation in dtype other than float not supported yet"); } -#else - ET_CHECK_MSG( - false, - "Quantized SDPA is not enabled. Check ENABLE_CUSTOM_QUANTIZED_SDPA compile flag"); -#endif } else { ::executorch::cpublas::gemm( ::executorch::cpublas::TransposeType::Transpose, @@ -141,7 +133,6 @@ void _qk_at_v_gemm( const int64_t o_stride_m, const accum_t beta) { if (v_data.dtype == ScalarType::Char) { -#if defined(ENABLE_CUSTOM_QUANTIZED_SDPA) if constexpr (std::is_same::value) { int a_stride_m_tmp, b_stride_n_tmp; auto kernel = torchao::kernels::cpu::quantized_matmul:: @@ -165,11 +156,6 @@ void _qk_at_v_gemm( ET_CHECK_MSG( false, "Accumulation in dtype other than float not supported yet"); } -#else - ET_CHECK_MSG( - false, - "Quantized SDPA is not enabled. Check ENABLE_CUSTOM_QUANTIZED_SDPA compile flag"); -#endif } else { ::executorch::cpublas::gemm( ::executorch::cpublas::TransposeType::NoTranspose, @@ -487,9 +473,7 @@ void cpu_flash_attention( } bool is_quantized_sdpa = false; -#if defined(ENABLE_CUSTOM_QUANTIZED_SDPA) is_quantized_sdpa = query.scalar_type() == ScalarType::Char; -#endif auto strides = query.strides(); int64_t qStrideB = strides[0]; diff --git a/third-party/ao b/third-party/ao index 8b264ce1e15..9516764a971 160000 --- a/third-party/ao +++ b/third-party/ao @@ -1 +1 @@ -Subproject commit 8b264ce1e1597f4c3f6476609334be05d294f092 +Subproject commit 9516764a97147231c72377bc1067c5e997de02f5 From 0d5b74247b5a37936b44a8e628c5c1071ab1e203 Mon Sep 17 00:00:00 2001 From: Max Ren <40742183+mcr229@users.noreply.github.com> Date: Thu, 10 Apr 2025 10:35:17 -0700 Subject: [PATCH 073/244] Fix up some Docs (#10038) Propagating some changes made to the release/0.6 docs so that future release can get them too --- docs/source/using-executorch-building-from-source.md | 4 ++-- docs/source/using-executorch-faqs.md | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/source/using-executorch-building-from-source.md b/docs/source/using-executorch-building-from-source.md index 8fe67984445..18d860cbc14 100644 --- a/docs/source/using-executorch-building-from-source.md +++ b/docs/source/using-executorch-building-from-source.md @@ -29,7 +29,7 @@ Windows (x86_64) - Otherwise, Python's built-in virtual environment manager `python venv` is a good alternative. * `g++` version 7 or higher, `clang++` version 5 or higher, or another C++17-compatible toolchain. -* `python` version 3.10-3.12 +* `python` version 3.10-3.12 Note that the cross-compilable core runtime code supports a wider range of toolchains, down to C++17. See the [Runtime Overview](./runtime-overview.md) for @@ -231,7 +231,7 @@ Assuming Android NDK is available, run: mkdir cmake-android-out && cd cmake-android-out # point -DCMAKE_TOOLCHAIN_FILE to the location where ndk is installed -cmake -DCMAKE_TOOLCHAIN_FILE=/Users/{user_name}/Library/Android/sdk/ndk/27.2.12479018/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a .. +cmake -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a .. cd .. cmake --build cmake-android-out -j9 diff --git a/docs/source/using-executorch-faqs.md b/docs/source/using-executorch-faqs.md index 56384c8015e..f639524d69c 100644 --- a/docs/source/using-executorch-faqs.md +++ b/docs/source/using-executorch-faqs.md @@ -18,7 +18,7 @@ if you are using Ubuntu, or use an equivalent install command. ### Missing out variants: { _ } -The model likely contains torch custom operators. Custom ops need an Executorch implementation and need to be loaded at export time. See the [ExecuTorch Custom Ops Documentation](https://pytorch.org/executorch/main/kernel-library-custom-aten-kernel.html#apis) for details on how to do this. +The model likely contains torch custom operators. Custom ops need an Executorch implementation and need to be loaded at export time. See the [ExecuTorch Custom Ops Documentation](kernel-library-custom-aten-kernel.md#apis) for details on how to do this. ### RuntimeError: PyTorch convert function for op _ not implemented @@ -32,7 +32,7 @@ ExecuTorch error codes are defined in [executorch/core/runtime/error.h](https:// If building the runtime from source, ensure that the build is done in release mode. For CMake builds, this can be done by passing `-DCMAKE_BUILD_TYPE=Release`. -Ensure the model is delegated. If not targeting a specific accelerator, use the XNNPACK delegate for CPU performance. Undelegated operators will typically fall back to the ExecuTorch portable library, which is designed as a fallback, and is not intended for performance sensitive operators. To target XNNPACK, pass an `XnnpackPartitioner` to `to_edge_transform_and_lower`. See [Building and Running ExecuTorch with XNNPACK Backend](https://pytorch.org/executorch/main/tutorial-xnnpack-delegate-lowering.html) for more information. +Ensure the model is delegated. If not targeting a specific accelerator, use the XNNPACK delegate for CPU performance. Undelegated operators will typically fall back to the ExecuTorch portable library, which is designed as a fallback, and is not intended for performance sensitive operators. To target XNNPACK, pass an `XnnpackPartitioner` to `to_edge_transform_and_lower`. See [Building and Running ExecuTorch with XNNPACK Backend](tutorial-xnnpack-delegate-lowering.md) for more information. Thread count can have a significant impact on CPU performance. The optimal thread count may depend on the model and application. By default, ExecuTorch will currently use as many threads as there are cores. Consider setting the thread count to cores / 2, or just set to 4 on mobile CPUs. @@ -41,11 +41,11 @@ Thread count can be set with the following function. Ensure this is done prior t ::executorch::extension::threadpool::get_threadpool()->_unsafe_reset_threadpool(num_threads); ``` -For a deeper investgiation into model performance, ExecuTorch supports operator-level performance profiling. See [Using the ExecuTorch Developer Tools to Profile a Model](https://pytorch.org/executorch/main/tutorials/devtools-integration-tutorial.html) for more information. +For a deeper investgiation into model performance, ExecuTorch supports operator-level performance profiling. See [Using the ExecuTorch Developer Tools to Profile a Model](devtools-integration-tutorial.md) for more information. ### Missing Logs -ExecuTorch provides hooks to route runtime logs. By default, logs are sent to stdout/stderr, but users can override `et_pal_emit_log_message` to route logs to a custom destination. The Android and iOS extensions also provide out-of-box log routing to the appropriate platform logs. See [Runtime Platform Abstraction Layer (PAL)](https://pytorch.org/executorch/main/runtime-platform-abstraction-layer.html) for more information. +ExecuTorch provides hooks to route runtime logs. By default, logs are sent to stdout/stderr, but users can override `et_pal_emit_log_message` to route logs to a custom destination. The Android and iOS extensions also provide out-of-box log routing to the appropriate platform logs. See [Runtime Platform Abstraction Layer (PAL)](runtime-platform-abstraction-layer.md) for more information. ### Error setting input: 0x10 / Attempted to resize a bounded tensor... From 61c00df1a82fcd5f906d06d4725c19f14a66b8da Mon Sep 17 00:00:00 2001 From: DannyYuyang-quic Date: Fri, 11 Apr 2025 01:38:46 +0800 Subject: [PATCH 074/244] Qualcomm AI Engine Direct - oss model enablement (EfficientSAM) (#9266) ### Summary - e2e script for https://github.com/yformer/EfficientSAM - Fastvit breakage fix - Add support for cum_sum - Add bicubic interpolate transform pass - Fix stack op ### Test plan ``` bash python ./examples/qualcomm/oss_scripts/efficientSAM/efficientSAM.py -m ${soc} -b build-android -H ${host_id} -s ${device_id} --oss_repo ${Path_to_oss_repo} --pretrained_weight ${Path_to_pretrained_weight} -d ${Path_to_dataset_dir} ``` --- backends/qualcomm/_passes/__init__.py | 2 + .../_passes/convert_upsample_bicubic2d.py | 27 ++ backends/qualcomm/_passes/layout_transform.py | 1 + backends/qualcomm/_passes/qnn_pass_manager.py | 2 + .../_passes/recompose_pixel_unshuffle.py | 12 +- backends/qualcomm/_passes/utils.py | 5 +- backends/qualcomm/builders/__init__.py | 2 + backends/qualcomm/builders/op_cos.py | 1 - backends/qualcomm/builders/op_cum_sum.py | 84 +++++ backends/qualcomm/builders/op_sin.py | 1 - backends/qualcomm/builders/op_stack.py | 2 +- backends/qualcomm/builders/qnn_constants.py | 8 + backends/qualcomm/partition/common_defs.py | 1 + backends/qualcomm/partition/utils.py | 1 + backends/qualcomm/quantizer/annotators.py | 5 + backends/qualcomm/tests/models.py | 8 + backends/qualcomm/tests/test_qnn_delegate.py | 52 ++- backends/qualcomm/tests/utils.py | 2 + examples/qualcomm/oss_scripts/dino_v2.py | 8 + .../oss_scripts/efficientSAM/efficientSAM.py | 357 ++++++++++++++++++ .../source_transformation/__init__.py | 17 + .../source_transformation/mask_decoder.py | 125 ++++++ .../source_transformation/pos_emb.py | 64 ++++ examples/qualcomm/oss_scripts/fastvit.py | 6 +- examples/qualcomm/utils.py | 9 + 25 files changed, 788 insertions(+), 14 deletions(-) create mode 100644 backends/qualcomm/_passes/convert_upsample_bicubic2d.py create mode 100644 backends/qualcomm/builders/op_cum_sum.py create mode 100644 examples/qualcomm/oss_scripts/efficientSAM/efficientSAM.py create mode 100644 examples/qualcomm/oss_scripts/efficientSAM/source_transformation/__init__.py create mode 100644 examples/qualcomm/oss_scripts/efficientSAM/source_transformation/mask_decoder.py create mode 100644 examples/qualcomm/oss_scripts/efficientSAM/source_transformation/pos_emb.py diff --git a/backends/qualcomm/_passes/__init__.py b/backends/qualcomm/_passes/__init__.py index 9c884d7ab93..6d50bbb51b2 100644 --- a/backends/qualcomm/_passes/__init__.py +++ b/backends/qualcomm/_passes/__init__.py @@ -9,6 +9,7 @@ from .annotate_unbind import AnnotateUnbind from .convert_bmm_to_matmul import ConvertBmmToMatmul from .convert_conv1d_to_conv2d import ConvertConv1dToConv2d +from .convert_upsample_bicubic2d import ConvertUpsampleBicubicWithBilinear from .decompose_any import DecomposeAny from .decompose_einsum import DecomposeEinsum from .decompose_expm1 import DecomposeExpM1 @@ -40,6 +41,7 @@ ConvertBmmToMatmul, ConvertConv1dToConv2d, DecomposeAny, + ConvertUpsampleBicubicWithBilinear, DecomposeEinsum, DecomposeExpM1, DecomposeLinalgVectorNorm, diff --git a/backends/qualcomm/_passes/convert_upsample_bicubic2d.py b/backends/qualcomm/_passes/convert_upsample_bicubic2d.py new file mode 100644 index 00000000000..367e9155c77 --- /dev/null +++ b/backends/qualcomm/_passes/convert_upsample_bicubic2d.py @@ -0,0 +1,27 @@ +# Copyright (c) Qualcomm Innovation Center, Inc. +# All rights reserved +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +from executorch.exir.dialects._ops import ops as exir_ops +from executorch.exir.pass_base import ExportPass + + +class ConvertUpsampleBicubicWithBilinear(ExportPass): + """ + Qnn does not support bicubic interpolation, so we need to convert it to bilinear. + This pass will convert bicubic interpolation to bilinear interpolation. + """ + + bicubic_op_targets = { + exir_ops.edge.aten.upsample_bicubic2d.vec, + } + upsample_bilinear_op = exir_ops.edge.aten.upsample_bilinear2d.default + + def __init__(self): + super(ConvertUpsampleBicubicWithBilinear, self).__init__() + + def call_operator(self, op, args, kwargs, meta): + if op not in self.bicubic_op_targets: + return super().call_operator(op, args, kwargs, meta) + return super().call_operator(self.upsample_bilinear_op, args[:-1], kwargs, meta) diff --git a/backends/qualcomm/_passes/layout_transform.py b/backends/qualcomm/_passes/layout_transform.py index 4d47c38bc03..19c5417f8f8 100644 --- a/backends/qualcomm/_passes/layout_transform.py +++ b/backends/qualcomm/_passes/layout_transform.py @@ -55,6 +55,7 @@ class LayoutTransform(ExportPass): exir_ops.edge.aten.ceil.default, exir_ops.edge.aten.clamp.default, exir_ops.edge.aten.constant_pad_nd.default, + exir_ops.edge.aten.cumsum.default, exir_ops.edge.aten.div.Tensor, exir_ops.edge.aten.elu.default, exir_ops.edge.aten.eq.Tensor, diff --git a/backends/qualcomm/_passes/qnn_pass_manager.py b/backends/qualcomm/_passes/qnn_pass_manager.py index ab2c86102df..ed3c8eb217e 100644 --- a/backends/qualcomm/_passes/qnn_pass_manager.py +++ b/backends/qualcomm/_passes/qnn_pass_manager.py @@ -14,6 +14,7 @@ AnnotateUnbind, ConvertBmmToMatmul, ConvertConv1dToConv2d, + ConvertUpsampleBicubicWithBilinear, DecomposeAny, DecomposeEinsum, DecomposeExpM1, @@ -74,6 +75,7 @@ def get_capture_program_passes(): (AnnotateUnbind, True), (ConvertBmmToMatmul, True), (ConvertConv1dToConv2d, True), + (ConvertUpsampleBicubicWithBilinear, False), (DecomposeAny, True), (ExpandBroadcastTensorShape, False), (FixedLinearKeepDim, True), diff --git a/backends/qualcomm/_passes/recompose_pixel_unshuffle.py b/backends/qualcomm/_passes/recompose_pixel_unshuffle.py index 7aac4fb823e..81214facc3a 100644 --- a/backends/qualcomm/_passes/recompose_pixel_unshuffle.py +++ b/backends/qualcomm/_passes/recompose_pixel_unshuffle.py @@ -45,13 +45,11 @@ def call(self, graph_module: torch.fx.GraphModule): continue view_node = premute_node.args[0] - if any( - [ - view_node.op != "call_function", - view_node.target != self.view_target, - len(view_node.args[1]) != 6, - len(premute_node.args[1]) != 6, - ] + if ( + view_node.op != "call_function" + or view_node.target != self.view_target + or len(view_node.args[1]) != 6 + or len(premute_node.args[1]) != 6 ): continue diff --git a/backends/qualcomm/_passes/utils.py b/backends/qualcomm/_passes/utils.py index a8eb6b192ee..6b7dc8c16fa 100755 --- a/backends/qualcomm/_passes/utils.py +++ b/backends/qualcomm/_passes/utils.py @@ -78,6 +78,7 @@ def get_passes_dependency_for_capture_program(): AnnotateUnbind, ConvertBmmToMatmul, ConvertConv1dToConv2d, + ConvertUpsampleBicubicWithBilinear, DecomposeAny, DecomposeLinalgVectorNorm, ExpandBroadcastTensorShape, @@ -96,18 +97,20 @@ def get_passes_dependency_for_capture_program(): AnnotateQuantAttrs: [ RecomposePixelUnshuffle, ConvertBmmToMatmul, + ConvertUpsampleBicubicWithBilinear, RemoveRedundancy, ], AnnotateStack: [RemoveRedundancy], AnnotateUnbind: [RemoveRedundancy], ConvertBmmToMatmul: [RecomposePixelUnshuffle], ConvertConv1dToConv2d: [FoldQDQ], + ConvertUpsampleBicubicWithBilinear: [RemoveRedundancy], DecomposeAny: [RemoveRedundancy], DecomposeLinalgVectorNorm: [RemoveRedundancy], ExpandBroadcastTensorShape: [RemoveRedundancy], FixedLinearKeepDim: [FoldQDQ], FoldQDQ: [AnnotateQuantAttrs, AnnotateStack, AnnotateUnbind], - I64toI32: [RemoveRedundancy], + I64toI32: [ConvertUpsampleBicubicWithBilinear, RemoveRedundancy], LayoutTransform: [ AnnotateQuantAttrs, ConvertConv1dToConv2d, diff --git a/backends/qualcomm/builders/__init__.py b/backends/qualcomm/builders/__init__.py index 645b823d0e5..705d5d163cd 100644 --- a/backends/qualcomm/builders/__init__.py +++ b/backends/qualcomm/builders/__init__.py @@ -21,6 +21,7 @@ op_clamp, op_conv2d, op_cos, + op_cum_sum, op_depth_to_space, op_dequantize, op_div, @@ -108,6 +109,7 @@ op_clamp, op_conv2d, op_cos, + op_cum_sum, op_depth_to_space, op_dequantize, op_div, diff --git a/backends/qualcomm/builders/op_cos.py b/backends/qualcomm/builders/op_cos.py index 3858a947d93..589bf3ef88e 100644 --- a/backends/qualcomm/builders/op_cos.py +++ b/backends/qualcomm/builders/op_cos.py @@ -3,7 +3,6 @@ # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. - from typing import Dict import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper diff --git a/backends/qualcomm/builders/op_cum_sum.py b/backends/qualcomm/builders/op_cum_sum.py new file mode 100644 index 00000000000..f62485bc519 --- /dev/null +++ b/backends/qualcomm/builders/op_cum_sum.py @@ -0,0 +1,84 @@ +# Copyright (c) Qualcomm Innovation Center, Inc. +# All rights reserved +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +from typing import cast, Dict + +import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper + +import numpy as np +import torch +from executorch.backends.qualcomm.utils.constants import QCOM_AXIS_ORDER, QCOM_DATA + +from .node_visitor import NodeVisitor, register_node_visitor +from .qnn_constants import OpCumulativeSum, QNN_OP_PACKAGE_NAME_QTI_AISW + + +@register_node_visitor +class CumulativeSum(NodeVisitor): + target = ["aten.cumsum.default"] + + def __init__(self, *args) -> None: + super().__init__(*args) + + def get_param(self, node, input_tensor): + dim = node.args[1] + + if dim < 0: + dim = dim % len(input_tensor.shape) + if QCOM_AXIS_ORDER in node.meta: + dim = node.meta[QCOM_AXIS_ORDER].index(dim) + + return cast(np.uint32, dim) + + def define_node( + self, + node: torch.fx.Node, + nodes_to_wrappers: Dict[torch.fx.Node, PyQnnWrapper.TensorWrapper], + ) -> PyQnnWrapper.PyQnnOpWrapper: + input_node = node.args[0] + input_tensor = self.get_tensor(input_node, node) + input_tensor_wrapper = self.define_tensor( + input_node, + node, + input_tensor, + PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE, + nodes_to_wrappers, + ) + + dim = self.get_param(node, input_tensor) + + output_tensor = self.get_tensor(node, node) + output_tensor_wrapper = self.define_tensor( + node, + node, + output_tensor, + PyQnnWrapper.Qnn_TensorType_t.QNN_TENSOR_TYPE_NATIVE, + nodes_to_wrappers, + ) + + cumsum_op = PyQnnWrapper.PyQnnOpWrapper( + node.name, + QNN_OP_PACKAGE_NAME_QTI_AISW, + OpCumulativeSum.op_name, + ) + cumsum_op.AddInputTensors([input_tensor_wrapper]) + cumsum_op.AddOutputTensors([output_tensor_wrapper]) + cumsum_op.AddScalarParam( + OpCumulativeSum.param_axis, + PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_UINT_32, + {QCOM_DATA: dim}, + ) + cumsum_op.AddScalarParam( + OpCumulativeSum.param_exclusive, + PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_BOOL_8, + {QCOM_DATA: False}, + ) + cumsum_op.AddScalarParam( + OpCumulativeSum.param_reverse, + PyQnnWrapper.Qnn_DataType_t.QNN_DATATYPE_BOOL_8, + {QCOM_DATA: False}, + ) + + return cumsum_op diff --git a/backends/qualcomm/builders/op_sin.py b/backends/qualcomm/builders/op_sin.py index 89fce6bee9c..8828685ac9e 100644 --- a/backends/qualcomm/builders/op_sin.py +++ b/backends/qualcomm/builders/op_sin.py @@ -3,7 +3,6 @@ # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. - from typing import Dict import executorch.backends.qualcomm.python.PyQnnWrapperAdaptor as PyQnnWrapper diff --git a/backends/qualcomm/builders/op_stack.py b/backends/qualcomm/builders/op_stack.py index 616d0ee0ccc..fdef148ad4d 100644 --- a/backends/qualcomm/builders/op_stack.py +++ b/backends/qualcomm/builders/op_stack.py @@ -51,7 +51,7 @@ def define_node( dim = 0 if len(node.args) == 1 else cast(int, node.args[1]) if dim < 0: - dim = dim % len(input_tensor.shape) + dim = dim % len(output_tensor.shape) if QCOM_AXIS_ORDER in node.meta: dim = node.meta[QCOM_AXIS_ORDER].index(dim) stack_op = PyQnnWrapper.PyQnnOpWrapper( diff --git a/backends/qualcomm/builders/qnn_constants.py b/backends/qualcomm/builders/qnn_constants.py index 31822a174b9..6398fbba1ed 100644 --- a/backends/qualcomm/builders/qnn_constants.py +++ b/backends/qualcomm/builders/qnn_constants.py @@ -57,6 +57,14 @@ class OpConvert: op_name: str = "Convert" +@dataclass(init=False, frozen=True) +class OpCumulativeSum: + op_name = "CumulativeSum" + param_axis = "axis" + param_exclusive = "exclusive" + param_reverse = "reverse" + + @dataclass(init=False, frozen=True) class OpDepthToSpace: op_name: str = "DepthToSpace" diff --git a/backends/qualcomm/partition/common_defs.py b/backends/qualcomm/partition/common_defs.py index b427c59ce07..6326f4d1210 100644 --- a/backends/qualcomm/partition/common_defs.py +++ b/backends/qualcomm/partition/common_defs.py @@ -13,6 +13,7 @@ exir_ops.edge.aten.clone.default, exir_ops.edge.aten.slice_scatter.default, exir_ops.edge.aten.copy.default, + exir_ops.edge.aten.upsample_bicubic2d.vec, exir_ops.edge.quantized_decomposed.embedding_4bit.dtype, ] diff --git a/backends/qualcomm/partition/utils.py b/backends/qualcomm/partition/utils.py index 1e2b17b2a69..6931e35e6e3 100644 --- a/backends/qualcomm/partition/utils.py +++ b/backends/qualcomm/partition/utils.py @@ -39,6 +39,7 @@ def get_skip_decomp_table() -> List[torch._ops.OperatorBase]: torch.ops.aten.rms_norm.default, torch.ops.aten._safe_softmax.default, torch.ops.aten.stack.default, + torch.ops.aten.upsample_bicubic2d.vec, # This request is ignored because it is in a blocklist. Refer to exir/program/_program.py # torch.ops.aten.unbind.int, torch.ops.pt2e_quant.quantize_affine.default, diff --git a/backends/qualcomm/quantizer/annotators.py b/backends/qualcomm/quantizer/annotators.py index 52662202795..f59df46b3c1 100644 --- a/backends/qualcomm/quantizer/annotators.py +++ b/backends/qualcomm/quantizer/annotators.py @@ -976,6 +976,11 @@ def annotate_conv2d(node: Node, quantization_config: QuantizationConfig) -> None ) +@register_annotator([torch.ops.aten.cumsum.default]) +def annotate_cumsum(node: Node, quantization_config: QuantizationConfig) -> None: + annotate_single_in_single_out(node, quantization_config) + + @register_annotator([torch.ops.aten.linear.default]) def annotate_linear(node: Node, quantization_config: QuantizationConfig) -> None: act_node = node.args[0] diff --git a/backends/qualcomm/tests/models.py b/backends/qualcomm/tests/models.py index f1171b129e6..6570b4befcb 100644 --- a/backends/qualcomm/tests/models.py +++ b/backends/qualcomm/tests/models.py @@ -568,6 +568,14 @@ def forward(self, x): return torch.cos(x) +class CumSum(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + return x.cumsum(dim=0) + + class Div(torch.nn.Module): def __init__(self): super().__init__() diff --git a/backends/qualcomm/tests/test_qnn_delegate.py b/backends/qualcomm/tests/test_qnn_delegate.py index 9aba5a059e0..895326c743a 100644 --- a/backends/qualcomm/tests/test_qnn_delegate.py +++ b/backends/qualcomm/tests/test_qnn_delegate.py @@ -233,6 +233,11 @@ def test_qnn_backend_cos(self): sample_input = (torch.randn(2, 5, 1, 3),) self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_cumsum(self): + module = CumSum() # noqa: F405 + sample_input = (torch.randn(4),) + self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_einsum_outer_product(self): module = EinsumOuterProduct() # noqa: F405 x = torch.randn(5) @@ -1297,6 +1302,12 @@ def test_qnn_backend_cos(self): module = self.get_qdq_module(module, sample_input) self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_cumsum(self): + module = CumSum() # noqa: F405 + sample_input = (torch.randn(4),) + module = self.get_qdq_module(module, sample_input) + self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_einsum_outer_product(self): module = EinsumOuterProduct() # noqa: F405 x = torch.randn(5) @@ -3537,7 +3548,6 @@ def test_conv_former(self): self.assertGreaterEqual(msg["top_1"], 60) self.assertGreaterEqual(msg["top_5"], 80) - @unittest.skip("bicubic resize is not supported") def test_dino_v2(self): if not self.required_envs([self.image_dataset]): self.skipTest("missing required envs") @@ -3573,6 +3583,46 @@ def test_dino_v2(self): self.assertGreaterEqual(msg["top_1"], 70) self.assertGreaterEqual(msg["top_5"], 85) + def test_efficientSAM(self): + if not self.required_envs( + [self.image_dataset, self.pretrained_weight, self.oss_repo] + ): + self.skipTest("missing required envs") + cmds = [ + "python", + f"{self.executorch_root}/examples/qualcomm/oss_scripts/efficientSAM.py", + "--dataset", + self.image_dataset, + "--artifact", + self.artifact_dir, + "--build_folder", + self.build_folder, + "--device", + self.device, + "--model", + self.model, + "--oss_repo", + self.oss_repo, + "--pretrained_weight", + self.pretrained_weight, + "--ip", + self.ip, + "--port", + str(self.port), + ] + if self.host: + cmds.extend(["--host", self.host]) + + p = subprocess.Popen(cmds, stdout=subprocess.DEVNULL) + with Listener((self.ip, self.port)) as listener: + conn = listener.accept() + p.communicate() + msg = json.loads(conn.recv()) + if "Error" in msg: + self.fail(msg["Error"]) + else: + self.assertGreaterEqual(msg["MIoU"], 0.55) + def test_esrgan(self): if not self.required_envs(): self.skipTest("missing required envs") diff --git a/backends/qualcomm/tests/utils.py b/backends/qualcomm/tests/utils.py index 42eec15891c..a3006eff8f6 100644 --- a/backends/qualcomm/tests/utils.py +++ b/backends/qualcomm/tests/utils.py @@ -438,12 +438,14 @@ def lower_module_and_test_output( skip_node_id_set: set = None, skip_node_op_set: set = None, dynamic_shapes: Dict = None, + passes_job: collections.OrderedDict = None, ): delegated_program = to_edge_transform_and_lower_to_qnn( module, sample_inputs, self.compiler_specs, dynamic_shapes=dynamic_shapes, + passes_job=passes_job, skip_node_id_set=skip_node_id_set, skip_node_op_set=skip_node_op_set, ) diff --git a/examples/qualcomm/oss_scripts/dino_v2.py b/examples/qualcomm/oss_scripts/dino_v2.py index 2eb26e6cece..18b5ade8b35 100644 --- a/examples/qualcomm/oss_scripts/dino_v2.py +++ b/examples/qualcomm/oss_scripts/dino_v2.py @@ -10,7 +10,12 @@ import numpy as np import torch +from executorch.backends.qualcomm._passes import ConvertUpsampleBicubicWithBilinear +from executorch.backends.qualcomm._passes.qnn_pass_manager import ( + get_capture_program_passes, +) from executorch.backends.qualcomm.quantizer.quantizer import QuantDtype +from executorch.backends.qualcomm.utils.constants import QCOM_PASS_ACTIVATE_KEY from executorch.examples.qualcomm.utils import ( build_executorch_binary, @@ -56,6 +61,8 @@ def main(args): pte_filename = "dino_v2" instance = get_instance() + passes_job = get_capture_program_passes() + passes_job[ConvertUpsampleBicubicWithBilinear][QCOM_PASS_ACTIVATE_KEY] = True build_executorch_binary( instance, sample_input, @@ -65,6 +72,7 @@ def main(args): skip_node_id_set=skip_node_id_set, skip_node_op_set=skip_node_op_set, quant_dtype=QuantDtype.use_8a8w, + passes_job=passes_job, shared_buffer=args.shared_buffer, ) diff --git a/examples/qualcomm/oss_scripts/efficientSAM/efficientSAM.py b/examples/qualcomm/oss_scripts/efficientSAM/efficientSAM.py new file mode 100644 index 00000000000..ea65917dcd9 --- /dev/null +++ b/examples/qualcomm/oss_scripts/efficientSAM/efficientSAM.py @@ -0,0 +1,357 @@ +# Copyright (c) Qualcomm Innovation Center, Inc. +# All rights reserved +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import getpass +import json +import os +import zipfile +from multiprocessing.connection import Client +from typing import Callable, List + +import numpy as np +import torch +from executorch.backends.qualcomm._passes import ( + ConvertUpsampleBicubicWithBilinear, + ExpandBroadcastTensorShape, +) +from executorch.backends.qualcomm._passes.qnn_pass_manager import ( + get_capture_program_passes, +) +from executorch.backends.qualcomm.utils.constants import QCOM_PASS_ACTIVATE_KEY +from executorch.examples.qualcomm.oss_scripts.efficientSAM.source_transformation import ( + replace_maskdecoder_with_custom_op, + replace_pos_emb_with_custom_op, +) + +from executorch.examples.qualcomm.utils import ( + build_executorch_binary, + class_agnostic_mIoU, + make_output_dir, + parse_skip_delegation_node, + setup_common_args_and_variables, + SimpleADB, +) +from PIL import Image, ImageDraw +from scipy.ndimage import label +from torch.utils.data import DataLoader, Dataset +from torchvision import datasets, transforms + + +def load_dataset(dataset_path): + image_shape = (224, 224) + preprocess = transforms.Compose( + [ + transforms.Resize(image_shape), + transforms.ToTensor(), + ] + ) + imagenet_data = datasets.ImageFolder(dataset_path, transform=preprocess) + + return list(imagenet_data) + + +class EfficientSAMDataset(Dataset): + def __init__(self, dataset_path, data_size=1) -> None: + self.to_tensor = transforms.ToTensor() + dataset = load_dataset(dataset_path) + self.inputs = self.get_val_dataset(dataset, data_size) + self.data_size = data_size + + def get_val_dataset(self, dataset, data_size): + imgs, pt_prompts, pt_labels = [], [], [] + for i, data in enumerate(dataset): + if i >= data_size: + break + img = data[0] + h, w = img.shape[-2:] + + # Assuming the main object usually appears in the middle of the image, this default value is set for better demo visualization. + # Users can modify/add the point prompt here. + pt_prompt = torch.tensor([[w / 2, (h * 2 / 3)]], dtype=torch.float32)[ + None, ... + ] + # Users can increase the tensor size by adding more labels (0 for negative samples, 1 for positive samples) to label the corresponding points. + # The default label is [[1]], indicating that the point is a positive sample. + pt_label = torch.tensor([[1]], dtype=torch.float32) + + imgs.append(img) + pt_prompts.append(pt_prompt) + pt_labels.append(pt_label) + + imgs = torch.stack(imgs) + pt_prompts = torch.stack(pt_prompts) + pt_labels = torch.stack(pt_labels) + inputs = (imgs, pt_prompts, pt_labels) + return inputs + + def __getitem__(self, idx): + return self.inputs[0][idx], self.inputs[1][idx], self.inputs[2][idx] + + def __len__(self): + return self.data_size + + +def get_dataset(dataset_path, data_size=1): + + dataset = EfficientSAMDataset(dataset_path, data_size=data_size) + dataloader = DataLoader(dataset) + + # prepare input data + inputs, input_list = [], "" + for index, data in enumerate(dataloader): + if index >= data_size: + break + inputs.append(tuple(data)) + num_feature = len(data) + for idx, _ in enumerate(data): + input_name = f"input_{index}_{idx}.raw" + input_list += input_name + " " if idx < num_feature - 1 else input_name + + input_list = input_list + "\n" + + return inputs, input_list + + +def source_transform( + model, transforms: List[Callable[[torch.nn.Module], torch.nn.Module]] +): + for transform in transforms: + model = transform(model) + return model + + +def get_instance(args): + import sys + + sys.path.insert(0, args.oss_repo) + from efficient_sam.efficient_sam import build_efficient_sam + + ckpt = args.pretrained_weight + file_path, file_extension = os.path.splitext(ckpt) + file_dir, filename = os.path.split(file_path) + + if file_extension == ".zip": + with zipfile.ZipFile(ckpt, "r") as zip_ref: + zip_ref.extractall(file_dir) + ckpt = file_path + filename = os.path.splitext(filename)[0] + + model_arch = filename.split("_")[-1] + + if model_arch == "vitt": + encoder_patch_embed_dim, encoder_num_heads = (192, 3) + elif model_arch == "vits": + encoder_patch_embed_dim, encoder_num_heads = (384, 6) + else: + raise ValueError(f"Unsupported model architecture: {model_arch}") + + model = build_efficient_sam( + encoder_patch_embed_dim=encoder_patch_embed_dim, + encoder_num_heads=encoder_num_heads, + checkpoint=ckpt, + ).eval() + + return model + + +def generate_mask(predicted_logits, predicted_iou): + sorted_ids = torch.argsort(predicted_iou, dim=-1, descending=True) + predicted_iou = torch.take_along_dim(predicted_iou, sorted_ids, dim=2) + predicted_logits = torch.take_along_dim( + predicted_logits, sorted_ids[..., None, None], dim=2 + ) + + # The masks are already sorted by their predicted IOUs. + # We use the first mask. + mask = torch.ge(predicted_logits[0, 0, 0, :, :], 0).cpu().detach().numpy() + return mask + + +def save_mask(mask, input, save_path): + image, prompt, pt_label = input + original_image_tensor = image[0] + + # Convert tensor to numpy array if necessary + if not isinstance(original_image_tensor, np.ndarray): + original_image_tensor = original_image_tensor.detach().numpy() + + # Transpose if the image has 3 channels + if original_image_tensor.shape[0] == 3: + original_image_tensor = original_image_tensor.transpose(1, 2, 0) + + original_img = Image.fromarray( + (original_image_tensor * 255).astype(np.uint8) + ).convert("RGBA") + + # Create an empty RGBA image for the mask + mask_img = np.ones((mask.shape[0], mask.shape[1], 4)) + mask_img[:, :, 3] = 0 + + colors = [ + [1, 0, 0, 0.5], + [0, 1, 0, 0.5], + [0, 0, 1, 0.5], + [1, 1, 0, 0.5], + [1, 0, 1, 0.5], + [0, 1, 1, 0.5], + ] + + # Apply mask + labeled_mask, num_feature = label(mask) + for i in range(1, num_feature + 1): + mask_img[labeled_mask == i] = colors[(i - 1) % len(colors)] + + mask_img = Image.fromarray((mask_img * 255).astype(np.uint8), "RGBA") + + # Combine original image with mask + combined_img = Image.alpha_composite(original_img, mask_img) + + # Draw prompts point ("green" for positive samples, "red" for negative samples) + draw = ImageDraw.Draw(combined_img) + for pt, l in zip(prompt[0][0], pt_label[0][0]): + color = "green" if l else "red" + point_size = 3 + x1, y1 = max(0, int(pt[0]) - point_size), max(0, int(pt[1]) - point_size) + x2, y2 = min(combined_img.size[0], int(pt[0]) + point_size), min( + combined_img.size[1], int(pt[1]) + point_size + ) + draw.ellipse((x1, y1, x2, y2), fill=color, outline=color) + + combined_img.save(save_path) + + +def main(args): + skip_node_id_set, skip_node_op_set = parse_skip_delegation_node(args) + + os.makedirs(args.artifact, exist_ok=True) + + data_size = 1 + inputs, input_list = get_dataset(args.dataset, data_size) + assert args.pretrained_weight, "Checkpoint params can't be empty" + + # Get the EfficientSAM model. + model = get_instance(args) + model = source_transform( + model, + [ + replace_maskdecoder_with_custom_op, + replace_pos_emb_with_custom_op, + ], + ) + + pte_filename = "efficientSAM_qnn" + + # lower to QNN + passes_job = get_capture_program_passes() + passes_job[ConvertUpsampleBicubicWithBilinear][QCOM_PASS_ACTIVATE_KEY] = True + passes_job[ExpandBroadcastTensorShape][QCOM_PASS_ACTIVATE_KEY] = True + build_executorch_binary( + model, + inputs[0], + args.model, + f"{args.artifact}/{pte_filename}", + dataset=inputs, + skip_node_id_set=skip_node_id_set, + skip_node_op_set=skip_node_op_set, + passes_job=passes_job, + shared_buffer=args.shared_buffer, + ) + + if args.compile_only: + return + + workspace = f"/data/local/tmp/{getpass.getuser()}/executorch/{pte_filename}" + pte_path = f"{args.artifact}/{pte_filename}.pte" + + adb = SimpleADB( + qnn_sdk=os.getenv("QNN_SDK_ROOT"), + build_path=f"{args.build_folder}", + pte_path=pte_path, + workspace=workspace, + device_id=args.device, + host_id=args.host, + soc_model=args.model, + ) + adb.push(inputs=inputs, input_list=input_list) + adb.execute() + + # collect output data + output_data_folder = f"{args.artifact}/outputs" + make_output_dir(output_data_folder) + outputs = [] + + def post_process(): + for i, f in enumerate(sorted(os.listdir(output_data_folder))): + filename = os.path.join(output_data_folder, f) + output = np.fromfile(filename, dtype=np.float32) + output_shape = [1, 1, 3] if i % 2 else [1, 1, 3, 224, 224] + output = torch.from_numpy(output).reshape(output_shape) + outputs.append(output) + + adb.pull(output_path=args.artifact, callback=post_process) + + # MIoU analysis + miou = 0 + targets = [model(img, pt, pt_label) for img, pt, pt_label in inputs] + for i in range(data_size): + pred_mask = generate_mask(outputs[i * 2], outputs[i * 2 + 1]) + save_mask(pred_mask, inputs[i], f"{args.artifact}/output_{i}.png") + target_mask = generate_mask(targets[i][0], targets[i][1]) + miou += class_agnostic_mIoU([pred_mask], [target_mask]) + miou /= data_size + + if args.ip and args.port != -1: + with Client((args.ip, args.port)) as conn: + conn.send(json.dumps({"MIoU": miou})) + else: + print(f"MIoU->{miou}") + + +if __name__ == "__main__": + parser = setup_common_args_and_variables() + parser.add_argument( + "-a", + "--artifact", + help="path for storing generated artifacts and output by this example. Default ./EfficientSAM_qnn", + default="./EfficientSAM_qnn", + type=str, + ) + + parser.add_argument( + "--pretrained_weight", + help="Path to ESAM checkpoint, such as ./efficient_sam_vitt.pt or ./efficient_sam_vits.pt.zip", + type=str, + required=True, + ) + + parser.add_argument( + "-d", + "--dataset", + help=( + "path to the validation folder of ImageNet dataset. " + "e.g. --dataset imagenet-mini/val " + "for https://www.kaggle.com/datasets/ifigotin/imagenetmini-1000)" + ), + type=str, + required=True, + ) + + parser.add_argument( + "--oss_repo", + help="Path to clone https://github.com/yformer/EfficientSAM", + type=str, + required=True, + ) + + args = parser.parse_args() + try: + main(args) + except Exception as e: + if args.ip and args.port != -1: + with Client((args.ip, args.port)) as conn: + conn.send(json.dumps({"Error": str(e)})) + else: + raise Exception(e) diff --git a/examples/qualcomm/oss_scripts/efficientSAM/source_transformation/__init__.py b/examples/qualcomm/oss_scripts/efficientSAM/source_transformation/__init__.py new file mode 100644 index 00000000000..fd54a727136 --- /dev/null +++ b/examples/qualcomm/oss_scripts/efficientSAM/source_transformation/__init__.py @@ -0,0 +1,17 @@ +# Copyright (c) Qualcomm Innovation Center, Inc. +# All rights reserved +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +from executorch.examples.qualcomm.oss_scripts.efficientSAM.source_transformation.mask_decoder import ( + replace_maskdecoder_with_custom_op, +) +from executorch.examples.qualcomm.oss_scripts.efficientSAM.source_transformation.pos_emb import ( + replace_pos_emb_with_custom_op, +) + + +__all__ = [ + replace_maskdecoder_with_custom_op, + replace_pos_emb_with_custom_op, +] diff --git a/examples/qualcomm/oss_scripts/efficientSAM/source_transformation/mask_decoder.py b/examples/qualcomm/oss_scripts/efficientSAM/source_transformation/mask_decoder.py new file mode 100644 index 00000000000..c70d51a48fe --- /dev/null +++ b/examples/qualcomm/oss_scripts/efficientSAM/source_transformation/mask_decoder.py @@ -0,0 +1,125 @@ +# Copyright (c) Qualcomm Innovation Center, Inc. +# All rights reserved +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +from typing import List, Tuple + +import torch +import torch.nn as nn + + +class MaskDecoderCustom(nn.Module): + def forward( + self, + image_embeddings: torch.Tensor, + image_pe: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + multimask_output: bool, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Predict masks given image and prompt embeddings. + + Arguments: + image_embeddings: A tensor of shape [B, C, H, W] or [B*max_num_queries, C, H, W] + image_pe (torch.Tensor): positional encoding with the shape of image_embeddings (the batch dimension is broadcastable). + sparse_prompt_embeddings (torch.Tensor): the embeddings of the points and boxes + multimask_output (bool): Whether to return multiple masks or a single + mask. + + Returns: + torch.Tensor: batched predicted masks + torch.Tensor: batched predictions of mask quality + """ + + ( + batch_size, + max_num_queries, + sparse_embed_dim_1, + sparse_embed_dim_2, + ) = sparse_prompt_embeddings.shape + + ( + _, + image_embed_dim_c, + image_embed_dim_h, + image_embed_dim_w, + ) = image_embeddings.shape + + # QNN don't support dim greater than 4 + image_embeddings_expanded = image_embeddings.expand(max_num_queries, -1, -1, -1) + image_embeddings_tiled = image_embeddings_expanded.contiguous().view( + batch_size * max_num_queries, + image_embed_dim_c, + image_embed_dim_h, + image_embed_dim_w, + ) + sparse_prompt_embeddings = sparse_prompt_embeddings.reshape( + batch_size * max_num_queries, sparse_embed_dim_1, sparse_embed_dim_2 + ) + masks, iou_pred = self.predict_masks( + image_embeddings=image_embeddings_tiled, + image_pe=image_pe, + sparse_prompt_embeddings=sparse_prompt_embeddings, + ) + if multimask_output and self.num_multimask_outputs > 1: + return masks[:, 1:, :], iou_pred[:, 1:] + else: + return masks[:, :1, :], iou_pred[:, :1] + + def predict_masks( + self, + image_embeddings: torch.Tensor, + image_pe: torch.Tensor, + sparse_prompt_embeddings: torch.Tensor, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """Predicts masks. See 'forward' for more details.""" + # Concatenate output tokens + output_tokens = torch.cat( + [self.iou_token.weight, self.mask_tokens.weight], dim=0 + ) + output_tokens = output_tokens.unsqueeze(0).expand( + sparse_prompt_embeddings.size(0), -1, -1 + ) + tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1) + # Expand per-image data in batch direction to be per-mask + # QNN don't support dim greater than 4, + pos_src = image_pe.expand([tokens.shape[0]] + [*image_pe.shape[1:]]) + b, c, h, w = image_embeddings.shape + hs, src = self.transformer(image_embeddings, pos_src, tokens) + iou_token_out = hs[:, 0, :] + mask_tokens_out = hs[:, 1 : (1 + self.num_mask_tokens), :] + + # Upscale mask embeddings and predict masks using the mask tokens + upscaled_embedding = src.transpose(1, 2).view(b, c, h, w) + + for upscaling_layer in self.final_output_upscaling_layers: + upscaled_embedding = upscaling_layer(upscaled_embedding) + hyper_in_list: List[torch.Tensor] = [] + for i, output_hypernetworks_mlp in enumerate(self.output_hypernetworks_mlps): + hyper_in_list.append(output_hypernetworks_mlp(mask_tokens_out[:, i, :])) + hyper_in = torch.stack(hyper_in_list, dim=1) + b, c, h, w = upscaled_embedding.shape + masks = (hyper_in @ upscaled_embedding.view(b, c, h * w)).view(b, -1, h, w) + # Generate mask quality predictions + iou_pred = self.iou_prediction_head(iou_token_out) + return masks, iou_pred + + +def _replace_maskdecoder_with_custom_op(module: torch.nn.Module): + from efficient_sam.efficient_sam_decoder import MaskDecoder # B007 + + for _, child in module.named_children(): + if isinstance(child, MaskDecoder): + child.forward = MaskDecoderCustom.forward.__get__(child, MaskDecoder) + child.predict_masks = MaskDecoderCustom.predict_masks.__get__( + child, MaskDecoder + ) + else: + _replace_maskdecoder_with_custom_op(child) + + +def replace_maskdecoder_with_custom_op(module: torch.nn.Module) -> torch.nn.Module: + + _replace_maskdecoder_with_custom_op(module) + return module diff --git a/examples/qualcomm/oss_scripts/efficientSAM/source_transformation/pos_emb.py b/examples/qualcomm/oss_scripts/efficientSAM/source_transformation/pos_emb.py new file mode 100644 index 00000000000..7a3a91c7607 --- /dev/null +++ b/examples/qualcomm/oss_scripts/efficientSAM/source_transformation/pos_emb.py @@ -0,0 +1,64 @@ +# Copyright (c) Qualcomm Innovation Center, Inc. +# All rights reserved +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +from typing import Tuple + +import numpy as np + +import torch +import torch.nn as nn + + +class PositionEmbeddingRandomCustom(nn.Module): + """ + Positional encoding using random spatial frequencies. + """ + + def _pe_encoding(self, coords: torch.Tensor) -> torch.Tensor: + """Positionally encode points that are normalized to [0,1].""" + # assuming coords are in [0, 1]^2 square and have d_1 x ... x d_n x 2 shape + coords = 2 * coords - 1 + coords = coords.unsqueeze(0) + coords = torch.matmul( + coords, self.positional_encoding_gaussian_matrix.unsqueeze(0) + ) + coords = coords.squeeze(0) + coords = 2 * np.pi * coords + # outputs d_1 x ... x d_n x C shape + return torch.cat([torch.sin(coords), torch.cos(coords)], dim=-1) + + def forward_with_coords( + self, coords_input: torch.Tensor, image_size: Tuple[int, int] + ) -> torch.Tensor: + """Positionally encode points that are not normalized to [0,1].""" + coords = coords_input.clone() + coords_0 = coords[:, :, 0] / image_size[1] + coords_1 = coords[:, :, 1] / image_size[0] + coords = torch.stack((coords_0, coords_1), dim=-1) + + return self._pe_encoding(coords.to(torch.float)) # B x N x C + + +def _replace_pos_emb_with_custom_op(module: torch.nn.Module): + from efficient_sam.efficient_sam_decoder import PositionEmbeddingRandom # B007 + + for _, child in module.named_children(): + if isinstance(child, PositionEmbeddingRandom): + child._pe_encoding = PositionEmbeddingRandomCustom._pe_encoding.__get__( + child, PositionEmbeddingRandom + ) + child.forward_with_coords = ( + PositionEmbeddingRandomCustom.forward_with_coords.__get__( + child, PositionEmbeddingRandom + ) + ) + else: + _replace_pos_emb_with_custom_op(child) + + +def replace_pos_emb_with_custom_op(module: torch.nn.Module) -> torch.nn.Module: + + _replace_pos_emb_with_custom_op(module) + return module diff --git a/examples/qualcomm/oss_scripts/fastvit.py b/examples/qualcomm/oss_scripts/fastvit.py index 501ea522acd..b15e7d7267f 100644 --- a/examples/qualcomm/oss_scripts/fastvit.py +++ b/examples/qualcomm/oss_scripts/fastvit.py @@ -107,14 +107,16 @@ def main(args): weight=weight_qspec, bias=_derived_bias_quant_spec, ) + # rewrite default ptq config - q_config = quantizer.bit8_quant_config - quantizer.bit8_quant_config = QuantizationConfig( + q_config = quantizer.quant_config + quantizer.quant_config = QuantizationConfig( input_activation=act_qspec, output_activation=act_qspec, weight=q_config.weight, bias=q_config.bias, ) + # lower to QNN passes_job = get_capture_program_passes() passes_job[ExpandBroadcastTensorShape][QCOM_PASS_ACTIVATE_KEY] = True diff --git a/examples/qualcomm/utils.py b/examples/qualcomm/utils.py index b17bc8f98bd..3aecf405fff 100755 --- a/examples/qualcomm/utils.py +++ b/examples/qualcomm/utils.py @@ -428,6 +428,15 @@ def histogram(golden, predict): return (pa, mpa, miou, cls_iou) +def class_agnostic_mIoU(predictions, targets): + total_iou = 0 + for pred, tar in zip(predictions, targets): + inter = np.count_nonzero(pred & tar) + union = np.count_nonzero(pred | tar) + total_iou += inter / (union + 1e-10) + return total_iou / len(predictions) + + def get_imagenet_dataset( dataset_path, data_size, image_shape, crop_size=None, shuffle=True ): From 0722ec0880340f1456cdd49c1714b591b9ea581b Mon Sep 17 00:00:00 2001 From: Jacob Szwejbka Date: Thu, 10 Apr 2025 10:39:03 -0700 Subject: [PATCH 075/244] memory_planning algos take the specs as inputs instead of calculating them themselves Differential Revision: D72600295 Pull Request resolved: https://github.com/pytorch/executorch/pull/9952 --- backends/cadence/aot/memory_planning.py | 22 +-- backends/vulkan/vulkan_preprocess.py | 6 +- exir/memory_planning.py | 251 ++++++++++++++---------- exir/passes/memory_planning_pass.py | 10 +- exir/tests/test_memory_planning.py | 10 +- 5 files changed, 168 insertions(+), 131 deletions(-) diff --git a/backends/cadence/aot/memory_planning.py b/backends/cadence/aot/memory_planning.py index cfe1b9ab9d8..3c6c518f16a 100644 --- a/backends/cadence/aot/memory_planning.py +++ b/backends/cadence/aot/memory_planning.py @@ -12,7 +12,7 @@ import math import typing from functools import partial -from typing import Iterable, List, Optional, Tuple +from typing import Iterable, List, Optional, Set, Tuple import torch from executorch.backends.cadence.aot.memory_constraints import ( @@ -73,11 +73,11 @@ def collect_specs_from_graph_module( # the fastest memory available # flake8: noqa 'position_based_greedy_with_hierarchy' is too complex (13) def position_based_greedy_with_hierarchy( - graph_module: torch.fx.GraphModule, alignment: int, + specs: Set[TensorSpec], + graph_module: torch.fx.GraphModule, graph_signature: ExportGraphSignature, - alloc_graph_input: bool, - alloc_graph_output: bool, + extra_padding: int = 0, *, memory_config: MemoryConfig, mem_constraints: MemConstraints, @@ -119,9 +119,7 @@ def memory_available(spec: TensorSpec) -> bool: # Iterate over all the specs in sorted order for spec in sorted( - collect_specs_from_graph_module( - graph_module, graph_signature, alloc_graph_input, alloc_graph_output - ), + specs, key=lambda spec: spec.allocated_memory, reverse=True, ): @@ -167,11 +165,11 @@ def memory_available(spec: TensorSpec) -> bool: # Greedy tensor placement with the heuristics from arxiv.org/pdf/2001.03288.pdf def greedy_by_size_for_offset_calculation_with_hierarchy( - graph_module: torch.fx.GraphModule, alignment: int, + specs: Set[TensorSpec], + graph_module: torch.fx.GraphModule, graph_signature: ExportGraphSignature, - alloc_graph_input: bool, - alloc_graph_output: bool, + extra_padding: int = 0, *, memory_config: MemoryConfig, mem_constraints: MemConstraints, @@ -199,9 +197,7 @@ def greedy_by_size_for_offset_calculation_with_hierarchy( # Iterate over all the specs in sorted order for spec in sorted( - collect_specs_from_graph_module( - graph_module, graph_signature, alloc_graph_input, alloc_graph_output - ), + specs, key=lambda spec: spec.allocated_memory, reverse=True, ): diff --git a/backends/vulkan/vulkan_preprocess.py b/backends/vulkan/vulkan_preprocess.py index 1c1c51bb58a..188311e5f2c 100644 --- a/backends/vulkan/vulkan_preprocess.py +++ b/backends/vulkan/vulkan_preprocess.py @@ -47,7 +47,7 @@ ) from executorch.exir.backend.utils import DelegateMappingBuilder -from executorch.exir.memory_planning import greedy, memory_planning_algorithm_suite +from executorch.exir.memory_planning import greedy, MemoryPlanningAlgorithmSuite from executorch.exir.pass_base import ExportPass, PassBase from executorch.exir.passes import MemoryPlanningPass, SpecPropPass @@ -199,8 +199,8 @@ def preprocess( # noqa: C901 # Finally, apply dynamic shape passes and memory planning pass. These passes # must be applied only when the graph structure is finalized. greedy_memory_planning = partial(greedy, allow_overlapping_allocations=False) - mem_planning_suite = partial( - memory_planning_algorithm_suite, algo_list=[greedy_memory_planning] + mem_planning_suite = MemoryPlanningAlgorithmSuite( + algo_list=[greedy_memory_planning] ) program = apply_passes( program, diff --git a/exir/memory_planning.py b/exir/memory_planning.py index 3f45276c9e2..17640a9f7aa 100644 --- a/exir/memory_planning.py +++ b/exir/memory_planning.py @@ -731,53 +731,43 @@ def _contains_xnnpack_delegate(graph_module: torch.fx.GraphModule) -> bool: def greedy( - graph_module: torch.fx.GraphModule, alignment: int, - graph_signature: Optional[ExportGraphSignature] = None, - alloc_graph_input: bool = True, - alloc_graph_output: bool = True, + specs: Set[TensorSpec], + graph_module: torch.fx.GraphModule, + graph_signature: ExportGraphSignature, + extra_padding: int = 0, + *, allow_overlapping_allocations: bool = True, ) -> MemoryAlgoResult: r"""Greedy algorithm to allocate memory for tensors in the graph. - alloc_graph_input: If set to true, the algorithm will allocate memory for graph input. - alloc_graph_output: If set to true, the algorithm will allocate memory for graph output. - allow_overlapping_allocations: If set to true, allows for allocations that overlap - in their lifetime but are at different offsets in the storage. By default true. - This flag is added to allow for Vulkan to use MemoryPlanningPass with overlapping - allocations disabled + + Args: + alignment: Memory alignment requirement + specs: Set of TensorSpec objects with updated lifetimes + graph_module: Graph module + graph_signature: Graph signature + extra_padding: Additional padding to add to each memory buffer (in bytes) + allow_overlapping_allocations: If set to true, allows for allocations that overlap + in their lifetime but are at different offsets in the storage. By default true. + This flag is added to allow for Vulkan to use MemoryPlanningPass with overlapping + allocations disabled + + Returns: + MemoryAlgoResult containing the allocation decisions """ greedy_result = MemoryAlgoResult({}, []) - # padding allocation with 64 bytes. - # this requirement is really for XNNPACK backend which can read tensors - # beyond the end of the tensor. This is done for performance - # optimizations in XNNPACK. - # While accounting for backend specific requirement is not the right choice - # in backend agnostic memory planning, we do it here as it seems most appropriate. - # Right now this applies to greedy only so any other - # algorithm that plans memory for XNNPACK backend will - # not have this. - extra_padded_bytes = 0 - if _contains_xnnpack_delegate(graph_module): - extra_padded_bytes = 64 spec2obj = {} shared_objects = defaultdict(list) - # Don't do assertion in collect_specs_from_nodes if we have already encountered - # and ignored some to_out_variant errors. - do_assertion = not getattr(graph_module, "encounter_to_out_var_failure", False) + # For each tensor, pick the available shared object with closest size to # the tensor. If there are no available shared object left, create a new # one. import bisect sorted_specs = [] - for spec in collect_specs_from_nodes( - graph_module.graph.nodes, - graph_signature, - do_assertion=do_assertion, - ignore_graph_input=not alloc_graph_input, - ignore_graph_output=not alloc_graph_output, - ): + for spec in specs: bisect.insort(sorted_specs, spec, key=lambda x: x.allocated_memory) + sorted_specs.reverse() for spec in sorted_specs: @@ -806,15 +796,13 @@ def greedy( for mem_id in shared_objects: input_total_size = 0 if bufsizes := getattr(graph_module, "input_mem_buffer_sizes", None): - # pyre-fixme[6]: For 1st argument expected - # `pyre_extensions.ReadOnly[Sized]` but got `Union[Tensor, Module]`. + assert isinstance(bufsizes, list) if len(bufsizes) > mem_id: - # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.Ten... input_total_size = bufsizes[mem_id] total_sizes[mem_id] = materialize_buffer( shared_objects[mem_id], input_total_size ) - total_sizes[mem_id] += extra_padded_bytes + total_sizes[mem_id] += extra_padding # Since we now know the number of shared objects we need and the size of # each shared object, we can assign offset in the memory buffer for each @@ -838,72 +826,107 @@ def greedy( return greedy_result -def memory_planning_algorithm_suite( - graph_module: torch.fx.GraphModule, - alignment: int, - graph_signature: Optional[ExportGraphSignature] = None, - alloc_graph_input: bool = True, - alloc_graph_output: bool = True, - allow_overlapping_allocations: bool = True, - algo_list: Optional[List[Callable[..., MemoryAlgoResult]]] = None, -) -> List[int]: - r""" - Memory planning algorithm suite that runs a list of memory planning algorithms - and returns the result of the algorithm that minimizes the total memory usage. - """ - if algo_list is None: - algo_list = [greedy] - mem_algo_results = {} - for algo in algo_list: - if isinstance(algo, functools.partial): - name = algo.func.__name__ - else: - name = getattr(algo, "__name__", None) - # Run this memory planning algorithm and store the result in mem_algo_results - # with the name of the algorithm as the key. - mem_algo_results[name] = algo( - graph_module, - alignment, - graph_signature, - alloc_graph_input, - alloc_graph_output, - ) +class MemoryPlanningAlgorithmSuite: + def __init__( + self, + algo_list: Optional[List[Callable[..., MemoryAlgoResult]]] = None, + ) -> None: + if algo_list is None: + algo_list = [greedy] + self.algo_list: List[Callable[..., MemoryAlgoResult]] = algo_list - # All the algorithms should have the same number of buffers allocated. - assert ( - len( - { - len(mem_algo_result.bufsizes) - for mem_algo_result in mem_algo_results.values() - } + def __call__( + self, + alignment: int, + specs: Set[TensorSpec], + graph_module: torch.fx.GraphModule, + graph_signature: ExportGraphSignature, + extra_padding: int, + ) -> List[int]: + r""" + Memory planning algorithm suite that runs a list of memory planning algorithms + and returns the result of the algorithm that minimizes the total memory usage. + + Args: + graph_module: The graph module to allocate memory for + alignment: Memory alignment requirement + graph_signature: Optional graph signature + alloc_graph_input: Whether to allocate memory for graph input + alloc_graph_output: Whether to allocate memory for graph output + allow_overlapping_allocations: Whether to allow overlapping allocations + algo_list: List of memory planning algorithms to run + specs: Optional set of TensorSpec objects with updated lifetimes. If None, they will be + calculated from the graph_module. + + Returns: + List of buffer sizes for each memory hierarchy + """ + + mem_algo_results = {} + for algo in self.algo_list: + if isinstance(algo, functools.partial): + name = algo.func.__name__ + else: + name = getattr(algo, "__name__", None) + + mem_algo_results[name] = algo( + alignment, + specs, + graph_module, + graph_signature, + extra_padding, + ) + + # All the algorithms should have the same number of buffers allocated. + assert ( + len( + { + len(mem_algo_result.bufsizes) + for mem_algo_result in mem_algo_results.values() + } + ) + == 1 + ), "Different memory planning algorithms should have the same number of buffers allocated." + + # Find the algorithm that minimizes the total memory usage. + best_algo = min( + mem_algo_results, key=lambda k: sum(mem_algo_results[k].bufsizes) ) - == 1 - ), "Different memory planning algorithms should have the same number of buffers allocated." - - # Find the algorithm that minimizes the total memory usage. - best_algo = min(mem_algo_results, key=lambda k: sum(mem_algo_results[k].bufsizes)) - logging.debug(f"Best memory planning algo for this model is {best_algo}") - bufsizes = mem_algo_results[best_algo].bufsizes - - # Update the mem_id and mem_offset for each spec in the graph module based on the - # values provided by the best memory planning algorithm. - for spec in mem_algo_results[best_algo].spec_dict: - spec_alloc_result = mem_algo_results[best_algo].spec_dict[spec] - spec.mem_id = spec_alloc_result.mem_id - spec.mem_offset = spec_alloc_result.mem_offset - spec.mem_obj_id = spec_alloc_result.mem_obj_id + logging.debug(f"Best memory planning algo for this model is {best_algo}") + bufsizes = mem_algo_results[best_algo].bufsizes - return bufsizes + # Update the mem_id and mem_offset for each spec in the graph module based on the + # values provided by the best memory planning algorithm. + for spec in mem_algo_results[best_algo].spec_dict: + spec_alloc_result = mem_algo_results[best_algo].spec_dict[spec] + spec.mem_id = spec_alloc_result.mem_id + spec.mem_offset = spec_alloc_result.mem_offset + spec.mem_obj_id = spec_alloc_result.mem_obj_id + + return bufsizes def naive( - graph_module: torch.fx.GraphModule, alignment: int, - graph_signature: Optional[ExportGraphSignature] = None, - alloc_graph_input: bool = True, - alloc_graph_output: bool = True, + specs: Set[TensorSpec], + graph_module: torch.fx.GraphModule, + graph_signature: ExportGraphSignature, + extra_padding: int, ) -> MemoryAlgoResult: + """Naive algorithm to allocate memory for tensors in the graph. + This algorithm simply allocates memory for each tensor sequentially without reusing memory. + + Args: + alignment: Memory alignment requirement + specs: Set of TensorSpec objects with updated lifetimes + graph_module: Graph module + graph_signature: Graph signature + extra_padding: Additional padding to add to each memory buffer (in bytes) + + Returns: + MemoryAlgoResult containing the allocation decisions + """ naive_result = MemoryAlgoResult({}, []) # allocate 'allocated' bytes from buffer with id mem_id. @@ -918,14 +941,9 @@ def _allocate_buf(bufsizes: List[int], mem_id: int, allocated: int) -> int: bufsizes = getattr(graph_module, "input_mem_buffer_sizes", None) if bufsizes is None: bufsizes = [0, 0] - bufsizes = typing.cast(List[int], bufsizes) - for spec in collect_specs_from_nodes( - graph_module.graph.nodes, - graph_signature, - ignore_graph_input=not alloc_graph_input, - ignore_graph_output=not alloc_graph_output, - ): + + for spec in specs: spec_alloc_result = naive_result.spec_dict.get(spec, SpecAllocResult(0, 0, 0)) # assume a single memory layer which has mem_id 1 if spec.mem_id is None: @@ -1027,7 +1045,7 @@ def insert_calls_to_free( def apply_algo( algo: Callable[ - [torch.fx.GraphModule, int, Optional[ExportGraphSignature], bool, bool], + ..., List[int], ], graph_module: torch.fx.GraphModule, @@ -1048,11 +1066,35 @@ def apply_algo( TODO: make these optimizations once we have some baseline working. """ - specs = update_all_tensors_lifetime(graph_module, graph_signature) + # Extract the nodes and their lifespans from the graph_module + # Difficult to just filter the list of specs returned by this due to + # how we flag trainable weights. + _ = update_all_tensors_lifetime(graph_module, graph_signature) + + # Filter specs based on alloc_graph_input and alloc_graph_output + specs = collect_specs_from_nodes( + graph_module.graph.nodes, + graph_signature, + do_assertion=False, + ignore_graph_input=not alloc_graph_input, + ignore_graph_output=not alloc_graph_output, + ) + + # Get extra padding for XNNPACK if needed + extra_padding = 0 + if _contains_xnnpack_delegate(graph_module): + extra_padding = 64 + + # Pass the filtered specs to the algorithm bufsizes: List[int] = algo( - graph_module, alignment, graph_signature, alloc_graph_input, alloc_graph_output + alignment, + specs, + graph_module, + graph_signature, + extra_padding, ) - insert_calls_to_free(graph_module, specs) + + insert_calls_to_free(graph_module, set(specs)) def handle_submodule( submodule_nd: torch.fx.Node, alloc_graph_input: bool = False @@ -1063,6 +1105,7 @@ def handle_submodule( # memory planning for submodule need to be aware of the amount of # buffer already allocated. submodule.input_mem_buffer_sizes = bufsizes + bufsizes = apply_algo( algo, submodule, diff --git a/exir/passes/memory_planning_pass.py b/exir/passes/memory_planning_pass.py index f4881e7ab71..f4b3ad8a8a7 100644 --- a/exir/passes/memory_planning_pass.py +++ b/exir/passes/memory_planning_pass.py @@ -17,7 +17,7 @@ _is_out_var_node, apply_algo, get_node_tensor_specs, - memory_planning_algorithm_suite, + MemoryPlanningAlgorithmSuite, Verifier, ) from executorch.exir.operator.convert import get_out_args_from_opoverload @@ -40,9 +40,7 @@ def _callable_name(any_callable: Callable[..., Any]) -> str: class MemoryPlanningPass(PassBase): def __init__( self, - memory_planning_algo: Callable[ - ..., List[int] - ] = memory_planning_algorithm_suite, + memory_planning_algo: Optional[Callable[..., List[int]]] = None, allow_lifetime_and_storage_overlap: bool = False, alloc_graph_input: bool = True, alloc_graph_output: bool = True, @@ -54,6 +52,8 @@ def __init__( the graph input/output. The default behavior is the algorithm will allocate memory for both graph input and output. """ + if memory_planning_algo is None: + memory_planning_algo = MemoryPlanningAlgorithmSuite() self.memory_planning_algo = memory_planning_algo self.allow_lifetime_and_storage_overlap = allow_lifetime_and_storage_overlap self.alloc_graph_input = alloc_graph_input @@ -125,7 +125,7 @@ def run( # passes/stages is quite natural and avoid yet another 'context' data structure # to do the job. _ = apply_algo( - self.memory_planning_algo, + self.memory_planning_algo, # pyre-ignore[6] graph_module, self.alignment, graph_signature, diff --git a/exir/tests/test_memory_planning.py b/exir/tests/test_memory_planning.py index 8df0cfed0bf..52986aaa04c 100644 --- a/exir/tests/test_memory_planning.py +++ b/exir/tests/test_memory_planning.py @@ -8,7 +8,6 @@ import itertools import unittest -from functools import partial from typing import Any, Callable, List, Optional, Tuple, Type import executorch.exir as exir @@ -20,8 +19,8 @@ filter_nodes, get_node_tensor_specs, greedy, - memory_planning_algorithm_suite, MemoryAlgoResult, + MemoryPlanningAlgorithmSuite, naive, Verifier, ) @@ -269,7 +268,7 @@ def wrapper(self: "TestMemoryPlanning") -> None: .exported_program() .graph_module ) - mem_algo = partial(memory_planning_algorithm_suite, algo_list=[algo]) + mem_algo = MemoryPlanningAlgorithmSuite(algo_list=[algo]) graph_module = PassManager( passes=[ SpecPropPass(), @@ -497,7 +496,6 @@ def quantize(self, eager_model: nn.Module) -> nn.Module: ) return quantized_model - # pyre-ignore @parameterized.expand( [ ( @@ -514,7 +512,7 @@ def quantize(self, eager_model: nn.Module) -> nn.Module: ) def test_multiple_pools( self, - algo: Callable[..., List[int]], + algo: Callable[..., MemoryAlgoResult], expected_allocs: List[Tuple[int, int]], expected_bufsizes: List[int], ) -> None: @@ -522,7 +520,7 @@ def test_multiple_pools( export(MultiplePoolsToyModel(), (torch.ones(1),), strict=True) ) - mem_algo = partial(memory_planning_algorithm_suite, algo_list=[algo]) + mem_algo = MemoryPlanningAlgorithmSuite(algo_list=[algo]) edge_program.to_executorch( exir.ExecutorchBackendConfig( memory_planning_pass=CustomPoolMemoryPlanningPass( From 57f4dc3c65cbe69e5879a745b16440ef9a5e8cd8 Mon Sep 17 00:00:00 2001 From: Manuel Candales <42380156+manuelcandales@users.noreply.github.com> Date: Thu, 10 Apr 2025 13:52:13 -0400 Subject: [PATCH 076/244] Update kernels readme Differential Revision: D72796889 Pull Request resolved: https://github.com/pytorch/executorch/pull/10067 --- kernels/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/kernels/README.md b/kernels/README.md index e1c6d02afa8..58931beb984 100644 --- a/kernels/README.md +++ b/kernels/README.md @@ -351,9 +351,11 @@ Once you have your operator and corresponding tests in place, we can try it out. cmake . \ -DCMAKE_INSTALL_PREFIX=cmake-out \ -DEXECUTORCH_USE_CPP_CODE_COVERAGE=ON \ + -DEXECUTORCH_BUILD_KERNELS_CUSTOM=ON \ -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON \ -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON \ -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=ON \ + -DEXECUTORCH_BUILD_EXTENSION_FLAT_TENSOR=ON \ -DEXECUTORCH_BUILD_EXTENSION_MODULE=ON \ -DEXECUTORCH_BUILD_EXTENSION_RUNNER_UTIL=ON \ -DEXECUTORCH_BUILD_EXTENSION_TENSOR=ON \ @@ -369,6 +371,7 @@ cmake --build cmake-out -j9 --target install ``` ./cmake-out/kernels/test/portable_kernels_test ./cmake-out/kernels/test/optimized_kernels_test +./cmake-out/kernels/test/quantized_kernels_test ``` #### Implementation restrictions From e876a8088b59b9e7968fb2dd1816dccaba550688 Mon Sep 17 00:00:00 2001 From: pytorchbot Date: Thu, 10 Apr 2025 14:49:18 -0400 Subject: [PATCH 077/244] [#9971] Gracefully error out in ETDump for get_flatbuffer_scalar_type (#10076) Following #9971 - Update get_flatbuffer_scalar_type return type to Result - Iteratively update functions that calling the functions with result type changed: - Check returns, if with an error, pass above the error. - If unable to pass error, update the return type as Result Differential Revision: [D72771753](https://our.internmc.facebook.com/intern/diff/D72771753/) --- devtools/etdump/etdump_flatcc.cpp | 51 ++++++++++++++++++------- devtools/etdump/etdump_flatcc.h | 2 +- runtime/core/event_tracer.h | 5 ++- runtime/core/test/event_tracer_test.cpp | 4 +- 4 files changed, 45 insertions(+), 17 deletions(-) diff --git a/devtools/etdump/etdump_flatcc.cpp b/devtools/etdump/etdump_flatcc.cpp index 4e300ee4065..8bff9d7ada4 100644 --- a/devtools/etdump/etdump_flatcc.cpp +++ b/devtools/etdump/etdump_flatcc.cpp @@ -42,7 +42,7 @@ namespace executorch { namespace etdump { namespace { -executorch_flatbuffer_ScalarType_enum_t get_flatbuffer_scalar_type( +Result get_flatbuffer_scalar_type( executorch::aten::ScalarType tensor_scalar_type) { switch (tensor_scalar_type) { case executorch::aten::ScalarType::Byte: @@ -66,21 +66,26 @@ executorch_flatbuffer_ScalarType_enum_t get_flatbuffer_scalar_type( case executorch::aten::ScalarType::UInt16: return executorch_flatbuffer_ScalarType_UINT16; default: - ET_CHECK_MSG( + ET_CHECK_OR_RETURN_ERROR( 0, + InvalidArgument, "This ScalarType = %hhd is not yet supported in ETDump", static_cast(tensor_scalar_type)); } } -etdump_Tensor_ref_t add_tensor_entry( +Result add_tensor_entry( flatcc_builder_t* builder_, const executorch::aten::Tensor& tensor, long offset) { etdump_Tensor_start(builder_); - etdump_Tensor_scalar_type_add( - builder_, get_flatbuffer_scalar_type(tensor.scalar_type())); + Result scalar_type = + get_flatbuffer_scalar_type(tensor.scalar_type()); + if (!scalar_type.ok()) { + return scalar_type.error(); + } + etdump_Tensor_scalar_type_add(builder_, scalar_type.get()); etdump_Tensor_sizes_start(builder_); for (auto dim : tensor.sizes()) { @@ -390,18 +395,26 @@ Result ETDumpGen::log_intermediate_output_delegate_helper( // Check the type of `output` then call the corresponding logging functions if constexpr (std::is_same::value) { long offset = write_tensor_or_raise_error(output); - etdump_Tensor_ref_t tensor_ref = add_tensor_entry(builder_, output, offset); + Result tensor_ref = + add_tensor_entry(builder_, output, offset); + if (!tensor_ref.ok()) { + return tensor_ref.error(); + } etdump_Value_start(builder_); etdump_Value_val_add(builder_, etdump_ValueType_Tensor); - etdump_Value_tensor_add(builder_, tensor_ref); + etdump_Value_tensor_add(builder_, tensor_ref.get()); } else if constexpr (std::is_same>::value) { etdump_Tensor_vec_start(builder_); for (size_t i = 0; i < output.size(); ++i) { long offset = write_tensor_or_raise_error(output[i]); - etdump_Tensor_vec_push( - builder_, add_tensor_entry(builder_, output[i], offset)); + Result tensor_ref = + add_tensor_entry(builder_, output[i], offset); + if (!tensor_ref.ok()) { + return tensor_ref.error(); + } + etdump_Tensor_vec_push(builder_, tensor_ref.get()); } etdump_Tensor_vec_ref_t tensor_vec_ref = etdump_Tensor_vec_end(builder_); etdump_TensorList_ref_t tensor_list_ref = @@ -538,7 +551,9 @@ void ETDumpGen::set_data_sink(DataSinkBase* data_sink) { data_sink_ = data_sink; } -void ETDumpGen::log_evalue(const EValue& evalue, LoggedEValueType evalue_type) { +Result ETDumpGen::log_evalue( + const EValue& evalue, + LoggedEValueType evalue_type) { check_ready_to_add_events(); etdump_DebugEvent_start(builder_); @@ -550,12 +565,15 @@ void ETDumpGen::log_evalue(const EValue& evalue, LoggedEValueType evalue_type) { case Tag::Tensor: { executorch::aten::Tensor tensor = evalue.toTensor(); long offset = write_tensor_or_raise_error(tensor); - etdump_Tensor_ref_t tensor_ref = + Result tensor_ref = add_tensor_entry(builder_, tensor, offset); + if (!tensor_ref.ok()) { + return tensor_ref.error(); + } etdump_Value_start(builder_); etdump_Value_val_add(builder_, etdump_ValueType_Tensor); - etdump_Value_tensor_add(builder_, tensor_ref); + etdump_Value_tensor_add(builder_, tensor_ref.get()); if (evalue_type == LoggedEValueType::kProgramOutput) { auto bool_ref = etdump_Bool_create(builder_, FLATBUFFERS_TRUE); etdump_Value_output_add(builder_, bool_ref); @@ -572,8 +590,12 @@ void ETDumpGen::log_evalue(const EValue& evalue, LoggedEValueType evalue_type) { etdump_Tensor_vec_start(builder_); for (size_t i = 0; i < tensors.size(); ++i) { long offset = write_tensor_or_raise_error(tensors[i]); - etdump_Tensor_vec_push( - builder_, add_tensor_entry(builder_, tensors[i], offset)); + Result tensor_ref = + add_tensor_entry(builder_, tensors[i], offset); + if (!tensor_ref.ok()) { + return tensor_ref.error(); + } + etdump_Tensor_vec_push(builder_, tensor_ref.get()); } etdump_Tensor_vec_ref_t tensor_vec_ref = etdump_Tensor_vec_end(builder_); etdump_TensorList_ref_t tensor_list_ref = @@ -645,6 +667,7 @@ void ETDumpGen::log_evalue(const EValue& evalue, LoggedEValueType evalue_type) { etdump_RunData_events_push_start(builder_); etdump_Event_debug_event_add(builder_, debug_event); etdump_RunData_events_push_end(builder_); + return true; } size_t ETDumpGen::get_num_blocks() { diff --git a/devtools/etdump/etdump_flatcc.h b/devtools/etdump/etdump_flatcc.h index dc0f822f922..3a186b5bbc3 100644 --- a/devtools/etdump/etdump_flatcc.h +++ b/devtools/etdump/etdump_flatcc.h @@ -102,7 +102,7 @@ class ETDumpGen : public ::executorch::runtime::EventTracer { size_t size) override; virtual ::executorch::runtime::AllocatorID track_allocator( const char* name) override; - virtual void log_evalue( + virtual Result log_evalue( const ::executorch::runtime::EValue& evalue, ::executorch::runtime::LoggedEValueType evalue_type = ::executorch::runtime::LoggedEValueType::kIntermediateOutput) diff --git a/runtime/core/event_tracer.h b/runtime/core/event_tracer.h index e810437f7d2..5bcdd0cfb1f 100644 --- a/runtime/core/event_tracer.h +++ b/runtime/core/event_tracer.h @@ -313,8 +313,11 @@ class EventTracer { * @param[in] evalue The value to be logged. * @param[in] evalue_type Indicates what type of output this is logging e.g. * an intermediate output, program output etc. + * @return A Result indicating the status of the logging operation. + * - True if the evalue output was successfully logged. + * - An error code if an error occurs during logging. */ - virtual void log_evalue( + virtual Result log_evalue( const EValue& evalue, LoggedEValueType evalue_type) = 0; diff --git a/runtime/core/test/event_tracer_test.cpp b/runtime/core/test/event_tracer_test.cpp index ee16aa924a6..1c9e1a446b9 100644 --- a/runtime/core/test/event_tracer_test.cpp +++ b/runtime/core/test/event_tracer_test.cpp @@ -161,9 +161,11 @@ class DummyEventTracer : public EventTracer { return true; } - void log_evalue(const EValue& evalue, LoggedEValueType evalue_type) override { + Result log_evalue(const EValue& evalue, LoggedEValueType evalue_type) + override { logged_evalue_ = evalue; logged_evalue_type_ = evalue_type; + return true; } EValue logged_evalue() { From 8845477ec5177ac2a397627e0509d9d05b46c87c Mon Sep 17 00:00:00 2001 From: Mergen Nachin Date: Thu, 10 Apr 2025 15:34:27 -0400 Subject: [PATCH 078/244] Llama exported model from executorch-community (#10064) We have the recipe and .pte file on ExecuTorch-Community on HF. So let's just use that. --- .../android/LlamaDemo/docs/delegates/xnnpack_README.md | 4 ++++ .../apple_ios/LLaMA/docs/delegates/xnnpack_README.md | 3 +++ examples/models/llama/README.md | 4 ++++ 3 files changed, 11 insertions(+) diff --git a/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md b/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md index c00ecb93154..cd4d3f0c7fb 100644 --- a/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md +++ b/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md @@ -57,6 +57,7 @@ Meta has released prequantized INT4 SpinQuant Llama 3.2 models that ExecuTorch s ``` python -m examples.models.llama.export_llama --model "llama3_2" --checkpoint --params -kv --use_sdpa_with_kv_cache -X -d fp32 --xnnpack-extended-ops --preq_mode 8da4w_output_8da8w --preq_group_size 32 --max_seq_length 2048 --max_context_length 2048 --preq_embedding_quantize 8,0 --use_spin_quant native --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' --output_name "llama3_2_spinquant.pte" ``` +For convenience, an [exported ExecuTorch SpinQuant model](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8-ET/blob/main/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8.pte) is available on Hugging Face. The export was created using [this detailed recipe notebook](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8-ET/blob/main/Export_Recipe_Llama_3_2_1B_Instruct_SpinQuant_INT4_EO8.ipynb). ### For Llama 3.2 1B and 3B QAT+LoRA models Meta has released prequantized INT4 QAT+LoRA Llama 3.2 models that ExecuTorch supports on the XNNPACK backend. @@ -64,6 +65,8 @@ Meta has released prequantized INT4 QAT+LoRA Llama 3.2 models that ExecuTorch su ``` python -m examples.models.llama.export_llama --model "llama3_2" --checkpoint --params -qat -lora 16 -kv --use_sdpa_with_kv_cache -X -d fp32 --xnnpack-extended-ops --preq_mode 8da4w_output_8da8w --preq_group_size 32 --max_seq_length 2048 --max_context_length 2048--preq_embedding_quantize 8,0 --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' --output_name "llama3_2_qat_lora.pte" ``` +For convenience, an [exported ExecuTorch QAT+LoRA model](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-QLORA_INT4_EO8-ET/blob/main/Llama-3.2-1B-Instruct-QLORA_INT4_EO8.pte) is available on Hugging Face. The export was created using [this detailed recipe notebook](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-QLORA_INT4_EO8-ET/blob/main/Export_Recipe_Llama_3_2_1B_Instruct_QLORA_INT4_EO8.ipynb). + ### For Llama 3.2 1B and 3B BF16 models We have supported BF16 as a data type on the XNNPACK backend for Llama 3.2 1B/3B models. @@ -73,6 +76,7 @@ We have supported BF16 as a data type on the XNNPACK backend for Llama 3.2 1B/3B ``` python -m examples.models.llama.export_llama --model "llama3_2" --checkpoint --params -kv --use_sdpa_with_kv_cache -X -d bf16 --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' --output_name="llama3_2_bf16.pte" ``` +For convenience, an [exported ExecuTorch bf16 model](https://huggingface.co/executorch-community/Llama-3.2-1B-ET/blob/main/llama3_2-1B.pte) is available on Hugging Face. The export was created using [this detailed recipe notebook](https://huggingface.co/executorch-community/Llama-3.2-1B-ET/blob/main/ExportRecipe_1B.ipynb). For more detail using Llama 3.2 lightweight models including prompt template, please go to our official [website](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_2#-llama-3.2-lightweight-models-(1b/3b)-). diff --git a/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md b/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md index f9cc3da1641..ffcb9f894b7 100644 --- a/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md +++ b/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md @@ -53,6 +53,7 @@ Meta has released prequantized INT4 SpinQuant Llama 3.2 models that ExecuTorch s ``` python -m examples.models.llama.export_llama --model "llama3_2" --checkpoint --params -kv --use_sdpa_with_kv_cache -X -d fp32 --xnnpack-extended-ops --preq_mode 8da4w_output_8da8w --preq_group_size 32 --max_seq_length 2048 --max_context_length 2048 --preq_embedding_quantize 8,0 --use_spin_quant native --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' --output_name "llama3_2_spinquant.pte" ``` +For convenience, an [exported ExecuTorch SpinQuant model](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8-ET/blob/main/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8.pte) is available on Hugging Face. The export was created using [this detailed recipe notebook](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8-ET/blob/main/Export_Recipe_Llama_3_2_1B_Instruct_SpinQuant_INT4_EO8.ipynb). ### For Llama 3.2 1B and 3B QAT+LoRA models Meta has released prequantized INT4 QAT+LoRA Llama 3.2 models that ExecuTorch supports on the XNNPACK backend. @@ -60,6 +61,7 @@ Meta has released prequantized INT4 QAT+LoRA Llama 3.2 models that ExecuTorch su ``` python -m examples.models.llama.export_llama --model "llama3_2" --checkpoint --params -qat -lora 16 -kv --use_sdpa_with_kv_cache -X -d fp32 --xnnpack-extended-ops --preq_mode 8da4w_output_8da8w --preq_group_size 32 --max_seq_length 2048 --max_context_length 2048 --preq_embedding_quantize 8,0 --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' --output_name "llama3_2_qat_lora.pte" ``` +For convenience, an [exported ExecuTorch QAT+LoRA model](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-QLORA_INT4_EO8-ET/blob/main/Llama-3.2-1B-Instruct-QLORA_INT4_EO8.pte) is available on Hugging Face. The export was created using [this detailed recipe notebook](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-QLORA_INT4_EO8-ET/blob/main/Export_Recipe_Llama_3_2_1B_Instruct_QLORA_INT4_EO8.ipynb). ### For Llama 3.2 1B and 3B BF16 models We have supported BF16 as a data type on the XNNPACK backend for Llama 3.2 1B/3B models. @@ -69,6 +71,7 @@ We have supported BF16 as a data type on the XNNPACK backend for Llama 3.2 1B/3B ``` python -m examples.models.llama.export_llama --model "llama3_2" --checkpoint --params -kv --use_sdpa_with_kv_cache -X -d bf16 --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' --output_name="llama3_2_bf16.pte" ``` +For convenience, an [exported ExecuTorch bf16 model](https://huggingface.co/executorch-community/Llama-3.2-1B-ET/blob/main/llama3_2-1B.pte) is available on Hugging Face. The export was created using [this detailed recipe notebook](https://huggingface.co/executorch-community/Llama-3.2-1B-ET/blob/main/ExportRecipe_1B.ipynb). For more detail using Llama 3.2 lightweight models including prompt template, please go to our official [website](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_2#-llama-3.2-lightweight-models-(1b/3b)-). diff --git a/examples/models/llama/README.md b/examples/models/llama/README.md index 39e4c79873b..a5f513e931d 100644 --- a/examples/models/llama/README.md +++ b/examples/models/llama/README.md @@ -177,6 +177,7 @@ python -m examples.models.llama.export_llama \ --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' \ --output_name="llama3_2.pte" ``` +For convenience, an [exported ExecuTorch bf16 model](https://huggingface.co/executorch-community/Llama-3.2-1B-ET/blob/main/llama3_2-1B.pte) is available on Hugging Face. The export was created using [this detailed recipe notebook](https://huggingface.co/executorch-community/Llama-3.2-1B-ET/blob/main/ExportRecipe_1B.ipynb). - To use **SpinQuant**, here are two ways: - Download directly from [Llama website](https://www.llama.com/llama-downloads). The model weights are prequantized and can be exported to `pte` file directly. @@ -206,6 +207,8 @@ python -m examples.models.llama.export_llama \ --use_spin_quant native \ --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' ``` +For convenience, an [exported ExecuTorch SpinQuant model](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8-ET/blob/main/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8.pte) is available on Hugging Face. The export was created using [this detailed recipe notebook](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8-ET/blob/main/Export_Recipe_Llama_3_2_1B_Instruct_SpinQuant_INT4_EO8.ipynb). + - To use **QAT+LoRA**, download directly from [Llama website](https://www.llama.com/llama-downloads). The model weights are prequantized and can be exported to `pte` file directly by: @@ -234,6 +237,7 @@ python -m examples.models.llama.export_llama \ --output_name "llama3_2.pte" \ --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001]}' ``` +For convenience, an [exported ExecuTorch QAT+LoRA model](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-QLORA_INT4_EO8-ET/blob/main/Llama-3.2-1B-Instruct-QLORA_INT4_EO8.pte) is available on Hugging Face. The export was created using [this detailed recipe notebook](https://huggingface.co/executorch-community/Llama-3.2-1B-Instruct-QLORA_INT4_EO8-ET/blob/main/Export_Recipe_Llama_3_2_1B_Instruct_QLORA_INT4_EO8.ipynb). ### Option B: Download and export Llama 3 8B instruct model From bdef23f728795168b3526be513cb04245feac59e Mon Sep 17 00:00:00 2001 From: DannyYuyang-quic Date: Fri, 11 Apr 2025 04:15:11 +0800 Subject: [PATCH 079/244] Qualcomm AI Engine Direct - Support tile op for different I/O rank (#10054) Summary: - Support if the rank of input tensor is less than the rank of output tensor. - make_quantizer kwargs alignment. - Remove module.eval() since calling eval() is not supported for exported models. ### Test plan ``` bash python -m backends.qualcomm.tests.test_qnn_delegate TestQNNQuantizedOperator.test_qnn_backend_expand -s ${device_id} -H ${host_id} -m ${soc} -b build-android ``` --- .../_passes/expand_broadcast_tensor_shape.py | 4 ++ backends/qualcomm/_passes/utils.py | 2 +- backends/qualcomm/tests/test_qnn_delegate.py | 44 ++++++++++++++----- backends/qualcomm/tests/utils.py | 4 +- examples/qualcomm/utils.py | 6 +-- 5 files changed, 44 insertions(+), 16 deletions(-) diff --git a/backends/qualcomm/_passes/expand_broadcast_tensor_shape.py b/backends/qualcomm/_passes/expand_broadcast_tensor_shape.py index 277fc9c6ce8..829b3757e06 100644 --- a/backends/qualcomm/_passes/expand_broadcast_tensor_shape.py +++ b/backends/qualcomm/_passes/expand_broadcast_tensor_shape.py @@ -22,12 +22,16 @@ def __init__(self): exir_ops.edge.aten.sub.Tensor, exir_ops.edge.aten.mul.Tensor, exir_ops.edge.aten.div.Tensor, + # Support if the rank of input tensor: {input_dims} is less than the rank of output tensor: {output_dims}. + exir_ops.edge.aten.expand_copy.default, ] def traverse_broadcast_node(self, graph_module: torch.fx.GraphModule): for node in graph_module.graph.nodes: if node.target in self.broadcast_op_targets: for arg in node.args: + if not isinstance(arg, torch.fx.Node): + continue input_rank = len(arg.meta["val"].shape) output_rank = len(node.meta["val"].shape) if input_rank != output_rank: diff --git a/backends/qualcomm/_passes/utils.py b/backends/qualcomm/_passes/utils.py index 6b7dc8c16fa..46d9e0cde76 100755 --- a/backends/qualcomm/_passes/utils.py +++ b/backends/qualcomm/_passes/utils.py @@ -107,7 +107,7 @@ def get_passes_dependency_for_capture_program(): ConvertUpsampleBicubicWithBilinear: [RemoveRedundancy], DecomposeAny: [RemoveRedundancy], DecomposeLinalgVectorNorm: [RemoveRedundancy], - ExpandBroadcastTensorShape: [RemoveRedundancy], + ExpandBroadcastTensorShape: [FoldQDQ], FixedLinearKeepDim: [FoldQDQ], FoldQDQ: [AnnotateQuantAttrs, AnnotateStack, AnnotateUnbind], I64toI32: [ConvertUpsampleBicubicWithBilinear, RemoveRedundancy], diff --git a/backends/qualcomm/tests/test_qnn_delegate.py b/backends/qualcomm/tests/test_qnn_delegate.py index 895326c743a..6579dc7c468 100644 --- a/backends/qualcomm/tests/test_qnn_delegate.py +++ b/backends/qualcomm/tests/test_qnn_delegate.py @@ -69,7 +69,11 @@ from collections import defaultdict from typing import List -from executorch.backends.qualcomm._passes import FoldQDQ, TagQuantIO +from executorch.backends.qualcomm._passes import ( + ExpandBroadcastTensorShape, + FoldQDQ, + TagQuantIO, +) from executorch.backends.qualcomm.builders.node_visitor import get_node_visitors from executorch.backends.qualcomm.debugger.utils import DrawGraph from executorch.examples.models.deeplab_v3 import DeepLabV3ResNet101Model @@ -435,10 +439,20 @@ def test_qnn_backend_equal(self): def test_qnn_backend_expand(self): modules = [ExpandAs(), ExpandCopy()] # noqa: F405 - sample_input = (torch.randn([3, 1]),) - for i, module in enumerate(modules): - with self.subTest(i=i): - self.lower_module_and_test_output(module, sample_input) + sample_inputs = [ + (torch.randn([3, 1]),), + (torch.randn([4]),), + ] + passes_job = get_capture_program_passes() + passes_job[ExpandBroadcastTensorShape][QCOM_PASS_ACTIVATE_KEY] = True + index = 0 + for module in modules: + for sample_input in sample_inputs: + with self.subTest(i=index): + self.lower_module_and_test_output( + module, sample_input, passes_job=passes_job + ) + index += 1 def test_qnn_backend_expm1(self): sample_input = (torch.randn(3, 4, 5),) @@ -1517,11 +1531,21 @@ def test_qnn_backend_equal(self): def test_qnn_backend_expand(self): modules = [ExpandAs(), ExpandCopy()] # noqa: F405 - sample_input = (torch.randn([3, 1]),) - for i, module in enumerate(modules): - with self.subTest(i=i): - module = self.get_qdq_module(module, sample_input) - self.lower_module_and_test_output(module, sample_input) + sample_inputs = [ + (torch.randn([3, 1]),), + (torch.randn([4]),), + ] + passes_job = get_capture_program_passes() + passes_job[ExpandBroadcastTensorShape][QCOM_PASS_ACTIVATE_KEY] = True + index = 0 + for module in modules: + for sample_input in sample_inputs: + with self.subTest(i=index): + module = self.get_qdq_module(module, sample_input) + self.lower_module_and_test_output( + module, sample_input, passes_job=passes_job + ) + index += 1 def test_qnn_backend_expm1(self): sample_input = (torch.randn(3, 4, 5),) diff --git a/backends/qualcomm/tests/utils.py b/backends/qualcomm/tests/utils.py index a3006eff8f6..1d8040ef988 100644 --- a/backends/qualcomm/tests/utils.py +++ b/backends/qualcomm/tests/utils.py @@ -9,7 +9,7 @@ import subprocess import tempfile import unittest -from typing import Callable, Dict, List, Optional, Tuple +from typing import Callable, Dict, List, Optional, OrderedDict, Tuple import numpy as np import torch @@ -435,6 +435,7 @@ def lower_module_and_test_output( expected_profile_events: int = -1, expected_intermediate_events: int = -1, assert_output_equal: bool = True, + passes_job: Optional[OrderedDict] = None, skip_node_id_set: set = None, skip_node_op_set: set = None, dynamic_shapes: Dict = None, @@ -508,7 +509,6 @@ def get_qdq_module( block_size_map: Dict[str, Tuple] = None, submodule_qconfig_list: Optional[List[Tuple[Callable, ModuleQConfig]]] = None, ) -> torch.fx.GraphModule: - module = module.eval() m = torch.export.export( module, inputs, dynamic_shapes=dynamic_shapes, strict=True ).module() diff --git a/examples/qualcomm/utils.py b/examples/qualcomm/utils.py index 3aecf405fff..83e5b9a0442 100755 --- a/examples/qualcomm/utils.py +++ b/examples/qualcomm/utils.py @@ -262,7 +262,7 @@ def make_quantizer( per_channel_linear=False, act_observer=MovingAverageMinMaxObserver, is_qat=False, - callback_qconfig_list: Optional[List[Tuple[Callable, ModuleQConfig]]] = None, + submodule_qconfig_list: Optional[List[Tuple[Callable, ModuleQConfig]]] = None, ): quantizer = QnnQuantizer() quantizer.add_custom_quant_annotations(custom_annotations) @@ -273,8 +273,8 @@ def make_quantizer( is_linear_per_channel=per_channel_linear, act_observer=act_observer, ) - callback_qconfig_list = callback_qconfig_list or [] - quantizer.set_submodule_qconfig_list(callback_qconfig_list) + submodule_qconfig_list = submodule_qconfig_list or [] + quantizer.set_submodule_qconfig_list(submodule_qconfig_list) return quantizer From ab2a2f382b40c5f56bf28801f31dc480f4f358d5 Mon Sep 17 00:00:00 2001 From: lucylq Date: Thu, 10 Apr 2025 14:47:16 -0700 Subject: [PATCH 080/244] llama doc update (#10082) See error from: https://github.com/pytorch/executorch/issues/10063 --- examples/models/llama/README.md | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/examples/models/llama/README.md b/examples/models/llama/README.md index a5f513e931d..93ef71769b5 100644 --- a/examples/models/llama/README.md +++ b/examples/models/llama/README.md @@ -548,3 +548,22 @@ clang: error: linker command failed with exit code 1 (use -v to see invocation) ``` It's a known issue for Xcode version 15.1. Mitigation: update to most recent Xcode version, clean and rebuild. + +- If you encounter issues with missing abseil-cpp or re2, try running `git submodule update --init --recursive` to pull in those submodules. +Example error: +``` +CMake Error at runner/CMakeLists.txt:68 (add_subdirectory): + The source directory + + /Users/../executorch/extension/llm/tokenizers/third-party/abseil-cpp + + does not contain a CMakeLists.txt file. + + +CMake Error at runner/CMakeLists.txt:72 (add_subdirectory): + The source directory + + /Users/../executorch/extension/llm/tokenizers/third-party/re2 + + does not contain a CMakeLists.txt file. +``` From 2707f96d5bf4eaa827670036fea48b9d45547035 Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Thu, 10 Apr 2025 15:36:07 -0700 Subject: [PATCH 081/244] Update using-executorch-android.md (#10084) scripts/build_android_library.sh will no longer build demo app. --- docs/source/using-executorch-android.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/docs/source/using-executorch-android.md b/docs/source/using-executorch-android.md index 4528de55219..2b0d04da6c7 100644 --- a/docs/source/using-executorch-android.md +++ b/docs/source/using-executorch-android.md @@ -96,8 +96,7 @@ Now you can compile your app with the ExecuTorch Android library. ## Building from Source -`scripts/build_android_library.sh` is a helper script to build the Java library (into .jar), native library (into .so), and the packaged AAR file. It can also build -demo apps to showcase the AAR is integrated into a user app correctly. +`scripts/build_android_library.sh` is a helper script to build the Java library (into .jar), native library (into .so), and the packaged AAR file. You need Android [SDK](https://developer.android.com/studio) and [NDK](https://developer.android.com/ndk/downloads) to use it. From fa07c0d070cb0869f1c3fa6faca0cba65b95c489 Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Thu, 10 Apr 2025 15:49:32 -0700 Subject: [PATCH 082/244] Update LlamaDemo AAR build docs (#10086) --- .../docs/delegates/mediatek_README.md | 22 ++++++++++- .../docs/delegates/qualcomm_README.md | 38 +++++++++++-------- .../docs/delegates/xnnpack_README.md | 31 ++++++++------- 3 files changed, 61 insertions(+), 30 deletions(-) diff --git a/examples/demo-apps/android/LlamaDemo/docs/delegates/mediatek_README.md b/examples/demo-apps/android/LlamaDemo/docs/delegates/mediatek_README.md index dcfd07918cd..abd3c5f31b9 100644 --- a/examples/demo-apps/android/LlamaDemo/docs/delegates/mediatek_README.md +++ b/examples/demo-apps/android/LlamaDemo/docs/delegates/mediatek_README.md @@ -126,13 +126,31 @@ The Mediatek runner (`examples/mediatek/executor_runner/mtk_llama_runner.cpp`) c ## Build AAR Library +1. Open a terminal window and navigate to the root directory of the executorch +2. Set the following environment variables: +```sh +export ANDROID_NDK= +export ANDROID_ABIS=arm64-v8a +export NEURON_BUFFER_ALLOCATOR_LIB= +``` +*Note: is the root for the NDK, which is usually under ~/Library/Android/sdk/ndk/XX.Y.ZZZZZ for macOS, and contains NOTICE and README.md. We use /build/cmake/android.toolchain.cmake for CMake to cross-compile.* -Next we need to build and compile the MediaTek backend and MediaTek Llama runner. By setting `NEURON_BUFFER_ALLOCATOR_LIB`, the script will build the MediaTek backend. +3. Create a directory to hold the AAR +```sh +mkdir -p aar-out +export BUILD_AAR_DIR=aar-out ``` + +4. Run the following command to build the AAR: +```sh sh scripts/build_android_library.sh ``` -**Output**: This will generate an .aar file that is already imported into the expected directory for the Android app. It will live in `examples/demo-apps/android/Llamademo/app/libs`. +5. Copy the AAR to the app: +```sh +mkdir -p examples/demo-apps/android/LlamaDemo/app/libs +cp aar-out/executorch.aar examples/demo-apps/android/LlamaDemo/app/libs/executorch.aar +``` If you were to unzip the .aar file or open it in Android Studio, verify it contains the following related to MediaTek backend: * libneuron_buffer_allocator.so diff --git a/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md b/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md index e8bc1dc1d26..fb9df3c3375 100644 --- a/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md +++ b/examples/demo-apps/android/LlamaDemo/docs/delegates/qualcomm_README.md @@ -178,30 +178,38 @@ adb push tokenizer.bin /data/local/tmp/llama ## Build AAR Library -Open a terminal window and navigate to the root directory of the executorch. +1. Open a terminal window and navigate to the root directory of the executorch Set the following environment variables: -``` +```sh export ANDROID_NDK= -export ANDROID_ABI=arm64-v8a -``` -Note: is the root for the NDK, which is usually under ~/Library/Android/sdk/ndk/XX.Y.ZZZZZ for macOS, and contains NOTICE and README.md. We use /build/cmake/android.toolchain.cmake for CMake to cross-compile. -Build the Android Java extension code: +export ANDROID_ABIS=arm64-v8a +export QNN_SDK_ROOT= ``` -pushd extension/android -./gradlew build -popd + +*Note: is the root for the NDK, which is usually under ~/Library/Android/sdk/ndk/XX.Y.ZZZZZ for macOS, and contains NOTICE and README.md. We use /build/cmake/android.toolchain.cmake for CMake to cross-compile.* + +3. Create a directory to hold the AAR +```sh +mkdir -p aar-out +export BUILD_AAR_DIR=aar-out ``` -Run the following command set up the required JNI library: + +4. Run the following command to build the AAR: +```sh +sh scripts/build_android_library.sh ``` -pushd examples/demo-apps/android/LlamaDemo -./gradlew :app:setupQnn -popd + +5. Copy the AAR to the app: +```sh +mkdir -p examples/demo-apps/android/LlamaDemo/app/libs +cp aar-out/executorch.aar examples/demo-apps/android/LlamaDemo/app/libs/executorch.aar ``` + Alternative you can also just run the shell script directly as in the root directory: -``` +```sh sh examples/demo-apps/android/LlamaDemo/setup-with-qnn.sh ``` -This is running the shell script which configures the required core ExecuTorch, Llama2/3, and Android libraries, builds them, and copies them to jniLibs. +This is running the shell script which configures the required core ExecuTorch, Llama2/3, and Android libraries, builds them into AAR, and copies it to the app. Note: If you are building the Android app mentioned in the next section on a separate machine (i.e. MacOS but building and exporting for QNN backend on Linux), make sure you copy the aar file generated from setup-with-qnn script to “examples/demo-apps/android/LlamaDemo/app/libs” before building the Android app. diff --git a/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md b/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md index cd4d3f0c7fb..cce21749979 100644 --- a/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md +++ b/examples/demo-apps/android/LlamaDemo/docs/delegates/xnnpack_README.md @@ -132,30 +132,35 @@ adb push tokenizer.bin /data/local/tmp/llama ## Build AAR Library 1. Open a terminal window and navigate to the root directory of the executorch 2. Set the following environment variables: -``` +```sh export ANDROID_NDK= -export ANDROID_ABI=arm64-v8a +export ANDROID_ABIS=arm64-v8a ``` *Note: is the root for the NDK, which is usually under ~/Library/Android/sdk/ndk/XX.Y.ZZZZZ for macOS, and contains NOTICE and README.md. We use /build/cmake/android.toolchain.cmake for CMake to cross-compile.* -3. Build the Android Java extension code: -``` -pushd extension/android -./gradlew build -popd +3. Create a directory to hold the AAR +```sh +mkdir -p aar-out +export BUILD_AAR_DIR=aar-out ``` -4. Run the following command set up the required JNI library: + +4. Run the following command to build the AAR: +```sh +sh scripts/build_android_library.sh ``` -pushd examples/demo-apps/android/LlamaDemo -./gradlew :app:setup -popd + +5. Copy the AAR to the app: +```sh +mkdir -p examples/demo-apps/android/LlamaDemo/app/libs +cp aar-out/executorch.aar examples/demo-apps/android/LlamaDemo/app/libs/executorch.aar ``` + Alternative you can also just run the shell script directly as in the root directory: -``` +```sh sh examples/demo-apps/android/LlamaDemo/setup.sh ``` -This is running the shell script which configures the required core ExecuTorch, Llama2/3, and Android libraries, builds them, and copies them to jniLibs. +This is running the shell script which configures the required core ExecuTorch, Llama2/3, and Android libraries, builds them into AAR, and copies it to the app. **Output**: The executorch.aar file will be generated in a newly created folder in the example/demo-apps/android/LlamaDemo/app/libs directory. This is the path that the Android app expects it to be in. From 614b35cc579a437c54c7a581c3f017eb557b5855 Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Thu, 10 Apr 2025 15:53:05 -0700 Subject: [PATCH 083/244] Improve android instrumentation test experience and add docs (#9989) Add README for Android Make instrumentation test easier for users on their local development. --- .ci/scripts/build_android_instrumentation.sh | 21 --------- .github/workflows/_android.yml | 3 +- extension/android/README.md | 46 +++++++++++++++++++ .../executorch_android/android_test_setup.sh | 28 +++++++++++ .../android/executorch_android/build.gradle | 4 ++ .../LlmModuleInstrumentationTest.java | 2 +- scripts/build_android_library.sh | 2 + 7 files changed, 83 insertions(+), 23 deletions(-) delete mode 100644 .ci/scripts/build_android_instrumentation.sh create mode 100644 extension/android/README.md create mode 100644 extension/android/executorch_android/android_test_setup.sh diff --git a/.ci/scripts/build_android_instrumentation.sh b/.ci/scripts/build_android_instrumentation.sh deleted file mode 100644 index 747dd7063a2..00000000000 --- a/.ci/scripts/build_android_instrumentation.sh +++ /dev/null @@ -1,21 +0,0 @@ -#!/bin/bash -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD-style license found in the -# LICENSE file in the root directory of this source tree. - -set -ex - -if [[ -z "${PYTHON_EXECUTABLE:-}" ]]; then - PYTHON_EXECUTABLE=python3 -fi -which "${PYTHON_EXECUTABLE}" - -mkdir -p extension/android/executorch_android/src/androidTest/resources -cp extension/module/test/resources/add.pte extension/android/executorch_android/src/androidTest/resources - -pushd extension/android -ANDROID_HOME="${ANDROID_SDK:-/opt/android/sdk}" ./gradlew :executorch_android:testDebugUnitTest -ANDROID_HOME="${ANDROID_SDK:-/opt/android/sdk}" ./gradlew :executorch_android:assembleAndroidTest -popd diff --git a/.github/workflows/_android.yml b/.github/workflows/_android.yml index 6598b6f3b42..8378d7ed4d9 100644 --- a/.github/workflows/_android.yml +++ b/.github/workflows/_android.yml @@ -36,7 +36,8 @@ jobs: cp ${BUILD_AAR_DIR}/executorch.aar $ARTIFACTS_DIR_NAME mkdir -p ${ARTIFACTS_DIR_NAME}/library_test_dir - bash .ci/scripts/build_android_instrumentation.sh + bash extension/android/executorch_android/android_test_setup.sh + (cd extension/android; ANDROID_HOME="${ANDROID_SDK:-/opt/android/sdk}" ./gradlew :executorch_android:assembleAndroidTest) cp extension/android/executorch_android/build/outputs/apk/androidTest/debug/executorch_android-debug-androidTest.apk "${ARTIFACTS_DIR_NAME}/library_test_dir" mkdir -p ${ARTIFACTS_DIR_NAME}/fp32-xnnpack-custom diff --git a/extension/android/README.md b/extension/android/README.md new file mode 100644 index 00000000000..8972e615173 --- /dev/null +++ b/extension/android/README.md @@ -0,0 +1,46 @@ +# ExecuTorch Android + +This directory contains the Android Java/Kotlin binding. The final product is an AAR, +which contains the `.so` libraries for c++ runtime, and `.jar` for Java API, and required +metadata `AndroidManifest.xml`. + +## Core contents + +Under `extension/android/`, + +- `executorch_android/` is the root for the Java `org.pytorch.executorch` package + - `src/` + - `androidTest/` contains the android instrumentation test source + - `main/` contains the Java source + - `test/` contains the Java unit test source + - `build.gradle` is the rule to build the Java package. +- `jni/` contains the JNI layer code, which depends on the ExecuTorch c++ runtime library. +- `CMakeLists.txt` is the rule for building the JNI library. + +## Build + +`scripts/build_android_library.sh` is a helper script to build the Java library (into .jar), native library (into .so), and the packaged AAR file. + +The usage is: +```sh +export ANDROID_HOME=/path/to/sdk +export ANDROID_NDK=/path/to/ndk +sh scripts/build_android_library.sh +``` + +Please see [Android building from source](https://pytorch.org/executorch/main/using-executorch-android.html#building-from-source) for details + +## Test + +After the library is built, + +```sh +# Set up models for testing +sh executorch_android/android_test_setup.sh + +# Run unit test +./gradlew :executorch_android:testDebugUnitTest + +# Run instrumentation test +./gradlew :executorch_android:connectedAndroidTest +``` diff --git a/extension/android/executorch_android/android_test_setup.sh b/extension/android/executorch_android/android_test_setup.sh new file mode 100644 index 00000000000..68f4d9dd6f6 --- /dev/null +++ b/extension/android/executorch_android/android_test_setup.sh @@ -0,0 +1,28 @@ +#!/bin/bash +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +set -ex + +if [[ -z "${PYTHON_EXECUTABLE:-}" ]]; then + PYTHON_EXECUTABLE=python3 +fi +which "${PYTHON_EXECUTABLE}" + +BASEDIR=$(dirname "$0") +cp "${BASEDIR}/../../../extension/module/test/resources/add.pte" "${BASEDIR}/src/androidTest/resources" + +pushd "${BASEDIR}/../../../" +curl -Ls "https://huggingface.co/karpathy/tinyllamas/resolve/main/stories110M.pt" --output stories110M.pt +curl -Ls "https://raw.githubusercontent.com/karpathy/llama2.c/master/tokenizer.model" --output tokenizer.model +# Create params.json file +touch params.json +echo '{"dim": 768, "multiple_of": 32, "n_heads": 12, "n_layers": 12, "norm_eps": 1e-05, "vocab_size": 32000}' > params.json +python -m examples.models.llama.export_llama -c stories110M.pt -p params.json -X -kv --model=stories110m + +cp *.pte "${BASEDIR}/src/androidTest/resources/stories.pte" +cp tokenizer.model "${BASEDIR}/src/androidTest/resources/tokenizer.bin" +popd diff --git a/extension/android/executorch_android/build.gradle b/extension/android/executorch_android/build.gradle index 70e18289442..b1bc090759a 100644 --- a/extension/android/executorch_android/build.gradle +++ b/extension/android/executorch_android/build.gradle @@ -36,6 +36,10 @@ android { } } +task copyTestRes(type: Exec) { + commandLine 'bash', 'android_test_setup.sh' +} + dependencies { implementation 'com.facebook.fbjni:fbjni:0.5.1' implementation 'com.facebook.soloader:nativeloader:0.10.5' diff --git a/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/LlmModuleInstrumentationTest.java b/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/LlmModuleInstrumentationTest.java index b3b515d7ed0..ae81a21f420 100644 --- a/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/LlmModuleInstrumentationTest.java +++ b/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/LlmModuleInstrumentationTest.java @@ -40,7 +40,7 @@ /** Unit tests for {@link org.pytorch.executorch.extension.llm.LlmModule}. */ @RunWith(AndroidJUnit4.class) public class LlmModuleInstrumentationTest implements LlmCallback { - private static String TEST_FILE_NAME = "/tinyllama_portable_fp16_h.pte"; + private static String TEST_FILE_NAME = "/stories.pte"; private static String TOKENIZER_FILE_NAME = "/tokenizer.bin"; private static String TEST_PROMPT = "Hello"; private static int OK = 0x00; diff --git a/scripts/build_android_library.sh b/scripts/build_android_library.sh index 20450218de6..a32ac5f4e5b 100755 --- a/scripts/build_android_library.sh +++ b/scripts/build_android_library.sh @@ -119,6 +119,8 @@ build_aar() { fi pushd extension/android/ ANDROID_HOME="${ANDROID_SDK:-/opt/android/sdk}" ./gradlew build + # Use java unit test as sanity check + ANDROID_HOME="${ANDROID_SDK:-/opt/android/sdk}" ./gradlew :executorch_android:testDebugUnitTest popd cp extension/android/executorch_android/build/outputs/aar/executorch_android-debug.aar "${BUILD_AAR_DIR}/executorch.aar" } From 7e3dfed07d3af53a9af239c5d51074be2df9dc48 Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Thu, 10 Apr 2025 16:23:55 -0700 Subject: [PATCH 084/244] Update backends-xnnpack.md (#10024) Update XNNPACK docs --- docs/source/backends-xnnpack.md | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/docs/source/backends-xnnpack.md b/docs/source/backends-xnnpack.md index 45055ea4e13..db1c055dc9c 100644 --- a/docs/source/backends-xnnpack.md +++ b/docs/source/backends-xnnpack.md @@ -28,6 +28,7 @@ the core ExecuTorch runtime. To target the XNNPACK backend during the export and lowering process, pass an instance of the `XnnpackPartitioner` to `to_edge_transform_and_lower`. The example below demonstrates this process using the MobileNet V2 model from torchvision. ```python +import torch import torchvision.models as models from torchvision.models.mobilenetv2 import MobileNet_V2_Weights from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner @@ -47,10 +48,10 @@ with open("mv2_xnnpack.pte", "wb") as file: ### Partitioner API -The XNNPACK partitioner API allows for configuration of the model delegation to XNNPACK. Passing an `XnnpackPartitioner` instance with no additional parameters will run as much of the model as possible on the XNNPACK backend. This is the most common use-case. For advanced use cases, the partitioner exposes the following options via the [constructor](https://github.com/pytorch/executorch/blob/14ff52ff89a89c074fc6c14d3f01683677783dcd/backends/xnnpack/partition/xnnpack_partitioner.py#L31): +The XNNPACK partitioner API allows for configuration of the model delegation to XNNPACK. Passing an `XnnpackPartitioner` instance with no additional parameters will run as much of the model as possible on the XNNPACK backend. This is the most common use-case. For advanced use cases, the partitioner exposes the following options via the [constructor](https://github.com/pytorch/executorch/blob/release/0.6/backends/xnnpack/partition/xnnpack_partitioner.py#L31): - - `configs`: Control which operators are delegated to XNNPACK. By default, all available operators all delegated. See [../config/\_\_init\_\_.py](https://github.com/pytorch/executorch/blob/14ff52ff89a89c074fc6c14d3f01683677783dcd/backends/xnnpack/partition/config/__init__.py#L66) for an exhaustive list of available operator configs. - - `config_precisions`: Filter operators by data type. By default, delegate all precisions. One or more of `ConfigPrecisionType.FP32`, `ConfigPrecisionType.STATIC_QUANT`, or `ConfigPrecisionType.DYNAMIC_QUANT`. See [ConfigPrecisionType](https://github.com/pytorch/executorch/blob/14ff52ff89a89c074fc6c14d3f01683677783dcd/backends/xnnpack/partition/config/xnnpack_config.py#L24). + - `configs`: Control which operators are delegated to XNNPACK. By default, all available operators all delegated. See [../config/\_\_init\_\_.py](https://github.com/pytorch/executorch/blob/release/0.6/backends/xnnpack/partition/config/__init__.py#L66) for an exhaustive list of available operator configs. + - `config_precisions`: Filter operators by data type. By default, delegate all precisions. One or more of `ConfigPrecisionType.FP32`, `ConfigPrecisionType.STATIC_QUANT`, or `ConfigPrecisionType.DYNAMIC_QUANT`. See [ConfigPrecisionType](https://github.com/pytorch/executorch/blob/release/0.6/backends/xnnpack/partition/config/xnnpack_config.py#L24). - `per_op_mode`: If true, emit individual delegate calls for every operator. This is an advanced option intended to reduce memory overhead in some contexts at the cost of a small amount of runtime overhead. Defaults to false. - `verbose`: If true, print additional information during lowering. @@ -87,15 +88,23 @@ To perform 8-bit quantization with the PT2E flow, perform the following steps pr The output of `convert_pt2e` is a PyTorch model which can be exported and lowered using the normal flow. As it is a regular PyTorch model, it can also be used to evaluate the accuracy of the quantized model using standard PyTorch techniques. ```python +import torch +import torchvision.models as models +from torchvision.models.mobilenetv2 import MobileNet_V2_Weights from executorch.backends.xnnpack.quantizer.xnnpack_quantizer import XNNPACKQuantizer +from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner +from executorch.exir import to_edge_transform_and_lower from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e from torch.ao.quantization.quantizer.xnnpack_quantizer import get_symmetric_quantization_config +model = models.mobilenetv2.mobilenet_v2(weights=MobileNet_V2_Weights.DEFAULT).eval() +sample_inputs = (torch.randn(1, 3, 224, 224), ) + qparams = get_symmetric_quantization_config(is_per_channel=True) # (1) quantizer = XNNPACKQuantizer() quantizer.set_global(qparams) -training_ep = torch.export.export_for_training(model, sample_inputs).module(), # (2) +training_ep = torch.export.export_for_training(model, sample_inputs).module() # (2) prepared_model = prepare_pt2e(training_ep, quantizer) # (3) for cal_sample in [torch.randn(1, 3, 224, 224)]: # Replace with representative model inputs From defd83b97036aa2fa20d13477394a630191970aa Mon Sep 17 00:00:00 2001 From: Digant Desai Date: Thu, 10 Apr 2025 18:53:09 -0500 Subject: [PATCH 085/244] Arm backend: Tosa tools update (#9451) Differential Revision: D72071739 Pull Request resolved: https://github.com/pytorch/executorch/pull/10075 --- .mypy.ini | 3 + backends/arm/operators/node_visitor.py | 3 +- backends/arm/operators/op_abs.py | 8 +- backends/arm/operators/op_add.py | 7 +- backends/arm/operators/op_amax.py | 5 +- backends/arm/operators/op_amin.py | 5 +- backends/arm/operators/op_any.py | 5 +- backends/arm/operators/op_avg_pool2d.py | 3 +- backends/arm/operators/op_bmm.py | 6 +- backends/arm/operators/op_cat.py | 8 +- backends/arm/operators/op_clamp.py | 7 +- backends/arm/operators/op_constant_pad_nd.py | 8 +- backends/arm/operators/op_conv2d.py | 3 +- backends/arm/operators/op_eq.py | 5 +- backends/arm/operators/op_erf.py | 4 +- backends/arm/operators/op_exp.py | 6 +- backends/arm/operators/op_full.py | 2 +- backends/arm/operators/op_ge.py | 5 +- backends/arm/operators/op_get_item.py | 6 +- backends/arm/operators/op_gt.py | 5 +- backends/arm/operators/op_le.py | 5 +- backends/arm/operators/op_log.py | 6 +- backends/arm/operators/op_lt.py | 5 +- backends/arm/operators/op_max_pool2d.py | 6 +- backends/arm/operators/op_maximum.py | 6 +- backends/arm/operators/op_minimum.py | 6 +- backends/arm/operators/op_mul.py | 9 +- backends/arm/operators/op_permute.py | 6 +- backends/arm/operators/op_pow.py | 5 +- backends/arm/operators/op_reciprocal.py | 8 +- backends/arm/operators/op_repeat.py | 7 +- backends/arm/operators/op_rescale.py | 4 +- backends/arm/operators/op_rshift_tensor.py | 6 +- backends/arm/operators/op_rsqrt.py | 6 +- backends/arm/operators/op_sigmoid.py | 6 +- backends/arm/operators/op_slice.py | 5 +- backends/arm/operators/op_sub.py | 7 +- backends/arm/operators/op_sum.py | 7 +- backends/arm/operators/op_table.py | 7 +- backends/arm/operators/op_tanh.py | 5 +- backends/arm/operators/op_to_copy.py | 5 +- .../arm/operators/op_to_dim_order_copy.py | 5 +- backends/arm/operators/op_transpose.py | 6 +- .../arm/operators/op_upsample_nearest2d.py | 8 +- backends/arm/operators/op_view.py | 5 +- backends/arm/operators/op_where.py | 4 +- backends/arm/operators/ops_binary.py | 18 +- backends/arm/operators/ops_unary.py | 10 +- backends/arm/process_node.py | 2 +- .../arm/scripts/install_reference_model.sh | 52 ++++ backends/arm/scripts/utils.sh | 3 +- backends/arm/test/conftest.py | 2 +- backends/arm/test/runner_utils.py | 4 +- backends/arm/test/tester/arm_tester.py | 4 +- ...to-be-namespaced-into-tosa-tools.v0_.patch | 154 ++++++++++ ...-serializer-lib-to-be-self-contained.patch | 283 ++++++++++++++++++ backends/arm/tosa_backend.py | 2 +- backends/arm/tosa_mapping.py | 3 +- backends/arm/tosa_quant_utils.py | 24 +- backends/arm/tosa_utils.py | 5 +- examples/arm/setup.sh | 14 +- 61 files changed, 653 insertions(+), 186 deletions(-) create mode 100755 backends/arm/scripts/install_reference_model.sh create mode 100644 backends/arm/third-party/reference_model/patches/v0.80/reference_model/0001-Move-tosa-tools-to-be-namespaced-into-tosa-tools.v0_.patch create mode 100644 backends/arm/third-party/reference_model/patches/v0.80/serialization_lib/0001-Make-TOSA-serializer-lib-to-be-self-contained.patch diff --git a/.mypy.ini b/.mypy.ini index 8c1c9dbcadc..5ee07ddb2bf 100644 --- a/.mypy.ini +++ b/.mypy.ini @@ -80,6 +80,9 @@ ignore_missing_imports = True [mypy-serializer.*] ignore_missing_imports = True +[mypy-tosa_tools.*] +ignore_missing_imports = True + [mypy-setuptools.*] ignore_missing_imports = True diff --git a/backends/arm/operators/node_visitor.py b/backends/arm/operators/node_visitor.py index f2c7ce9f9ce..72fb58f582c 100644 --- a/backends/arm/operators/node_visitor.py +++ b/backends/arm/operators/node_visitor.py @@ -7,8 +7,9 @@ from typing import Dict, List -import serializer.tosa_serializer as ts # type: ignore import torch + +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification from torch.export import ExportedProgram diff --git a/backends/arm/operators/op_abs.py b/backends/arm/operators/op_abs.py index 886a96fd520..648edde04f4 100644 --- a/backends/arm/operators/op_abs.py +++ b/backends/arm/operators/op_abs.py @@ -9,15 +9,13 @@ import executorch.backends.arm.tosa_quant_utils as tqutils import executorch.backends.arm.tosa_utils as tutils -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification - -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -70,7 +68,7 @@ def define_node( # Do the INT32 Abs tosa_graph.addOperator( - TosaOp.Op().ABS, + ts.TosaOp.Op().ABS, [ rescaled_inputs[0].name, ], @@ -126,7 +124,7 @@ def define_node( # MI lowering tosa_graph.addOperator( - TosaOp.Op().ABS, + ts.TosaOp.Op().ABS, [inputs[0].name], [output.name], None, diff --git a/backends/arm/operators/op_add.py b/backends/arm/operators/op_add.py index 1be4a218232..904a2405047 100644 --- a/backends/arm/operators/op_add.py +++ b/backends/arm/operators/op_add.py @@ -10,14 +10,13 @@ import executorch.backends.arm.tosa_quant_utils as tqutils import executorch.backends.arm.tosa_utils as tutils -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -82,7 +81,7 @@ def define_node( # Do the INT32 Add tosa_graph.addOperator( - TosaOp.Op().ADD, + ts.TosaOp.Op().ADD, [input1.name, input2.name], [add_output.name], None, @@ -135,7 +134,7 @@ def define_node( # MI lowering tosa_graph.addOperator( - TosaOp.Op().ADD, + ts.TosaOp.Op().ADD, [input1.name, input2.name], [output.name], None, diff --git a/backends/arm/operators/op_amax.py b/backends/arm/operators/op_amax.py index 7347648c454..059f6c1e553 100644 --- a/backends/arm/operators/op_amax.py +++ b/backends/arm/operators/op_amax.py @@ -4,14 +4,13 @@ # LICENSE file in the root directory of this source tree. from typing import List -import serializer.tosa_serializer as ts +import tosa_tools.v0_80.serializer.tosa_serializer as ts from executorch.backends.arm._passes.arm_pass_utils import get_first_fake_tensor from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -48,5 +47,5 @@ def define_node( attr.AxisAttribute(input.dim_order.index(dim)) tosa_graph.addOperator( - TosaOp.Op().REDUCE_MAX, [input.name], [output.name], attr + ts.TosaOp.Op().REDUCE_MAX, [input.name], [output.name], attr ) diff --git a/backends/arm/operators/op_amin.py b/backends/arm/operators/op_amin.py index 37625cfcc52..85e43b76c4c 100644 --- a/backends/arm/operators/op_amin.py +++ b/backends/arm/operators/op_amin.py @@ -4,14 +4,13 @@ # LICENSE file in the root directory of this source tree. from typing import List -import serializer.tosa_serializer as ts +import tosa_tools.v0_80.serializer.tosa_serializer as ts from executorch.backends.arm._passes.arm_pass_utils import get_first_fake_tensor from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -48,5 +47,5 @@ def define_node( attr.AxisAttribute(input.dim_order.index(dim)) tosa_graph.addOperator( - TosaOp.Op().REDUCE_MIN, [input.name], [output.name], attr + ts.TosaOp.Op().REDUCE_MIN, [input.name], [output.name], attr ) diff --git a/backends/arm/operators/op_any.py b/backends/arm/operators/op_any.py index ffb2e8a3c5d..b65ebb2ac5d 100644 --- a/backends/arm/operators/op_any.py +++ b/backends/arm/operators/op_any.py @@ -6,14 +6,13 @@ # pyre-unsafe from typing import cast, List -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( # type: ignore NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg # type: ignore -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -49,5 +48,5 @@ def define_node( attr.AxisAttribute(inputs[0].dim_order.index(dim)) tosa_graph.addOperator( - TosaOp.Op().REDUCE_ANY, [inputs[0].name], [output.name], attr + ts.TosaOp.Op().REDUCE_ANY, [inputs[0].name], [output.name], attr ) diff --git a/backends/arm/operators/op_avg_pool2d.py b/backends/arm/operators/op_avg_pool2d.py index 772f8353565..bdd3425fda5 100644 --- a/backends/arm/operators/op_avg_pool2d.py +++ b/backends/arm/operators/op_avg_pool2d.py @@ -6,9 +6,10 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore + from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import ( get_input_qparams, get_output_qparams, diff --git a/backends/arm/operators/op_bmm.py b/backends/arm/operators/op_bmm.py index af02fc30dd8..6dc0ec8002d 100644 --- a/backends/arm/operators/op_bmm.py +++ b/backends/arm/operators/op_bmm.py @@ -7,9 +7,10 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore + from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import ( get_input_qparams, get_output_qparams, @@ -20,7 +21,6 @@ ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_quant_utils import build_rescale -from serializer.tosa_serializer import TosaOp @register_node_visitor @@ -64,7 +64,7 @@ def define_node( attr.MatMulAttribute(A_zp=input0_zp, B_zp=input1_zp) tosa_graph.addOperator( - TosaOp.Op().MATMUL, + ts.TosaOp.Op().MATMUL, [inputs[0].name, inputs[1].name], [bmm_output_name], attr, diff --git a/backends/arm/operators/op_cat.py b/backends/arm/operators/op_cat.py index f786395cc39..6b1710301b1 100644 --- a/backends/arm/operators/op_cat.py +++ b/backends/arm/operators/op_cat.py @@ -7,13 +7,12 @@ from typing import List -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -42,5 +41,8 @@ def define_node( attr.AxisAttribute(dim) tosa_graph.addOperator( - TosaOp.Op().CONCAT, [tensor.name for tensor in tensors], [output.name], attr + ts.TosaOp.Op().CONCAT, + [tensor.name for tensor in tensors], + [output.name], + attr, ) diff --git a/backends/arm/operators/op_clamp.py b/backends/arm/operators/op_clamp.py index 7c4ad8682fa..67fff8b8a60 100644 --- a/backends/arm/operators/op_clamp.py +++ b/backends/arm/operators/op_clamp.py @@ -8,9 +8,9 @@ from typing import Any, List, Tuple -import serializer.tosa_serializer as ts # type: ignore - import torch + +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, @@ -18,7 +18,6 @@ from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -51,7 +50,7 @@ def _create_clamp_node( min_fp32, max_fp32, ) - tosa_graph.addOperator(TosaOp.Op().CLAMP, [input_name], [output_name], attr) + tosa_graph.addOperator(ts.TosaOp.Op().CLAMP, [input_name], [output_name], attr) def _get_min_max_arguments( self, node: Node, dtype_min: int | float, dtype_max: int | float diff --git a/backends/arm/operators/op_constant_pad_nd.py b/backends/arm/operators/op_constant_pad_nd.py index 73f6d2751c5..b2c31df96ab 100644 --- a/backends/arm/operators/op_constant_pad_nd.py +++ b/backends/arm/operators/op_constant_pad_nd.py @@ -7,9 +7,10 @@ from typing import List -import serializer.tosa_serializer as ts import torch +import tosa_tools.v0_80.serializer.tosa_serializer as ts + from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import ( get_input_qparams, ) @@ -18,7 +19,6 @@ register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp @register_node_visitor @@ -71,4 +71,6 @@ def define_node( attr = ts.TosaSerializerAttribute() attr.PadAttribute(tosa_graph.builder, output_pad, pad_const_qs, pad_const_fp) - tosa_graph.addOperator(TosaOp.Op().PAD, [inputs[0].name], [output.name], attr) + tosa_graph.addOperator( + ts.TosaOp.Op().PAD, [inputs[0].name], [output.name], attr + ) diff --git a/backends/arm/operators/op_conv2d.py b/backends/arm/operators/op_conv2d.py index 2fe00b6758f..90475af1476 100644 --- a/backends/arm/operators/op_conv2d.py +++ b/backends/arm/operators/op_conv2d.py @@ -6,9 +6,10 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore + from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import ( get_input_qparams, get_output_qparams, diff --git a/backends/arm/operators/op_eq.py b/backends/arm/operators/op_eq.py index de49b267641..7f87fb5a81d 100644 --- a/backends/arm/operators/op_eq.py +++ b/backends/arm/operators/op_eq.py @@ -9,13 +9,12 @@ import executorch.backends.arm.tosa_quant_utils as tqutils -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -53,7 +52,7 @@ def define_node( # Do the equal comparison tosa_graph.addOperator( - TosaOp.Op().EQUAL, + ts.TosaOp.Op().EQUAL, [input_nodes[0].name, input_nodes[1].name], output.name, None, diff --git a/backends/arm/operators/op_erf.py b/backends/arm/operators/op_erf.py index d0dc2af572f..01243716129 100644 --- a/backends/arm/operators/op_erf.py +++ b/backends/arm/operators/op_erf.py @@ -5,15 +5,15 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch.fx +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.tosa.Op as TosaOp # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification -from serializer.tosa_serializer import TosaOp @register_node_visitor diff --git a/backends/arm/operators/op_exp.py b/backends/arm/operators/op_exp.py index d9f0e4f5197..ca067b3b8be 100644 --- a/backends/arm/operators/op_exp.py +++ b/backends/arm/operators/op_exp.py @@ -6,15 +6,13 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification - -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -46,4 +44,4 @@ def define_node( f"{inputs[0].dtype} and output dtype: {output.dtype}" ) - tosa_graph.addOperator(TosaOp.Op().EXP, [inputs[0].name], [output.name]) + tosa_graph.addOperator(ts.TosaOp.Op().EXP, [inputs[0].name], [output.name]) diff --git a/backends/arm/operators/op_full.py b/backends/arm/operators/op_full.py index f06b9873e63..e68610a37a0 100644 --- a/backends/arm/operators/op_full.py +++ b/backends/arm/operators/op_full.py @@ -8,7 +8,7 @@ import numpy as np -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, diff --git a/backends/arm/operators/op_ge.py b/backends/arm/operators/op_ge.py index d18156e1a50..b2193a2e7ed 100644 --- a/backends/arm/operators/op_ge.py +++ b/backends/arm/operators/op_ge.py @@ -9,13 +9,12 @@ import executorch.backends.arm.tosa_quant_utils as tqutils -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -52,7 +51,7 @@ def define_node( input_nodes = rescaled_inputs tosa_graph.addOperator( - TosaOp.Op().GREATER_EQUAL, + ts.TosaOp.Op().GREATER_EQUAL, [input_nodes[0].name, input_nodes[1].name], [output.name], None, diff --git a/backends/arm/operators/op_get_item.py b/backends/arm/operators/op_get_item.py index 577a8c8d2ea..0e1192b3bef 100644 --- a/backends/arm/operators/op_get_item.py +++ b/backends/arm/operators/op_get_item.py @@ -6,14 +6,14 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch + +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp @register_node_visitor @@ -32,4 +32,4 @@ def define_node( ) -> None: item_name = inputs[0].name ## Simply add an identityOp - tosa_graph.addOperator(TosaOp.Op().IDENTITY, [item_name], [output.name]) + tosa_graph.addOperator(ts.TosaOp.Op().IDENTITY, [item_name], [output.name]) diff --git a/backends/arm/operators/op_gt.py b/backends/arm/operators/op_gt.py index 25ff2463c5c..06f29e4505c 100644 --- a/backends/arm/operators/op_gt.py +++ b/backends/arm/operators/op_gt.py @@ -9,13 +9,12 @@ import executorch.backends.arm.tosa_quant_utils as tqutils -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -52,7 +51,7 @@ def define_node( input_nodes = rescaled_inputs tosa_graph.addOperator( - TosaOp.Op().GREATER, + ts.TosaOp.Op().GREATER, [input_nodes[0].name, input_nodes[1].name], [output.name], None, diff --git a/backends/arm/operators/op_le.py b/backends/arm/operators/op_le.py index 8ed5539034b..fadf4848359 100644 --- a/backends/arm/operators/op_le.py +++ b/backends/arm/operators/op_le.py @@ -9,13 +9,12 @@ import executorch.backends.arm.tosa_quant_utils as tqutils -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -52,7 +51,7 @@ def define_node( input_nodes = rescaled_inputs tosa_graph.addOperator( - TosaOp.Op().GREATER_EQUAL, + ts.TosaOp.Op().GREATER_EQUAL, [input_nodes[1].name, input_nodes[0].name], [output.name], None, diff --git a/backends/arm/operators/op_log.py b/backends/arm/operators/op_log.py index d8a136e37f8..34911075065 100644 --- a/backends/arm/operators/op_log.py +++ b/backends/arm/operators/op_log.py @@ -6,15 +6,13 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification - -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -38,4 +36,4 @@ def define_node( assert len(node.all_input_nodes) == 1 assert inputs[0].dtype == output.dtype == ts.DType.FP32 - tosa_graph.addOperator(TosaOp.Op().LOG, [inputs[0].name], [output.name]) + tosa_graph.addOperator(ts.TosaOp.Op().LOG, [inputs[0].name], [output.name]) diff --git a/backends/arm/operators/op_lt.py b/backends/arm/operators/op_lt.py index 3bb71f611a5..a261cd2db9f 100644 --- a/backends/arm/operators/op_lt.py +++ b/backends/arm/operators/op_lt.py @@ -9,13 +9,12 @@ import executorch.backends.arm.tosa_quant_utils as tqutils -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -52,7 +51,7 @@ def define_node( input_nodes = rescaled_inputs tosa_graph.addOperator( - TosaOp.Op().GREATER, + ts.TosaOp.Op().GREATER, [input_nodes[1].name, input_nodes[0].name], [output.name], None, diff --git a/backends/arm/operators/op_max_pool2d.py b/backends/arm/operators/op_max_pool2d.py index 9dd627a3e4f..fcf2636977d 100644 --- a/backends/arm/operators/op_max_pool2d.py +++ b/backends/arm/operators/op_max_pool2d.py @@ -6,9 +6,10 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore + from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import ( get_input_qparams, get_output_qparams, @@ -18,7 +19,6 @@ register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp @register_node_visitor @@ -75,7 +75,7 @@ def define_node( ) tosa_graph.addOperator( - TosaOp.Op().MAX_POOL2D, + ts.TosaOp.Op().MAX_POOL2D, [input_tensor.name], [output.name], attr, diff --git a/backends/arm/operators/op_maximum.py b/backends/arm/operators/op_maximum.py index 4eb7e47fac8..fdee4e61855 100644 --- a/backends/arm/operators/op_maximum.py +++ b/backends/arm/operators/op_maximum.py @@ -8,7 +8,7 @@ from typing import List import executorch.backends.arm.tosa_quant_utils as tqutils -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import ( get_input_qparams, @@ -19,8 +19,6 @@ ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_utils import tosa_shape - -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -62,7 +60,7 @@ def define_node( operand_inputs = inputs tosa_graph.addOperator( - TosaOp.Op().MAXIMUM, + ts.TosaOp.Op().MAXIMUM, [ operand_inputs[0].name, operand_inputs[1].name, diff --git a/backends/arm/operators/op_minimum.py b/backends/arm/operators/op_minimum.py index 1b8c1960411..dd3afe90cb5 100644 --- a/backends/arm/operators/op_minimum.py +++ b/backends/arm/operators/op_minimum.py @@ -9,7 +9,7 @@ import executorch.backends.arm.tosa_quant_utils as tqutils -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import ( get_input_qparams, @@ -20,8 +20,6 @@ ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_utils import tosa_shape - -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -63,7 +61,7 @@ def define_node( operand_inputs = inputs tosa_graph.addOperator( - TosaOp.Op().MINIMUM, + ts.TosaOp.Op().MINIMUM, [ operand_inputs[0].name, operand_inputs[1].name, diff --git a/backends/arm/operators/op_mul.py b/backends/arm/operators/op_mul.py index 2f6c7e7130c..dcceb36b0ab 100644 --- a/backends/arm/operators/op_mul.py +++ b/backends/arm/operators/op_mul.py @@ -9,10 +9,10 @@ import executorch.backends.arm.tosa_quant_utils as tqutils import executorch.backends.arm.tosa_utils as tutils - -import serializer.tosa_serializer as ts # type: ignore import torch +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore + from executorch.backends.arm._passes.fold_qdq_with_annotated_qparams_pass import ( get_input_qparams, ) @@ -24,7 +24,6 @@ from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification from executorch.backends.arm.tosa_utils import reshape_for_broadcast -from serializer.tosa_serializer import TosaOp @register_node_visitor @@ -87,7 +86,7 @@ def define_node( attr = ts.TosaSerializerAttribute() attr.MulAttribute(shift=0) tosa_graph.addOperator( - TosaOp.Op().MUL, + ts.TosaOp.Op().MUL, [input1.name, input2.name], [mul_output.name], attr, @@ -119,5 +118,5 @@ def define_node( attr = ts.TosaSerializerAttribute() attr.MulAttribute(shift=0) tosa_graph.addOperator( - TosaOp.Op().MUL, [input1.name, input2.name], [output.name], attr + ts.TosaOp.Op().MUL, [input1.name, input2.name], [output.name], attr ) diff --git a/backends/arm/operators/op_permute.py b/backends/arm/operators/op_permute.py index e659918baf2..c92a008a281 100644 --- a/backends/arm/operators/op_permute.py +++ b/backends/arm/operators/op_permute.py @@ -7,14 +7,14 @@ from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch + +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp def permutation_vector_to_matrix(permutation_vector: list[int]) -> torch.Tensor: @@ -117,5 +117,5 @@ def define_node( attr = ts.TosaSerializerAttribute() attr.TransposeAttribute(permutation_vector) tosa_graph.addOperator( - TosaOp.Op().TRANSPOSE, [inputs[0].name], [output.name], attr + ts.TosaOp.Op().TRANSPOSE, [inputs[0].name], [output.name], attr ) diff --git a/backends/arm/operators/op_pow.py b/backends/arm/operators/op_pow.py index 0f251a8aa6d..d3b92feff12 100644 --- a/backends/arm/operators/op_pow.py +++ b/backends/arm/operators/op_pow.py @@ -7,14 +7,13 @@ from typing import List -import serializer.tosa_serializer as ts +import tosa_tools.v0_80.serializer.tosa_serializer as ts from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -47,7 +46,7 @@ def define_node( ) tosa_graph.addOperator( - TosaOp.Op().POW, + ts.TosaOp.Op().POW, [ inputs[0].name, inputs[1].name, diff --git a/backends/arm/operators/op_reciprocal.py b/backends/arm/operators/op_reciprocal.py index 5410e1dd99a..11d2cbc2cc1 100644 --- a/backends/arm/operators/op_reciprocal.py +++ b/backends/arm/operators/op_reciprocal.py @@ -6,15 +6,15 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch + +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification -from serializer.tosa_serializer import TosaOp @register_node_visitor @@ -35,4 +35,6 @@ def define_node( output: TosaArg, ) -> None: assert inputs[0].dtype == output.dtype == ts.DType.FP32 - tosa_graph.addOperator(TosaOp.Op().RECIPROCAL, [inputs[0].name], [output.name]) + tosa_graph.addOperator( + ts.TosaOp.Op().RECIPROCAL, [inputs[0].name], [output.name] + ) diff --git a/backends/arm/operators/op_repeat.py b/backends/arm/operators/op_repeat.py index b97d7023ef0..142ccb1d25a 100644 --- a/backends/arm/operators/op_repeat.py +++ b/backends/arm/operators/op_repeat.py @@ -5,15 +5,14 @@ # pyre-unsafe -import serializer.tosa_serializer as ts # type: ignore import torch +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_utils import tosa_shape -from serializer.tosa_serializer import TosaOp @register_node_visitor @@ -35,4 +34,6 @@ def define_node( attr = ts.TosaSerializerAttribute() attr.TileAttribute(tosa_shape(multiples, output.dim_order)) - tosa_graph.addOperator(TosaOp.Op().TILE, [inputs[0].name], [output.name], attr) + tosa_graph.addOperator( + ts.TosaOp.Op().TILE, [inputs[0].name], [output.name], attr + ) diff --git a/backends/arm/operators/op_rescale.py b/backends/arm/operators/op_rescale.py index 098fbeccce1..c59015dcc14 100644 --- a/backends/arm/operators/op_rescale.py +++ b/backends/arm/operators/op_rescale.py @@ -8,10 +8,10 @@ from typing import cast, List import executorch.backends.arm.tosa_quant_utils as tosa_quant_utils -import serializer.tosa_serializer as ts # type: ignore import torch +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore -import tosa.Op as TosaOp # type: ignore +import tosa_tools.v0_80.tosa.Op as TosaOp # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, diff --git a/backends/arm/operators/op_rshift_tensor.py b/backends/arm/operators/op_rshift_tensor.py index 8ea0343faaa..125f5493a29 100644 --- a/backends/arm/operators/op_rshift_tensor.py +++ b/backends/arm/operators/op_rshift_tensor.py @@ -7,15 +7,15 @@ from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch + +import tosa_tools.v0_80.serializer.tosa_serializer as ts from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import Tosa_0_80 -from serializer.tosa_serializer import TosaOp @register_node_visitor @@ -39,7 +39,7 @@ def define_node( attr.ArithmeticRightShiftAttribute(round=round) tosa_graph.addOperator( - TosaOp.Op().ARITHMETIC_RIGHT_SHIFT, + ts.TosaOp.Op().ARITHMETIC_RIGHT_SHIFT, [inputs[0].name, inputs[1].name], [output.name], attr, diff --git a/backends/arm/operators/op_rsqrt.py b/backends/arm/operators/op_rsqrt.py index 0fbb203b081..52bcc937c96 100644 --- a/backends/arm/operators/op_rsqrt.py +++ b/backends/arm/operators/op_rsqrt.py @@ -6,15 +6,15 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch + +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification -from serializer.tosa_serializer import TosaOp @register_node_visitor @@ -35,4 +35,4 @@ def define_node( output: TosaArg, ) -> None: assert inputs[0].dtype == output.dtype == ts.DType.FP32 - tosa_graph.addOperator(TosaOp.Op().RSQRT, [inputs[0].name], [output.name]) + tosa_graph.addOperator(ts.TosaOp.Op().RSQRT, [inputs[0].name], [output.name]) diff --git a/backends/arm/operators/op_sigmoid.py b/backends/arm/operators/op_sigmoid.py index abf60bf747f..9a002036fee 100644 --- a/backends/arm/operators/op_sigmoid.py +++ b/backends/arm/operators/op_sigmoid.py @@ -6,15 +6,13 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification - -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -46,4 +44,4 @@ def define_node( f"{inputs[0].dtype} and output_dtype: {output.dtype}" ) - tosa_graph.addOperator(TosaOp.Op().SIGMOID, [inputs[0].name], [output.name]) + tosa_graph.addOperator(ts.TosaOp.Op().SIGMOID, [inputs[0].name], [output.name]) diff --git a/backends/arm/operators/op_slice.py b/backends/arm/operators/op_slice.py index a3ce80c5b24..a9e1a6cdbe2 100644 --- a/backends/arm/operators/op_slice.py +++ b/backends/arm/operators/op_slice.py @@ -7,13 +7,12 @@ from typing import List -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -58,5 +57,5 @@ def define_node( attr.SliceAttribute(start_attr, size_attr) tosa_graph.addOperator( - TosaOp.Op().SLICE, [input_node.name], [output.name], attr + ts.TosaOp.Op().SLICE, [input_node.name], [output.name], attr ) diff --git a/backends/arm/operators/op_sub.py b/backends/arm/operators/op_sub.py index 6cd422095ab..1f701f29b1e 100644 --- a/backends/arm/operators/op_sub.py +++ b/backends/arm/operators/op_sub.py @@ -10,14 +10,13 @@ import executorch.backends.arm.tosa_quant_utils as tqutils import executorch.backends.arm.tosa_utils as tutils -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -63,7 +62,7 @@ def define_node( # Do the INT32 Sub tosa_graph.addOperator( - TosaOp.Op().SUB, + ts.TosaOp.Op().SUB, [ rescaled_inputs[0].name, rescaled_inputs[1].name, @@ -110,7 +109,7 @@ def define_node( # MI lowering tosa_graph.addOperator( - TosaOp.Op().SUB, + ts.TosaOp.Op().SUB, [inputs[0].name, inputs[1].name], [output.name], None, diff --git a/backends/arm/operators/op_sum.py b/backends/arm/operators/op_sum.py index b5b388b3352..135566e48ac 100644 --- a/backends/arm/operators/op_sum.py +++ b/backends/arm/operators/op_sum.py @@ -10,14 +10,13 @@ import executorch.backends.arm.tosa_quant_utils as tqutils import executorch.backends.arm.tosa_utils as tutils -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -69,7 +68,7 @@ def define_node( ) tosa_graph.addOperator( - TosaOp.Op().REDUCE_SUM, [prev_node.name], [next_node.name], attr + ts.TosaOp.Op().REDUCE_SUM, [prev_node.name], [next_node.name], attr ) prev_node = next_node @@ -120,7 +119,7 @@ def define_node( ).name tosa_graph.addOperator( - TosaOp.Op().REDUCE_SUM, [input_name], [output_name], attr + ts.TosaOp.Op().REDUCE_SUM, [input_name], [output_name], attr ) input_name = output_name diff --git a/backends/arm/operators/op_table.py b/backends/arm/operators/op_table.py index 40214b265f0..6a2053bea0d 100644 --- a/backends/arm/operators/op_table.py +++ b/backends/arm/operators/op_table.py @@ -8,15 +8,14 @@ from typing import List import numpy as np - -import serializer.tosa_serializer as ts # type: ignore import torch + +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp @register_node_visitor @@ -49,5 +48,5 @@ def define_node( table_attr.TableAttribute(np.array(table)) tosa_graph.addOperator( - TosaOp.Op().TABLE, [inputs[0].name], [output.name], table_attr + ts.TosaOp.Op().TABLE, [inputs[0].name], [output.name], table_attr ) diff --git a/backends/arm/operators/op_tanh.py b/backends/arm/operators/op_tanh.py index 89dd15c97d6..51cf1ee786b 100644 --- a/backends/arm/operators/op_tanh.py +++ b/backends/arm/operators/op_tanh.py @@ -6,14 +6,13 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification -from serializer.tosa_serializer import TosaOp from torch.fx import Node @@ -44,4 +43,4 @@ def define_node( f"{inputs[0].dtype} and output_dtype: {output.dtype}" ) - tosa_graph.addOperator(TosaOp.Op().TANH, [inputs[0].name], [output.name]) + tosa_graph.addOperator(ts.TosaOp.Op().TANH, [inputs[0].name], [output.name]) diff --git a/backends/arm/operators/op_to_copy.py b/backends/arm/operators/op_to_copy.py index feaec3a41e9..90485b71d50 100644 --- a/backends/arm/operators/op_to_copy.py +++ b/backends/arm/operators/op_to_copy.py @@ -6,9 +6,10 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch -import tosa.Op as TosaOp # type: ignore + +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.tosa.Op as TosaOp # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, diff --git a/backends/arm/operators/op_to_dim_order_copy.py b/backends/arm/operators/op_to_dim_order_copy.py index 397979a439d..f144beba29f 100644 --- a/backends/arm/operators/op_to_dim_order_copy.py +++ b/backends/arm/operators/op_to_dim_order_copy.py @@ -6,9 +6,10 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch -import tosa.Op as TosaOp # type: ignore + +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.tosa.Op as TosaOp # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, diff --git a/backends/arm/operators/op_transpose.py b/backends/arm/operators/op_transpose.py index 54a79297dd6..b909aef2ac9 100644 --- a/backends/arm/operators/op_transpose.py +++ b/backends/arm/operators/op_transpose.py @@ -7,14 +7,14 @@ from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch + +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp @register_node_visitor @@ -39,5 +39,5 @@ def define_node( attr = ts.TosaSerializerAttribute() attr.TransposeAttribute(perms) tosa_graph.addOperator( - TosaOp.Op().TRANSPOSE, [inputs[0].name], [output.name], attr + ts.TosaOp.Op().TRANSPOSE, [inputs[0].name], [output.name], attr ) diff --git a/backends/arm/operators/op_upsample_nearest2d.py b/backends/arm/operators/op_upsample_nearest2d.py index 38e4087d38d..23d24b78339 100644 --- a/backends/arm/operators/op_upsample_nearest2d.py +++ b/backends/arm/operators/op_upsample_nearest2d.py @@ -6,17 +6,17 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch + +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_utils import get_resize_parameters, tosa_shape -from serializer.tosa_serializer import TosaOp -from tosa.ResizeMode import ResizeMode # type: ignore +from tosa_tools.v0_80.tosa.ResizeMode import ResizeMode # type: ignore @register_node_visitor @@ -65,5 +65,5 @@ def in_int16_range(x): ) tosa_graph.addOperator( - TosaOp.Op().RESIZE, [inputs[0].name], [output.name], attr + ts.TosaOp.Op().RESIZE, [inputs[0].name], [output.name], attr ) diff --git a/backends/arm/operators/op_view.py b/backends/arm/operators/op_view.py index 119e32fa58f..e063b8e39ec 100644 --- a/backends/arm/operators/op_view.py +++ b/backends/arm/operators/op_view.py @@ -6,9 +6,10 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch -import tosa.Op as TosaOp # type: ignore + +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.tosa.Op as TosaOp # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, diff --git a/backends/arm/operators/op_where.py b/backends/arm/operators/op_where.py index c8b35e831d4..ba2469e74e1 100644 --- a/backends/arm/operators/op_where.py +++ b/backends/arm/operators/op_where.py @@ -5,7 +5,8 @@ from typing import List, Sequence -import serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore +import tosa_tools.v0_80.tosa.Op as TosaOp # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, @@ -13,7 +14,6 @@ ) from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification -from serializer.tosa_serializer import TosaOp from torch.fx import Node diff --git a/backends/arm/operators/ops_binary.py b/backends/arm/operators/ops_binary.py index 307710e38e9..a17da41f767 100644 --- a/backends/arm/operators/ops_binary.py +++ b/backends/arm/operators/ops_binary.py @@ -7,16 +7,16 @@ from typing import List -import serializer.tosa_serializer as ts import torch import torch.fx +import tosa_tools.v0_80.serializer.tosa_serializer as ts + from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp def binary_operator_factory(bw_target: str, tosa_op): @@ -46,12 +46,12 @@ def define_node( register_node_visitor(BinaryOperator) -binary_operator_factory("aten.bitwise_and.Tensor", TosaOp.Op().BITWISE_AND) -binary_operator_factory("aten.bitwise_xor.Tensor", TosaOp.Op().BITWISE_XOR) -binary_operator_factory("aten.bitwise_or.Tensor", TosaOp.Op().BITWISE_OR) -binary_operator_factory("aten.logical_and.default", TosaOp.Op().LOGICAL_AND) -binary_operator_factory("aten.logical_xor.default", TosaOp.Op().LOGICAL_XOR) -binary_operator_factory("aten.logical_or.default", TosaOp.Op().LOGICAL_OR) +binary_operator_factory("aten.bitwise_and.Tensor", ts.TosaOp.Op().BITWISE_AND) +binary_operator_factory("aten.bitwise_xor.Tensor", ts.TosaOp.Op().BITWISE_XOR) +binary_operator_factory("aten.bitwise_or.Tensor", ts.TosaOp.Op().BITWISE_OR) +binary_operator_factory("aten.logical_and.default", ts.TosaOp.Op().LOGICAL_AND) +binary_operator_factory("aten.logical_xor.default", ts.TosaOp.Op().LOGICAL_XOR) +binary_operator_factory("aten.logical_or.default", ts.TosaOp.Op().LOGICAL_OR) binary_operator_factory( - "aten.bitwise_left_shift.Tensor", TosaOp.Op().LOGICAL_LEFT_SHIFT + "aten.bitwise_left_shift.Tensor", ts.TosaOp.Op().LOGICAL_LEFT_SHIFT ) diff --git a/backends/arm/operators/ops_unary.py b/backends/arm/operators/ops_unary.py index 0a7d45ffe98..3f713e086e6 100644 --- a/backends/arm/operators/ops_unary.py +++ b/backends/arm/operators/ops_unary.py @@ -6,15 +6,15 @@ # pyre-unsafe from typing import List -import serializer.tosa_serializer as ts # type: ignore import torch.fx + +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import ( NodeVisitor, register_node_visitor, ) from executorch.backends.arm.tosa_mapping import TosaArg -from serializer.tosa_serializer import TosaOp def unary_operator_factory(unary_target: str, tosa_op): @@ -53,6 +53,6 @@ def define_node( register_node_visitor(UnaryOperator) -unary_operator_factory("aten.ceil.default", TosaOp.Op().CEIL) -unary_operator_factory("aten.floor.default", TosaOp.Op().FLOOR) -unary_operator_factory("aten.logical_not.default", TosaOp.Op().LOGICAL_NOT) +unary_operator_factory("aten.ceil.default", ts.TosaOp.Op().CEIL) +unary_operator_factory("aten.floor.default", ts.TosaOp.Op().FLOOR) +unary_operator_factory("aten.logical_not.default", ts.TosaOp.Op().LOGICAL_NOT) diff --git a/backends/arm/process_node.py b/backends/arm/process_node.py index f9b77e28493..07bbfaa37b9 100644 --- a/backends/arm/process_node.py +++ b/backends/arm/process_node.py @@ -8,9 +8,9 @@ from typing import cast, Dict import numpy as np -import serializer.tosa_serializer as ts # type: ignore import torch import torch.fx +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import NodeVisitor from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification diff --git a/backends/arm/scripts/install_reference_model.sh b/backends/arm/scripts/install_reference_model.sh new file mode 100755 index 00000000000..796a1ed418e --- /dev/null +++ b/backends/arm/scripts/install_reference_model.sh @@ -0,0 +1,52 @@ +#!/usr/bin/env bash +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +set -euo pipefail + +# Installation script to manage transition to 1.0 + +# TOSA reference model +tosa_reference_model_url="https://git.gitlab.arm.com/tosa/tosa-reference-model.git" +tosa_reference_model_0_80_branch="v0.80" +tosa_reference_model_0_80_rev="70ed0b40fa831387e36abdb4f7fb9670a3464f5a" +tosa_serialization_lib_0_80_rev="v0.80.1" +tosa_reference_model_1_0_rev="v1.0" + +script_dir=$(cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd) + +source ${script_dir}/utils.sh + + +function setup_tosa_reference_model() { + local work_dir="$1" + + if [[ -z "$work_dir" ]]; then + echo "Error: work_dir parameter is required." + return 1 + fi + + mkdir -p "$work_dir" + pushd "$work_dir" || exit 1 + + # Install a patched version of TOSA reference model v0.80.1 to make it co-exist with 1.0 during the transition period + if [[ ! -d "reference_model" ]]; then + git clone --recurse-submodules --branch ${tosa_reference_model_0_80_branch} "$tosa_reference_model_url" reference_model + fi + + patches_dir=${script_dir}/../third-party/reference_model/patches/v0.80 + patch_repo reference_model ${tosa_reference_model_0_80_rev} ${patches_dir} + patch_repo reference_model/thirdparty/serialization_lib ${tosa_serialization_lib_0_80_rev} ${patches_dir} + + pushd reference_model + rm -rf build + # reference_model flatbuffers version clashes with Vela. + # go with Vela's since it newer. + # Vela's flatbuffer requirement is expected to loosen, then remove this. MLETORCH-565 + CMAKE_POLICY_VERSION_MINIMUM=3.5 pip install . --no-dependencies flatbuffers + popd +} + +setup_tosa_reference_model $1 diff --git a/backends/arm/scripts/utils.sh b/backends/arm/scripts/utils.sh index e3ed04ffa22..8b4c8d4f96f 100644 --- a/backends/arm/scripts/utils.sh +++ b/backends/arm/scripts/utils.sh @@ -46,7 +46,7 @@ function patch_repo() { local patch_dir="${3}/$name" echo -e "[${FUNCNAME[0]}] Patching ${name} repo_dir:${repo_dir} base_rev:${base_rev} patch_dir:${patch_dir}" - cd $repo_dir + pushd $repo_dir git fetch git reset --hard ${base_rev} @@ -54,4 +54,5 @@ function patch_repo() { git am -3 ${patch_dir}/*.patch echo -e "[${FUNCNAME[0]}] Patched ${name} @ $(git describe --all --long 2> /dev/null) in ${repo_dir} dir.\n" + popd } diff --git a/backends/arm/test/conftest.py b/backends/arm/test/conftest.py index e5d7783fea3..12220acbae9 100644 --- a/backends/arm/test/conftest.py +++ b/backends/arm/test/conftest.py @@ -15,7 +15,7 @@ import pytest try: - import tosa_reference_model + import tosa_tools.v0_80.tosa_reference_model as tosa_reference_model except ImportError: logging.warning("tosa_reference_model not found, can't run reference model tests") tosa_reference_model = None diff --git a/backends/arm/test/runner_utils.py b/backends/arm/test/runner_utils.py index 28bbee052f9..7fc4db2273a 100644 --- a/backends/arm/test/runner_utils.py +++ b/backends/arm/test/runner_utils.py @@ -20,7 +20,7 @@ logger = logging.getLogger(__name__) try: - import tosa_reference_model + import tosa_tools.v0_80.tosa_reference_model as tosa_reference_model except ImportError: tosa_reference_model = None from executorch.backends.arm.arm_backend import get_tosa_spec, is_tosa @@ -34,7 +34,7 @@ from torch.fx.node import Node from torch.overrides import TorchFunctionMode -from tosa import TosaGraph +from tosa_tools.v0_80.tosa import TosaGraph logger = logging.getLogger(__name__) logger.setLevel(logging.CRITICAL) diff --git a/backends/arm/test/tester/arm_tester.py b/backends/arm/test/tester/arm_tester.py index 7b74603cfb2..6346a53edef 100644 --- a/backends/arm/test/tester/arm_tester.py +++ b/backends/arm/test/tester/arm_tester.py @@ -14,10 +14,10 @@ import executorch.backends.xnnpack.test.tester.tester as tester -import serializer.tosa_serializer as ts # type: ignore[import-untyped] - import torch.fx import torch.utils._pytree as pytree + +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore[import-untyped] from executorch.backends.arm._passes.arm_pass_manager import ArmPassManager from executorch.backends.arm.arm_backend import ( diff --git a/backends/arm/third-party/reference_model/patches/v0.80/reference_model/0001-Move-tosa-tools-to-be-namespaced-into-tosa-tools.v0_.patch b/backends/arm/third-party/reference_model/patches/v0.80/reference_model/0001-Move-tosa-tools-to-be-namespaced-into-tosa-tools.v0_.patch new file mode 100644 index 00000000000..512c105bda2 --- /dev/null +++ b/backends/arm/third-party/reference_model/patches/v0.80/reference_model/0001-Move-tosa-tools-to-be-namespaced-into-tosa-tools.v0_.patch @@ -0,0 +1,154 @@ +From 20c2059723d5c6952cecfb7fcde92601639ef825 Mon Sep 17 00:00:00 2001 +From: =?UTF-8?q?Per=20=C3=85strand?= +Date: Wed, 5 Feb 2025 12:31:47 +0100 +Subject: [PATCH 1/2] Move tosa-tools to be namespaced into tosa-tools.v0_80 + +--- + CMakeLists.txt | 4 ++- + pyproject.toml | 3 ++- + setup.cfg | 70 +++++++++++++++++++++++++------------------------- + setup.py | 3 ++- + 4 files changed, 42 insertions(+), 38 deletions(-) + +diff --git a/CMakeLists.txt b/CMakeLists.txt +index 68e8d8a..34becd0 100644 +--- a/CMakeLists.txt ++++ b/CMakeLists.txt +@@ -1,4 +1,6 @@ +-cmake_minimum_required (VERSION 3.4) ++cmake_minimum_required (VERSION 3.19) ++ ++cmake_policy(SET CMP0077 NEW) + + set(CMAKE_INSTALL_PREFIX ".") + project(tosa_tools LANGUAGES CXX) +diff --git a/pyproject.toml b/pyproject.toml +index 7565f93..60448e7 100644 +--- a/pyproject.toml ++++ b/pyproject.toml +@@ -6,7 +6,8 @@ requires = [ + "setuptools>=42", + "wheel", + "setuptools_scm[toml]>=6.0", +- "cmake" ++ "cmake", ++ "ninja", + ] + build-backend = "setuptools.build_meta" + +diff --git a/setup.cfg b/setup.cfg +index 82ec9b8..c1bd1a8 100644 +--- a/setup.cfg ++++ b/setup.cfg +@@ -2,7 +2,7 @@ + # SPDX-License-Identifier: Apache-2.0 + + [metadata] +-name = tosa-tools ++name = tosa-tools-v0.80 + # version = done by setuptools_scm in pyproject.toml + author = Arm Limited + #author_email = +@@ -25,44 +25,44 @@ install_requires = + python_requires = >=3.6 + include_package_data = True + packages = +- runner +- generator +- checker +- frameworks +- tests +- conformance +- xunit +- json2fbbin +- json2numpy +- schemavalidation +- convert2conformance +- tosa +- serializer +- tosa_reference_model ++ tosa_tools.v0_80.verif.runner ++ tosa_tools.v0_80.verif.generator ++ tosa_tools.v0_80.verif.checker ++ tosa_tools.v0_80.verif.frameworks ++ tosa_tools.v0_80.verif.tests ++ tosa_tools.v0_80.verif.conformance ++ tosa_tools.v0_80.xunit ++ tosa_tools.v0_80.json2fbbin ++ tosa_tools.v0_80.json2numpy ++ tosa_tools.v0_80.schemavalidation ++ tosa_tools.v0_80.convert2conformance ++ tosa_tools.v0_80.tosa ++ tosa_tools.v0_80.serializer ++ tosa_tools.v0_80.tosa_reference_model + package_dir = +- = verif +- xunit = scripts/xunit +- json2fbbin = scripts/json2fbbin +- json2numpy = scripts/json2numpy +- convert2conformance = scripts/convert2conformance +- tosa = thirdparty/serialization_lib/python/tosa +- serializer = thirdparty/serialization_lib/python/serializer +- tosa_reference_model = py_package +- schemavalidation = scripts/schemavalidation ++ tosa_tools.v0_80.verif = verif ++ tosa_tools.v0_80.xunit = scripts/xunit ++ tosa_tools.v0_80.json2fbbin = scripts/json2fbbin ++ tosa_tools.v0_80.json2numpy = scripts/json2numpy ++ tosa_tools.v0_80.convert2conformance = scripts/convert2conformance ++ tosa_tools.v0_80.tosa = thirdparty/serialization_lib/python/tosa ++ tosa_tools.v0_80.serializer = thirdparty/serialization_lib/python/serializer ++ tosa_tools.v0_80.tosa_reference_model = py_package ++ tosa_tools.v0_80.schemavalidation = scripts/schemavalidation + + [options.entry_points] + console_scripts = +- tosa_verif_run_ref = runner.tosa_verif_run_tests:main +- tosa_verif_run_tests = runner.tosa_verif_run_tests:main +- tosa_verif_build_tests = generator.tosa_verif_build_tests:main +- tosa_json2numpy = json2numpy.json2numpy:main +- tosa_json2fbbin = json2fbbin.json2fbbin:main +- tosa_verif_result_check = checker.tosa_result_checker:main +- tosa_convert2conformance = convert2conformance.convert2conformance:main +- tosa_verif_framework_generator = frameworks.tosa_verif_framework_generator:main +- tosa_verif_framework_compiler_runner = frameworks.tosa_verif_framework_compiler_runner:main +- tosa_verif_conformance_generator = conformance.tosa_verif_conformance_generator:main +- tosa_schemavalidation = schemavalidation.schemavalidation:main ++ tosa_verif_run_ref = tosa_tools.v0_80.verif.runner.tosa_verif_run_tests:main ++ tosa_verif_run_tests = tosa_tools.v0_80.verif.runner.tosa_verif_run_tests:main ++ tosa_verif_build_tests = tosa_tools.v0_80.verif.generator.tosa_verif_build_tests:main ++ tosa_json2numpy = tosa_tools.v0_80.verif.json2numpy.json2numpy:main ++ tosa_json2fbbin = tosa_tools.v0_80.verif.json2fbbin.json2fbbin:main ++ tosa_verif_result_check = tosa_tools.v0_80.verif.checker.tosa_result_checker:main ++ tosa_convert2conformance = tosa_tools.v0_80.verif.convert2conformance.convert2conformance:main ++ tosa_verif_framework_generator = tosa_tools.v0_80.verif.frameworks.tosa_verif_framework_generator:main ++ tosa_verif_framework_compiler_runner = tosa_tools.v0_80.verif.frameworks.tosa_verif_framework_compiler_runner:main ++ tosa_verif_conformance_generator = tosa_tools.v0_80.verif.conformance.tosa_verif_conformance_generator:main ++ tosa_schemavalidation = tosa_tools.v0_80.verif.schemavalidation.schemavalidation:main + + [options.package_data] + schemavalidation= +diff --git a/setup.py b/setup.py +index 8c6b4cd..95896ad 100644 +--- a/setup.py ++++ b/setup.py +@@ -20,7 +20,7 @@ class CMakeBuild(build_py): + root_dir = Path(__file__).parent + build_dir = root_dir / "build" + build_dir.mkdir(exist_ok=True) +- package_dir = root_dir / "py_package" ++ package_dir = root_dir / "build/lib/tosa_tools/v0_80/tosa_reference_model/" + + cmake_cmd = [ + "cmake", +@@ -90,6 +90,7 @@ class CMakeBuild(build_py): + # Python will know which one to import + copied_so = False + so_dir = build_dir / "reference_model" ++ package_dir.mkdir(parents=True, exist_ok=True) + print(f"copying .so files from '{so_dir}' to '{package_dir}'") + for so_file in so_dir.glob("tosa_reference_model.*.so"): + shutil.copy(so_file, package_dir) +-- +2.39.5 (Apple Git-154) + diff --git a/backends/arm/third-party/reference_model/patches/v0.80/serialization_lib/0001-Make-TOSA-serializer-lib-to-be-self-contained.patch b/backends/arm/third-party/reference_model/patches/v0.80/serialization_lib/0001-Make-TOSA-serializer-lib-to-be-self-contained.patch new file mode 100644 index 00000000000..cc9cbc4edad --- /dev/null +++ b/backends/arm/third-party/reference_model/patches/v0.80/serialization_lib/0001-Make-TOSA-serializer-lib-to-be-self-contained.patch @@ -0,0 +1,283 @@ +From b3c8c3f779a7e051826f317598fb831fa9cfe923 Mon Sep 17 00:00:00 2001 +From: =?UTF-8?q?Per=20=C3=85strand?= +Date: Wed, 5 Feb 2025 12:30:09 +0100 +Subject: [PATCH] Make TOSA serializer lib to be self contained + +--- + CMakeLists.txt | 4 ++ + python/serializer/tosa_serializer.py | 57 ++++++++++++++-------------- + 2 files changed, 32 insertions(+), 29 deletions(-) + +diff --git a/CMakeLists.txt b/CMakeLists.txt +index ac34b75..5e191aa 100644 +--- a/CMakeLists.txt ++++ b/CMakeLists.txt +@@ -19,6 +19,8 @@ + cmake_minimum_required(VERSION 3.13.4) + project(TosaSerialization) + ++cmake_policy(SET CMP0077 NEW) ++ + set(CMAKE_CXX_STANDARD 14 CACHE STRING "C++ standard to conform to") + set(CMAKE_CXX_STANDARD_REQUIRED YES) + +@@ -27,6 +29,8 @@ set(CMAKE_VERBOSE_MAKEFILE ON) + option(BUILD_TESTS "Build test applications" ON) + option(FLATBUFFERS_ROOT "Location where the flatbuffers 'include' and 'lib' folders to be found" Off) + ++message(STATUS "FLATBUFFERS_ROOT set to: ${FLATBUFFERS_ROOT}") ++ + include_directories(${PROJECT_SOURCE_DIR}/third_party/half/include) + + include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include) +diff --git a/python/serializer/tosa_serializer.py b/python/serializer/tosa_serializer.py +index 7bc75f0..d191997 100644 +--- a/python/serializer/tosa_serializer.py ++++ b/python/serializer/tosa_serializer.py +@@ -14,12 +14,11 @@ + + import os + import struct +-import serializer.tosa_serializer as ts + import json + import flatbuffers + import numpy as np + from enum import IntEnum, unique +-from tosa import ( ++from ..tosa import ( + TosaGraph, + TosaRegion, + TosaBasicBlock, +@@ -27,8 +26,8 @@ from tosa import ( + TosaOperator, + Version, + ) +-import tosa.DType as TosaDType +-import tosa.Op as TosaOp ++from ..tosa import DType as TosaDType ++from ..tosa import Op as TosaOp + + # Keep version number in sync with the version default value with schema/tosa.fbs + TOSA_VERSION_MAJOR = 0 +@@ -159,7 +158,7 @@ class TosaSerializerAttribute(TosaSerializerUnion): + output_zp, + accum_dtype, + ): +- from tosa import PoolAttribute as a, Attribute ++ from ..tosa import PoolAttribute as a, Attribute + + self.utype = Attribute.Attribute().PoolAttribute + +@@ -172,7 +171,7 @@ class TosaSerializerAttribute(TosaSerializerUnion): + self.ints.append((a.AddAccumDtype, accum_dtype)) + + def ConvAttribute(self, pad, stride, dilation, input_zp, weight_zp, local_bound): +- from tosa import ConvAttribute as a, Attribute ++ from ..tosa import ConvAttribute as a, Attribute + + self.utype = Attribute.Attribute().ConvAttribute + self.optFcns = (a.Start, a.End) +@@ -187,7 +186,7 @@ class TosaSerializerAttribute(TosaSerializerUnion): + def TransposeConvAttribute( + self, outpad, stride, output_shape, input_zp, weight_zp, local_bound + ): +- from tosa import TransposeConvAttribute as a, Attribute ++ from ..tosa import TransposeConvAttribute as a, Attribute + + self.utype = Attribute.Attribute().TransposeConvAttribute + self.optFcns = (a.Start, a.End) +@@ -200,7 +199,7 @@ class TosaSerializerAttribute(TosaSerializerUnion): + self.bools.append((a.AddLocalBound, local_bound)) + + def PadAttribute(self, serializer_builder, padding, pad_const_int, pad_const_fp): +- from tosa import PadAttribute as a, Attribute ++ from ..tosa import PadAttribute as a, Attribute + + self.utype = Attribute.Attribute().PadAttribute + self.optFcns = (a.Start, a.End) +@@ -210,14 +209,14 @@ class TosaSerializerAttribute(TosaSerializerUnion): + + # pad_const_fp attribute serialized as uint8 vector + pad_const_float_as_bytes = struct.pack(" tuple[list[TosaSerializerTensor], float]: +) -> tuple[list[ts.TosaSerializerTensor], float]: """Rescales all 'nodes' to int32, adding suitable RESCALE ops to 'tosa_graph'. The scales are adjusted using the smallest scale of all 'nodes'. @@ -61,7 +61,7 @@ def insert_rescale_ops_to_int32( min_scale = min([qarg.scale for qarg in qargs]) scales = [qarg.scale / min_scale for qarg in qargs] - rescaled_nodes: list[TosaSerializerTensor] = [] + rescaled_nodes: list[ts.TosaSerializerTensor] = [] for tensor, qarg, scale in zip(tensors, qargs, scales): rescaled_nodes.append( build_rescale_to_int32( @@ -198,9 +198,9 @@ def compute_multiplier_and_shift( def build_rescale( - tosa_fb: TosaSerializer, + tosa_fb: ts.TosaSerializer, scale: list[float], - input_node: TosaSerializerTensor, + input_node: ts.TosaSerializerTensor, output_name: str, output_type: ts.DType, output_shape: List[int], @@ -233,14 +233,14 @@ def build_rescale( def build_rescale_to_int32( - tosa_fb: TosaSerializer, + tosa_fb: ts.TosaSerializer, input_arg: executorch.backends.arm.tosa_mapping.TosaArg, input_zp: int, rescale_scale: list[float], is_scale32: bool = True, is_double_round: bool = False, per_channel: bool = False, -) -> TosaSerializerTensor: +) -> ts.TosaSerializerTensor: multipliers, shifts = compute_multiplier_and_shift(rescale_scale) attr_rescale = ts.TosaSerializerAttribute() attr_rescale.RescaleAttribute( @@ -266,7 +266,7 @@ def build_rescale_to_int32( def build_rescale_from_int32( - tosa_fb: TosaSerializer, + tosa_fb: ts.TosaSerializer, input_name: str, output_name: str, output_zp: int, @@ -300,8 +300,8 @@ def build_rescale_from_int32( def build_rescale_conv_output( - tosa_fb: TosaSerializer, - op: TosaSerializerTensor, + tosa_fb: ts.TosaSerializer, + op: ts.TosaSerializerTensor, output_name: str, output_type: ts.DType, input_scale: list[float], diff --git a/backends/arm/tosa_utils.py b/backends/arm/tosa_utils.py index 5fa603ea683..4d0f33003bc 100644 --- a/backends/arm/tosa_utils.py +++ b/backends/arm/tosa_utils.py @@ -9,14 +9,15 @@ import os from typing import Any, Optional, Tuple -import serializer.tosa_serializer as ts # type: ignore import torch + +import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.tosa_mapping import TosaArg from executorch.exir.dialects._ops import ops as exir_ops from executorch.exir.print_program import inspect_node -from serializer.tosa_serializer import TosaOp from torch.fx import Node +from tosa_tools.v0_80.serializer.tosa_serializer import TosaOp logger = logging.getLogger(__name__) diff --git a/examples/arm/setup.sh b/examples/arm/setup.sh index 016e03f04a2..8d77eabce0f 100755 --- a/examples/arm/setup.sh +++ b/examples/arm/setup.sh @@ -55,10 +55,6 @@ else echo "[main] Error: only x86-64 & aarch64/arm64 architecture is supported for now!"; exit 1; fi -# tosa reference model -tosa_reference_model_url="https://git.gitlab.arm.com/tosa/tosa-reference-model.git" -tosa_reference_model_rev="70ed0b40fa831387e36abdb4f7fb9670a3464f5a" - # vela vela_repo_url="https://gitlab.arm.com/artificial-intelligence/ethos-u/ethos-u-vela" vela_rev="425541302c7e4b6fbeca7c0061286b131ee507c3" @@ -156,14 +152,6 @@ function setup_toolchain() { tar xf "${toolchain_dir}.tar.xz" } -function setup_tosa_reference_model() { - # reference_model flatbuffers version clashes with Vela. - # go with Vela's since it newer. - # Vela's flatbuffer requirement is expected to loosen, then remove this. MLETORCH-565 - CMAKE_POLICY_VERSION_MINIMUM=3.5 pip install tosa-tools@git+${tosa_reference_model_url}@${tosa_reference_model_rev} --no-dependencies flatbuffers - -} - function setup_vela() { pip install ethos-u-vela@git+${vela_repo_url}@${vela_rev} } @@ -233,7 +221,7 @@ if [[ $is_script_sourced -eq 0 ]] create_setup_path # Setup the tosa_reference_model - setup_tosa_reference_model + $et_dir/backends/arm/scripts/install_reference_model.sh ${root_dir} # Setup vela and patch in codegen fixes setup_vela From 05fe758736f6b5915456dc3cca5ca2cfb45fa82e Mon Sep 17 00:00:00 2001 From: jhelsby <11036537+jhelsby@users.noreply.github.com> Date: Fri, 11 Apr 2025 02:13:26 +0200 Subject: [PATCH 086/244] New Contributor Guide documentation for newcomers to ExecuTorch or open-source (#9977) --- CONTRIBUTING.md | 2 + .../_static/img/new-contributor-guide/ci1.png | Bin 0 -> 167466 bytes .../img/new-contributor-guide/cla1.png | Bin 0 -> 203640 bytes .../img/new-contributor-guide/cla2.png | Bin 0 -> 58897 bytes .../end_of_draft_pr1.png | Bin 0 -> 222870 bytes .../end_of_draft_pr2.png | Bin 0 -> 244596 bytes .../good_first_issues.png | Bin 0 -> 480285 bytes .../new-contributor-guide/how_to_clone.png | Bin 0 -> 429764 bytes .../how_to_draft_pr1.png | Bin 0 -> 263629 bytes .../how_to_draft_pr2.png | Bin 0 -> 181393 bytes .../how_to_draft_pr3.png | Bin 0 -> 400983 bytes .../new-contributor-guide/how_to_fork1.png | Bin 0 -> 254210 bytes .../new-contributor-guide/how_to_fork2.png | Bin 0 -> 256238 bytes .../img/new-contributor-guide/how_to_pr1.png | Bin 0 -> 235771 bytes .../img/new-contributor-guide/how_to_pr2.png | Bin 0 -> 231916 bytes .../img/new-contributor-guide/how_to_pr3.png | Bin 0 -> 356944 bytes .../new-contributor-guide/release_notes.png | Bin 0 -> 129460 bytes .../img/new-contributor-guide/synced_fork.png | Bin 0 -> 412684 bytes .../new-contributor-guide/unsynced_fork.png | Bin 0 -> 493202 bytes docs/source/new-contributor-guide.md | 276 ++++++++++++++++++ 20 files changed, 278 insertions(+) create mode 100644 docs/source/_static/img/new-contributor-guide/ci1.png create mode 100644 docs/source/_static/img/new-contributor-guide/cla1.png create mode 100644 docs/source/_static/img/new-contributor-guide/cla2.png create mode 100644 docs/source/_static/img/new-contributor-guide/end_of_draft_pr1.png create mode 100644 docs/source/_static/img/new-contributor-guide/end_of_draft_pr2.png create mode 100644 docs/source/_static/img/new-contributor-guide/good_first_issues.png create mode 100644 docs/source/_static/img/new-contributor-guide/how_to_clone.png create mode 100644 docs/source/_static/img/new-contributor-guide/how_to_draft_pr1.png create mode 100644 docs/source/_static/img/new-contributor-guide/how_to_draft_pr2.png create mode 100644 docs/source/_static/img/new-contributor-guide/how_to_draft_pr3.png create mode 100644 docs/source/_static/img/new-contributor-guide/how_to_fork1.png create mode 100644 docs/source/_static/img/new-contributor-guide/how_to_fork2.png create mode 100644 docs/source/_static/img/new-contributor-guide/how_to_pr1.png create mode 100644 docs/source/_static/img/new-contributor-guide/how_to_pr2.png create mode 100644 docs/source/_static/img/new-contributor-guide/how_to_pr3.png create mode 100644 docs/source/_static/img/new-contributor-guide/release_notes.png create mode 100644 docs/source/_static/img/new-contributor-guide/synced_fork.png create mode 100644 docs/source/_static/img/new-contributor-guide/unsynced_fork.png create mode 100644 docs/source/new-contributor-guide.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 801375b589b..c7b1e0d5756 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -102,6 +102,8 @@ executorch ## Contributing workflow We actively welcome your pull requests (PRs). +If you're completely new to open-source projects, GitHub, or ExecuTorch, please see our [New Contributor Guide](./docs/source/new-contributor-guide.md) for a step-by-step walkthrough on making your first contribution. Otherwise, read on. + 1. [Claim an issue](#claiming-issues), if present, before starting work. 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ExecuTorch is proudly open-source and welcomes contributions from developers of all backgrounds. + +If you're new to ExecuTorch, open-source projects, or GitHub, this guide is for you. We're excited to have you on board! + +If you have any questions, issues, comments, or just want to say hello to our community, please feel free to introduce yourselves on our **[Discord Server](https://discord.com/invite/Dh43CKSAdc)**. We'd love to speak with you. + +--- + +## 🔑 Prerequisites + +### Git + +This guide assumes a basic knowledge of Git, and how to run Git commands in your terminal. If you've never used Git before, you can read [this quick guide](https://www.freecodecamp.org/news/learn-the-basics-of-git-in-under-10-minutes-da548267cc91/), [git guide](https://rogerdudler.github.io/git-guide/), [cheat sheet](https://towardsdatascience.com/git-commands-cheat-sheet-software-developer-54f6aedc1c46/), the [Setup Git](https://docs.github.com/en/get-started/git-basics/set-up-git) page from GitHub’s documentation, or watch one of the many tutorials on YouTube. + +Git is a powerful version control system for coding projects — it enables you to collaborate, record code changes, and avoid losing hours of work when you make a mistake. It is essential for projects like ExecuTorch with large codebases and many collaborators. Without it, the complexity of tracking everyone's changes, reviewing their code, and identifying bugs, quickly becomes unmanageable. + +Git is an industry standard in the coding world, and particularly in open-source. It can take a while to get used to at first, but we promise you it's well worth the effort! We believe that learning Git can make you a significantly stronger and more effective developer. + +### A GitHub Account + +We also assume that you have a GitHub account. If you don't, please [register here](https://github.com/signup), [verify your email address](https://docs.github.com/en/account-and-profile/setting-up-and-managing-your-personal-account-on-github/managing-email-preferences/verifying-your-email-address#verifying-your-email-address) (required for the steps below to work!), then [login](https://github.com/login) to your new account before proceeding further. + +--- + +## 🧑‍💻 Your First Contribution + +The first step towards making a contribution is finding something you want to work on. If you're new to ExecuTorch or the wider world of open-source, it might seem hard to know where to start. + +To help you out with this, we've gathered together some beginner-friendly suggestions. These are self-contained pieces of work — "issues" in GitHub parlance — specifically designed to help people new to ExecuTorch get started contributing code. We call these "good first issues", and you can view all of them here: [New Contributors Projects and Issues](https://github.com/orgs/pytorch/projects/102/views/1). + +Here's what the list looks like at the time of writing — you can see that they all have a purple `good first issue` label in the right-hand column: + +![](./_static/img/new-contributor-guide/good_first_issues.png) + +Please check it out and see if anything interests you! New issues are added to this list all the time. + +Once you've found an issue you like the look of, read our [Contribution Guide](https://github.com/pytorch/executorch/blob/main/CONTRIBUTING.md). This comprehensive manual will help you: +* build ExecuTorch on your machine. +* understand the structure of the ExecuTorch codebase. +* format, test, and document your code according to ExecuTorch best practices. +* and finally, submit your code for review, so it can be polished, approved, and merged into the main codebase. + +If that seems like a lot of information, please read on — we'll walk you through your first contribution right now. + +--- + +## 📤 Contributing Code, Step-By-Step + +### Prepare Your Workspace + +Before you can start writing any code, you need to get a copy of ExecuTorch codebase onto your GitHub account, and download it onto your dev machine. You'll want to build it, too — otherwise, you won't be able to test your solution. + +1. Fork the main ExecuTorch repository into your GitHub account. This creates a clone of the repository in your own space, so you can modify it freely. To do this, visit the [main repository page](https://github.com/pytorch/executorch) and click `Fork`: + + ![](./_static/img/new-contributor-guide/how_to_fork1.png) + + This will take you to another page. Click `Create fork`: + + ![](./_static/img/new-contributor-guide/how_to_fork2.png) + +2. Clone your fork locally. This downloads a copy of your fork onto your dev machine, ready for you to make your changes. + + In the example below, we clone using HTTP, but any of the provided methods on the `Local` tab are fine. For HTTP, copy the URL given here: + + ![](./_static/img/new-contributor-guide/how_to_clone.png) + + Then go to your terminal, enter the directory you want to clone the fork to, and run: + + ```bash + git clone https://github.com/pytorch/executorch.git + ``` + + This will create an `executorch` folder in your directory containing your forked codebase. + +3. Set the `upstream` pointing to the main ExecuTorch repository. This will allow you to easily synchronize with the latest development. + + Assuming you're in the same directory you cloned into, run: + + ```bash + cd executorch # enter the cloned project + git remote add upstream https://github.com/pytorch/executorch.git + ``` + + To see if it worked, run: + + ```bash + git remote -v + ``` + + Depending on how you cloned your repo (HTTP, SSH, etc.), this should print something like: + + ```bash + origin https://github.com/YOUR_GITHUB_USERNAME/executorch.git (fetch) + origin https://github.com/YOUR_GITHUB_USERNAME/executorch.git (push) + upstream https://github.com/pytorch/executorch.git (fetch) + upstream https://github.com/pytorch/executorch.git (push) + ``` + + What does this mean? Well: + + * The `origin` entries show your forked GitHub repository. They tell you that when you run `git pull` or `git push`, your changes will go from/to your GitHub fork. + + * The `upstream` entries show the main ExecuTorch repository. If you want to sync the latest changes from there, you can run `git fetch upstream`. + + Let's sync from both your fork _and_ the main ExecuTorch branch, getting the latest changes from each of them. To do this, run: + + ```bash + git fetch --all --prune + ``` + +4. If you just cloned your fork, your GitHub repository will tell you your branch is up-to-date: + + ![](./_static/img/new-contributor-guide/synced_fork.png) + + However, ExecuTorch updates frequently — if it's been a while you visited your fork, you might not have the latest version anymore. It's important to keep your fork as up-to-date as possible. Otherwise, the code changes you're making might fix your issue for an old version of the codebase, but _not_ fix it for the current version. + + GitHub will tell you if your fork is out-of-date. To synchronise the necessary changes, click `Sync fork`, then `Update branch` as shown: + + ![](./_static/img/new-contributor-guide/unsynced_fork.png) + +5. Now you have the latest fork on your GitHub account, it's time to download it onto your dev machine. For this, you can run the following commands in your terminal: + + ```bash + git fetch --all --prune # pull all branches from GitHub + git checkout main # enter your local main branch + git merge upstream/main # merge latest state from GitHub parent repo + git push # push updated local main to your GitHub fork + ``` + +6. [Build the project](https://pytorch.org/executorch/main/using-executorch-building-from-source.html) and [run the tests](https://github.com/pytorch/executorch/blob/main/CONTRIBUTING.md#testing). + + Unfortunately, this step is too long to detail here. If you get stuck at any point, please feel free to ask for help on our [Discord server](https://discord.com/invite/Dh43CKSAdc) — we're always eager to help newcomers get onboarded. + +One final note before we finish this section. It's very important to get your tests running at this stage, for two reasons: + +* If they work, it's a great sign that you've got things set up correctly. + +* As we'll discuss later, you'll want to run the tests _after_ making your changes to ensure you haven't broken existing functionality. Running them _before_ making your changes gives you a baseline you can compare with later test results. + +### Implement your changes + +Great job — you're all set up. Now you can actually start coding! + +1. Before making any changes, we recommend creating a new branch. To do this, just run: + ```bash + git checkout -b YOUR_NEW_BRANCH_NAME + ``` + + You can follow this naming convention: `type/`, where the types are: `bugfix`, `feature`, `docs`, `tests`, etc. — or use something similarly descriptive. By way of example, here are a few branch names that were actually merged to ExecuTorch: + + * [bugfix/op_eq](https://github.com/pytorch/executorch/pull/9794) + + * [error-handling-log-intermediate-output-delegate](https://github.com/pytorch/executorch/pull/9759) + + * [add-datasink-try-before-set-tests](https://github.com/pytorch/executorch/pull/9762) + + Creating a new branch means that any changes you make will be isolated to your branch, allowing you to work on multiple issues in parallel. It also means that, if your fork gets behind the main repository and you have to synchronise, you won't need to deal with any merge conflicts — accidentally blocking your `main` branch can be very time-consuming. + +2. Make your changes. For bugfixes, we recommend a test-driven workflow: + - Find a test case that demonstrates your bug. + - Verify that your new test case fails on the `main` branch. + - Add that example as an automated test, and assert the expected failing results. If you can, try to make this test as minimal as possible to reduce interference with some other issue. + + Once you have a failing test, you can keep working on the issue and running the test until it passes. + + **Note:** Even if you do not find the solution, sending a PR with a test covering the issue is a valid contribution. From this point, we can help you find the solution, or even finish it with you. + +3. After every set of edits, checkpoint and commit your code changes with a "commit" message that describes the changes you made. For example, in terminal: + + ```bash + git add my_changed_file1 my_new_test_case # Pick the files you changed + git commit -m "Fixed bug X and added a passing test case" # Describe your change + ``` + + Try to make your commit messages as descriptive as possible. This helps to maintain a clear project history. Not only will this help your own development, but it will make your code vastly easier for other developers to review and maintain. + + Here are some example commit messages that were merged to ExecuTorch: + + * [Delete examples/demo-apps/apple_ios/ExecuTorchDemo directory](https://github.com/pytorch/executorch/pull/9991/commits/df2f451e5e8fc217231975d7a0065a8cc36709cb) + * [[ET-VK][ez] Allow logit linear layer to be lowered to Vulkan](https://github.com/pytorch/executorch/pull/9951/commits/3fdd8cab8c58db0be666f3454c41f73ad5964743) + * [Allow emitting mutable buffer names in schema](https://github.com/pytorch/executorch/pull/9935/commits/773a34725afea6c0bf1b99d02a9cefb91c4960e1) + +4. When you are done making changes and the test case you added is passing, [run the same tests](https://github.com/pytorch/executorch/blob/main/CONTRIBUTING.md#testing) you ran earlier (at the end of the [Prepare Your Workspace](#prepare-your-workspace) section). + + If any tests fail now which were working before, it means your changes have broken some existing functionality. You'll need to dig back into your code to figure out what's gone wrong. + +5. Once your new test _and_ the old tests are all working as intended, upload/push these changes to your fork: + + ```bash + # Make sure you've committed all your changes first, then run: + git push + ``` + +### Submit a PR + +Once you've successfully finished local development, it's time to send out your pull request. This is the final phase — here, we'll help you finetune your changes to get merged into the main repository. + +1. After pushing your last edit to remote, your GitHub fork will show your new changed branch — click `Compare & pull request`: + + ![](./_static/img/new-contributor-guide/how_to_pr1.png) + + Alternatively, you can click the same `Compare & pull request` button on the main ExecuTorch repo: + + ![](./_static/img/new-contributor-guide/how_to_pr2.png) + + Another way still is via the `Pull request` tab on the main repo — we won't go into that here though, as it takes a few more steps. + +2. This will take you to a page where you can format your PR and explain your changes. You'll see all the required details in our PR template. You should choose a title describing the proposed fix and fill in all the required details. + + ![](./_static/img/new-contributor-guide/how_to_pr3.png) + + In the description, you’ll describe all the changes you’ve made. + +3. If you want to submit your PR right away, you can go ahead and click the Green `Create pull request` button. However, please note that this will immediately notify all reviewers. We strongly recommend creating a Draft PR first. This will allow you to perform some extra checks first: + + * You can get some early feedback on your PR without notifying everybody. + + * It prevents anyone from accidentally merging your unfinished PR. + + * Creating it will start CI (["Continuous Integration"](https://en.wikipedia.org/wiki/Continuous_integration)) checks to verify that all tests pass under various configurations. If some tests fail, you can fix them before creating the final PR. + + To do submit a draft, click the arrow next to the `Create Pull Request` button, then click `Create draft pull request` in the dropdown menu: + + ![](./_static/img/new-contributor-guide/how_to_draft_pr1.png) + + This will change the green button's text to `Draft pull request`: + + ![](./_static/img/new-contributor-guide/how_to_draft_pr2.png) + + Click it to create your draft PR. + +4. This will take you to your Draft PR page. It might look something like this: + + ![](./_static/img/new-contributor-guide/how_to_draft_pr3.png) + + As you scroll down, you might see a number of comments and automated checks, some of which may come with alarming red warning signs and the word "Failure"! There's no need to panic, though — they are here to help. Let's go through some common checks one-by-one. + + * The `pytorch-bot` will probably be the first comment. It runs regular CI checks. When your PR is passing, this comment will automatically update to let you know. + + ![](./_static/img/new-contributor-guide/ci1.png) + + * If this is your very first contribution to a Meta Open Source project, and you've not signed Meta's contributor license agreement (CLA), you may have a comment like this from `facebook-github-bot`: + + ![](./_static/img/new-contributor-guide/cla1.png) + + You will need to sign the linked CLA to contribute your code. Once your signature has been processed, the bot will let you know in another comment: + + ![](./_static/img/new-contributor-guide/cla2.png) + + * You may see a comment from `github-actions` requesting a "release notes" label: + + ![](./_static/img/new-contributor-guide/release_notes.png) + + Please ignore this message. It's directed at the ExecuTorch maintainers — they'll label your PR for you. + + * At the end of your Draft PR, you'll see something like this: + + ![](./_static/img/new-contributor-guide/end_of_draft_pr1.png) + + This is a summary of all the CI checks and requirements which need to be satisfied before your PR can be merged. Ensure that all tests are passing. If not, click on a failing test to see what went wrong and make the required changes. + + Once you're happy with your draft, you can click the `Ready for review` button to create your PR: + + ![](./_static/img/new-contributor-guide/end_of_draft_pr2.png) + +5. Now you've created your PR, it's time for your changes to be reviewed by the ExecuTorch community and maintainers. + + You'll need approval from one of our core contributors for your request to be merged. They may have questions or suggestions for you to address or respond to. Be aware that the review process may take a couple of iterations... Nevertheless, we hope that you'll find this feedback helpful. Code reviews can be a fantastic way to learn more about ExecuTorch and coding best practices from other contributors. + + Those reviewers/maintainers are here to finetune your contribution and eventually catch some issues before we merge the PR. We aim for this process to be pleasing on both sides: we try to give and get the best. + + Once the reviewers are happy, one of our core contributors will merge your PR. Congratulations — you're now an official ExecuTorch contributor! + +Great job making it to the end of our guide — we hope you enjoy contributing. Once again, please check out **[Discord Server](https://discord.com/invite/Dh43CKSAdc)** if you want to say hello, ask any questions, or talk about any and all things ExecuTorch. We look forward to receiving your contributions! From 508a5aef2ac1f326060a36830cc9c7beb79414d2 Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Thu, 10 Apr 2025 17:24:56 -0700 Subject: [PATCH 087/244] Remove unused lines in build_android_library.sh (#10088) For CI, check out recursively. --- .github/workflows/_android.yml | 2 +- .github/workflows/android-perf.yml | 2 +- .github/workflows/android-release-artifacts.yml | 2 +- scripts/build_android_library.sh | 5 ----- 4 files changed, 3 insertions(+), 8 deletions(-) diff --git a/.github/workflows/_android.yml b/.github/workflows/_android.yml index 8378d7ed4d9..a38ed4527e9 100644 --- a/.github/workflows/_android.yml +++ b/.github/workflows/_android.yml @@ -14,7 +14,7 @@ jobs: with: runner: linux.2xlarge docker-image: executorch-ubuntu-22.04-clang12-android - submodules: 'true' + submodules: 'recursive' ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }} timeout: 90 upload-artifact: android-apps diff --git a/.github/workflows/android-perf.yml b/.github/workflows/android-perf.yml index 09a6453094f..931bdbfe22d 100644 --- a/.github/workflows/android-perf.yml +++ b/.github/workflows/android-perf.yml @@ -345,7 +345,7 @@ jobs: with: runner: linux.2xlarge docker-image: executorch-ubuntu-22.04-clang12-android - submodules: 'true' + submodules: 'recursive' ref: ${{ github.event_name == 'pull_request' && github.event.pull_request.head.sha || github.sha }} timeout: 90 upload-artifact: android-apps diff --git a/.github/workflows/android-release-artifacts.yml b/.github/workflows/android-release-artifacts.yml index 2503bf2214b..9add3116236 100644 --- a/.github/workflows/android-release-artifacts.yml +++ b/.github/workflows/android-release-artifacts.yml @@ -45,7 +45,7 @@ jobs: secrets-env: EXECUTORCH_MAVEN_SIGNING_KEYID EXECUTORCH_MAVEN_SIGNING_PASSWORD EXECUTORCH_MAVEN_CENTRAL_PASSWORD EXECUTORCH_MAVEN_CENTRAL_USERNAME EXECUTORCH_MAVEN_SIGNING_GPG_KEY_CONTENTS runner: linux.2xlarge docker-image: executorch-ubuntu-22.04-clang12-android - submodules: 'true' + submodules: 'recursive' ref: ${{ github.sha }} timeout: 90 upload-artifact: android-apps diff --git a/scripts/build_android_library.sh b/scripts/build_android_library.sh index a32ac5f4e5b..c96793a126a 100755 --- a/scripts/build_android_library.sh +++ b/scripts/build_android_library.sh @@ -65,11 +65,6 @@ build_android_native_library() { fi cmake --build "${CMAKE_OUT}" -j "${CMAKE_JOBS}" --target install --config "${EXECUTORCH_CMAKE_BUILD_TYPE}" - # Update tokenizers submodule - pushd extension/llm/tokenizers - echo "Update tokenizers submodule" - git submodule update --init - popd cmake extension/android \ -DCMAKE_TOOLCHAIN_FILE=${ANDROID_NDK}/build/cmake/android.toolchain.cmake \ -DANDROID_ABI="${ANDROID_ABI}" \ From c89d037a1072cf8547c48706c011f35b4262ac11 Mon Sep 17 00:00:00 2001 From: Mengwei Liu Date: Thu, 10 Apr 2025 17:36:10 -0700 Subject: [PATCH 088/244] Return None as dynamic shape when enable_dynamic_shape is False Differential Revision: D72805966 Pull Request resolved: https://github.com/pytorch/executorch/pull/10073 --- extension/llm/export/TARGETS | 1 + extension/llm/export/builder.py | 14 +-- extension/llm/export/test/TARGETS | 18 ++++ extension/llm/export/test/__init__.py | 0 extension/llm/export/test/test_builder.py | 117 ++++++++++++++++++++++ 5 files changed, 143 insertions(+), 7 deletions(-) create mode 100644 extension/llm/export/test/TARGETS create mode 100644 extension/llm/export/test/__init__.py create mode 100644 extension/llm/export/test/test_builder.py diff --git a/extension/llm/export/TARGETS b/extension/llm/export/TARGETS index 40f8599e9e0..298a64ba328 100644 --- a/extension/llm/export/TARGETS +++ b/extension/llm/export/TARGETS @@ -22,6 +22,7 @@ runtime.python_library( "//bento/...", "//bento_kernels/...", "//executorch/examples/...", + "//executorch/extension/llm/...", "//meta_intern_odllm/...", ], deps = [ diff --git a/extension/llm/export/builder.py b/extension/llm/export/builder.py index 8fcb5798327..9be7bbeee4b 100644 --- a/extension/llm/export/builder.py +++ b/extension/llm/export/builder.py @@ -178,13 +178,13 @@ def _get_dynamic_shape(self) -> Any: return self.dynamic_shapes dim = torch.export.Dim("token_dim", max=self.max_seq_len - 1) - - if not self.use_kv_cache: - # Only one input argument: tokens - self.dynamic_shapes = ({1: dim},) - elif self.enable_dynamic_shape: - # Two input arguments: tokens and input_pos but input_pos is static shape - self.dynamic_shapes = ({1: dim}, {"input_pos": {0: 1}}) + if self.enable_dynamic_shape: + if not self.use_kv_cache: + # Only one input argument: tokens + self.dynamic_shapes = ({1: dim},) + else: + # Two input arguments: tokens and input_pos but input_pos is static shape + self.dynamic_shapes = ({1: dim}, {"input_pos": {0: 1}}) else: # Two input arguments: tokens and input_pos but both are of static shape self.dynamic_shapes = None diff --git a/extension/llm/export/test/TARGETS b/extension/llm/export/test/TARGETS new file mode 100644 index 00000000000..63efce84119 --- /dev/null +++ b/extension/llm/export/test/TARGETS @@ -0,0 +1,18 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +load("@fbsource//xplat/executorch/build:runtime_wrapper.bzl", "runtime") + +oncall("executorch") + +runtime.python_test( + name = "test_builder", + srcs = ["test_builder.py"], + deps = [ + "//executorch/extension/llm/export:export_lib", + "//caffe2:torch", + ], +) diff --git a/extension/llm/export/test/__init__.py b/extension/llm/export/test/__init__.py new file mode 100644 index 00000000000..e69de29bb2d diff --git a/extension/llm/export/test/test_builder.py b/extension/llm/export/test/test_builder.py new file mode 100644 index 00000000000..7883480c1e7 --- /dev/null +++ b/extension/llm/export/test/test_builder.py @@ -0,0 +1,117 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +# pyre-strict +import unittest +from unittest.mock import MagicMock + +import torch + +from executorch.extension.llm.export.builder import DType, LLMEdgeManager + + +class TestLLMEdgeManager(unittest.TestCase): + def setUp(self) -> None: + # Create a mock model + self.mock_model = MagicMock() + self.modelname = "test_model" + self.max_seq_len = 2048 + self.dtype = DType.fp32 + self.example_inputs = (torch.zeros((1, 10), dtype=torch.long),) + self.example_kwarg_inputs = {"input_pos": torch.tensor([0])} + + def test_get_dynamic_shape_with_preset_dynamic_shapes(self) -> None: + """Test that _get_dynamic_shape returns preset dynamic_shapes if available.""" + # Create a manager with preset dynamic_shapes + preset_dynamic_shapes = {"preset": "shapes"} + manager = LLMEdgeManager( + model=self.mock_model, + modelname=self.modelname, + max_seq_len=self.max_seq_len, + dtype=self.dtype, + use_kv_cache=False, + example_inputs=self.example_inputs, + dynamic_shapes=preset_dynamic_shapes, + ) + + # Call _get_dynamic_shape and verify it returns the preset value + result = manager._get_dynamic_shape() + self.assertEqual(result, preset_dynamic_shapes) + + def test_get_dynamic_shape_with_dynamic_shape_enabled_no_kv_cache(self) -> None: + """Test _get_dynamic_shape when enable_dynamic_shape=True and use_kv_cache=False.""" + # Create a manager with enable_dynamic_shape=True and use_kv_cache=False + manager = LLMEdgeManager( + model=self.mock_model, + modelname=self.modelname, + max_seq_len=self.max_seq_len, + dtype=self.dtype, + use_kv_cache=False, + example_inputs=self.example_inputs, + enable_dynamic_shape=True, + ) + + # Call _get_dynamic_shape + result = manager._get_dynamic_shape() + + # Verify the result has the expected structure + self.assertIsInstance(result, tuple) + self.assertEqual(len(result), 1) + self.assertIsInstance(result[0], dict) + self.assertIn(1, result[0]) + # Check that the value at key 1 is a torch.export.Dim with the correct max value + self.assertEqual(result[0][1].max, self.max_seq_len - 1) + + def test_get_dynamic_shape_with_dynamic_shape_enabled_with_kv_cache(self) -> None: + """Test _get_dynamic_shape when enable_dynamic_shape=True and use_kv_cache=True.""" + # Create a manager with enable_dynamic_shape=True and use_kv_cache=True + manager = LLMEdgeManager( + model=self.mock_model, + modelname=self.modelname, + max_seq_len=self.max_seq_len, + dtype=self.dtype, + use_kv_cache=True, + example_inputs=self.example_inputs, + enable_dynamic_shape=True, + ) + + # Call _get_dynamic_shape + result = manager._get_dynamic_shape() + + # Verify the result has the expected structure + self.assertIsInstance(result, tuple) + self.assertEqual(len(result), 2) + + # Check first element (tokens dimension) + self.assertIsInstance(result[0], dict) + self.assertIn(1, result[0]) + self.assertEqual(result[0][1].max, self.max_seq_len - 1) + + # Check second element (input_pos dimension) + self.assertIsInstance(result[1], dict) + self.assertIn("input_pos", result[1]) + self.assertIsInstance(result[1]["input_pos"], dict) + self.assertIn(0, result[1]["input_pos"]) + self.assertEqual(result[1]["input_pos"][0], 1) + + def test_get_dynamic_shape_with_dynamic_shape_disabled(self) -> None: + """Test _get_dynamic_shape when enable_dynamic_shape=False.""" + # Create a manager with enable_dynamic_shape=False + manager = LLMEdgeManager( + model=self.mock_model, + modelname=self.modelname, + max_seq_len=self.max_seq_len, + dtype=self.dtype, + use_kv_cache=True, # Doesn't matter for this test + example_inputs=self.example_inputs, + enable_dynamic_shape=False, + ) + + # Call _get_dynamic_shape + result = manager._get_dynamic_shape() + + # Verify the result is None + self.assertIsNone(result) From eca87186484475ba1b0e211cb62494fd8e013a32 Mon Sep 17 00:00:00 2001 From: cccclai Date: Thu, 10 Apr 2025 22:06:10 -0700 Subject: [PATCH 089/244] fix lint (#10090) Fix the lint error from https://github.com/pytorch/executorch/pull/10054 --- backends/qualcomm/tests/utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/backends/qualcomm/tests/utils.py b/backends/qualcomm/tests/utils.py index 1d8040ef988..77cbed5cc99 100644 --- a/backends/qualcomm/tests/utils.py +++ b/backends/qualcomm/tests/utils.py @@ -439,7 +439,6 @@ def lower_module_and_test_output( skip_node_id_set: set = None, skip_node_op_set: set = None, dynamic_shapes: Dict = None, - passes_job: collections.OrderedDict = None, ): delegated_program = to_edge_transform_and_lower_to_qnn( module, From 739768dd9b13819479041dfa67f6452d0dd3fce9 Mon Sep 17 00:00:00 2001 From: Kimish Patel Date: Thu, 10 Apr 2025 23:22:42 -0700 Subject: [PATCH 090/244] In quantized sdpa dequant v Differential Revision: D71833063 Pull Request resolved: https://github.com/pytorch/executorch/pull/10097 --- extension/llm/custom_ops/op_sdpa_impl.h | 122 +++++++++++++++++++++--- 1 file changed, 108 insertions(+), 14 deletions(-) diff --git a/extension/llm/custom_ops/op_sdpa_impl.h b/extension/llm/custom_ops/op_sdpa_impl.h index 1f19fa75de7..76dbf776700 100644 --- a/extension/llm/custom_ops/op_sdpa_impl.h +++ b/extension/llm/custom_ops/op_sdpa_impl.h @@ -120,6 +120,88 @@ void _q_at_k_gemm( } } +// Refactor op_dequantize.cpp to avoid code duplication +void dequantize_optimized( + const int8_t* in, + const float scale, + const int8_t zero_point, + float* out, + int64_t quant_min, + int64_t quant_max, + size_t numel) { + size_t i = 0; +#if defined(__aarch64__) || defined(__ARM_NEON) + int8x8_t zero_point_vec = vdup_n_s8(zero_point); + float32x4_t scales = vdupq_n_f32(static_cast(scale)); + constexpr int32_t kVecSize = 16; + const size_t num_vecs = numel / kVecSize; + const int8_t* in_copy = in; + float* out_copy = out; + for (; i < num_vecs; i++) { + int8x16_t in_vec = vld1q_s8(in_copy); + int16x8_t sub_vec_0_7 = vsubl_s8(vget_low_s8(in_vec), zero_point_vec); + int32x4_t sub_vec_0_3 = vmovl_s16(vget_low_s16(sub_vec_0_7)); + int32x4_t sub_vec_4_7 = vmovl_s16(vget_high_s16(sub_vec_0_7)); + float32x4_t out_vec_0_3 = vmulq_f32(vcvtq_f32_s32(sub_vec_0_3), scales); + float32x4_t out_vec_4_7 = vmulq_f32(vcvtq_f32_s32(sub_vec_4_7), scales); + + int16x8_t sub_vec_8_15 = vsubl_s8(vget_high_s8(in_vec), zero_point_vec); + int32x4_t sub_vec_8_11 = vmovl_s16(vget_low_s16(sub_vec_8_15)); + int32x4_t sub_vec_12_15 = vmovl_s16(vget_high_s16(sub_vec_8_15)); + float32x4_t out_vec_8_11 = vmulq_f32(vcvtq_f32_s32(sub_vec_8_11), scales); + float32x4_t out_vec_12_15 = vmulq_f32(vcvtq_f32_s32(sub_vec_12_15), scales); + vst1q_f32(out_copy + 0, out_vec_0_3); + vst1q_f32(out_copy + 4, out_vec_4_7); + vst1q_f32(out_copy + 8, out_vec_8_11); + vst1q_f32(out_copy + 12, out_vec_12_15); + in_copy += kVecSize; + out_copy += kVecSize; + } + i = i * kVecSize; +#endif + for (; i < numel; i++) { + out[i] = (static_cast(in[i]) - static_cast(zero_point)) * + scale; + } +} + +void dequantize_per_channel_optimized( + const int8_t* in_data, + const float* scales_data, + const int8_t* zero_points_data, + float* out_data, + int64_t quant_min, + int64_t quant_max, + size_t outer_size, + size_t in_outer_stride, + size_t out_outer_stride, + size_t num_channels, + size_t in_channel_stride, + size_t out_channel_stride, + size_t channel_size, + size_t qparams_stride) { + for (size_t outer_idx = 0; outer_idx < outer_size; ++outer_idx) { + // Loop through dim + for (size_t channel_idx = 0; channel_idx < num_channels; ++channel_idx) { + const int8_t* in_data_local = in_data + outer_idx * in_outer_stride + + channel_idx * in_channel_stride; + const float scale = *(scales_data + channel_idx * qparams_stride); + const int8_t zero_point = + *(zero_points_data + channel_idx * qparams_stride); + float* out_data_local = out_data + outer_idx * out_outer_stride + + channel_idx * out_channel_stride; + dequantize_optimized( + in_data_local, + scale, + zero_point, + out_data_local, + quant_min, + quant_max, + channel_size); + } + } +} + template void _qk_at_v_gemm( const int64_t m, @@ -134,24 +216,36 @@ void _qk_at_v_gemm( const accum_t beta) { if (v_data.dtype == ScalarType::Char) { if constexpr (std::is_same::value) { - int a_stride_m_tmp, b_stride_n_tmp; - auto kernel = torchao::kernels::cpu::quantized_matmul:: - get_fp32_a_input_channelwise_8bit_b_f32_c_matmul( - m, n, k, false, false, a_stride_m_tmp, b_stride_n_tmp); - kernel( - m, + std::vector dequantized_v_data(v_data.m * v_data.n); + dequantize_per_channel_optimized( + static_cast(v_data.data), + static_cast(v_data.scales), + static_cast(v_data.zero_points), + dequantized_v_data.data(), + -128, + 127, + 1, + 0, + 0, + v_data.m, + v_stride_n, + v_data.n, + v_data.n, + v_data.zero_points_stride); + ::executorch::cpublas::gemm( + ::executorch::cpublas::TransposeType::NoTranspose, + ::executorch::cpublas::TransposeType::NoTranspose, n, + m, k, + static_cast(1), + dequantized_v_data.data(), + v_data.n, qk_data, - qk_stride_m /*lhs_stride_m*/, - static_cast(v_data.data), - v_stride_n /*rhs_stride_n*/, - o_data, - o_stride_m /*out_stride_n*/, - static_cast(v_data.zero_points), - static_cast(v_data.scales), + qk_stride_m, beta, - v_data.zero_points_stride); + o_data, + o_stride_m); } else { ET_CHECK_MSG( false, "Accumulation in dtype other than float not supported yet"); From f19069ed22ae783ac303c9f23dd83acee47e6232 Mon Sep 17 00:00:00 2001 From: Sebastian Larsson <38941629+Sebastian-Larsson@users.noreply.github.com> Date: Fri, 11 Apr 2025 09:17:02 +0200 Subject: [PATCH 091/244] Arm backend: Convert asserts to raise errors in op_minimum (#10055) Asserts are converted to proper raises to ensure graph integrity. Improve error messages and add additional check that both inputs and outputs have the same data type. Signed-off-by: Sebastian Larsson --- backends/arm/operators/op_minimum.py | 23 +++++++++++++++-------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/backends/arm/operators/op_minimum.py b/backends/arm/operators/op_minimum.py index dd3afe90cb5..88cb8d376fe 100644 --- a/backends/arm/operators/op_minimum.py +++ b/backends/arm/operators/op_minimum.py @@ -37,20 +37,27 @@ def define_node( inputs: List[TosaArg], output: TosaArg, ) -> None: - assert inputs[0].dtype == inputs[1].dtype + if inputs[0].dtype != inputs[1].dtype and inputs[0].dtype != output.dtype: + raise TypeError( + f"Data type of inputs and output must be the same. Got input 0 dtype: " + f"{inputs[0].dtype}, input 1 dtype: {inputs[1].dtype} and output " + f"dtype: {output.dtype}" + ) scale_back = 1.0 min_output = output if inputs[0].dtype == ts.DType.INT8: input_qparams = get_input_qparams(node) - assert ( - len(input_qparams) == 2 - ), f"Both inputs needs to have quantization information for {node}" - # insert RESCALEs to int32 - assert ( - input_qparams[0] == input_qparams[1] - ), "Both inputs must have same quantization for MIN" + if len(input_qparams) != 2: + raise ValueError( + f"Both inputs need to have quantization information for {node}" + ) + if input_qparams[0] != input_qparams[1]: + raise ValueError( + "Both inputs must have the same quantization parameters for MIN" + ) + # insert RESCALEs to int32 operand_inputs, scale_back = tqutils.insert_rescale_ops_to_int32( tosa_graph, inputs, node ) From ac8946431a5356b7c4e5445d29e8d2775ce64f47 Mon Sep 17 00:00:00 2001 From: Yufeng Shi Date: Fri, 11 Apr 2025 08:17:44 +0100 Subject: [PATCH 092/244] Arm backend: Add TOSA support for gt.Scalar and lt.Scalar (#9908) - Convert gt.Scalar and lt.Scalar to gt.Tensor and lt.Tensor - Expand the scalar operands to match the shape of the tensor operands - Rename the eq test names to include full aten op name Signed-off-by: Yufeng Shi --- backends/arm/_passes/match_arg_ranks_pass.py | 2 + .../replace_scalar_with_tensor_pass.py | 4 + .../arm/operator_support/ethos_u55_support.py | 2 + .../tosa_supported_operators.py | 2 + backends/arm/test/ops/test_eq.py | 35 ++++- backends/arm/test/ops/test_gt.py | 126 ++++++++++++------ backends/arm/test/ops/test_lt.py | 126 ++++++++++++------ backends/arm/test/ops/test_where.py | 35 +---- 8 files changed, 212 insertions(+), 120 deletions(-) diff --git a/backends/arm/_passes/match_arg_ranks_pass.py b/backends/arm/_passes/match_arg_ranks_pass.py index 2cfc9b2b86a..9f966596de3 100644 --- a/backends/arm/_passes/match_arg_ranks_pass.py +++ b/backends/arm/_passes/match_arg_ranks_pass.py @@ -48,6 +48,8 @@ def __init__(self, exported_program): exir_ops.edge.aten.bitwise_right_shift.Tensor, exir_ops.edge.aten.bitwise_left_shift.Tensor, exir_ops.edge.aten.eq.Tensor, + exir_ops.edge.aten.gt.Tensor, + exir_ops.edge.aten.lt.Tensor, exir_ops.edge.aten.pow.Tensor_Tensor, exir_ops.edge.aten.where.self, ] diff --git a/backends/arm/_passes/replace_scalar_with_tensor_pass.py b/backends/arm/_passes/replace_scalar_with_tensor_pass.py index 97e89132979..f24d53215f5 100644 --- a/backends/arm/_passes/replace_scalar_with_tensor_pass.py +++ b/backends/arm/_passes/replace_scalar_with_tensor_pass.py @@ -26,6 +26,8 @@ exir_ops.edge.aten.__rshift__.Scalar: exir_ops.edge.aten.bitwise_right_shift.Tensor, exir_ops.edge.aten.__lshift__.Scalar: exir_ops.edge.aten.bitwise_left_shift.Tensor, exir_ops.edge.aten.eq.Scalar: exir_ops.edge.aten.eq.Tensor, + exir_ops.edge.aten.gt.Scalar: exir_ops.edge.aten.gt.Tensor, + exir_ops.edge.aten.lt.Scalar: exir_ops.edge.aten.lt.Tensor, torch.ops.aten.add.Scalar: torch.ops.aten.add.Tensor, torch.ops.aten.sub.Scalar: torch.ops.aten.sub.Tensor, torch.ops.aten.mul.Scalar: torch.ops.aten.mul.Tensor, @@ -33,6 +35,8 @@ torch.ops.aten.__rshift__.Scalar: torch.ops.aten.bitwise_right_shift.Tensor, torch.ops.aten.__lshift__.Scalar: torch.ops.aten.bitwise_left_shift.Tensor, torch.ops.aten.eq.Scalar: torch.ops.aten.eq.Tensor, + torch.ops.aten.gt.Scalar: torch.ops.aten.gt.Tensor, + torch.ops.aten.lt.Scalar: torch.ops.aten.lt.Tensor, } diff --git a/backends/arm/operator_support/ethos_u55_support.py b/backends/arm/operator_support/ethos_u55_support.py index 69fda636423..b716acd0867 100644 --- a/backends/arm/operator_support/ethos_u55_support.py +++ b/backends/arm/operator_support/ethos_u55_support.py @@ -135,8 +135,10 @@ class EthosU55NotSupported(OperatorSupportBase): exir_ops.edge.aten.eq.Scalar, exir_ops.edge.aten.ge.Tensor, exir_ops.edge.aten.gt.Tensor, + exir_ops.edge.aten.gt.Scalar, exir_ops.edge.aten.le.Tensor, exir_ops.edge.aten.lt.Tensor, + exir_ops.edge.aten.lt.Scalar, exir_ops.edge.aten.flip.default, # REVERSE exir_ops.edge.aten.grid_sampler_2d, # GATHER exir_ops.edge.aten.scatter.src, diff --git a/backends/arm/operator_support/tosa_supported_operators.py b/backends/arm/operator_support/tosa_supported_operators.py index 09230e44257..24f8b1deeec 100644 --- a/backends/arm/operator_support/tosa_supported_operators.py +++ b/backends/arm/operator_support/tosa_supported_operators.py @@ -176,8 +176,10 @@ def is_node_supported( exir_ops.edge.aten.full_like.default, exir_ops.edge.aten.ge.Tensor, exir_ops.edge.aten.gt.Tensor, + exir_ops.edge.aten.gt.Scalar, exir_ops.edge.aten.le.Tensor, exir_ops.edge.aten.lt.Tensor, + exir_ops.edge.aten.lt.Scalar, exir_ops.edge.aten.mul.Tensor, exir_ops.edge.aten.add.Scalar, exir_ops.edge.aten.sub.Scalar, diff --git a/backends/arm/test/ops/test_eq.py b/backends/arm/test/ops/test_eq.py index 7cbba632696..e3bcf877ffe 100644 --- a/backends/arm/test/ops/test_eq.py +++ b/backends/arm/test/ops/test_eq.py @@ -96,8 +96,16 @@ def test_eq_scalar_tosa_MI(test_module): pipeline.run() -@common.parametrize("test_module", test_data_tensor | test_data_scalar) -def test_eq_tosa_BI(test_module): +@common.parametrize("test_module", test_data_tensor) +def test_eq_tensor_tosa_BI(test_module): + pipeline = TosaPipelineBI[input_t]( + test_module, test_module.get_inputs(), Equal.aten_op_Tensor, Equal.exir_op + ) + pipeline.run() + + +@common.parametrize("test_module", test_data_scalar) +def test_eq_scalar_tosa_BI(test_module): pipeline = TosaPipelineBI[input_t]( test_module, test_module.get_inputs(), Equal.aten_op_Tensor, Equal.exir_op ) @@ -133,15 +141,34 @@ def test_eq_scalar_u55_BI(test_module): @common.parametrize( "test_module", - test_data_tensor | test_data_scalar, + test_data_tensor, xfails={ "eq_tensor_rank4_randn": "MLETORCH-847: Boolean eq result unstable on U85", + }, + strict=False, +) +@common.XfailIfNoCorstone320 +def test_eq_tensor_u85_BI(test_module): + pipeline = EthosU85PipelineBI[input_t]( + test_module, + test_module.get_inputs(), + Equal.aten_op_Tensor, + Equal.exir_op, + run_on_fvp=True, + ) + pipeline.run() + + +@common.parametrize( + "test_module", + test_data_scalar, + xfails={ "eq_scalar_rank4_randn": "MLETORCH-847: Boolean eq result unstable on U85", }, strict=False, ) @common.XfailIfNoCorstone320 -def test_eq_u85_BI(test_module): +def test_eq_scalar_u85_BI(test_module): pipeline = EthosU85PipelineBI[input_t]( test_module, test_module.get_inputs(), diff --git a/backends/arm/test/ops/test_gt.py b/backends/arm/test/ops/test_gt.py index 2095f781bdb..15515958c85 100644 --- a/backends/arm/test/ops/test_gt.py +++ b/backends/arm/test/ops/test_gt.py @@ -5,7 +5,6 @@ from typing import Tuple -import pytest import torch from executorch.backends.arm.test import common @@ -16,13 +15,15 @@ TosaPipelineMI, ) -aten_op = "torch.ops.aten.gt.Tensor" -exir_op = "executorch_exir_dialects_edge__ops_aten_gt_Tensor" input_t = Tuple[torch.Tensor] class Greater(torch.nn.Module): + aten_op_tensor = "torch.ops.aten.gt.Tensor" + aten_op_scalar = "torch.ops.aten.gt.Scalar" + exir_op = "executorch_exir_dialects_edge__ops_aten_gt_Tensor" + def __init__(self, input, other): super().__init__() self.input_ = input @@ -31,7 +32,7 @@ def __init__(self, input, other): def forward( self, input_: torch.Tensor, - other_: torch.Tensor, + other_: torch.Tensor | int | float, ): return input_ > other_ @@ -39,98 +40,135 @@ def get_inputs(self): return (self.input_, self.other_) -op_gt_rank1_ones = Greater( +op_gt_tensor_rank1_ones = Greater( torch.ones(5), torch.ones(5), ) -op_gt_rank2_rand = Greater( +op_gt_tensor_rank2_rand = Greater( torch.rand(4, 5), torch.rand(1, 5), ) -op_gt_rank3_randn = Greater( +op_gt_tensor_rank3_randn = Greater( torch.randn(10, 5, 2), torch.randn(10, 5, 2), ) -op_gt_rank4_randn = Greater( +op_gt_tensor_rank4_randn = Greater( torch.randn(3, 2, 2, 2), torch.randn(3, 2, 2, 2), ) -test_data_common = { - "gt_rank1_ones": op_gt_rank1_ones, - "gt_rank2_rand": op_gt_rank2_rand, - "gt_rank3_randn": op_gt_rank3_randn, - "gt_rank4_randn": op_gt_rank4_randn, +op_gt_scalar_rank1_ones = Greater(torch.ones(5), 1.0) +op_gt_scalar_rank2_rand = Greater(torch.rand(4, 5), 0.2) +op_gt_scalar_rank3_randn = Greater(torch.randn(10, 5, 2), -0.1) +op_gt_scalar_rank4_randn = Greater(torch.randn(3, 2, 2, 2), 0.3) + +test_data_tensor = { + "gt_tensor_rank1_ones": op_gt_tensor_rank1_ones, + "gt_tensor_rank2_rand": op_gt_tensor_rank2_rand, + "gt_tensor_rank3_randn": op_gt_tensor_rank3_randn, + "gt_tensor_rank4_randn": op_gt_tensor_rank4_randn, +} + +test_data_scalar = { + "gt_scalar_rank1_ones": op_gt_scalar_rank1_ones, + "gt_scalar_rank2_rand": op_gt_scalar_rank2_rand, + "gt_scalar_rank3_randn": op_gt_scalar_rank3_randn, + "gt_scalar_rank4_randn": op_gt_scalar_rank4_randn, } -@common.parametrize("test_module", test_data_common) -def test_gt_tosa_MI(test_module): +@common.parametrize("test_module", test_data_tensor) +def test_gt_tensor_tosa_MI(test_module): + pipeline = TosaPipelineMI[input_t]( + test_module, test_module.get_inputs(), Greater.aten_op_tensor, Greater.exir_op + ) + pipeline.run() + + +@common.parametrize("test_module", test_data_scalar) +def test_gt_scalar_tosa_MI(test_module): pipeline = TosaPipelineMI[input_t]( - test_module, test_module.get_inputs(), aten_op, exir_op + test_module, test_module.get_inputs(), Greater.aten_op_scalar, Greater.exir_op + ) + pipeline.run() + + +@common.parametrize("test_module", test_data_tensor) +def test_gt_tensor_tosa_BI(test_module): + pipeline = TosaPipelineBI[input_t]( + test_module, test_module.get_inputs(), Greater.aten_op_tensor, Greater.exir_op ) pipeline.run() -@common.parametrize("test_module", test_data_common) -def test_gt_tosa_BI(test_module): +@common.parametrize("test_module", test_data_scalar) +def test_gt_scalar_tosa_BI(test_module): pipeline = TosaPipelineBI[input_t]( - test_module, test_module.get_inputs(), aten_op, exir_op + test_module, test_module.get_inputs(), Greater.aten_op_tensor, Greater.exir_op ) pipeline.run() -@common.parametrize("test_module", test_data_common) -def test_gt_u55_BI(test_module): - # GREATER is not supported on U55. +@common.parametrize("test_module", test_data_tensor) +@common.XfailIfNoCorstone300 +def test_gt_tensor_u55_BI(test_module): + # Greater is not supported on U55. pipeline = OpNotSupportedPipeline[input_t]( test_module, test_module.get_inputs(), "TOSA-0.80+BI+u55", - {exir_op: 1}, + {Greater.exir_op: 1}, ) pipeline.run() -@common.parametrize("test_module", test_data_common) -def test_gt_u85_BI(test_module): - pipeline = EthosU85PipelineBI[input_t]( +@common.parametrize("test_module", test_data_scalar) +@common.XfailIfNoCorstone300 +def test_gt_scalar_u55_BI(test_module): + # Greater is not supported on U55. + pipeline = OpNotSupportedPipeline[input_t]( test_module, test_module.get_inputs(), - aten_op, - exir_op, - run_on_fvp=False, - use_to_edge_transform_and_lower=True, + "TOSA-0.80+BI+u55", + {Greater.exir_op: 1}, + n_expected_delegates=1, ) pipeline.run() -@common.parametrize("test_module", test_data_common) -@pytest.mark.skip(reason="The same as test_gt_u55_BI") -def test_gt_u55_BI_on_fvp(test_module): - # GREATER is not supported on U55. - pipeline = OpNotSupportedPipeline[input_t]( +@common.parametrize( + "test_module", + test_data_tensor, + xfails={ + "gt_tensor_rank4_randn": "MLETORCH-847: Boolean eq result unstable on U85", + }, +) +@common.XfailIfNoCorstone320 +def test_gt_tensor_u85_BI(test_module): + pipeline = EthosU85PipelineBI[input_t]( test_module, test_module.get_inputs(), - "TOSA-0.80+BI+u55", - {exir_op: 1}, + Greater.aten_op_tensor, + Greater.exir_op, + run_on_fvp=True, ) pipeline.run() @common.parametrize( "test_module", - test_data_common, - xfails={"gt_rank4_randn": "4D fails because boolean Tensors can't be subtracted"}, + test_data_scalar, + xfails={ + "gt_scalar_rank4_randn": "MLETORCH-847: Boolean eq result unstable on U85", + }, ) -@common.SkipIfNoCorstone320 -def test_gt_u85_BI_on_fvp(test_module): +@common.XfailIfNoCorstone320 +def test_gt_scalar_u85_BI(test_module): pipeline = EthosU85PipelineBI[input_t]( test_module, test_module.get_inputs(), - aten_op, - exir_op, + Greater.aten_op_tensor, + Greater.exir_op, run_on_fvp=True, - use_to_edge_transform_and_lower=True, ) pipeline.run() diff --git a/backends/arm/test/ops/test_lt.py b/backends/arm/test/ops/test_lt.py index cae119cd7a8..f5664b7895d 100644 --- a/backends/arm/test/ops/test_lt.py +++ b/backends/arm/test/ops/test_lt.py @@ -5,7 +5,6 @@ from typing import Tuple -import pytest import torch from executorch.backends.arm.test import common @@ -16,13 +15,15 @@ TosaPipelineMI, ) -aten_op = "torch.ops.aten.lt.Tensor" -exir_op = "executorch_exir_dialects_edge__ops_aten_lt_Tensor" input_t = Tuple[torch.Tensor] class LessThan(torch.nn.Module): + aten_op_tensor = "torch.ops.aten.lt.Tensor" + aten_op_scalar = "torch.ops.aten.lt.Scalar" + exir_op = "executorch_exir_dialects_edge__ops_aten_lt_Tensor" + def __init__(self, input, other): super().__init__() self.input_ = input @@ -31,7 +32,7 @@ def __init__(self, input, other): def forward( self, input_: torch.Tensor, - other_: torch.Tensor, + other_: torch.Tensor | int | float, ): return input_ < other_ @@ -39,98 +40,135 @@ def get_inputs(self): return (self.input_, self.other_) -op_lt_rank1_ones = LessThan( +op_lt_tensor_rank1_ones = LessThan( torch.ones(5), torch.ones(5), ) -op_lt_rank2_rand = LessThan( +op_lt_tensor_rank2_rand = LessThan( torch.rand(4, 5), torch.rand(1, 5), ) -op_lt_rank3_randn = LessThan( +op_lt_tensor_rank3_randn = LessThan( torch.randn(10, 5, 2), torch.randn(10, 5, 2), ) -op_lt_rank4_randn = LessThan( +op_lt_tensor_rank4_randn = LessThan( torch.randn(3, 2, 2, 2), torch.randn(3, 2, 2, 2), ) -test_data_common = { - "lt_rank1_ones": op_lt_rank1_ones, - "lt_rank2_rand": op_lt_rank2_rand, - "lt_rank3_randn": op_lt_rank3_randn, - "lt_rank4_randn": op_lt_rank4_randn, +op_lt_scalar_rank1_ones = LessThan(torch.ones(5), 1.0) +op_lt_scalar_rank2_rand = LessThan(torch.rand(4, 5), 0.2) +op_lt_scalar_rank3_randn = LessThan(torch.randn(10, 5, 2), -0.1) +op_lt_scalar_rank4_randn = LessThan(torch.randn(3, 2, 2, 2), 0.3) + +test_data_tensor = { + "lt_tensor_rank1_ones": op_lt_tensor_rank1_ones, + "lt_tensor_rank2_rand": op_lt_tensor_rank2_rand, + "lt_tensor_rank3_randn": op_lt_tensor_rank3_randn, + "lt_tensor_rank4_randn": op_lt_tensor_rank4_randn, +} + +test_data_scalar = { + "lt_scalar_rank1_ones": op_lt_scalar_rank1_ones, + "lt_scalar_rank2_rand": op_lt_scalar_rank2_rand, + "lt_scalar_rank3_randn": op_lt_scalar_rank3_randn, + "lt_scalar_rank4_randn": op_lt_scalar_rank4_randn, } -@common.parametrize("test_module", test_data_common) -def test_lt_tosa_MI(test_module): +@common.parametrize("test_module", test_data_tensor) +def test_lt_tensor_tosa_MI(test_module): + pipeline = TosaPipelineMI[input_t]( + test_module, test_module.get_inputs(), LessThan.aten_op_tensor, LessThan.exir_op + ) + pipeline.run() + + +@common.parametrize("test_module", test_data_scalar) +def test_lt_scalar_tosa_MI(test_module): pipeline = TosaPipelineMI[input_t]( - test_module, test_module.get_inputs(), aten_op, exir_op + test_module, test_module.get_inputs(), LessThan.aten_op_scalar, LessThan.exir_op + ) + pipeline.run() + + +@common.parametrize("test_module", test_data_tensor) +def test_lt_tensor_tosa_BI(test_module): + pipeline = TosaPipelineBI[input_t]( + test_module, test_module.get_inputs(), LessThan.aten_op_tensor, LessThan.exir_op ) pipeline.run() -@common.parametrize("test_module", test_data_common) -def test_lt_tosa_BI(test_module): +@common.parametrize("test_module", test_data_scalar) +def test_lt_scalar_tosa_BI(test_module): pipeline = TosaPipelineBI[input_t]( - test_module, test_module.get_inputs(), aten_op, exir_op + test_module, test_module.get_inputs(), LessThan.aten_op_tensor, LessThan.exir_op ) pipeline.run() -@common.parametrize("test_module", test_data_common) -def test_lt_u55_BI(test_module): - # GREATER is not supported on U55. LT uses the GREATER Tosa operator. +@common.parametrize("test_module", test_data_tensor) +@common.XfailIfNoCorstone300 +def test_lt_tensor_u55_BI(test_module): + # LessThan is not supported on U55. pipeline = OpNotSupportedPipeline[input_t]( test_module, test_module.get_inputs(), "TOSA-0.80+BI+u55", - {exir_op: 1}, + {LessThan.exir_op: 1}, ) pipeline.run() -@common.parametrize("test_module", test_data_common) -def test_lt_u85_BI(test_module): - pipeline = EthosU85PipelineBI[input_t]( +@common.parametrize("test_module", test_data_scalar) +@common.XfailIfNoCorstone300 +def test_lt_scalar_u55_BI(test_module): + # LessThan is not supported on U55. + pipeline = OpNotSupportedPipeline[input_t]( test_module, test_module.get_inputs(), - aten_op, - exir_op, - run_on_fvp=False, - use_to_edge_transform_and_lower=True, + "TOSA-0.80+BI+u55", + {LessThan.exir_op: 1}, + n_expected_delegates=1, ) pipeline.run() -@common.parametrize("test_module", test_data_common) -@pytest.mark.skip(reason="The same as test_lt_u55_BI") -def test_lt_u55_BI_on_fvp(test_module): - # GREATER is not supported on U55. LT uses the GREATER Tosa operator. - pipeline = OpNotSupportedPipeline[input_t]( +@common.parametrize( + "test_module", + test_data_tensor, + xfails={ + "lt_tensor_rank4_randn": "MLETORCH-847: Boolean eq result unstable on U85", + }, +) +@common.XfailIfNoCorstone320 +def test_lt_tensor_u85_BI(test_module): + pipeline = EthosU85PipelineBI[input_t]( test_module, test_module.get_inputs(), - "TOSA-0.80+BI+u55", - {exir_op: 1}, + LessThan.aten_op_tensor, + LessThan.exir_op, + run_on_fvp=True, ) pipeline.run() @common.parametrize( "test_module", - test_data_common, - xfails={"lt_rank4_randn": "4D fails because boolean Tensors can't be subtracted"}, + test_data_scalar, + xfails={ + "lt_scalar_rank4_randn": "MLETORCH-847: Boolean eq result unstable on U85", + }, ) -@common.SkipIfNoCorstone320 -def test_lt_u85_BI_on_fvp(test_module): +@common.XfailIfNoCorstone320 +def test_lt_scalar_u85_BI(test_module): pipeline = EthosU85PipelineBI[input_t]( test_module, test_module.get_inputs(), - aten_op, - exir_op, + LessThan.aten_op_tensor, + LessThan.exir_op, run_on_fvp=True, - use_to_edge_transform_and_lower=True, ) pipeline.run() diff --git a/backends/arm/test/ops/test_where.py b/backends/arm/test/ops/test_where.py index bf127460f3e..dd4f3326f8e 100644 --- a/backends/arm/test/ops/test_where.py +++ b/backends/arm/test/ops/test_where.py @@ -173,14 +173,9 @@ def test_where_u55_BI(test_module): get_symmetric_quantization_config() ) - # If condition is tensor_condition then there will be one full_like op which will be - # delegated. - if test_module.condition == tensor_condition: - num_delegates = 1 - num_exir = 0 - else: - num_delegates = 0 - num_exir = 0 + # There will be one full_like op which will be delegated. + num_delegates = 1 + num_exir = 0 pipeline = OpNotSupportedPipeline[input_t]( test_module, @@ -223,14 +218,9 @@ def test_where_u55_BI_on_fvp(test_module): get_symmetric_quantization_config() ) - # If condition is tensor_condition then there will be one full_like op which will be - # delegated. - if test_module.condition == tensor_condition: - num_delegates = 1 - num_exir = 0 - else: - num_delegates = 0 - num_exir = 0 + # There will be one full_like op which will be delegated. + num_delegates = 1 + num_exir = 0 pipeline = OpNotSupportedPipeline[input_t]( test_module, @@ -249,18 +239,7 @@ def test_where_u55_BI_on_fvp(test_module): pipeline.run() -@common.parametrize( - "test_module", - test_modules_BI, - xfails={ - "two_dim_scalar_cond": "E [executorch:method.cpp:601] Missing operator: " - "[2] aten::gt.Scalar_out", - "three_dim_scalar_cond": "E [executorch:method.cpp:601] Missing operator: " - "[2] aten::gt.Scalar_out", - "float32_scalar_cond": "E [executorch:method.cpp:601] Missing operator: " - "[2] aten::gt.Scalar_out", - }, -) +@common.parametrize("test_module", test_modules_BI) @common.XfailIfNoCorstone320 def test_where_u85_BI_on_fvp(test_module): compile_spec = common.get_u85_compile_spec() From 2b386959b702ea9fb2c279fc46c7fbcda5df822b Mon Sep 17 00:00:00 2001 From: fumchin Date: Fri, 11 Apr 2025 08:19:50 +0100 Subject: [PATCH 093/244] Arm backend: Add support for sqrt (#9928) The sqrt operation is implemented via the pow operator, sqrt(x) = pow(x, 0.5). Signed-off-by: Fang-Ching --- backends/arm/_passes/__init__.py | 1 + backends/arm/_passes/arm_pass_manager.py | 3 + backends/arm/_passes/decompose_sqrt_pass.py | 39 ++++++++++ .../tosa_supported_operators.py | 2 + backends/arm/test/ops/test_sqrt.py | 78 +++++++++++++++++++ 5 files changed, 123 insertions(+) create mode 100644 backends/arm/_passes/decompose_sqrt_pass.py create mode 100644 backends/arm/test/ops/test_sqrt.py diff --git a/backends/arm/_passes/__init__.py b/backends/arm/_passes/__init__.py index dd7f3d02518..2b935172a7f 100644 --- a/backends/arm/_passes/__init__.py +++ b/backends/arm/_passes/__init__.py @@ -27,6 +27,7 @@ from .decompose_select import DecomposeSelectPass # noqa from .decompose_softmax_pass import DecomposeSoftmaxPass # noqa from .decompose_softmax_unstable_pass import DecomposeSoftmaxUnstablePass # noqa +from .decompose_sqrt_pass import DecomposeSqrtPass # noqa from .decompose_var_pass import DecomposeVarPass # noqa from .fold_qdq_with_annotated_qparams_pass import ( # noqa FoldAndAnnotateQParamsPass, diff --git a/backends/arm/_passes/arm_pass_manager.py b/backends/arm/_passes/arm_pass_manager.py index 703c6ff214c..85004686ebe 100644 --- a/backends/arm/_passes/arm_pass_manager.py +++ b/backends/arm/_passes/arm_pass_manager.py @@ -32,6 +32,7 @@ DecomposeSelectPass, DecomposeSoftmaxPass, DecomposeSoftmaxUnstablePass, + DecomposeSqrtPass, DecomposeVarPass, FoldAndAnnotateQParamsPass, FuseBatchnorm2DPass, @@ -115,6 +116,7 @@ def _tosa_080_BI_pipeline(self, exported_program: ExportedProgram) -> GraphModul return self._transform(exported_program.graph_module) def _tosa_080_MI_pipeline(self, exported_program: ExportedProgram) -> GraphModule: + self.add_pass(DecomposeSqrtPass()) self.add_pass(ReplaceScalarWithTensorArgPassTOSAMI()) self.add_pass(FuseQuantizedActivationPass()) self.add_pass(RemoveGetItemPass()) @@ -181,6 +183,7 @@ def transform_for_annotation_pipeline(self, graph_module: GraphModule): self.add_pass(DecomposeMeanDimPass()) self.add_pass(DecomposeDivPass()) self.add_pass(DecomposeLeakyReLUPass()) + self.add_pass(DecomposeSqrtPass()) if isinstance(self.tosa_spec, Tosa_0_80) and self.tosa_spec.is_U55_subset: # Numerically stable softmax uses amax which is not supported on Ethos-U55 diff --git a/backends/arm/_passes/decompose_sqrt_pass.py b/backends/arm/_passes/decompose_sqrt_pass.py new file mode 100644 index 00000000000..d4a678affea --- /dev/null +++ b/backends/arm/_passes/decompose_sqrt_pass.py @@ -0,0 +1,39 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +# pyre-unsafe +import torch +from executorch.exir.dialects._ops import ops as exir_ops +from executorch.exir.pass_base import ExportPass + +edge_sqrt_ops = (exir_ops.edge.aten.sqrt.default,) +aten_sqrt_ops = ( + torch.ops.aten.sqrt.default, + torch.ops.aten.sqrt_.default, +) + + +def get_sqrt_decomposition(op) -> tuple: + # TODO : "MLETORCH-863 : Replace current sqrt -> pow.Tensor_Scalar workaround with pow.Tensor_Tensor" + if op in edge_sqrt_ops: + return exir_ops.edge.aten.pow.Tensor_Scalar + if op in aten_sqrt_ops: + return torch.ops.aten.pow.Tensor_Scalar + raise RuntimeError(f"Can't get sqrt decomposition for op {op}") + + +class DecomposeSqrtPass(ExportPass): + + def call_operator(self, op, args, kwargs, meta): + """ + Decomposes `sqrt(x)` into `pow(x, 0.5)` for backend support. + """ + + if op not in (edge_sqrt_ops + aten_sqrt_ops): + return super().call_operator(op, args, kwargs, meta) + + pow_op = get_sqrt_decomposition(op) + + return super().call_operator(pow_op, (args[0], 0.5), {}, meta) diff --git a/backends/arm/operator_support/tosa_supported_operators.py b/backends/arm/operator_support/tosa_supported_operators.py index 24f8b1deeec..f76b30ce2a1 100644 --- a/backends/arm/operator_support/tosa_supported_operators.py +++ b/backends/arm/operator_support/tosa_supported_operators.py @@ -196,6 +196,7 @@ def is_node_supported( exir_ops.edge.aten.reciprocal.default, exir_ops.edge.aten.relu.default, exir_ops.edge.aten.leaky_relu.default, + exir_ops.edge.aten.sqrt.default, exir_ops.edge.aten.rsqrt.default, exir_ops.edge.aten._softmax.default, exir_ops.edge.aten.select_copy.int, @@ -258,6 +259,7 @@ def is_node_supported( exir_ops.edge.aten.var.correction, exir_ops.edge.aten.var.dim, exir_ops.edge.aten.add.Scalar, + exir_ops.edge.aten.sqrt.default, exir_ops.edge.aten.sub.Scalar, exir_ops.edge.aten.mul.Scalar, exir_ops.edge.aten.div.Scalar, diff --git a/backends/arm/test/ops/test_sqrt.py b/backends/arm/test/ops/test_sqrt.py new file mode 100644 index 00000000000..53a1e79c0a8 --- /dev/null +++ b/backends/arm/test/ops/test_sqrt.py @@ -0,0 +1,78 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + + +from typing import Dict, Tuple + +import torch +from executorch.backends.arm.test import common +from executorch.backends.arm.test.tester.test_pipeline import ( + EthosU55PipelineBI, + EthosU85PipelineBI, + TosaPipelineBI, + TosaPipelineMI, +) + + +class Sqrt(torch.nn.Module): + input_t = Tuple[torch.Tensor] + aten_op_MI = "torch.ops.aten.sqrt.default" + exir_op_MI = "executorch_exir_dialects_edge__ops_aten_pow_Tensor_Tensor" + + aten_op_BI = "torch.ops.aten.pow.Tensor_Scalar" + exir_op_BI = "executorch_exir_dialects_edge__ops_aten_pow_Tensor_Scalar" + + def __init__(self): + super().__init__() + + def forward(self, x): + return torch.sqrt(x) + + test_data: Dict[str, input_t] = { + "sqrt_tensor_rank1_ones": (torch.ones(10),), + "sqrt_tensor_rank2_random": (torch.rand(5, 10),), + "sqrt_tensor_rank3_ones": (torch.ones(2, 3, 4),), + "sqrt_tensor_rank4_random": (torch.rand(1, 3, 8, 8),), + "sqrt_tensor_rank4_multibatch": (torch.rand(2, 3, 4, 4),), + } + + +fvp_xfails = { + "sqrt_tensor_rank4_multibatch": "MLETORCH-517 : Multiple batches not supported", +} + + +@common.parametrize("test_data", Sqrt.test_data) +def test_sqrt_tosa_MI(test_data: Sqrt.input_t): + pipeline = TosaPipelineMI[Sqrt.input_t]( + Sqrt(), test_data, Sqrt.aten_op_MI, Sqrt.exir_op_MI + ) + pipeline.run() + + +@common.parametrize("test_data", Sqrt.test_data) +def test_sqrt_tosa_BI(test_data: Sqrt.input_t): + pipeline = TosaPipelineBI[Sqrt.input_t]( + Sqrt(), test_data, Sqrt.aten_op_BI, Sqrt.exir_op_BI + ) + pipeline.run() + + +@common.parametrize("test_data", Sqrt.test_data, fvp_xfails) +@common.XfailIfNoCorstone300 +def test_sqrt_u55_BI(test_data: Sqrt.input_t): + pipeline = EthosU55PipelineBI[Sqrt.input_t]( + Sqrt(), test_data, Sqrt.aten_op_BI, Sqrt.exir_op_BI, run_on_fvp=True + ) + pipeline.run() + + +@common.parametrize("test_data", Sqrt.test_data, fvp_xfails) +@common.XfailIfNoCorstone320 +def test_sqrt_u85_BI(test_data: Sqrt.input_t): + pipeline = EthosU85PipelineBI[Sqrt.input_t]( + Sqrt(), test_data, Sqrt.aten_op_BI, Sqrt.exir_op_BI, run_on_fvp=True + ) + pipeline.run() From 0d8006f7fd17dfd08951f1b81ac5d943e4efe2ff Mon Sep 17 00:00:00 2001 From: Iliyan Georgiev Date: Fri, 11 Apr 2025 08:22:35 +0100 Subject: [PATCH 094/244] Arm backend: Placeholder processing now handles non-persistent buffers (#9994) - Update process_placeholder and called functions to use _export.utils - Add test to validate non-persistent input buffers going forward - Remove workaround from test_llama Signed-off-by: Iliyan Georgiev --- backends/arm/process_node.py | 27 +++++----- .../test/misc/test_non_persistent_buffers.py | 49 +++++++++++++++++++ backends/arm/test/models/test_llama.py | 18 ------- 3 files changed, 64 insertions(+), 30 deletions(-) create mode 100644 backends/arm/test/misc/test_non_persistent_buffers.py diff --git a/backends/arm/process_node.py b/backends/arm/process_node.py index 07bbfaa37b9..930c834f232 100644 --- a/backends/arm/process_node.py +++ b/backends/arm/process_node.py @@ -15,6 +15,14 @@ from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification from executorch.backends.arm.tosa_utils import getNodeArgs, tosa_shape +from torch._export.utils import ( + get_buffer, + get_lifted_tensor_constant, + get_param, + is_buffer, + is_lifted_tensor_constant, + is_param, +) from torch.export.exported_program import ExportedProgram @@ -99,8 +107,7 @@ def process_inputs_to_parameters( f"Failed processing parameter placeholder: {node.name}. " "Is the original torch function supported?" ) from e - parameter_name = edge_program.graph_signature.inputs_to_parameters[tosa_arg.name] - parameter_data = edge_program.state_dict[parameter_name] + parameter_data = get_param(edge_program, node) assert isinstance(parameter_data, torch.Tensor), "Expect Attr to be tensor" parameter_values = parameter_data.detach().numpy() @@ -128,8 +135,7 @@ def process_inputs_to_buffers( f"Failed processing buffer placeholder: {node.name}. " "Is the original torch function supported?" ) from e - buffer_name = edge_program.graph_signature.inputs_to_buffers[node.name] - buffer_data = edge_program.state_dict[buffer_name] + buffer_data = get_buffer(edge_program, node) assert isinstance(buffer_data, torch.Tensor), "Expect Attr to be tensor" buffer_values = buffer_data.detach().numpy() @@ -156,11 +162,8 @@ def process_inputs_to_lifted_tensor_constants( f"Failed processing lifted tensor constant placeholder: {node.name}. " "Is the original torch function supported?" ) from e - tensor_name = edge_program.graph_signature.inputs_to_lifted_tensor_constants[ - tosa_arg.name - ] - tensor = edge_program.tensor_constants[tensor_name] - tensor_data = tensor.detach().numpy() + tensor = get_lifted_tensor_constant(edge_program, node) + tensor_data = tensor.detach().numpy() # type: ignore[union-attr] tosa_graph.addConst( tensor_data.shape, tosa_arg.dtype, tensor_data, name=tosa_arg.name @@ -179,11 +182,11 @@ def process_placeholder( if node.name in edge_program.graph_signature.user_inputs: process_inputs(node, tosa_graph, tosa_spec) - elif node.name in edge_program.graph_signature.inputs_to_parameters: + elif is_param(edge_program, node): process_inputs_to_parameters(node, tosa_graph, edge_program, tosa_spec) - elif node.name in edge_program.graph_signature.inputs_to_buffers: + elif is_buffer(edge_program, node): process_inputs_to_buffers(node, tosa_graph, edge_program) - elif node.name in edge_program.graph_signature.inputs_to_lifted_tensor_constants: + elif is_lifted_tensor_constant(edge_program, node): process_inputs_to_lifted_tensor_constants(node, tosa_graph, edge_program) elif node.name in edge_program.graph_signature.inputs_to_lifted_custom_objs: raise NotImplementedError( diff --git a/backends/arm/test/misc/test_non_persistent_buffers.py b/backends/arm/test/misc/test_non_persistent_buffers.py new file mode 100644 index 00000000000..1b9456ae470 --- /dev/null +++ b/backends/arm/test/misc/test_non_persistent_buffers.py @@ -0,0 +1,49 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import torch +import torch.nn as nn + +from executorch.backends.arm.test.common import parametrize +from executorch.backends.arm.test.tester.test_pipeline import ( + TosaPipelineBI, + TosaPipelineMI, +) + + +class NonPersistentBuffer(nn.Module): + """ + Min code version registering a non-persistent input buffer. + """ + + def __init__(self): + super().__init__() + self.register_buffer("test_buff", torch.rand(2, 2, 2, 2), persistent=False) + + def forward(self, x): + return x - self.test_buff + + +test_input = {"input": (torch.ones(2, 2, 2, 2),)} + +input_t = tuple[torch.Tensor] + + +@parametrize("test_data", test_input) +def test_non_persistent_buffer_MI(test_data: input_t): + """ + Test validates Arm backend handling of non-persistent buffers + and ensures that there are no asserts or errors when they are used. + """ + TosaPipelineMI[input_t](NonPersistentBuffer(), test_data, "").run() + + +@parametrize("test_data", test_input) +def test_non_persistent_buffer_BI(test_data: input_t): + """ + Test validates Arm backend handling of non-persistent buffers + and ensures that there are no asserts or errors when they are used. + """ + TosaPipelineBI[input_t](NonPersistentBuffer(), test_data, "").run() diff --git a/backends/arm/test/models/test_llama.py b/backends/arm/test/models/test_llama.py index 378c86cb420..c27303094f6 100644 --- a/backends/arm/test/models/test_llama.py +++ b/backends/arm/test/models/test_llama.py @@ -79,24 +79,6 @@ def prepare_model(self): llama_model, llama_inputs, llama_meta = get_llama_model(args) - # TODO: Remove workaround since attention mask should not be persistent, - # it only works if input shape is always the same - freqs_c = "freqs_cos" - freqs_s = "freqs_sin" - for i in range(llama_model.n_layers): - val = llama_model.layers[i].attention.get_buffer("mask") - llama_model.layers[i].attention.register_buffer( - "mask", val, persistent=True - ) - val = llama_model.layers[i].attention.rope.get_buffer(freqs_c) - llama_model.layers[i].attention.rope.register_buffer( - freqs_c, val, persistent=True - ) - val = llama_model.layers[i].attention.rope.get_buffer(freqs_s) - llama_model.layers[i].attention.rope.register_buffer( - freqs_s, val, persistent=True - ) - return llama_model, llama_inputs, llama_meta def test_llama_tosa_MI(self): From 51d03bcab6c2deae46e06f9f15ca028ba16e4928 Mon Sep 17 00:00:00 2001 From: per held Date: Fri, 11 Apr 2025 09:28:42 +0200 Subject: [PATCH 095/244] Arm backend: Add test_arm_baremetal.sh checks (#10057) Test_arm_baremetal.sh didnt warn about missing setup_path.sh like other scripts. This patch makes it more like others. --- backends/arm/test/test_arm_baremetal.sh | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/backends/arm/test/test_arm_baremetal.sh b/backends/arm/test/test_arm_baremetal.sh index b995341a586..cc140cc9db5 100755 --- a/backends/arm/test/test_arm_baremetal.sh +++ b/backends/arm/test/test_arm_baremetal.sh @@ -12,10 +12,19 @@ script_dir=$(cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd) et_root_dir=$(cd ${script_dir}/../../.. && pwd) cd "${et_root_dir}" pwd +setup_path_script=${et_root_dir}/examples/arm/ethos-u-scratch/setup_path.sh +_setup_msg="please refer to ${et_root_dir}/examples/arm/setup.sh to properly install necessary tools." TEST_SUITE=$1 +# Source the tools +# This should be prepared by the setup.sh +[[ -f ${setup_path_script} ]] \ + || { echo "Missing ${setup_path_script}. ${_setup_msg}"; exit 1; } + +source ${setup_path_script} + help() { echo "Usage:" echo " $0 " @@ -66,7 +75,6 @@ test_pytest() { # Test ops and other things ./examples/models/llama3_2_vision/install_requirements.sh cd "${et_root_dir}" - source examples/arm/ethos-u-scratch/setup_path.sh backends/arm/scripts/build_quantized_ops_aot_lib.sh # Run arm baremetal pytest tests without FVP @@ -78,7 +86,6 @@ test_pytest_ethosu_fvp() { # Same as test_pytest but also sometime verify using echo "${TEST_SUITE_NAME}: Run pytest with fvp" ./examples/models/llama3_2_vision/install_requirements.sh - source examples/arm/ethos-u-scratch/setup_path.sh # Prepare Corstone-3x0 FVP for pytest examples/arm/run.sh --model_name=add --build_only @@ -92,8 +99,6 @@ test_pytest_ethosu_fvp() { # Same as test_pytest but also sometime verify using test_run_ethosu_fvp() { # End to End model tests using run.sh echo "${TEST_SUITE_NAME}: Test ethos-u delegate examples with run.sh" - source examples/arm/ethos-u-scratch/setup_path.sh - # TOSA quantized echo "${TEST_SUITE_NAME}: Test ethos-u target TOSA" examples/arm/run.sh --et_build_root=arm_test/test_run --target=TOSA --model_name=add @@ -114,8 +119,6 @@ test_run_ethosu_fvp() { # End to End model tests using run.sh test_models_ethosu_fvp() { # End to End model tests using model_test.py echo "${TEST_SUITE_NAME}: Test ethos-u delegate models with test_model.py" - source examples/arm/ethos-u-scratch/setup_path.sh - # Build common libs once python3 backends/arm/test/test_model.py --test_output=arm_test/test_model --build_libs From 10e0f8941a1c8a7542472cf45ed01a7f25314b38 Mon Sep 17 00:00:00 2001 From: Sebastian Larsson <38941629+Sebastian-Larsson@users.noreply.github.com> Date: Fri, 11 Apr 2025 09:30:28 +0200 Subject: [PATCH 096/244] Arm backend: Convert assert to raise ValueError in op_clamp (#9932) Asserts are converted to proper raises to ensure graph integrity. It should not be possible for clamp to have more than 1 input for a correctly formatted graph, but in the node visitor we cannot know for sure that the graph is formatted correctly. Signed-off-by: Sebastian Larsson --- backends/arm/operators/op_clamp.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/backends/arm/operators/op_clamp.py b/backends/arm/operators/op_clamp.py index 67fff8b8a60..b18ed640b5f 100644 --- a/backends/arm/operators/op_clamp.py +++ b/backends/arm/operators/op_clamp.py @@ -63,7 +63,8 @@ def cast_type(value: Any) -> int | float: # Attempt to cast to float return float(value) - assert 2 <= len(node.args) <= 3 + if len(node.args) != 2 and len(node.args) != 3: + raise ValueError(f"Expected len(node.args) to be 2 or 3, got {node.args}") min_arg = dtype_min max_arg = dtype_max @@ -84,7 +85,10 @@ def define_node( inputs: List[TosaArg], output: TosaArg, ) -> None: - assert len(node.all_input_nodes) == 1 + if len(node.all_input_nodes) != 1: + raise ValueError( + f"Expected 1 input for {self.target}, got {len(node.all_input_nodes)}" + ) min_int8, max_int8 = self._get_min_max_arguments( node, @@ -122,7 +126,10 @@ def define_node( inputs: List[TosaArg], output: TosaArg, ) -> None: - assert len(node.all_input_nodes) == 1 + if len(node.all_input_nodes) != 1: + raise ValueError( + f"Expected 1 input for {self.target}, got {len(node.all_input_nodes)}" + ) if inputs[0].dtype == ts.DType.INT8: # Call the inherited define_node for handling integers From 85abbd53dc7ea0b15fd873a3d04e024d76707a4f Mon Sep 17 00:00:00 2001 From: Sebastian Larsson <38941629+Sebastian-Larsson@users.noreply.github.com> Date: Fri, 11 Apr 2025 11:10:43 +0200 Subject: [PATCH 097/244] Arm backend: Convert asserts to raise errors in op_maximum (#10102) Asserts are converted to proper raises to ensure graph integrity. Improve error messages and add additional check that both inputs and outputs have the same data type. Signed-off-by: Sebastian Larsson --- backends/arm/operators/op_maximum.py | 23 +++++++++++++++-------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/backends/arm/operators/op_maximum.py b/backends/arm/operators/op_maximum.py index fdee4e61855..ee52e5276cd 100644 --- a/backends/arm/operators/op_maximum.py +++ b/backends/arm/operators/op_maximum.py @@ -36,20 +36,27 @@ def define_node( inputs: List[TosaArg], output: TosaArg, ) -> None: - assert inputs[0].dtype == inputs[1].dtype + if inputs[0].dtype != inputs[1].dtype and inputs[0].dtype != output.dtype: + raise TypeError( + f"Data type of inputs and output must be the same. Got input 0 dtype: " + f"{inputs[0].dtype}, input 1 dtype: {inputs[1].dtype} and output " + f"dtype: {output.dtype}" + ) scale_back = 1.0 max_output = output if inputs[0].dtype == ts.DType.INT8: input_qparams = get_input_qparams(node) - assert ( - len(input_qparams) == 2 - ), f"Both inputs needs to have quantization information for {node}" - # insert RESCALEs to int32 - assert ( - input_qparams[0] == input_qparams[1] - ), "Both inputs must have same quantization for MAX" + if len(input_qparams) != 2: + raise ValueError( + f"Both inputs need to have quantization information for {node}" + ) + if input_qparams[0] != input_qparams[1]: + raise ValueError( + "Both inputs must have the same quantization parameters for MAX" + ) + # insert RESCALEs to int32 operand_inputs, scale_back = tqutils.insert_rescale_ops_to_int32( tosa_graph, inputs, node ) From ce9a98aeff9f6caa5962f5b60927110e2c971acb Mon Sep 17 00:00:00 2001 From: Sebastian Larsson <38941629+Sebastian-Larsson@users.noreply.github.com> Date: Fri, 11 Apr 2025 11:11:28 +0200 Subject: [PATCH 098/244] Arm backend: Convert assert to raise TypeError in op_sub (#9958) Asserts are converted to proper raises to ensure graph integrity. Signed-off-by: Sebastian Larsson --- backends/arm/operators/op_sub.py | 32 +++++++++++++++++++++++++++----- 1 file changed, 27 insertions(+), 5 deletions(-) diff --git a/backends/arm/operators/op_sub.py b/backends/arm/operators/op_sub.py index 1f701f29b1e..ef9ed31c88d 100644 --- a/backends/arm/operators/op_sub.py +++ b/backends/arm/operators/op_sub.py @@ -40,9 +40,19 @@ def define_node( ) -> None: # Specification (0.80) states that input and output types # should all be the same - assert inputs[0].dtype == inputs[1].dtype == output.dtype + if inputs[0].dtype != inputs[1].dtype or inputs[0].dtype != output.dtype: + raise TypeError( + f"All IO needs to have the same data type, got input 1: " + f"{inputs[0].dtype}, input 2: {inputs[1].dtype} and output: " + f"{output.dtype}" + ) + # Handle int8 (quantized) and int32 - assert inputs[0].dtype in [ts.DType.INT8, ts.DType.INT32] + supported_dtypes = [ts.DType.INT8, ts.DType.INT32] + if inputs[0].dtype not in supported_dtypes: + raise TypeError( + f'IO data type needs to be {supported_dtypes}, got "{inputs[0].dtype}"' + ) if inputs[0].dtype == ts.DType.INT8: rescaled_inputs, scale_back = tqutils.insert_rescale_ops_to_int32( @@ -97,15 +107,27 @@ def define_node( ) -> None: # Specification (0.80) states that input and output types # should all be the same - assert inputs[0].dtype == inputs[1].dtype == output.dtype + if inputs[0].dtype != inputs[1].dtype or inputs[0].dtype != output.dtype: + raise TypeError( + f"All IO needs to have the same data type, got input 1: " + f"{inputs[0].dtype}, input 2: {inputs[1].dtype} and output: " + f"{output.dtype}" + ) if inputs[0].dtype in [ts.DType.INT8, ts.DType.INT32]: # Call the inherited define_node for handling integers super().define_node(node, tosa_graph, inputs, output) else: # FP32 Sub lowering - assert inputs[0].dtype == ts.DType.FP32 - assert output.dtype == ts.DType.FP32 + if ( + inputs[0].dtype != ts.DType.FP32 + or inputs[1].dtype != ts.DType.FP32 + or output.dtype != ts.DType.FP32 + ): + raise TypeError( + f"All IO needs to have data type fp32. Got: {inputs[0].dtype}, " + f"input 2: {inputs[1].dtype} and output: {output.dtype}" + ) # MI lowering tosa_graph.addOperator( From de8fe99070219e349969985ae9ede30d29d48366 Mon Sep 17 00:00:00 2001 From: Oscar Andersson <87121123+oscarandersson8218@users.noreply.github.com> Date: Fri, 11 Apr 2025 12:28:06 +0200 Subject: [PATCH 099/244] Arm backend: Remove logger.setLevel (#10103) We don't want to set logging levels for separate files. This should be controlled from a top level, e.g. pytest flag or similar. Signed-off-by: Oscar Andersson --- backends/arm/_passes/cast_int64_pass.py | 1 - backends/arm/arm_backend.py | 6 ------ backends/arm/operator_support/right_shift_support.py | 1 - backends/arm/operator_support/slice_copy_support.py | 1 - backends/arm/test/misc/test_debug_feats.py | 6 ------ backends/arm/test/models/test_conformer.py | 5 ----- backends/arm/test/models/test_llama.py | 1 - backends/arm/test/models/test_w2l_arm.py | 5 ----- backends/arm/test/ops/test_batch_norm.py | 3 --- backends/arm/test/ops/test_conv_combos.py | 3 --- backends/arm/test/ops/test_div.py | 3 --- backends/arm/test/ops/test_linear.py | 4 ---- backends/arm/test/ops/test_max_pool.py | 3 --- backends/arm/test/ops/test_sigmoid.py | 6 +----- backends/arm/test/runner_utils.py | 2 -- backends/arm/util/arm_model_evaluator.py | 1 + 16 files changed, 2 insertions(+), 49 deletions(-) diff --git a/backends/arm/_passes/cast_int64_pass.py b/backends/arm/_passes/cast_int64_pass.py index 3b97b944fd4..87512f9fb3c 100644 --- a/backends/arm/_passes/cast_int64_pass.py +++ b/backends/arm/_passes/cast_int64_pass.py @@ -12,7 +12,6 @@ from torch._export.utils import is_buffer logger = logging.getLogger(__name__) -logger.setLevel(logging.WARNING) class CastInt64BuffersToInt32Pass(ExportPass): diff --git a/backends/arm/arm_backend.py b/backends/arm/arm_backend.py index e6a885b43fa..05b101bef7d 100644 --- a/backends/arm/arm_backend.py +++ b/backends/arm/arm_backend.py @@ -11,8 +11,6 @@ # JIT compiler flows. # -import logging - from typing import List, Optional from executorch.backends.arm.tosa_specification import TosaSpecification @@ -20,10 +18,6 @@ from executorch.exir.backend.compile_spec_schema import CompileSpec -logger = logging.getLogger(__name__) -logger.setLevel(logging.WARNING) - - class ArmCompileSpecBuilder: def __init__(self): self.compile_spec: List[CompileSpec] = [] diff --git a/backends/arm/operator_support/right_shift_support.py b/backends/arm/operator_support/right_shift_support.py index 6c61347ba68..212b877a59f 100644 --- a/backends/arm/operator_support/right_shift_support.py +++ b/backends/arm/operator_support/right_shift_support.py @@ -17,7 +17,6 @@ from executorch.exir.dialects._ops import ops as exir_ops logger = logging.getLogger(__name__) -logger.setLevel(logging.WARNING) @register_tosa_support_check diff --git a/backends/arm/operator_support/slice_copy_support.py b/backends/arm/operator_support/slice_copy_support.py index 1f5ace91cde..477b3bfae4a 100644 --- a/backends/arm/operator_support/slice_copy_support.py +++ b/backends/arm/operator_support/slice_copy_support.py @@ -16,7 +16,6 @@ from executorch.exir.dialects._ops import ops as exir_ops logger = logging.getLogger(__name__) -logger.setLevel(logging.WARNING) @register_tosa_support_check diff --git a/backends/arm/test/misc/test_debug_feats.py b/backends/arm/test/misc/test_debug_feats.py index 945c940a20b..60bf89b6e17 100644 --- a/backends/arm/test/misc/test_debug_feats.py +++ b/backends/arm/test/misc/test_debug_feats.py @@ -4,7 +4,6 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -import logging import os import shutil import tempfile @@ -15,9 +14,6 @@ from executorch.backends.arm.test.tester.arm_tester import ArmTester -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) - class Linear(torch.nn.Module): def __init__( @@ -205,7 +201,6 @@ def test_collate_tosa_BI_tests(self): def test_dump_tosa_ops(caplog): - caplog.set_level(logging.INFO) model = Linear(20, 30) ( ArmTester( @@ -222,7 +217,6 @@ def test_dump_tosa_ops(caplog): def test_fail_dump_tosa_ops(caplog): - caplog.set_level(logging.INFO) class Add(torch.nn.Module): def forward(self, x): diff --git a/backends/arm/test/models/test_conformer.py b/backends/arm/test/models/test_conformer.py index b293555e66b..fb0d5eb75d3 100644 --- a/backends/arm/test/models/test_conformer.py +++ b/backends/arm/test/models/test_conformer.py @@ -3,7 +3,6 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -import logging import unittest import torch @@ -14,10 +13,6 @@ from torchaudio.models import Conformer -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) - - def get_test_inputs(dim, lengths, num_examples): return (torch.rand(num_examples, int(lengths.max()), dim), lengths) diff --git a/backends/arm/test/models/test_llama.py b/backends/arm/test/models/test_llama.py index c27303094f6..644ad69222c 100644 --- a/backends/arm/test/models/test_llama.py +++ b/backends/arm/test/models/test_llama.py @@ -28,7 +28,6 @@ sys.path.append(project_dir) logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) class TestLlama(unittest.TestCase): diff --git a/backends/arm/test/models/test_w2l_arm.py b/backends/arm/test/models/test_w2l_arm.py index fb491ca2250..8cd2ff22b75 100644 --- a/backends/arm/test/models/test_w2l_arm.py +++ b/backends/arm/test/models/test_w2l_arm.py @@ -5,7 +5,6 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -import logging import unittest from typing import Tuple @@ -19,10 +18,6 @@ from torchaudio import models -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) - - def get_test_inputs(batch_size, num_features, input_frames): return (torch.randn(batch_size, num_features, input_frames),) diff --git a/backends/arm/test/ops/test_batch_norm.py b/backends/arm/test/ops/test_batch_norm.py index 360429d3d6c..980ab28df64 100644 --- a/backends/arm/test/ops/test_batch_norm.py +++ b/backends/arm/test/ops/test_batch_norm.py @@ -5,7 +5,6 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -import logging import unittest from typing import Tuple @@ -15,8 +14,6 @@ from executorch.backends.arm.test.tester.arm_tester import ArmTester from parameterized import parameterized -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) test_data_suite = [ # (test_name, test_data, [num_features, affine, track_running_stats, weight, bias, running_mean, running_var,] ) diff --git a/backends/arm/test/ops/test_conv_combos.py b/backends/arm/test/ops/test_conv_combos.py index f6e13a2222e..0fb3c2675e9 100644 --- a/backends/arm/test/ops/test_conv_combos.py +++ b/backends/arm/test/ops/test_conv_combos.py @@ -4,7 +4,6 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -import logging import unittest from typing import Tuple @@ -18,8 +17,6 @@ from parameterized import parameterized from torch.nn.parameter import Parameter -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) """ This file contain unit tests where conv are combined with other ops. diff --git a/backends/arm/test/ops/test_div.py b/backends/arm/test/ops/test_div.py index 062dbfacaef..d200a753ce5 100644 --- a/backends/arm/test/ops/test_div.py +++ b/backends/arm/test/ops/test_div.py @@ -5,7 +5,6 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -import logging import unittest from typing import Optional, Tuple, Union @@ -17,8 +16,6 @@ from executorch.backends.arm.test.tester.arm_tester import ArmTester from parameterized import parameterized -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) test_data_suite = [ # (test_name, input, other, rounding_mode) See torch.div() for info diff --git a/backends/arm/test/ops/test_linear.py b/backends/arm/test/ops/test_linear.py index 33bf9932b5a..9a289909bae 100644 --- a/backends/arm/test/ops/test_linear.py +++ b/backends/arm/test/ops/test_linear.py @@ -5,7 +5,6 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -import logging import unittest from typing import Tuple @@ -19,9 +18,6 @@ from executorch.exir.backend.compile_spec_schema import CompileSpec from parameterized import parameterized -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) - test_data_suite_rank1 = [ # (test_name, test_data, out_features, has_bias) diff --git a/backends/arm/test/ops/test_max_pool.py b/backends/arm/test/ops/test_max_pool.py index a31c12be3a0..2f3426f2dda 100644 --- a/backends/arm/test/ops/test_max_pool.py +++ b/backends/arm/test/ops/test_max_pool.py @@ -5,7 +5,6 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -import logging import unittest from typing import Tuple @@ -26,8 +25,6 @@ from executorch.exir.backend.backend_details import CompileSpec from parameterized import parameterized -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) test_data_suite = [ # (test_name, test_data, [kernel_size, stride, padding]) diff --git a/backends/arm/test/ops/test_sigmoid.py b/backends/arm/test/ops/test_sigmoid.py index a5c6c86c52b..b3411475e24 100644 --- a/backends/arm/test/ops/test_sigmoid.py +++ b/backends/arm/test/ops/test_sigmoid.py @@ -1,11 +1,10 @@ # Copyright (c) Meta Platforms, Inc. and affiliates. -# Copyright 2024 Arm Limited and/or its affiliates. # All rights reserved. +# Copyright 2024-2025 Arm Limited and/or its affiliates. # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -import logging import unittest from typing import Tuple @@ -16,9 +15,6 @@ from executorch.exir.backend.compile_spec_schema import CompileSpec from parameterized import parameterized -logger = logging.getLogger(__name__) -logger.setLevel(logging.INFO) - test_data_suite = [ # (test_name, test_data) diff --git a/backends/arm/test/runner_utils.py b/backends/arm/test/runner_utils.py index 7fc4db2273a..16f2e85e1d2 100644 --- a/backends/arm/test/runner_utils.py +++ b/backends/arm/test/runner_utils.py @@ -18,7 +18,6 @@ import numpy as np import torch -logger = logging.getLogger(__name__) try: import tosa_tools.v0_80.tosa_reference_model as tosa_reference_model except ImportError: @@ -37,7 +36,6 @@ from tosa_tools.v0_80.tosa import TosaGraph logger = logging.getLogger(__name__) -logger.setLevel(logging.CRITICAL) # Copied from PyTorch. # From torch/testing/_internal/common_utils.py:torch_to_numpy_dtype_dict diff --git a/backends/arm/util/arm_model_evaluator.py b/backends/arm/util/arm_model_evaluator.py index c371d794376..5fbc6f7e894 100644 --- a/backends/arm/util/arm_model_evaluator.py +++ b/backends/arm/util/arm_model_evaluator.py @@ -24,6 +24,7 @@ # Logger for outputting progress for longer running evaluation logger = logging.getLogger(__name__) +# Explicitly set logging level: MLETORCH-893 logger.setLevel(logging.INFO) From 93e0535d5ebd79db1b83bcfcdd33fcfca1568a61 Mon Sep 17 00:00:00 2001 From: Sebastian Larsson <38941629+Sebastian-Larsson@users.noreply.github.com> Date: Fri, 11 Apr 2025 14:24:39 +0200 Subject: [PATCH 100/244] Arm backend: Convert asserts to raise errors in op_reciprocal (#10105) Asserts are converted to proper raises to ensure graph integrity. Improve error messages and add additional check that there's only one input. Signed-off-by: Sebastian Larsson --- backends/arm/operators/op_reciprocal.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/backends/arm/operators/op_reciprocal.py b/backends/arm/operators/op_reciprocal.py index 11d2cbc2cc1..c75fb99977e 100644 --- a/backends/arm/operators/op_reciprocal.py +++ b/backends/arm/operators/op_reciprocal.py @@ -34,7 +34,16 @@ def define_node( inputs: List[TosaArg], output: TosaArg, ) -> None: - assert inputs[0].dtype == output.dtype == ts.DType.FP32 + if len(node.all_input_nodes) != 1: + raise ValueError( + f"Expected 1 input for {self.target}, got {len(node.all_input_nodes)}" + ) + if inputs[0].dtype != ts.DType.FP32 or output.dtype != ts.DType.FP32: + raise ValueError( + f"Input and output for {self.target} need to be FP32, got " + f"{inputs[0].dtype=} and {output.dtype=}" + ) + tosa_graph.addOperator( ts.TosaOp.Op().RECIPROCAL, [inputs[0].name], [output.name] ) From 5a5c4817a12e211b08c494d08229879fba5ba5b6 Mon Sep 17 00:00:00 2001 From: Yufeng Shi Date: Fri, 11 Apr 2025 13:27:40 +0100 Subject: [PATCH 101/244] Arm backend: Add check to not partition ops with float64 input (#10106) - Float64 placeholders are not supported in the Arm backend. They will cause a crash when processed in the process_placeholder function. This patch rejects Float64 placeholders early to prevent crashes during the partition. Signed-off-by: Yufeng Shi --- .../tosa_supported_operators.py | 24 +++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/backends/arm/operator_support/tosa_supported_operators.py b/backends/arm/operator_support/tosa_supported_operators.py index f76b30ce2a1..1cec35923de 100644 --- a/backends/arm/operator_support/tosa_supported_operators.py +++ b/backends/arm/operator_support/tosa_supported_operators.py @@ -112,6 +112,7 @@ def tosa_support_factory( # Negative checks: Remove nodes from partitioning negative_checks: list[OperatorSupportBase] = [ CheckInt64Inputs(exported_program, reporter), + CheckFloat64Inputs(exported_program, reporter), *[ reporter.wrap_check(check, f"Rejected by {check.__class__.__name__}") for check in (additional_checks if additional_checks else []) @@ -443,3 +444,26 @@ def is_node_supported( ) return False return True + + +class CheckFloat64Inputs(OperatorSupportBase): + + def __init__( + self, exported_program: ExportedProgram, reporter: WhyNoPartitionReporter + ): + self.reporter = reporter + super().__init__() + + def is_node_supported( + self, submodules: typing.Mapping[str, torch.nn.Module], node: fx.Node + ) -> bool: + + for input_node in node.all_input_nodes: + tensor = get_first_fake_tensor(input_node) + if tensor.dtype == torch.float64: + self.reporter.report_reject( + node, + f"Had float64 input {input_node.name} that couldn't be handled.", + ) + return False + return True From 0e0945e87dc5a6863140e3da316e361a4e113bf4 Mon Sep 17 00:00:00 2001 From: Sebastian Larsson <38941629+Sebastian-Larsson@users.noreply.github.com> Date: Fri, 11 Apr 2025 17:17:16 +0200 Subject: [PATCH 102/244] Arm backend: Convert asserts to raise errors in op_rsqrt (#10107) Asserts are converted to proper raises to ensure graph integrity. Improve error messages and add additional check that both input and output are of data type fp32. Signed-off-by: Sebastian Larsson --- backends/arm/operators/op_rsqrt.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/backends/arm/operators/op_rsqrt.py b/backends/arm/operators/op_rsqrt.py index 52bcc937c96..e3937f8c44a 100644 --- a/backends/arm/operators/op_rsqrt.py +++ b/backends/arm/operators/op_rsqrt.py @@ -34,5 +34,14 @@ def define_node( inputs: List[TosaArg], output: TosaArg, ) -> None: - assert inputs[0].dtype == output.dtype == ts.DType.FP32 + if len(node.all_input_nodes) != 1: + raise ValueError( + f"Expected 1 input for {self.target}, got {len(node.all_input_nodes)}" + ) + if inputs[0].dtype != ts.DType.FP32 or output.dtype != ts.DType.FP32: + raise ValueError( + f"Input and output for {self.target} need to be FP32, got " + f"{inputs[0].dtype=} and {output.dtype=}" + ) + tosa_graph.addOperator(ts.TosaOp.Op().RSQRT, [inputs[0].name], [output.name]) From 7649d574406b16b552637dd09d0500676fbf101d Mon Sep 17 00:00:00 2001 From: Val Tarasyuk Date: Fri, 11 Apr 2025 10:08:24 -0700 Subject: [PATCH 103/244] Prevent decomposing RMSNorm in Jarvis (#10074) Summary: D72276082 added support for RMSNorm in the Turing compiler, this diff does the same for Jarvis and removes redundant op registrations. Differential Revision: D72802423 --- backends/cadence/aot/compiler.py | 9 ++++++--- backends/cadence/aot/ops_registrations.py | 4 ---- 2 files changed, 6 insertions(+), 7 deletions(-) diff --git a/backends/cadence/aot/compiler.py b/backends/cadence/aot/compiler.py index 4d51d1fa34c..32a4427278b 100644 --- a/backends/cadence/aot/compiler.py +++ b/backends/cadence/aot/compiler.py @@ -31,11 +31,11 @@ EdgeProgramManager, ExecutorchBackendConfig, ExecutorchProgramManager, - to_edge, ) from executorch.exir.pass_base import PassResult from executorch.exir.passes import ToOutVarPass from executorch.exir.passes.sym_shape_eval_pass import HintBasedSymShapeEvalPass +from executorch.exir.program._program import to_edge_with_preserved_ops from torch._inductor.decomposition import remove_decompositions from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e @@ -80,6 +80,7 @@ def convert_pt2( torch.ops.aten.layer_norm.default, torch.ops.aten.linear.default, torch.ops.aten.matmul.default, + torch.ops.aten.rms_norm.default, ] # Remove decompositions for the ops we want to keep # pyre-fixme[6]: For 1st argument expected `Dict[typing.Callable[..., typing.Any @@ -201,9 +202,9 @@ def lower_ep_to_edge( """ Lower an ExportedProgram to an EdgeProgramManager (in edge IR). """ - # Call to_edge to convert the graph to edge IR. + # Call to_edge_with_preserved_ops to convert the graph to edge IR. # Note: dim_order is skipped (https://github.com/pytorch/executorch/issues/3704) - edge_prog_manager = to_edge( + edge_prog_manager = to_edge_with_preserved_ops( expo_program, compile_config=EdgeCompileConfig( _skip_dim_order=True, @@ -216,9 +217,11 @@ def lower_ep_to_edge( torch.ops.aten.linalg_vector_norm.default, torch.ops.aten.unfold.default, torch.ops.aten.angle.default, + torch.ops.aten.rms_norm.default, ], ), constant_methods=constant_methods, + preserve_ops=(torch.ops.aten.rms_norm.default,), ) if dump_graphs: diff --git a/backends/cadence/aot/ops_registrations.py b/backends/cadence/aot/ops_registrations.py index 368f499aa85..aca4965083d 100644 --- a/backends/cadence/aot/ops_registrations.py +++ b/backends/cadence/aot/ops_registrations.py @@ -139,7 +139,6 @@ "int in_zero_point, bool channel_last=False) -> (Tensor out)" ) lib.define("linalg_vector_norm(Tensor X) -> (Tensor Y)") -lib.define("rms_norm(Tensor X, float eps, Tensor W) -> (Tensor Y)") lib.define( "transposed_im2row(Tensor input, int[2] kernel_size, int[2] dilation, int[2] padding, int[2] stride, " "int[2] output_padding, Tensor in_zero_point, bool channel_last=False) -> (Tensor out)" @@ -211,9 +210,6 @@ "fully_connected.out(Tensor input, Tensor weight, Tensor? bias=None, *, Tensor(a!) out) -> Tensor(a!)" ) lib.define("linalg_vector_norm.out(Tensor X, *, Tensor(a!) out) -> Tensor(a!)") -lib.define( - "rms_norm.out(Tensor X, float eps, Tensor W, *, Tensor(a!) out) -> Tensor(a!)" -) lib.define( "quantized_fully_connected.out(Tensor src, Tensor weight, Tensor bias, int src_zero_point, " "Tensor weight_zero_point, Tensor out_multiplier, Tensor out_shift, int out_zero_point, Tensor? offset, *, Tensor(a!) out) -> Tensor(a!)" From 02234e80387dad0100377bdcd11e5c94ae86cfb9 Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Fri, 11 Apr 2025 10:34:15 -0700 Subject: [PATCH 104/244] Remove partitioner/quantizer discussion from readme and link to docs (#10033) To keep information consistent and in-sync, link to CoreML docs rather than discuss the partioner/quantizer. --- backends/apple/coreml/README.md | 107 +------------------------------- 1 file changed, 1 insertion(+), 106 deletions(-) diff --git a/backends/apple/coreml/README.md b/backends/apple/coreml/README.md index e8a062774d0..d063dfc8b71 100644 --- a/backends/apple/coreml/README.md +++ b/backends/apple/coreml/README.md @@ -1,8 +1,7 @@ # ExecuTorch Core ML Delegate - This subtree contains the Core ML Delegate implementation for ExecuTorch. -Core ML is an optimized framework for running machine learning models on Apple devices. The delegate is the mechanism for leveraging the Core ML framework to accelerate operators when running on Apple devices. +Core ML is an optimized framework for running machine learning models on Apple devices. The delegate is the mechanism for leveraging the Core ML framework to accelerate operators when running on Apple devices. To learn how to use the CoreML delegate, see the [documentation](https://github.com/pytorch/executorch/blob/main/docs/source/backends-coreml.md). ## Layout - `compiler/` : Lowers a module to Core ML backend. @@ -19,110 +18,6 @@ Core ML is an optimized framework for running machine learning models on Apple d - `workspace` : Xcode workspace for the runtime. - `third-party/`: External dependencies. -## Partition and Delegation - -To delegate a Program to the **Core ML** backend, the client must call `to_backend` with the **CoreMLPartitioner**. - -```python -import torch -import executorch.exir - -from executorch.backends.apple.coreml.compiler import CoreMLBackend -from executorch.backends.apple.coreml.partition import CoreMLPartitioner - -class Model(torch.nn.Module): - def __init__(self): - super().__init__() - - def forward(self, x): - return torch.sin(x) - -source_model = Model() -example_inputs = (torch.ones(1), ) - -# Export the source model to Edge IR representation -aten_program = torch.export.export(source_model, example_inputs) -edge_program_manager = executorch.exir.to_edge(aten_program) - -# Delegate to Core ML backend -delegated_program_manager = edge_program_manager.to_backend(CoreMLPartitioner()) - -# Serialize delegated program -executorch_program = delegated_program_manager.to_executorch() -with open("model.pte", "wb") as f: - f.write(executorch_program.buffer) -``` - -The module will be fully or partially delegated to **Core ML**, depending on whether all or part of ops are supported by the **Core ML** backend. User may force skip certain ops by `CoreMLPartitioner(skip_ops_for_coreml_delegation=...)` - -The `to_backend` implementation is a thin wrapper over [coremltools](https://apple.github.io/coremltools/docs-guides/), `coremltools` is responsible for converting an **ExportedProgram** to a **MLModel**. The converted **MLModel** data is saved, flattened, and returned as bytes to **ExecuTorch**. - -## Quantization - -To quantize a Program in a Core ML favored way, the client may utilize **CoreMLQuantizer**. - -```python -import torch -import executorch.exir - -from torch.export import export_for_training -from torch.ao.quantization.quantize_pt2e import ( - convert_pt2e, - prepare_pt2e, - prepare_qat_pt2e, -) - -from executorch.backends.apple.coreml.quantizer import CoreMLQuantizer -from coremltools.optimize.torch.quantization.quantization_config import ( - LinearQuantizerConfig, - QuantizationScheme, -) - -class Model(torch.nn.Module): - def __init__(self) -> None: - super().__init__() - self.conv = torch.nn.Conv2d( - in_channels=3, out_channels=16, kernel_size=3, padding=1 - ) - self.relu = torch.nn.ReLU() - - def forward(self, x: torch.Tensor) -> torch.Tensor: - a = self.conv(x) - return self.relu(a) - -source_model = Model() -example_inputs = (torch.randn((1, 3, 256, 256)), ) - -pre_autograd_aten_dialect = export_for_training(source_model, example_inputs).module() - -quantization_config = LinearQuantizerConfig.from_dict( - { - "global_config": { - "quantization_scheme": QuantizationScheme.symmetric, - "activation_dtype": torch.quint8, - "weight_dtype": torch.qint8, - "weight_per_channel": True, - } - } -) -quantizer = CoreMLQuantizer(quantization_config) - -# For post-training quantization, use `prepare_pt2e` -# For quantization-aware trainin,g use `prepare_qat_pt2e` -prepared_graph = prepare_pt2e(pre_autograd_aten_dialect, quantizer) - -prepared_graph(*example_inputs) -converted_graph = convert_pt2e(prepared_graph) -``` - -The `converted_graph` is the quantized torch model, and can be delegated to **Core ML** similarly through **CoreMLPartitioner** - -## Runtime - -To execute a Core ML delegated program, the application must link to the `coremldelegate` library. Once linked there are no additional steps required, ExecuTorch when running the program would call the Core ML runtime to execute the Core ML delegated part of the program. - -Please follow the instructions described in the [Core ML setup](/backends/apple/coreml/setup.md) to link the `coremldelegate` library. - ## Help & Improvements If you have problems or questions or have suggestions for ways to make implementation and testing better, please create an issue on [github](https://www.github.com/pytorch/executorch/issues). From 14b7ba2763602b300df3dde446f1e873cc5e369f Mon Sep 17 00:00:00 2001 From: Jacob Szwejbka Date: Fri, 11 Apr 2025 10:47:57 -0700 Subject: [PATCH 105/244] allow not memory planning mutable buffers Differential Revision: D72749868 Pull Request resolved: https://github.com/pytorch/executorch/pull/10071 --- exir/emit/_emitter.py | 25 ++++++++++++++++----- exir/emit/test/test_emit.py | 34 ++++++++++++++++++++++++++++- exir/memory_planning.py | 12 ++++++++-- exir/passes/memory_planning_pass.py | 9 ++++++-- exir/tests/test_memory_planning.py | 27 ++++++++++++++++++----- 5 files changed, 90 insertions(+), 17 deletions(-) diff --git a/exir/emit/_emitter.py b/exir/emit/_emitter.py index 9cc8a1e809c..fe18e49a623 100644 --- a/exir/emit/_emitter.py +++ b/exir/emit/_emitter.py @@ -1640,13 +1640,26 @@ def placeholder( # noqa: C901 else: spec.extra_tensor_info.fully_qualified_name = fqn spec.extra_tensor_info.location = TensorDataLocation.EXTERNAL - if self.emitter_state.emit_mutable_buffer_names and is_mutable_buffer: - if spec.extra_tensor_info is None: - spec.extra_tensor_info = ExtraTensorInfo( - fully_qualified_name=fqn, location=TensorDataLocation.SEGMENT + + if is_mutable_buffer: + # Emit names if we are supposed to. + if self.emitter_state.emit_mutable_buffer_names: + if spec.extra_tensor_info is None: + spec.extra_tensor_info = ExtraTensorInfo( + fully_qualified_name=fqn, + location=TensorDataLocation.SEGMENT, + ) + else: + spec.extra_tensor_info.fully_qualified_name = fqn + # if We aren't emitting the name then it needs to be memory planned. + elif spec.mem_id is None or spec.mem_offset is None: + raise InternalError( + self._emit_node_specific_error( + self.node, + # [2:] to remove the b_ prefix buffers get + f'Mutable buffer "{target[2:]}" must have a memory id and offset if we are emitting it without a name. Please either memory plan your mutable buffers or call to_executorch with config=ExecutorchBackendConfig(emit_mutable_buffer_names=True)', + ) ) - else: - spec.extra_tensor_info.fully_qualified_name = fqn # From the fqn find the corresponding tensor real_tensor = None diff --git a/exir/emit/test/test_emit.py b/exir/emit/test/test_emit.py index a2df53e8cf3..186c5a402ab 100644 --- a/exir/emit/test/test_emit.py +++ b/exir/emit/test/test_emit.py @@ -1838,8 +1838,40 @@ def forward(self, x): ep = to_edge(ep) # Lower the graph to executorch. ep = ep.to_executorch( - config=ExecutorchBackendConfig(emit_mutable_buffer_names=True) + config=ExecutorchBackendConfig( + emit_mutable_buffer_names=True, + memory_planning_pass=MemoryPlanningPass(alloc_mutable_buffers=False), + ) ) for val in ep.executorch_program.execution_plan[0].values: if isinstance(val, Tensor) and val.extra_tensor_info: self.assertEqual(val.extra_tensor_info.fully_qualified_name, "buffer") + self.assertEqual(val.allocation_info, None) + + def test_emit_mutable_buffer_names_fails(self) -> None: + class Net(nn.Module): + def __init__(self): + super().__init__() + self.linear = nn.Linear(2, 2) + self.register_buffer("buffer", torch.zeros(1, 2)) + + def forward(self, x): + self.buffer.add_(1) + return self.linear(x) + self.buffer + + net = Net() + + ep = export(net, (torch.randn(1, 2),), strict=True) + # Lower the graph to edge dialect. + ep = to_edge(ep) + # Lower the graph to executorch. + # Must emit mutable buffer names if we don't allocate mutable buffers + with self.assertRaises(InternalError): + ep.to_executorch( + config=ExecutorchBackendConfig( + emit_mutable_buffer_names=False, + memory_planning_pass=MemoryPlanningPass( + alloc_mutable_buffers=False + ), + ) + ) diff --git a/exir/memory_planning.py b/exir/memory_planning.py index 17640a9f7aa..83598940882 100644 --- a/exir/memory_planning.py +++ b/exir/memory_planning.py @@ -44,12 +44,14 @@ def __init__( graph_module: torch.fx.GraphModule, alloc_graph_input: bool, alloc_graph_output: bool, + alloc_mutable_buffers: bool, graph_signature: Optional[ExportGraphSignature] = None, ) -> None: self.graph_module = graph_module self.graph_signature = graph_signature self.alloc_graph_input = alloc_graph_input self.alloc_graph_output = alloc_graph_output + self.alloc_mutable_buffers = alloc_mutable_buffers @classmethod def mem_obj_id_match( @@ -149,6 +151,7 @@ def verify_storage_reuse( ignore_const=True, ignore_graph_input=not self.alloc_graph_input, ignore_graph_output=not self.alloc_graph_output, + ignore_mutable_buffers=not self.alloc_mutable_buffers, do_assertion=False, ignore_out_var_node=False, dedup=True, @@ -374,6 +377,7 @@ def collect_specs_from_nodes( # noqa: C901 graph_signature: Optional[ExportGraphSignature] = None, ignore_graph_input: bool = False, ignore_graph_output: bool = False, + ignore_mutable_buffers: bool = False, ignore_const: bool = True, ignore_out_var_node: bool = True, dedup: bool = True, @@ -414,6 +418,9 @@ def collect_specs_from_nodes( # noqa: C901 if _is_inplace_node(node): continue + if _is_mutable_buffer(node, graph_signature) and ignore_mutable_buffers: + continue + if do_assertion: internal_assert( node.op in ("placeholder", "output") @@ -469,6 +476,7 @@ def update_all_tensors_lifetime( Set the lifetime for all the tensors encountered in the Fx graph. """ specs = set() + for node_idx, node in enumerate(graph_module.graph.nodes): for spec in collect_specs_from_nodes( filter_nodes(itertools.chain([node], node.args, node.kwargs.values())), @@ -1053,6 +1061,7 @@ def apply_algo( graph_signature: Optional[ExportGraphSignature] = None, alloc_graph_input: bool = True, alloc_graph_output: bool = True, + alloc_mutable_buffers: bool = True, ) -> List[int]: """ Recursively apply algo to graph_module and its submodules for control flow. @@ -1065,12 +1074,10 @@ def apply_algo( storage with tensors in the outer module. TODO: make these optimizations once we have some baseline working. """ - # Extract the nodes and their lifespans from the graph_module # Difficult to just filter the list of specs returned by this due to # how we flag trainable weights. _ = update_all_tensors_lifetime(graph_module, graph_signature) - # Filter specs based on alloc_graph_input and alloc_graph_output specs = collect_specs_from_nodes( graph_module.graph.nodes, @@ -1078,6 +1085,7 @@ def apply_algo( do_assertion=False, ignore_graph_input=not alloc_graph_input, ignore_graph_output=not alloc_graph_output, + ignore_mutable_buffers=not alloc_mutable_buffers, ) # Get extra padding for XNNPACK if needed diff --git a/exir/passes/memory_planning_pass.py b/exir/passes/memory_planning_pass.py index f4b3ad8a8a7..9bd4ab20bf5 100644 --- a/exir/passes/memory_planning_pass.py +++ b/exir/passes/memory_planning_pass.py @@ -44,6 +44,7 @@ def __init__( allow_lifetime_and_storage_overlap: bool = False, alloc_graph_input: bool = True, alloc_graph_output: bool = True, + alloc_mutable_buffers: bool = True, alignment: int = ALIGNMENT, ) -> None: r""" @@ -54,10 +55,11 @@ def __init__( """ if memory_planning_algo is None: memory_planning_algo = MemoryPlanningAlgorithmSuite() - self.memory_planning_algo = memory_planning_algo + self.memory_planning_algo: Callable[..., List[int]] = memory_planning_algo self.allow_lifetime_and_storage_overlap = allow_lifetime_and_storage_overlap self.alloc_graph_input = alloc_graph_input self.alloc_graph_output = alloc_graph_output + self.alloc_mutable_buffers = alloc_mutable_buffers self.alignment = alignment def _set_alloc_node_spec(self, graph_module: torch.fx.GraphModule) -> None: @@ -124,13 +126,15 @@ def run( # customized fields. Using the graph_module object to convey information across # passes/stages is quite natural and avoid yet another 'context' data structure # to do the job. + _ = apply_algo( - self.memory_planning_algo, # pyre-ignore[6] + self.memory_planning_algo, graph_module, self.alignment, graph_signature, self.alloc_graph_input, self.alloc_graph_output, + self.alloc_mutable_buffers, ) # TODO: make the verifier do the work recursively to handle @@ -139,6 +143,7 @@ def run( graph_module, self.alloc_graph_input, self.alloc_graph_output, + self.alloc_mutable_buffers, graph_signature, ) diff --git a/exir/tests/test_memory_planning.py b/exir/tests/test_memory_planning.py index 52986aaa04c..b87ae2dfb58 100644 --- a/exir/tests/test_memory_planning.py +++ b/exir/tests/test_memory_planning.py @@ -241,6 +241,7 @@ def maketest( use_functionalization: bool = True, alloc_graph_input: bool = True, alloc_graph_output: bool = True, + alloc_mutable_buffer: bool = True, has_unused_graph_input: bool = False, ) -> Callable[..., None]: # parameterized.expand is not compatible with maketest. I'll just loop thru @@ -282,10 +283,17 @@ def wrapper(self: "TestMemoryPlanning") -> None: )(graph_module).graph_module self.verify_reuse( - graph_module, expect_reuse, alloc_graph_input, alloc_graph_output + graph_module, + expect_reuse, + alloc_graph_input, + alloc_graph_output, + alloc_mutable_buffer, ) self.verify_graph_input_output( - graph_module, alloc_graph_input, alloc_graph_output + graph_module, + alloc_graph_input, + alloc_graph_output, + alloc_mutable_buffer, ) self.verify_overlap_placeholders(has_unused_graph_input, graph_module) @@ -306,6 +314,7 @@ def verify_reuse( expect_reuse: bool, alloc_graph_input: bool, alloc_graph_output: bool, + alloc_mutable_buffer: bool, ) -> None: r""" Do sanity check and verify tensor storage reuse. @@ -321,6 +330,7 @@ def verify_reuse( graph_module, alloc_graph_input=alloc_graph_input, alloc_graph_output=alloc_graph_output, + alloc_mutable_buffers=alloc_mutable_buffer, ).verify_storage_reuse() print(f"num_reuse_pairs is {num_reuse_pairs}") @@ -334,9 +344,10 @@ def verify_graph_input_output( graph_module: torch.fx.GraphModule, alloc_graph_input: bool, alloc_graph_output: bool, + alloc_mutable_buffers: bool, ) -> None: Verifier( - graph_module, alloc_graph_input, alloc_graph_output + graph_module, alloc_graph_input, alloc_graph_output, alloc_mutable_buffers ).verify_graph_input_output() def verify_overlap_placeholders( @@ -404,13 +415,16 @@ def verify_overlap_placeholders( ) def test_graph_input_output(self) -> None: - for alloc_graph_input, alloc_graph_output in itertools.product( - [True, False], [True, False] - ): + for ( + alloc_graph_input, + alloc_graph_output, + alloc_mutable_buffers, + ) in itertools.product([True, False], [True, False], [True, False]): case = maketest( ModelWithDifferentTensorSizes, alloc_graph_input=alloc_graph_input, alloc_graph_output=alloc_graph_output, + alloc_mutable_buffer=alloc_mutable_buffers, ) case(self) @@ -535,6 +549,7 @@ def test_multiple_pools( graph_module, alloc_graph_input=True, alloc_graph_output=True, + alloc_mutable_buffers=True, ) verifier.verify_storage_reuse() verifier.verify_graph_input_output() From 3be783356ed6c8faaa782c2a92d7d4b593edc600 Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Fri, 11 Apr 2025 12:54:43 -0700 Subject: [PATCH 106/244] Create daily AAR snapshot (#10092) Download daily snapshots like https://ossci-android.s3.amazonaws.com/executorch/release/snapshot-20250411/executorch.aar --- .github/workflows/android-release-artifacts.yml | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/.github/workflows/android-release-artifacts.yml b/.github/workflows/android-release-artifacts.yml index 9add3116236..e40b53a148b 100644 --- a/.github/workflows/android-release-artifacts.yml +++ b/.github/workflows/android-release-artifacts.yml @@ -11,6 +11,8 @@ on: description: Upload the AAR to maven staging repository required: false type: boolean + schedule: + - cron: 0 10 * * * concurrency: group: ${{ github.workflow }}-${{ github.ref }} @@ -26,6 +28,10 @@ jobs: shell: bash run: | VERSION="${{ inputs.version }}" + if [ -z "$VERSION" ]; then + echo "No version name specified. Will create a snapshot AAR" + exit 0 + fi if curl -I "https://ossci-android.s3.amazonaws.com/executorch/release/${VERSION}/executorch.aar" | grep "200 OK"; then echo "AAR already exists at https://ossci-android.s3.amazonaws.com/executorch/release/${VERSION}/executorch.aar" echo "Will skip build/upload" @@ -107,6 +113,8 @@ jobs: pip install awscli==1.32.18 AWS_CMD="aws s3 cp" VERSION="${{ inputs.version }}" - VERSION_NAME="${VERSION:-temp_snapshot}" - ${AWS_CMD} executorch.aar s3://ossci-android/executorch/release/${VERSION_NAME}/executorch.aar --acl public-read - ${AWS_CMD} executorch.aar.sha256sums s3://ossci-android/executorch/release/${VERSION_NAME}/executorch.aar.sha256sums --acl public-read + if [ -z "$VERSION" ]; then + VERSION="snapshot-$(date +"%Y%m%d")" + fi + ${AWS_CMD} executorch.aar s3://ossci-android/executorch/release/${VERSION}/executorch.aar --acl public-read + ${AWS_CMD} executorch.aar.sha256sums s3://ossci-android/executorch/release/${VERSION}/executorch.aar.sha256sums --acl public-read From a85f0431ecc8d387c43af1a1b5accce960217503 Mon Sep 17 00:00:00 2001 From: Digant Desai Date: Fri, 11 Apr 2025 15:04:21 -0500 Subject: [PATCH 107/244] Arm Ethos-u: Buckify Slice tests Differential Revision: D72766924 Pull Request resolved: https://github.com/pytorch/executorch/pull/10099 --- backends/arm/test/ops/test_slice.py | 19 ++++++++++++++----- backends/arm/test/targets.bzl | 3 ++- 2 files changed, 16 insertions(+), 6 deletions(-) diff --git a/backends/arm/test/ops/test_slice.py b/backends/arm/test/ops/test_slice.py index 7cb82e3a828..6900fda3abe 100644 --- a/backends/arm/test/ops/test_slice.py +++ b/backends/arm/test/ops/test_slice.py @@ -7,9 +7,11 @@ import unittest from typing import Tuple +import pytest + import torch -from executorch.backends.arm.test import common +from executorch.backends.arm.test import common, conftest from executorch.backends.arm.test.tester.arm_tester import ArmTester from executorch.exir.backend.compile_spec_schema import CompileSpec from parameterized import parameterized @@ -35,7 +37,7 @@ def forward(self, x: torch.Tensor): def _test_slice_tosa_MI_pipeline( self, module: torch.nn.Module, test_data: torch.Tensor ): - ( + tester = ( ArmTester( module, example_inputs=test_data, @@ -48,14 +50,16 @@ def _test_slice_tosa_MI_pipeline( .partition() .check_count({"torch.ops.higher_order.executorch_call_delegate": 1}) .to_executorch() - .run_method_and_compare_outputs(inputs=test_data) ) + if conftest.is_option_enabled("tosa_ref_model"): + tester.run_method_and_compare_outputs(inputs=test_data) + def _test_slice_tosa_BI_pipeline( self, module: torch.nn.Module, test_data: Tuple[torch.Tensor] ): - ( + tester = ( ArmTester( module, example_inputs=test_data, @@ -68,9 +72,11 @@ def _test_slice_tosa_BI_pipeline( .partition() .check_count({"torch.ops.higher_order.executorch_call_delegate": 1}) .to_executorch() - .run_method_and_compare_outputs(inputs=test_data, qtol=1) ) + if conftest.is_option_enabled("tosa_ref_model"): + tester.run_method_and_compare_outputs(inputs=test_data, qtol=1) + def _test_slice_ethos_BI_pipeline( self, compile_spec: list[CompileSpec], @@ -107,14 +113,17 @@ def _test_slice_u85_BI_pipeline( ) @parameterized.expand(Slice.test_tensors) + @pytest.mark.tosa_ref_model def test_slice_tosa_MI(self, tensor): self._test_slice_tosa_MI_pipeline(self.Slice(), (tensor,)) @parameterized.expand(Slice.test_tensors[:2]) + @pytest.mark.tosa_ref_model def test_slice_nchw_tosa_BI(self, test_tensor: torch.Tensor): self._test_slice_tosa_BI_pipeline(self.Slice(), (test_tensor,)) @parameterized.expand(Slice.test_tensors[2:]) + @pytest.mark.tosa_ref_model def test_slice_nhwc_tosa_BI(self, test_tensor: torch.Tensor): self._test_slice_tosa_BI_pipeline(self.Slice(), (test_tensor,)) diff --git a/backends/arm/test/targets.bzl b/backends/arm/test/targets.bzl index e97b46cb977..4eece953f2d 100644 --- a/backends/arm/test/targets.bzl +++ b/backends/arm/test/targets.bzl @@ -12,7 +12,8 @@ def define_arm_tests(): test_files.remove("passes/test_ioquantization_pass.py") # Operators - test_files += native.glob(["ops/test_linear.py"]) + test_files += ["ops/test_linear.py"] + test_files += ["ops/test_slice.py"] TESTS = {} From 4495b637fe67018de497ab20301f1b5122bd6f2b Mon Sep 17 00:00:00 2001 From: Iliyan Georgiev Date: Fri, 11 Apr 2025 21:13:00 +0100 Subject: [PATCH 108/244] Arm backend: Add GELU operator (#10109) - Add GELU decomposition pass - BI handled by table op pass - Add tests - xfail partitioning test with task to revisit Signed-off-by: Iliyan Georgiev --- backends/arm/_passes/__init__.py | 1 + backends/arm/_passes/arm_pass_manager.py | 2 + backends/arm/_passes/decompose_gelu_pass.py | 149 ++++++++++++++++++ backends/arm/_passes/insert_table_ops.py | 14 ++ .../tosa_supported_operators.py | 2 + .../arm/quantizer/quantization_annotator.py | 1 + ...test_partition_decomposed_quantized_ops.py | 7 +- backends/arm/test/ops/test_gelu.py | 125 +++++++++++++++ 8 files changed, 300 insertions(+), 1 deletion(-) create mode 100644 backends/arm/_passes/decompose_gelu_pass.py create mode 100644 backends/arm/test/ops/test_gelu.py diff --git a/backends/arm/_passes/__init__.py b/backends/arm/_passes/__init__.py index 2b935172a7f..1a1719bf8ae 100644 --- a/backends/arm/_passes/__init__.py +++ b/backends/arm/_passes/__init__.py @@ -20,6 +20,7 @@ from .convert_to_clamp import ConvertToClampPass # noqa from .decompose_batchnorm_pass import DecomposeBatchNormPass # noqa from .decompose_div_pass import DecomposeDivPass # noqa +from .decompose_gelu_pass import DecomposeGeluPass # noqa from .decompose_layernorm_pass import DecomposeLayerNormPass # noqa from .decompose_leaky_relu_pass import DecomposeLeakyReLUPass # noqa from .decompose_linear_pass import DecomposeLinearPass # noqa diff --git a/backends/arm/_passes/arm_pass_manager.py b/backends/arm/_passes/arm_pass_manager.py index 85004686ebe..261ee045790 100644 --- a/backends/arm/_passes/arm_pass_manager.py +++ b/backends/arm/_passes/arm_pass_manager.py @@ -25,6 +25,7 @@ ConvertToClampPass, DecomposeBatchNormPass, DecomposeDivPass, + DecomposeGeluPass, DecomposeLayerNormPass, DecomposeLeakyReLUPass, DecomposeLinearPass, @@ -132,6 +133,7 @@ def _tosa_080_MI_pipeline(self, exported_program: ExportedProgram) -> GraphModul self.add_pass(ConvertMeanDimToAveragePoolPass()) self.add_pass(DecomposeDivPass()) self.add_pass(DecomposeSoftmaxPass()) + self.add_pass(DecomposeGeluPass()) self.add_pass(ConvertFullLikeToFullPass()) self.add_pass(ConvertToClampPass()) self.add_pass(ConvertMinMaxPass()) diff --git a/backends/arm/_passes/decompose_gelu_pass.py b/backends/arm/_passes/decompose_gelu_pass.py new file mode 100644 index 00000000000..6e72175e68b --- /dev/null +++ b/backends/arm/_passes/decompose_gelu_pass.py @@ -0,0 +1,149 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from executorch.backends.arm._passes.arm_pass_utils import get_node_arg +from executorch.exir.dialects._ops import ops as exir_ops +from executorch.exir.pass_base import ExportPass + +torch_gelu = (torch.ops.aten.gelu.default,) + +edge_gelu = (exir_ops.edge.aten.gelu.default,) + + +def _get_gelu_ops(op) -> tuple: + """ + Returns the operators needed to decompose GELU + """ + + if op in edge_gelu: + return ( + exir_ops.edge.aten.full.default, + exir_ops.edge.aten.add.Tensor, + exir_ops.edge.aten.mul.Tensor, + exir_ops.edge.aten.tanh.default, + exir_ops.edge.aten.erf.default, + ) + if op in torch_gelu: + return ( + torch.ops.aten.full.default, + torch.ops.aten.add.Tensor, + torch.ops.aten.mul.Tensor, + torch.ops.aten.tanh.default, + torch.ops.aten.erf.default, + ) + raise RuntimeError(f"Can't get GeLU decomposition ops for op {op}") + + +class DecomposeGeluPass(ExportPass): + """ + This pass decomposes the GELU operator into primitive ops. + Aiming to adhere closely to the reference implementations built into + ExecuTorch. Including using the same pre-calculated constants. + + This operator has two formulae depending on the value of the + approximate argument. Examples below include the added full + operators necessary for the initialization for constants used in + each respective formula. + + aten.gelu(x, approximate="none") becomes: + %FULL_0_5 = full() + %FULL_1 = full() + %FULL_SQRT1_2 = full() + %op1 = mul(x, %FULL_SQRT1_2) + %op2 = erf(%op1) + %op3 = add(%op2, %FULL_1) + %op4 = mul(%op3, %FULL_0_5) + %op5 = mul(%x, %op4) + + aten.gelu(x, approximate="tanh") becomes: + %FULL_0_5 = full() + %FULL_1 = full() + %FULL_SQRT2 = full() + %FULL_2_SQRTPI = full() + %FULL_CUBE_COEFF = full() + %SQRT_MUL = mul(%FULL_SQRT2, %FULL_2_SQRTPI) + %SQRT_2_PI = mul(%SQRT_MUL, %FULL_0_5) + %sqr_x = mul(x, x) + %cube_x = mul(sqr_x, x) + %op1 = mul(%cube_x, %FULL_CUBE_COEFF) + %op2 = add(%x, %op1) + %op3 = mul(%op2, %SQRT_2_PI) + %op4 = tanh(%op3) + %op5 = add(%op4, %FULL_1) + %op6 = mul(%x, %op5) + %op7 = mul(%op6, %FULL_0_5) + """ + + def call_operator(self, op, args, kwargs, meta): + if op not in torch_gelu + edge_gelu: + return super().call_operator(op, args, kwargs, meta) + + full_op, add_op, mul_op, tanh_op, erf_op = _get_gelu_ops(op) + + input = get_node_arg(args, 0) + # If approximate is default (none) it does not appear in kwargs + approximate = get_node_arg(kwargs, "approximate", "none") + + shape = meta["val"].size() + dtype = meta["val"].dtype + + FULL_0_5 = super().call_operator( + full_op, ([1] * len(shape), 0.5), {"dtype": dtype}, meta + ) + FULL_1 = super().call_operator( + full_op, ([1] * len(shape), 1), {"dtype": dtype}, meta + ) + + if approximate == "none": + # Constant mirrors ExecuTorch implementation for parity. + FULL_SQRT1_2 = super().call_operator( + full_op, ([1] * len(shape), 0.70710678118654752440), {}, meta + ) + + op1 = super().call_operator(mul_op, (input, FULL_SQRT1_2), {}, meta) + op2 = super().call_operator(erf_op, (op1,), {}, meta) + op3 = super().call_operator(add_op, (op2, FULL_1), {}, meta) + op4 = super().call_operator(mul_op, (op3, FULL_0_5), {}, meta) + return super().call_operator(mul_op, (input, op4), {}, meta) + + elif approximate == "tanh": + # Constants mirror ExecuTorch implementation for parity. + FULL_SQRT2 = super().call_operator( + full_op, + ([1] * len(shape), 1.41421356237309504880), + {"dtype": dtype}, + meta, + ) + FULL_2_SQRTPI = super().call_operator( + full_op, + ([1] * len(shape), 1.12837916709551257390), + {"dtype": dtype}, + meta, + ) + FULL_CUBE_COEFF = super().call_operator( + full_op, ([1] * len(shape), 0.044715), {"dtype": dtype}, meta + ) + + # Mirrors ExecuTorch implementations for calculating this value + SQRT_MUL = super().call_operator( + mul_op, (FULL_SQRT2, FULL_2_SQRTPI), {}, meta + ) + SQRT_2_PI = super().call_operator(mul_op, (SQRT_MUL, FULL_0_5), {}, meta) + + # Avoiding using POW in order to reduce pass order reliance. + sqr_x = super().call_operator(mul_op, (input, input), {}, meta) + cube_x = super().call_operator(mul_op, (sqr_x, input), {}, meta) + op1 = super().call_operator(mul_op, (cube_x, FULL_CUBE_COEFF), {}, meta) + op2 = super().call_operator(add_op, (input, op1), {}, meta) + op3 = super().call_operator(mul_op, (op2, SQRT_2_PI), {}, meta) + op4 = super().call_operator(tanh_op, (op3,), {}, meta) + op5 = super().call_operator(add_op, (op4, FULL_1), {}, meta) + op6 = super().call_operator(mul_op, (input, op5), {}, meta) + return super().call_operator(mul_op, (op6, FULL_0_5), {}, meta) + else: + raise RuntimeError( + f"approximate argument expected 'none' or 'tanh' but got {approximate}" + ) diff --git a/backends/arm/_passes/insert_table_ops.py b/backends/arm/_passes/insert_table_ops.py index 02510600d82..a5f66829da5 100644 --- a/backends/arm/_passes/insert_table_ops.py +++ b/backends/arm/_passes/insert_table_ops.py @@ -56,6 +56,7 @@ class TableOps: # Targets that must be treated explicitly special_table_ops: Set[EdgeOpOverload] = { exir_ops.edge.aten.pow.Tensor_Scalar, + exir_ops.edge.aten.gelu.default, } def __init__(self, exported_program: ExportedProgram): @@ -76,6 +77,19 @@ def __getitem__(self, node: Node): # Exponent is a constant. Embed it into a lambda. exp = cast(int, node.args[1]) return lambda x: torch.pow(x, exp).flatten() + case exir_ops.edge.aten.gelu.default: + # If kwargs not present it is default "none" + approximate = cast( + str, + ( + node.kwargs["approximate"] + if "approximate" in node.kwargs + else "none" + ), + ) + return lambda x: torch.nn.functional.gelu( + x, approximate=approximate + ).flatten() case _: # Op must be handled if it's inside self.special_ops raise AssertionError("Unhandled table operation") diff --git a/backends/arm/operator_support/tosa_supported_operators.py b/backends/arm/operator_support/tosa_supported_operators.py index 1cec35923de..f84bde7fadc 100644 --- a/backends/arm/operator_support/tosa_supported_operators.py +++ b/backends/arm/operator_support/tosa_supported_operators.py @@ -225,6 +225,7 @@ def is_node_supported( exir_ops.edge.aten.bitwise_left_shift.Tensor, exir_ops.edge.aten.__lshift__.Scalar, torch.ops.aten.scalar_tensor.default, + exir_ops.edge.aten.gelu.default, ] return supported @@ -361,6 +362,7 @@ def is_node_supported( exir_ops.edge.aten.sub.Tensor, exir_ops.edge.aten.tanh.default, exir_ops.edge.aten.upsample_nearest2d.vec, + exir_ops.edge.aten.gelu.default, ): return True elif node.target in ( diff --git a/backends/arm/quantizer/quantization_annotator.py b/backends/arm/quantizer/quantization_annotator.py index b0f9e90b10f..1575d59fd77 100644 --- a/backends/arm/quantizer/quantization_annotator.py +++ b/backends/arm/quantizer/quantization_annotator.py @@ -178,6 +178,7 @@ def _match_pattern( torch.ops.aten.hardswish_.default, torch.ops.aten.full_like.default, torch.ops.aten.pow.Tensor_Scalar, + torch.ops.aten.gelu.default, ] _one_to_one_shared_input_qspec = [ diff --git a/backends/arm/test/misc/test_partition_decomposed_quantized_ops.py b/backends/arm/test/misc/test_partition_decomposed_quantized_ops.py index f69d9d34462..49efbbb4a9c 100644 --- a/backends/arm/test/misc/test_partition_decomposed_quantized_ops.py +++ b/backends/arm/test/misc/test_partition_decomposed_quantized_ops.py @@ -117,7 +117,12 @@ def test_softplus_tosa_BI(test_data: input_t1): # Since GELU will not be quantized by TosaQuantizer, the Dropout's input will not be quantized either. # If so, the Dropout should not be partitioned by TosaPartitioner for TOSA BI profile. This test tests that the # partitioner indeed does not partition the Dropout (clone) for TOSA BI. -@common.parametrize("test_data", test_data) +@common.parametrize( + "test_data", + test_data, + {"3d_rand": "MLETORCH-909: Partition test to not rely on unsupported ops"}, + strict=False, +) def test_linear_residaul_tosa_MI(test_data: input_t1): pipeline = TosaPipelineMI[input_t1]( LinearResidualModule(), diff --git a/backends/arm/test/ops/test_gelu.py b/backends/arm/test/ops/test_gelu.py new file mode 100644 index 00000000000..fb1253fdb0c --- /dev/null +++ b/backends/arm/test/ops/test_gelu.py @@ -0,0 +1,125 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Tuple + +import torch +from executorch.backends.arm.test import common +from executorch.backends.arm.test.tester.test_pipeline import ( + EthosU55PipelineBI, + EthosU85PipelineBI, + TosaPipelineBI, + TosaPipelineMI, +) + +input_t1 = Tuple[torch.Tensor] + + +class Gelu(torch.nn.Module): + aten_op = "torch.ops.aten.gelu.default" + exir_op = "executorch_exir_dialects_edge__ops_aten_gelu_default" + + test_data: dict[str, Tuple[str, input_t1]] = { + "zeros_none": ( + "none", + torch.zeros(1, 10, 10, 10), + ), + "ones_none": ( + "none", + torch.ones(10, 10, 10), + ), + "rand_none": ( + "none", + (torch.rand(10, 10) - 0.5), + ), + "randn_pos_none": ( + "none", + (torch.randn(1, 4, 4, 4) + 10), + ), + "randn_neg_none": ( + "none", + (torch.randn(1, 4, 4, 4) - 10), + ), + "ramp_none": ( + "none", + torch.arange(-16, 16, 0.2), + ), + "zeros_tanh": ( + "tanh", + torch.zeros(1, 10, 10, 10), + ), + "ones_tanh": ( + "tanh", + torch.ones(10, 10, 10), + ), + "rand_tanh": ( + "tanh", + (torch.rand(10, 10) - 0.5), + ), + "randn_pos_tanh": ( + "tanh", + (torch.randn(1, 4, 4, 4) + 10), + ), + "randn_neg_tanh": ( + "tanh", + (torch.randn(1, 4, 4, 4) - 10), + ), + "ramp_tanh": ( + "tanh", + torch.arange(-16, 16, 0.2), + ), + } + + def __init__(self, approximate: str = "none"): + super().__init__() + self.gelu = torch.nn.GELU(approximate) + + def forward(self, x: torch.Tensor): + return self.gelu(x) + + +@common.parametrize("test_data", Gelu.test_data) +def test_gelu_tosa_MI(test_data: input_t1): + approximate = test_data[0] + TosaPipelineMI[input_t1]( + Gelu(approximate), + (test_data[1],), + Gelu.aten_op, + Gelu.exir_op, + use_to_edge_transform_and_lower=False, + ).run() + + +@common.parametrize("test_data", Gelu.test_data) +def test_gelu_tosa_BI(test_data: input_t1): + approximate = test_data[0] + TosaPipelineBI[input_t1]( + Gelu(approximate), + (test_data[1],), + Gelu.aten_op, + Gelu.exir_op, + ).run() + + +@common.parametrize("test_data", Gelu.test_data) +def test_gelu_u55_BI(test_data: input_t1): + approximate = test_data[0] + EthosU55PipelineBI[input_t1]( + Gelu(approximate), + (test_data[1],), + Gelu.aten_op, + Gelu.exir_op, + ).run() + + +@common.parametrize("test_data", Gelu.test_data) +def test_gelu_u85_BI(test_data: input_t1): + approximate = test_data[0] + EthosU85PipelineBI[input_t1]( + Gelu(approximate), + (test_data[1],), + Gelu.aten_op, + Gelu.exir_op, + ).run() From e9e1e86e50e5a8fe1377c22c4d0a99e4e6a7df2c Mon Sep 17 00:00:00 2001 From: jhelsby <11036537+jhelsby@users.noreply.github.com> Date: Fri, 11 Apr 2025 22:39:51 +0200 Subject: [PATCH 109/244] Corrected New Contributor Guide PR merging details (#10101) ### Summary The New Contributor Guide added in [this PR](https://github.com/pytorch/executorch/pull/9977) had an inaccuracy, mentioned in [this comment](https://github.com/pytorch/executorch/pull/9977#discussion_r2036181251) from @byjlw. The guide mistakenly said that only core contributors can merge PRs. This PR corrects that error. It encourages the reader to merge their PR themselves once approved, and provides illustrative screenshots. 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zBXi@TJ02gLVZ0@7ZxY8~8{M%b&s+Xjk-_Tc+(kfjmc~ybvDIPzD;zQV4qxx^2A|tngZn5#> z6WomBZ~>3f?pS&X{mlgdr=c*rP7ed9U1yfNX#f8Pfd4U@H;~_ODtN>lgxVg5#X2yh zCj~hDQe7!bU<}>uf(_BP5L}MpFmNQa5k{h!dUzQ{LZqLf5kx5-2T#({sP2hk{{zTw+M^I*;kSWJEOb) zG%T&I7ICKp7Xn56t%K#0fXTWC8W-hrCNGu$AtSml3yQGxTx1*Y_YiqPHIYZ>zn&yI z{_{0J$d<=TL#oL8iXP{tA=@VEdvmk@-D%(u0Lq}<90r_rJs#Ls_0PxnM^S!=JfdyN z7)f^o4$b5{{ZIM*mE=wIHy%6~%*a-#_|&CTfRL z_WP$21D$nRE#Ora+r_U`{5jIA=8??x&q(Na?n@;_?jyLh$0C Date: Fri, 11 Apr 2025 14:49:06 -0700 Subject: [PATCH 110/244] Simplify AAR copy in build_android_library.sh (#10121) Copy AAR only if $BUILD_AAR_DIR is set --- scripts/build_android_library.sh | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/scripts/build_android_library.sh b/scripts/build_android_library.sh index c96793a126a..cbde7ae3d43 100755 --- a/scripts/build_android_library.sh +++ b/scripts/build_android_library.sh @@ -117,15 +117,12 @@ build_aar() { # Use java unit test as sanity check ANDROID_HOME="${ANDROID_SDK:-/opt/android/sdk}" ./gradlew :executorch_android:testDebugUnitTest popd - cp extension/android/executorch_android/build/outputs/aar/executorch_android-debug.aar "${BUILD_AAR_DIR}/executorch.aar" + if [ ! -z $BUILD_AAR_DIR ]; then + cp extension/android/executorch_android/build/outputs/aar/executorch_android-debug.aar "${BUILD_AAR_DIR}/executorch.aar" + fi } main() { - if [[ -z "${BUILD_AAR_DIR:-}" ]]; then - BUILD_AAR_DIR="$(mktemp -d)" - fi - export BUILD_AAR_DIR - mkdir -p $BUILD_AAR_DIR if [ -z "$ANDROID_ABIS" ]; then ANDROID_ABIS=("arm64-v8a" "x86_64") fi From dd046cf24801e4099f47c17761849ce120dd2096 Mon Sep 17 00:00:00 2001 From: jhelsby <11036537+jhelsby@users.noreply.github.com> Date: Sat, 12 Apr 2025 00:19:53 +0200 Subject: [PATCH 111/244] Update CONTRIBUTING.md and New Contributors Guide to reflect that anyone can merge PRs after checks and approvals (#10123) ### Summary As discussed in [this PR](https://github.com/pytorch/executorch/pull/10118), anyone can merge PRs once approved and all CI checks have passed. `CONTRIBUTING.md` was outdated, saying only people with write-access could merge. This change: * Updates CONTRIBUTING.md to reflect the fact anyone can merge their PR after required approvals and checks. * Adds step-by-step instructions on this to the New Contributors Guide. --------- Co-authored-by: jhels <11036537+jhels@users.noreply.github.com> --- CONTRIBUTING.md | 15 +++------------ .../new-contributor-guide/how_to_merge1.png | Bin 0 -> 269952 bytes .../new-contributor-guide/how_to_merge2.png | Bin 0 -> 40602 bytes .../new-contributor-guide/how_to_merge3.png | Bin 0 -> 67004 bytes docs/source/new-contributor-guide.md | 16 ++++++++++++++-- 5 files changed, 17 insertions(+), 14 deletions(-) create mode 100644 docs/source/_static/img/new-contributor-guide/how_to_merge1.png create mode 100644 docs/source/_static/img/new-contributor-guide/how_to_merge2.png create mode 100644 docs/source/_static/img/new-contributor-guide/how_to_merge3.png diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index c7b1e0d5756..64c6f1d249e 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -409,18 +409,9 @@ for basics. - If the reviewers have requests or questions, follow up with them. - The goal of the reviewer is to ensure that the code in the `main` branch of the repo is consistent, maintainable, and of high quality. -1. Once the PR has been approved, - - If you have the "write permission" in this repo, you can merge it yourself - by clicking the "Squash and merge" button once it is green and all CI - signals are passing. - - If you don't have "write permission" in this repo, the reviewer will take - care of the PR. The reviewer may import the PR into Meta's internal system - to validate it against internal CI. - - If the PR is approved but not merged within 5 business days, please comment - on the PR to ask about its status. - - Note that if the `main` [CI](#continuous-integration) jobs are broken, we - will only merge PRs that fix the broken jobs until all critical jobs are - fixed. +1. 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z0HZO;z(w0;?i2qRNh#^xV|!cR@r#U14S6|N7ty0TrCke>Qc~AAKlCRB=abr=kW|tQ zOu(Jtbhl~Fm$Fq-l!nllp7oJr1R|>smE|}Z&N^>7lbiEOR@bCBT+Dxf-tXyY(dJb1 zwZ7p^JSl&zPk9StuWZP_r~4eOk(x$K*gRWW1}m9C6))y*0o(l*!()YH=0?PQ+)k3e zK{C(xeTw@UM01&s(SQ1y4Iz4l4-l!X?@4cWp5^a{I&PwSM0;=c7B;oAoWa?6p$wj zP9u&y{X!^RC-?^Ph?2EISExDp#WA`wJa6ooBj{zZc?Dy0Ar+ltcLyHRp4}t79OAF& zZ}N(lSWPnHM()Bls}!$|(q6Zv+eZrJA50w~v5frjE0J#kI0i4K_#G)_#cu=8D;-yI zSu-ATxpZL_fgU(eF*de2{ZJXoPCU5`;6i>v+S}LPHST!PqhXjnU13<$ zuR^ieoC^_*c<&nBNF_cTiB4h4;>qQeP3}0 zO=(6}dbIzlV|%8|Ph>dy=oztZ9&Q#d|F-%!xc~L`*Nv~VK^mKB%=TaH{kQrz<}{r~ ziy8_i_YUr#t!a+ZL%w1!2<;D_5gUhi4@n>u7v~qx-CYicjV7o|};UUAo78-`0|E&coLFB3Ii5!+!DF;>7M< zg$4X^IZ2_t2WbC=QhbcI+JG`kZmJ2E-((A&o|fG=g$~d+-TG*2@fQz(X?O3n)ZfaneW_;ywAv8BAFDa6 zv44IE8|EUQF05Swl6-Kui4BzBe@$;W{9RzKsn?> zL7q;^ggJ@??A?+Hv=oi)LY`LLbWL`2@0g2J+?mHEuw0BPSp zV(XJ)zYy|?HL*%IC{Oa$>iY1?1VZS0MdI7|^mOT#b$;p`|`H zIafSeK`Z{XAd9Y>{sPve%`~Iksx7r+H%=yqYF;~BX|6yV5k_yi!0UdER{zP0zi=k_ zDO>M{bm8c4{JLq4{XJ!{>oEM%np&aA>0p#vVG{5DX}}?F8~oz5H7Ixgen2xr>u2Y8 zNLBLJJN^GONBn1jyozBl1MLgAX57zd08Ss1XZuz@!(Ljy^6cT!qQLzs#DU<`e+V0z zoSDx%zHceWcu%X!T Date: Sat, 12 Apr 2025 09:28:36 -0700 Subject: [PATCH 112/244] llava cmakelists (#10127) See issue: https://github.com/pytorch/executorch/issues/10096 Copy llama Cmakelists.txt to link custom ops into the binary: https://github.com/pytorch/executorch/blob/409447d75a1524c1acc8f8ea894c2e13dd723a79/examples/models/llama/CMakeLists.txt#L114 Test plan: Build: ``` cmake -DPYTHON_EXECUTABLE=python -DCMAKE_INSTALL_PREFIX=${BUILD_DIR} -DCMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE} -DEXECUTORCH_BUILD_KERNELS_CUSTOM=ON -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON -DEXECUTORCH_BUILD_XNNPACK=ON -DCMAKE_PREFIX_PATH="/home/lfq/.conda/envs/executorch/lib/python3.10/site-packages" -Bcmake-out/examples/models/llava examples/models/llava cmake --build cmake-out/examples/models/llava/ -j8 --config Debug ``` Run: ``` cmake-out/examples/models/llava/llava_main --model_path=llava.pte --tokenizer_path=tokenizer.bin --image_path=image.pt --prompt="ASSISTANT:" --temperature=0 --seq_len=650 age.pt --prompt="ASSISTANT:" --temperature=0 --seq_len=650 I 00:00:00.001282 executorch:cpuinfo_utils.cpp:62] Reading file /sys/devices/soc0/image_version I 00:00:00.001330 executorch:cpuinfo_utils.cpp:78] Failed to open midr file /sys/devices/soc0/image_version I 00:00:00.001353 executorch:cpuinfo_utils.cpp:91] Reading file /sys/devices/system/cpu/cpu0/regs/identification/midr_el1 I 00:00:00.001380 executorch:cpuinfo_utils.cpp:100] Failed to open midr file /sys/devices/system/cpu/cpu0/regs/identification/midr_el1 I 00:00:00.001390 executorch:cpuinfo_utils.cpp:116] CPU info and manual query on # of cpus dont match. I 00:00:00.001397 executorch:main.cpp:77] Resetting threadpool with num threads = 0 I 00:00:00.001412 executorch:multimodal_runner.h:45] Creating Multimodal LLM runner: model_path=llava.pte, tokenizer_path=tokenizer.bin I 00:00:00.025122 executorch:main.cpp:107] image size(0): 3, size(1): 240, size(2): 336 I 00:00:21.793359 executorch:llava_runner.cpp:142] RSS after loading model: 6123.457031 MiB (0 if unsupported) I 00:00:23.059576 executorch:text_prefiller.cpp:95] Prefill token result numel(): 32064 I 00:00:33.459186 executorch:llava_runner.cpp:166] RSS after prompt and image prefill: 6255.707031 MiB (0 if unsupported) ASSISTANT:I 00:00:33.948606 executorch:text_prefiller.cpp:95] Prefill token result numel(): 32064 image captures a basketball game in progress, with several players on the court. One player is in the middle of a dunk, while another player is attempting toPyTorchObserver {"prompt_tokens":616,"generated_tokens":33,"model_load_start_ms":1744415212709,"model_load_end_ms":1744415234476,"inference_start_ms":1744415234477,"inference_end_ms":1744415259787,"prompt_eval_end_ms":1744415246632,"first_token_ms":1744415246632,"aggregate_sampling_time_ms":2883588,"SCALING_FACTOR_UNITS_PER_SECOND":1000} I 00:00:47.103512 executorch:stats.h:104] Prompt Tokens: 616 Generated Tokens: 33 I 00:00:47.103520 executorch:stats.h:110] Model Load Time: 21.767000 (seconds) I 00:00:47.103528 executorch:stats.h:117] Total inference time: 25.310000 (seconds) Rate: 1.303832 (tokens/second) I 00:00:47.103533 executorch:stats.h:127] Prompt evaluation: 12.155000 (seconds) Rate: 50.678733 (tokens/second) I 00:00:47.103538 executorch:stats.h:136] Generated 33 tokens: 13.155000 (seconds) Rate: 2.508552 (tokens/second) I 00:00:47.103542 executorch:stats.h:147] Time to first generated token: 12.155000 (seconds) I 00:00:47.103545 executorch:stats.h:153] Sampling time over 649 tokens: 2883.588000 (seconds) I 00:00:47.103549 executorch:llava_runner.cpp:178] RSS after finishing text generation: 6255.707031 MiB (0 if unsupported) ``` --- .ci/scripts/test_llava.sh | 2 +- examples/models/llava/CMakeLists.txt | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.ci/scripts/test_llava.sh b/.ci/scripts/test_llava.sh index 15df725f9c1..8a1d5683b33 100644 --- a/.ci/scripts/test_llava.sh +++ b/.ci/scripts/test_llava.sh @@ -154,7 +154,7 @@ run_and_verify() { EXPECTED_PREFIX="ASSISTANT: image captures a basketball game in progress, with several players on the court. One of the players is dribbling the ball, while the others are in various" else # set the expected prefix to be the same as prompt because there's a bug in sdpa_with_kv_cache that causes tokens. - EXPECTED_PREFIX="ASSISTANT:" + EXPECTED_PREFIX="ASSISTANT: image" fi if [[ "${RESULT}" == *"${EXPECTED_PREFIX}"* ]]; then echo "Expected result prefix: ${EXPECTED_PREFIX}" diff --git a/examples/models/llava/CMakeLists.txt b/examples/models/llava/CMakeLists.txt index 6003f3a000d..eeb6c296dd5 100644 --- a/examples/models/llava/CMakeLists.txt +++ b/examples/models/llava/CMakeLists.txt @@ -15,7 +15,7 @@ # ~~~ # It should also be cmake-lint clean. # -cmake_minimum_required(VERSION 3.19) +cmake_minimum_required(VERSION 3.24) # 3.24 is required for WHOLE_ARCHIVE project(llava) # Duplicating options as root CMakeLists.txt @@ -124,7 +124,7 @@ target_link_options_shared_lib(quantized_ops_lib) list(APPEND link_libraries quantized_kernels quantized_ops_lib) if(EXECUTORCH_BUILD_KERNELS_CUSTOM) - list(APPEND link_libraries custom_ops) + list(APPEND link_libraries $) endif() set(XNNPACK_ROOT ${CMAKE_CURRENT_SOURCE_DIR}/../../../backends/xnnpack) From d9caa79a44bef3cd30f5f63055bf47a6d29281b1 Mon Sep 17 00:00:00 2001 From: Eli Amesefe Date: Sun, 13 Apr 2025 17:02:08 -0700 Subject: [PATCH 113/244] Fixup op_slice negative start arguments Differential Revision: D72728353 Pull Request resolved: https://github.com/pytorch/executorch/pull/10122 --- backends/arm/operators/op_slice.py | 28 +++++++++++--- backends/arm/test/ops/test_slice.py | 58 +++++++++++++++-------------- 2 files changed, 52 insertions(+), 34 deletions(-) diff --git a/backends/arm/operators/op_slice.py b/backends/arm/operators/op_slice.py index a9e1a6cdbe2..27ae977a5bc 100644 --- a/backends/arm/operators/op_slice.py +++ b/backends/arm/operators/op_slice.py @@ -23,6 +23,18 @@ class SliceVisitor(NodeVisitor): def __init__(self, *args): super().__init__(*args) + def _fixup_start(self, start, shape, dim): + if start.number < 0: + return start.number % shape[dim] + else: + return start.number + + def _fixup_end(self, end, shape, dim): + if end.number < 0: + return end.number % shape[dim] + else: + return min(end.number, shape[dim]) + def define_node( self, node: Node, @@ -42,17 +54,21 @@ def define_node( # Translate and check parameters in Pytorch dim order. shape = input_node.shape dim = dim.number - if end.number < 0: - end_index = end.number % shape[dim] - else: - end_index = min(end.number, shape[dim]) - size = end_index - start.number + + start_index = self._fixup_start(start, shape, dim) + end_index = self._fixup_end(end, shape, dim) + size = end_index - start_index + assert size > 0 assert size <= shape[dim] # Convert aten args to Tosa's start and size attributes and in TOSA dim order. attr = ts.TosaSerializerAttribute() - start_attr = [start.number if i == dim else 0 for i in input_node.dim_order] + + start_attr = [ + self._fixup_start(start, shape, dim) if i == dim else 0 + for i in input_node.dim_order + ] size_attr = [size if i == dim else shape[i] for i in input_node.dim_order] attr.SliceAttribute(start_attr, size_attr) diff --git a/backends/arm/test/ops/test_slice.py b/backends/arm/test/ops/test_slice.py index 6900fda3abe..91ef51cc2a2 100644 --- a/backends/arm/test/ops/test_slice.py +++ b/backends/arm/test/ops/test_slice.py @@ -16,23 +16,21 @@ from executorch.exir.backend.compile_spec_schema import CompileSpec from parameterized import parameterized +test_data_suite = [ + (torch.ones(10), [(3, -3)]), + (torch.ones(10), [(-8, 3)]), + (torch.ones(10, 10), [(1, 3), (3, None)]), + (torch.ones(10, 10, 10), [(0, 7), (0, None), (0, 8)]), + (torch.ones((1, 12, 10, 10)), [(None, None), (None, 5), (3, 5), (4, 10)]), +] + class TestSimpleSlice(unittest.TestCase): class Slice(torch.nn.Module): - - sizes = [(10), (10, 10), (10, 10, 10), ((1, 12, 10, 10))] - test_tensors = [(torch.ones(n),) for n in sizes] - - def forward(self, x: torch.Tensor): - if x.dim() == 1: - return x[3:-3] - elif x.dim() == 2: - return x[1:3, 3:] - elif x.dim() == 3: - return x[0:7, 0:, 0:8] - elif x.dim() == 4: - return x[:, :5, 3:5, 4:10] + def forward(self, x: torch.Tensor, s: list[tuple[int, int]]): + slices = [slice(*i) for i in s] + return x[slices] def _test_slice_tosa_MI_pipeline( self, module: torch.nn.Module, test_data: torch.Tensor @@ -112,25 +110,29 @@ def _test_slice_u85_BI_pipeline( common.get_u85_compile_spec(), module, test_data ) - @parameterized.expand(Slice.test_tensors) + @parameterized.expand(test_data_suite) @pytest.mark.tosa_ref_model - def test_slice_tosa_MI(self, tensor): - self._test_slice_tosa_MI_pipeline(self.Slice(), (tensor,)) + def test_slice_tosa_MI(self, tensor: torch.Tensor, slices: list[tuple[int, int]]): + self._test_slice_tosa_MI_pipeline(self.Slice(), (tensor, slices)) - @parameterized.expand(Slice.test_tensors[:2]) + @parameterized.expand(test_data_suite) @pytest.mark.tosa_ref_model - def test_slice_nchw_tosa_BI(self, test_tensor: torch.Tensor): - self._test_slice_tosa_BI_pipeline(self.Slice(), (test_tensor,)) + def test_slice_nchw_tosa_BI( + self, tensor: torch.Tensor, slices: list[tuple[int, int]] + ): + self._test_slice_tosa_BI_pipeline(self.Slice(), (tensor, slices)) - @parameterized.expand(Slice.test_tensors[2:]) + @parameterized.expand(test_data_suite) @pytest.mark.tosa_ref_model - def test_slice_nhwc_tosa_BI(self, test_tensor: torch.Tensor): - self._test_slice_tosa_BI_pipeline(self.Slice(), (test_tensor,)) + def test_slice_nhwc_tosa_BI( + self, tensor: torch.Tensor, slices: list[tuple[int, int]] + ): + self._test_slice_tosa_BI_pipeline(self.Slice(), (tensor, slices)) - @parameterized.expand(Slice.test_tensors) - def test_slice_u55_BI(self, test_tensor: torch.Tensor): - self._test_slice_u55_BI_pipeline(self.Slice(), (test_tensor,)) + @parameterized.expand(test_data_suite) + def test_slice_u55_BI(self, tensor: torch.Tensor, slices: list[tuple[int, int]]): + self._test_slice_u55_BI_pipeline(self.Slice(), (tensor, slices)) - @parameterized.expand(Slice.test_tensors) - def test_slice_u85_BI(self, test_tensor: torch.Tensor): - self._test_slice_u85_BI_pipeline(self.Slice(), (test_tensor,)) + @parameterized.expand(test_data_suite) + def test_slice_u85_BI(self, tensor: torch.Tensor, slices: list[tuple[int, int]]): + self._test_slice_u85_BI_pipeline(self.Slice(), (tensor, slices)) From 08e8e17df014bb1e14ca7e01a75a22e0e4edbb65 Mon Sep 17 00:00:00 2001 From: Kimish Patel Date: Sun, 13 Apr 2025 17:12:15 -0700 Subject: [PATCH 114/244] {Executorch][llm] quantized sdpa. update attn_scores @ v gemm Differential Revision: D71833065 Pull Request resolved: https://github.com/pytorch/executorch/pull/10108 --- extension/llm/custom_ops/op_sdpa_impl.h | 108 +++++++++++++++++------- 1 file changed, 78 insertions(+), 30 deletions(-) diff --git a/extension/llm/custom_ops/op_sdpa_impl.h b/extension/llm/custom_ops/op_sdpa_impl.h index 76dbf776700..689537923d5 100644 --- a/extension/llm/custom_ops/op_sdpa_impl.h +++ b/extension/llm/custom_ops/op_sdpa_impl.h @@ -202,6 +202,49 @@ void dequantize_per_channel_optimized( } } +void dequant_and_gemm( + const int64_t m, + const int64_t n, + const int64_t k, + float* qk_data, + const int64_t qk_stride_m, + const MaybeQuantizedMatrixData& v_data, + const int64_t v_stride_n, + float* o_data, + const int64_t o_stride_m, + const float beta) { + std::vector dequantized_v_data(v_data.m * v_data.n); + dequantize_per_channel_optimized( + static_cast(v_data.data), + static_cast(v_data.scales), + static_cast(v_data.zero_points), + dequantized_v_data.data(), + -128, + 127, + 1, + 0, + 0, + v_data.m, + v_stride_n, + v_data.n, + v_data.n, + v_data.zero_points_stride); + ::executorch::cpublas::gemm( + ::executorch::cpublas::TransposeType::NoTranspose, + ::executorch::cpublas::TransposeType::NoTranspose, + n, + m, + k, + static_cast(1), + dequantized_v_data.data(), + v_data.n, + qk_data, + qk_stride_m, + beta, + o_data, + o_stride_m); +} + template void _qk_at_v_gemm( const int64_t m, @@ -216,36 +259,41 @@ void _qk_at_v_gemm( const accum_t beta) { if (v_data.dtype == ScalarType::Char) { if constexpr (std::is_same::value) { - std::vector dequantized_v_data(v_data.m * v_data.n); - dequantize_per_channel_optimized( - static_cast(v_data.data), - static_cast(v_data.scales), - static_cast(v_data.zero_points), - dequantized_v_data.data(), - -128, - 127, - 1, - 0, - 0, - v_data.m, - v_stride_n, - v_data.n, - v_data.n, - v_data.zero_points_stride); - ::executorch::cpublas::gemm( - ::executorch::cpublas::TransposeType::NoTranspose, - ::executorch::cpublas::TransposeType::NoTranspose, - n, - m, - k, - static_cast(1), - dequantized_v_data.data(), - v_data.n, - qk_data, - qk_stride_m, - beta, - o_data, - o_stride_m); + if (m > 4) { + // For larger batch sizes, dequantize and use BLAS for better + // performance + dequant_and_gemm( + m, + n, + k, + const_cast(qk_data), + qk_stride_m, + v_data, + v_stride_n, + o_data, + o_stride_m, + beta); + } else { + // For smaller batch sizes, use quantized gemm + int a_stride_m_tmp, b_stride_n_tmp; + auto kernel = torchao::kernels::cpu::quantized_matmul:: + get_fp32_a_input_channelwise_8bit_b_f32_c_matmul( + m, n, k, false, false, a_stride_m_tmp, b_stride_n_tmp); + kernel( + m, + n, + k, + qk_data, + qk_stride_m /*lhs_stride_m*/, + static_cast(v_data.data), + v_stride_n /*rhs_stride_n*/, + o_data, + o_stride_m /*out_stride_n*/, + static_cast(v_data.zero_points), + static_cast(v_data.scales), + beta, + v_data.zero_points_stride); + } } else { ET_CHECK_MSG( false, "Accumulation in dtype other than float not supported yet"); From a41403b2012f5421163c98935f0aeb1074344097 Mon Sep 17 00:00:00 2001 From: Michiel Olieslagers <44864547+Michiel-Olieslagers@users.noreply.github.com> Date: Mon, 14 Apr 2025 10:19:54 +0100 Subject: [PATCH 115/244] Arm backend: Added 8 new unit tests for testing various passes. (#9037) Refactored tests cases to use TestPassPipeline instead of ArmTester. Signed-off-by: Michiel Olieslagers --- .../test_convert_expand_copy_to_repeat.py | 51 +++++++++ .../passes/test_convert_split_to_slice.py | 67 ++++++++++++ .../test/passes/test_decompose_div_pass.py | 65 +++++++++++ .../passes/test_decompose_layernorm_pass.py | 69 ++++++++++++ .../passes/test_decompose_meandim_pass.py | 73 +++++++++++++ .../passes/test_decompose_softmax_pass.py | 103 ++++++++++++++++++ .../test/passes/test_decompose_var_pass.py | 84 ++++++++++++++ .../arm/test/passes/test_remove_clone_pass.py | 43 ++++++++ 8 files changed, 555 insertions(+) create mode 100644 backends/arm/test/passes/test_convert_expand_copy_to_repeat.py create mode 100644 backends/arm/test/passes/test_convert_split_to_slice.py create mode 100644 backends/arm/test/passes/test_decompose_div_pass.py create mode 100644 backends/arm/test/passes/test_decompose_layernorm_pass.py create mode 100644 backends/arm/test/passes/test_decompose_meandim_pass.py create mode 100644 backends/arm/test/passes/test_decompose_softmax_pass.py create mode 100644 backends/arm/test/passes/test_decompose_var_pass.py create mode 100755 backends/arm/test/passes/test_remove_clone_pass.py diff --git a/backends/arm/test/passes/test_convert_expand_copy_to_repeat.py b/backends/arm/test/passes/test_convert_expand_copy_to_repeat.py new file mode 100644 index 00000000000..5d83bc82f22 --- /dev/null +++ b/backends/arm/test/passes/test_convert_expand_copy_to_repeat.py @@ -0,0 +1,51 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Tuple + +import torch +from executorch.backends.arm._passes.convert_expand_copy_to_repeat import ( + ConvertExpandCopyToRepeatPass, +) + +from executorch.backends.arm.test.tester.test_pipeline import PassPipeline + +input_t = Tuple[torch.Tensor] # Input x + + +class Expand(torch.nn.Module): + """ + Basic expand model using torch.Tensor.expand function + """ + + def __init__(self): + super(Expand, self).__init__() + + def forward(self, x): + return x.expand(3, 4) + + def get_inputs(self) -> input_t: + return (torch.rand(3, 1),) + + +def test_expand_to_repeat_tosa_BI(): + module = Expand() + pipeline = PassPipeline[input_t]( + module, + module.get_inputs(), + tosa_version="TOSA-0.80+BI", + ops_before_pass={ + "executorch_exir_dialects_edge__ops_aten_expand_copy_default": 1, + }, + ops_not_before_pass=["executorch_exir_dialects_edge__ops_aten_repeat_default"], + ops_after_pass={ + "executorch_exir_dialects_edge__ops_aten_repeat_default": 1, + }, + ops_not_after_pass=[ + "executorch_exir_dialects_edge__ops_aten_expand_copy_default" + ], + pass_list=[ConvertExpandCopyToRepeatPass], + ) + pipeline.run() diff --git a/backends/arm/test/passes/test_convert_split_to_slice.py b/backends/arm/test/passes/test_convert_split_to_slice.py new file mode 100644 index 00000000000..d4fdffe3b01 --- /dev/null +++ b/backends/arm/test/passes/test_convert_split_to_slice.py @@ -0,0 +1,67 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Tuple + +import torch +from executorch.backends.arm._passes.convert_split_to_slice import ( + ConvertSplitToSlicePass, +) + +from executorch.backends.arm.test import common + +from executorch.backends.arm.test.tester.test_pipeline import PassPipeline + +input_t = Tuple[torch.Tensor] # Input x + + +class Split(torch.nn.Module): + """ + Basic split model using torch.split function + """ + + def get_inputs(self) -> input_t: + return (torch.rand(10),) + + def forward(self, x): + return torch.split(x, 2) + + +class SplitTensor(torch.nn.Module): + """ + Basic split model using torch.Tensor.split function + """ + + def get_inputs(self) -> input_t: + return (torch.rand(10),) + + def forward(self, x): + return x.split(2) + + +modules = {"split_basic": Split(), "split_tensor": SplitTensor()} + + +@common.parametrize("module", modules) +def test_split_to_slice_tosa_BI(module): + pipeline = PassPipeline[input_t]( + module, + module.get_inputs(), + tosa_version="TOSA-0.80+BI", + ops_before_pass={ + "executorch_exir_dialects_edge__ops_aten_split_with_sizes_copy_default": 1, + }, + ops_not_before_pass=[ + "executorch_exir_dialects_edge__ops_aten_slice_copy_Tensor" + ], + ops_after_pass={ + "executorch_exir_dialects_edge__ops_aten_slice_copy_Tensor": 5, + }, + ops_not_after_pass=[ + "executorch_exir_dialects_edge__ops_aten_split_with_sizes_copy_default" + ], + pass_list=[ConvertSplitToSlicePass], + ) + pipeline.run() diff --git a/backends/arm/test/passes/test_decompose_div_pass.py b/backends/arm/test/passes/test_decompose_div_pass.py new file mode 100644 index 00000000000..71d586c0029 --- /dev/null +++ b/backends/arm/test/passes/test_decompose_div_pass.py @@ -0,0 +1,65 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Tuple + +import torch +from executorch.backends.arm._passes.decompose_div_pass import DecomposeDivPass + +from executorch.backends.arm.test import common + +from executorch.backends.arm.test.tester.test_pipeline import PassPipeline + +input_t = Tuple[torch.Tensor] # Input x + + +class Div(torch.nn.Module): + """ + Basic div model using torch.div + """ + + def get_inputs(self) -> input_t: + return (torch.rand(10),) + + def forward(self, x): + return torch.div(x, 2) + + +class DivTensor(torch.nn.Module): + """ + Basic div model using torch.Tensor.div + """ + + def get_inputs(self) -> input_t: + return (torch.rand(10),) + + def forward(self, x): + return x.div(2) + + +modules = {"div_basic": Div(), "div_tensor": DivTensor()} + + +@common.parametrize("module", modules) +def test_decompose_div_tosa_MI(module): + pipeline = PassPipeline[input_t]( + module, + module.get_inputs(), + tosa_version="TOSA-0.80+MI", + ops_before_pass={ + "executorch_exir_dialects_edge__ops_aten_div_Tensor": 1, + }, + ops_not_before_pass=[ + "executorch_exir_dialects_edge__ops_aten_mul_Tensor", + "executorch_exir_dialects_edge__ops_aten_reciprocal_default", + ], + ops_after_pass={ + "executorch_exir_dialects_edge__ops_aten_mul_Tensor": 1, + "executorch_exir_dialects_edge__ops_aten_reciprocal_default": 1, + }, + ops_not_after_pass=["executorch_exir_dialects_edge__ops_aten_div_Tensor"], + pass_list=[DecomposeDivPass], + ) + pipeline.run() diff --git a/backends/arm/test/passes/test_decompose_layernorm_pass.py b/backends/arm/test/passes/test_decompose_layernorm_pass.py new file mode 100644 index 00000000000..40e49e15bc5 --- /dev/null +++ b/backends/arm/test/passes/test_decompose_layernorm_pass.py @@ -0,0 +1,69 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Tuple + +import torch +from executorch.backends.arm._passes.decompose_layernorm_pass import ( + DecomposeLayerNormPass, +) + +from executorch.backends.arm.test.tester.test_pipeline import PassPipeline + +input_t = Tuple[torch.Tensor] # Input x + + +class LayerNorm(torch.nn.Module): + """ + Basic layer_norm model using torch.nn.layer_norm layer + """ + + def __init__(self): + super(LayerNorm, self).__init__() + self.layer_norm = torch.nn.LayerNorm(10) + + def forward(self, x): + x = self.layer_norm(x) + return x + + def get_inputs(self) -> input_t: + return (torch.rand(10),) + + +def test_decompose_layernorm_tosa_MI(): + module = LayerNorm() + pipeline = PassPipeline[input_t]( + module, + module.get_inputs(), + tosa_version="TOSA-0.80+MI", + ops_before_pass={ + "executorch_exir_dialects_edge__ops_aten_native_layer_norm_default": 1, + }, + ops_not_before_pass=[ + "executorch_exir_dialects_edge__ops_aten_add_Tensor", + "executorch_exir_dialects_edge__ops_aten_view_copy_default", + "executorch_exir_dialects_edge__ops_aten_mul_Tensor", + "executorch_exir_dialects_edge__ops_aten_full_default", + "executorch_exir_dialects_edge__ops_aten_rsqrt_default", + "executorch_exir_dialects_edge__ops_aten_var_correction", + "executorch_exir_dialects_edge__ops_aten_sub_Tensor", + "executorch_exir_dialects_edge__ops_aten_mean_dim", + ], + ops_after_pass={ + "executorch_exir_dialects_edge__ops_aten_add_Tensor": 2, + "executorch_exir_dialects_edge__ops_aten_view_copy_default": 2, + "executorch_exir_dialects_edge__ops_aten_mul_Tensor": 2, + "executorch_exir_dialects_edge__ops_aten_full_default": 1, + "executorch_exir_dialects_edge__ops_aten_rsqrt_default": 1, + "executorch_exir_dialects_edge__ops_aten_var_correction": 1, + "executorch_exir_dialects_edge__ops_aten_sub_Tensor": 1, + "executorch_exir_dialects_edge__ops_aten_mean_dim": 1, + }, + ops_not_after_pass=[ + "executorch_exir_dialects_edge__ops_aten_expand_copy_default" + ], + pass_list=[DecomposeLayerNormPass], + ) + pipeline.run() diff --git a/backends/arm/test/passes/test_decompose_meandim_pass.py b/backends/arm/test/passes/test_decompose_meandim_pass.py new file mode 100644 index 00000000000..6ba9ceff3a7 --- /dev/null +++ b/backends/arm/test/passes/test_decompose_meandim_pass.py @@ -0,0 +1,73 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Tuple + +import torch +from executorch.backends.arm._passes.decompose_meandim_pass import DecomposeMeanDimPass + +from executorch.backends.arm.test import common + +from executorch.backends.arm.test.tester.test_pipeline import PassPipeline + +input_t = Tuple[torch.Tensor] # Input x + + +class MeanDim(torch.nn.Module): + """ + Basic mean model using torch.mean function making sure keepdim=True (keepdim=False doesnt work for this pass for some reason) + """ + + def __init__(self): + super(MeanDim, self).__init__() + + def forward(self, x): + return torch.mean(x, 1, True) + + def get_inputs(self) -> input_t: + return (torch.rand(4, 4),) + + +class MeanDimTensor(torch.nn.Module): + """ + Basic mean model using torch.Tensor.mean function making sure keepdim=True (keepdim=False doesnt work for this pass for some reason) + """ + + def __init__(self): + super(MeanDimTensor, self).__init__() + + def forward(self, x): + return x.mean(1, True) + + def get_inputs(self) -> input_t: + return (torch.rand(4, 4),) + + +modules = {"meandim_basic": MeanDim(), "meandim_tensor": MeanDimTensor()} + + +@common.parametrize("module", modules) +def test_decompose_meandim_tosa_MI(module): + pipeline = PassPipeline[input_t]( + module, + module.get_inputs(), + tosa_version="TOSA-0.80+MI", + ops_before_pass={ + "executorch_exir_dialects_edge__ops_aten_mean_dim": 1, + }, + ops_not_before_pass=[ + "executorch_exir_dialects_edge__ops_aten_mul_Tensor", + "executorch_exir_dialects_edge__ops_aten_full_default", + "executorch_exir_dialects_edge__ops_aten_sum_dim_IntList", + ], + ops_after_pass={ + "executorch_exir_dialects_edge__ops_aten_mul_Tensor": 1, + "executorch_exir_dialects_edge__ops_aten_full_default": 1, + "executorch_exir_dialects_edge__ops_aten_sum_dim_IntList": 1, + }, + ops_not_after_pass=["executorch_exir_dialects_edge__ops_aten_mean_dim"], + pass_list=[DecomposeMeanDimPass], + ) + pipeline.run() diff --git a/backends/arm/test/passes/test_decompose_softmax_pass.py b/backends/arm/test/passes/test_decompose_softmax_pass.py new file mode 100644 index 00000000000..efb911f03aa --- /dev/null +++ b/backends/arm/test/passes/test_decompose_softmax_pass.py @@ -0,0 +1,103 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Tuple + +import torch +from executorch.backends.arm._passes.decompose_softmax_pass import DecomposeSoftmaxPass + +from executorch.backends.arm.test.tester.test_pipeline import PassPipeline + +input_t = Tuple[torch.Tensor] # Input x + + +class Softmax(torch.nn.Module): + """ + Basic torch.nn.softmax layer model + """ + + def __init__(self): + super(Softmax, self).__init__() + self.softmax = torch.nn.Softmax(dim=1) + + def forward(self, x): + x = self.softmax(x) + return x + + def get_inputs(self) -> input_t: + return (torch.rand(2, 3),) + + +class SoftmaxLog(torch.nn.Module): + """ + Basic torch.nn.log_softmax layer model + """ + + def __init__(self): + super(SoftmaxLog, self).__init__() + self.softmax = torch.nn.LogSoftmax(dim=1) + + def forward(self, x): + x = self.softmax(x) + return x + + def get_inputs(self) -> input_t: + return (torch.rand(2, 3),) + + +def test_softmax_basic_tosa_MI(): + module = Softmax() + pipeline = PassPipeline[input_t]( + module, + module.get_inputs(), + tosa_version="TOSA-0.80+MI", + ops_before_pass={ + "executorch_exir_dialects_edge__ops_aten__softmax_default": 1, + }, + ops_not_before_pass=[ + "executorch_exir_dialects_edge__ops_aten_mul_Tensor", + "executorch_exir_dialects_edge__ops_aten_reciprocal_default", + "executorch_exir_dialects_edge__ops_aten_sum_dim_IntList", + "executorch_exir_dialects_edge__ops_aten_exp_default", + ], + ops_after_pass={ + "executorch_exir_dialects_edge__ops_aten_mul_Tensor": 1, + "executorch_exir_dialects_edge__ops_aten_exp_default": 1, + "executorch_exir_dialects_edge__ops_aten_reciprocal_default": 1, + "executorch_exir_dialects_edge__ops_aten_sum_dim_IntList": 1, + }, + ops_not_after_pass=["executorch_exir_dialects_edge__ops_aten__softmax_default"], + pass_list=[DecomposeSoftmaxPass], + ) + pipeline.run() + + +def test_softmax_log_tosa_MI(): + module = SoftmaxLog() + pipeline = PassPipeline[input_t]( + module, + module.get_inputs(), + tosa_version="TOSA-0.80+MI", + ops_before_pass={ + "executorch_exir_dialects_edge__ops_aten__log_softmax_default": 1, + }, + ops_not_before_pass=[ + "executorch_exir_dialects_edge__ops_aten_mul_Tensor", + "executorch_exir_dialects_edge__ops_aten_reciprocal_default", + "executorch_exir_dialects_edge__ops_aten_sum_dim_IntList", + "executorch_exir_dialects_edge__ops_aten_exp_default", + ], + ops_after_pass={ + "executorch_exir_dialects_edge__ops_aten_mul_Tensor": 1, + "executorch_exir_dialects_edge__ops_aten_exp_default": 1, + "executorch_exir_dialects_edge__ops_aten_reciprocal_default": 1, + "executorch_exir_dialects_edge__ops_aten_sum_dim_IntList": 1, + }, + ops_not_after_pass=[ + "executorch_exir_dialects_edge__ops_aten__log_softmax_default" + ], + pass_list=[DecomposeSoftmaxPass], + ) + pipeline.run() diff --git a/backends/arm/test/passes/test_decompose_var_pass.py b/backends/arm/test/passes/test_decompose_var_pass.py new file mode 100644 index 00000000000..fe793dba14b --- /dev/null +++ b/backends/arm/test/passes/test_decompose_var_pass.py @@ -0,0 +1,84 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Tuple + +import torch +from executorch.backends.arm._passes.decompose_var_pass import DecomposeVarPass + +from executorch.backends.arm.test import common + +from executorch.backends.arm.test.tester.test_pipeline import PassPipeline + +input_t = Tuple[torch.Tensor] # Input x + + +class VarDim(torch.nn.Module): + """ + Basic variance model using torch.Tensor.var function. + """ + + def __init__(self, keepdim): + super(VarDim, self).__init__() + self.keepdim = keepdim + + def forward(self, x): + return x.var(dim=-1, keepdim=self.keepdim) + + def get_inputs(self) -> input_t: + return (torch.rand(4, 4),) + + +class VarCorrection(torch.nn.Module): + """ + Basic variance model using torch.var function. + """ + + def __init__(self, keepdim): + super(VarCorrection, self).__init__() + self.keepdim = keepdim + + def forward(self, x): + return torch.var(x, -1, keepdim=self.keepdim) + + def get_inputs(self) -> input_t: + return (torch.rand(4, 4),) + + +modules = { + "vardim_keepdim": VarDim(True), + "vardim_no_keepdim": VarDim(False), + "varcorrection_keepdim": VarCorrection(True), + "varcorrection_no_keepdim": VarCorrection(False), +} + + +@common.parametrize("module", modules) +def test_decompose_var_tosa_MI(module): + pipeline = PassPipeline[input_t]( + module, + module.get_inputs(), + tosa_version="TOSA-0.80+MI", + ops_before_pass={ + "executorch_exir_dialects_edge__ops_aten_var_correction": 1, + }, + ops_not_before_pass=[ + "executorch_exir_dialects_edge__ops_aten_mul_Tensor", + "executorch_exir_dialects_edge__ops_aten_full_default", + "executorch_exir_dialects_edge__ops_aten_sum_dim_IntList", + "executorch_exir_dialects_edge__ops_aten_mean_dim", + "executorch_exir_dialects_edge__ops_aten_sub_Tensor", + ], + ops_after_pass={ + "executorch_exir_dialects_edge__ops_aten_mul_Tensor": 2, + "executorch_exir_dialects_edge__ops_aten_mean_dim": 1, + "executorch_exir_dialects_edge__ops_aten_sub_Tensor": 1, + "executorch_exir_dialects_edge__ops_aten_full_default": 1, + "executorch_exir_dialects_edge__ops_aten_sum_dim_IntList": 1, + }, + ops_not_after_pass=["executorch_exir_dialects_edge__ops_aten_var_correction"], + pass_list=[DecomposeVarPass], + ) + pipeline.run() diff --git a/backends/arm/test/passes/test_remove_clone_pass.py b/backends/arm/test/passes/test_remove_clone_pass.py new file mode 100755 index 00000000000..e586edd323d --- /dev/null +++ b/backends/arm/test/passes/test_remove_clone_pass.py @@ -0,0 +1,43 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Tuple + +import torch +from executorch.backends.arm._passes.remove_clone_pass import RemoveClonePass + +from executorch.backends.arm.test.tester.test_pipeline import PassPipeline + +input_t = Tuple[torch.Tensor] # Input x + + +class Clone(torch.nn.Module): + """ + Basic remove layer model to test RemoveClonePass + """ + + def __init__(self): + super(Clone, self).__init__() + + def forward(self, x): + return torch.clone(x) + + def get_inputs(self) -> input_t: + return (torch.rand(3, 1),) + + +def test_remove_clone_tosa_BI(): + module = Clone() + pipeline = PassPipeline[input_t]( + module, + module.get_inputs(), + tosa_version="TOSA-0.80+BI", + ops_before_pass={ + "executorch_exir_dialects_edge__ops_aten_clone_default": 1, + }, + ops_not_after_pass=["executorch_exir_dialects_edge__ops_aten_clone_default"], + pass_list=[RemoveClonePass], + ) + pipeline.run() From eca61ff430edb66e3a0f95b5541583efe1af90cb Mon Sep 17 00:00:00 2001 From: George Gekov Date: Mon, 14 Apr 2025 12:33:02 +0100 Subject: [PATCH 116/244] Arm backend: Remove node vistor for full (#9904) The full operator is now decomposed via the ComputeConstantOpsAOT pass --- backends/arm/operators/__init__.py | 1 - backends/arm/operators/op_full.py | 49 ------------------------------ 2 files changed, 50 deletions(-) delete mode 100644 backends/arm/operators/op_full.py diff --git a/backends/arm/operators/__init__.py b/backends/arm/operators/__init__.py index b62e8940ed2..861ed9c7701 100644 --- a/backends/arm/operators/__init__.py +++ b/backends/arm/operators/__init__.py @@ -21,7 +21,6 @@ op_eq, op_erf, op_exp, - op_full, op_ge, op_get_item, op_gt, diff --git a/backends/arm/operators/op_full.py b/backends/arm/operators/op_full.py deleted file mode 100644 index e68610a37a0..00000000000 --- a/backends/arm/operators/op_full.py +++ /dev/null @@ -1,49 +0,0 @@ -# Copyright 2024-2025 Arm Limited and/or its affiliates. -# -# This source code is licensed under the BSD-style license found in the -# LICENSE file in the root directory of this source tree. - -# pyre-unsafe -from typing import List - -import numpy as np - -import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore -from executorch.backends.arm.operators.node_visitor import ( - NodeVisitor, - register_node_visitor, -) -from executorch.backends.arm.tosa_mapping import TosaArg -from executorch.backends.arm.tosa_utils import tosa_shape -from torch.fx import Node - - -@register_node_visitor -class FullVisitor(NodeVisitor): - target = "aten.full.default" - - def __init__(self, *args): - super().__init__(*args) - - def define_node( - self, - node: Node, - tosa_graph: ts.TosaSerializer, - inputs: List[TosaArg], - output: TosaArg, - ) -> None: - - shape = tosa_shape(inputs[0].special, output.dim_order) - - value = inputs[1].number - - if output.dtype == ts.DType.INT8: - fill_dtype = np.int8 - else: - fill_dtype = np.float32 # type: ignore[assignment] - data = np.full(shape, value, dtype=fill_dtype) - - tosa_graph.addConst(shape, output.dtype, data, node.name + "full-const") - tosa_graph.addOperator( - ts.TosaOp.Op.IDENTITY, [node.name + "full-const"], [output.name] - ) From 8b9662c70bfa593becfe044cd6fdb8668aff07ce Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Per=20=C3=85strand?= Date: Mon, 14 Apr 2025 15:16:45 +0200 Subject: [PATCH 117/244] Arm backend: Add support for TOSA 1.0 serializer (#10135) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Adapt serialization and TOSA graph handling to be able to handle 1.0. Also install TOSA pip package for 1.0 alongside 0.80. ### Test plan Validate that 0.80 TOSA version test still work with the 1.0 package installed. Signed-off-by: Per Åstrand --- backends/arm/_passes/arm_pass_manager.py | 10 +++ .../operator_support/convolution_support.py | 2 + .../arm/operator_support/minmax_support.py | 1 + .../arm/operator_support/pool_2d_support.py | 4 ++ .../operator_support/reduce_sum_support.py | 2 + .../operator_support/right_shift_support.py | 2 + .../operator_support/slice_copy_support.py | 2 + .../arm/operator_support/to_copy_support.py | 2 + .../tosa_supported_operators.py | 2 + backends/arm/operators/node_visitor.py | 16 +++-- backends/arm/process_node.py | 31 ++++++--- .../arm/scripts/install_reference_model.sh | 5 +- backends/arm/test/runner_utils.py | 64 +++++++++++-------- backends/arm/tosa_backend.py | 17 ++++- backends/arm/tosa_specification.py | 2 +- 15 files changed, 118 insertions(+), 44 deletions(-) diff --git a/backends/arm/_passes/arm_pass_manager.py b/backends/arm/_passes/arm_pass_manager.py index 261ee045790..dd9f8b7bed0 100644 --- a/backends/arm/_passes/arm_pass_manager.py +++ b/backends/arm/_passes/arm_pass_manager.py @@ -166,12 +166,22 @@ def _tosa_080_MI_pipeline(self, exported_program: ExportedProgram) -> GraphModul return self._transform(exported_program.graph_module) + def _tosa_1_0_int_quantized_pipeline(self, exported_program: ExportedProgram): + return self._tosa_080_BI_pipeline(exported_program) + + def _tosa_1_0_fp_pipeline(self, exported_program: ExportedProgram): + return self._tosa_080_MI_pipeline(exported_program) + def transform_to_backend_pipeline(self, exported_program: ExportedProgram): """Apply passes before transforming program to backend""" if self.tosa_spec == TosaSpecification.create_from_string("TOSA-0.80.0+BI"): return self._tosa_080_BI_pipeline(exported_program) elif self.tosa_spec == TosaSpecification.create_from_string("TOSA-0.80.0+MI"): return self._tosa_080_MI_pipeline(exported_program) + elif self.tosa_spec == TosaSpecification.create_from_string("TOSA-1.0+FP"): + return self._tosa_1_0_fp_pipeline(exported_program) + elif self.tosa_spec == TosaSpecification.create_from_string("TOSA-1.0+INT"): + return self._tosa_1_0_int_quantized_pipeline(exported_program) else: raise NotImplementedError( f"No pass pipeline implemented for {self.tosa_spec=}" diff --git a/backends/arm/operator_support/convolution_support.py b/backends/arm/operator_support/convolution_support.py index 9e13babe23a..75899eb7425 100644 --- a/backends/arm/operator_support/convolution_support.py +++ b/backends/arm/operator_support/convolution_support.py @@ -22,6 +22,8 @@ class ConvolutionSupported(SupportedTOSAOperatorCheck): tosa_specs = [ TosaSpecification.create_from_string("TOSA-0.80+BI"), TosaSpecification.create_from_string("TOSA-0.80+MI"), + TosaSpecification.create_from_string("TOSA-1.0+INT"), + TosaSpecification.create_from_string("TOSA-1.0+FP"), ] def is_node_tosa_supported(self, node: fx.Node, tosa_spec: TosaSpecification): diff --git a/backends/arm/operator_support/minmax_support.py b/backends/arm/operator_support/minmax_support.py index bdff368a5ce..86b949082eb 100644 --- a/backends/arm/operator_support/minmax_support.py +++ b/backends/arm/operator_support/minmax_support.py @@ -22,6 +22,7 @@ class MinMaxSupported(SupportedTOSAOperatorCheck): # TODO : "MLETORCH-718 : Quantization of indices in arm_quantizer" tosa_specs = [ TosaSpecification.create_from_string("TOSA-0.80+MI"), + TosaSpecification.create_from_string("TOSA-1.0+FP"), ] def is_node_tosa_supported(self, node: fx.Node, tosa_spec: TosaSpecification): diff --git a/backends/arm/operator_support/pool_2d_support.py b/backends/arm/operator_support/pool_2d_support.py index 8291ede8ad9..750fab2730d 100644 --- a/backends/arm/operator_support/pool_2d_support.py +++ b/backends/arm/operator_support/pool_2d_support.py @@ -41,6 +41,8 @@ class AvgPool2dSupported(SupportedTOSAOperatorCheck): tosa_specs = [ TosaSpecification.create_from_string("TOSA-0.80+BI"), TosaSpecification.create_from_string("TOSA-0.80+MI"), + TosaSpecification.create_from_string("TOSA-1.0+INT"), + TosaSpecification.create_from_string("TOSA-1.0+FP"), ] def is_node_tosa_supported(self, node: fx.Node, tosa_spec: TosaSpecification): @@ -94,6 +96,8 @@ class MaxPool2dSupported(SupportedTOSAOperatorCheck): tosa_specs = [ TosaSpecification.create_from_string("TOSA-0.80+BI"), TosaSpecification.create_from_string("TOSA-0.80+MI"), + TosaSpecification.create_from_string("TOSA-1.0+INT"), + TosaSpecification.create_from_string("TOSA-1.0+FP"), ] def is_node_tosa_supported(self, node: fx.Node, tosa_spec: TosaSpecification): diff --git a/backends/arm/operator_support/reduce_sum_support.py b/backends/arm/operator_support/reduce_sum_support.py index 37a71d7264c..a50bcbceab7 100644 --- a/backends/arm/operator_support/reduce_sum_support.py +++ b/backends/arm/operator_support/reduce_sum_support.py @@ -21,6 +21,8 @@ class SumSupported(SupportedTOSAOperatorCheck): tosa_specs = [ TosaSpecification.create_from_string("TOSA-0.80+BI"), TosaSpecification.create_from_string("TOSA-0.80+MI"), + TosaSpecification.create_from_string("TOSA-1.0+INT"), + TosaSpecification.create_from_string("TOSA-1.0+FP"), ] def is_node_tosa_supported(self, node: fx.Node, tosa_spec: TosaSpecification): diff --git a/backends/arm/operator_support/right_shift_support.py b/backends/arm/operator_support/right_shift_support.py index 212b877a59f..49976b2346f 100644 --- a/backends/arm/operator_support/right_shift_support.py +++ b/backends/arm/operator_support/right_shift_support.py @@ -29,6 +29,8 @@ class RightShiftSupported(SupportedTOSAOperatorCheck): tosa_specs = [ TosaSpecification.create_from_string("TOSA-0.80+BI"), TosaSpecification.create_from_string("TOSA-0.80+MI"), + TosaSpecification.create_from_string("TOSA-1.0+INT"), + TosaSpecification.create_from_string("TOSA-1.0+FP"), ] def is_node_tosa_supported(self, node: fx.Node, tosa_spec: TosaSpecification): diff --git a/backends/arm/operator_support/slice_copy_support.py b/backends/arm/operator_support/slice_copy_support.py index 477b3bfae4a..ea18c408149 100644 --- a/backends/arm/operator_support/slice_copy_support.py +++ b/backends/arm/operator_support/slice_copy_support.py @@ -25,6 +25,8 @@ class SliceCopySupported(SupportedTOSAOperatorCheck): tosa_specs = [ TosaSpecification.create_from_string("TOSA-0.80+BI"), TosaSpecification.create_from_string("TOSA-0.80+MI"), + TosaSpecification.create_from_string("TOSA-1.0+INT"), + TosaSpecification.create_from_string("TOSA-1.0+FP"), ] def is_node_tosa_supported(self, node: fx.Node, tosa_spec: TosaSpecification) -> bool: # type: ignore[override, misc] diff --git a/backends/arm/operator_support/to_copy_support.py b/backends/arm/operator_support/to_copy_support.py index 7926b3dc053..aa0be8cfcd0 100644 --- a/backends/arm/operator_support/to_copy_support.py +++ b/backends/arm/operator_support/to_copy_support.py @@ -30,6 +30,8 @@ class ToCopySupported(SupportedTOSAOperatorCheck): tosa_specs = [ TosaSpecification.create_from_string("TOSA-0.80+BI"), TosaSpecification.create_from_string("TOSA-0.80+MI"), + TosaSpecification.create_from_string("TOSA-1.0+INT"), + TosaSpecification.create_from_string("TOSA-1.0+FP"), ] SupportedTypeDict = dict[torch.dtype, list[torch.dtype]] diff --git a/backends/arm/operator_support/tosa_supported_operators.py b/backends/arm/operator_support/tosa_supported_operators.py index f84bde7fadc..0f500bd759e 100644 --- a/backends/arm/operator_support/tosa_supported_operators.py +++ b/backends/arm/operator_support/tosa_supported_operators.py @@ -66,6 +66,8 @@ def is_node_tosa_supported( _tosa_spec_support: dict[TosaSpecification, list[Type[SupportedTOSAOperatorCheck]]] = { TosaSpecification.create_from_string("TOSA-0.80+BI"): [], TosaSpecification.create_from_string("TOSA-0.80+MI"): [], + TosaSpecification.create_from_string("TOSA-1.0+INT"): [], + TosaSpecification.create_from_string("TOSA-1.0+FP"): [], } diff --git a/backends/arm/operators/node_visitor.py b/backends/arm/operators/node_visitor.py index 72fb58f582c..5056c5f7f54 100644 --- a/backends/arm/operators/node_visitor.py +++ b/backends/arm/operators/node_visitor.py @@ -5,11 +5,10 @@ # pyre-unsafe -from typing import Dict, List +from typing import Any, Dict, List import torch -import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.tosa_mapping import TosaArg from executorch.backends.arm.tosa_specification import TosaSpecification from torch.export import ExportedProgram @@ -25,11 +24,18 @@ class NodeVisitor: # a specific TOSA version. # When all node_visitors has been refactored to target a specific # version, this list should be removed. - tosa_specs = [ + tosa_specs_1_00 = [ + TosaSpecification.create_from_string("TOSA-1.0+INT"), + TosaSpecification.create_from_string("TOSA-1.0+FP"), + ] + + tosa_specs_0_80 = [ TosaSpecification.create_from_string("TOSA-0.80+BI"), TosaSpecification.create_from_string("TOSA-0.80+MI"), ] + tosa_specs = tosa_specs_0_80 + tosa_specs_1_00 + def __init__(self, exported_program: ExportedProgram, tosa_spec: TosaSpecification): self._exported_program = exported_program self.tosa_spec = tosa_spec @@ -37,7 +43,7 @@ def __init__(self, exported_program: ExportedProgram, tosa_spec: TosaSpecificati def define_node( self, node: torch.fx.Node, - tosa_graph: ts.TosaSerializer, + tosa_graph: Any, inputs: List[TosaArg], output: TosaArg, ) -> None: @@ -48,6 +54,8 @@ def define_node( _node_visitor_dicts: Dict[TosaSpecification, Dict] = { TosaSpecification.create_from_string("TOSA-0.80+BI"): {}, TosaSpecification.create_from_string("TOSA-0.80+MI"): {}, + TosaSpecification.create_from_string("TOSA-1.0+INT"): {}, + TosaSpecification.create_from_string("TOSA-1.0+FP"): {}, } diff --git a/backends/arm/process_node.py b/backends/arm/process_node.py index 930c834f232..6692b75c892 100644 --- a/backends/arm/process_node.py +++ b/backends/arm/process_node.py @@ -5,15 +5,18 @@ # # pyre-unsafe -from typing import cast, Dict +from typing import Any, cast, Dict import numpy as np import torch import torch.fx -import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore from executorch.backends.arm.operators.node_visitor import NodeVisitor from executorch.backends.arm.tosa_mapping import TosaArg -from executorch.backends.arm.tosa_specification import TosaSpecification +from executorch.backends.arm.tosa_specification import ( + Tosa_0_80, + Tosa_1_00, + TosaSpecification, +) from executorch.backends.arm.tosa_utils import getNodeArgs, tosa_shape from torch._export.utils import ( get_buffer, @@ -28,7 +31,7 @@ def process_call_function( node: torch.fx.Node, - tosa_graph: ts.TosaSerializer, + tosa_graph: Any, node_visitors: Dict[str, NodeVisitor], tosa_spec: TosaSpecification, ): @@ -63,7 +66,7 @@ def process_call_function( def process_inputs( node: torch.fx.Node, - tosa_graph: ts.TosaSerializer, + tosa_graph: Any, tosa_spec: TosaSpecification, ): """Serialize an input node""" @@ -81,6 +84,14 @@ def process_inputs( f"Failed processing input placeholder: {node.name}. " "Is the original torch function supported?" ) from e + + if isinstance(tosa_spec, Tosa_0_80): + import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore + elif isinstance(tosa_spec, Tosa_1_00): + import serializer.tosa_serializer as ts + else: + raise ValueError(f"Unsupported TOSA spec: {tosa_spec}") + input_shape = tosa_arg.shape input_dim_order = tosa_arg.dim_order tensor = ts.TosaSerializerTensor( @@ -95,7 +106,7 @@ def process_inputs( def process_inputs_to_parameters( node: torch.fx.Node, - tosa_graph: ts.TosaSerializer, + tosa_graph: Any, edge_program: ExportedProgram, tosa_spec: TosaSpecification, ): @@ -124,7 +135,7 @@ def process_inputs_to_parameters( def process_inputs_to_buffers( node: torch.fx.Node, - tosa_graph: ts.TosaSerializer, + tosa_graph: Any, edge_program: ExportedProgram, ): """Serialize quantized weights""" @@ -152,7 +163,7 @@ def process_inputs_to_buffers( def process_inputs_to_lifted_tensor_constants( node: torch.fx.Node, - tosa_graph: ts.TosaSerializer, + tosa_graph: Any, edge_program: ExportedProgram, ): try: @@ -172,7 +183,7 @@ def process_inputs_to_lifted_tensor_constants( def process_placeholder( node: torch.fx.Node, - tosa_graph: ts.TosaSerializer, + tosa_graph: Any, edge_program: ExportedProgram, tosa_spec: TosaSpecification, ): @@ -198,7 +209,7 @@ def process_placeholder( def process_output( node: torch.fx.Node, - tosa_graph: ts.TosaSerializer, + tosa_graph: Any, ): for output in cast(tuple[torch.fx.Node, ...], node.args[0]): tosa_graph.addOutputTensor( diff --git a/backends/arm/scripts/install_reference_model.sh b/backends/arm/scripts/install_reference_model.sh index 796a1ed418e..0141b195a0d 100755 --- a/backends/arm/scripts/install_reference_model.sh +++ b/backends/arm/scripts/install_reference_model.sh @@ -13,7 +13,7 @@ tosa_reference_model_url="https://git.gitlab.arm.com/tosa/tosa-reference-model.g tosa_reference_model_0_80_branch="v0.80" tosa_reference_model_0_80_rev="70ed0b40fa831387e36abdb4f7fb9670a3464f5a" tosa_serialization_lib_0_80_rev="v0.80.1" -tosa_reference_model_1_0_rev="v1.0" +tosa_reference_model_1_0_rev="f9b4ceb850964be03a39e965ad7a0546dc6c57ae" script_dir=$(cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd) @@ -47,6 +47,9 @@ function setup_tosa_reference_model() { # Vela's flatbuffer requirement is expected to loosen, then remove this. MLETORCH-565 CMAKE_POLICY_VERSION_MINIMUM=3.5 pip install . --no-dependencies flatbuffers popd + + # Install the 1.0 branch from upstream + CMAKE_POLICY_VERSION_MINIMUM=3.5 BUILD_PYBIND=1 pip install "tosa-tools@git+${tosa_reference_model_url}@${tosa_reference_model_1_0_rev}" ml_dtypes==0.5.1 --no-dependencies flatbuffers } setup_tosa_reference_model $1 diff --git a/backends/arm/test/runner_utils.py b/backends/arm/test/runner_utils.py index 16f2e85e1d2..4481a9c7cc2 100644 --- a/backends/arm/test/runner_utils.py +++ b/backends/arm/test/runner_utils.py @@ -13,27 +13,24 @@ from pathlib import Path -from typing import cast, Dict, List, Literal, Optional, Tuple +from typing import Any, cast, Dict, List, Literal, Optional, Tuple import numpy as np import torch -try: - import tosa_tools.v0_80.tosa_reference_model as tosa_reference_model -except ImportError: - tosa_reference_model = None from executorch.backends.arm.arm_backend import get_tosa_spec, is_tosa - from executorch.backends.arm.test.conftest import is_option_enabled -from executorch.backends.arm.tosa_specification import TosaSpecification +from executorch.backends.arm.tosa_specification import ( + Tosa_0_80, + Tosa_1_00, + TosaSpecification, +) from executorch.exir import ExecutorchProgramManager, ExportedProgram from executorch.exir.backend.compile_spec_schema import CompileSpec from executorch.exir.lowered_backend_module import LoweredBackendModule -from packaging.version import Version from torch.fx.node import Node from torch.overrides import TorchFunctionMode -from tosa_tools.v0_80.tosa import TosaGraph logger = logging.getLogger(__name__) @@ -566,7 +563,7 @@ def arm_executor_runner_exists(target_board): def run_tosa_graph( - graph: TosaGraph, + graph: Any, tosa_version: TosaSpecification, inputs: list[torch.Tensor], ) -> list[torch.Tensor]: @@ -574,25 +571,38 @@ def run_tosa_graph( inputs_np = [input.numpy() for input in inputs] transpose_data_format(inputs_np, to="NHWC") - tosa_release = tosa_version.version - - if tosa_release > Version("0.80"): - logger.warning("The reference model is only tested for TOSA v0.80") - - # tosa_profile: 0 = Base Inference, 1 = Main Inference, 2 = Main Training. - tosa_profile = 1 if tosa_version.support_float() else 0 - debug_mode = "ALL" if logger.level <= logging.DEBUG else None - outputs_np, status = tosa_reference_model.run( - graph, - inputs_np, - verbosity=_tosa_refmodel_loglevel(logger.level), - tosa_profile=tosa_profile, - initialize_variable_tensor_from_numpy=1, # True - debug_mode=debug_mode, - ) + if isinstance(tosa_version, Tosa_0_80): + import tosa_tools.v0_80.tosa_reference_model as reference_model + + # tosa_profile: 0 = Base Inference, 1 = Main Inference, 2 = Main Training. + tosa_profile = 1 if tosa_version.support_float() else 0 + debug_mode = "ALL" if logger.level <= logging.DEBUG else None + outputs_np, status = reference_model.run( + graph, + inputs_np, + verbosity=_tosa_refmodel_loglevel(logger.level), + tosa_profile=tosa_profile, + initialize_variable_tensor_from_numpy=True, + debug_mode=debug_mode, + ) + elif isinstance(tosa_version, Tosa_1_00): + import tosa_reference_model as reference_model + + debug_mode = "ALL" if logger.level <= logging.DEBUG else None + outputs_np, status = reference_model.run( + graph, + inputs_np, + verbosity=_tosa_refmodel_loglevel(logger.level), + initialize_variable_tensor_from_numpy=True, + debug_mode=debug_mode, + ) + else: + raise ValueError( + f"Unknown TOSA specification: {tosa_version}. No refererence model available to run for this specification version" + ) assert ( - status == tosa_reference_model.GraphStatus.TOSA_VALID + status == reference_model.GraphStatus.TOSA_VALID ), "Non-valid TOSA given to reference model." transpose_data_format(outputs_np, to="NCHW") diff --git a/backends/arm/tosa_backend.py b/backends/arm/tosa_backend.py index adb4fba1fc8..ad16b0d84df 100644 --- a/backends/arm/tosa_backend.py +++ b/backends/arm/tosa_backend.py @@ -13,7 +13,7 @@ import logging from typing import cast, final, List -import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore +import executorch.backends.arm.tosa_specification as tosa_specification from executorch.backends.arm.arm_backend import get_tosa_spec from executorch.backends.arm.operators.node_visitor import get_node_visitors @@ -88,7 +88,22 @@ def preprocess( # noqa: C901 # Converted output for this subgraph, serializer needs path early as it emits # const data directly. Path created and data written only in debug builds. + if isinstance(tosa_spec, tosa_specification.Tosa_0_80): + import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore + elif isinstance(tosa_spec, tosa_specification.Tosa_1_00): + import serializer.tosa_serializer as ts # type: ignore + else: + raise RuntimeError( + f"Unknown TOSA version {tosa_spec}, no pip package installed to handle serialization to that version." + ) + tosa_graph = ts.TosaSerializer(artifact_path) + + assert ( + tosa_spec.version.major == ts.TOSA_VERSION_MAJOR + and tosa_spec.version.minor == ts.TOSA_VERSION_MINOR + ), f"TOSA serializer version ({ts.TOSA_VERSION_MAJOR}.{ts.TOSA_VERSION_MINOR}) doesn't match specification {tosa_spec}" + graph_module = ArmPassManager(tosa_spec).transform_to_backend_pipeline( # type: ignore exported_program=edge_program ) diff --git a/backends/arm/tosa_specification.py b/backends/arm/tosa_specification.py index 94c307d440c..640361e059c 100644 --- a/backends/arm/tosa_specification.py +++ b/backends/arm/tosa_specification.py @@ -142,7 +142,7 @@ class Tosa_1_00(TosaSpecification): available_profiles = ["INT", "FP"] valid_extensions = { - "INT": ["int16", "int4", "var", "cf"], + "INT": ["int16", "int4", "var", "cf", "u55"], "FP": ["bf16", "fp8e4m3", "fp8e5m2", "fft", "var", "cf"], } From 3849366c02f072a995b44536db4ab1f1abb6933e Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Mon, 14 Apr 2025 10:21:38 -0700 Subject: [PATCH 118/244] Fix paths in LLaMa project.pbxproj (#10132) --- .../apple_ios/LLaMA/LLaMA.xcodeproj/project.pbxproj | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/demo-apps/apple_ios/LLaMA/LLaMA.xcodeproj/project.pbxproj b/examples/demo-apps/apple_ios/LLaMA/LLaMA.xcodeproj/project.pbxproj index b4719e70e9a..efaf851da82 100644 --- a/examples/demo-apps/apple_ios/LLaMA/LLaMA.xcodeproj/project.pbxproj +++ b/examples/demo-apps/apple_ios/LLaMA/LLaMA.xcodeproj/project.pbxproj @@ -128,8 +128,8 @@ F292B07F2D88B0D200BE6839 /* result.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = result.h; sourceTree = ""; }; F292B0812D88B0D200BE6839 /* tiktoken.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = tiktoken.h; sourceTree = ""; }; F292B0832D88B0D200BE6839 /* tokenizer.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; path = tokenizer.h; sourceTree = ""; }; - F292B0FF2D88B20C00BE6839 /* llama_tiktoken.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; name = llama_tiktoken.h; path = /Users/larryliu/CLionProjects/executorch/examples/models/llama/tokenizer/llama_tiktoken.h; sourceTree = ""; }; - F292B1002D88B20C00BE6839 /* llama_tiktoken.cpp */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.cpp.cpp; name = llama_tiktoken.cpp; path = /Users/larryliu/CLionProjects/executorch/examples/models/llama/tokenizer/llama_tiktoken.cpp; sourceTree = ""; }; + F292B0FF2D88B20C00BE6839 /* llama_tiktoken.h */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.c.h; name = llama_tiktoken.h; path = ../../../../../executorch/examples/models/llama/tokenizer/llama_tiktoken.h; sourceTree = ""; }; + F292B1002D88B20C00BE6839 /* llama_tiktoken.cpp */ = {isa = PBXFileReference; lastKnownFileType = sourcecode.cpp.cpp; name = llama_tiktoken.cpp; path = ../../../../../executorch/examples/models/llama/tokenizer/llama_tiktoken.cpp; sourceTree = ""; }; /* End PBXFileReference section */ /* Begin PBXFrameworksBuildPhase section */ @@ -852,7 +852,7 @@ isa = XCRemoteSwiftPackageReference; repositoryURL = "https://github.com/pytorch/executorch"; requirement = { - branch = "swiftpm-0.7.0.20250401"; + branch = "swiftpm-0.6.0"; kind = branch; }; }; From fe695c93b624160f2ae234eaf15bff28c5376fdf Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Mon, 14 Apr 2025 10:24:06 -0700 Subject: [PATCH 119/244] Make llama model search case insensitive for benchmark app (#10133) --- extension/benchmark/apple/Benchmark/Tests/LLaMA/LLaMATests.mm | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/extension/benchmark/apple/Benchmark/Tests/LLaMA/LLaMATests.mm b/extension/benchmark/apple/Benchmark/Tests/LLaMA/LLaMATests.mm index 332c3986b0b..7cb871c08a7 100644 --- a/extension/benchmark/apple/Benchmark/Tests/LLaMA/LLaMATests.mm +++ b/extension/benchmark/apple/Benchmark/Tests/LLaMA/LLaMATests.mm @@ -60,7 +60,7 @@ @implementation LLaMATests + (NSDictionary *)predicates { return @{ @"model" : ^BOOL(NSString *filename){ - return [filename hasSuffix:@".pte"] && [filename containsString:@"llama"]; + return [filename hasSuffix:@".pte"] && [filename.lowercaseString containsString:@"llama"]; }, @"tokenizer" : ^BOOL(NSString *filename) { return [filename isEqual:@"tokenizer.bin"] || [filename isEqual:@"tokenizer.model"]; From bf1001157d825b9a29ae4de44202e37037dd5fb1 Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Mon, 14 Apr 2025 11:04:42 -0700 Subject: [PATCH 120/244] Clone submodules recursively in install_executorch.py (#10131) Somehow we still don't clone submodules recursively for some submodules, like tokenizers --- install_executorch.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/install_executorch.py b/install_executorch.py index 1d3fe8af1fb..4c7b51ef239 100644 --- a/install_executorch.py +++ b/install_executorch.py @@ -120,8 +120,10 @@ def check_folder(folder: str, file: str) -> bool: if missing_submodules: logger.warning("Some required submodules are missing. Updating submodules...") try: - subprocess.check_call(["git", "submodule", "sync"]) - subprocess.check_call(["git", "submodule", "update", "--init"]) + subprocess.check_call(["git", "submodule", "sync", "--recursive"]) + subprocess.check_call( + ["git", "submodule", "update", "--init", "--recursive"] + ) except subprocess.CalledProcessError as e: logger.error(f"Error updating submodules: {e}") exit(1) @@ -130,13 +132,10 @@ def check_folder(folder: str, file: str) -> bool: for path, file in missing_submodules.items(): if not check_folder(path, file): logger.error(f"{file} not found in {path}.") - logger.error("Please run `git submodule update --init`.") + logger.error( + "Submodule update failed. Please run `git submodule update --init --recursive` manually." + ) exit(1) - # Go into tokenizers submodule and install its submodules - tokenizers_path = get_required_submodule_paths().get("tokenizers", None) - if tokenizers_path: - with pushd(tokenizers_path): - subprocess.check_call(["git", "submodule", "update", "--init"]) logger.info("All required submodules are present.") From 64a362a6b615ffa16546172d1eb6ddf632828019 Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Mon, 14 Apr 2025 11:29:16 -0700 Subject: [PATCH 121/244] LLM custom ops tutorial should direct to general custom ops (#10139) Instead of having two pages using the same custom example, consolidate into one. Also move Source-to-source transformation to kernel-library-custom-aten-kernel.md --- .../kernel-library-custom-aten-kernel.md | 20 ++++ docs/source/llm/getting-started.md | 92 +------------------ 2 files changed, 23 insertions(+), 89 deletions(-) diff --git a/docs/source/kernel-library-custom-aten-kernel.md b/docs/source/kernel-library-custom-aten-kernel.md index 838b0f69d3b..11aaa3b1e99 100644 --- a/docs/source/kernel-library-custom-aten-kernel.md +++ b/docs/source/kernel-library-custom-aten-kernel.md @@ -299,6 +299,26 @@ torch.ops.load_library("libcustom_linear.so/dylib") op = torch.ops.myop.custom_linear.default ``` +#### Using a Custom Operator in a Model + +The custom operator can explicitly used in the PyTorch model, or you can write a transformation to replace instances of a core operator with the custom variant. For this example, you could find +all instances of `torch.nn.Linear` and replace them with `CustomLinear`. + +```python +def replace_linear_with_custom_linear(module): + for name, child in module.named_children(): + if isinstance(child, nn.Linear): + setattr( + module, + name, + CustomLinear(child.in_features, child.out_features, child.bias), + ) + else: + replace_linear_with_custom_linear(child) +``` + +The remaining steps are the same as the normal flow. Now you can run this module in eager mode as well as export to ExecuTorch. + ### Custom Ops API Best Practices Given that we have 2 kernel registration APIs for custom ops, which API should we use? Here are some pros and cons for each API: diff --git a/docs/source/llm/getting-started.md b/docs/source/llm/getting-started.md index f19982660ae..25eaa9339ef 100644 --- a/docs/source/llm/getting-started.md +++ b/docs/source/llm/getting-started.md @@ -855,99 +855,13 @@ With the ExecuTorch custom operator APIs, custom operator and kernel authors can There are three steps to use custom kernels in ExecuTorch: -1. Write the custom kernel using ExecuTorch types. -2. Compile and link the custom kernel to both AOT Python environment as well as the runtime binary. -3. Source-to-source transformation to swap an operator with a custom op. - -### Writing a Custom Kernel - -Define your custom operator schema for both functional variant (used in AOT compilation) and out variant (used in ExecuTorch runtime). The schema needs to follow PyTorch ATen convention (see [native_functions.yaml](https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/native_functions.yaml)). - -``` -custom_linear(Tensor weight, Tensor input, Tensor(?) bias) -> Tensor - -custom_linear.out(Tensor weight, Tensor input, Tensor(?) bias, *, Tensor(a!) out) -> Tensor(a!) -``` - -Write your custom kernel according to the schema defined above. Use the `EXECUTORCH_LIBRARY` macro to make the kernel available to the ExecuTorch runtime. - -```cpp -// custom_linear.h / custom_linear.cpp -#include - -Tensor& custom_linear_out(const Tensor& weight, const Tensor& input, optional bias, Tensor& out) { - // calculation - return out; -} - -// Register as myop::custom_linear.out -EXECUTORCH_LIBRARY(myop, "custom_linear.out", custom_linear_out); -``` - -To make this operator available in PyTorch, you can define a wrapper around the ExecuTorch custom kernel. Note that the ExecuTorch -implementation uses ExecuTorch tensor types, while the PyTorch wrapper uses ATen tensors. - -```cpp -// custom_linear_pytorch.cpp - -#include "custom_linear.h" -#include - -at::Tensor custom_linear(const at::Tensor& weight, const at::Tensor& input, std::optional bias) { - - // initialize out - at::Tensor out = at::empty({weight.size(1), input.size(1)}); - - // wrap kernel in custom_linear.cpp into ATen kernel - WRAP_TO_ATEN(custom_linear_out, 3)(weight, input, bias, out); - - return out; -} - -// Register the operator with PyTorch. -TORCH_LIBRARY(myop, m) { - m.def("custom_linear(Tensor weight, Tensor input, Tensor(?) bias) -> Tensor", custom_linear); - m.def("custom_linear.out(Tensor weight, Tensor input, Tensor(?) bias, *, Tensor(a!) out) -> Tensor(a!)", WRAP_TO_ATEN(custom_linear_out, 3)); -} -``` - -### Compile and Link the Custom Kernel - -To make it available to the ExecuTorch runtime, compile custom_linear.h/cpp into the binary target. You can also build the kernel as a dynamically loaded library (.so or .dylib) and link it as well. - -To make it available to PyTorch, package custom_linear.h, custom_linear.cpp and custom_linear_pytorch.cpp into a dynamically loaded library (.so or .dylib) and load it into the python environment. -This is needed to make PyTorch aware of the custom operator at the time of export. - -```python -import torch -torch.ops.load_library("libcustom_linear.so") -``` - -Once loaded, you can use the custom operator in PyTorch code. +1. [Write the custom kernel](../kernel-library-custom-aten-kernel.md#c-api-for-custom-ops) using ExecuTorch types. +2. [Compile and link the custom kernel](../kernel-library-custom-aten-kernel.md#compile-and-link-the-custom-kernel) to both AOT Python environment as well as the runtime binary. +3. [Source-to-source transformation](../kernel-library-custom-aten-kernel.md#using-a-custom-operator-in-a-model) to swap an operator with a custom op. For more information, see [PyTorch Custom Operators](https://pytorch.org/tutorials/advanced/torch_script_custom_ops.html) and and [ExecuTorch Kernel Registration](../kernel-library-custom-aten-kernel.md). -### Using a Custom Operator in a Model - -The custom operator can explicitly used in the PyTorch model, or you can write a transformation to replace instances of a core operator with the custom variant. For this example, you could find -all instances of `torch.nn.Linear` and replace them with `CustomLinear`. - -```python -def replace_linear_with_custom_linear(module): - for name, child in module.named_children(): - if isinstance(child, nn.Linear): - setattr( - module, - name, - CustomLinear(child.in_features, child.out_features, child.bias), - ) - else: - replace_linear_with_custom_linear(child) -``` - -The remaining steps are the same as the normal flow. Now you can run this module in eager mode as well as export to ExecuTorch. - ## How to Build Mobile Apps See the instructions for building and running LLMs using ExecuTorch on iOS and Android. From 1c6264fb237a8a351f3184a8da50e0fcdc12b1c6 Mon Sep 17 00:00:00 2001 From: Mingyuan Zhong Date: Mon, 14 Apr 2025 11:57:48 -0700 Subject: [PATCH 122/244] [#9971] Gracefully error out in ETDump for set_debug_buffer (#10130) ## Summary Following https://github.com/pytorch/executorch/issues/9971 Start with a relatively simple method. Update set_debug_bugffer. Use ET_CHECK_OR_RETURN_ERROR instead. If unable to pass error, update the return type as Result Note: Not sure if InvalidArgument is a good one ( maybe memory not allocated?). Please feel free to comment. ##Test test/run_oss_cpp_tests.sh @Gasoonjia @zhenyan-zhang-meta Thanks --- devtools/etdump/etdump_flatcc.cpp | 6 ++++-- devtools/etdump/etdump_flatcc.h | 2 +- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/devtools/etdump/etdump_flatcc.cpp b/devtools/etdump/etdump_flatcc.cpp index 8bff9d7ada4..4b5da78550e 100644 --- a/devtools/etdump/etdump_flatcc.cpp +++ b/devtools/etdump/etdump_flatcc.cpp @@ -536,15 +536,17 @@ ETDumpResult ETDumpGen::get_etdump_data() { return result; } -void ETDumpGen::set_debug_buffer(Span buffer) { +Result ETDumpGen::set_debug_buffer(Span buffer) { Result bds_ret = BufferDataSink::create(buffer); - ET_CHECK_MSG( + ET_CHECK_OR_RETURN_ERROR( bds_ret.ok(), + InvalidArgument, "Failed to create data sink from debug buffer with error 0x%" PRIx32, static_cast(bds_ret.error())); buffer_data_sink_ = std::move(bds_ret.get()); data_sink_ = &buffer_data_sink_; + return true; } void ETDumpGen::set_data_sink(DataSinkBase* data_sink) { diff --git a/devtools/etdump/etdump_flatcc.h b/devtools/etdump/etdump_flatcc.h index 3a186b5bbc3..ea0c1cb653d 100644 --- a/devtools/etdump/etdump_flatcc.h +++ b/devtools/etdump/etdump_flatcc.h @@ -154,7 +154,7 @@ class ETDumpGen : public ::executorch::runtime::EventTracer { virtual void set_delegation_intermediate_output_filter( EventTracerFilterBase* event_tracer_filter) override; - void set_debug_buffer(::executorch::runtime::Span buffer); + Result set_debug_buffer(::executorch::runtime::Span buffer); void set_data_sink(DataSinkBase* data_sink); ETDumpResult get_etdump_data(); size_t get_num_blocks(); From 1ea5aff190728f36f47a8cc46fb2444a7931955d Mon Sep 17 00:00:00 2001 From: "Mengtao (Martin) Yuan" Date: Mon, 14 Apr 2025 12:21:35 -0700 Subject: [PATCH 123/244] Mimi: sqnr and test without streaming Differential Revision: D72978040 Pull Request resolved: https://github.com/pytorch/executorch/pull/10004 --- examples/models/moshi/mimi/test_mimi.py | 24 ++++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/examples/models/moshi/mimi/test_mimi.py b/examples/models/moshi/mimi/test_mimi.py index 69859fa39bc..7e2cfb14c49 100644 --- a/examples/models/moshi/mimi/test_mimi.py +++ b/examples/models/moshi/mimi/test_mimi.py @@ -135,16 +135,28 @@ def test_streaming_encoding_decoding(self): all_codes_th = torch.cat(all_codes, dim=-1) + pcm_ref = self.mimi.decode(all_codes_th) + all_pcms = [] + for i in range(all_codes_th.shape[-1]): + codes = all_codes_th[..., i : i + 1] + pcm = self.mimi.decode(codes) + all_pcms.append(pcm) + all_pcms = torch.cat(all_pcms, dim=-1) + sqnr = compute_sqnr(pcm_ref, all_pcms) + print(f"sqnr = {sqnr} dB") + self.assertTrue(sqnr > 4) + + all_pcms_streaming = [] with self.mimi.streaming(1): for i in range(all_codes_th.shape[-1]): codes = all_codes_th[..., i : i + 1] - pcm = self.mimi.decode(codes) - all_pcms.append(pcm) - all_pcms = torch.cat(all_pcms, dim=-1) - - pcm_ref = self.mimi.decode(all_codes_th) - self.assertTrue(torch.allclose(pcm_ref, all_pcms, atol=1e-5)) + pcm_streaming = self.mimi.decode(codes) + all_pcms_streaming.append(pcm_streaming) + all_pcms_streaming = torch.cat(all_pcms_streaming, dim=-1) + sqnr_streaming = compute_sqnr(pcm_ref, all_pcms_streaming) + print(f"sqnr_streaming = {sqnr_streaming} dB") + self.assertTrue(sqnr_streaming > 100) def test_exported_encoding(self): """Ensure exported encoding model is consistent with reference output.""" From dd5581c1f391457dabf218f5436ecc89d882ca16 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Luk=C3=A1=C5=A1=20Sztefek?= Date: Mon, 14 Apr 2025 21:26:21 +0200 Subject: [PATCH 124/244] NXP backend: Add NeutronQuantizer (#9876) Unrelated test failure --- backends/nxp/quantizer/neutron_quantizer.py | 205 ++++++++++++ backends/nxp/quantizer/patterns.py | 342 ++++++++++++++++++++ backends/nxp/quantizer/utils.py | 151 +++++++++ backends/nxp/tests/models.py | 238 ++++++++++++++ backends/nxp/tests/test_quantizer.py | 273 ++++++++++++++++ 5 files changed, 1209 insertions(+) create mode 100644 backends/nxp/quantizer/neutron_quantizer.py create mode 100644 backends/nxp/quantizer/patterns.py create mode 100644 backends/nxp/quantizer/utils.py create mode 100644 backends/nxp/tests/models.py create mode 100644 backends/nxp/tests/test_quantizer.py diff --git a/backends/nxp/quantizer/neutron_quantizer.py b/backends/nxp/quantizer/neutron_quantizer.py new file mode 100644 index 00000000000..eff7f513cb9 --- /dev/null +++ b/backends/nxp/quantizer/neutron_quantizer.py @@ -0,0 +1,205 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Copyright 2024-2025 NXP +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from typing import List, Optional, Tuple, Union + +import torch + +from executorch.backends.nxp.quantizer.patterns import ( + AddmmPattern, + AvgPoolPattern, + Conv1dPattern, + Conv2dPattern, + LinearPattern, + MaxPoolPattern, + PadPattern, + PermutePattern, + QuantizationPattern, + ReluInPlacePattern, + ReluPattern, + ReshapePattern, + SoftMaxPattern, +) +from executorch.backends.nxp.quantizer.utils import ( + find_sequential_partitions_aten, + is_annotated, + no_outside_users, +) +from executorch.backends.xnnpack.quantizer.xnnpack_quantizer_utils import ( + OperatorConfig, + QuantizationAnnotation, + QuantizationConfig, + QuantizationSpec, +) +from torch import fx +from torch.ao.quantization.observer import HistogramObserver, MinMaxObserver +from torch.ao.quantization.quantizer import DerivedQuantizationSpec, Quantizer +from torch.ao.quantization.quantizer.composable_quantizer import ComposableQuantizer + + +class NeutronAtenQuantizer(Quantizer): + def __init__( + self, pattern: QuantizationPattern, quantization_config: QuantizationConfig + ) -> None: + super().__init__() + self.pattern = pattern + self.quantization_config = quantization_config + + def annotate(self, model: torch.fx.GraphModule) -> torch.fx.GraphModule: + fused_partitions = find_sequential_partitions_aten( + model, + self.pattern.partition_types(), + ) + + input_act_qspec = self.quantization_config.input_activation + weight_qspec = self.quantization_config.weight + bias_qspec = self.quantization_config.bias + output_act_qspec = self.quantization_config.output_activation + + for fused_partition in fused_partitions: + if not no_outside_users(fused_partition): + continue + + anchors = self.pattern.get_anchors(model, fused_partition) + if not anchors or anchors.empty: + continue + if is_annotated( + [ + x[0] + for x in anchors.inputs + + anchors.weights + + anchors.biases + + anchors.output + ] + ): + continue + + for output, *custom_spec in anchors.output: + # pyre-ignore[16]: no attribute + output.meta["quantization_annotation"] = QuantizationAnnotation( + # pyre-ignore[6]: incompatible parameter type + output_qspec=(custom_spec[0] if custom_spec else output_act_qspec), + _annotated=True, + ) + + def annotate_inputs( + inputs: Union[ + List[Tuple[fx.Node, int]], + List[Tuple[fx.Node, int, DerivedQuantizationSpec],], + ], + spec: Optional[QuantizationSpec], + ) -> None: + for node, idx, *custom_spec in inputs: + # pyre-ignore[16]: no attribute + annotation = node.meta.get( + "quantization_annotation", + QuantizationAnnotation(_annotated=True), + ) + arg = ( + # pyre-ignore[16]: no attribute + node.args[idx] + if isinstance(idx, int) + # pyre-ignore[16]: no attribute + else node.args[idx[0]][idx[1]] + ) + annotation.input_qspec_map[arg] = ( + custom_spec[0] if custom_spec else spec + ) + # pyre-ignore[16]: no attribute + node.meta["quantization_annotation"] = annotation + + def annotate_weights_or_biases( + weights_or_biases: List[Tuple[fx.Node, int]], + spec: Optional[QuantizationSpec], + ) -> None: + for node, idx, *custom_spec in weights_or_biases: + annotation = node.meta.get( + "quantization_annotation", + QuantizationAnnotation(_annotated=True), + ) + annotation.input_qspec_map[node.args[idx]] = ( + custom_spec[0] if custom_spec else spec + ) + node.meta["quantization_annotation"] = annotation + + # pyre-ignore[6]: incompatible parameter type + annotate_inputs(anchors.inputs, input_act_qspec) + annotate_weights_or_biases(anchors.weights, weight_qspec) + # pyre-ignore[6]: incompatible parameter type + annotate_weights_or_biases(anchors.biases, bias_qspec) + return model + + def validate(self, model: fx.GraphModule) -> None: + pass + + @classmethod + def get_supported_operators(cls) -> List[OperatorConfig]: + return [] + + +# Quantization Specification used by Neutron NPU +act_qspec = QuantizationSpec( + dtype=torch.int8, + quant_min=-128, + quant_max=127, + qscheme=torch.per_tensor_affine, + is_dynamic=False, + observer_or_fake_quant_ctr=HistogramObserver.with_args(eps=2**-12), +) + +wgt_qspec = QuantizationSpec( + dtype=torch.int8, + quant_min=-127, + quant_max=127, + qscheme=torch.per_tensor_symmetric, + is_dynamic=False, + observer_or_fake_quant_ctr=MinMaxObserver, + ch_axis=0, +) + +wgt_fc_qspec = QuantizationSpec( + dtype=torch.int8, + quant_min=-127, + quant_max=127, + qscheme=torch.per_tensor_symmetric, + is_dynamic=False, + observer_or_fake_quant_ctr=MinMaxObserver, +) + +# Is set by the *PatternQuantizer directly. +bias_qspec = None + + +class NeutronQuantizer(ComposableQuantizer): + def __init__(self): + static_qconfig = QuantizationConfig( + act_qspec, + act_qspec, + wgt_qspec, + None, + ) + static_fc_qconfig = QuantizationConfig(act_qspec, act_qspec, wgt_fc_qspec, None) + super().__init__( + [ + NeutronAtenQuantizer(AddmmPattern(), static_fc_qconfig), + NeutronAtenQuantizer(Conv1dPattern(), static_qconfig), + NeutronAtenQuantizer(Conv2dPattern(), static_qconfig), + NeutronAtenQuantizer(LinearPattern(), static_fc_qconfig), + NeutronAtenQuantizer(MaxPoolPattern(), static_qconfig), + NeutronAtenQuantizer(SoftMaxPattern(), static_qconfig), + NeutronAtenQuantizer(ReshapePattern(), static_qconfig), + NeutronAtenQuantizer(PermutePattern(), static_qconfig), + NeutronAtenQuantizer(PadPattern(), static_qconfig), + NeutronAtenQuantizer(ReluPattern(), static_qconfig), + NeutronAtenQuantizer(ReluInPlacePattern(), static_qconfig), + NeutronAtenQuantizer(AvgPoolPattern(), static_qconfig), + ] + ) + + def transform_for_annotation( + self, model: torch.fx.GraphModule + ) -> torch.fx.GraphModule: + return model diff --git a/backends/nxp/quantizer/patterns.py b/backends/nxp/quantizer/patterns.py new file mode 100644 index 00000000000..6797447c50c --- /dev/null +++ b/backends/nxp/quantizer/patterns.py @@ -0,0 +1,342 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Copyright 2025 NXP +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from abc import ABC, abstractmethod +from dataclasses import dataclass, field +from typing import List, Optional, Tuple, Type, Union + +import torch + +from executorch.backends.nxp.quantizer.utils import get_bias_qparams +from torch import fx +from torch._ops import OpOverload +from torch.ao.quantization.quantizer import ( + DerivedQuantizationSpec, + FixedQParamsQuantizationSpec, + SharedQuantizationSpec, +) + + +@dataclass +class PartitionAnchors: + """ + All fields except output are lists of (node, args_index) pair, where node is from + the given partition and node.args[args_index] is an input to the partition. Assumes + a single output. + + Quantizer uses inputs, weights and biases for quantization annotation. The others + field contains tensor inputs that aren't quantized, and the literals fields contains + is used for other types of input values as well as handling default parameters. + """ + + # Inputs can share quantization parameters + inputs: List[ + Union[ + Tuple[fx.Node, Union[int, Tuple[int, int]]], + Tuple[ + fx.Node, + Union[int, Tuple[int, int]], + SharedQuantizationSpec, + ], + ] + ] = field(default_factory=list) + weights: List[Tuple[fx.Node, int]] = field(default_factory=list) + biases: List[ + Union[Tuple[fx.Node, int], Tuple[fx.Node, int, DerivedQuantizationSpec]] + ] = field(default_factory=list) + others: List[Tuple[fx.Node, int]] = field(default_factory=list) + literals: List[Tuple[fx.Node, int]] = field(default_factory=list) + output: List[Union[Tuple[fx.Node], Tuple[fx.Node, SharedQuantizationSpec]]] = field( + default_factory=list + ) + empty: bool = False + + +class QuantizationPattern(ABC): + @abstractmethod + def partition_types(self) -> list[OpOverload]: + """ + List of types to be passed to find_sequential_partitions_aten. + """ + pass + + @abstractmethod + def get_anchors( + self, gm: torch.fx.GraphModule, fused_partition: List[fx.GraphModule] + ) -> Optional[PartitionAnchors]: + pass + + +class SharedSpecPattern(QuantizationPattern): + """ + Quantization pattern for shared quantization. + + The quantization is derived from the previous node quantization and the input and output shares the same + quantization parameters (scale and zero-point). + """ + + def partition_types(self) -> List[Type[torch.nn.Module]]: + pass + + def get_anchors( + self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule] + ) -> PartitionAnchors | None: + node = fused_partition[0].nodes[-1] + assert len(fused_partition[0].input_nodes) == 1 + prev_node = fused_partition[0].input_nodes[0] + + # Previous node was not quantized => we are not able to share q-params + if "quantization_annotation" not in prev_node.meta: + return None + + qspec = SharedQuantizationSpec(prev_node) + + return PartitionAnchors( + inputs=[(node, 0)], + weights=[], + biases=[], + output=[ + (node, qspec), + ], + ) + + +class AddmmPattern(QuantizationPattern): + def partition_types(self) -> List[OpOverload]: + return [torch.ops.aten.addmm.default] + + def get_anchors( + self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule] + ) -> PartitionAnchors: + # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorBase.__ge... + addmm_node = fused_partition[0].nodes[-1] + + bias_qspec = DerivedQuantizationSpec( + derived_from=[ + (addmm_node.args[1], addmm_node), + (addmm_node.args[2], addmm_node), + ], + derive_qparams_fn=get_bias_qparams, + dtype=torch.int32, + quant_min=-(2**31), + quant_max=2**31 - 1, + qscheme=torch.per_tensor_affine, + ) + + return PartitionAnchors( + inputs=[(addmm_node, 1)], + weights=[(addmm_node, 2)], + biases=[(addmm_node, 0, bias_qspec)], + output=[(addmm_node,)], + ) + + +class AvgPoolPattern(SharedSpecPattern): + """ + Quantizer for AvgPool2D operator. + """ + + def partition_types(self): + return [torch.ops.aten.avg_pool2d.default] + + +class Conv1dPattern(QuantizationPattern): + def partition_types(self) -> List[OpOverload]: + return [torch.ops.aten.conv1d.default] + + def get_anchors( + self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule] + ) -> PartitionAnchors: + # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorBase.__ge... + conv1d_node = fused_partition[0].nodes[-1] + + bias_qspec = DerivedQuantizationSpec( + derived_from=[ + (conv1d_node.args[0], conv1d_node), + (conv1d_node.args[1], conv1d_node), + ], + derive_qparams_fn=get_bias_qparams, + dtype=torch.int32, + quant_min=-(2**31), + quant_max=2**31 - 1, + qscheme=torch.per_tensor_affine, + ) + + # Keep bias empty if not supplied + bias = [] + if len(conv1d_node.args) > 2 and conv1d_node.args[2] is not None: + bias = [(conv1d_node, 2, bias_qspec)] + + return PartitionAnchors( + inputs=[(conv1d_node, 0)], + weights=[(conv1d_node, 1)], + # pyre-fixme[6]: Incompatible parameter type + biases=bias, + output=[(conv1d_node,)], + ) + + +class Conv2dPattern(QuantizationPattern): + def partition_types(self) -> List[OpOverload]: + return [torch.ops.aten.conv2d.default] + + def get_anchors( + self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule] + ) -> PartitionAnchors: + # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorBase.__ge... + conv2d_node = fused_partition[0].nodes[-1] + + bias_qspec = DerivedQuantizationSpec( + derived_from=[ + (conv2d_node.args[0], conv2d_node), + (conv2d_node.args[1], conv2d_node), + ], + derive_qparams_fn=get_bias_qparams, + dtype=torch.int32, + quant_min=-(2**31), + quant_max=2**31 - 1, + qscheme=torch.per_tensor_affine, + ) + + # Keep bias empty if not supplied + bias = [] + if len(conv2d_node.args) > 2 and conv2d_node.args[2] is not None: + bias = [(conv2d_node, 2, bias_qspec)] + + return PartitionAnchors( + inputs=[(conv2d_node, 0)], + weights=[(conv2d_node, 1)], + # pyre-fixme[6]: Incompatible parameter type + biases=bias, + output=[(conv2d_node,)], + ) + + +class LinearPattern(QuantizationPattern): + def partition_types(self) -> List[OpOverload]: + return [torch.ops.aten.linear.default] + + def get_anchors( + self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule] + ) -> PartitionAnchors: + # pyre-fixme[29]: `Union[BoundMethod[typing.Callable(torch._C.TensorBase.__ge... + linear_node = fused_partition[0].nodes[-1] + + bias_qspec = DerivedQuantizationSpec( + derived_from=[ + (linear_node.args[0], linear_node), + (linear_node.args[1], linear_node), + ], + derive_qparams_fn=get_bias_qparams, + dtype=torch.int32, + quant_min=-(2**31), + quant_max=2**31 - 1, + qscheme=torch.per_tensor_affine, + ) + + # Keep bias empty if not supplied + bias = [] + if len(linear_node.args) > 2: + bias = [(linear_node, 2, bias_qspec)] + + return PartitionAnchors( + inputs=[(linear_node, 0)], + weights=[(linear_node, 1)], + # pyre-fixme[6]: Incompatible parameter type + biases=bias, + output=[(linear_node,)], + ) + + +class MaxPoolPattern(SharedSpecPattern): + """ + Quantizer for MaxPool2D operator. + """ + + def partition_types(self): + return [torch.ops.aten.max_pool2d.default] + + +class PadPattern(SharedSpecPattern): + """ + Quantizer for Pad operator. + """ + + def partition_types(self): + return [torch.ops.aten.pad.default] + + +class PermutePattern(SharedSpecPattern): + """ + Quantizer for Permute operator. + """ + + def partition_types(self): + return [torch.ops.aten.permute.default] + + +class ReluPattern(SharedSpecPattern): + """ + Quantizer for Relu operator. Shared quantization spec is selected, as ReLU usually follows computation layer. + """ + + def partition_types(self): + return [torch.ops.aten.relu.default] + + +class ReluInPlacePattern(SharedSpecPattern): + """ + Quantizer for Relu operator with param inplace=True. Shared quantization spec is selected, as ReLU usually + follows computation layer. + """ + + def partition_types(self): + return [torch.ops.aten.relu_.default] + + +class ReshapePattern(SharedSpecPattern): + """ + Quantizer for Reshape operator. + """ + + def partition_types(self): + return [torch.ops.aten.reshape.default] + + +class SoftMaxPattern(QuantizationPattern): + """ + Quantizer for Softmax operator. + + The quantization of Softmax output is fixed to scale 1/256, zero point -128, dtype int8. + """ + + def partition_types(self) -> List[OpOverload]: + return [torch.ops.aten.softmax.int] + + def get_anchors( + self, gm: fx.GraphModule, fused_partition: List[fx.GraphModule] + ) -> PartitionAnchors: + node = fused_partition[0].nodes[-1] + assert len(fused_partition[0].input_nodes) == 1 + + qspec = FixedQParamsQuantizationSpec( + dtype=torch.int8, + scale=1.0 / 256.0, + zero_point=-128, + quant_min=-128, + quant_max=127, + qscheme=torch.per_tensor_affine, + ) + + return PartitionAnchors( + inputs=[(node, 0)], + weights=[], + biases=[], + output=[ + (node, qspec), + ], + ) diff --git a/backends/nxp/quantizer/utils.py b/backends/nxp/quantizer/utils.py new file mode 100644 index 00000000000..1effcdff25a --- /dev/null +++ b/backends/nxp/quantizer/utils.py @@ -0,0 +1,151 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# Copyright 2024-2025 NXP +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +# pyre-unsafe + +import itertools +from collections import OrderedDict +from typing import Any, Dict, List, Tuple, Type + +import torch +from torch import fx +from torch._ops import OpOverload +from torch.ao.quantization import ObserverOrFakeQuantize +from torch.fx.passes.utils.source_matcher_utils import ( + check_subgraphs_connected, + SourcePartition, +) + + +def is_annotated(nodes: List[fx.Node]) -> bool: + annotated = False + for node in nodes: + annotated = annotated or ( + "quantization_annotation" in node.meta + and node.meta["quantization_annotation"]._annotated + ) + return annotated + + +def no_outside_users(fused_partition) -> bool: + """ + Checks if each partition other than the last does not have any outside users. + """ + for source_partition in fused_partition[:-1]: + if len(source_partition.output_nodes) != 1: + return False + if len(source_partition.output_nodes[0].users) != 1: + return False + return True + + +def get_bias_qparams( + obs_or_fqs: List[ObserverOrFakeQuantize], +) -> Tuple[torch.Tensor, torch.Tensor]: + act_scale, _ = obs_or_fqs[0].calculate_qparams() + weight_scale, _ = obs_or_fqs[1].calculate_qparams() + bias_scale = act_scale * weight_scale + bias_zero_point = torch.zeros_like(bias_scale, dtype=torch.int32) + return bias_scale, bias_zero_point + + +def get_aten_node_target_partitions( + graph: torch.fx.Graph, + wanted_original_aten_op: List[OpOverload], +): + """ + Args: + graph: The graph we want to partition + wanted_original_aten_op: List of original_aten ops (OpOverload) + + Returns: + Dictionary mapping aten ops that were given to a list of SourcePartitions + that correspond to the list of nodes that were decomposed from the given + aten ops. + """ + modules: Dict[Type, Dict[str, List[torch.fx.Node]]] = {} + + for node in graph.nodes: + # The metadata source_fn should contain a tuple of a unique name for the + # source, and the source function if the node is decomposed from a + # function, or the type of module if the node is decomposed from a leaf + # module + # TODO(matthiascremon): look into ways to avoid using source_fn_stack + if (source_fn_st := node.meta.get("source_fn_stack")) is None: + continue + + source_fn = source_fn_st[-1] + if node.target not in wanted_original_aten_op: + continue + + diff_modules = modules.setdefault(source_fn[1], {}) + partition = diff_modules.setdefault(node.name, []) + partition.append(node) + + def make_partition( + nodes: List[torch.fx.Node], module_type: Type + ) -> SourcePartition: + input_nodes = set() + output_nodes = set() + params = set() + for node in nodes: + for arg in node.args: + if isinstance(arg, torch.fx.Node) and arg not in nodes: + input_nodes.add(arg) + + if node.op == "get_attr": + params.add(node) + + for user in node.users.keys(): + if user not in nodes: + output_nodes.add(node) + + return SourcePartition( + nodes, + module_type, + list(input_nodes), + list(output_nodes), + list(params), # type: ignore[arg-type] + ) + + ret: Dict[Type[Any], List[SourcePartition]] = {} + + for k, v in modules.items(): + ret[k] = [make_partition(partition, k) for partition in v.values()] + + return ret + + +def _partitions_sequential(partitions: Tuple[SourcePartition]) -> bool: + prev_partition = None + for partition in partitions: + if prev_partition is not None and not check_subgraphs_connected( + prev_partition, partition + ): + return False + prev_partition = partition + return True + + +def find_sequential_partitions_aten( + gm: torch.fx.GraphModule, + partition_types: List[Any], +): + typed_partitions: OrderedDict[Any, List[SourcePartition]] = OrderedDict() + for partition_type in partition_types: + partitions = get_aten_node_target_partitions(gm.graph, [partition_type]) + typed_partitions[partition_type] = list( + itertools.chain.from_iterable(partitions.values()) + ) + + typed_partitions_list = list(typed_partitions.values()) + fusion_candidates = itertools.product(*typed_partitions_list) + fused_partitions = [] + for candidate in fusion_candidates: + if _partitions_sequential(candidate): + fused_partitions.append(candidate) + return fused_partitions diff --git a/backends/nxp/tests/models.py b/backends/nxp/tests/models.py new file mode 100644 index 00000000000..741e64a28a1 --- /dev/null +++ b/backends/nxp/tests/models.py @@ -0,0 +1,238 @@ +# Copyright 2024 NXP +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Collection, Union + +import torch + + +class Conv2dModule(torch.nn.Module): + def __init__( + self, + bias: bool = True, + dilation: Union[int, tuple[int, int]] = 1, + in_channels: int = 4, + kernel_size: Union[int, tuple[int, int]] = 3, + out_channels: int = 8, + padding: Union[str, int, Collection[int]] = 0, + stride: Union[int, tuple[int, int]] = 2, + ): + super().__init__() + + self.conv = torch.nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + bias=bias, + ) + + def forward(self, x): + return self.conv(x) + + +class Conv2dAndMaxPool2DModule(torch.nn.Module): + def __init__(self): + super().__init__() + + self.conv = torch.nn.Conv2d( + in_channels=8, out_channels=32, kernel_size=5, bias=True + ) + self.maxpool = torch.nn.MaxPool2d(kernel_size=2, stride=2) + + def forward(self, x): + x = self.conv(x) + return self.maxpool(x) + + +class Conv2dConstantPadNDModule(torch.nn.Module): + def __init__(self, paddings: Collection[int], constant: float | int | None = None): + super().__init__() + self.pad = ConstantPadNDModule(paddings, constant) + self.conv = Conv2dModule() + + def forward(self, x): + x = self.conv(x) + return self.pad(x) + + +class SoftmaxModule(torch.nn.Module): + def __init__(self, dim: int): + super().__init__() + + self.softmax = torch.nn.Softmax(dim=dim) + + def forward(self, x): + return self.softmax(x) + + +class SoftmaxConvModule(torch.nn.Module): + def __init__(self, dim: int): + super().__init__() + + self.conv = Conv2dModule() + self.softmax = SoftmaxModule(dim=dim) + + def forward(self, x): + x = self.conv(x) + return self.softmax(x) + + +class LinearModule(torch.nn.Module): + def __init__(self, bias: bool): + super().__init__() + self.linear = torch.nn.Linear(32, 16, bias=bias) + + def forward(self, x): + return self.linear(x) + + +class LinearSoftmaxModule(torch.nn.Module): + def __init__(self): + super().__init__() + + self.linear = torch.nn.Linear(12, 10) + self.softmax = torch.nn.Softmax(1) + + def forward(self, x): + x = self.linear(x) + x = self.softmax(x) + + return x + + +class ConvFCSoftmaxModule(torch.nn.Module): + def __init__(self): + super().__init__() + + self.conv = torch.nn.Conv2d(4, 64, 2, bias=False) + self.fc = torch.nn.Linear(1024, 10) + self.softmax = torch.nn.Softmax(1) + + def forward(self, x): + x = self.conv(x) + x = torch.reshape(x, (-1, 1024)) + x = self.fc(x) + x = self.softmax(x) + + return x + + +class ConstantPadNDModule(torch.nn.Module): + def __init__(self, paddings: Collection[int], constant: float | int | None = None): + super().__init__() + self.paddings = paddings + self.constant = constant + + def forward(self, x): + if self.constant is None: + return torch.nn.functional.pad(x, tuple(self.paddings), "constant") + else: + return torch.nn.functional.pad( + x, tuple(self.paddings), "constant", self.constant + ) + + +class ConstantPadNDConvModule(torch.nn.Module): + def __init__(self, paddings: Collection[int], constant: float | int | None = None): + super().__init__() + self.pad = ConstantPadNDModule(paddings, constant) + self.conv = Conv2dModule() + + def forward(self, x): + x = self.pad(x) + return self.conv(x) + + +class MaxPool2dModule(torch.nn.Module): + def __init__(self, padding=0): + super().__init__() + + self.max_pool2d = torch.nn.MaxPool2d( + kernel_size=3, stride=2, padding=padding, dilation=1 + ) + + def forward(self, x): + return self.max_pool2d(x) + + +class MaxPool2dConvModule(torch.nn.Module): + def __init__(self, padding=0): + super().__init__() + + self.conv = Conv2dModule() + self.max_pool2d = torch.nn.MaxPool2d( + kernel_size=3, stride=2, padding=padding, dilation=1 + ) + + def forward(self, x): + x = self.conv(x) + return self.max_pool2d(x) + + +class AvgPool2dModule(torch.nn.Module): + def __init__(self, count_include_pad, padding=0): + super().__init__() + + self.avg_pool = torch.nn.AvgPool2d( + kernel_size=3, + stride=2, + padding=padding, + count_include_pad=count_include_pad, + ) + + def forward(self, x): + return self.avg_pool(x) + + +class AvgPool2dConvModule(torch.nn.Module): + def __init__(self, count_include_pad, padding=0): + super().__init__() + + self.conv = Conv2dModule() + self.avg_pool = torch.nn.AvgPool2d( + kernel_size=3, + stride=1, + padding=padding, + count_include_pad=count_include_pad, + ) + + def forward(self, x): + x = self.conv(x) + return self.avg_pool(x) + + +class ReLUModule(torch.nn.Module): + def __init__(self): + super().__init__() + + self.relu = torch.nn.ReLU() + + def forward(self, x): + return self.relu(x) + + +class Conv2dReLUModule(torch.nn.Module): + def __init__(self): + super().__init__() + + self.conv = torch.nn.Conv2d(4, 64, 2, bias=False) + self.relu = torch.nn.ReLU() + + def forward(self, x): + x = self.conv(x) + return self.relu(x) + + +class Conv2dPermuteModule(torch.nn.Module): + def __init__(self): + super().__init__() + self.conv = torch.nn.Conv2d(4, 64, 2, bias=False) + + def forward(self, x): + x = self.conv(x) + return torch.permute(x, [0, 2, 1, 3]) diff --git a/backends/nxp/tests/test_quantizer.py b/backends/nxp/tests/test_quantizer.py new file mode 100644 index 00000000000..868a94059b5 --- /dev/null +++ b/backends/nxp/tests/test_quantizer.py @@ -0,0 +1,273 @@ +# Copyright 2024 NXP +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +# Tests for NeutronQuantizer. + +import executorch.backends.nxp.tests.models as models +import torch +from executorch.backends.nxp.quantizer.neutron_quantizer import NeutronQuantizer +from torch.ao.quantization.quantize_pt2e import convert_pt2e, prepare_pt2e + + +def _get_target_name(node): + return node._pretty_print_target(node.target) + + +def test_quantizer_conv2d(): + model = models.Conv2dModule() + model.eval() + + example_input = (torch.ones(1, 4, 32, 32),) + quantizer = NeutronQuantizer() + graph_module = torch.export.export_for_training( + model, example_input, strict=True + ).module() + + # noinspection PyTypeChecker + m = prepare_pt2e(graph_module, quantizer) + m(*example_input) + m = convert_pt2e(m) + + # Dry run + m(*example_input) + + nodes = list(m.graph.nodes) + assert len(nodes) == 11 + assert nodes[7].name == "conv2d" + # [0]: Input, [1] : weights, [2]: bias + assert ( + _get_target_name(nodes[7].args[0]) + == "torch.ops.quantized_decomposed.dequantize_per_tensor.default" + ) + assert ( + _get_target_name(nodes[7].args[1]) + == "torch.ops.quantized_decomposed.dequantize_per_tensor.default" + ) + assert ( + _get_target_name(nodes[7].args[2]) + == "torch.ops.quantized_decomposed.dequantize_per_tensor.default" + ) + assert ( + _get_target_name(nodes[8]) + == "torch.ops.quantized_decomposed.quantize_per_tensor.default" + ) + assert nodes[8].args[0].name == "conv2d" + + +def test_quantizer_linear(): + model = models.LinearModule(bias=True) + model.eval() + + example_input = (torch.ones(10, 32),) + quantizer = NeutronQuantizer() + graph_module = torch.export.export_for_training( + model, example_input, strict=True + ).module() + + # noinspection PyTypeChecker + m = prepare_pt2e(graph_module, quantizer) + m(*example_input) + m = convert_pt2e(m) + + # Dry run + m(*example_input) + + nodes = list(m.graph.nodes) + assert len(nodes) == 11 + assert nodes[7].name == "linear" + # [0]: Input, [1] : weights, [2]: bias + assert ( + _get_target_name(nodes[7].args[0]) + == "torch.ops.quantized_decomposed.dequantize_per_tensor.default" + ) + assert ( + _get_target_name(nodes[7].args[1]) + == "torch.ops.quantized_decomposed.dequantize_per_tensor.default" + ) + assert ( + _get_target_name(nodes[7].args[2]) + == "torch.ops.quantized_decomposed.dequantize_per_tensor.default" + ) + assert ( + _get_target_name(nodes[8]) + == "torch.ops.quantized_decomposed.quantize_per_tensor.default" + ) + assert nodes[8].args[0].name == "linear" + + +def test_quantizer_maxpool2d(): + model = models.Conv2dAndMaxPool2DModule() + model.eval() + + example_input = (torch.ones(1, 8, 32, 32),) + quantizer = NeutronQuantizer() + graph_module = torch.export.export_for_training( + model, example_input, strict=True + ).module() + + # noinspection PyTypeChecker + m = prepare_pt2e(graph_module, quantizer) + m(*example_input) + m = convert_pt2e(m) + + # Dry run + m(*example_input) + + nodes = list(m.graph.nodes) + assert len(nodes) == 14 + # Check if QDQ pattern: + assert nodes[10].name == "max_pool2d" + assert ( + _get_target_name(nodes[10].args[0]) + == "torch.ops.quantized_decomposed.dequantize_per_tensor.default" + ) + assert ( + _get_target_name(nodes[11]) + == "torch.ops.quantized_decomposed.quantize_per_tensor.default" + ) + assert nodes[11].args[0].name == "max_pool2d" + + # Check if input and output quantization is same + input_quant = nodes[10].args[0].args[1:] + output_quant = nodes[11].args[1:] + assert input_quant == output_quant + + +def test_quantizer_softmax(): + model = models.SoftmaxModule(dim=0) + model.eval() + + example_input = (torch.ones(1, 10),) + quantizer = NeutronQuantizer() + graph_module = torch.export.export_for_training( + model, example_input, strict=True + ).module() + + # noinspection PyTypeChecker + m = prepare_pt2e(graph_module, quantizer) + m(*example_input) + m = convert_pt2e(m) + + # Dry run + m(*example_input) + + nodes = list(m.graph.nodes) + assert len(nodes) == 7 + # Check if QDQ pattern: + assert nodes[3].name == "softmax" + assert ( + _get_target_name(nodes[3].args[0]) + == "torch.ops.quantized_decomposed.dequantize_per_tensor.default" + ) + assert ( + _get_target_name(nodes[4]) + == "torch.ops.quantized_decomposed.quantize_per_tensor.default" + ) + assert nodes[4].args[0].name == "softmax" + + # Check output quantization + scale, zp, _, _, dtype = nodes[4].args[1:] + assert scale == 1.0 / 256.0 + assert zp == -128 + assert dtype == torch.int8 + + +def test_quantizer_single_maxpool2d(): + model = models.MaxPool2dModule() + model.eval() + + example_input = (torch.ones(1, 4, 32, 32),) + quantizer = NeutronQuantizer() + graph_module = torch.export.export_for_training( + model, example_input, strict=True + ).module() + + # noinspection PyTypeChecker + m = prepare_pt2e(graph_module, quantizer) + m(*example_input) + m = convert_pt2e(m) + + # Dry run + m(*example_input) + + nodes = list(m.graph.nodes) + assert len(nodes) == 3 + assert nodes[1].name == "max_pool2d" + assert "quantization_annotation" not in nodes[1].meta + + +def test_quantizer_conv2d_relu(): + model = models.Conv2dReLUModule() + model.eval() + + example_input = (torch.ones(1, 4, 32, 32),) + quantizer = NeutronQuantizer() + graph_module = torch.export.export_for_training( + model, example_input, strict=True + ).module() + + # noinspection PyTypeChecker + m = prepare_pt2e(graph_module, quantizer) + m(*example_input) + m = convert_pt2e(m) + + # Dry run + m(*example_input) + + nodes = list(m.graph.nodes) + assert len(nodes) == 12 + assert nodes[7].name == "dequantize_per_tensor_default_2" + assert nodes[8].name == "relu" + assert nodes[9].name == "quantize_per_tensor_default_3" + + +def test_quantizer_conv2d_avg_pool2d(): + model = models.AvgPool2dConvModule(count_include_pad=False) + model.eval() + + example_input = (torch.ones(1, 4, 16, 16),) + quantizer = NeutronQuantizer() + graph_module = torch.export.export_for_training( + model, example_input, strict=True + ).module() + + # noinspection PyTypeChecker + m = prepare_pt2e(graph_module, quantizer) + m(*example_input) + m = convert_pt2e(m) + + # Dry run + m(*example_input) + + nodes = list(m.graph.nodes) + assert len(nodes) == 14 + assert nodes[9].name == "dequantize_per_tensor_default_3" + assert nodes[10].name == "avg_pool2d" + assert nodes[11].name == "quantize_per_tensor_default_4" + + +def test_quantizer_conv2d_permute(): + model = models.Conv2dPermuteModule() + model.eval() + + example_input = (torch.ones(1, 4, 16, 16),) + quantizer = NeutronQuantizer() + graph_module = torch.export.export_for_training( + model, example_input, strict=True + ).module() + + # noinspection PyTypeChecker + m = prepare_pt2e(graph_module, quantizer) + m(*example_input) + m = convert_pt2e(m) + + # Dry run + m(*example_input) + + nodes = list(m.graph.nodes) + assert len(nodes) == 12 + assert nodes[7].name == "dequantize_per_tensor_default_2" + assert nodes[8].name == "permute" + assert nodes[9].name == "quantize_per_tensor_default_3" From 341e6df09f546a9b76192be155a76bf209279f3b Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Mon, 14 Apr 2025 12:48:14 -0700 Subject: [PATCH 125/244] Update demo-apps-ios.md (#10146) --- docs/source/demo-apps-ios.md | 34 +++++++++++++--------------------- 1 file changed, 13 insertions(+), 21 deletions(-) diff --git a/docs/source/demo-apps-ios.md b/docs/source/demo-apps-ios.md index 09371e32918..b74701afdf3 100644 --- a/docs/source/demo-apps-ios.md +++ b/docs/source/demo-apps-ios.md @@ -38,10 +38,9 @@ python3 --version pip --version ``` -### 3. Getting Started Tutorial +### 3. Set Up ExecuTorch -Follow the [Setting Up ExecuTorch](https://pytorch.org/executorch/stable/getting-started-setup) -tutorial to configure the basic environment: +Clone ExecuTorch and set up the environment as explained in the [Building from Source](using-executorch-building-from-source.md) tutorial: ```bash git clone -b viable/strict https://github.com/pytorch/executorch.git && cd executorch @@ -51,22 +50,19 @@ python3 -m venv .venv && source .venv/bin/activate && pip install --upgrade pip ./install_executorch.sh ``` -### 4. Clone the Demo App +### 4. Backend Dependencies + +Install additional dependencies for [Core ML](backends-coreml.md) and [MPS](backends-mps.md) backends: ```bash -git clone --depth 1 https://github.com/pytorch-labs/executorch-examples.git +./backends/apple/coreml/scripts/install_requirements.sh +./backends/apple/mps/install_requirements.sh ``` -### 5. Backend Dependencies - -Also, follow the corresponding sections from [Core ML](https://pytorch.org/executorch/stable/build-run-coreml) and -[MPS](https://pytorch.org/executorch/stable/build-run-mps) tutorials to install additional dependencies for those -backends: +### 5. Clone the Demo App ```bash -./backends/apple/coreml/scripts/install_requirements.sh - -./backends/apple/mps/install_requirements.sh +git clone --depth 1 https://github.com/pytorch-labs/executorch-examples.git ``` ## Models and Labels @@ -101,10 +97,9 @@ curl https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt \ -o "$APP_PATH/Resources/Models/MobileNet/imagenet_classes.txt" ``` -## Final Steps +## Build and Run the App -We're almost done! Now, we just need to open the project in Xcode, run the -tests, and finally run the app. +We're almost done! Now, we just need to open the project in Xcode, run the tests, and finally run the app. ### 1. Open Project in Xcode @@ -116,8 +111,7 @@ open $APP_PATH.xcodeproj ### 2. Run Tests -You can run tests on Simulaltor directly in Xcode with `Cmd + U` or use the -command line: +You can run tests on Simulaltor directly in Xcode with `Cmd + U` or use the command line: ```bash xcrun simctl create executorch "iPhone 15" @@ -136,6 +130,4 @@ using `Cmd + R`. Try installing a Release build for better performance. Congratulations! You've successfully set up the ExecuTorch iOS Demo App. Now, you can explore and enjoy the power of ExecuTorch on your iOS device! -Learn more about integrating and running [ExecuTorch on Apple](https://pytorch.org/executorch/stable/apple-runtime) platforms. - -![](_static/img/demo_ios_xcode.jpg) +Learn more about [Using ExecuTorch on iOS](using-executorch-ios.md). From 7cb48601ae78121fe595fa4a38acf82f934f9ef8 Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Mon, 14 Apr 2025 12:48:26 -0700 Subject: [PATCH 126/244] Update using-executorch-ios.md (#10128) --- docs/source/using-executorch-ios.md | 458 +++++++++++++++++++++++++++- 1 file changed, 450 insertions(+), 8 deletions(-) diff --git a/docs/source/using-executorch-ios.md b/docs/source/using-executorch-ios.md index 1d03284ec2c..08c862341b5 100644 --- a/docs/source/using-executorch-ios.md +++ b/docs/source/using-executorch-ios.md @@ -171,29 +171,471 @@ You can assign such a config file to your target in Xcode: ## Runtime API -Check out the [C++ Runtime API](extension-module.md) and [Tensors](extension-tensor.md) tutorials to learn more about how to load and run an exported model. It is recommended to use the C++ API for macOS or iOS, wrapped with Objective-C++ and Swift code if needed to expose it for other components. Please refer to the [Demo App](demo-apps-ios.md) as an example of such a setup. +ExecuTorch provides native Objective-C APIs, automatically bridged to Swift, for interacting with the runtime. These APIs act as wrappers around the core C++ components found in [extension/tensor](extension-tensor.md) and [extension/module](extension-module.md), offering a more idiomatic experience for Apple platform developers. -Once linked against the `executorch` runtime framework, the target can now import all ExecuTorch public headers. For example, in Objective-C++: +**Note:** These Objective-C/Swift APIs are currently experimental and subject to change. -```objectivecpp +### Importing + +Once linked against the `executorch` framework, you can import the necessary components. + +Objective-C (Objective-C++): + +```objectivec +// Import the main umbrella header for Module/Tensor/Value wrappers. #import + +// If using C++ directly alongside Objective-C++, you might still need C++ headers. #import #import ``` -Or in Swift: +Swift: + +```swift +import ExecuTorch +``` + +#### Example + +Here's a concise example demonstrating how to load a model, prepare input, run inference, and process output using the Objective-C and Swift API. Imagine you have a MobileNet v3 model (`mv3.pte`) that takes a `[1, 3, 224, 224]` float tensor as input and outputs logits. + +Objective-C: + +```objectivec +NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"mv3" ofType:@"pte"]; + +// Create a module with the model file path. Nothing gets loaded into memory just yet. +ExecuTorchModule *module = [[ExecuTorchModule alloc] initWithFilePath:modelPath]; + +NSError *error; // Optional error output argument to learn about failures. + +// Force-load the program and 'forward' method. Otherwise, it's loaded at the first execution. +[module loadMethod:@"forward" error:&error]; + +float *imageBuffer = ...; // Existing image buffer. + +// Create an input tensor referencing the buffer and assuming the given shape and data type. +ExecuTorchTensor *inputTensor = [[ExecuTorchTensor alloc] initWithBytesNoCopy:imageBuffer + shape:@[@1, @3, @224, @224] + dataType:ExecuTorchDataTypeFloat]; + +// Execute the 'forward' method with the given input tensor and get output values back. +NSArray *outputs = [module forwardWithTensor:inputTensor error:&error]; + +// Get the first output value assuming it's a tensor. +ExecuTorchTensor *outputTensor = outputs.firstObject.tensor; + +// Access the output tensor data. +[outputTensor bytesWithHandler:^(const void *pointer, NSInteger count, ExecuTorchDataType dataType) { + float *logits = (float *)pointer; + // Use logits... +}]; +``` + +Swift: + +```swift +let modelPath = Bundle.main.path(forResource: "mv3", ofType: "pte")! + +// Create a module with the model file path. Nothing gets loaded into memory just yet. +let module = Module(filePath: modelPath) + +// Force-load the program and 'forward' method. Otherwise, it's loaded at the first execution. +try module.load("forward") + +let imageBuffer: UnsafeMutableRawPointer = ... // Existing image buffer + +// Create an input tensor referencing the buffer and assuming the given shape and data type. +let inputTensor = Tensor( + bytesNoCopy: imageBuffer, + shape: [1, 3, 224, 224], + dataType: .float +) + +// Execute the 'forward' method with the given input tensor and get output values back. +let outputs = try module.forward(inputTensor) + +// Get the first output value assuming it's a tensor. +if let outputTensor = outputs.first?.tensor { + // Access the output tensor data. + outputTensor.bytes { pointer, count, dataType in + // Copy the tensor data into logits array for easier access. + let logits = Array(UnsafeBufferPointer( + start: pointer.assumingMemoryBound(to: Float.self), + count: count + )) + // Use logits... + } +} +``` + +### Tensor + +The `Tensor` class (exposed as `ExecuTorchTensor` in Objective-C) represents a multi-dimensional array of elements (such as floats or ints) and includes metadata like shape (dimensions) and data type. Tensors are used to feed inputs to a model and retrieve outputs, or for any computation you need to do on raw data. You can create tensors from simple arrays of numbers, inspect their properties, read or modify their contents, and even reshape or copy them. + +#### Key Properties: + +- dataType: The element type (e.g., `.float`, `.int`, `.byte`). +- shape: An array of `NSNumber` describing the size of each dimension. +- count: The total number of elements. +- strides: The jump in memory needed to advance one element along each dimension. +- dimensionOrder: The order of dimensions in memory. +- shapeDynamism: Indicates if the tensor shape can change (`.static`, `.dynamicBound`, `.dynamicUnbound`). + +#### Initialization: + +You can create tensors in various ways: + +From existing memory buffers: +- `init(bytesNoCopy:shape:dataType:...)`: Creates a tensor that references an existing memory buffer without copying. The buffer's lifetime must exceed the tensor's. +- `init(bytes:shape:dataType:...)`: Creates a tensor by copying data from a memory buffer. + +From `NSData` / `Data`: +- `init(data:shape:dataType:...)`: Creates a tensor using an `NSData` object, referencing its bytes without copying. + +From scalar arrays: +- `init(_:shape:dataType:...)`: Creates a tensor from an array of `NSNumber` scalars. Convenience initializers exist to infer shape or data type. + +From single scalars: +- `init(_:)`, `init(_:dataType:)`, `init(float:)`, `init(int:)`, etc.: Create 0-dimensional tensors (scalars). + +Objective-C: + +```objectivec +#import + +// Create from copying bytes. +float data[] = {1.0f, 2.0f, 3.0f, 4.0f}; +NSArray *shape = @[@2, @2]; +ExecuTorchTensor *tensorFromBytes = [[ExecuTorchTensor alloc] initWithBytes:data + shape:shape + dataType:ExecuTorchDataTypeFloat]; + +// Create from scalars. +NSArray *scalars = @[@(1), @(2), @(3)]; +ExecuTorchTensor *tensorFromScalars = [[ExecuTorchTensor alloc] initWithScalars:scalars + dataType:ExecuTorchDataTypeInt]; + +// Create a float scalar tensor. +ExecuTorchTensor *scalarTensor = [[ExecuTorchTensor alloc] initWithFloat:3.14f]; +``` + +Swift: +```swift +import ExecuTorch + +// Create from existing buffer without copying. +var mutableData: [Float] = [1.0, 2.0, 3.0, 4.0] +let tensorNoCopy = mutableData.withUnsafeMutableBytes { bufferPointer in + Tensor( + bytesNoCopy: bufferPointer.baseAddress!, + shape: [2, 2], + dataType: .float + ) +} + +// Create from Data (no copy). +let data = Data(bytes: mutableData, count: mutableData.count * MemoryLayout.size) +let tensorFromData = Tensor(data: data, shape: [2, 2], dataType: .float) + +// Create from scalars (infers float type). +let tensorFromScalars = Tensor([1.0, 2.0, 3.0, 4.0], shape: [4]) + +// Create an Int scalar tensor. +let scalarTensor = Tensor(42) // Infers Int as .long data type (64-bit integer) +``` + +#### Accessing Data: + +Use `bytes(_:)` for immutable access and `mutableBytes(_:)` for mutable access to the tensor's underlying data buffer. + +Objective-C: + +```objectivec +[tensor bytesWithHandler:^(const void *pointer, NSInteger count, ExecuTorchDataType dataType) { + if (dataType == ExecuTorchDataTypeFloat) { + const float *floatPtr = (const float *)pointer; + NSLog(@"First float element: %f", floatPtr[0]); + } +}]; + +[tensor mutableBytesWithHandler:^(void *pointer, NSInteger count, ExecuTorchDataType dataType) { + if (dataType == ExecuTorchDataTypeFloat) { + float *floatPtr = (float *)pointer; + floatPtr[0] = 100.0f; // Modify the original mutableData buffer. + } +}]; +``` + +Swift: +```swift +tensor.bytes { pointer, count, dataType in + if dataType == .float { + let buffer = UnsafeBufferPointer(start: pointer.assumingMemoryBound(to: Float.self), count: count) + print("First float element: \(buffer.first ?? 0.0)") + } +} + +tensor.mutableBytes { pointer, count, dataType in + if dataType == .float { + let buffer = UnsafeMutableBufferPointer(start: pointer.assumingMemoryBound(to: Float.self), count: count) + buffer[1] = 200.0 // Modify the original mutableData buffer. + } +} +``` + +#### Resizing: + +Tensors can be resized if their underlying memory allocation allows it (typically requires ShapeDynamism other than Static or sufficient capacity). + +Objective-C: + +```objectivec +NSError *error; +BOOL success = [tensor resizeToShape:@[@4, @1] error:&error]; +if (success) { + NSLog(@"Resized shape: %@", tensor.shape); +} else { + NSLog(@"Resize failed: %@", error); +} +``` + +Swift: +```swift +do { + try tensor.resize(to: [4, 1]) + print("Resized shape: \(tensor.shape)") +} catch { + print("Resize failed: \(error)") +} +``` + +### Value + +The `Value` class (exposed as `ExecuTorchValue` in Objective-C) is a dynamic container that can hold different types of data, primarily used for model inputs and outputs. ExecuTorch methods accept and return arrays of `Value` objects. + +#### Key Properties: + +- `tag`: Indicates the type of data held (e.g., `.tensor`, `.integer`, `.string`, `.boolean`). +- `isTensor`, `isInteger`, `isString`, etc.: Boolean checks for the type. +- `tensor`, `integer`, `string`, `boolean`, `double`: Accessors for the underlying data (return `nil` or a default value if the tag doesn't match). + +#### Initialization: + +Create Value objects directly from the data they should hold. + +Objective-C: + +```objectivec +#import + +ExecuTorchTensor *tensor = [[ExecuTorchTensor alloc] initWithFloat:1.0f]; + +ExecuTorchValue *tensorValue = [[ExecuTorchValue alloc] valueWithTensor:tensor]; +ExecuTorchValue *intValue = [[ExecuTorchValue alloc] valueWithInteger:100]; +ExecuTorchValue *stringValue = [[ExecuTorchValue alloc] valueWithString:@"hello"]; +ExecuTorchValue *boolValue = [[ExecuTorchValue alloc] valueWithBoolean:YES]; +ExecuTorchValue *doubleValue = [[ExecuTorchValue alloc] valueWithDouble:3.14]; +``` + +Swift: + +```swift +import ExecuTorch + +let tensor = Tensor(2.0) + +let tensorValue = Value(tensor) +let intValue = Value(200) +let stringValue = Value("world") +let boolValue = Value(false) +let doubleValue = Value(2.718) +``` + +### Module +The `Module` class (exposed as `ExecuTorchModule` in Objective-C) represents a loaded ExecuTorch model (`.pte` file). It provides methods to load the model program and execute its internal methods (like `forward`). + +#### Initialization: + +Create a `Module` instance by providing the file path to the `.pte` model. Initialization itself is lightweight and doesn't load the program data immediately. + +Objective-C: + +```objectivec +#import + +NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"model" ofType:@"pte"]; +ExecuTorchModule *module = [[ExecuTorchModule alloc] initWithFilePath:modelPath]; +// Optional: specify load mode, e.g., memory mapping. +// ExecuTorchModule *moduleMmap = [[ExecuTorchModule alloc] initWithFilePath:modelPath +// loadMode:ExecuTorchModuleLoadModeMmap]; +``` + +Swift: ```swift import ExecuTorch + +let modelPath = Bundle.main.path(forResource: "model", ofType: "pte") +let module = Module(filePath: modelPath!) +// Optional: specify load mode, e.g., memory mapping. +// let moduleMmap = Module(filePath: modelPath, loadMode: .mmap) ``` -**Note:** Importing the ExecuTorch umbrella header (or ExecuTorch module in Swift) provides access to the logging API only. You still need to import the other runtime headers explicitly as needed, e.g., `module.h`. There is no support for other runtime APIs in Objective-C or Swift beyond logging described below. +#### Loading: -**Note:** Logs are stripped in the release builds of ExecuTorch frameworks. To preserve logging, use debug builds during development. +Model loading is deferred until explicitly requested or needed for execution. While execution calls can trigger loading automatically, it's often more efficient to load methods explicitly beforehand. + +- `load()`: Loads the basic program structure. Minimal verification is used by default. +- `load(_:)`: Loads the program structure and prepares a specific method (e.g., "forward") for execution. This performs necessary setup like backend delegation and is recommended if you know which method you'll run. +- `isLoaded()` / `isLoaded(_:)`: Check loading status. + +Objective-C: + +```objectivec +NSError *error; +// Loads program and prepares 'forward' for execution. +BOOL success = [module loadMethod:@"forward" error:&error]; +if (success) { + NSLog(@"Forward method loaded: %d", [module isMethodLoaded:@"forward"]); +} else { + NSLog(@"Failed to load method: %@", error); +} +``` + +Swift: + +```swift +do { + // Loads program and prepares 'forward' for execution. + try module.load("forward") + print("Forward method loaded: \(module.isLoaded("forward"))") +} catch { + print("Failed to load method: \(error)") +} +``` + +#### Execution: + +The `Module` class offers flexible ways to execute methods within the loaded program. + +- Named Execution: You can execute any available method by name using `execute(methodName:inputs:)`. +- Forward Shortcut: For the common case of running the primary inference method, use the `forward(inputs:)` shortcut, which is equivalent to calling execute with the method name "forward". +- Input Flexibility: Inputs can be provided in several ways: + - As an array of `Value` objects. This is the most general form. + - As an array of `Tensor` objects. This is a convenience where tensors are automatically wrapped into `Value` objects. + - As a single `Value` or `Tensor` object if the method expects only one input. + - With no inputs if the method takes none. + +Outputs are always returned as an array of `Value`. + +Objective-C: + +```objectivec +ExecuTorchTensor *inputTensor1 = [[ExecuTorchTensor alloc] initWithScalars:@[@1.0f, @2.0f]]; +ExecuTorchTensor *inputTensor2 = [[ExecuTorchTensor alloc] initWithScalars:@[@3.0f, @4.0f]]; +ExecuTorchTensor *singleInputTensor = [[ExecuTorchTensor alloc] initWithFloat:5.0f]; +NSError *error; + +// Execute "forward" using the shortcut with an array of Tensors. +NSArray *outputs1 = [module forwardWithTensors:@[inputTensor1, inputTensor2] error:&error]; +if (outputs1) { + NSLog(@"Forward output count: %lu", (unsigned long)outputs1.count); +} else { + NSLog(@"Execution failed: %@", error); +} + +// Execute "forward" with a single Tensor input. +NSArray *outputs2 = [module forwardWithTensor:singleInputTensor error:&error]; +if (outputs2) { + NSLog(@"Forward single input output count: %lu", (unsigned long)outputs2.count); +} else { + NSLog(@"Execution failed: %@", error); +} + +// Execute a potentially different method by name. +NSArray *outputs3 = [module executeMethod:@"another_method" + withInput:[[ExecuTorchValue alloc] valueWithTensor:inputTensor1] + error:&error]; + +// Process outputs (assuming first output is a tensor). +if (outputs1) { + ExecuTorchValue *firstOutput = outputs1.firstObject; + if (firstOutput.isTensor) { + ExecuTorchTensor *resultTensor = firstOutput.tensorValue; + // Process resultTensor. + } +} +``` + +Swift: + +```swift +let inputTensor1 = Tensor([1.0, 2.0], dataType: .float) +let inputTensor2 = Tensor([3.0, 4.0], dataType: .float) +let singleInputTensor = Tensor([5.0], dataType: .float) + +do { + // Execute "forward" using the shortcut with an array of Tensors. + let outputs1 = try module.forward([inputTensor1, inputTensor2]) + print("Forward output count: \(outputs1.count)") + + // Execute "forward" with a single Tensor input. + let outputs2 = try module.forward(singleInputTensor) + print("Forward single input output count: \(outputs2.count)") + + // Execute a potentially different method by name. + let outputs3 = try module.execute("another_method", inputs: [Value(inputTensor1)]) + + // Process outputs (assuming first output is a tensor). + if let resultTensor = outputs1.first?.tensor { + resultTensor.bytes { ptr, count, dtype in + // Access result data. + } + } +} catch { + print("Execution failed: \(error)") +} +``` + +#### Method Names: + +You can query the available method names in the model after the program is loaded. + +Objective-C: + +```objectivec +NSError *error; + +// Note: methodNames: will load the program if not already loaded. +NSSet *names = [module methodNames:&error]; +if (names) { + NSLog(@"Available methods: %@", names); +} else { + NSLog(@"Could not get method names: %@", error); +} +``` + +Swift: + +```swift +do { + // Note: methodNames() will load the program if not already loaded. + let names = try module.methodNames() + print("Available methods: \(names)") // Output: e.g., {"forward"} +} catch { + print("Could not get method names: \(error)") +} +``` ### Logging -ExecuTorch provides extra APIs for logging in Objective-C and Swift as a lightweight wrapper of the internal ExecuTorch machinery. To use it, just import the main framework header in Objective-C. Then use the `ExecuTorchLog` interface (or the `Log` class in Swift) to subscribe your own implementation of the `ExecuTorchLogSink` protocol (or `LogSink` in Swift) to listen to log events. +ExecuTorch provides APIs for logging in Objective-C and Swift via the `ExecuTorchLog` (`Log` in Swift) singleton. You can subscribe custom log sinks conforming to the `ExecuTorchLogSink` (`LogSink` in Swift) protocol to receive internal ExecuTorch log messages. + +**Note:** Logs are stripped in the Release builds of ExecuTorch frameworks. To capture logs, link against the Debug builds (e.g., `executorch_debug`) during development. + +Objective-C: ```objectivec #import @@ -250,7 +692,7 @@ ExecuTorch provides extra APIs for logging in Objective-C and Swift as a lightwe @end ``` -Swift version: +Swift: ```swift import ExecuTorch From 062429868b0ee87a4490e9d9e61f0d8806565f2e Mon Sep 17 00:00:00 2001 From: pytorchbot Date: Mon, 14 Apr 2025 16:07:18 -0400 Subject: [PATCH 127/244] [ET-VK][ez] Support convolutions with padding > 0 and dilation > 1 (#10148) ## Context As title. ## Changes Modified convolution shader to set starting input position correctly when padding and dilation are both greater than 0. Removed safeguard check for padding = 0 when dilation > 1 in C++ implementation. Differential Revision: [D72879342](https://our.internmc.facebook.com/intern/diff/D72879342/) --- .../vulkan/runtime/graph/ops/glsl/conv2d.glsl | 12 +- .../runtime/graph/ops/impl/Convolution.cpp | 5 - backends/vulkan/test/op_tests/cases.py | 327 ++++++++++-------- 3 files changed, 193 insertions(+), 151 deletions(-) diff --git a/backends/vulkan/runtime/graph/ops/glsl/conv2d.glsl b/backends/vulkan/runtime/graph/ops/glsl/conv2d.glsl index 1c2ffe7afe4..c0ed9204227 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/conv2d.glsl +++ b/backends/vulkan/runtime/graph/ops/glsl/conv2d.glsl @@ -47,7 +47,17 @@ void main() { // Compute the start and end of the input indices to load. Padding is assumed // to be constant 0 padding, so reads from the padding region are skipped. - const ivec2 start = max(ivec2(0), ipos); + ivec2 start = ipos; + if (start.x < 0) { + // number of "steps" to get to >= zero is div_up(-start, dilation) + int num_steps = ((-ipos.x) + dilation.x - 1) / dilation.x; + start.x = ipos.x + num_steps * dilation.x; + } + if (start.y < 0) { + // number of "steps" to get to >= zero is div_up(-start, dilation) + int num_steps = ((-ipos.y) + dilation.y - 1) / dilation.y; + start.y = ipos.y + num_steps * dilation.y; + } const ivec2 end = min(ipos + overlay_region.xy, ivec2(in_sizes.xy)); // Compute the start of the kernel based on how far we are skipping ahead when // reading the input. Note that these are "canonical" indices. diff --git a/backends/vulkan/runtime/graph/ops/impl/Convolution.cpp b/backends/vulkan/runtime/graph/ops/impl/Convolution.cpp index 18599ed4ba6..060f5028c02 100644 --- a/backends/vulkan/runtime/graph/ops/impl/Convolution.cpp +++ b/backends/vulkan/runtime/graph/ops/impl/Convolution.cpp @@ -262,11 +262,6 @@ void check_conv2d_params(const Kernel2dParams& p, const bool transposed) { "aten.convolution.default: transposed = true, dilation > 1 is not supported yet!"); } } - if ((p.padding[0] > 0 && p.kernel_size[0] > 1 && p.dilation[0] > 1) || - (p.padding[1] > 0 && p.kernel_size[1] > 1 && p.dilation[1] > 1)) { - VK_THROW( - "aten.convolution.default: padding > 0 while dilation, kernel_size > 1 is not supported yet!"); - } } Conv2dMethod get_conv2d_method( diff --git a/backends/vulkan/test/op_tests/cases.py b/backends/vulkan/test/op_tests/cases.py index 85008a52ff0..a1b03db27c9 100644 --- a/backends/vulkan/test/op_tests/cases.py +++ b/backends/vulkan/test/op_tests/cases.py @@ -226,153 +226,190 @@ def get_max_pool2d_inputs(): @register_test_suite("aten.convolution.default") def get_conv_inputs(): - test_suite = VkTestSuite( + Test = namedtuple( + "ConvTest", [ - ( - (1, 6, 40, 50), - (8, 6, 3, 3), - (8,), - [1, 2], - [2, 3], - [1, 1], - False, - [0, 0], - 1, - ), - ( - (1, 6, 40, 50), - (6, 8, 3, 3), - (8,), - [1, 2], - [2, 3], - [1, 1], - True, - [0, 1], - 1, - ), - ( - (1, 8, 72, 96), - (8, 1, 3, 3), - (8,), - [1, 1], - [1, 1], - [1, 1], - False, - [0, 0], - 8, - ), - ( - (1, 8, 72, 96), - (8, 8, 1, 1), - (8,), - [1, 1], - [1, 1], - [1, 1], - False, - [0, 0], - 1, - ), - ( - (1, 6, 40, 50), - (8, 6, 3, 3), - None, - [1, 2], - [2, 3], - [1, 1], - False, - [0, 0], - 1, - ), - ( - (1, 6, 7), - (6, 1, 3), - (6,), - [1], - [0], - [1], - False, - [0], - 6, - ), - ( - (2, 20, 30), - (10, 4, 6), - (10,), - [5], - [5], - [3], - False, - [0], - 5, - ), - ( - (1, 9, 11), - (9, 1, 3), - None, - [1], - [0], - [1], - False, - [0], - 9, - ), - ( - (5, 15, 30), - (20, 3, 3), - None, - [3], - [5], - [7], - False, - [0], - 5, - ), - ( - (1, 16, 672, 512), - (64, 16, 1, 1), - (64,), - [1, 1], - [0, 0], - [1, 1], - False, - [0, 0], - 1, - ), - ( - (1, 4, 234, 234), - (4, 1, 3, 3), - (4,), - [2, 1], - [1, 1], - [1, 1], - False, - [0, 0], - 4, - ), - ( - (1, 4, 234, 234), - (4, 1, 3, 3), - (4,), - [1, 2], - [1, 1], - [1, 1], - False, - [0, 0], - 4, - ), - ( - (1, 4, 234, 234), - (4, 1, 3, 3), - (4,), - [2, 2], - [1, 1], - [1, 1], - False, - [0, 0], - 4, - ), - ] + "self", + "weight", + "bias", + "stride", + "padding", + "dilation", + "transposed", + "output_padding", + "groups", + ], + ) + Test.__new__.__defaults__ = ( + None, + None, + None, + [1, 1], + [0, 0], + [1, 1], + False, + [9, 0], + 1, ) + test_cases = [] + test_cases = [ + Test( + self=(1, 6, 40, 50), + weight=(8, 6, 3, 3), + bias=(8,), + stride=[1, 2], + padding=[2, 3], + dilation=[1, 1], + transposed=False, + output_padding=[0, 0], + groups=1, + ), + Test( + self=(1, 6, 40, 50), + weight=(6, 8, 3, 3), + bias=(8,), + stride=[1, 2], + padding=[2, 3], + dilation=[1, 1], + transposed=True, + output_padding=[0, 1], + groups=1, + ), + Test( + self=(1, 8, 72, 96), + weight=(8, 1, 3, 3), + bias=(8,), + stride=[1, 1], + padding=[1, 1], + dilation=[1, 1], + transposed=False, + output_padding=[0, 0], + groups=8, + ), + Test( + self=(1, 8, 72, 96), + weight=(8, 8, 1, 1), + bias=(8,), + stride=[1, 1], + padding=[1, 1], + dilation=[1, 1], + transposed=False, + output_padding=[0, 0], + groups=1, + ), + Test( + self=(1, 6, 40, 50), + weight=(8, 6, 3, 3), + bias=None, + stride=[1, 2], + padding=[2, 3], + dilation=[1, 1], + transposed=False, + output_padding=[0, 0], + groups=1, + ), + Test( + self=(1, 6, 7), + weight=(6, 1, 3), + bias=(6,), + stride=[1], + padding=[0], + dilation=[1], + transposed=False, + output_padding=[0], + groups=6, + ), + Test( + self=(2, 20, 30), + weight=(10, 4, 6), + bias=(10,), + stride=[5], + padding=[5], + dilation=[3], + transposed=False, + output_padding=[0], + groups=5, + ), + Test( + self=(1, 9, 11), + weight=(9, 1, 3), + bias=None, + stride=[1], + padding=[0], + dilation=[1], + transposed=False, + output_padding=[0], + groups=9, + ), + Test( + self=(5, 15, 30), + weight=(20, 3, 3), + bias=None, + stride=[3], + padding=[5], + dilation=[7], + transposed=False, + output_padding=[0], + groups=5, + ), + Test( + self=(1, 16, 672, 512), + weight=(64, 16, 1, 1), + bias=(64,), + stride=[1, 1], + padding=[0, 0], + dilation=[1, 1], + transposed=False, + output_padding=[0, 0], + groups=1, + ), + Test( + self=(1, 4, 234, 234), + weight=(4, 1, 3, 3), + bias=(4,), + stride=[2, 1], + padding=[1, 1], + dilation=[1, 1], + transposed=False, + output_padding=[0, 0], + groups=4, + ), + Test( + self=(1, 4, 234, 234), + weight=(4, 1, 3, 3), + bias=(4,), + stride=[1, 2], + padding=[1, 1], + dilation=[1, 1], + transposed=False, + output_padding=[0, 0], + groups=4, + ), + Test( + self=(1, 4, 234, 234), + weight=(4, 1, 3, 3), + bias=(4,), + stride=[2, 2], + padding=[1, 1], + dilation=[1, 1], + transposed=False, + output_padding=[0, 0], + groups=4, + ), + Test( + self=(1, 8, 90, 77), + weight=(1, 8, 3, 3), + bias=(1,), + stride=[1, 1], + padding=[2, 2], + dilation=[2, 2], + transposed=False, + output_padding=[0, 0], + groups=1, + ), + ] + + test_suite = VkTestSuite(test_cases) return test_suite From f9423fc7f89c6acde016ef7cb574fe2f66910c43 Mon Sep 17 00:00:00 2001 From: Mengwei Liu Date: Mon, 14 Apr 2025 13:47:58 -0700 Subject: [PATCH 128/244] Add memory requirement and clarify image format for llava example Differential Revision: D72988591 Pull Request resolved: https://github.com/pytorch/executorch/pull/10153 --- examples/models/llava/README.md | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/examples/models/llava/README.md b/examples/models/llava/README.md index d0dc71c0a85..2a60ab4f884 100644 --- a/examples/models/llava/README.md +++ b/examples/models/llava/README.md @@ -26,17 +26,26 @@ model) for general-purpose visual and language understanding, achieving impressive chat capabilities mimicking spirits of the cutting edge multimodal models and setting a high bar for accuracy on Science QA. -## Instructions +## Instructions to run Llava on Android/iOS First you need to generate a .PTE file for the model, along with input image, and other artifacts. Then you need either a C++ runner, or Android or iOS application to test things out on device. +### Host machine requirements + +The biggest requirement is to have a host machine with at least 32GiB memory, preferably 64GiB. + +The model weights is 15GiB, and the other memory usage at export stage (`export_llava`) is around 10GiB. So you need at least 25GiB memory to run the export script. + + ### Generate ExecuTorch .PTE and other artifacts Run the following command to generate `llava.pte`, `tokenizer.bin` and an image tensor (serialized in TorchScript) `image.pt`. +> **Warning**: The C++ runner `llava_main` binary cannot process raw image inputs such as JPEG, PNG, or BMP files directly. You must convert these images to a `.pt` file format using the `examples/models/llava/image_util.py` script before using them with `llava_main`. + Prerequisite: run `install_executorch.sh` to install ExecuTorch and run `examples/models/llava/install_requirements.sh` to install dependencies. @@ -69,6 +78,13 @@ cmake-out/examples/models/llava/llava_main ### Build Mobile Apps +#### Device Requirements + +To run the Android/iOS apps, you need a device with at least 12GiB memory. + +- iPhone 13 Pro or above +- Samsung Galaxy S23 or above + #### Android We can run LLAVA using the LLAMA Demo Apps. Please refer to [this From 72dfc6b870c0015f15d0142e14095028da899371 Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Mon, 14 Apr 2025 14:38:26 -0700 Subject: [PATCH 129/244] Delete obsolete docs (#10159) --- docs/source/sdk-bundled-io.md | 3 --- docs/source/sdk-debugging.md | 3 --- docs/source/sdk-delegate-integration.md | 3 --- docs/source/sdk-etdump.md | 3 --- docs/source/sdk-etrecord.rst | 4 ---- docs/source/sdk-inspector.rst | 4 ---- docs/source/sdk-overview.md | 3 --- docs/source/sdk-profiling.md | 3 --- docs/source/sdk-tutorial.md | 3 --- .../tutorials_source/sdk-integration-tutorial.py | 13 ------------- 10 files changed, 42 deletions(-) delete mode 100644 docs/source/sdk-bundled-io.md delete mode 100644 docs/source/sdk-debugging.md delete mode 100644 docs/source/sdk-delegate-integration.md delete mode 100644 docs/source/sdk-etdump.md delete mode 100644 docs/source/sdk-etrecord.rst delete mode 100644 docs/source/sdk-inspector.rst delete mode 100644 docs/source/sdk-overview.md delete mode 100644 docs/source/sdk-profiling.md delete mode 100644 docs/source/sdk-tutorial.md delete mode 100644 docs/source/tutorials_source/sdk-integration-tutorial.py diff --git a/docs/source/sdk-bundled-io.md b/docs/source/sdk-bundled-io.md deleted file mode 100644 index 488ade7bac8..00000000000 --- a/docs/source/sdk-bundled-io.md +++ /dev/null @@ -1,3 +0,0 @@ -# Bundled Program -- a Tool for ExecuTorch Model Validation - -Please update your link to . This URL will be deleted after v0.4.0. diff --git a/docs/source/sdk-debugging.md b/docs/source/sdk-debugging.md deleted file mode 100644 index 3e975875f21..00000000000 --- a/docs/source/sdk-debugging.md +++ /dev/null @@ -1,3 +0,0 @@ -# Debugging Models in ExecuTorch - -Please update your link to . This URL will be deleted after v0.4.0. diff --git a/docs/source/sdk-delegate-integration.md b/docs/source/sdk-delegate-integration.md deleted file mode 100644 index 7c2c9e92a90..00000000000 --- a/docs/source/sdk-delegate-integration.md +++ /dev/null @@ -1,3 +0,0 @@ -# Debug in Delegates - -Please update your link to . This URL will be deleted after v0.4.0. diff --git a/docs/source/sdk-etdump.md b/docs/source/sdk-etdump.md deleted file mode 100644 index a765d4cf1b4..00000000000 --- a/docs/source/sdk-etdump.md +++ /dev/null @@ -1,3 +0,0 @@ -# Prerequisite | ETDump - ExecuTorch Dump - -Please update your link to . This URL will be deleted after v0.4.0. diff --git a/docs/source/sdk-etrecord.rst b/docs/source/sdk-etrecord.rst deleted file mode 100644 index ee8f9b2b2d2..00000000000 --- a/docs/source/sdk-etrecord.rst +++ /dev/null @@ -1,4 +0,0 @@ -Prerequisite | ETRecord - ExecuTorch Record -=========================================== - -Please update your link to . This URL will be deleted after v0.4.0. diff --git a/docs/source/sdk-inspector.rst b/docs/source/sdk-inspector.rst deleted file mode 100644 index 0019528f419..00000000000 --- a/docs/source/sdk-inspector.rst +++ /dev/null @@ -1,4 +0,0 @@ -Inspector APIs -============== - -Please update your link to . This URL will be deleted after v0.4.0. diff --git a/docs/source/sdk-overview.md b/docs/source/sdk-overview.md deleted file mode 100644 index 1e8f1fae1ba..00000000000 --- a/docs/source/sdk-overview.md +++ /dev/null @@ -1,3 +0,0 @@ -# Introduction to the ExecuTorch Developer Tools - -Please update your link to . This URL will be deleted after v0.4.0. diff --git a/docs/source/sdk-profiling.md b/docs/source/sdk-profiling.md deleted file mode 100644 index 9c99a979757..00000000000 --- a/docs/source/sdk-profiling.md +++ /dev/null @@ -1,3 +0,0 @@ -# Profiling Models in ExecuTorch - -Please update your link to . This URL will be deleted after v0.4.0. diff --git a/docs/source/sdk-tutorial.md b/docs/source/sdk-tutorial.md deleted file mode 100644 index 457d3b47ebf..00000000000 --- a/docs/source/sdk-tutorial.md +++ /dev/null @@ -1,3 +0,0 @@ -## Developer Tools Usage Tutorial - -Please update your link to . This URL will be deleted after v0.4.0. diff --git a/docs/source/tutorials_source/sdk-integration-tutorial.py b/docs/source/tutorials_source/sdk-integration-tutorial.py deleted file mode 100644 index b9a8009c646..00000000000 --- a/docs/source/tutorials_source/sdk-integration-tutorial.py +++ /dev/null @@ -1,13 +0,0 @@ -# -*- coding: utf-8 -*- -# Copyright (c) Meta Platforms, Inc. and affiliates. -# All rights reserved. -# -# This source code is licensed under the BSD-style license found in the -# LICENSE file in the root directory of this source tree. - -""" -Using the ExecuTorch Developer Tools to Profile a Model -======================== - -Please update your link to . This URL will be deleted after v0.4.0. -""" From 49918f059b1da7d3fa95496f9f3a2e1a3566b249 Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Mon, 14 Apr 2025 15:02:28 -0700 Subject: [PATCH 130/244] Update doc links to relative markdown files (#10164) --- CONTRIBUTING.md | 2 +- docs/source/build-run-openvino.md | 2 +- docs/source/memory-planning-inspection.md | 10 +++++----- docs/source/new-contributor-guide.md | 2 +- docs/source/using-executorch-android.md | 20 +++++++++---------- docs/source/using-executorch-ios.md | 6 +++--- .../docs/delegates/mediatek_README.md | 2 +- examples/demo-apps/apple_ios/LLaMA/README.md | 2 +- .../LLaMA/docs/delegates/mps_README.md | 4 ++-- .../LLaMA/docs/delegates/xnnpack_README.md | 4 ++-- 10 files changed, 27 insertions(+), 27 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 64c6f1d249e..32681cdb08f 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -58,7 +58,7 @@ executorch │ ├── verification - IR verification. ├── extension - Extensions built on top of the runtime. │ ├── android - ExecuTorch wrappers for Android apps. Please refer to the Android documentation and Javadoc for more information. -│ ├── apple - ExecuTorch wrappers for iOS apps. Please refer to the iOS documentation and how to integrate into Apple platform for more information. +│ ├── apple - ExecuTorch wrappers for iOS apps. Please refer to the iOS documentation and how to integrate into Apple platform for more information. │ ├── aten_util - Converts to and from PyTorch ATen types. │ ├── data_loader - 1st party data loader implementations. │ ├── evalue_util - Helpers for working with EValue objects. diff --git a/docs/source/build-run-openvino.md b/docs/source/build-run-openvino.md index f9ea5df0862..db3d221ffc7 100644 --- a/docs/source/build-run-openvino.md +++ b/docs/source/build-run-openvino.md @@ -61,7 +61,7 @@ For more information about OpenVINO build, refer to the [OpenVINO Build Instruct Follow the steps below to setup your build environment: -1. **Setup ExecuTorch Environment**: Refer to the [Environment Setup](https://pytorch.org/executorch/stable/getting-started-setup#environment-setup) guide for detailed instructions on setting up the ExecuTorch environment. +1. **Setup ExecuTorch Environment**: Refer to the [Environment Setup](getting-started-setup.md#environment-setup) guide for detailed instructions on setting up the ExecuTorch environment. 2. **Setup OpenVINO Backend Environment** - Install the dependent libs. Ensure that you are inside `executorch/backends/openvino/` directory diff --git a/docs/source/memory-planning-inspection.md b/docs/source/memory-planning-inspection.md index 47951a72038..9f7d6d6b688 100644 --- a/docs/source/memory-planning-inspection.md +++ b/docs/source/memory-planning-inspection.md @@ -1,9 +1,9 @@ # Memory Planning Inspection in ExecuTorch -After the [Memory Planning](https://pytorch.org/executorch/main/concepts.html#memory-planning) pass of ExecuTorch, memory allocation information is stored on the nodes of the [`ExportedProgram`](https://pytorch.org/executorch/main/concepts.html#exportedprogram). Here, we present a tool designed to inspect memory allocation and visualize all active tensor objects. +After the [Memory Planning](concepts.md#memory-planning) pass of ExecuTorch, memory allocation information is stored on the nodes of the [`ExportedProgram`](concepts.md#exportedprogram). Here, we present a tool designed to inspect memory allocation and visualize all active tensor objects. ## Usage -User should add this code after they call [to_executorch()](https://pytorch.org/executorch/main/export-to-executorch-api-reference.html#executorch.exir.EdgeProgramManager.to_executorch), and it will write memory allocation information stored on the nodes to the file path "memory_profile.json". The file is compatible with the Chrome trace viewer; see below for more information about interpreting the results. +User should add this code after they call [to_executorch()](export-to-executorch-api-reference.rst#executorch.exir.EdgeProgramManager.to_executorch), and it will write memory allocation information stored on the nodes to the file path "memory_profile.json". The file is compatible with the Chrome trace viewer; see below for more information about interpreting the results. ```python from executorch.util.activation_memory_profiler import generate_memory_trace @@ -13,18 +13,18 @@ generate_memory_trace( enable_memory_offsets=True, ) ``` -* `prog` is an instance of [`ExecuTorchProgramManager`](https://pytorch.org/executorch/main/export-to-executorch-api-reference.html#executorch.exir.ExecutorchProgramManager), returned by [to_executorch()](https://pytorch.org/executorch/main/export-to-executorch-api-reference.html#executorch.exir.EdgeProgramManager.to_executorch). +* `prog` is an instance of [`ExecuTorchProgramManager`](export-to-executorch-api-reference.rst#executorch.exir.ExecutorchProgramManager), returned by [to_executorch()](export-to-executorch-api-reference.rst#executorch.exir.EdgeProgramManager.to_executorch). * Set `enable_memory_offsets` to `True` to show the location of each tensor on the memory space. ## Chrome Trace Open a Chrome browser tab and navigate to . Upload the generated `.json` to view. Example of a [MobileNet V2](https://pytorch.org/vision/main/models/mobilenetv2.html) model: -![Memory planning Chrome trace visualization](/_static/img/memory_planning_inspection.png) +![Memory planning Chrome trace visualization](_static/img/memory_planning_inspection.png) Note that, since we are repurposing the Chrome trace tool, the axes in this context may have different meanings compared to other Chrome trace graphs you may have encountered previously: * The horizontal axis, despite being labeled in seconds (s), actually represents megabytes (MBs). * The vertical axis has a 2-level hierarchy. The first level, "pid", represents memory space. For CPU, everything is allocated on one "space"; other backends may have multiple. In the second level, each row represents one time step. Since nodes will be executed sequentially, each node represents one time step, thus you will have as many nodes as there are rows. ## Further Reading -* [Memory Planning](https://pytorch.org/executorch/main/compiler-memory-planning.html) +* [Memory Planning](compiler-memory-planning.md) diff --git a/docs/source/new-contributor-guide.md b/docs/source/new-contributor-guide.md index 3b2eebfa5f5..cc33e81d508 100644 --- a/docs/source/new-contributor-guide.md +++ b/docs/source/new-contributor-guide.md @@ -129,7 +129,7 @@ Before you can start writing any code, you need to get a copy of ExecuTorch code git push # push updated local main to your GitHub fork ``` -6. [Build the project](https://pytorch.org/executorch/main/using-executorch-building-from-source.html) and [run the tests](https://github.com/pytorch/executorch/blob/main/CONTRIBUTING.md#testing). +6. [Build the project](using-executorch-building-from-source.md) and [run the tests](https://github.com/pytorch/executorch/blob/main/CONTRIBUTING.md#testing). Unfortunately, this step is too long to detail here. If you get stuck at any point, please feel free to ask for help on our [Discord server](https://discord.com/invite/Dh43CKSAdc) — we're always eager to help newcomers get onboarded. diff --git a/docs/source/using-executorch-android.md b/docs/source/using-executorch-android.md index 2b0d04da6c7..207da320ba9 100644 --- a/docs/source/using-executorch-android.md +++ b/docs/source/using-executorch-android.md @@ -2,7 +2,7 @@ To use from Android, ExecuTorch provides Java/Kotlin API bindings and Android platform integration, available as an AAR file. -Note: This page covers Android app integration through the AAR library. The ExecuTorch C++ APIs can also be used from Android native, and the documentation can be found on [this page about cross compilation](https://pytorch.org/executorch/main/using-executorch-building-from-source.html#cross-compilation). +Note: This page covers Android app integration through the AAR library. The ExecuTorch C++ APIs can also be used from Android native, and the documentation can be found on [this page about cross compilation](using-executorch-building-from-source.md#cross-compilation). ## Installation @@ -41,8 +41,8 @@ dependencies { Note: If you want to use release v0.5.0, please use dependency `org.pytorch:executorch-android:0.5.1`. Click the screenshot below to watch the *demo video* on how to add the package and run a simple ExecuTorch model with Android Studio. - - Integrating and Running ExecuTorch on Android + + Integrating and Running ExecuTorch on Android ## Using AAR file directly @@ -130,17 +130,17 @@ Set environment variable `EXECUTORCH_CMAKE_BUILD_TYPE` to `Release` or `Debug` b #### Using MediaTek backend -To use [MediaTek backend](https://pytorch.org/executorch/main/backends-mediatek.html), +To use [MediaTek backend](backends-mediatek.md), after installing and setting up the SDK, set `NEURON_BUFFER_ALLOCATOR_LIB` and `NEURON_USDK_ADAPTER_LIB` to the corresponding path. #### Using Qualcomm AI Engine Backend -To use [Qualcomm AI Engine Backend](https://pytorch.org/executorch/main/backends-qualcomm.html#qualcomm-ai-engine-backend), +To use [Qualcomm AI Engine Backend](backends-qualcomm.md#qualcomm-ai-engine-backend), after installing and setting up the SDK, set `QNN_SDK_ROOT` to the corresponding path. #### Using Vulkan Backend -To use [Vulkan Backend](https://pytorch.org/executorch/main/backends-vulkan.html#vulkan-backend), +To use [Vulkan Backend](backends-vulkan.md#vulkan-backend), set `EXECUTORCH_BUILD_VULKAN` to `ON`. ## Android Backends @@ -149,10 +149,10 @@ The following backends are available for Android: | Backend | Type | Doc | | ------- | -------- | --- | -| [XNNPACK](https://github.com/google/XNNPACK) | CPU | [Doc](./backends-xnnpack.md) | -| [MediaTek NeuroPilot](https://neuropilot.mediatek.com/) | NPU | [Doc](./backends-mediatek.md) | -| [Qualcomm AI Engine](https://www.qualcomm.com/developer/software/qualcomm-ai-engine-direct-sdk) | NPU | [Doc](./backends-qualcomm.md) | -| [Vulkan](https://www.vulkan.org/) | GPU | [Doc](./backends-vulkan.md) | +| [XNNPACK](https://github.com/google/XNNPACK) | CPU | [Doc](backends-xnnpack.md) | +| [MediaTek NeuroPilot](https://neuropilot.mediatek.com/) | NPU | [Doc](backends-mediatek.md) | +| [Qualcomm AI Engine](https://www.qualcomm.com/developer/software/qualcomm-ai-engine-direct-sdk) | NPU | [Doc](backends-qualcomm.md) | +| [Vulkan](https://www.vulkan.org/) | GPU | [Doc](backends-vulkan.md) | ## Runtime Integration diff --git a/docs/source/using-executorch-ios.md b/docs/source/using-executorch-ios.md index 08c862341b5..61b260f4a00 100644 --- a/docs/source/using-executorch-ios.md +++ b/docs/source/using-executorch-ios.md @@ -35,8 +35,8 @@ Then select which ExecuTorch framework should link against which target. Click the screenshot below to watch the *demo video* on how to add the package and run a simple ExecuTorch model on iOS. - - Integrating and Running ExecuTorch on Apple Platforms + + Integrating and Running ExecuTorch on Apple Platforms #### CLI @@ -293,7 +293,7 @@ From existing memory buffers: From `NSData` / `Data`: - `init(data:shape:dataType:...)`: Creates a tensor using an `NSData` object, referencing its bytes without copying. - + From scalar arrays: - `init(_:shape:dataType:...)`: Creates a tensor from an array of `NSNumber` scalars. Convenience initializers exist to infer shape or data type. diff --git a/examples/demo-apps/android/LlamaDemo/docs/delegates/mediatek_README.md b/examples/demo-apps/android/LlamaDemo/docs/delegates/mediatek_README.md index abd3c5f31b9..2ad87df0653 100644 --- a/examples/demo-apps/android/LlamaDemo/docs/delegates/mediatek_README.md +++ b/examples/demo-apps/android/LlamaDemo/docs/delegates/mediatek_README.md @@ -65,7 +65,7 @@ export ANDROID_ABIS=arm64-v8a MTK currently supports Llama 3 exporting. ### Set up Environment -1. Follow the ExecuTorch set-up environment instructions found on the [Getting Started](https://pytorch.org/executorch/stable/getting-started-setup.html) page +1. Follow the ExecuTorch set-up environment instructions found on the [Getting Started](https://pytorch.org/executorch/main/getting-started-setup.html) page 2. Set-up MTK AoT environment ``` // Ensure that you are inside executorch/examples/mediatek directory diff --git a/examples/demo-apps/apple_ios/LLaMA/README.md b/examples/demo-apps/apple_ios/LLaMA/README.md index 5ac8c80ca78..e44b4502cac 100644 --- a/examples/demo-apps/apple_ios/LLaMA/README.md +++ b/examples/demo-apps/apple_ios/LLaMA/README.md @@ -56,7 +56,7 @@ Link your binary with the ExecuTorch runtime and any backends or kernels used by Note: To access logs, link against the Debug build of the ExecuTorch runtime, i.e., the executorch_debug framework. For optimal performance, always link against the Release version of the deliverables (those without the _debug suffix), which have all logging overhead removed. -For more details integrating and Running ExecuTorch on Apple Platforms, checkout this [link](https://pytorch.org/executorch/main/apple-runtime.html). +For more details integrating and Running ExecuTorch on Apple Platforms, checkout this [link](https://pytorch.org/executorch/main/using-executorch-ios.html). ### XCode * Open XCode and select "Open an existing project" to open `examples/demo-apps/apple_ios/LLama`. diff --git a/examples/demo-apps/apple_ios/LLaMA/docs/delegates/mps_README.md b/examples/demo-apps/apple_ios/LLaMA/docs/delegates/mps_README.md index bffe4465eee..8601774f0e8 100644 --- a/examples/demo-apps/apple_ios/LLaMA/docs/delegates/mps_README.md +++ b/examples/demo-apps/apple_ios/LLaMA/docs/delegates/mps_README.md @@ -9,7 +9,7 @@ More specifically, it covers: ## Prerequisites * [Xcode 15](https://developer.apple.com/xcode) * [iOS 18 SDK](https://developer.apple.com/ios) -* Set up your ExecuTorch repo and environment if you haven’t done so by following the [Setting up ExecuTorch](https://pytorch.org/executorch/stable/getting-started-setup) to set up the repo and dev environment: +* Set up your ExecuTorch repo and environment if you haven’t done so by following the [Setting up ExecuTorch](https://pytorch.org/executorch/stable/using-executorch-building-from-source) to set up the repo and dev environment: ## Setup ExecuTorch In this section, we will need to set up the ExecuTorch repo first with Conda environment management. Make sure you have Conda available in your system (or follow the instructions to install it [here](https://conda.io/projects/conda/en/latest/user-guide/install/index.html)). The commands below are running on Linux (CentOS). @@ -85,7 +85,7 @@ Link your binary with the ExecuTorch runtime and any backends or kernels used by Note: To access logs, link against the Debug build of the ExecuTorch runtime, i.e., the executorch_debug framework. For optimal performance, always link against the Release version of the deliverables (those without the _debug suffix), which have all logging overhead removed. -For more details integrating and Running ExecuTorch on Apple Platforms, checkout this [link](https://pytorch.org/executorch/main/apple-runtime.html). +For more details integrating and Running ExecuTorch on Apple Platforms, checkout this [link](https://pytorch.org/executorch/using-executorch-ios.html).

iOS LLaMA App Swift PM diff --git a/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md b/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md index ffcb9f894b7..7e5090410a3 100644 --- a/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md +++ b/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md @@ -163,7 +163,7 @@ If you cannot add the package into your app target (it's greyed out), it might h - More details on integrating and Running ExecuTorch on Apple Platforms, check out the detailed guide [here](https://pytorch.org/executorch/main/apple-runtime.html#local-build). + More details on integrating and Running ExecuTorch on Apple Platforms, check out the detailed guide [here](https://pytorch.org/executorch/main/using-executorch-ios.html#local-build). ### 3. Configure Build Schemes @@ -175,7 +175,7 @@ Navigate to `Product --> Scheme --> Edit Scheme --> Info --> Build Configuration We recommend that you only use the Debug build scheme during development, where you might need to access additional logs. Debug build has logging overhead and will impact inferencing performance, while release build has compiler optimizations enabled and all logging overhead removed. -For more details integrating and Running ExecuTorch on Apple Platforms or building the package locally, checkout this [link](https://pytorch.org/executorch/main/apple-runtime.html). +For more details integrating and Running ExecuTorch on Apple Platforms or building the package locally, checkout this [link](https://pytorch.org/executorch/main/using-executorch-ios.html). ### 4. Build and Run the project From 4bce90f16c6409fd93a49d4e7dfd09d5d0fcfc5c Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Mon, 14 Apr 2025 15:17:54 -0700 Subject: [PATCH 131/244] Update android docs for nightly snapshots After 2025-04-12, user can use any date of AAR they want. --- docs/source/using-executorch-android.md | 17 ++++++++++++++--- 1 file changed, 14 insertions(+), 3 deletions(-) diff --git a/docs/source/using-executorch-android.md b/docs/source/using-executorch-android.md index 207da320ba9..e141140692a 100644 --- a/docs/source/using-executorch-android.md +++ b/docs/source/using-executorch-android.md @@ -58,9 +58,20 @@ You can also directly specify an AAR file in the app. We upload pre-built AAR to ### Snapshots from main branch -| Date | AAR | SHASUMS | -| ------- | --- | ------- | -| 2025-02-27 | [executorch.aar](https://ossci-android.s3.amazonaws.com/executorch/release/executorch-20250227/executorch.aar) | [executorch.aar.sha256sums](https://ossci-android.s3.amazonaws.com/executorch/release/executorch-20250227/executorch.aar.sha256sums) | +Starting from 2025-04-12, you can download nightly `main` branch snapshots: +* `executorch.aar`: `https://ossci-android.s3.amazonaws.com/executorch/release/snapshot-YYYYMMDD/executorch.aar` +* `executorch.aar.sha256sums`: `https://ossci-android.s3.amazonaws.com/executorch/release/snapshot-YYYYMMDD/executorch.aar.sha256sums` +* Replace `YYYYMMDD` with the actual date you want to use. +* AAR file is generated by [this workflow](https://github.com/pytorch/executorch/blob/c66b37d010c88a113560693b14dc6bd112593c11/.github/workflows/android-release-artifacts.yml#L14-L15). + +For example: + +```sh +curl -O https://ossci-android.s3.amazonaws.com/executorch/release/snapshot-20250412/executorch.aar +curl -O https://ossci-android.s3.amazonaws.com/executorch/release/snapshot-20250412/executorch.aar.sha256sums +``` + +We aim to make every daily snapshot available and useable. However, for best stability, please use releases, not snapshots. ## Using AAR file From a306f4f1c5eeaf727ee87537844fef0526049001 Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Mon, 14 Apr 2025 15:43:49 -0700 Subject: [PATCH 132/244] Update mps_README.md (#10167) --- examples/demo-apps/apple_ios/LLaMA/docs/delegates/mps_README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/demo-apps/apple_ios/LLaMA/docs/delegates/mps_README.md b/examples/demo-apps/apple_ios/LLaMA/docs/delegates/mps_README.md index 8601774f0e8..5c1a7437435 100644 --- a/examples/demo-apps/apple_ios/LLaMA/docs/delegates/mps_README.md +++ b/examples/demo-apps/apple_ios/LLaMA/docs/delegates/mps_README.md @@ -85,7 +85,7 @@ Link your binary with the ExecuTorch runtime and any backends or kernels used by Note: To access logs, link against the Debug build of the ExecuTorch runtime, i.e., the executorch_debug framework. For optimal performance, always link against the Release version of the deliverables (those without the _debug suffix), which have all logging overhead removed. -For more details integrating and Running ExecuTorch on Apple Platforms, checkout this [link](https://pytorch.org/executorch/using-executorch-ios.html). +For more details integrating and Running ExecuTorch on Apple Platforms, checkout this [link](https://pytorch.org/executorch/main/using-executorch-ios.html).

iOS LLaMA App Swift PM From f76bb50a721771b83478fe80815887849fa3eba9 Mon Sep 17 00:00:00 2001 From: Guang Yang <42389959+guangy10@users.noreply.github.com> Date: Mon, 14 Apr 2025 15:46:42 -0700 Subject: [PATCH 133/244] [doc] Link Hugging Face models to the ExecuTorch doc (#10154) ### Summary ExecuTorch doc and repo have been successfully linked to the main entry on [Hugging Face doc](https://huggingface.co/docs/optimum/main/en/index), now we need to link the Hugging Face doc and repo ([`huggingface/optimum-executorch`](https://github.com/huggingface/optimum-executorch)) to ExecuTorch doc for seamless experience. ### Test plan Doc build on CI Co-authored-by: Guang Yang --- docs/source/getting-started.md | 5 ++++- docs/source/index.md | 1 + 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/source/getting-started.md b/docs/source/getting-started.md index e0d87d7d87c..c4ccd66c5e8 100644 --- a/docs/source/getting-started.md +++ b/docs/source/getting-started.md @@ -43,7 +43,8 @@ ExecuTorch provides hardware acceleration for a wide variety of hardware. The mo For mobile use cases, consider using XNNPACK for Android and Core ML or XNNPACK for iOS as a first step. See [Hardware Backends](backends-overview.md) for more information. ### Exporting -Exporting is done using Python APIs. ExecuTorch provides a high degree of customization during the export process, but the typical flow is as follows. This example uses the MobileNet V2 image classification model implementation in torchvision, but the process supports any [export-compliant](https://pytorch.org/docs/stable/export.html) PyTorch model. +Exporting is done using Python APIs. ExecuTorch provides a high degree of customization during the export process, but the typical flow is as follows. This example uses the MobileNet V2 image classification model implementation in torchvision, but the process supports any [export-compliant](https://pytorch.org/docs/stable/export.html) PyTorch model. For users working with Hugging Face models, +you can find a list of supported models in the [*huggingface/optimum-executorch*](https://github.com/huggingface/optimum-executorch) repo. ```python import torch @@ -101,6 +102,8 @@ print(torch.allclose(output[0], eager_reference_output, rtol=1e-3, atol=1e-5)) For complete examples of exporting and running the model, please refer to our [examples GitHub repository](https://github.com/pytorch-labs/executorch-examples/tree/main/mv2/python). +Additionally, if you work with Hugging Face models, the [*huggingface/optimum-executorch*](https://github.com/huggingface/optimum-executorch) library simplifies running these models end-to-end with ExecuTorch, using familiar Hugging Face APIs. Visit the repository for specific examples and supported models. +

## Running on Device diff --git a/docs/source/index.md b/docs/source/index.md index 47ea42a21ea..01f883020f3 100644 --- a/docs/source/index.md +++ b/docs/source/index.md @@ -43,6 +43,7 @@ ExecuTorch provides support for: #### Examples - [Android Demo Apps](https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo#executorch-android-demo-app) - [iOS Demo Apps](demo-apps-ios.md) +- [Hugging Face Models](https://github.com/huggingface/optimum-executorch/blob/main/README.md) #### Backends - [Overview](backends-overview) - [XNNPACK](backends-xnnpack) From 22c69ff27c18e059983834f43426d64aaf82c75d Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Mon, 14 Apr 2025 15:47:23 -0700 Subject: [PATCH 134/244] Update README.md (#10168) --- backends/xnnpack/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/backends/xnnpack/README.md b/backends/xnnpack/README.md index 967a852599a..0bb5ffe7383 100644 --- a/backends/xnnpack/README.md +++ b/backends/xnnpack/README.md @@ -131,6 +131,6 @@ create an issue on [github](https://www.github.com/pytorch/executorch/issues). ## See Also -For more information about the XNNPACK Delegate, please check out the following resources: -- [ExecuTorch XNNPACK Delegate](https://pytorch.org/executorch/0.2/native-delegates-executorch-xnnpack-delegate.html) -- [Building and Running ExecuTorch with XNNPACK Backend](https://pytorch.org/executorch/0.2/native-delegates-executorch-xnnpack-delegate.html) +For more information about the XNNPACK Backend, please check out the following resources: +- [XNNPACK Backend](https://pytorch.org/executorch/main/backends-xnnpack.html) +- [XNNPACK Backend Internals](https://pytorch.org/executorch/main/backend-delegates-xnnpack-reference.html) From 64ad4cfa769a98379d75fede251906498edb2ba7 Mon Sep 17 00:00:00 2001 From: Gregory Comer Date: Mon, 14 Apr 2025 15:57:08 -0700 Subject: [PATCH 135/244] Fix compiler warnings in a few places (#10165) Summary: There are two places that are emitting warnings, causing builds with -Werror to fail. Neither are correctness issues, but this PR cleans them up to enable build with -Werror. Differential Revision: D72996608 cc @larryliu0820 @JacobSzwejbka @lucylq --- runtime/core/exec_aten/testing_util/tensor_factory.h | 2 +- test/utils/DeathTest.h | 4 ++++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/runtime/core/exec_aten/testing_util/tensor_factory.h b/runtime/core/exec_aten/testing_util/tensor_factory.h index 367db09285a..1e29b220251 100644 --- a/runtime/core/exec_aten/testing_util/tensor_factory.h +++ b/runtime/core/exec_aten/testing_util/tensor_factory.h @@ -133,7 +133,7 @@ inline bool check_dim_order( size_t gauss_sum = 0; std::vector count(dim_order.size(), 0); for (int i = 0; i < dim_order.size(); i++) { - if (dim_order[i] < 0 || dim_order[i] >= sizes.size()) { + if (dim_order[i] >= sizes.size()) { return false; } gauss_sum += static_cast(dim_order[i]) + 1; diff --git a/test/utils/DeathTest.h b/test/utils/DeathTest.h index 2ba9bd61bd9..be83593adf1 100644 --- a/test/utils/DeathTest.h +++ b/test/utils/DeathTest.h @@ -15,6 +15,10 @@ #include +#ifndef ET_BUILD_MODE_COV +#define ET_BUILD_MODE_COV 0 +#endif // ET_BUILD_MODE_COV + #if ET_BUILD_MODE_COV /** From 83dbad6d0fcbdc90fa75daaab1dbbf44d9b92eb4 Mon Sep 17 00:00:00 2001 From: mcremon-meta <134334895+mcremon-meta@users.noreply.github.com> Date: Mon, 14 Apr 2025 16:36:35 -0700 Subject: [PATCH 136/244] Add approximate gelu replacement to opt level 2 Differential Revision: D72935935 Pull Request resolved: https://github.com/pytorch/executorch/pull/10129 --- backends/cadence/aot/replace_ops.py | 97 +++++++++++++++++++ .../aot/tests/test_replace_ops_passes.py | 36 +++++++ 2 files changed, 133 insertions(+) diff --git a/backends/cadence/aot/replace_ops.py b/backends/cadence/aot/replace_ops.py index 5a4922ae069..f2cda53981d 100644 --- a/backends/cadence/aot/replace_ops.py +++ b/backends/cadence/aot/replace_ops.py @@ -2110,6 +2110,102 @@ def call_operator( return super().call_operator(op, args, kwargs, meta) +@register_cadence_pass(CadencePassAttribute(opt_level=2)) +class ReplaceGeluWithApproximateGeluPass(ExportPass): + """ + Replace the gelu op with an approximate gelu op. The approximate gelu op + is more efficient on DSP backends. + """ + + def call_operator( + self, + op, + args: Tuple[Argument, ...], + kwargs: Dict[str, Argument], + meta: NodeMetadata, + ) -> ProxyValue: + if op not in { + exir_ops.edge.aten.gelu.default, + }: + return super().call_operator(op, args, kwargs, meta) + + # compute the approximate gelu (0.7978845608028654 is sqrt(2 / pi)) + # as 0.5 * x * (1 + torch.tanh(0.7978845608028654 * ( x + 0.044715 * x^3))) + + # Get 0.5 * x + half = super().call_operator( + exir_ops.edge.aten.mul.Tensor, + (args[0], 0.5), + {}, + meta, + ) + + scaled = super().call_operator( + exir_ops.edge.aten.mul.Tensor, + (args[0], 0.044715), + {}, + meta, + ) + + # Get x^2 (note that we use mul.Tensor twice instead of pow.Tensor because + # it is much more efficient on DSP backends) + scaled_square = super().call_operator( + exir_ops.edge.aten.mul.Tensor, + (scaled, args[0]), + {}, + meta, + ) + + # Get x^3 + scaled_cubed = super().call_operator( + exir_ops.edge.aten.mul.Tensor, + (scaled_square, args[0]), + {}, + meta, + ) + + # Get x + 0.044715 * x^3 + inner_sum = super().call_operator( + exir_ops.edge.aten.add.Tensor, + (scaled_cubed, args[0]), + {}, + meta, + ) + + # Get 0.7978845608028654 * ( x + 0.044715 * x^3) + scaled_sum = super().call_operator( + exir_ops.edge.aten.mul.Tensor, + (inner_sum, 0.7978845608028654), + {}, + meta, + ) + + # Get torch.tanh(0.7978845608028654 * ( x + 0.044715 * x^3)) + tanh = super().call_operator( + exir_ops.edge.aten.tanh.default, + (scaled_sum,), + {}, + meta, + ) + + # Get 1 + torch.tanh(0.79788456 * ( x + 0.044715 * x^3)) + # TODO(): Check why this is not working properly with integer values (e.g. 1 instead of 1.) + outer_sum = super().call_operator( + exir_ops.edge.aten.add.Tensor, + (tanh, 1.0), + {}, + meta, + ) + + # Retunr the final result + return super().call_operator( + exir_ops.edge.aten.mul.Tensor, + (half, outer_sum), + {}, + meta, + ) + + # This class encapsulates all the functions that replace/switch one op in the # graph with another. class CadenceReplaceOpsInGraph: @@ -2149,4 +2245,5 @@ class CadenceReplaceOpsInGraph: ReplaceAtenAvgPoolWithJarvisAvgPoolPass, ReplaceAtenLinalgVectorNormWithCadenceLinalgVectorNormPass, ReplaceWhereWithFullArgsWithWhereScalar, + # ReplaceGeluWithApproximateGeluPass, ] diff --git a/backends/cadence/aot/tests/test_replace_ops_passes.py b/backends/cadence/aot/tests/test_replace_ops_passes.py index e40c26c0f4e..c22dd947d5d 100644 --- a/backends/cadence/aot/tests/test_replace_ops_passes.py +++ b/backends/cadence/aot/tests/test_replace_ops_passes.py @@ -29,6 +29,7 @@ ReplaceConvWithIm2RowAndLinear, ReplaceEmptyTensorsWithFullPass, ReplaceFunctionallyEquivalentOpTargets, + ReplaceGeluWithApproximateGeluPass, ReplaceIm2RowWithViewPass, ReplaceLinearWithFullyConnectedOpPass, ReplaceMMWithAddMMPass, @@ -1301,6 +1302,41 @@ def forward(self, cond: torch.Tensor): 1, ) + def test_replace_aten_gelu_with_approximate_gelu(self): + class Gelu(torch.nn.Module): + def forward(self, input): + return torch.nn.functional.gelu(input) + + inputs = torch.randn(2, 1, 64) + + graph_module = export_to_edge(Gelu(), (inputs,)).exported_program().graph_module + + p = ReplaceGeluWithApproximateGeluPass() + graph_after_passes = cast(PassResult, p(graph_module)).graph_module + + # Assert that aten.gelu op was decomposed + self.assertEqual( + count_node( + graph_after_passes, + exir_ops.edge.aten.gelu.default, + ), + 0, + ) + + # The decomposition should have one tanh, 2 add and 6 mul + self.assertEqual( + count_node(graph_after_passes, exir_ops.edge.aten.tanh.default), + 1, + ) + self.assertEqual( + count_node(graph_after_passes, exir_ops.edge.aten.add.Tensor), + 2, + ) + self.assertEqual( + count_node(graph_after_passes, exir_ops.edge.aten.mul.Tensor), + 6, + ) + class TestReplaceIm2rowWithViewPass(unittest.TestCase): def test_no_replacement_for_conv(self): From 264942ceee84fc6657c5dcca6969a65bdeaee5e9 Mon Sep 17 00:00:00 2001 From: lucylq Date: Mon, 14 Apr 2025 17:10:09 -0700 Subject: [PATCH 137/244] Add '--recursive' to git submodule update --init (#10178) See edit here: https://github.com/pytorch/executorch/pull/10082/files --- docs/source/using-executorch-building-from-source.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/using-executorch-building-from-source.md b/docs/source/using-executorch-building-from-source.md index 18d860cbc14..69127225fbf 100644 --- a/docs/source/using-executorch-building-from-source.md +++ b/docs/source/using-executorch-building-from-source.md @@ -113,7 +113,7 @@ Or alternatively, [install conda on your machine](https://conda.io/projects/cond > # From the root of the executorch repo: > ./install_executorch.sh --clean > git submodule sync -> git submodule update --init +> git submodule update --init --recursive > ``` ## Build ExecuTorch C++ runtime from source From 8fff36c4fa331f64b2cb6cb556ec8db70a31235e Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Mon, 14 Apr 2025 17:53:22 -0700 Subject: [PATCH 138/244] Consolidate references in docs (#10175) Stick to the main branch on main and remove .html suffixes where possible --- CONTRIBUTING.md | 26 ++++++++--------- Package.swift | 2 +- README-wheel.md | 29 ++++++------------- README.md | 10 +++---- backends/apple/mps/setup.md | 2 +- backends/mediatek/README.md | 2 +- backends/openvino/README.md | 2 +- backends/qualcomm/README.md | 2 +- docs/source/_templates/layout.html | 8 ++--- examples/README.md | 4 +-- examples/apple/coreml/README.md | 2 +- examples/apple/mps/README.md | 2 +- examples/arm/README.md | 2 +- .../LLaMA/docs/delegates/mps_README.md | 4 +-- examples/devtools/README.md | 2 +- examples/models/llama/UTILS.md | 2 +- examples/models/llama/non_cpu_backends.md | 4 +-- examples/models/phi-3-mini-lora/README.md | 2 +- examples/portable/README.md | 2 +- examples/portable/custom_ops/README.md | 2 +- examples/qualcomm/README.md | 2 +- examples/qualcomm/oss_scripts/llama/README.md | 2 +- .../qualcomm/qaihub_scripts/llama/README.md | 4 +-- .../qaihub_scripts/stable_diffusion/README.md | 2 +- examples/selective_build/README.md | 2 +- examples/xnnpack/README.md | 6 ++-- exir/program/_program.py | 4 +-- extension/android/README.md | 2 +- extension/pybindings/pybindings.pyi | 4 +-- runtime/COMPATIBILITY.md | 2 +- 30 files changed, 65 insertions(+), 76 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 32681cdb08f..a96a5fe00d3 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -5,10 +5,10 @@ it easy to contribute to this project. ## Dev Install Set up your environment by following the instructions at -https://pytorch.org/executorch/stable/getting-started-setup.html to clone +https://pytorch.org/executorch/main/getting-started-setup to clone the repo and install the necessary requirements. -Refer to this [document](https://pytorch.org/executorch/main/using-executorch-building-from-source.html) to build ExecuTorch from source. +Refer to this [document](docs/source/using-executorch-building-from-source.md) to build ExecuTorch from source. ### Dev Setup for Android For Android, please refer to the [Android documentation](docs/source/using-executorch-android.md). @@ -40,8 +40,8 @@ executorch ├── devtools - Model profiling, debugging, and inspection. Please refer to the tools documentation for more information. │ ├── bundled_program - a tool for validating ExecuTorch model. See doc. │ ├── etdump - ETDump - a format for saving profiling and debugging data from runtime. See doc. -│ ├── etrecord - ETRecord - AOT debug artifact for ExecuTorch. See doc. -│ ├── inspector - Python API to inspect ETDump and ETRecord. See doc. +│ ├── etrecord - ETRecord - AOT debug artifact for ExecuTorch. See doc. +│ ├── inspector - Python API to inspect ETDump and ETRecord. See doc. │ └── visualization - Visualization tools for representing model structure and performance metrics. ├── docs - Static docs tooling and documentation source files. ├── examples - Examples of various user flows, such as model export, delegates, and runtime execution. @@ -57,8 +57,8 @@ executorch │ ├── serde - Graph module serialization/deserialization. │ ├── verification - IR verification. ├── extension - Extensions built on top of the runtime. -│ ├── android - ExecuTorch wrappers for Android apps. Please refer to the Android documentation and Javadoc for more information. -│ ├── apple - ExecuTorch wrappers for iOS apps. Please refer to the iOS documentation and how to integrate into Apple platform for more information. +│ ├── android - ExecuTorch wrappers for Android apps. Please refer to the Android documentation and Javadoc for more information. +│ ├── apple - ExecuTorch wrappers for iOS apps. Please refer to the iOS documentation on how to integrate into Apple platform for more information. │ ├── aten_util - Converts to and from PyTorch ATen types. │ ├── data_loader - 1st party data loader implementations. │ ├── evalue_util - Helpers for working with EValue objects. @@ -68,10 +68,10 @@ executorch │ ├── memory_allocator - 1st party memory allocator implementations. │ ├── module - A simplified C++ wrapper for the runtime. An abstraction that deserializes and executes an ExecuTorch artifact (.pte file). Refer to the module documentation for more information. │ ├── parallel - C++ threadpool integration. -│ ├── pybindings - Python API for executorch runtime. This is powering up the runtime Python API for ExecuTorch. +│ ├── pybindings - Python API for executorch runtime. This is powering up the runtime Python API for ExecuTorch. │ ├── pytree - C++ and Python flattening and unflattening lib for pytrees. │ ├── runner_util - Helpers for writing C++ PTE-execution tools. -│ ├── tensor - Tensor maker and TensorPtr, details in this documentation. For how to use TensorPtr and Module, please refer to the "Using ExecuTorch with C++" doc. +│ ├── tensor - Tensor maker and TensorPtr, details in this documentation. For how to use TensorPtr and Module, please refer to the "Using ExecuTorch with C++" doc. │ ├── testing_util - Helpers for writing C++ tests. │ ├── threadpool - Threadpool. │ └── training - Experimental libraries for on-device training. @@ -85,7 +85,7 @@ executorch ├── runtime - Core C++ runtime. These components are used to execute the ExecuTorch program. Please refer to the runtime documentation for more information. │ ├── backend - Backend delegate runtime APIs. │ ├── core - Core structures used across all levels of the runtime. Basic components such as Tensor, EValue, Error and Result etc. -│ ├── executor - Model loading, initialization, and execution. Runtime components that execute the ExecuTorch program, such as Program, Method. Refer to the runtime API documentation for more information. +│ ├── executor - Model loading, initialization, and execution. Runtime components that execute the ExecuTorch program, such as Program, Method. Refer to the runtime API documentation for more information. │ ├── kernel - Kernel registration and management. │ └── platform - Layer between architecture specific code and portable C++. ├── schema - ExecuTorch PTE file format flatbuffer schemas. @@ -102,7 +102,7 @@ executorch ## Contributing workflow We actively welcome your pull requests (PRs). -If you're completely new to open-source projects, GitHub, or ExecuTorch, please see our [New Contributor Guide](./docs/source/new-contributor-guide.md) for a step-by-step walkthrough on making your first contribution. Otherwise, read on. +If you're completely new to open-source projects, GitHub, or ExecuTorch, please see our [New Contributor Guide](docs/source/new-contributor-guide.md) for a step-by-step walkthrough on making your first contribution. Otherwise, read on. 1. [Claim an issue](#claiming-issues), if present, before starting work. If an issue doesn't cover the work you plan to do, consider creating one to provide @@ -245,7 +245,7 @@ modifications to the Google C++ style guide. ### C++ Portability Guidelines -See also [Portable C++ Programming](/docs/source/portable-cpp-programming.md) +See also [Portable C++ Programming](docs/source/portable-cpp-programming.md) for detailed advice. #### C++ language version @@ -417,9 +417,9 @@ for basics. ## For Backend Delegate Authors -- Use [this](/docs/source/backend-delegates-integration.md) guide when +- Use [this](docs/source/backend-delegates-integration.md) guide when integrating your delegate with ExecuTorch. -- Refer to [this](/docs/source/backend-delegates-dependencies.md) set of +- Refer to [this](docs/source/backend-delegates-dependencies.md) set of guidelines when including a third-party depenency for your delegate.   diff --git a/Package.swift b/Package.swift index 1322b918c07..b8a8b7d064b 100644 --- a/Package.swift +++ b/Package.swift @@ -15,7 +15,7 @@ // // For details on building frameworks locally or using prebuilt binaries, // see the documentation: -// https://pytorch.org/executorch/main/using-executorch-ios.html +// https://pytorch.org/executorch/main/using-executorch-ios import PackageDescription diff --git a/README-wheel.md b/README-wheel.md index 9f074ab5ee3..12752bcabfa 100644 --- a/README-wheel.md +++ b/README-wheel.md @@ -10,32 +10,21 @@ The `executorch` pip package is in beta. The prebuilt `executorch.runtime` module included in this package provides a way to run ExecuTorch `.pte` files, with some restrictions: -* Only [core ATen - operators](https://pytorch.org/executorch/stable/ir-ops-set-definition.html) - are linked into the prebuilt module -* Only the [XNNPACK backend - delegate](https://pytorch.org/executorch/main/native-delegates-executorch-xnnpack-delegate.html) - is linked into the prebuilt module. -* \[macOS only] [Core ML](https://pytorch.org/executorch/main/build-run-coreml.html) - and [MPS](https://pytorch.org/executorch/main/build-run-mps.html) backend - delegates are also linked into the prebuilt module. +* Only [core ATen operators](docs/source/ir-ops-set-definition.md) are linked into the prebuilt module +* Only the [XNNPACK backend delegate](docs/source/backends-xnnpack.md) is linked into the prebuilt module. +* \[macOS only] [Core ML](docs/source/backends-coreml.md) and [MPS](docs/source/backends-mps.md) backend + are also linked into the prebuilt module. -Please visit the [ExecuTorch website](https://pytorch.org/executorch/) for +Please visit the [ExecuTorch website](https://pytorch.org/executorch) for tutorials and documentation. Here are some starting points: -* [Getting - Started](https://pytorch.org/executorch/stable/getting-started-setup.html) +* [Getting Started](https://pytorch.org/executorch/main/getting-started-setup) * Set up the ExecuTorch environment and run PyTorch models locally. -* [Working with - local LLMs](https://pytorch.org/executorch/stable/llm/getting-started.html) +* [Working with local LLMs](docs/source/llm/getting-started.md) * Learn how to use ExecuTorch to export and accelerate a large-language model from scratch. -* [Exporting to - ExecuTorch](https://pytorch.org/executorch/main/tutorials/export-to-executorch-tutorial.html) +* [Exporting to ExecuTorch](https://pytorch.org/executorch/main/tutorials/export-to-executorch-tutorial) * Learn the fundamentals of exporting a PyTorch `nn.Module` to ExecuTorch, and optimizing its performance using quantization and hardware delegation. -* Running LLaMA on - [iOS](https://pytorch.org/executorch/stable/llm/llama-demo-ios.html) and - [Android](https://pytorch.org/executorch/stable/llm/llama-demo-android.html) - devices. +* Running LLaMA on [iOS](docs/source/llm/llama-demo-ios) and [Android](docs/source/llm/llama-demo-android) devices. * Build and run LLaMA in a demo mobile app, and learn how to integrate models with your own apps. diff --git a/README.md b/README.md index 025a8780739..c0d594e7733 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@
- Logo + Logo

ExecuTorch: A powerful on-device AI Framework

@@ -8,7 +8,7 @@ Contributors Stargazers Join our Discord community - Check out the documentation + Check out the documentation
@@ -49,9 +49,9 @@ Key value propositions of ExecuTorch are: ## Getting Started To get started you can: -- Visit the [Step by Step Tutorial](https://pytorch.org/executorch/main/index.html) to get things running locally and deploy a model to a device -- Use this [Colab Notebook](https://pytorch.org/executorch/stable/getting-started-setup.html#quick-setup-colab-jupyter-notebook-prototype) to start playing around right away -- Jump straight into LLM use cases by following specific instructions for [Llama](./examples/models/llama/README.md) and [Llava](./examples/models/llava/README.md) +- Visit the [Step by Step Tutorial](https://pytorch.org/executorch/main/index) to get things running locally and deploy a model to a device +- Use this [Colab Notebook](https://pytorch.org/executorch/main/getting-started-setup#quick-setup-colab-jupyter-notebook-prototype) to start playing around right away +- Jump straight into LLM use cases by following specific instructions for [Llama](examples/models/llama/README.md) and [Llava](examples/models/llava/README.md) ## Feedback and Engagement diff --git a/backends/apple/mps/setup.md b/backends/apple/mps/setup.md index 5c14ad673df..f0c4c378e3f 100644 --- a/backends/apple/mps/setup.md +++ b/backends/apple/mps/setup.md @@ -40,7 +40,7 @@ In order to be able to successfully build and run a model using the MPS backend ## Setting up Developer Environment -***Step 1.*** Please finish tutorial [Setting up ExecuTorch](https://pytorch.org/executorch/stable/getting-started-setup). +***Step 1.*** Please finish tutorial [Setting up ExecuTorch](https://pytorch.org/executorch/main/getting-started-setup). ***Step 2.*** Install dependencies needed to lower MPS delegate: diff --git a/backends/mediatek/README.md b/backends/mediatek/README.md index ec4c392eb46..0a756a7bf1a 100644 --- a/backends/mediatek/README.md +++ b/backends/mediatek/README.md @@ -43,7 +43,7 @@ Download [NeuroPilot Express SDK](https://neuropilot.mediatek.com/resources/publ Follow the steps below to setup your build environment: -1. **Setup ExecuTorch Environment**: Refer to the [Setting up ExecuTorch](https://pytorch.org/executorch/stable/getting-started-setup) guide for detailed instructions on setting up the ExecuTorch environment. +1. **Setup ExecuTorch Environment**: Refer to the [Setting up ExecuTorch](https://pytorch.org/executorch/main/getting-started-setup) guide for detailed instructions on setting up the ExecuTorch environment. 2. **Setup MediaTek Backend Environment** - Install the dependent libs. Ensure that you are inside backends/mediatek/ directory diff --git a/backends/openvino/README.md b/backends/openvino/README.md index 95a5f4c364e..b79689c2b56 100644 --- a/backends/openvino/README.md +++ b/backends/openvino/README.md @@ -63,7 +63,7 @@ For more information about OpenVINO build, refer to the [OpenVINO Build Instruct Follow the steps below to setup your build environment: -1. **Setup ExecuTorch Environment**: Refer to the [Environment Setup](https://pytorch.org/executorch/stable/getting-started-setup#environment-setup) guide for detailed instructions on setting up the ExecuTorch environment. +1. **Setup ExecuTorch Environment**: Refer to the [Environment Setup](https://pytorch.org/executorch/main/getting-started-setup#environment-setup) guide for detailed instructions on setting up the ExecuTorch environment. 2. **Setup OpenVINO Backend Environment** - Install the dependent libs. Ensure that you are inside `executorch/backends/openvino/` directory diff --git a/backends/qualcomm/README.md b/backends/qualcomm/README.md index 85019add313..5cde568957e 100644 --- a/backends/qualcomm/README.md +++ b/backends/qualcomm/README.md @@ -8,7 +8,7 @@ This backend is implemented on the top of [Qualcomm AI Engine Direct SDK](https://developer.qualcomm.com/software/qualcomm-ai-engine-direct-sdk). Please follow [tutorial](../../docs/source/build-run-qualcomm-ai-engine-direct-backend.md) to setup environment, build, and run executorch models by this backend (Qualcomm AI Engine Direct is also referred to as QNN in the source and documentation). -A website version of the tutorial is [here](https://pytorch.org/executorch/stable/build-run-qualcomm-ai-engine-direct-backend.html). +A website version of the tutorial is [here](https://pytorch.org/executorch/main/build-run-qualcomm-ai-engine-direct-backend). ## Delegate Options diff --git a/docs/source/_templates/layout.html b/docs/source/_templates/layout.html index 210153e123c..155ae5eaf06 100644 --- a/docs/source/_templates/layout.html +++ b/docs/source/_templates/layout.html @@ -131,14 +131,14 @@ $(".main-menu a:contains('GitHub')").each(overwrite); // Overwrite link to Tutorials and Get Started top navigation. If these sections are moved // this overrides need to be updated. - $(".main-menu a:contains('Tutorials')").attr("href", "https://pytorch.org/executorch/stable/index.html#tutorials-and-examples"); - $(".main-menu a:contains('Get Started')").attr("href", "https://pytorch.org/executorch/stable/getting-started-setup.html"); + $(".main-menu a:contains('Tutorials')").attr("href", "https://pytorch.org/executorch/main/index#tutorials-and-examples"); + $(".main-menu a:contains('Get Started')").attr("href", "https://pytorch.org/executorch/main/getting-started-setup"); // Mobile $(".mobile-menu a:contains('Github')").each(overwrite); // Overwrite link to Tutorials and Get Started top navigation. If these sections are moved // this overrides need to be updated. - $(".mobile-menu a:contains('Tutorials')").attr("href", "https://pytorch.org/executorch/stable/index.html#tutorials-and-examples"); - $(".mobile-menu a:contains('Get Started')").attr("href", "https://pytorch.org/executorch/stable/getting-started-setup.html"); + $(".mobile-menu a:contains('Tutorials')").attr("href", "https://pytorch.org/executorch/main/index#tutorials-and-examples"); + $(".mobile-menu a:contains('Get Started')").attr("href", "https://pytorch.org/executorch/main/getting-started-setup"); }); diff --git a/examples/README.md b/examples/README.md index 17999b15423..b6a4e0a0472 100644 --- a/examples/README.md +++ b/examples/README.md @@ -9,7 +9,7 @@ ExecuTorch's extensive support spans from simple modules like "Add" to comprehen ## Directory structure ``` examples -├── llm_manual # A storage place for the files that [LLM Maunal](https://pytorch.org/executorch/main/llm/getting-started.html) needs +├── llm_manual # A storage place for the files that [LLM Maunal](https://pytorch.org/executorch/main/llm/getting-started) needs ├── models # Contains a set of popular and representative PyTorch models ├── portable # Contains end-to-end demos for ExecuTorch in portable mode ├── selective_build # Contains demos of selective build for optimizing the binary size of the ExecuTorch runtime @@ -75,7 +75,7 @@ The [`Cadence/`](./cadence) directory hosts a demo that showcases the process of ## Dependencies -Various models and workflows listed in this directory have dependencies on some other packages. You need to follow the setup guide in [Setting up ExecuTorch from GitHub](https://pytorch.org/executorch/stable/getting-started-setup) to have appropriate packages installed. +Various models and workflows listed in this directory have dependencies on some other packages. You need to follow the setup guide in [Setting up ExecuTorch from GitHub](https://pytorch.org/executorch/main/getting-started-setup) to have appropriate packages installed. # Disclaimer diff --git a/examples/apple/coreml/README.md b/examples/apple/coreml/README.md index 4dba5031358..a4234e72c2d 100644 --- a/examples/apple/coreml/README.md +++ b/examples/apple/coreml/README.md @@ -15,7 +15,7 @@ coreml We will walk through an example model to generate a Core ML delegated binary file from a python `torch.nn.module` then we will use the `coreml_executor_runner` to run the exported binary file. -1. Following the setup guide in [Setting Up ExecuTorch](https://pytorch.org/executorch/stable/getting-started-setup) +1. Following the setup guide in [Setting Up ExecuTorch](https://pytorch.org/executorch/main/getting-started-setup) you should be able to get the basic development environment for ExecuTorch working. diff --git a/examples/apple/mps/README.md b/examples/apple/mps/README.md index dc01d585f84..2eafb86f1c6 100644 --- a/examples/apple/mps/README.md +++ b/examples/apple/mps/README.md @@ -8,7 +8,7 @@ This README gives some examples on backend-specific model workflow. ## Prerequisite Please finish the following tutorials: -- [Setting up ExecuTorch](https://pytorch.org/executorch/stable/getting-started-setup). +- [Setting up ExecuTorch](https://pytorch.org/executorch/main/getting-started-setup). - [Setting up MPS backend](../../../backends/apple/mps/setup.md). ## Delegation to MPS backend diff --git a/examples/arm/README.md b/examples/arm/README.md index 8762e7ccdd1..25f2fd5a316 100644 --- a/examples/arm/README.md +++ b/examples/arm/README.md @@ -44,6 +44,6 @@ jupyter notebook ethos_u_minimal_example.ipynb ### Online Tutorial -We also have a [tutorial](https://pytorch.org/executorch/stable/executorch-arm-delegate-tutorial.html) explaining the steps performed in these +We also have a [tutorial](https://pytorch.org/executorch/main/executorch-arm-delegate-tutorial) explaining the steps performed in these scripts, expected results, possible problems and more. It is a step-by-step guide you can follow to better understand this delegate. diff --git a/examples/demo-apps/apple_ios/LLaMA/docs/delegates/mps_README.md b/examples/demo-apps/apple_ios/LLaMA/docs/delegates/mps_README.md index 5c1a7437435..b16f27410af 100644 --- a/examples/demo-apps/apple_ios/LLaMA/docs/delegates/mps_README.md +++ b/examples/demo-apps/apple_ios/LLaMA/docs/delegates/mps_README.md @@ -9,7 +9,7 @@ More specifically, it covers: ## Prerequisites * [Xcode 15](https://developer.apple.com/xcode) * [iOS 18 SDK](https://developer.apple.com/ios) -* Set up your ExecuTorch repo and environment if you haven’t done so by following the [Setting up ExecuTorch](https://pytorch.org/executorch/stable/using-executorch-building-from-source) to set up the repo and dev environment: +* Set up your ExecuTorch repo and environment if you haven’t done so by following the [Setting up ExecuTorch](https://pytorch.org/executorch/main/using-executorch-building-from-source) to set up the repo and dev environment: ## Setup ExecuTorch In this section, we will need to set up the ExecuTorch repo first with Conda environment management. Make sure you have Conda available in your system (or follow the instructions to install it [here](https://conda.io/projects/conda/en/latest/user-guide/install/index.html)). The commands below are running on Linux (CentOS). @@ -85,7 +85,7 @@ Link your binary with the ExecuTorch runtime and any backends or kernels used by Note: To access logs, link against the Debug build of the ExecuTorch runtime, i.e., the executorch_debug framework. For optimal performance, always link against the Release version of the deliverables (those without the _debug suffix), which have all logging overhead removed. -For more details integrating and Running ExecuTorch on Apple Platforms, checkout this [link](https://pytorch.org/executorch/main/using-executorch-ios.html). +For more details integrating and Running ExecuTorch on Apple Platforms, checkout this [link](https://pytorch.org/executorch/main/using-executorch-ios).

iOS LLaMA App Swift PM diff --git a/examples/devtools/README.md b/examples/devtools/README.md index e4fbadfcca0..0b516ad629e 100644 --- a/examples/devtools/README.md +++ b/examples/devtools/README.md @@ -17,7 +17,7 @@ examples/devtools We will use an example model (in `torch.nn.Module`) and its representative inputs, both from [`models/`](../models) directory, to generate a [BundledProgram(`.bpte`)](../../docs/source/bundled-io.md) file using the [script](scripts/export_bundled_program.py). Then we will use [devtools/example_runner](example_runner/example_runner.cpp) to execute the `.bpte` model on the ExecuTorch runtime and verify the model on BundledProgram API. -1. Sets up the basic development environment for ExecuTorch by [Setting up ExecuTorch from GitHub](https://pytorch.org/executorch/stable/getting-started-setup). +1. Sets up the basic development environment for ExecuTorch by [Setting up ExecuTorch from GitHub](https://pytorch.org/executorch/main/getting-started-setup). 2. Using the [script](scripts/export_bundled_program.py) to generate a BundledProgram binary file by retreiving a `torch.nn.Module` model and its representative inputs from the list of available models in the [`models/`](../models) dir. diff --git a/examples/models/llama/UTILS.md b/examples/models/llama/UTILS.md index dd014240ace..2d4d4dfd788 100644 --- a/examples/models/llama/UTILS.md +++ b/examples/models/llama/UTILS.md @@ -25,7 +25,7 @@ From `executorch` root: ## Smaller model delegated to other backends Currently we supported lowering the stories model to other backends, including, CoreML, MPS and QNN. Please refer to the instruction -for each backend ([CoreML](https://pytorch.org/executorch/main/build-run-coreml.html), [MPS](https://pytorch.org/executorch/main/build-run-mps.html), [QNN](https://pytorch.org/executorch/main/build-run-qualcomm-ai-engine-direct-backend.html)) before trying to lower them. After the backend library is installed, the script to export a lowered model is +for each backend ([CoreML](https://pytorch.org/executorch/main/backends-coreml), [MPS](https://pytorch.org/executorch/main/backends-mps), [QNN](https://pytorch.org/executorch/main/backend-qualcomm)) before trying to lower them. After the backend library is installed, the script to export a lowered model is - Lower to CoreML: `python -m examples.models.llama.export_llama -kv --disable_dynamic_shape --coreml -c stories110M.pt -p params.json ` - MPS: `python -m examples.models.llama.export_llama -kv --disable_dynamic_shape --mps -c stories110M.pt -p params.json ` diff --git a/examples/models/llama/non_cpu_backends.md b/examples/models/llama/non_cpu_backends.md index 1ee594ebd83..f414582a3c1 100644 --- a/examples/models/llama/non_cpu_backends.md +++ b/examples/models/llama/non_cpu_backends.md @@ -2,7 +2,7 @@ # Running Llama 3/3.1 8B on non-CPU backends ### QNN -Please follow [the instructions](https://pytorch.org/executorch/stable/llm/build-run-llama3-qualcomm-ai-engine-direct-backend.html) to deploy Llama 3 8B to an Android smartphone with Qualcomm SoCs. +Please follow [the instructions](https://pytorch.org/executorch/main/llm/build-run-llama3-qualcomm-ai-engine-direct-backend) to deploy Llama 3 8B to an Android smartphone with Qualcomm SoCs. ### MPS Export: @@ -10,7 +10,7 @@ Export: python -m examples.models.llama2.export_llama --checkpoint llama3.pt --params params.json -kv --disable_dynamic_shape --mps --use_sdpa_with_kv_cache -d fp32 -qmode 8da4w -G 32 --embedding-quantize 4,32 ``` -After exporting the MPS model .pte file, the [iOS LLAMA](https://pytorch.org/executorch/main/llm/llama-demo-ios.html) app can support running the model. ` --embedding-quantize 4,32` is an optional args for quantizing embedding to reduce the model size. +After exporting the MPS model .pte file, the [iOS LLAMA](https://pytorch.org/executorch/main/llm/llama-demo-ios) app can support running the model. ` --embedding-quantize 4,32` is an optional args for quantizing embedding to reduce the model size. ### CoreML Export: diff --git a/examples/models/phi-3-mini-lora/README.md b/examples/models/phi-3-mini-lora/README.md index 2b7cc0ba401..5bc48bd48f4 100644 --- a/examples/models/phi-3-mini-lora/README.md +++ b/examples/models/phi-3-mini-lora/README.md @@ -16,7 +16,7 @@ To see how you can use the model exported for training in a fully involved finet python export_model.py ``` -2. Run the inference model using an example runtime. For more detailed steps on this, check out [Build & Run](https://pytorch.org/executorch/stable/getting-started-setup.html#build-run). +2. Run the inference model using an example runtime. For more detailed steps on this, check out [Build & Run](https://pytorch.org/executorch/main/getting-started-setup#build-run). ``` # Clean and configure the CMake build system. Compiled programs will appear in the executorch/cmake-out directory we create here. ./install_executorch.sh --clean diff --git a/examples/portable/README.md b/examples/portable/README.md index a6658197da3..6bfc9aa7281 100644 --- a/examples/portable/README.md +++ b/examples/portable/README.md @@ -20,7 +20,7 @@ We will walk through an example model to generate a `.pte` file in [portable mod from the [`models/`](../models) directory using scripts in the `portable/scripts` directory. Then we will run on the `.pte` model on the ExecuTorch runtime. For that we will use `executor_runner`. -1. Following the setup guide in [Setting up ExecuTorch](https://pytorch.org/executorch/stable/getting-started-setup) +1. Following the setup guide in [Setting up ExecuTorch](https://pytorch.org/executorch/main/getting-started-setup) you should be able to get the basic development environment for ExecuTorch working. 2. Using the script `portable/scripts/export.py` generate a model binary file by selecting a diff --git a/examples/portable/custom_ops/README.md b/examples/portable/custom_ops/README.md index db517e84a0c..bf17d6a6753 100644 --- a/examples/portable/custom_ops/README.md +++ b/examples/portable/custom_ops/README.md @@ -3,7 +3,7 @@ This folder contains examples to register custom operators into PyTorch as well ## How to run -Prerequisite: finish the [setting up wiki](https://pytorch.org/executorch/stable/getting-started-setup). +Prerequisite: finish the [setting up wiki](https://pytorch.org/executorch/main/getting-started-setup). Run: diff --git a/examples/qualcomm/README.md b/examples/qualcomm/README.md index bdac58d2bfc..1f7e2d1e476 100644 --- a/examples/qualcomm/README.md +++ b/examples/qualcomm/README.md @@ -22,7 +22,7 @@ Here are some general information and limitations. ## Prerequisite -Please finish tutorial [Setting up executorch](https://pytorch.org/executorch/stable/getting-started-setup). +Please finish tutorial [Setting up executorch](https://pytorch.org/executorch/main/getting-started-setup). Please finish [setup QNN backend](../../docs/source/build-run-qualcomm-ai-engine-direct-backend.md). diff --git a/examples/qualcomm/oss_scripts/llama/README.md b/examples/qualcomm/oss_scripts/llama/README.md index 9b6ec9574eb..27abd5689a0 100644 --- a/examples/qualcomm/oss_scripts/llama/README.md +++ b/examples/qualcomm/oss_scripts/llama/README.md @@ -28,7 +28,7 @@ Hybrid Mode: Hybrid mode leverages the strengths of both AR-N model and KV cache ### Step 1: Setup 1. Follow the [tutorial](https://pytorch.org/executorch/main/getting-started-setup) to set up ExecuTorch. -2. Follow the [tutorial](https://pytorch.org/executorch/stable/build-run-qualcomm-ai-engine-direct-backend.html) to build Qualcomm AI Engine Direct Backend. +2. Follow the [tutorial](https://pytorch.org/executorch/main/build-run-qualcomm-ai-engine-direct-backend) to build Qualcomm AI Engine Direct Backend. ### Step 2: Prepare Model diff --git a/examples/qualcomm/qaihub_scripts/llama/README.md b/examples/qualcomm/qaihub_scripts/llama/README.md index 0fec6ea867f..4d010b5d474 100644 --- a/examples/qualcomm/qaihub_scripts/llama/README.md +++ b/examples/qualcomm/qaihub_scripts/llama/README.md @@ -12,7 +12,7 @@ Note that the pre-compiled context binaries could not be futher fine-tuned for o ### Instructions #### Step 1: Setup 1. Follow the [tutorial](https://pytorch.org/executorch/main/getting-started-setup) to set up ExecuTorch. -2. Follow the [tutorial](https://pytorch.org/executorch/stable/build-run-qualcomm-ai-engine-direct-backend.html) to build Qualcomm AI Engine Direct Backend. +2. Follow the [tutorial](https://pytorch.org/executorch/main/build-run-qualcomm-ai-engine-direct-backend) to build Qualcomm AI Engine Direct Backend. #### Step2: Prepare Model 1. Create account for https://aihub.qualcomm.com/ @@ -40,7 +40,7 @@ Note that the pre-compiled context binaries could not be futher fine-tuned for o ### Instructions #### Step 1: Setup 1. Follow the [tutorial](https://pytorch.org/executorch/main/getting-started-setup) to set up ExecuTorch. -2. Follow the [tutorial](https://pytorch.org/executorch/stable/build-run-qualcomm-ai-engine-direct-backend.html) to build Qualcomm AI Engine Direct Backend. +2. Follow the [tutorial](https://pytorch.org/executorch/main/build-run-qualcomm-ai-engine-direct-backend) to build Qualcomm AI Engine Direct Backend. #### Step2: Prepare Model 1. Create account for https://aihub.qualcomm.com/ diff --git a/examples/qualcomm/qaihub_scripts/stable_diffusion/README.md b/examples/qualcomm/qaihub_scripts/stable_diffusion/README.md index b008d3135d4..998c97d78e3 100644 --- a/examples/qualcomm/qaihub_scripts/stable_diffusion/README.md +++ b/examples/qualcomm/qaihub_scripts/stable_diffusion/README.md @@ -11,7 +11,7 @@ The model architecture, scheduler, and time embedding are from the [stabilityai/ ### Instructions #### Step 1: Setup 1. Follow the [tutorial](https://pytorch.org/executorch/main/getting-started-setup) to set up ExecuTorch. -2. Follow the [tutorial](https://pytorch.org/executorch/stable/build-run-qualcomm-ai-engine-direct-backend.html) to build Qualcomm AI Engine Direct Backend. +2. Follow the [tutorial](https://pytorch.org/executorch/main/build-run-qualcomm-ai-engine-direct-backend) to build Qualcomm AI Engine Direct Backend. #### Step2: Prepare Model 1. Download the context binaries for TextEncoder, UNet, and VAEDecoder under https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/tree/main diff --git a/examples/selective_build/README.md b/examples/selective_build/README.md index 6c655e18a3d..97706d70c48 100644 --- a/examples/selective_build/README.md +++ b/examples/selective_build/README.md @@ -3,7 +3,7 @@ To optimize binary size of ExecuTorch runtime, selective build can be used. This ## How to run -Prerequisite: finish the [setting up wiki](https://pytorch.org/executorch/stable/getting-started-setup). +Prerequisite: finish the [setting up wiki](https://pytorch.org/executorch/main/getting-started-setup). Run: diff --git a/examples/xnnpack/README.md b/examples/xnnpack/README.md index 179e47004a1..56deff928af 100644 --- a/examples/xnnpack/README.md +++ b/examples/xnnpack/README.md @@ -1,8 +1,8 @@ # XNNPACK Backend [XNNPACK](https://github.com/google/XNNPACK) is a library of optimized neural network operators for ARM and x86 CPU platforms. Our delegate lowers models to run using these highly optimized CPU operators. You can try out lowering and running some example models in the demo. Please refer to the following docs for information on the XNNPACK Delegate -- [XNNPACK Backend Delegate Overview](https://pytorch.org/executorch/stable/native-delegates-executorch-xnnpack-delegate.html) -- [XNNPACK Delegate Export Tutorial](https://pytorch.org/executorch/stable/tutorial-xnnpack-delegate-lowering.html) +- [XNNPACK Backend Delegate Overview](https://pytorch.org/executorch/main/native-delegates-executorch-xnnpack-delegate) +- [XNNPACK Delegate Export Tutorial](https://pytorch.org/executorch/main/tutorial-xnnpack-delegate-lowering) ## Directory structure @@ -60,7 +60,7 @@ Now finally you should be able to run this model with the following command ``` ## Quantization -First, learn more about the generic PyTorch 2 Export Quantization workflow in the [Quantization Flow Docs](https://pytorch.org/executorch/stable/quantization-overview.html), if you are not familiar already. +First, learn more about the generic PyTorch 2 Export Quantization workflow in the [Quantization Flow Docs](https://pytorch.org/executorch/main/quantization-overview), if you are not familiar already. Here we will discuss quantizing a model suitable for XNNPACK delegation using XNNPACKQuantizer. diff --git a/exir/program/_program.py b/exir/program/_program.py index ef857ffd011..da5ca06c927 100644 --- a/exir/program/_program.py +++ b/exir/program/_program.py @@ -1325,7 +1325,7 @@ def to_edge( class EdgeProgramManager: """ Package of one or more `ExportedPrograms` in Edge dialect. Designed to simplify - lowering to ExecuTorch. See: https://pytorch.org/executorch/stable/ir-exir.html + lowering to ExecuTorch. See: https://pytorch.org/executorch/main/ir-exir Allows easy applications of transforms across a collection of exported programs including the delegation of subgraphs. @@ -1565,7 +1565,7 @@ def to_executorch( class ExecutorchProgramManager: """ Package of one or more `ExportedPrograms` in Execution dialect. Designed to simplify - lowering to ExecuTorch. See: https://pytorch.org/executorch/stable/ir-exir.html + lowering to ExecuTorch. See: https://pytorch.org/executorch/main/ir-exir When the ExecutorchProgramManager is constructed the ExportedPrograms in execution dialect are used to form the executorch binary (in a process called emission) and then serialized diff --git a/extension/android/README.md b/extension/android/README.md index 8972e615173..1274a18e447 100644 --- a/extension/android/README.md +++ b/extension/android/README.md @@ -28,7 +28,7 @@ export ANDROID_NDK=/path/to/ndk sh scripts/build_android_library.sh ``` -Please see [Android building from source](https://pytorch.org/executorch/main/using-executorch-android.html#building-from-source) for details +Please see [Android building from source](https://pytorch.org/executorch/main/using-executorch-android#building-from-source) for details ## Test diff --git a/extension/pybindings/pybindings.pyi b/extension/pybindings/pybindings.pyi index 64ea14f08ff..7aede1c29a9 100644 --- a/extension/pybindings/pybindings.pyi +++ b/extension/pybindings/pybindings.pyi @@ -161,7 +161,7 @@ def _load_for_executorch( Args: path: File path to the ExecuTorch program as a string. enable_etdump: If true, enables an ETDump which can store profiling information. - See documentation at https://pytorch.org/executorch/stable/etdump.html + See documentation at https://pytorch.org/executorch/main/etdump for how to use it. debug_buffer_size: If non-zero, enables a debug buffer which can store intermediate results of each instruction in the ExecuTorch program. @@ -192,7 +192,7 @@ def _load_for_executorch_from_bundled_program( ) -> ExecuTorchModule: """Same as _load_for_executorch, but takes a bundled program instead of a file path. - See https://pytorch.org/executorch/stable/bundled-io.html for documentation. + See https://pytorch.org/executorch/main/bundled-io for documentation. .. warning:: diff --git a/runtime/COMPATIBILITY.md b/runtime/COMPATIBILITY.md index 7d9fd47c590..583dab172cc 100644 --- a/runtime/COMPATIBILITY.md +++ b/runtime/COMPATIBILITY.md @@ -1,7 +1,7 @@ # Runtime Compatibility Policy This document describes the compatibility guarantees between the [PTE file -format](https://pytorch.org/executorch/stable/pte-file-format.html) and the +format](https://pytorch.org/executorch/main/pte-file-format) and the ExecuTorch runtime. > [!IMPORTANT] From ee37fba901717e3d2703c80467f1bcc05f5bcd91 Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Mon, 14 Apr 2025 18:13:42 -0700 Subject: [PATCH 139/244] Fix android instrumentation (#10125) For CI, just run module test. LLM test won't work on emulator. OOM. Locally, use the instructions and it will work. --- .github/workflows/_android.yml | 3 ++- .../executorch_android/android_test_setup.sh | 13 +++++++------ .../LlmModuleInstrumentationTest.java | 1 - scripts/run_android_emulator.sh | 19 ++++++++----------- 4 files changed, 17 insertions(+), 19 deletions(-) diff --git a/.github/workflows/_android.yml b/.github/workflows/_android.yml index a38ed4527e9..2a91fb45ffa 100644 --- a/.github/workflows/_android.yml +++ b/.github/workflows/_android.yml @@ -131,7 +131,8 @@ jobs: # https://github.com/ReactiveCircus/android-emulator-runner. The max number # of cores we can set is 6, any higher number will be reduced to 6. cores: 6 - ram-size: 12288M + ram-size: 16384M + heap-size: 12288M force-avd-creation: false disable-animations: true emulator-options: -no-snapshot-save -no-window -gpu swiftshader_indirect -noaudio -no-boot-anim -camera-back none diff --git a/extension/android/executorch_android/android_test_setup.sh b/extension/android/executorch_android/android_test_setup.sh index 68f4d9dd6f6..fff8ba33618 100644 --- a/extension/android/executorch_android/android_test_setup.sh +++ b/extension/android/executorch_android/android_test_setup.sh @@ -12,17 +12,18 @@ if [[ -z "${PYTHON_EXECUTABLE:-}" ]]; then fi which "${PYTHON_EXECUTABLE}" -BASEDIR=$(dirname "$0") +BASEDIR=$(dirname "$(realpath $0)") cp "${BASEDIR}/../../../extension/module/test/resources/add.pte" "${BASEDIR}/src/androidTest/resources" pushd "${BASEDIR}/../../../" -curl -Ls "https://huggingface.co/karpathy/tinyllamas/resolve/main/stories110M.pt" --output stories110M.pt -curl -Ls "https://raw.githubusercontent.com/karpathy/llama2.c/master/tokenizer.model" --output tokenizer.model +curl -C - -Ls "https://huggingface.co/karpathy/tinyllamas/resolve/main/stories110M.pt" --output stories110M.pt +curl -C - -Ls "https://raw.githubusercontent.com/karpathy/llama2.c/master/tokenizer.model" --output tokenizer.model # Create params.json file touch params.json echo '{"dim": 768, "multiple_of": 32, "n_heads": 12, "n_layers": 12, "norm_eps": 1e-05, "vocab_size": 32000}' > params.json -python -m examples.models.llama.export_llama -c stories110M.pt -p params.json -X -kv --model=stories110m +python -m examples.models.llama.export_llama -c stories110M.pt -p params.json -d fp16 -n stories110m_h.pte -kv +python -m pytorch_tokenizers.tools.llama2c.convert -t tokenizer.model -o tokenizer.bin -cp *.pte "${BASEDIR}/src/androidTest/resources/stories.pte" -cp tokenizer.model "${BASEDIR}/src/androidTest/resources/tokenizer.bin" +cp stories110m_h.pte "${BASEDIR}/src/androidTest/resources/stories.pte" +cp tokenizer.bin "${BASEDIR}/src/androidTest/resources/tokenizer.bin" popd diff --git a/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/LlmModuleInstrumentationTest.java b/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/LlmModuleInstrumentationTest.java index ae81a21f420..e95b42f2650 100644 --- a/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/LlmModuleInstrumentationTest.java +++ b/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/LlmModuleInstrumentationTest.java @@ -86,7 +86,6 @@ public void testGenerate() throws IOException, URISyntaxException{ @Test public void testGenerateAndStop() throws IOException, URISyntaxException{ - int seqLen = 32; mModule.generate(TEST_PROMPT, SEQ_LEN, new LlmCallback() { @Override public void onResult(String result) { diff --git a/scripts/run_android_emulator.sh b/scripts/run_android_emulator.sh index fe73ec8a1d7..52f5fe55bd8 100755 --- a/scripts/run_android_emulator.sh +++ b/scripts/run_android_emulator.sh @@ -18,17 +18,14 @@ $ADB_PATH wait-for-device shell 'while [[ -z $(getprop sys.boot_completed) ]]; d echo "List all running emulators" $ADB_PATH devices -adb uninstall com.example.executorchllamademo || true -adb uninstall com.example.executorchllamademo.test || true -adb install -t app-debug.apk -adb install -t app-debug-androidTest.apk - -adb shell mkdir -p /data/local/tmp/llama -adb push model.pte /data/local/tmp/llama -adb push tokenizer.bin /data/local/tmp/llama -adb shell am instrument -w -r com.example.executorchllamademo.test/androidx.test.runner.AndroidJUnitRunner - adb uninstall org.pytorch.executorch.test || true adb install -t android-test-debug-androidTest.apk -adb shell am instrument -w -r org.pytorch.executorch.test/androidx.test.runner.AndroidJUnitRunner +adb logcat -c +adb shell am instrument -w -r -e class org.pytorch.executorch.ModuleInstrumentationTest \ + org.pytorch.executorch.test/androidx.test.runner.AndroidJUnitRunner >result.txt 2>&1 +adb logcat -d > logcat.txt +cat logcat.txt +grep -q FAILURES result.txt && cat result.txt +grep -q FAILURES result.txt && exit -1 +exit 0 From fab63dc70b330daf69788a2144e3ab16fbad9a2e Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Mon, 14 Apr 2025 18:14:10 -0700 Subject: [PATCH 140/244] Update instrumentation test docs (#10173) CI won't test this due to OOM. Now we rely on local instrumentation test. --- .../demo-apps/android/LlamaDemo/README.md | 28 +++++++++++++++++++ .../LlamaDemo/run_instrumentation_test.sh | 28 +++++++++++++++++++ 2 files changed, 56 insertions(+) create mode 100644 examples/demo-apps/android/LlamaDemo/run_instrumentation_test.sh diff --git a/examples/demo-apps/android/LlamaDemo/README.md b/examples/demo-apps/android/LlamaDemo/README.md index a735b48dee1..fae91857fbe 100644 --- a/examples/demo-apps/android/LlamaDemo/README.md +++ b/examples/demo-apps/android/LlamaDemo/README.md @@ -141,5 +141,33 @@ Ensure you have the following functions in your callback class that you provided ``` +## Instrumentation Test +You can run the instrumentation test for sanity check. The test loads a model pte file and tokenizer.bin file +under `/data/local/tmp/llama`. + +### Model preparation +Go to ExecuTorch root, +```sh +curl -C - -Ls "https://huggingface.co/karpathy/tinyllamas/resolve/main/stories110M.pt" --output stories110M.pt +curl -C - -Ls "https://raw.githubusercontent.com/karpathy/llama2.c/master/tokenizer.model" --output tokenizer.model +# Create params.json file +touch params.json +echo '{"dim": 768, "multiple_of": 32, "n_heads": 12, "n_layers": 12, "norm_eps": 1e-05, "vocab_size": 32000}' > params.json +python -m examples.models.llama.export_llama -c stories110M.pt -p params.json -d fp16 -n stories110m_h.pte -kv +python -m pytorch_tokenizers.tools.llama2c.convert -t tokenizer.model -o tokenizer.bin +``` +### Push model +```sh +adb mkdir -p /data/local/tmp/llama +adb push stories110m_h.pte /data/local/tmp/llama +adb push tokenizer.bin /data/local/tmp/llama +``` + +### Run test +Go to `examples/demo-apps/android/LlamaDemo`, +```sh +./gradlew connectedAndroidTest +``` + ## Reporting Issues If you encountered any bugs or issues following this tutorial please file a bug/issue here on [Github](https://github.com/pytorch/executorch/issues/new). diff --git a/examples/demo-apps/android/LlamaDemo/run_instrumentation_test.sh b/examples/demo-apps/android/LlamaDemo/run_instrumentation_test.sh new file mode 100644 index 00000000000..5933b44b0a8 --- /dev/null +++ b/examples/demo-apps/android/LlamaDemo/run_instrumentation_test.sh @@ -0,0 +1,28 @@ +#!/usr/bin/env bash +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +set -eu + +BASEDIR=$(dirname "$0") +pushd "$BASEDIR"/../../../../ +curl -C - -Ls "https://huggingface.co/karpathy/tinyllamas/resolve/main/stories110M.pt" --output stories110M.pt +curl -C - -Ls "https://raw.githubusercontent.com/karpathy/llama2.c/master/tokenizer.model" --output tokenizer.model +# Create params.json file +touch params.json +echo '{"dim": 768, "multiple_of": 32, "n_heads": 12, "n_layers": 12, "norm_eps": 1e-05, "vocab_size": 32000}' > params.json +python -m examples.models.llama.export_llama -c stories110M.pt -p params.json -d fp16 -n stories110m_h.pte -kv +python -m pytorch_tokenizers.tools.llama2c.convert -t tokenizer.model -o tokenizer.bin + +adb mkdir -p /data/local/tmp/llama +adb push stories110m_h.pte /data/local/tmp/llama +adb push tokenizer.bin /data/local/tmp/llama +popd + +pushd "$BASEDIR" +./gradlew connectedAndroidTest +popd + From 2f0f732bbaf6664e6a3ffad5ff113ca5a4bdcfa5 Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Mon, 14 Apr 2025 18:20:54 -0700 Subject: [PATCH 141/244] Add docs for $BUILD_AAR_DIR (#10174) This helps users locate their AAR file. --- extension/android/README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/extension/android/README.md b/extension/android/README.md index 1274a18e447..5fc4ba4429d 100644 --- a/extension/android/README.md +++ b/extension/android/README.md @@ -28,6 +28,10 @@ export ANDROID_NDK=/path/to/ndk sh scripts/build_android_library.sh ``` +The AAR file will be `extension/android/executorch_android/build/outputs/aar/executorch_android-debug.aar`. +If you set an environment variable `BUILD_AAR_DIR`, then the AAR will be copied to `$BUILD_AAR_DIR/executorch.aar`. +Later, you can copy `$BUILD_AAR_DIR/executorch.aar` to your app directory to use as a library. + Please see [Android building from source](https://pytorch.org/executorch/main/using-executorch-android#building-from-source) for details ## Test From c95666459f1cf4a82580abf66a46f8d8c4ac33b1 Mon Sep 17 00:00:00 2001 From: Gyanendra Sinha Date: Mon, 14 Apr 2025 18:43:57 -0700 Subject: [PATCH 142/244] [Core ML] Improve error logging Differential Revision: D72251859 Pull Request resolved: https://github.com/pytorch/executorch/pull/9801 --- .../coreml/runtime/delegate/ETCoreMLAsset.h | 2 +- .../coreml/runtime/delegate/ETCoreMLAsset.mm | 12 +- .../runtime/delegate/ETCoreMLAssetManager.h | 2 +- .../runtime/delegate/ETCoreMLAssetManager.mm | 40 +++--- .../delegate/ETCoreMLDefaultModelExecutor.h | 8 +- .../delegate/ETCoreMLDefaultModelExecutor.mm | 21 +-- .../coreml/runtime/delegate/ETCoreMLLogging.h | 94 ++++++------ .../runtime/delegate/ETCoreMLLogging.mm | 4 +- .../coreml/runtime/delegate/ETCoreMLModel.mm | 24 ++-- .../runtime/delegate/ETCoreMLModelCompiler.mm | 25 ++-- .../runtime/delegate/ETCoreMLModelLoader.mm | 20 +-- .../runtime/delegate/ETCoreMLModelManager.mm | 135 ++++++++++-------- .../runtime/delegate/MLModel_Prewarm.mm | 47 +++++- .../runtime/delegate/MLMultiArray_Copy.mm | 24 ++-- .../apple/coreml/runtime/delegate/asset.mm | 6 +- .../runtime/delegate/backend_delegate.h | 3 +- .../runtime/delegate/backend_delegate.mm | 28 ++-- .../delegate/coreml_backend_delegate.mm | 23 +-- .../coreml/runtime/delegate/model_metadata.h | 2 +- .../coreml/runtime/delegate/multiarray.mm | 5 +- .../coreml/runtime/delegate/serde_json.mm | 8 +- .../runtime/sdk/ETCoreMLModelAnalyzer.mm | 10 +- .../runtime/sdk/ETCoreMLModelDebugger.mm | 39 ++--- .../runtime/sdk/ETCoreMLModelProfiler.h | 1 + .../runtime/sdk/ETCoreMLModelProfiler.mm | 9 +- .../sdk/ETCoreMLOperationProfilingInfo.h | 2 +- .../runtime/sdk/model_event_logger_impl.mm | 6 +- .../coreml/runtime/sdk/model_package_info.mm | 2 +- .../coreml/runtime/test/ETCoreMLTestUtils.mm | 9 +- .../coreml/scripts/build_executor_runner.sh | 26 +++- 30 files changed, 368 insertions(+), 269 deletions(-) diff --git a/backends/apple/coreml/runtime/delegate/ETCoreMLAsset.h b/backends/apple/coreml/runtime/delegate/ETCoreMLAsset.h index d97b3cf9b76..01655ca06c1 100644 --- a/backends/apple/coreml/runtime/delegate/ETCoreMLAsset.h +++ b/backends/apple/coreml/runtime/delegate/ETCoreMLAsset.h @@ -7,7 +7,7 @@ #import -#import +#import "asset.h" NS_ASSUME_NONNULL_BEGIN diff --git a/backends/apple/coreml/runtime/delegate/ETCoreMLAsset.mm b/backends/apple/coreml/runtime/delegate/ETCoreMLAsset.mm index 6b1723f7113..455edf89480 100644 --- a/backends/apple/coreml/runtime/delegate/ETCoreMLAsset.mm +++ b/backends/apple/coreml/runtime/delegate/ETCoreMLAsset.mm @@ -5,15 +5,15 @@ // // Please refer to the license found in the LICENSE file in the root directory of the source tree. -#import +#import "ETCoreMLAsset.h" + +#import "ETCoreMLLogging.h" +#import "objc_safe_cast.h" #import #import #import #import - -#import - namespace { using namespace executorchcoreml; @@ -85,6 +85,10 @@ - (void)dealloc { - (BOOL)_keepAliveAndReturnError:(NSError * __autoreleasing *)error { if (!_isValid) { + ETCoreMLLogErrorAndSetNSError(error, + ETCoreMLErrorCorruptedModel, + "The asset with identifier = %@ is invalid. Some required asset files appear to be missing.", + _identifier); return NO; } diff --git a/backends/apple/coreml/runtime/delegate/ETCoreMLAssetManager.h b/backends/apple/coreml/runtime/delegate/ETCoreMLAssetManager.h index 04fef204e1a..11d957044e9 100644 --- a/backends/apple/coreml/runtime/delegate/ETCoreMLAssetManager.h +++ b/backends/apple/coreml/runtime/delegate/ETCoreMLAssetManager.h @@ -7,7 +7,7 @@ #import -#import +#import "database.hpp" @class ETCoreMLAsset; diff --git a/backends/apple/coreml/runtime/delegate/ETCoreMLAssetManager.mm b/backends/apple/coreml/runtime/delegate/ETCoreMLAssetManager.mm index 73e9cc0f33b..256026e1f09 100644 --- a/backends/apple/coreml/runtime/delegate/ETCoreMLAssetManager.mm +++ b/backends/apple/coreml/runtime/delegate/ETCoreMLAssetManager.mm @@ -6,12 +6,14 @@ // Please refer to the license found in the LICENSE file in the root directory of the source tree. #import "ETCoreMLAssetManager.h" -#import -#import -#import + +#import "ETCoreMLAsset.h" +#import "ETCoreMLLogging.h" +#import "database.hpp" +#import "json_key_value_store.hpp" +#import "serde_json.h" + #import -#import -#import #import namespace { @@ -365,8 +367,7 @@ - (void)cleanupAssetIfNeeded:(ETCoreMLAsset *)asset { NSError *cleanupError = nil; if (![self _removeAssetWithIdentifier:asset.identifier error:&cleanupError]) { ETCoreMLLogError(cleanupError, - "%@: Failed to remove asset with identifier = %@", - NSStringFromClass(ETCoreMLAssetManager.class), + "Failed to remove asset with identifier = %@", identifier); } }); @@ -440,9 +441,7 @@ - (void)triggerCompaction { dispatch_async(self.syncQueue, ^{ NSError *localError = nil; if (![weakSelf _compact:self.maxAssetsSizeInBytes error:&localError]) { - ETCoreMLLogError(localError, - "%@: Failed to compact asset store.", - NSStringFromClass(ETCoreMLAssetManager.class)); + ETCoreMLLogError(localError, "Failed to compact asset store."); } }); } @@ -486,11 +485,11 @@ - (nullable ETCoreMLAsset *)assetWithIdentifier:(NSString *)identifier if ([result keepAliveAndReturnError:error]) { [self.assetsInUseMap setObject:result forKey:identifier]; - } else { - [self cleanupAssetIfNeeded:result]; - } + return result; + } - return result; + [self cleanupAssetIfNeeded:result]; + return nil; } - (BOOL)_containsAssetWithIdentifier:(NSString *)identifier @@ -587,8 +586,7 @@ - (BOOL)removeAssetWithIdentifier:(NSString *)identifier [assets addObject:asset]; } else if (localError) { ETCoreMLLogError(localError, - "%@: Failed to retrieve asset with identifier = %@", - NSStringFromClass(ETCoreMLAssetManager.class), + "Failed to retrieve asset with identifier = %@.", identifier); } @@ -647,8 +645,7 @@ - (NSUInteger)_compact:(NSUInteger)sizeInBytes error:(NSError * __autoreleasing NSString *identifier = @(asset.identifier.c_str()); if (![self _removeAssetWithIdentifier:identifier error:&cleanupError] && cleanupError) { ETCoreMLLogError(cleanupError, - "%@: Failed to remove asset with identifier = %@", - NSStringFromClass(ETCoreMLAssetManager.class), + "Failed to remove asset with identifier = %@.", identifier); } } @@ -689,8 +686,7 @@ - (void)removeFilesInTrashDirectory { for (NSURL *itemURL in enumerator) { if (![fileManager removeItemAtURL:itemURL error:&localError]) { ETCoreMLLogError(localError, - "%@: Failed to remove item in trash directory with name = %@", - NSStringFromClass(ETCoreMLAssetManager.class), + "Failed to remove item in trash directory with name = %@", itemURL.lastPathComponent); } } @@ -720,9 +716,7 @@ - (BOOL)_purge:(NSError * __autoreleasing *)error { NSError *localError = nil; // Create the assets directory, if we fail here it's okay. if (![self.fileManager createDirectoryAtURL:self.assetsDirectoryURL withIntermediateDirectories:NO attributes:@{} error:&localError]) { - ETCoreMLLogError(localError, - "%@: Failed to create assets directory", - NSStringFromClass(ETCoreMLAssetManager.class)); + ETCoreMLLogError(localError, "Failed to create assets directory."); } return true; diff --git a/backends/apple/coreml/runtime/delegate/ETCoreMLDefaultModelExecutor.h b/backends/apple/coreml/runtime/delegate/ETCoreMLDefaultModelExecutor.h index 13b1023bcbc..3cf9e3df5f4 100644 --- a/backends/apple/coreml/runtime/delegate/ETCoreMLDefaultModelExecutor.h +++ b/backends/apple/coreml/runtime/delegate/ETCoreMLDefaultModelExecutor.h @@ -1,13 +1,13 @@ // -// ETCoreMLDefaultModelExecutor.h -// executorchcoreml_tests +// ETCoreMLDefaultModelExecutor.h // -// Created by Gyan Sinha on 2/25/24. +// Copyright © 2024 Apple Inc. All rights reserved. // +// Please refer to the license found in the LICENSE file in the root directory of the source tree. #import -#import +#import "ETCoreMLModelExecutor.h" @class ETCoreMLModel; diff --git a/backends/apple/coreml/runtime/delegate/ETCoreMLDefaultModelExecutor.mm b/backends/apple/coreml/runtime/delegate/ETCoreMLDefaultModelExecutor.mm index 226307f3c8f..63bc60695ce 100644 --- a/backends/apple/coreml/runtime/delegate/ETCoreMLDefaultModelExecutor.mm +++ b/backends/apple/coreml/runtime/delegate/ETCoreMLDefaultModelExecutor.mm @@ -1,14 +1,14 @@ // -// ETCoreMLDefaultModelExecutor.m -// executorchcoreml_tests +// ETCoreMLDefaultModelExecutor.mm // -// Created by Gyan Sinha on 2/25/24. +// Copyright © 2024 Apple Inc. All rights reserved. // +// Please refer to the license found in the LICENSE file in the root directory of the source tree. -#import -#import -#import -#import +#import "ETCoreMLAsset.h" +#import "ETCoreMLDefaultModelExecutor.h" +#import "ETCoreMLLogging.h" +#import "ETCoreMLModel.h" @implementation ETCoreMLDefaultModelExecutor @@ -27,7 +27,9 @@ - (instancetype)initWithModel:(ETCoreMLModel *)model { eventLogger:(const executorchcoreml::ModelEventLogger* _Nullable __unused)eventLogger error:(NSError * __autoreleasing *)error { if (self.ignoreOutputBackings) { - predictionOptions.outputBackings = @{}; + if (@available(macOS 11.0, iOS 16.0, tvOS 16.0, watchOS 9.0, *)) { + predictionOptions.outputBackings = @{}; + } } id outputs = [self.model predictionFromFeatures:inputs @@ -44,8 +46,7 @@ - (instancetype)initWithModel:(ETCoreMLModel *)model { if (!featureValue.multiArrayValue) { ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorBrokenModel, - "%@: Model is broken, expected multiarray for output=%@.", - NSStringFromClass(self.class), + "Model is broken, expected multiarray for output=%@.", outputName); return nil; } diff --git a/backends/apple/coreml/runtime/delegate/ETCoreMLLogging.h b/backends/apple/coreml/runtime/delegate/ETCoreMLLogging.h index d9c4d4ef638..d1bb7c2caa5 100644 --- a/backends/apple/coreml/runtime/delegate/ETCoreMLLogging.h +++ b/backends/apple/coreml/runtime/delegate/ETCoreMLLogging.h @@ -6,9 +6,9 @@ // Please refer to the license found in the LICENSE file in the root directory of the source tree. #import +#import #import -#import NS_ASSUME_NONNULL_BEGIN @@ -18,15 +18,15 @@ extern NSErrorDomain const ETCoreMLErrorDomain; /// The error codes that are exposed publicly. typedef NS_ERROR_ENUM(ETCoreMLErrorDomain, ETCoreMLError) { ETCoreMLErrorCorruptedData = 1, // AOT blob can't be parsed. - ETCoreMLErrorCorruptedMetadata, // AOT blob has incorrect or missing metadata. - ETCoreMLErrorCorruptedModel, // AOT blob has incorrect or missing CoreML model. - ETCoreMLErrorBrokenModel, // CoreML model doesn't match the input and output specification. - ETCoreMLErrorCompilationFailed, // CoreML model failed to compile. - ETCoreMLErrorModelCompilationNotSupported, // CoreML model compilation is not supported by the target. - ETCoreMLErrorModelProfilingNotSupported, // Model profiling is not supported by the target. - ETCoreMLErrorModelSaveFailed, // Failed to save CoreML model to disk. - ETCoreMLErrorModelCacheCreationFailed, // Failed to create model cache. - ETCoreMLErrorInternalError, // Internal error. + ETCoreMLErrorCorruptedMetadata = 2, // AOT blob has incorrect or missing metadata. + ETCoreMLErrorCorruptedModel = 3, // AOT blob has incorrect or missing CoreML model. + ETCoreMLErrorBrokenModel = 4, // CoreML model doesn't match the input and output specification. + ETCoreMLErrorCompilationFailed = 5, // CoreML model failed to compile. + ETCoreMLErrorModelCompilationNotSupported = 6, // CoreML model compilation is not supported by the target. + ETCoreMLErrorModelProfilingNotSupported = 7, // Model profiling is not supported by the target. + ETCoreMLErrorModelSaveFailed = 8, // Failed to save CoreML model to disk. + ETCoreMLErrorModelCacheCreationFailed = 9, // Failed to create model cache. + ETCoreMLErrorInternalError = 10, // Internal error. }; @interface ETCoreMLErrorUtils : NSObject @@ -47,47 +47,47 @@ typedef NS_ERROR_ENUM(ETCoreMLErrorDomain, ETCoreMLError) { #pragma clang diagnostic push #pragma clang diagnostic ignored "-Wgnu-zero-variadic-macro-arguments" +#if ET_LOG_ENABLED +#define ETCoreMLLogError(error, formatString, ...) \ + do { \ + NSString* message = error.localizedDescription; \ + message = [NSString stringWithFormat:@"[Core ML] " formatString " %@", ##__VA_ARGS__, message]; \ + ET_LOG(Error, "%s", message.UTF8String); \ + } while (0) +#else +#define ETCoreMLLogError(error, formatString, ...) \ + os_log_error(ETCoreMLErrorUtils.loggingChannel, formatString " %@", ##__VA_ARGS__, error.localizedDescription) +#endif + +#if ET_LOG_ENABLED +#define ETCoreMLLogInfo(formatString, ...) \ + ET_LOG(Info, "%s", [NSString stringWithFormat:@formatString, ##__VA_ARGS__].UTF8String) +#else +#define ETCoreMLLogInfo(formatString, ...) os_log_info(ETCoreMLErrorUtils.loggingChannel, formatString, ##__VA_ARGS__) +#endif + /// Record the error with `os_log_error` and fills `*errorOut` with `NSError`. -#define ETCoreMLLogErrorAndSetNSError(errorOut, errorCode, formatString, ...) \ - if (ET_LOG_ENABLED) { \ - ET_LOG(Error, "%s", [NSString stringWithFormat:@formatString, ##__VA_ARGS__].UTF8String); \ - } else { \ - os_log_error(ETCoreMLErrorUtils.loggingChannel, formatString, ##__VA_ARGS__); \ - } \ - if (errorOut) { \ - *errorOut = \ - [NSError errorWithDomain:ETCoreMLErrorDomain \ - code:errorCode \ - userInfo:@{ \ - NSLocalizedDescriptionKey : [NSString stringWithFormat:@formatString, ##__VA_ARGS__] \ - }]; \ - } +#define ETCoreMLLogErrorAndSetNSError(errorOut, errorCode, formatString, ...) \ + do { \ + NSDictionary* userInfo = \ + @{ NSLocalizedDescriptionKey : [NSString stringWithFormat:@formatString, ##__VA_ARGS__] }; \ + NSError* localError = [NSError errorWithDomain:ETCoreMLErrorDomain code:errorCode userInfo:userInfo]; \ + ETCoreMLLogError(localError, ""); \ + if (errorOut) { \ + *errorOut = localError; \ + } \ + } while (0) /// Record the error and its underlying error with `os_log_error` and fills `*errorOut` with `NSError`. #define ETCoreMLLogUnderlyingErrorAndSetNSError(errorOut, errorCode, underlyingNSError, formatString, ...) \ - if (ET_LOG_ENABLED) { \ - ET_LOG(Error, "%s", [NSString stringWithFormat:@formatString, ##__VA_ARGS__].UTF8String); \ - } else { \ - os_log_error(ETCoreMLErrorUtils.loggingChannel, \ - formatString ", with underlying error= %@.", \ - ##__VA_ARGS__, \ - (underlyingNSError).localizedDescription); \ - } \ - if (errorOut) { \ - *errorOut = [ETCoreMLErrorUtils errorWithCode:errorCode \ - underlyingError:underlyingNSError \ - format:@formatString, ##__VA_ARGS__]; \ - } - -#define ETCoreMLLogError(error, formatString, ...) \ - if (ET_LOG_ENABLED) { \ - ET_LOG(Error, "%s", [NSString stringWithFormat:@formatString, ##__VA_ARGS__].UTF8String); \ - } else { \ - os_log_error(ETCoreMLErrorUtils.loggingChannel, \ - formatString ", with error= %@.", \ - ##__VA_ARGS__, \ - (error).localizedDescription); \ - } + do { \ + ETCoreMLLogError(underlyingNSError, formatString, ##__VA_ARGS__); \ + if (errorOut) { \ + *errorOut = [ETCoreMLErrorUtils errorWithCode:errorCode \ + underlyingError:underlyingNSError \ + format:@formatString, ##__VA_ARGS__]; \ + } \ + } while (0) #pragma clang diagnostic pop diff --git a/backends/apple/coreml/runtime/delegate/ETCoreMLLogging.mm b/backends/apple/coreml/runtime/delegate/ETCoreMLLogging.mm index 15d60d35704..f76b86a36b3 100644 --- a/backends/apple/coreml/runtime/delegate/ETCoreMLLogging.mm +++ b/backends/apple/coreml/runtime/delegate/ETCoreMLLogging.mm @@ -5,9 +5,9 @@ // // Please refer to the license found in the LICENSE file in the root directory of the source tree. -#import +#import "ETCoreMLLogging.h" -#import +#import "ETCoreMLStrings.h" const NSErrorDomain ETCoreMLErrorDomain = @"com.apple.executorchcoreml"; diff --git a/backends/apple/coreml/runtime/delegate/ETCoreMLModel.mm b/backends/apple/coreml/runtime/delegate/ETCoreMLModel.mm index 6b39ae5f920..4201293d1c5 100644 --- a/backends/apple/coreml/runtime/delegate/ETCoreMLModel.mm +++ b/backends/apple/coreml/runtime/delegate/ETCoreMLModel.mm @@ -5,7 +5,7 @@ // // Please refer to the license found in the LICENSE file in the root directory of the source tree. -#import +#import "ETCoreMLModel.h" #import "ETCoreMLAsset.h" #import "ETCoreMLLogging.h" @@ -256,14 +256,23 @@ - (NSString *)identifier { } if (multiArrayArg && lCopyData) { - [multiArrayArg getMutableBytesWithHandler:^(void *_Nonnull mutableBytes, - NSInteger __unused size, - NSArray *strides) { - MultiArray buffer(mutableBytes, MultiArray::MemoryLayout(to_multiarray_data_type(constraint.dataType).value(), + void (^copy_data)(void *, NSArray *) = ^(void *bytes, NSArray *strides) { + MultiArray buffer(bytes, MultiArray::MemoryLayout(to_multiarray_data_type(constraint.dataType).value(), layout.shape(), to_vector(strides))); arg.copy(buffer); - }]; + }; + + + if (@available(macOS 12.3, iOS 15.4, tvOS 15.4, watchOS 8.5, *)) { + [multiArrayArg getMutableBytesWithHandler:^(void *_Nonnull mutableBytes, + NSInteger __unused size, + NSArray *strides) { + copy_data(mutableBytes, strides); + }]; + } else { + copy_data(multiArrayArg.dataPointer, multiArrayArg.strides); + } } [result addObject:multiArrayArg]; @@ -318,8 +327,7 @@ - (BOOL)prewarmAndReturnError:(NSError* __autoreleasing*)error { BOOL result = [self.mlModel prewarmUsingState:self.state error:error]; if (!result) { ETCoreMLLogError(localError, - "%@: Failed to prewarm model with identifier = %@", - NSStringFromClass(self.class), + "Failed to prewarm model with identifier = %@", self.identifier); } diff --git a/backends/apple/coreml/runtime/delegate/ETCoreMLModelCompiler.mm b/backends/apple/coreml/runtime/delegate/ETCoreMLModelCompiler.mm index c50bf3002fa..5b2c5a225a3 100644 --- a/backends/apple/coreml/runtime/delegate/ETCoreMLModelCompiler.mm +++ b/backends/apple/coreml/runtime/delegate/ETCoreMLModelCompiler.mm @@ -5,8 +5,10 @@ // // Please refer to the license found in the LICENSE file in the root directory of the source tree. -#import -#import +#import "ETCoreMLModelCompiler.h" + +#import "ETCoreMLLogging.h" + #import @implementation ETCoreMLModelCompiler @@ -20,8 +22,7 @@ + (nullable NSURL *)compileModelAtURL:(NSURL *)modelURL (void)error; ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorModelCompilationNotSupported, - "%@: Model compilation is not supported on the target, please make sure to export a compiled model.", - NSStringFromClass(ETCoreMLModelCompiler.class)); + "Model compilation is not supported on the target, please make sure to export a compiled model."); return nil; #else __block NSError *localError = nil; @@ -37,11 +38,10 @@ + (nullable NSURL *)compileModelAtURL:(NSURL *)modelURL long status = dispatch_semaphore_wait(sema, dispatch_time(DISPATCH_TIME_NOW, (int64_t)(maxWaitTimeInSeconds * NSEC_PER_SEC))); if (status != 0) { - ETCoreMLLogErrorAndSetNSError(error, - ETCoreMLErrorCompilationFailed, - "%@: Failed to compile model in %f seconds.", - NSStringFromClass(ETCoreMLModelCompiler.class), - maxWaitTimeInSeconds); + ETCoreMLLogErrorAndSetNSError(error, + ETCoreMLErrorCompilationFailed, + "Failed to compile model in %f seconds.", + maxWaitTimeInSeconds); return nil; } } else { @@ -50,10 +50,9 @@ + (nullable NSURL *)compileModelAtURL:(NSURL *)modelURL if (localError) { ETCoreMLLogErrorAndSetNSError(error, - ETCoreMLErrorCompilationFailed, - "%@: Failed to compile model, error: %@", - NSStringFromClass(ETCoreMLModelCompiler.class), - localError); + ETCoreMLErrorCompilationFailed, + "Failed to compile model, error = %@.", + localError); return nil; } else { return result; diff --git a/backends/apple/coreml/runtime/delegate/ETCoreMLModelLoader.mm b/backends/apple/coreml/runtime/delegate/ETCoreMLModelLoader.mm index 11690793baa..05aa910d954 100644 --- a/backends/apple/coreml/runtime/delegate/ETCoreMLModelLoader.mm +++ b/backends/apple/coreml/runtime/delegate/ETCoreMLModelLoader.mm @@ -5,14 +5,15 @@ // // Please refer to the license found in the LICENSE file in the root directory of the source tree. -#import -#import -#import -#import -#import -#import -#import -#import +#import "ETCoreMLModelLoader.h" + +#import "asset.h" +#import "ETCoreMLAsset.h" +#import "ETCoreMLAssetManager.h" +#import "ETCoreMLDefaultModelExecutor.h" +#import "ETCoreMLLogging.h" +#import "ETCoreMLModel.h" +#import "model_metadata.h" using namespace executorchcoreml; @@ -64,8 +65,7 @@ + (nullable ETCoreMLModel *)loadModelWithContentsOfURL:(NSURL *)compiledModelURL if (localError) { ETCoreMLLogError(localError, - "%@: Failed to load model from compiled asset with identifier = %@", - NSStringFromClass(ETCoreMLModelLoader.class), + "Failed to load model from compiled asset with identifier = %@", identifier); } diff --git a/backends/apple/coreml/runtime/delegate/ETCoreMLModelManager.mm b/backends/apple/coreml/runtime/delegate/ETCoreMLModelManager.mm index 3848f7c9b3c..c6da7750a11 100644 --- a/backends/apple/coreml/runtime/delegate/ETCoreMLModelManager.mm +++ b/backends/apple/coreml/runtime/delegate/ETCoreMLModelManager.mm @@ -5,6 +5,8 @@ // // Please refer to the license found in the LICENSE file in the root directory of the source tree. +#import "ETCoreMLModelManager.h" + #import "ETCoreMLAsset.h" #import "ETCoreMLAssetManager.h" #import "ETCoreMLDefaultModelExecutor.h" @@ -13,20 +15,20 @@ #import "ETCoreMLModelCompiler.h" #import "ETCoreMLModelExecutor.h" #import "ETCoreMLModelLoader.h" -#import "ETCoreMLModelManager.h" #import "ETCoreMLStrings.h" #import "MLModel_Prewarm.h" #import "MLMultiArray_Copy.h" -#import #import "inmemory_filesystem_utils.hpp" -#import -#import #import "model_metadata.h" #import "multiarray.h" #import "objc_array_util.h" +#import "serde_json.h" + +#import +#import +#import #import #import -#import "serde_json.h" #import #import #import @@ -73,11 +75,15 @@ BOOL is_backed_by_same_buffer(MLMultiArray *array1, MLMultiArray *array2) { __block BOOL result = NO; - [array1 getBytesWithHandler:^(const void *bytes1, NSInteger __unused size1){ - [array2 getBytesWithHandler:^(const void *bytes2, NSInteger __unused size2) { - result = (bytes1 == bytes2); + if (@available(macOS 12.3, iOS 15.4, tvOS 15.4, watchOS 8.5, *)) { + [array1 getBytesWithHandler:^(const void *bytes1, NSInteger __unused size1){ + [array2 getBytesWithHandler:^(const void *bytes2, NSInteger __unused size2) { + result = (bytes1 == bytes2); + }]; }]; - }]; + } else { + result = (array1.dataPointer == array2.dataPointer); + } return result; } @@ -86,17 +92,19 @@ BOOL is_backed_by_same_buffer(MLMultiArray *array1, MLMultiArray *array2) { NSOrderedSet *output_names, NSError * __autoreleasing *error) { MLPredictionOptions *options = [MLPredictionOptions new]; - NSMutableDictionary *output_backings = [NSMutableDictionary new]; - NSEnumerator *enumerator = [output_names objectEnumerator]; - for (MLMultiArray *output in outputs) { - NSString *output_name = [enumerator nextObject]; - if (output_name.length == 0) { - ETCoreMLLogErrorAndSetNSError(error, 0, "%@: Model is broken.", NSStringFromClass(ETCoreMLModelManager.class)); - return nil; + if (@available(macOS 11.0, iOS 16.0, tvOS 16.0, watchOS 9.0, *)) { + NSMutableDictionary *output_backings = [NSMutableDictionary dictionary]; + NSEnumerator *enumerator = [output_names objectEnumerator]; + for (MLMultiArray *output in outputs) { + NSString *output_name = [enumerator nextObject]; + if (output_name.length == 0) { + ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorCorruptedModel, "Model is broken."); + return nil; + } + output_backings[output_name] = output; } - output_backings[output_name] = output; + options.outputBackings = output_backings; } - options.outputBackings = output_backings; return options; } @@ -138,14 +146,25 @@ void set_outputs(NSArray *outputs, NSArray *mode } void copy(MLMultiArray *src, executorchcoreml::MultiArray& dst) { - [src getBytesWithHandler:^(const void * _Nonnull bytes, NSInteger size) { + void (^copy_data)(void *) = ^(void *bytes) { if (bytes == dst.data()) { return; } - - MultiArray::MemoryLayout src_layout(get_data_type(src.dataType).value(), to_vector(src.shape), to_vector(src.strides)); + + MultiArray::MemoryLayout src_layout( + get_data_type(src.dataType).value(), + to_vector(src.shape), + to_vector(src.strides) + ); MultiArray(const_cast(bytes), std::move(src_layout)).copy(dst); - }]; + }; + if (@available(macOS 12.3, iOS 15.4, tvOS 15.4, watchOS 8.5, *)) { + [src getBytesWithHandler:^(const void * _Nonnull bytes, NSInteger size) { + copy_data(const_cast(bytes)); + }]; + } else { + copy_data(src.dataPointer); + } } void set_outputs(std::vector& outputs, @@ -212,8 +231,7 @@ void set_outputs(std::vector& outputs, ETCoreMLLogUnderlyingErrorAndSetNSError(error, ETCoreMLErrorModelSaveFailed, local_error, - "%@: Failed to create directory when saving model with identifier = %@.", - NSStringFromClass(ETCoreMLModelManager.class), + "Failed to create directory when saving model with identifier = %@.", identifier); return nil; } @@ -236,8 +254,7 @@ void set_outputs(std::vector& outputs, if (!inmemory_fs->write_item_to_disk(file_path, model_path, true, ec)) { ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorModelSaveFailed, - "%@: Failed to write model files to disk when saving model with identifier = %@.", - NSStringFromClass(ETCoreMLModelManager.class), + "Failed to write model files to disk when saving model with identifier = %@.", identifier); return nil; } @@ -395,8 +412,7 @@ - (nullable ETCoreMLAsset *)assetWithIdentifier:(NSString *)identifier { modelAsset = [self.assetManager assetWithIdentifier:identifier error:&localError]; if (localError) { ETCoreMLLogError(localError, - "%@: Failed to retrieve asset with identifier = %@", - NSStringFromClass(self.assetManager.class), + "Failed to retrieve asset with identifier = %@.", identifier); } @@ -411,8 +427,7 @@ - (nullable NSURL *)compiledModelURLWithIdentifier:(NSString *)identifier if (!modelAssetType) { ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorCorruptedModel, - "%@: AOT blob is missing model file.", - NSStringFromClass(ETCoreMLModelManager.class)); + "AOT blob is missing model file."); return nil; } @@ -420,11 +435,12 @@ - (nullable NSURL *)compiledModelURLWithIdentifier:(NSString *)identifier NSURL *modelURL = ::write_model_files(dstURL, self.fileManager, identifier, modelAssetType.value(), inMemoryFS, error); switch (modelAssetType.value()) { case ModelAssetType::CompiledModel: { + // Model is already compiled. return modelURL; } case ModelAssetType::Model: { - // we need to compiled the model. + // Compile the model. NSURL *compiledModelURL = [ETCoreMLModelCompiler compileModelAtURL:modelURL maxWaitTimeInSeconds:(5 * 60) error:error]; @@ -442,6 +458,12 @@ - (nullable NSURL *)compiledModelURLWithIdentifier:(NSString *)identifier NSString *identifier = @(metadata.identifier.c_str()); // Otherwise try to retrieve the compiled asset. ETCoreMLAsset *compiledModelAsset = [self assetWithIdentifier:identifier]; + if (compiledModelAsset) { + ETCoreMLLogInfo("Cache Hit: Successfully retrieved model with identifier=%@ from the models cache.", identifier); + } else { + ETCoreMLLogInfo("Cache Miss: Model with identifier=%@ was not found in the models cache.", identifier); + } + // Create a unique directory for writing model files. NSURL *dstURL = [self.assetManager.trashDirectoryURL URLByAppendingPathComponent:[NSUUID UUID].UUIDString]; auto modelAssetType = get_model_asset_type(inMemoryFS); @@ -499,9 +521,11 @@ - (nullable NSURL *)compiledModelURLWithIdentifier:(NSString *)identifier ETCoreMLAsset *asset = [self assetWithIdentifier:identifier]; ETCoreMLModel *model = asset ? get_model_from_asset(asset, configuration, metadata, error) : nil; if (model) { + ETCoreMLLogInfo("Cache Hit: Successfully retrieved model with identifier=%@ from the models cache.", identifier); return [[ETCoreMLDefaultModelExecutor alloc] initWithModel:model]; } + ETCoreMLLogInfo("Cache Miss: Model with identifier=%@ was not found in the models cache.", identifier); // Compile the model. NSURL *compiledModelURL = [self compiledModelURLWithIdentifier:identifier inMemoryFS:inMemoryFS @@ -531,8 +555,7 @@ - (nullable NSURL *)compiledModelURLWithIdentifier:(NSString *)identifier if (!inMemoryFS) { ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorCorruptedModel, - "%@: Model data is corrupted.", - NSStringFromClass(ETCoreMLModelManager.class)); + "Model data is corrupted."); return nil; } @@ -540,8 +563,7 @@ - (nullable NSURL *)compiledModelURLWithIdentifier:(NSString *)identifier if (!metadata) { ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorCorruptedMetadata, - "%@: Metadata is invalid or missing.", - NSStringFromClass(ETCoreMLModelManager.class)); + "Metadata is invalid or missing."); return nil; } @@ -607,9 +629,7 @@ - (void)prewarmRecentlyUsedAssetsWithMaxCount:(NSUInteger)maxCount { NSArray *assets = [self.assetManager mostRecentlyUsedAssetsWithMaxCount:maxCount error:&localError]; if (localError) { - ETCoreMLLogError(localError, - "%@: Failed to retrieve recently used assets.", - NSStringFromClass(self.assetManager.class)); + ETCoreMLLogError(localError, "Failed to retrieve recently used assets."); } if (assets.count == 0) { @@ -627,8 +647,7 @@ - (void)prewarmRecentlyUsedAssetsWithMaxCount:(NSUInteger)maxCount { NSError *prewarmError = nil; if (![asset prewarmAndReturnError:&prewarmError]) { ETCoreMLLogError(prewarmError, - "%@: Failed to prewarm asset with identifier = %@", - NSStringFromClass(strongSelf.assetManager.class), + "Failed to prewarm asset with identifier = %@", asset.identifier); return; } @@ -664,18 +683,20 @@ - (void)addPrewarmedAsset:(ETCoreMLAsset *)asset { NSArray *modelOutputs = [executor executeModelWithInputs:inputFeatures predictionOptions:predictionOptions - loggingOptions:loggingOptions + loggingOptions:loggingOptions eventLogger:eventLogger error:&localError]; // Try without output backings. - if (!modelOutputs && predictionOptions.outputBackings.count > 0) { - executor.ignoreOutputBackings = YES; - localError = nil; - modelOutputs = [executor executeModelWithInputs:inputFeatures - predictionOptions:predictionOptions - loggingOptions:loggingOptions - eventLogger:eventLogger - error:&localError]; + if (@available(macOS 11.0, iOS 16.0, tvOS 16.0, watchOS 9.0, *)) { + if (!modelOutputs && predictionOptions.outputBackings.count > 0) { + executor.ignoreOutputBackings = YES; + localError = nil; + modelOutputs = [executor executeModelWithInputs:inputFeatures + predictionOptions:predictionOptions + loggingOptions:loggingOptions + eventLogger:eventLogger + error:&localError]; + } } if (error) { @@ -693,9 +714,8 @@ - (BOOL)executeModelWithHandle:(ModelHandle *)handle id executor = [self executorWithHandle:handle]; if (!executor) { ETCoreMLLogErrorAndSetNSError(error, - 0, - "%@: Model is already unloaded.", - NSStringFromClass(self.class)); + ETCoreMLErrorInternalError, + "Model is already unloaded."); return NO; } @@ -703,8 +723,7 @@ - (BOOL)executeModelWithHandle:(ModelHandle *)handle if (args.count != model.orderedInputNames.count + model.orderedOutputNames.count) { ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorCorruptedModel, - "%@: Model is invalid, expected args count to be %lu but got %lu.", - NSStringFromClass(self.class), + "Model is invalid, expected args count to be %lu but got %lu.", static_cast(model.orderedInputNames.count + model.orderedOutputNames.count), args.count); return NO; @@ -741,9 +760,8 @@ - (BOOL)executeModelWithHandle:(ModelHandle *)handle id executor = [self executorWithHandle:handle]; if (!executor) { ETCoreMLLogErrorAndSetNSError(error, - 0, - "%@: Model is already unloaded.", - NSStringFromClass(self.class)); + ETCoreMLErrorInternalError, + "Model is already unloaded."); return NO; } @@ -751,8 +769,7 @@ - (BOOL)executeModelWithHandle:(ModelHandle *)handle if (argsVec.size() != model.orderedInputNames.count + model.orderedOutputNames.count) { ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorCorruptedModel, - "%@: Model is invalid, expected args count to be %lu but got %lu.", - NSStringFromClass(self.class), + "Model is invalid, expected args count to be %lu but got %lu.", static_cast(model.orderedInputNames.count + model.orderedOutputNames.count), argsVec.size()); return NO; diff --git a/backends/apple/coreml/runtime/delegate/MLModel_Prewarm.mm b/backends/apple/coreml/runtime/delegate/MLModel_Prewarm.mm index d6f59666cf0..6a737d1e82b 100644 --- a/backends/apple/coreml/runtime/delegate/MLModel_Prewarm.mm +++ b/backends/apple/coreml/runtime/delegate/MLModel_Prewarm.mm @@ -5,10 +5,34 @@ // // Please refer to the license found in the LICENSE file in the root directory of the source tree. -#import +#import "MLModel_Prewarm.h" +#include #import +namespace { + size_t get_number_of_bytes(MLMultiArrayDataType data_type) { + switch (data_type) { + case MLMultiArrayDataTypeFloat16: { + return 2; + } + case MLMultiArrayDataTypeFloat32: { + return 4; + } + case MLMultiArrayDataTypeInt32: { + return 4; + } + case MLMultiArrayDataTypeFloat64: { + return 8; + } + default: { + return 0; + } + } + } + +} + @interface MLMultiArray (Prewarm) + (nullable MLMultiArray *)zeroedMultiArrayWithShape:(NSArray *)shape @@ -28,11 +52,22 @@ + (MLMultiArray *)zeroedMultiArrayWithShape:(NSArray *)shape return nil; } - [multiArray getMutableBytesWithHandler:^(void *mutableBytes, NSInteger size, NSArray * __unused strides) { - uint8_t *start = reinterpret_cast(mutableBytes); - uint8_t *end = start + size; - std::fill(start, end, uint8_t(0)); - }]; + + if (@available(macOS 12.3, iOS 15.4, tvOS 15.4, watchOS 8.5, *)) { + void (^fill_zeroes)(void *, NSInteger) = ^(void *bytes, NSInteger size) { + uint8_t *start = reinterpret_cast(bytes); + uint8_t *end = start + size; + std::fill(start, end, uint8_t(0)); + }; + + if (@available(macOS 12.3, iOS 15.4, tvOS 15.4, watchOS 8.5, *)) { + [multiArray getMutableBytesWithHandler:^(void *mutableBytes, NSInteger size, NSArray * __unused strides) { + fill_zeroes(mutableBytes, size); + }]; + } else { + fill_zeroes(multiArray.dataPointer, multiArray.count * get_number_of_bytes(multiArray.dataType)); + } + } return multiArray; } diff --git a/backends/apple/coreml/runtime/delegate/MLMultiArray_Copy.mm b/backends/apple/coreml/runtime/delegate/MLMultiArray_Copy.mm index b8a10fcbbbc..313ee3edaf9 100644 --- a/backends/apple/coreml/runtime/delegate/MLMultiArray_Copy.mm +++ b/backends/apple/coreml/runtime/delegate/MLMultiArray_Copy.mm @@ -5,10 +5,10 @@ // // Please refer to the license found in the LICENSE file in the root directory of the source tree. -#import +#import "MLMultiArray_Copy.h" -#import -#import +#import "objc_array_util.h" +#import "multiarray.h" namespace { using namespace executorchcoreml; @@ -27,13 +27,19 @@ MultiArray to_multi_array(void *data, @implementation MLMultiArray (Copy) - (void)copyInto:(MLMultiArray *)dstMultiArray { - [self getBytesWithHandler:^(const void *srcBytes, __unused NSInteger srcSize) { - [dstMultiArray getMutableBytesWithHandler:^(void *dstBytes, __unused NSInteger size, NSArray * strides) { - auto src = ::to_multi_array(const_cast(srcBytes), self.dataType, self.shape, self.strides); - auto dst = ::to_multi_array(dstBytes, dstMultiArray.dataType, dstMultiArray.shape, strides); - src.copy(dst); + if (@available(macOS 12.3, iOS 15.4, tvOS 15.4, watchOS 8.5, *)) { + [self getBytesWithHandler:^(const void *srcBytes, __unused NSInteger srcSize) { + [dstMultiArray getMutableBytesWithHandler:^(void *dstBytes, __unused NSInteger size, NSArray * strides) { + auto src = ::to_multi_array(const_cast(srcBytes), self.dataType, self.shape, self.strides); + auto dst = ::to_multi_array(dstBytes, dstMultiArray.dataType, dstMultiArray.shape, strides); + src.copy(dst); + }]; }]; - }]; + } else { + auto src = ::to_multi_array(self.dataPointer, self.dataType, self.shape, self.strides); + auto dst = ::to_multi_array(dstMultiArray.dataPointer, dstMultiArray.dataType, dstMultiArray.shape, dstMultiArray.strides); + src.copy(dst); + } } @end diff --git a/backends/apple/coreml/runtime/delegate/asset.mm b/backends/apple/coreml/runtime/delegate/asset.mm index c9a6e16d2af..6df2dfbd3c5 100644 --- a/backends/apple/coreml/runtime/delegate/asset.mm +++ b/backends/apple/coreml/runtime/delegate/asset.mm @@ -1,16 +1,16 @@ // -// ModelAsset.cpp +// asset.cpp // // Copyright © 2024 Apple Inc. All rights reserved. // // Please refer to the license found in the LICENSE file in the root directory of the source tree. -#import +#import "asset.h" #import -#import +#import "objc_safe_cast.h" namespace { diff --git a/backends/apple/coreml/runtime/delegate/backend_delegate.h b/backends/apple/coreml/runtime/delegate/backend_delegate.h index 9af3df01af2..93c420e11d2 100644 --- a/backends/apple/coreml/runtime/delegate/backend_delegate.h +++ b/backends/apple/coreml/runtime/delegate/backend_delegate.h @@ -7,7 +7,8 @@ #pragma once -#include +#include "model_logging_options.h" + #include #include #include diff --git a/backends/apple/coreml/runtime/delegate/backend_delegate.mm b/backends/apple/coreml/runtime/delegate/backend_delegate.mm index d8096e16781..2cb274f0a89 100644 --- a/backends/apple/coreml/runtime/delegate/backend_delegate.mm +++ b/backends/apple/coreml/runtime/delegate/backend_delegate.mm @@ -6,13 +6,15 @@ // Please refer to the license found in the LICENSE file in the root directory of the source tree. -#import -#import -#import -#import -#import -#import -#import +#import "backend_delegate.h" + +#import "ETCoreMLAssetManager.h" +#import "ETCoreMLLogging.h" +#import "ETCoreMLModel.h" +#import "ETCoreMLModelManager.h" +#import "ETCoreMLStrings.h" +#import "model_event_logger.h" +#import "multiarray.h" namespace { using namespace executorchcoreml; @@ -282,6 +284,9 @@ explicit BackendDelegateImpl(const Config& config) noexcept ModelHandle *modelHandle = [model_manager_ loadModelFromAOTData:data configuration:configuration error:&localError]; + if (localError != nil) { + ETCoreMLLogError(localError, "Model init failed"); + } return modelHandle; } @@ -290,13 +295,16 @@ bool execute(Handle* handle, const ModelLoggingOptions& logging_options, ModelEventLogger *event_logger, std::error_code& ec) const noexcept override { - NSError *error = nil; + NSError *localError = nil; if (![model_manager_ executeModelWithHandle:handle argsVec:args loggingOptions:logging_options eventLogger:event_logger - error:&error]) { - ec = static_cast(error.code); + error:&localError]) { + if (localError != nil) { + ETCoreMLLogError(localError, "Model execution failed"); + ec = static_cast(localError.code); + } return false; } diff --git a/backends/apple/coreml/runtime/delegate/coreml_backend_delegate.mm b/backends/apple/coreml/runtime/delegate/coreml_backend_delegate.mm index 380ec52b7d7..028191ce497 100644 --- a/backends/apple/coreml/runtime/delegate/coreml_backend_delegate.mm +++ b/backends/apple/coreml/runtime/delegate/coreml_backend_delegate.mm @@ -5,22 +5,25 @@ // // Please refer to the license found in the LICENSE file in the root directory of the source tree. -#import -#import -#import -#import -#import +#import "coreml_backend/delegate.h" + +#import "backend_delegate.h" +#import "ETCoreMLLogging.h" +#import "ETCoreMLModel.h" +#import "ETCoreMLStrings.h" +#import "model_event_logger.h" +#import "model_logging_options.h" +#import "multiarray.h" +#import "objc_safe_cast.h" + #import #import #import + +#include #import -#import -#import -#import -#import #import #import -#include #ifdef ET_EVENT_TRACER_ENABLED #import diff --git a/backends/apple/coreml/runtime/delegate/model_metadata.h b/backends/apple/coreml/runtime/delegate/model_metadata.h index 275aa39dd3b..8d0c1f0914d 100644 --- a/backends/apple/coreml/runtime/delegate/model_metadata.h +++ b/backends/apple/coreml/runtime/delegate/model_metadata.h @@ -10,7 +10,7 @@ #import #import -#import +#import "serde_json.h" namespace executorchcoreml { diff --git a/backends/apple/coreml/runtime/delegate/multiarray.mm b/backends/apple/coreml/runtime/delegate/multiarray.mm index de705991780..d38ac377799 100644 --- a/backends/apple/coreml/runtime/delegate/multiarray.mm +++ b/backends/apple/coreml/runtime/delegate/multiarray.mm @@ -6,13 +6,14 @@ // // Please refer to the license found in the LICENSE file in the root directory of the source tree. -#import +#import "multiarray.h" + +#import "objc_array_util.h" #import #import #import #import -#import #import #import diff --git a/backends/apple/coreml/runtime/delegate/serde_json.mm b/backends/apple/coreml/runtime/delegate/serde_json.mm index 3568ffe4ce8..e39df4d734e 100644 --- a/backends/apple/coreml/runtime/delegate/serde_json.mm +++ b/backends/apple/coreml/runtime/delegate/serde_json.mm @@ -5,11 +5,11 @@ // // Please refer to the license found in the LICENSE file in the root directory of the source tree. -#import +#import "serde_json.h" -#import -#import -#import +#import "asset.h" +#import "objc_json_serde.h" +#import "model_metadata.h" namespace { struct FileInfoKeys { diff --git a/backends/apple/coreml/runtime/sdk/ETCoreMLModelAnalyzer.mm b/backends/apple/coreml/runtime/sdk/ETCoreMLModelAnalyzer.mm index 988b5d808a0..87e086c5bbd 100644 --- a/backends/apple/coreml/runtime/sdk/ETCoreMLModelAnalyzer.mm +++ b/backends/apple/coreml/runtime/sdk/ETCoreMLModelAnalyzer.mm @@ -65,9 +65,7 @@ - (nullable instancetype)initWithCompiledModelAsset:(ETCoreMLAsset *)compiledMod assetManager:assetManager error:&localError]; if (!model) { - ETCoreMLLogError(localError, - "%@: Failed to create model profiler.", - NSStringFromClass(ETCoreMLAssetManager.class)); + ETCoreMLLogError(localError, "Failed to create model profiler."); } self = [super init]; @@ -98,8 +96,7 @@ - (nullable instancetype)initWithCompiledModelAsset:(ETCoreMLAsset *)compiledMod if (!self.profiler) { ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorModelProfilingNotSupported, - "%@: Model profiling is only available for macOS >= 14.4, iOS >= 17.4, tvOS >= 17.4 and watchOS >= 10.4.", - NSStringFromClass(ETCoreMLModelAnalyzer.class)); + "Model profiling is only available for macOS >= 14.4, iOS >= 17.4, tvOS >= 17.4 and watchOS >= 10.4."); return nil; } @@ -125,8 +122,7 @@ - (nullable instancetype)initWithCompiledModelAsset:(ETCoreMLAsset *)compiledMod if (!self.modelAsset) { ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorCorruptedData, - "%@: There is no mlpackage, mlpackage is required for debugging a model. Please check the export path.", - NSStringFromClass(ETCoreMLModelAnalyzer.class)); + "The AOT blob is missing an 'mlpackage', which is required for debugging the model. Please check the export path."); return nil; } diff --git a/backends/apple/coreml/runtime/sdk/ETCoreMLModelDebugger.mm b/backends/apple/coreml/runtime/sdk/ETCoreMLModelDebugger.mm index 3be28b56d66..1cac0de40f3 100644 --- a/backends/apple/coreml/runtime/sdk/ETCoreMLModelDebugger.mm +++ b/backends/apple/coreml/runtime/sdk/ETCoreMLModelDebugger.mm @@ -7,7 +7,6 @@ #import "ETCoreMLModelDebugger.h" -#import #import "ETCoreMLAsset.h" #import "ETCoreMLAssetManager.h" #import "ETCoreMLLogging.h" @@ -16,12 +15,14 @@ #import "ETCoreMLModelStructurePath.h" #import "ETCoreMLPair.h" #import "ETCoreMLStrings.h" -#import -#import -#import -#import +#import "format/MIL.pb.h" +#import "format/Model.pb.h" #import "model_package_info.h" #import "objc_json_serde.h" + +#import +#import +#import #import #import @@ -43,13 +44,19 @@ const auto& info_value = info.value(); auto it = info_value.items.find(info_value.root_model_identifier); if (it == info_value.items.end()) { - ETCoreMLLogErrorAndSetNSError(error, 0, "%@ is broken, root model info doesn't exist.", model_package_url.lastPathComponent); + ETCoreMLLogErrorAndSetNSError(error, + ETCoreMLErrorCorruptedModel, + "%@ is broken, root model info doesn't exist.", + model_package_url.lastPathComponent); return nil; } auto path = it->second.path; if (path.empty()) { - ETCoreMLLogErrorAndSetNSError(error, 0, "%@ is broken, root model path doesn't exist.", model_package_url.lastPathComponent); + ETCoreMLLogErrorAndSetNSError(error, + ETCoreMLErrorCorruptedModel, + "%@ is broken, root model path doesn't exist.", + model_package_url.lastPathComponent); return nil; } @@ -350,8 +357,8 @@ void set_model_outputs(id output_features, NSMutableArray *values = [NSMutableArray arrayWithCapacity:output_names.count]; for (NSString *output_name in output_names) { MLFeatureValue *feature_value = [output_features featureValueForName:output_name]; - NSCAssert(feature_value.multiArrayValue != nil, @"%@: Expected a multiarray value for output name=%@.", - NSStringFromClass(ETCoreMLModelDebugger.class), + NSCAssert(feature_value.multiArrayValue != nil, + @"Expected a multiarray value for output name=%@.", output_name); [values addObject:feature_value.multiArrayValue]; } @@ -570,8 +577,7 @@ - (nullable ETCoreMLAsset *)compiledModelAssetWithOutputsAtPaths:(NSArray #import diff --git a/backends/apple/coreml/runtime/sdk/ETCoreMLModelProfiler.mm b/backends/apple/coreml/runtime/sdk/ETCoreMLModelProfiler.mm index 5998701eb0f..e381bbb03d1 100644 --- a/backends/apple/coreml/runtime/sdk/ETCoreMLModelProfiler.mm +++ b/backends/apple/coreml/runtime/sdk/ETCoreMLModelProfiler.mm @@ -14,9 +14,10 @@ #import "ETCoreMLOperationProfilingInfo.h" #import "ETCoreMLPair.h" #import "ETCoreMLStrings.h" +#import "program_path.h" + #import #import -#import "program_path.h" namespace { using namespace executorchcoreml::modelstructure; @@ -42,8 +43,7 @@ ETCoreMLLogUnderlyingErrorAndSetNSError(error, ETCoreMLErrorCompilationFailed, local_error, - "%@: Failed to get compute plan of model with name=%@.", - NSStringFromClass(ETCoreMLModelProfiler.class), + "Failed to get compute plan of model with name=%@.", model_url.lastPathComponent); return nil; } @@ -288,8 +288,7 @@ - (nullable instancetype)initWithModel:(ETCoreMLModel *)model #endif ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorModelProfilingNotSupported, - "%@: Model profiling is only available for macOS >= 14.4, iOS >= 17.4, tvOS >= 17.4 and watchOS >= 10.4.", - NSStringFromClass(self.class)); + "Model profiling is only available for macOS >= 14.4, iOS >= 17.4, tvOS >= 17.4 and watchOS >= 10.4."); return nil; } diff --git a/backends/apple/coreml/runtime/sdk/ETCoreMLOperationProfilingInfo.h b/backends/apple/coreml/runtime/sdk/ETCoreMLOperationProfilingInfo.h index 80c49f8965e..13d4bb8e6ac 100644 --- a/backends/apple/coreml/runtime/sdk/ETCoreMLOperationProfilingInfo.h +++ b/backends/apple/coreml/runtime/sdk/ETCoreMLOperationProfilingInfo.h @@ -7,7 +7,7 @@ #import -#import +#import "ETCoreMLComputeUnits.h" NS_ASSUME_NONNULL_BEGIN diff --git a/backends/apple/coreml/runtime/sdk/model_event_logger_impl.mm b/backends/apple/coreml/runtime/sdk/model_event_logger_impl.mm index 12ac8ec15a3..be34e384b72 100644 --- a/backends/apple/coreml/runtime/sdk/model_event_logger_impl.mm +++ b/backends/apple/coreml/runtime/sdk/model_event_logger_impl.mm @@ -9,11 +9,13 @@ #import "ETCoreMLModelStructurePath.h" #import "ETCoreMLOperationProfilingInfo.h" -#import #import "objc_array_util.h" +#import "MLMultiArray_Copy.h" + +#import + #import #import -#import "MLMultiArray_Copy.h" namespace { diff --git a/backends/apple/coreml/runtime/sdk/model_package_info.mm b/backends/apple/coreml/runtime/sdk/model_package_info.mm index b7b26178fde..f4b13048718 100644 --- a/backends/apple/coreml/runtime/sdk/model_package_info.mm +++ b/backends/apple/coreml/runtime/sdk/model_package_info.mm @@ -66,7 +66,7 @@ static void from_json(id json, ModelPackageInfo& package_info) { NSURL *manifest_url = [model_package_url URLByAppendingPathComponent:@"manifest.json"].URLByStandardizingPath; BOOL is_directory = NO; if (![fm fileExistsAtPath:manifest_url.path isDirectory:&is_directory] || is_directory) { - ETCoreMLLogErrorAndSetNSError(error, 0, "%@ is broken, manifest doesn't exist.", model_package_url.lastPathComponent); + ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorCorruptedModel, "%@ is broken, manifest doesn't exist.", model_package_url.lastPathComponent); return std::nullopt; } diff --git a/backends/apple/coreml/runtime/test/ETCoreMLTestUtils.mm b/backends/apple/coreml/runtime/test/ETCoreMLTestUtils.mm index 3c0908201ac..50b0f2ec766 100644 --- a/backends/apple/coreml/runtime/test/ETCoreMLTestUtils.mm +++ b/backends/apple/coreml/runtime/test/ETCoreMLTestUtils.mm @@ -250,16 +250,14 @@ + (BOOL)extractModelAssetAndMetadataFromAOTData:(NSData *)data if (!inMemoryFS) { ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorCorruptedModel, - "%@: Model data is corrupted.", - NSStringFromClass(ETCoreMLTestUtils.class)); + "Model data is corrupted."); return NO; } if (!extract_model_metadata(*inMemoryFS, metadata) || !metadata.is_valid()) { ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorCorruptedMetadata, - "%@: Model metadata is corrupted.", - NSStringFromClass(ETCoreMLTestUtils.class)); + "Model metadata is corrupted."); return NO; } @@ -269,8 +267,7 @@ + (BOOL)extractModelAssetAndMetadataFromAOTData:(NSData *)data if (![fileManager createDirectoryAtURL:modelURL withIntermediateDirectories:NO attributes:@{} error:error]) { ETCoreMLLogErrorAndSetNSError(error, ETCoreMLErrorModelSaveFailed, - "%@: Failed to create directory when saving model with name = %@.", - NSStringFromClass(ETCoreMLTestUtils.class), + "Failed to create directory when saving model with name = %@.", modelURL.lastPathComponent); return NO; } diff --git a/examples/apple/coreml/scripts/build_executor_runner.sh b/examples/apple/coreml/scripts/build_executor_runner.sh index bc3f2872291..bdfbf24a0cf 100755 --- a/examples/apple/coreml/scripts/build_executor_runner.sh +++ b/examples/apple/coreml/scripts/build_executor_runner.sh @@ -5,11 +5,33 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. +set -e + +MODE="Release" + +usage() { + echo "Builds Core ML executor runner" + echo "Options:" + echo " --Debug Use Debug build mode. Default: 'Release'" + echo "Example:" + echo " $0 --Debug" + exit 0 +} + +for arg in "$@"; do + case $arg in + -h|--help) usage ;; + --Debug) MODE="Debug" ;; + *) + esac +done + SCRIPT_DIR_PATH="$( cd -- "$(dirname "$0")" >/dev/null 2>&1 pwd -P )" + EXECUTORCH_ROOT_PATH=$(realpath "$SCRIPT_DIR_PATH/../../../../") COREML_DIR_PATH="$EXECUTORCH_ROOT_PATH/backends/apple/coreml" EXAMPLES_COREML_DIR_PATH="$EXECUTORCH_ROOT_PATH/examples/apple/coreml" @@ -23,13 +45,13 @@ cd "$EXECUTORCH_ROOT_PATH" echo "ExecuTorch: Building executor_runner" -echo "ExecuTorch: Removing build directory $CMAKE_BUILD_DIR" +echo "ExecuTorch: Removing build directory $CMAKE_BUILD_DIR_PATH" rm -rf "$CMAKE_BUILD_DIR_PATH" # Build executorch echo "ExecuTorch: Building executorch" cmake "$EXECUTORCH_ROOT_PATH" -B"$CMAKE_BUILD_DIR_PATH" \ --DCMAKE_BUILD_TYPE=Release \ +-DCMAKE_BUILD_TYPE="$MODE" \ -DCMAKE_TOOLCHAIN_FILE="$IOS_TOOLCHAIN_PATH" \ -DPLATFORM=MAC_UNIVERSAL \ -DDEPLOYMENT_TARGET=13.0 \ From aa6b31d4da0cc02638c67349b08624118130871d Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Mon, 14 Apr 2025 19:22:22 -0700 Subject: [PATCH 143/244] Fix linter (#10183) --- examples/demo-apps/android/LlamaDemo/run_instrumentation_test.sh | 1 - 1 file changed, 1 deletion(-) diff --git a/examples/demo-apps/android/LlamaDemo/run_instrumentation_test.sh b/examples/demo-apps/android/LlamaDemo/run_instrumentation_test.sh index 5933b44b0a8..ff59fc56b2c 100644 --- a/examples/demo-apps/android/LlamaDemo/run_instrumentation_test.sh +++ b/examples/demo-apps/android/LlamaDemo/run_instrumentation_test.sh @@ -25,4 +25,3 @@ popd pushd "$BASEDIR" ./gradlew connectedAndroidTest popd - From 56f49558285d7b0fa9ee43d4a527c9b0bf182877 Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Mon, 14 Apr 2025 19:30:51 -0700 Subject: [PATCH 144/244] FIx links in docs (#10184) Some links didn't work --- backends/cadence/README.md | 2 +- backends/qualcomm/README.md | 4 ++-- backends/qualcomm/setup.md | 2 +- backends/xnnpack/README.md | 4 ++-- docs/README.md | 2 +- docs/source/llm/getting-started.md | 2 +- docs/source/tutorials_source/README.txt | 2 +- examples/README.md | 2 +- examples/arm/README.md | 2 +- examples/models/efficient_sam/README.md | 4 ++-- examples/models/llama/UTILS.md | 2 +- examples/models/phi-3-mini-lora/README.md | 3 ++- examples/qualcomm/README.md | 4 ++-- examples/qualcomm/oss_scripts/llama/README.md | 2 +- examples/qualcomm/qaihub_scripts/llama/README.md | 4 ++-- examples/qualcomm/qaihub_scripts/stable_diffusion/README.md | 2 +- examples/qualcomm/qaihub_scripts/utils/README.md | 4 ++-- examples/xnnpack/README.md | 2 +- extension/llm/export/partitioner_lib.py | 6 +++--- extension/llm/export/quantizer_lib.py | 4 ++-- 20 files changed, 30 insertions(+), 29 deletions(-) diff --git a/backends/cadence/README.md b/backends/cadence/README.md index 998ac55ddf0..3cefb71d945 100644 --- a/backends/cadence/README.md +++ b/backends/cadence/README.md @@ -6,7 +6,7 @@ ## Tutorial -Please follow the [tutorial](https://pytorch.org/executorch/main/build-run-xtensa.html) for more information on how to run models on Cadence/Xtensa DSPs. +Please follow the [tutorial](https://pytorch.org/executorch/main/backends-cadence) for more information on how to run models on Cadence/Xtensa DSPs. ## Directory Structure diff --git a/backends/qualcomm/README.md b/backends/qualcomm/README.md index 5cde568957e..2ec26fb937a 100644 --- a/backends/qualcomm/README.md +++ b/backends/qualcomm/README.md @@ -6,9 +6,9 @@ we reserve the right to modify interfaces and implementations. This backend is implemented on the top of [Qualcomm AI Engine Direct SDK](https://developer.qualcomm.com/software/qualcomm-ai-engine-direct-sdk). -Please follow [tutorial](../../docs/source/build-run-qualcomm-ai-engine-direct-backend.md) to setup environment, build, and run executorch models by this backend (Qualcomm AI Engine Direct is also referred to as QNN in the source and documentation). +Please follow [tutorial](../../docs/source/backends-qualcomm.md) to setup environment, build, and run executorch models by this backend (Qualcomm AI Engine Direct is also referred to as QNN in the source and documentation). -A website version of the tutorial is [here](https://pytorch.org/executorch/main/build-run-qualcomm-ai-engine-direct-backend). +A website version of the tutorial is [here](https://pytorch.org/executorch/main/backends-qualcomm). ## Delegate Options diff --git a/backends/qualcomm/setup.md b/backends/qualcomm/setup.md index 37d8e04c210..a7adb6d006d 100644 --- a/backends/qualcomm/setup.md +++ b/backends/qualcomm/setup.md @@ -1,6 +1,6 @@ # Setting up QNN Backend -Please refer to [Building and Running ExecuTorch with Qualcomm AI Engine Direct Backend](../../docs/source/build-run-qualcomm-ai-engine-direct-backend.md). +Please refer to [Building and Running ExecuTorch with Qualcomm AI Engine Direct Backend](../../docs/source/backends-qualcomm.md). That is a tutorial for building and running Qualcomm AI Engine Direct backend, including compiling a model on a x64 host and running the inference diff --git a/backends/xnnpack/README.md b/backends/xnnpack/README.md index 0bb5ffe7383..2328f8e4b90 100644 --- a/backends/xnnpack/README.md +++ b/backends/xnnpack/README.md @@ -132,5 +132,5 @@ create an issue on [github](https://www.github.com/pytorch/executorch/issues). ## See Also For more information about the XNNPACK Backend, please check out the following resources: -- [XNNPACK Backend](https://pytorch.org/executorch/main/backends-xnnpack.html) -- [XNNPACK Backend Internals](https://pytorch.org/executorch/main/backend-delegates-xnnpack-reference.html) +- [XNNPACK Backend](https://pytorch.org/executorch/main/backends-xnnpack) +- [XNNPACK Backend Internals](https://pytorch.org/executorch/main/backend-delegates-xnnpack-reference) diff --git a/docs/README.md b/docs/README.md index e6dc66d335e..b9997290e7e 100644 --- a/docs/README.md +++ b/docs/README.md @@ -130,7 +130,7 @@ Use the to contribute to the documentation. In addition to that, see -[Markdown in Sphinx Tips and Tricks](https://pytorch.org/executorch/markdown-sphinx-tips-tricks.html) +[Markdown in Sphinx Tips and Tricks](source/markdown-sphinx-tips-tricks.md) for tips on how to author high-quality markdown pages with Myst Parser. ## Adding Tutorials diff --git a/docs/source/llm/getting-started.md b/docs/source/llm/getting-started.md index 25eaa9339ef..a6b34adf99b 100644 --- a/docs/source/llm/getting-started.md +++ b/docs/source/llm/getting-started.md @@ -588,7 +588,7 @@ The delegated model should be noticeably faster compared to the non-delegated mo For more information regarding backend delegation, see the ExecuTorch guides for the [XNNPACK Backend](../backends-xnnpack.md), [Core ML -Backend](../backends-coreml.md) and [Qualcomm AI Engine Direct Backend](build-run-llama3-qualcomm-ai-engine-direct-backend.md). +Backend](../backends-coreml.md) and [Qualcomm AI Engine Direct Backend](../backends-qualcomm.md). ## Quantization diff --git a/docs/source/tutorials_source/README.txt b/docs/source/tutorials_source/README.txt index b8717197d14..2677c1ea882 100644 --- a/docs/source/tutorials_source/README.txt +++ b/docs/source/tutorials_source/README.txt @@ -3,4 +3,4 @@ Tutorials 1. tutorials/* Getting Started Tutorials - https://pytorch.org/executorch/tutorials/template_tutorial.html + https://github.com/pytorch/executorch/blob/main/docs/source/tutorials_source/template_tutorial.py diff --git a/examples/README.md b/examples/README.md index b6a4e0a0472..331722f384a 100644 --- a/examples/README.md +++ b/examples/README.md @@ -71,7 +71,7 @@ You will find demos of [ExecuTorch QNN Backend](./qualcomm) in the [`qualcomm/`] ### Cadence HiFi4 DSP -The [`Cadence/`](./cadence) directory hosts a demo that showcases the process of exporting and executing a model on Xtensa Hifi4 DSP. You can utilize [this tutorial](../docs/source/build-run-xtensa.md) to guide you in configuring the demo and running it. +The [`Cadence/`](./cadence) directory hosts a demo that showcases the process of exporting and executing a model on Xtensa Hifi4 DSP. You can utilize [this tutorial](../docs/source/backends-cadence.md) to guide you in configuring the demo and running it. ## Dependencies diff --git a/examples/arm/README.md b/examples/arm/README.md index 25f2fd5a316..0b487ebadb4 100644 --- a/examples/arm/README.md +++ b/examples/arm/README.md @@ -44,6 +44,6 @@ jupyter notebook ethos_u_minimal_example.ipynb ### Online Tutorial -We also have a [tutorial](https://pytorch.org/executorch/main/executorch-arm-delegate-tutorial) explaining the steps performed in these +We also have a [tutorial](https://pytorch.org/executorch/main/backends-arm-ethos-u) explaining the steps performed in these scripts, expected results, possible problems and more. It is a step-by-step guide you can follow to better understand this delegate. diff --git a/examples/models/efficient_sam/README.md b/examples/models/efficient_sam/README.md index bce1f7c5319..0e148a2bc3c 100644 --- a/examples/models/efficient_sam/README.md +++ b/examples/models/efficient_sam/README.md @@ -12,7 +12,7 @@ Follow the [tutorial](https://pytorch.org/executorch/main/getting-started-setup# ### Exporting to Core ML -Make sure to install the [required dependencies](https://pytorch.org/executorch/main/build-run-coreml.html#setting-up-your-developer-environment) for Core ML export. +Make sure to install the [required dependencies](https://pytorch.org/executorch/main/backends-coreml#development-requirements) for Core ML export. To export the model to Core ML, run the following command: @@ -32,7 +32,7 @@ python -m examples.xnnpack.aot_compiler -m efficient_sam # Performance -Tests were conducted on an Apple M1 Pro chip using the instructions for building and running Executorch with [Core ML](https://pytorch.org/executorch/main/build-run-coreml.html#runtime) and [XNNPACK](https://pytorch.org/executorch/main/tutorial-xnnpack-delegate-lowering.html#running-the-xnnpack-model-with-cmake) backends. +Tests were conducted on an Apple M1 Pro chip using the instructions for building and running Executorch with [Core ML](https://pytorch.org/executorch/main/https://pytorch.org/executorch/main/backends-coreml#runtime-integration) and [XNNPACK](https://pytorch.org/executorch/main/tutorial-xnnpack-delegate-lowering#running-the-xnnpack-model-with-cmake) backends. | Backend Configuration | Average Inference Time (seconds) | | ---------------------- | -------------------------------- | diff --git a/examples/models/llama/UTILS.md b/examples/models/llama/UTILS.md index 2d4d4dfd788..5f760ad7670 100644 --- a/examples/models/llama/UTILS.md +++ b/examples/models/llama/UTILS.md @@ -25,7 +25,7 @@ From `executorch` root: ## Smaller model delegated to other backends Currently we supported lowering the stories model to other backends, including, CoreML, MPS and QNN. Please refer to the instruction -for each backend ([CoreML](https://pytorch.org/executorch/main/backends-coreml), [MPS](https://pytorch.org/executorch/main/backends-mps), [QNN](https://pytorch.org/executorch/main/backend-qualcomm)) before trying to lower them. After the backend library is installed, the script to export a lowered model is +for each backend ([CoreML](https://pytorch.org/executorch/main/backends-coreml), [MPS](https://pytorch.org/executorch/main/backends-mps), [QNN](https://pytorch.org/executorch/main/backends-qualcomm)) before trying to lower them. After the backend library is installed, the script to export a lowered model is - Lower to CoreML: `python -m examples.models.llama.export_llama -kv --disable_dynamic_shape --coreml -c stories110M.pt -p params.json ` - MPS: `python -m examples.models.llama.export_llama -kv --disable_dynamic_shape --mps -c stories110M.pt -p params.json ` diff --git a/examples/models/phi-3-mini-lora/README.md b/examples/models/phi-3-mini-lora/README.md index 5bc48bd48f4..62efda6c3dc 100644 --- a/examples/models/phi-3-mini-lora/README.md +++ b/examples/models/phi-3-mini-lora/README.md @@ -16,8 +16,9 @@ To see how you can use the model exported for training in a fully involved finet python export_model.py ``` -2. Run the inference model using an example runtime. For more detailed steps on this, check out [Build & Run](https://pytorch.org/executorch/main/getting-started-setup#build-run). +2. Run the inference model using an example runtime. For more detailed steps on this, check out [Building from Source](https://pytorch.org/executorch/main/using-executorch-building-from-source). ``` + # Clean and configure the CMake build system. Compiled programs will appear in the executorch/cmake-out directory we create here. ./install_executorch.sh --clean (mkdir cmake-out && cd cmake-out && cmake ..) diff --git a/examples/qualcomm/README.md b/examples/qualcomm/README.md index 1f7e2d1e476..feaa170957b 100644 --- a/examples/qualcomm/README.md +++ b/examples/qualcomm/README.md @@ -24,13 +24,13 @@ Here are some general information and limitations. Please finish tutorial [Setting up executorch](https://pytorch.org/executorch/main/getting-started-setup). -Please finish [setup QNN backend](../../docs/source/build-run-qualcomm-ai-engine-direct-backend.md). +Please finish [setup QNN backend](../../docs/source/backends-qualcomm.md). ## Environment Please set up `QNN_SDK_ROOT` environment variable. Note that this version should be exactly same as building QNN backend. -Please check [setup](../../docs/source/build-run-qualcomm-ai-engine-direct-backend.md). +Please check [setup](../../docs/source/backends-qualcomm.md). Please set up `LD_LIBRARY_PATH` to `$QNN_SDK_ROOT/lib/x86_64-linux-clang`. Or, you could put QNN libraries to default search path of the dynamic linker. diff --git a/examples/qualcomm/oss_scripts/llama/README.md b/examples/qualcomm/oss_scripts/llama/README.md index 27abd5689a0..855fcef8d91 100644 --- a/examples/qualcomm/oss_scripts/llama/README.md +++ b/examples/qualcomm/oss_scripts/llama/README.md @@ -28,7 +28,7 @@ Hybrid Mode: Hybrid mode leverages the strengths of both AR-N model and KV cache ### Step 1: Setup 1. Follow the [tutorial](https://pytorch.org/executorch/main/getting-started-setup) to set up ExecuTorch. -2. Follow the [tutorial](https://pytorch.org/executorch/main/build-run-qualcomm-ai-engine-direct-backend) to build Qualcomm AI Engine Direct Backend. +2. Follow the [tutorial](https://pytorch.org/executorch/main/backends-qualcomm) to build Qualcomm AI Engine Direct Backend. ### Step 2: Prepare Model diff --git a/examples/qualcomm/qaihub_scripts/llama/README.md b/examples/qualcomm/qaihub_scripts/llama/README.md index 4d010b5d474..fbbf8827b4b 100644 --- a/examples/qualcomm/qaihub_scripts/llama/README.md +++ b/examples/qualcomm/qaihub_scripts/llama/README.md @@ -12,7 +12,7 @@ Note that the pre-compiled context binaries could not be futher fine-tuned for o ### Instructions #### Step 1: Setup 1. Follow the [tutorial](https://pytorch.org/executorch/main/getting-started-setup) to set up ExecuTorch. -2. Follow the [tutorial](https://pytorch.org/executorch/main/build-run-qualcomm-ai-engine-direct-backend) to build Qualcomm AI Engine Direct Backend. +2. Follow the [tutorial](https://pytorch.org/executorch/main/backends-qualcomm) to build Qualcomm AI Engine Direct Backend. #### Step2: Prepare Model 1. Create account for https://aihub.qualcomm.com/ @@ -40,7 +40,7 @@ Note that the pre-compiled context binaries could not be futher fine-tuned for o ### Instructions #### Step 1: Setup 1. Follow the [tutorial](https://pytorch.org/executorch/main/getting-started-setup) to set up ExecuTorch. -2. Follow the [tutorial](https://pytorch.org/executorch/main/build-run-qualcomm-ai-engine-direct-backend) to build Qualcomm AI Engine Direct Backend. +2. Follow the [tutorial](https://pytorch.org/executorch/main/backends-qualcomm) to build Qualcomm AI Engine Direct Backend. #### Step2: Prepare Model 1. Create account for https://aihub.qualcomm.com/ diff --git a/examples/qualcomm/qaihub_scripts/stable_diffusion/README.md b/examples/qualcomm/qaihub_scripts/stable_diffusion/README.md index 998c97d78e3..d2649cf72c2 100644 --- a/examples/qualcomm/qaihub_scripts/stable_diffusion/README.md +++ b/examples/qualcomm/qaihub_scripts/stable_diffusion/README.md @@ -11,7 +11,7 @@ The model architecture, scheduler, and time embedding are from the [stabilityai/ ### Instructions #### Step 1: Setup 1. Follow the [tutorial](https://pytorch.org/executorch/main/getting-started-setup) to set up ExecuTorch. -2. Follow the [tutorial](https://pytorch.org/executorch/main/build-run-qualcomm-ai-engine-direct-backend) to build Qualcomm AI Engine Direct Backend. +2. Follow the [tutorial](https://pytorch.org/executorch/main/backends-qualcomm) to build Qualcomm AI Engine Direct Backend. #### Step2: Prepare Model 1. Download the context binaries for TextEncoder, UNet, and VAEDecoder under https://huggingface.co/qualcomm/Stable-Diffusion-v2.1/tree/main diff --git a/examples/qualcomm/qaihub_scripts/utils/README.md b/examples/qualcomm/qaihub_scripts/utils/README.md index 61f465f3926..996b72f7937 100644 --- a/examples/qualcomm/qaihub_scripts/utils/README.md +++ b/examples/qualcomm/qaihub_scripts/utils/README.md @@ -1,6 +1,6 @@ # CLI Tool for Compile / Deploy Pre-Built QNN Artifacts -An easy-to-use tool for generating / executing .pte program from pre-built model libraries / context binaries from Qualcomm AI Engine Direct. Tool is verified with [host environement](../../../../docs/source/build-run-qualcomm-ai-engine-direct-backend.md#host-os). +An easy-to-use tool for generating / executing .pte program from pre-built model libraries / context binaries from Qualcomm AI Engine Direct. Tool is verified with [host environement](../../../../docs/source/backends-qualcomm.md#host-os). ## Description @@ -20,7 +20,7 @@ If users are interested in well-known applications, [Qualcomm AI HUB](https://ai ### Dependencies * Register for Qualcomm AI HUB. -* Download the corresponding QNN SDK via [link](https://www.qualcomm.com/developer/software/qualcomm-ai-engine-direct-sdk) which your favorite model is compiled with. Ths link will automatically download the latest version at this moment (users should be able to specify version soon, please refer to [this](../../../../docs/source/build-run-qualcomm-ai-engine-direct-backend.md#software) for earlier releases). +* Download the corresponding QNN SDK via [link](https://www.qualcomm.com/developer/software/qualcomm-ai-engine-direct-sdk) which your favorite model is compiled with. Ths link will automatically download the latest version at this moment (users should be able to specify version soon, please refer to [this](../../../../docs/source/backends-qualcomm.md#software) for earlier releases). ### Target Model diff --git a/examples/xnnpack/README.md b/examples/xnnpack/README.md index 56deff928af..5c307d34717 100644 --- a/examples/xnnpack/README.md +++ b/examples/xnnpack/README.md @@ -1,7 +1,7 @@ # XNNPACK Backend [XNNPACK](https://github.com/google/XNNPACK) is a library of optimized neural network operators for ARM and x86 CPU platforms. Our delegate lowers models to run using these highly optimized CPU operators. You can try out lowering and running some example models in the demo. Please refer to the following docs for information on the XNNPACK Delegate -- [XNNPACK Backend Delegate Overview](https://pytorch.org/executorch/main/native-delegates-executorch-xnnpack-delegate) +- [XNNPACK Backend Delegate Overview](https://pytorch.org/executorch/main/backends-xnnpack) - [XNNPACK Delegate Export Tutorial](https://pytorch.org/executorch/main/tutorial-xnnpack-delegate-lowering) diff --git a/extension/llm/export/partitioner_lib.py b/extension/llm/export/partitioner_lib.py index 76e8c357119..20604bbf635 100644 --- a/extension/llm/export/partitioner_lib.py +++ b/extension/llm/export/partitioner_lib.py @@ -57,7 +57,7 @@ def get_mps_partitioner(use_kv_cache: bool = False): ) except ImportError: raise ImportError( - "Please install the MPS backend follwing https://pytorch.org/executorch/main/build-run-mps.html" + "Please install the MPS backend follwing https://pytorch.org/executorch/main/backends-mps" ) compile_specs = [CompileSpec("use_fp16", bytes([True]))] @@ -81,7 +81,7 @@ def get_coreml_partitioner( ) except ImportError: raise ImportError( - "Please install the CoreML backend follwing https://pytorch.org/executorch/main/build-run-coreml.html" + "Please install the CoreML backend follwing https://pytorch.org/executorch/main/backends-coreml" + "; for buck users, please add example dependancies: //executorch/backends/apple/coreml:backend, and etc" ) @@ -195,7 +195,7 @@ def get_qnn_partitioner( ) except ImportError: raise ImportError( - "Please install the Qualcomm backend following https://pytorch.org/executorch/main/build-run-qualcomm-ai-engine-direct-backend.html" + "Please install the Qualcomm backend following https://pytorch.org/executorch/main/backends-qualcomm" ) use_fp16 = True diff --git a/extension/llm/export/quantizer_lib.py b/extension/llm/export/quantizer_lib.py index e516ae4c0a9..24c3be2e802 100644 --- a/extension/llm/export/quantizer_lib.py +++ b/extension/llm/export/quantizer_lib.py @@ -158,7 +158,7 @@ def get_qnn_quantizer( except ImportError: raise ImportError( - "Please install the Qualcomm backend follwing https://pytorch.org/executorch/main/build-run-qualcomm.html" + "Please install the Qualcomm backend follwing https://pytorch.org/executorch/main/backends-qualcomm" ) backend, quant_config = pt2e_quantize.split("_") @@ -226,7 +226,7 @@ def get_coreml_quantizer(pt2e_quantize: str): from executorch.backends.apple.coreml.quantizer import CoreMLQuantizer except ImportError: raise ImportError( - "Please install the CoreML backend follwing https://pytorch.org/executorch/main/build-run-coreml.html" + "Please install the CoreML backend follwing https://pytorch.org/executorch/main/backends-coreml" ) if pt2e_quantize == "coreml_8a_c8w": From 34ef971f2bd00a122a355139bc47a98699dc96e1 Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Mon, 14 Apr 2025 19:44:23 -0700 Subject: [PATCH 145/244] Update README.md (#10186) --- examples/models/efficient_sam/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/models/efficient_sam/README.md b/examples/models/efficient_sam/README.md index 0e148a2bc3c..c2ba18b2e16 100644 --- a/examples/models/efficient_sam/README.md +++ b/examples/models/efficient_sam/README.md @@ -32,7 +32,7 @@ python -m examples.xnnpack.aot_compiler -m efficient_sam # Performance -Tests were conducted on an Apple M1 Pro chip using the instructions for building and running Executorch with [Core ML](https://pytorch.org/executorch/main/https://pytorch.org/executorch/main/backends-coreml#runtime-integration) and [XNNPACK](https://pytorch.org/executorch/main/tutorial-xnnpack-delegate-lowering#running-the-xnnpack-model-with-cmake) backends. +Tests were conducted on an Apple M1 Pro chip using the instructions for building and running Executorch with [Core ML](https://pytorch.org/executorch/main/backends-coreml#runtime-integration) and [XNNPACK](https://pytorch.org/executorch/main/tutorial-xnnpack-delegate-lowering#running-the-xnnpack-model-with-cmake) backends. | Backend Configuration | Average Inference Time (seconds) | | ---------------------- | -------------------------------- | From 2484bca0a38f0364fee05a54b2fed5d04b9c3343 Mon Sep 17 00:00:00 2001 From: Manuel Candales <42380156+manuelcandales@users.noreply.github.com> Date: Tue, 15 Apr 2025 01:31:58 -0400 Subject: [PATCH 146/244] import complex.h from c10 Differential Revision: D72197508 Pull Request resolved: https://github.com/pytorch/executorch/pull/10155 --- .../core/portable_type/c10/c10/targets.bzl | 3 + .../core/portable_type/c10/c10/util/complex.h | 668 ++++++++++++++++++ .../portable_type/c10/c10/util/complex_math.h | 406 +++++++++++ .../c10/c10/util/complex_utils.h | 46 ++ runtime/core/portable_type/complex.h | 37 +- 5 files changed, 1129 insertions(+), 31 deletions(-) create mode 100644 runtime/core/portable_type/c10/c10/util/complex.h create mode 100644 runtime/core/portable_type/c10/c10/util/complex_math.h create mode 100644 runtime/core/portable_type/c10/c10/util/complex_utils.h diff --git a/runtime/core/portable_type/c10/c10/targets.bzl b/runtime/core/portable_type/c10/c10/targets.bzl index d9d72b5be3f..e9728745270 100644 --- a/runtime/core/portable_type/c10/c10/targets.bzl +++ b/runtime/core/portable_type/c10/c10/targets.bzl @@ -25,6 +25,9 @@ def define_common_targets(): "util/Half-inl.h", "util/TypeSafeSignMath.h", "util/bit_cast.h", + "util/complex.h", + "util/complex_math.h", + "util/complex_utils.h", "util/floating_point_utils.h", "util/irange.h", ], diff --git a/runtime/core/portable_type/c10/c10/util/complex.h b/runtime/core/portable_type/c10/c10/util/complex.h new file mode 100644 index 00000000000..b63710d9458 --- /dev/null +++ b/runtime/core/portable_type/c10/c10/util/complex.h @@ -0,0 +1,668 @@ +#pragma once + +#include + +#include +#include + +#if defined(__CUDACC__) || defined(__HIPCC__) +#include +#endif + +C10_CLANG_DIAGNOSTIC_PUSH() +#if C10_CLANG_HAS_WARNING("-Wimplicit-float-conversion") +C10_CLANG_DIAGNOSTIC_IGNORE("-Wimplicit-float-conversion") +#endif +#if C10_CLANG_HAS_WARNING("-Wfloat-conversion") +C10_CLANG_DIAGNOSTIC_IGNORE("-Wfloat-conversion") +#endif + +namespace c10 { + +// c10::complex is an implementation of complex numbers that aims +// to work on all devices supported by PyTorch +// +// Most of the APIs duplicates std::complex +// Reference: https://en.cppreference.com/w/cpp/numeric/complex +// +// [NOTE: Complex Operator Unification] +// Operators currently use a mix of std::complex, thrust::complex, and +// c10::complex internally. The end state is that all operators will use +// c10::complex internally. Until then, there may be some hacks to support all +// variants. +// +// +// [Note on Constructors] +// +// The APIs of constructors are mostly copied from C++ standard: +// https://en.cppreference.com/w/cpp/numeric/complex/complex +// +// Since C++14, all constructors are constexpr in std::complex +// +// There are three types of constructors: +// - initializing from real and imag: +// `constexpr complex( const T& re = T(), const T& im = T() );` +// - implicitly-declared copy constructor +// - converting constructors +// +// Converting constructors: +// - std::complex defines converting constructor between float/double/long +// double, +// while we define converting constructor between float/double. +// - For these converting constructors, upcasting is implicit, downcasting is +// explicit. +// - We also define explicit casting from std::complex/thrust::complex +// - Note that the conversion from thrust is not constexpr, because +// thrust does not define them as constexpr ???? +// +// +// [Operator =] +// +// The APIs of operator = are mostly copied from C++ standard: +// https://en.cppreference.com/w/cpp/numeric/complex/operator%3D +// +// Since C++20, all operator= are constexpr. Although we are not building with +// C++20, we also obey this behavior. +// +// There are three types of assign operator: +// - Assign a real value from the same scalar type +// - In std, this is templated as complex& operator=(const T& x) +// with specialization `complex& operator=(T x)` for float/double/long +// double Since we only support float and double, on will use `complex& +// operator=(T x)` +// - Copy assignment operator and converting assignment operator +// - There is no specialization of converting assignment operators, which type +// is +// convertible is solely dependent on whether the scalar type is convertible +// +// In addition to the standard assignment, we also provide assignment operators +// with std and thrust +// +// +// [Casting operators] +// +// std::complex does not have casting operators. We define casting operators +// casting to std::complex and thrust::complex +// +// +// [Operator ""] +// +// std::complex has custom literals `i`, `if` and `il` defined in namespace +// `std::literals::complex_literals`. We define our own custom literals in the +// namespace `c10::complex_literals`. Our custom literals does not follow the +// same behavior as in std::complex, instead, we define _if, _id to construct +// float/double complex literals. +// +// +// [real() and imag()] +// +// In C++20, there are two overload of these functions, one it to return the +// real/imag, another is to set real/imag, they are both constexpr. We follow +// this design. +// +// +// [Operator +=,-=,*=,/=] +// +// Since C++20, these operators become constexpr. In our implementation, they +// are also constexpr. +// +// There are two types of such operators: operating with a real number, or +// operating with another complex number. For the operating with a real number, +// the generic template form has argument type `const T &`, while the overload +// for float/double/long double has `T`. We will follow the same type as +// float/double/long double in std. +// +// [Unary operator +-] +// +// Since C++20, they are constexpr. We also make them expr +// +// [Binary operators +-*/] +// +// Each operator has three versions (taking + as example): +// - complex + complex +// - complex + real +// - real + complex +// +// [Operator ==, !=] +// +// Each operator has three versions (taking == as example): +// - complex == complex +// - complex == real +// - real == complex +// +// Some of them are removed on C++20, but we decide to keep them +// +// [Operator <<, >>] +// +// These are implemented by casting to std::complex +// +// +// +// TODO(@zasdfgbnm): c10::complex is not currently supported, +// because: +// - lots of members and functions of c10::Half are not constexpr +// - thrust::complex only support float and double + +template +struct alignas(sizeof(T) * 2) complex { + using value_type = T; + + T real_ = T(0); + T imag_ = T(0); + + constexpr complex() = default; + C10_HOST_DEVICE constexpr complex(const T& re, const T& im = T()) + : real_(re), imag_(im) {} + template + explicit constexpr complex(const std::complex& other) + : complex(other.real(), other.imag()) {} +#if defined(__CUDACC__) || defined(__HIPCC__) + template + explicit C10_HOST_DEVICE complex(const thrust::complex& other) + : real_(other.real()), imag_(other.imag()) {} +// NOTE can not be implemented as follow due to ROCm bug: +// explicit C10_HOST_DEVICE complex(const thrust::complex &other): +// complex(other.real(), other.imag()) {} +#endif + + // Use SFINAE to specialize casting constructor for c10::complex and + // c10::complex + template + C10_HOST_DEVICE explicit constexpr complex( + const std::enable_if_t, complex>& other) + : real_(other.real_), imag_(other.imag_) {} + template + C10_HOST_DEVICE constexpr complex( + const std::enable_if_t, complex>& other) + : real_(other.real_), imag_(other.imag_) {} + + constexpr complex& operator=(T re) { + real_ = re; + imag_ = 0; + return *this; + } + + constexpr complex& operator+=(T re) { + real_ += re; + return *this; + } + + constexpr complex& operator-=(T re) { + real_ -= re; + return *this; + } + + constexpr complex& operator*=(T re) { + real_ *= re; + imag_ *= re; + return *this; + } + + constexpr complex& operator/=(T re) { + real_ /= re; + imag_ /= re; + return *this; + } + + template + constexpr complex& operator=(const complex& rhs) { + real_ = rhs.real(); + imag_ = rhs.imag(); + return *this; + } + + template + constexpr complex& operator+=(const complex& rhs) { + real_ += rhs.real(); + imag_ += rhs.imag(); + return *this; + } + + template + constexpr complex& operator-=(const complex& rhs) { + real_ -= rhs.real(); + imag_ -= rhs.imag(); + return *this; + } + + template + constexpr complex& operator*=(const complex& rhs) { + // (a + bi) * (c + di) = (a*c - b*d) + (a * d + b * c) i + T a = real_; + T b = imag_; + U c = rhs.real(); + U d = rhs.imag(); + real_ = a * c - b * d; + imag_ = a * d + b * c; + return *this; + } + +#ifdef __APPLE__ +#define FORCE_INLINE_APPLE __attribute__((always_inline)) +#else +#define FORCE_INLINE_APPLE +#endif + template + constexpr FORCE_INLINE_APPLE complex& operator/=(const complex& rhs) + __ubsan_ignore_float_divide_by_zero__ { + // (a + bi) / (c + di) = (ac + bd)/(c^2 + d^2) + (bc - ad)/(c^2 + d^2) i + // the calculation below follows numpy's complex division + T a = real_; + T b = imag_; + U c = rhs.real(); + U d = rhs.imag(); + +#if defined(__GNUC__) && !defined(__clang__) + // std::abs is already constexpr by gcc + auto abs_c = std::abs(c); + auto abs_d = std::abs(d); +#else + auto abs_c = c < 0 ? -c : c; + auto abs_d = d < 0 ? -d : d; +#endif + + if (abs_c >= abs_d) { + if (abs_c == U(0) && abs_d == U(0)) { + /* divide by zeros should yield a complex inf or nan */ + real_ = a / abs_c; + imag_ = b / abs_d; + } else { + auto rat = d / c; + auto scl = U(1.0) / (c + d * rat); + real_ = (a + b * rat) * scl; + imag_ = (b - a * rat) * scl; + } + } else { + auto rat = c / d; + auto scl = U(1.0) / (d + c * rat); + real_ = (a * rat + b) * scl; + imag_ = (b * rat - a) * scl; + } + return *this; + } +#undef FORCE_INLINE_APPLE + + template + constexpr complex& operator=(const std::complex& rhs) { + real_ = rhs.real(); + imag_ = rhs.imag(); + return *this; + } + +#if defined(__CUDACC__) || defined(__HIPCC__) + template + C10_HOST_DEVICE complex& operator=(const thrust::complex& rhs) { + real_ = rhs.real(); + imag_ = rhs.imag(); + return *this; + } +#endif + + template + explicit constexpr operator std::complex() const { + return std::complex(std::complex(real(), imag())); + } + +#if defined(__CUDACC__) || defined(__HIPCC__) + template + C10_HOST_DEVICE explicit operator thrust::complex() const { + return static_cast>(thrust::complex(real(), imag())); + } +#endif + + // consistent with NumPy behavior + explicit constexpr operator bool() const { + return real() || imag(); + } + + C10_HOST_DEVICE constexpr T real() const { + return real_; + } + constexpr void real(T value) { + real_ = value; + } + C10_HOST_DEVICE constexpr T imag() const { + return imag_; + } + constexpr void imag(T value) { + imag_ = value; + } +}; + +namespace complex_literals { + +constexpr complex operator""_if(long double imag) { + return complex(0.0f, static_cast(imag)); +} + +constexpr complex operator""_id(long double imag) { + return complex(0.0, static_cast(imag)); +} + +constexpr complex operator""_if(unsigned long long imag) { + return complex(0.0f, static_cast(imag)); +} + +constexpr complex operator""_id(unsigned long long imag) { + return complex(0.0, static_cast(imag)); +} + +} // namespace complex_literals + +template +constexpr complex operator+(const complex& val) { + return val; +} + +template +constexpr complex operator-(const complex& val) { + return complex(-val.real(), -val.imag()); +} + +template +constexpr complex operator+(const complex& lhs, const complex& rhs) { + complex result = lhs; + return result += rhs; +} + +template +constexpr complex operator+(const complex& lhs, const T& rhs) { + complex result = lhs; + return result += rhs; +} + +template +constexpr complex operator+(const T& lhs, const complex& rhs) { + return complex(lhs + rhs.real(), rhs.imag()); +} + +template +constexpr complex operator-(const complex& lhs, const complex& rhs) { + complex result = lhs; + return result -= rhs; +} + +template +constexpr complex operator-(const complex& lhs, const T& rhs) { + complex result = lhs; + return result -= rhs; +} + +template +constexpr complex operator-(const T& lhs, const complex& rhs) { + complex result = -rhs; + return result += lhs; +} + +template +constexpr complex operator*(const complex& lhs, const complex& rhs) { + complex result = lhs; + return result *= rhs; +} + +template +constexpr complex operator*(const complex& lhs, const T& rhs) { + complex result = lhs; + return result *= rhs; +} + +template +constexpr complex operator*(const T& lhs, const complex& rhs) { + complex result = rhs; + return result *= lhs; +} + +template +constexpr complex operator/(const complex& lhs, const complex& rhs) { + complex result = lhs; + return result /= rhs; +} + +template +constexpr complex operator/(const complex& lhs, const T& rhs) { + complex result = lhs; + return result /= rhs; +} + +template +constexpr complex operator/(const T& lhs, const complex& rhs) { + complex result(lhs, T()); + return result /= rhs; +} + +// Define operators between integral scalars and c10::complex. std::complex does +// not support this when T is a floating-point number. This is useful because it +// saves a lot of "static_cast" when operate a complex and an integer. This +// makes the code both less verbose and potentially more efficient. +#define COMPLEX_INTEGER_OP_TEMPLATE_CONDITION \ + typename std::enable_if_t< \ + std::is_floating_point_v && std::is_integral_v, \ + int> = 0 + +template +constexpr c10::complex operator+(const c10::complex& a, const iT& b) { + return a + static_cast(b); +} + +template +constexpr c10::complex operator+(const iT& a, const c10::complex& b) { + return static_cast(a) + b; +} + +template +constexpr c10::complex operator-(const c10::complex& a, const iT& b) { + return a - static_cast(b); +} + +template +constexpr c10::complex operator-(const iT& a, const c10::complex& b) { + return static_cast(a) - b; +} + +template +constexpr c10::complex operator*(const c10::complex& a, const iT& b) { + return a * static_cast(b); +} + +template +constexpr c10::complex operator*(const iT& a, const c10::complex& b) { + return static_cast(a) * b; +} + +template +constexpr c10::complex operator/(const c10::complex& a, const iT& b) { + return a / static_cast(b); +} + +template +constexpr c10::complex operator/(const iT& a, const c10::complex& b) { + return static_cast(a) / b; +} + +#undef COMPLEX_INTEGER_OP_TEMPLATE_CONDITION + +template +constexpr bool operator==(const complex& lhs, const complex& rhs) { + return (lhs.real() == rhs.real()) && (lhs.imag() == rhs.imag()); +} + +template +constexpr bool operator==(const complex& lhs, const T& rhs) { + return (lhs.real() == rhs) && (lhs.imag() == T()); +} + +template +constexpr bool operator==(const T& lhs, const complex& rhs) { + return (lhs == rhs.real()) && (T() == rhs.imag()); +} + +template +constexpr bool operator!=(const complex& lhs, const complex& rhs) { + return !(lhs == rhs); +} + +template +constexpr bool operator!=(const complex& lhs, const T& rhs) { + return !(lhs == rhs); +} + +template +constexpr bool operator!=(const T& lhs, const complex& rhs) { + return !(lhs == rhs); +} + +template +std::basic_ostream& operator<<( + std::basic_ostream& os, + const complex& x) { + return (os << static_cast>(x)); +} + +template +std::basic_istream& operator>>( + std::basic_istream& is, + complex& x) { + std::complex tmp; + is >> tmp; + x = tmp; + return is; +} + +} // namespace c10 + +// std functions +// +// The implementation of these functions also follow the design of C++20 + +namespace std { + +template +constexpr T real(const c10::complex& z) { + return z.real(); +} + +template +constexpr T imag(const c10::complex& z) { + return z.imag(); +} + +template +C10_HOST_DEVICE T abs(const c10::complex& z) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return thrust::abs(static_cast>(z)); +#else + return std::abs(static_cast>(z)); +#endif +} + +#if defined(USE_ROCM) +#define ROCm_Bug(x) +#else +#define ROCm_Bug(x) x +#endif + +template +C10_HOST_DEVICE T arg(const c10::complex& z) { + return ROCm_Bug(std)::atan2(std::imag(z), std::real(z)); +} + +#undef ROCm_Bug + +template +constexpr T norm(const c10::complex& z) { + return z.real() * z.real() + z.imag() * z.imag(); +} + +// For std::conj, there are other versions of it: +// constexpr std::complex conj( float z ); +// template< class DoubleOrInteger > +// constexpr std::complex conj( DoubleOrInteger z ); +// constexpr std::complex conj( long double z ); +// These are not implemented +// TODO(@zasdfgbnm): implement them as c10::conj +template +constexpr c10::complex conj(const c10::complex& z) { + return c10::complex(z.real(), -z.imag()); +} + +// Thrust does not have complex --> complex version of thrust::proj, +// so this function is not implemented at c10 right now. +// TODO(@zasdfgbnm): implement it by ourselves + +// There is no c10 version of std::polar, because std::polar always +// returns std::complex. Use c10::polar instead; + +} // namespace std + +namespace c10 { + +template +C10_HOST_DEVICE complex polar(const T& r, const T& theta = T()) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>(thrust::polar(r, theta)); +#else + // std::polar() requires r >= 0, so spell out the explicit implementation to + // avoid a branch. + return complex(r * std::cos(theta), r * std::sin(theta)); +#endif +} + +template <> +struct alignas(4) complex { + Half real_; + Half imag_; + + // Constructors + complex() = default; + // Half constructor is not constexpr so the following constructor can't + // be constexpr + C10_HOST_DEVICE explicit inline complex(const Half& real, const Half& imag) + : real_(real), imag_(imag) {} + C10_HOST_DEVICE inline complex(const c10::complex& value) + : real_(value.real()), imag_(value.imag()) {} + + // Conversion operator + inline C10_HOST_DEVICE operator c10::complex() const { + return {real_, imag_}; + } + + constexpr C10_HOST_DEVICE Half real() const { + return real_; + } + constexpr C10_HOST_DEVICE Half imag() const { + return imag_; + } + + C10_HOST_DEVICE complex& operator+=(const complex& other) { + real_ = static_cast(real_) + static_cast(other.real_); + imag_ = static_cast(imag_) + static_cast(other.imag_); + return *this; + } + + C10_HOST_DEVICE complex& operator-=(const complex& other) { + real_ = static_cast(real_) - static_cast(other.real_); + imag_ = static_cast(imag_) - static_cast(other.imag_); + return *this; + } + + C10_HOST_DEVICE complex& operator*=(const complex& other) { + auto a = static_cast(real_); + auto b = static_cast(imag_); + auto c = static_cast(other.real()); + auto d = static_cast(other.imag()); + real_ = a * c - b * d; + imag_ = a * d + b * c; + return *this; + } +}; + +} // namespace c10 + +C10_CLANG_DIAGNOSTIC_POP() + +#define C10_INTERNAL_INCLUDE_COMPLEX_REMAINING_H +// math functions are included in a separate file +#include // IWYU pragma: keep +// utilities for complex types +#include // IWYU pragma: keep +#undef C10_INTERNAL_INCLUDE_COMPLEX_REMAINING_H diff --git a/runtime/core/portable_type/c10/c10/util/complex_math.h b/runtime/core/portable_type/c10/c10/util/complex_math.h new file mode 100644 index 00000000000..2b591026c94 --- /dev/null +++ b/runtime/core/portable_type/c10/c10/util/complex_math.h @@ -0,0 +1,406 @@ +#if !defined(C10_INTERNAL_INCLUDE_COMPLEX_REMAINING_H) +#error \ + "c10/util/complex_math.h is not meant to be individually included. Include c10/util/complex.h instead." +#endif + +namespace c10_complex_math { + +// Exponential functions + +template +C10_HOST_DEVICE inline c10::complex exp(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::exp(static_cast>(x))); +#else + return static_cast>( + std::exp(static_cast>(x))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex log(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::log(static_cast>(x))); +#else + return static_cast>( + std::log(static_cast>(x))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex log10(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::log10(static_cast>(x))); +#else + return static_cast>( + std::log10(static_cast>(x))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex log2(const c10::complex& x) { + const c10::complex log2 = c10::complex(::log(2.0), 0.0); + return c10_complex_math::log(x) / log2; +} + +// Power functions +// +#if defined(_LIBCPP_VERSION) || \ + (defined(__GLIBCXX__) && !defined(_GLIBCXX11_USE_C99_COMPLEX)) +namespace _detail { +C10_API c10::complex sqrt(const c10::complex& in); +C10_API c10::complex sqrt(const c10::complex& in); +C10_API c10::complex acos(const c10::complex& in); +C10_API c10::complex acos(const c10::complex& in); +} // namespace _detail +#endif + +template +C10_HOST_DEVICE inline c10::complex sqrt(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::sqrt(static_cast>(x))); +#elif !( \ + defined(_LIBCPP_VERSION) || \ + (defined(__GLIBCXX__) && !defined(_GLIBCXX11_USE_C99_COMPLEX))) + return static_cast>( + std::sqrt(static_cast>(x))); +#else + return _detail::sqrt(x); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex pow( + const c10::complex& x, + const c10::complex& y) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>(thrust::pow( + static_cast>(x), static_cast>(y))); +#else + return static_cast>(std::pow( + static_cast>(x), static_cast>(y))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex pow( + const c10::complex& x, + const T& y) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::pow(static_cast>(x), y)); +#else + return static_cast>( + std::pow(static_cast>(x), y)); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex pow( + const T& x, + const c10::complex& y) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::pow(x, static_cast>(y))); +#else + return static_cast>( + std::pow(x, static_cast>(y))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex pow( + const c10::complex& x, + const c10::complex& y) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>(thrust::pow( + static_cast>(x), static_cast>(y))); +#else + return static_cast>(std::pow( + static_cast>(x), static_cast>(y))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex pow( + const c10::complex& x, + const U& y) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::pow(static_cast>(x), y)); +#else + return static_cast>( + std::pow(static_cast>(x), y)); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex pow( + const T& x, + const c10::complex& y) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::pow(x, static_cast>(y))); +#else + return static_cast>( + std::pow(x, static_cast>(y))); +#endif +} + +// Trigonometric functions + +template +C10_HOST_DEVICE inline c10::complex sin(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::sin(static_cast>(x))); +#else + return static_cast>( + std::sin(static_cast>(x))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex cos(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::cos(static_cast>(x))); +#else + return static_cast>( + std::cos(static_cast>(x))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex tan(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::tan(static_cast>(x))); +#else + return static_cast>( + std::tan(static_cast>(x))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex asin(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::asin(static_cast>(x))); +#else + return static_cast>( + std::asin(static_cast>(x))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex acos(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::acos(static_cast>(x))); +#elif !defined(_LIBCPP_VERSION) + return static_cast>( + std::acos(static_cast>(x))); +#else + return _detail::acos(x); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex atan(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::atan(static_cast>(x))); +#else + return static_cast>( + std::atan(static_cast>(x))); +#endif +} + +// Hyperbolic functions + +template +C10_HOST_DEVICE inline c10::complex sinh(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::sinh(static_cast>(x))); +#else + return static_cast>( + std::sinh(static_cast>(x))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex cosh(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::cosh(static_cast>(x))); +#else + return static_cast>( + std::cosh(static_cast>(x))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex tanh(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::tanh(static_cast>(x))); +#else + return static_cast>( + std::tanh(static_cast>(x))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex asinh(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::asinh(static_cast>(x))); +#else + return static_cast>( + std::asinh(static_cast>(x))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex acosh(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::acosh(static_cast>(x))); +#else + return static_cast>( + std::acosh(static_cast>(x))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex atanh(const c10::complex& x) { +#if defined(__CUDACC__) || defined(__HIPCC__) + return static_cast>( + thrust::atanh(static_cast>(x))); +#else + return static_cast>( + std::atanh(static_cast>(x))); +#endif +} + +template +C10_HOST_DEVICE inline c10::complex log1p(const c10::complex& z) { +#if defined(__APPLE__) || defined(__MACOSX) || defined(__CUDACC__) || \ + defined(__HIPCC__) + // For Mac, the new implementation yielded a high relative error. Falling back + // to the old version for now. + // See https://github.com/numpy/numpy/pull/22611#issuecomment-1667945354 + // For CUDA we also use this one, as thrust::log(thrust::complex) takes + // *forever* to compile + + // log1p(z) = log(1 + z) + // Let's define 1 + z = r * e ^ (i * a), then we have + // log(r * e ^ (i * a)) = log(r) + i * a + // With z = x + iy, the term r can be written as + // r = ((1 + x) ^ 2 + y ^ 2) ^ 0.5 + // = (1 + x ^ 2 + 2 * x + y ^ 2) ^ 0.5 + // So, log(r) is + // log(r) = 0.5 * log(1 + x ^ 2 + 2 * x + y ^ 2) + // = 0.5 * log1p(x * (x + 2) + y ^ 2) + // we need to use the expression only on certain condition to avoid overflow + // and underflow from `(x * (x + 2) + y ^ 2)` + T x = z.real(); + T y = z.imag(); + T zabs = std::abs(z); + T theta = std::atan2(y, x + T(1)); + if (zabs < 0.5) { + T r = x * (T(2) + x) + y * y; + if (r == 0) { // handle underflow + return {x, theta}; + } + return {T(0.5) * std::log1p(r), theta}; + } else { + T z0 = std::hypot(x + 1, y); + return {std::log(z0), theta}; + } +#else + // CPU path + // Based on https://github.com/numpy/numpy/pull/22611#issuecomment-1667945354 + c10::complex u = z + T(1); + if (u == T(1)) { + return z; + } else { + auto log_u = log(u); + if (u - T(1) == z) { + return log_u; + } + return log_u * (z / (u - T(1))); + } +#endif +} + +template +C10_HOST_DEVICE inline c10::complex expm1(const c10::complex& z) { + // expm1(z) = exp(z) - 1 + // Define z = x + i * y + // f = e ^ (x + i * y) - 1 + // = e ^ x * e ^ (i * y) - 1 + // = (e ^ x * cos(y) - 1) + i * (e ^ x * sin(y)) + // = (e ^ x - 1) * cos(y) - (1 - cos(y)) + i * e ^ x * sin(y) + // = expm1(x) * cos(y) - 2 * sin(y / 2) ^ 2 + i * e ^ x * sin(y) + T x = z.real(); + T y = z.imag(); + T a = std::sin(y / 2); + T er = std::expm1(x) * std::cos(y) - T(2) * a * a; + T ei = std::exp(x) * std::sin(y); + return {er, ei}; +} + +} // namespace c10_complex_math + +using c10_complex_math::acos; +using c10_complex_math::acosh; +using c10_complex_math::asin; +using c10_complex_math::asinh; +using c10_complex_math::atan; +using c10_complex_math::atanh; +using c10_complex_math::cos; +using c10_complex_math::cosh; +using c10_complex_math::exp; +using c10_complex_math::expm1; +using c10_complex_math::log; +using c10_complex_math::log10; +using c10_complex_math::log1p; +using c10_complex_math::log2; +using c10_complex_math::pow; +using c10_complex_math::sin; +using c10_complex_math::sinh; +using c10_complex_math::sqrt; +using c10_complex_math::tan; +using c10_complex_math::tanh; + +namespace std { + +using c10_complex_math::acos; +using c10_complex_math::acosh; +using c10_complex_math::asin; +using c10_complex_math::asinh; +using c10_complex_math::atan; +using c10_complex_math::atanh; +using c10_complex_math::cos; +using c10_complex_math::cosh; +using c10_complex_math::exp; +using c10_complex_math::expm1; +using c10_complex_math::log; +using c10_complex_math::log10; +using c10_complex_math::log1p; +using c10_complex_math::log2; +using c10_complex_math::pow; +using c10_complex_math::sin; +using c10_complex_math::sinh; +using c10_complex_math::sqrt; +using c10_complex_math::tan; +using c10_complex_math::tanh; + +} // namespace std diff --git a/runtime/core/portable_type/c10/c10/util/complex_utils.h b/runtime/core/portable_type/c10/c10/util/complex_utils.h new file mode 100644 index 00000000000..1ca105f1d0a --- /dev/null +++ b/runtime/core/portable_type/c10/c10/util/complex_utils.h @@ -0,0 +1,46 @@ +#if !defined(C10_INTERNAL_INCLUDE_COMPLEX_REMAINING_H) +#error \ + "c10/util/complex_utils.h is not meant to be individually included. Include c10/util/complex.h instead." +#endif + +#include + +namespace c10 { + +template +struct is_complex : public std::false_type {}; + +template +struct is_complex> : public std::true_type {}; + +template +struct is_complex> : public std::true_type {}; + +// Extract double from std::complex; is identity otherwise +// TODO: Write in more idiomatic C++17 +template +struct scalar_value_type { + using type = T; +}; +template +struct scalar_value_type> { + using type = T; +}; +template +struct scalar_value_type> { + using type = T; +}; + +} // namespace c10 + +namespace std { + +template +class numeric_limits> : public numeric_limits {}; + +template +bool isnan(const c10::complex& v) { + return std::isnan(v.real()) || std::isnan(v.imag()); +} + +} // namespace std diff --git a/runtime/core/portable_type/complex.h b/runtime/core/portable_type/complex.h index e89a19e54d7..faf13a0432f 100644 --- a/runtime/core/portable_type/complex.h +++ b/runtime/core/portable_type/complex.h @@ -8,39 +8,14 @@ #pragma once -#include +#include -namespace executorch { -namespace runtime { -namespace etensor { +namespace executorch::runtime::etensor { +using c10::complex; +} // namespace executorch::runtime::etensor -/** - * An implementation of complex numbers, compatible with c10/util/complex.h from - * pytorch core. - */ -template -struct alignas(sizeof(T) * 2) complex { - T real_ = T(0); - T imag_ = T(0); -}; - -/** - * Specialization for Half, which is not a primitive C numeric type. - */ -template <> -struct alignas(4) complex { - Half real_; - Half imag_; -}; - -} // namespace etensor -} // namespace runtime -} // namespace executorch - -namespace torch { -namespace executor { +namespace torch::executor { // TODO(T197294990): Remove these deprecated aliases once all users have moved // to the new `::executorch` namespaces. using ::executorch::runtime::etensor::complex; -} // namespace executor -} // namespace torch +} // namespace torch::executor From 39fbde9a7e4e995f7dcebc31e7184622c1ee2659 Mon Sep 17 00:00:00 2001 From: Manuel Candales <42380156+manuelcandales@users.noreply.github.com> Date: Tue, 15 Apr 2025 01:43:15 -0400 Subject: [PATCH 147/244] Refactor internal switch cases Differential Revision: D72248082 Pull Request resolved: https://github.com/pytorch/executorch/pull/9802 --- .../core/exec_aten/util/scalar_type_util.h | 186 +++++++----------- 1 file changed, 72 insertions(+), 114 deletions(-) diff --git a/runtime/core/exec_aten/util/scalar_type_util.h b/runtime/core/exec_aten/util/scalar_type_util.h index d07052c2ec2..6f81146e925 100644 --- a/runtime/core/exec_aten/util/scalar_type_util.h +++ b/runtime/core/exec_aten/util/scalar_type_util.h @@ -921,55 +921,7 @@ struct promote_types { } \ }() -#define ET_INTERNAL_SWITCH_CASE_ALL_TYPES(CTYPE_ALIAS, ...) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Byte, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Char, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Short, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Int, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Long, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Half, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Float, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Double, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::ComplexHalf, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::ComplexFloat, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::ComplexDouble, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Bool, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::QInt8, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::QUInt8, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::QInt32, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::BFloat16, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::QUInt4x2, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::QUInt2x4, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Bits1x8, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Bits2x4, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Bits4x2, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Bits8, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Bits16, CTYPE_ALIAS, __VA_ARGS__) - -#define ET_INTERNAL_SWITCH_CASE_REAL_TYPES(CTYPE_ALIAS, ...) \ +#define ET_INTERNAL_SWITCH_CASE_INT_TYPES(CTYPE_ALIAS, ...) \ ET_INTERNAL_SWITCH_CASE( \ ::executorch::aten::ScalarType::Byte, CTYPE_ALIAS, __VA_ARGS__) \ ET_INTERNAL_SWITCH_CASE( \ @@ -979,12 +931,73 @@ struct promote_types { ET_INTERNAL_SWITCH_CASE( \ ::executorch::aten::ScalarType::Int, CTYPE_ALIAS, __VA_ARGS__) \ ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Long, CTYPE_ALIAS, __VA_ARGS__) \ + ::executorch::aten::ScalarType::Long, CTYPE_ALIAS, __VA_ARGS__) + +#define ET_INTERNAL_SWITCH_CASE_UINT_TYPES(CTYPE_ALIAS, ...) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::UInt16, CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::UInt32, CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::UInt64, CTYPE_ALIAS, __VA_ARGS__) + +#define ET_INTERNAL_SWITCH_CASE_FLOAT_TYPES(CTYPE_ALIAS, ...) \ ET_INTERNAL_SWITCH_CASE( \ ::executorch::aten::ScalarType::Float, CTYPE_ALIAS, __VA_ARGS__) \ ET_INTERNAL_SWITCH_CASE( \ ::executorch::aten::ScalarType::Double, CTYPE_ALIAS, __VA_ARGS__) +#define ET_INTERNAL_SWITCH_CASE_REAL_TYPES(CTYPE_ALIAS, ...) \ + ET_INTERNAL_SWITCH_CASE_INT_TYPES(CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE_FLOAT_TYPES(CTYPE_ALIAS, __VA_ARGS__) + +#define ET_INTERNAL_SWITCH_CASE_COMPLEX_TYPES(CTYPE_ALIAS, ...) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::ComplexFloat, CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::ComplexDouble, CTYPE_ALIAS, __VA_ARGS__) + +#define ET_INTERNAL_SWITCH_CASE_COMPLEXH_TYPES(CTYPE_ALIAS, ...) \ + ET_INTERNAL_SWITCH_CASE_COMPLEX_TYPES(CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::ComplexHalf, CTYPE_ALIAS, __VA_ARGS__) + +#define ET_INTERNAL_SWITCH_CASE_QINT_TYPES(CTYPE_ALIAS, ...) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::QInt8, CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::QUInt8, CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::QInt32, CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::QUInt4x2, CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::QUInt2x4, CTYPE_ALIAS, __VA_ARGS__) + +#define ET_INTERNAL_SWITCH_CASE_BITS_TYPES(CTYPE_ALIAS, ...) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::Bits1x8, CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::Bits2x4, CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::Bits4x2, CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::Bits8, CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::Bits16, CTYPE_ALIAS, __VA_ARGS__) + +#define ET_INTERNAL_SWITCH_CASE_ALL_TYPES(CTYPE_ALIAS, ...) \ + ET_INTERNAL_SWITCH_CASE_REAL_TYPES(CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::Half, CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::BFloat16, CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE( \ + ::executorch::aten::ScalarType::Bool, CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE_COMPLEXH_TYPES(CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE_QINT_TYPES(CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE_BITS_TYPES(CTYPE_ALIAS, __VA_ARGS__) + #define ET_INTERNAL_SWITCH_CASE_REAL_TYPES_AND(ADDITIONAL, CTYPE_ALIAS, ...) \ ET_INTERNAL_SWITCH_CASE_REAL_TYPES(CTYPE_ALIAS, __VA_ARGS__) \ ET_INTERNAL_SWITCH_CASE( \ @@ -1008,29 +1021,11 @@ struct promote_types { ET_INTERNAL_SWITCH_CASE( \ ::executorch::aten::ScalarType::ADDITIONAL3, CTYPE_ALIAS, __VA_ARGS__) -#define ET_INTERNAL_SWITCH_CASE_INT_TYPES(CTYPE_ALIAS, ...) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Byte, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Char, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Short, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Int, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Long, CTYPE_ALIAS, __VA_ARGS__) - #define ET_INTERNAL_SWITCH_CASE_INT_TYPES_AND(ADDITIONAL, CTYPE_ALIAS, ...) \ ET_INTERNAL_SWITCH_CASE_INT_TYPES(CTYPE_ALIAS, __VA_ARGS__) \ ET_INTERNAL_SWITCH_CASE( \ ::executorch::aten::ScalarType::ADDITIONAL, CTYPE_ALIAS, __VA_ARGS__) -#define ET_INTERNAL_SWITCH_CASE_FLOAT_TYPES(CTYPE_ALIAS, ...) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Double, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::Float, CTYPE_ALIAS, __VA_ARGS__) - #define ET_INTERNAL_SWITCH_CASE_FLOAT_TYPES_AND(ADDITIONAL, CTYPE_ALIAS, ...) \ ET_INTERNAL_SWITCH_CASE_FLOAT_TYPES(CTYPE_ALIAS, __VA_ARGS__) \ ET_INTERNAL_SWITCH_CASE( \ @@ -1050,32 +1045,6 @@ struct promote_types { ET_INTERNAL_SWITCH_CASE( \ ::executorch::aten::ScalarType::ADDITIONAL3, CTYPE_ALIAS, __VA_ARGS__) -#define ET_INTERNAL_SWITCH_CASE_QINT_TYPES(CTYPE_ALIAS, ...) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::QInt8, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::QUInt8, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::QInt32, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::QUInt4x2, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::QUInt2x4, CTYPE_ALIAS, __VA_ARGS__) - -#define ET_INTERNAL_SWITCH_CASE_COMPLEX_TYPES(CTYPE_ALIAS, ...) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::ComplexFloat, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::ComplexDouble, CTYPE_ALIAS, __VA_ARGS__) - -#define ET_INTERNAL_SWITCH_CASE_COMPLEXH_TYPES(CTYPE_ALIAS, ...) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::ComplexHalf, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::ComplexFloat, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::ComplexDouble, CTYPE_ALIAS, __VA_ARGS__) - #define ET_INTERNAL_SWITCH_CASE_SCALAR_OBJ_TYPES(CTYPE_ALIAS, ...) \ ET_INTERNAL_SWITCH_CASE( \ ::executorch::aten::ScalarType::Bool, CTYPE_ALIAS, __VA_ARGS__) \ @@ -1204,26 +1173,15 @@ struct promote_types { ET_SWITCH_REAL_TYPES_AND3( \ Half, Bool, BFloat16, TYPE, CONTEXT, NAME, CTYPE_ALIAS, __VA_ARGS__) -#define ET_SWITCH_REALHBBF16_AND_UINT_TYPES( \ - TYPE, CONTEXT, NAME, CTYPE_ALIAS, ...) \ - ET_INTERNAL_SWITCH( \ - TYPE, \ - CONTEXT, \ - NAME, \ - ET_INTERNAL_SWITCH_CASE_REAL_TYPES_AND3( \ - Half, Bool, BFloat16, CTYPE_ALIAS, __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::UInt16, \ - CTYPE_ALIAS, \ - __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::UInt32, \ - CTYPE_ALIAS, \ - __VA_ARGS__) \ - ET_INTERNAL_SWITCH_CASE( \ - ::executorch::aten::ScalarType::UInt64, \ - CTYPE_ALIAS, \ - __VA_ARGS__)) +#define ET_SWITCH_REALHBBF16_AND_UINT_TYPES( \ + TYPE, CONTEXT, NAME, CTYPE_ALIAS, ...) \ + ET_INTERNAL_SWITCH( \ + TYPE, \ + CONTEXT, \ + NAME, \ + ET_INTERNAL_SWITCH_CASE_REAL_TYPES_AND3( \ + Half, Bool, BFloat16, CTYPE_ALIAS, __VA_ARGS__) \ + ET_INTERNAL_SWITCH_CASE_UINT_TYPES(CTYPE_ALIAS, __VA_ARGS__)) #define ET_SWITCH_INT_TYPES(TYPE, CONTEXT, NAME, CTYPE_ALIAS, ...) \ ET_INTERNAL_SWITCH( \ From 71349dfa19510ee1ddb8cfbab35d438286613a58 Mon Sep 17 00:00:00 2001 From: winskuo-quic <143469905+winskuo-quic@users.noreply.github.com> Date: Tue, 15 Apr 2025 13:56:28 +0800 Subject: [PATCH 148/244] Qualcomm AI Engine Direct - Mimi Enablement Stage 2 (#10098) ### Summary - Support Mimi Encoder - Support Mimi Decoder - Support OP: CDist - Workaround for 0D tensor scenario since QNN does not support 0D tensor Commands to Execute the model 10 sec as a batch (1 inference for 10 sec audio): `python examples/qualcomm/oss_scripts/moshi/mimi.py -b build-android -s $DEVICE -m SM8650 --chunks_per_batch 125` 80s as a batch (125 inferences for 10 sec audio) `python examples/qualcomm/oss_scripts/moshi/mimi.py -b build-android -s $DEVICE -m SM8650` ### Test plan - UT for CDist - UT for 0D tensor --- backends/qualcomm/_passes/__init__.py | 6 +- backends/qualcomm/_passes/annotate_stack.py | 6 +- backends/qualcomm/_passes/annotate_unbind.py | 4 +- .../_passes/convert_conv1d_to_conv2d.py | 10 + backends/qualcomm/_passes/decompose_cdist.py | 81 ++++ .../_passes/lift_constant_scalar_operands.py | 8 +- backends/qualcomm/_passes/qnn_pass_manager.py | 34 +- backends/qualcomm/_passes/remove_0d_tensor.py | 36 ++ .../qualcomm/partition/qnn_partitioner.py | 5 +- backends/qualcomm/partition/utils.py | 17 +- backends/qualcomm/quantizer/annotators.py | 32 +- .../qualcomm/quantizer/custom_annotation.py | 37 +- backends/qualcomm/tests/models.py | 19 + backends/qualcomm/tests/test_qnn_delegate.py | 36 +- backends/qualcomm/utils/utils.py | 7 +- examples/qualcomm/oss_scripts/moshi/mimi.py | 402 ++++++++++++++++++ examples/qualcomm/utils.py | 6 +- 17 files changed, 701 insertions(+), 45 deletions(-) create mode 100644 backends/qualcomm/_passes/decompose_cdist.py create mode 100644 backends/qualcomm/_passes/remove_0d_tensor.py create mode 100644 examples/qualcomm/oss_scripts/moshi/mimi.py diff --git a/backends/qualcomm/_passes/__init__.py b/backends/qualcomm/_passes/__init__.py index 6d50bbb51b2..d8e6237ce32 100644 --- a/backends/qualcomm/_passes/__init__.py +++ b/backends/qualcomm/_passes/__init__.py @@ -11,6 +11,7 @@ from .convert_conv1d_to_conv2d import ConvertConv1dToConv2d from .convert_upsample_bicubic2d import ConvertUpsampleBicubicWithBilinear from .decompose_any import DecomposeAny +from .decompose_cdist import DecomposeCDist from .decompose_einsum import DecomposeEinsum from .decompose_expm1 import DecomposeExpM1 from .decompose_linalg_vector_norm import DecomposeLinalgVectorNorm @@ -27,6 +28,7 @@ from .recompose_pixel_unshuffle import RecomposePixelUnshuffle from .recompose_rms_norm import RecomposeRmsNorm from .reduce_dynamic_range import ReduceDynamicRange +from .remove_0d_tensor import Remove0DTensor from .remove_redundancy import RemoveRedundancy from .replace_arange_args import ReplaceArangeArgs from .replace_index_put_input import ReplaceIndexPutInput @@ -40,8 +42,9 @@ AnnotateUnbind, ConvertBmmToMatmul, ConvertConv1dToConv2d, - DecomposeAny, ConvertUpsampleBicubicWithBilinear, + DecomposeAny, + DecomposeCDist, DecomposeEinsum, DecomposeExpM1, DecomposeLinalgVectorNorm, @@ -58,6 +61,7 @@ RecomposePixelUnshuffle, RecomposeRmsNorm, ReduceDynamicRange, + Remove0DTensor, RemoveRedundancy, ReplaceArangeArgs, ReplaceIndexPutInput, diff --git a/backends/qualcomm/_passes/annotate_stack.py b/backends/qualcomm/_passes/annotate_stack.py index c42804af2f2..5fbfde058b2 100644 --- a/backends/qualcomm/_passes/annotate_stack.py +++ b/backends/qualcomm/_passes/annotate_stack.py @@ -17,14 +17,16 @@ class AnnotateStack(ExportPass): generated after quantization process. """ - decomp_ops = [torch.ops.aten.unbind.int] + decomp_ops = [torch.ops.aten.stack.default] def __init__(self, edge_program: torch.export.ExportedProgram): super(AnnotateStack, self).__init__() self.edge_program = edge_program def _annotate_stack(self, graph_module: torch.fx.GraphModule): - partitions = get_source_partitions(graph_module.graph, [torch.stack, "stack"]) + partitions = get_source_partitions( + graph_module.graph, [torch.stack, torch.ops.aten.stack.default, "stack"] + ) for _, src_partitions in partitions.items(): for src_partition in src_partitions: output = src_partition.output_nodes[0] diff --git a/backends/qualcomm/_passes/annotate_unbind.py b/backends/qualcomm/_passes/annotate_unbind.py index 0efa1638bc4..426285e872b 100644 --- a/backends/qualcomm/_passes/annotate_unbind.py +++ b/backends/qualcomm/_passes/annotate_unbind.py @@ -24,7 +24,9 @@ def __init__(self, edge_program: torch.export.ExportedProgram): self.edge_program = edge_program def _annotate_unbind(self, graph_module: torch.fx.GraphModule): - partitions = get_source_partitions(graph_module.graph, [torch.unbind, "unbind"]) + partitions = get_source_partitions( + graph_module.graph, [torch.unbind, torch.ops.aten.unbind.int, "unbind"] + ) for _, src_partitions in partitions.items(): for src_partition in src_partitions: if src_partition.input_nodes[0].target in dq_ops: diff --git a/backends/qualcomm/_passes/convert_conv1d_to_conv2d.py b/backends/qualcomm/_passes/convert_conv1d_to_conv2d.py index 947b631dbbf..72dc29c2880 100644 --- a/backends/qualcomm/_passes/convert_conv1d_to_conv2d.py +++ b/backends/qualcomm/_passes/convert_conv1d_to_conv2d.py @@ -7,6 +7,7 @@ import torch import torch.nn as nn from executorch.backends.qualcomm.builders.utils import get_parameter, set_parameter +from executorch.backends.qualcomm.utils.constants import QCOM_REQUANTIZE from executorch.exir.dialects._ops import ops as exir_ops from executorch.exir.pass_base import ExportPass, PassResult @@ -43,6 +44,7 @@ def call(self, graph_module: torch.fx.GraphModule): unsqueeze_node.meta = copy_meta( input_node.meta, lambda m: {**m, "val": m["val"].unsqueeze(2)} ) + with graph_module.graph.inserting_after(unsqueeze_node): filter_node = node.args[1] @@ -92,6 +94,14 @@ def call(self, graph_module: torch.fx.GraphModule): ), ) squeeze_node.meta = copy_meta(node.meta) + + if QCOM_REQUANTIZE in input_node.meta: + input_node.meta.pop(QCOM_REQUANTIZE) + if QCOM_REQUANTIZE in node.meta: + squeeze_node.meta[QCOM_REQUANTIZE] = node.meta[ + QCOM_REQUANTIZE + ] + conv2d_node.meta.pop(QCOM_REQUANTIZE, None) for user in node.users.copy(): user.replace_input_with(node, squeeze_node) graph.eliminate_dead_code() diff --git a/backends/qualcomm/_passes/decompose_cdist.py b/backends/qualcomm/_passes/decompose_cdist.py new file mode 100644 index 00000000000..d18a0295ffb --- /dev/null +++ b/backends/qualcomm/_passes/decompose_cdist.py @@ -0,0 +1,81 @@ +# Copyright (c) Qualcomm Innovation Center, Inc. +# All rights reserved +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from executorch.exir.pass_base import ExportPass, PassResult + + +class CDist(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x, y): + # Step 1: Compute differences + diff = x.unsqueeze(-2) - y.unsqueeze(-3) + + # Step 2: Square differences + sq_diff = diff**2 + + # Step 3: Sum of squares + sum_sq_diff = sq_diff.sum(dim=-1) + + # Step 4: Square root + distances = torch.sqrt(sum_sq_diff) + + return distances + + +class DecomposeCDist(ExportPass): + """ + Decompose for math equivalent op. + """ + + def __init__(self) -> None: + super().__init__() + + def call(self, graph_module: torch.fx.GraphModule) -> PassResult: + graph = graph_module.graph + for node in graph.nodes: + model = CDist() + if torch.ops.aten.cdist.default == node.target: + if len(node.args) > 2: + assert ( + node.args[2] == 2 + ), "Currently only p=2 is supported for CDist Decomposition" + decomposed_module = torch.export.export( + model, + (node.args[0].meta["val"], node.args[1].meta["val"]), + strict=True, + ).module() + with graph.inserting_before(node): + # remap is used to map original node values to new node values, + # which ensures that reference to nodes are correctly updated in the new graph + remap = {"x": node.args[0], "y": node.args[1]} + + for decomposed_node in decomposed_module.graph.nodes: + # no need to copy existent 'output' + if decomposed_node.op == "output": + for user in node.users.copy(): + # remap + user.replace_input_with( + node, + remap[decomposed_node.args[0][0]], + ) + # no need to copy existent placeholders + elif decomposed_node.op == "placeholder": + # replace node map from string to graph node + remap[decomposed_node] = remap.pop(decomposed_node.name) + else: + remap[decomposed_node] = graph.node_copy( + decomposed_node, + arg_transform=lambda x, remap=remap: remap[x], + ) + + graph.erase_node(node) + + graph.eliminate_dead_code() + graph_module.recompile() + return PassResult(graph_module, True) diff --git a/backends/qualcomm/_passes/lift_constant_scalar_operands.py b/backends/qualcomm/_passes/lift_constant_scalar_operands.py index 93abfe621bc..9b3a308813e 100644 --- a/backends/qualcomm/_passes/lift_constant_scalar_operands.py +++ b/backends/qualcomm/_passes/lift_constant_scalar_operands.py @@ -53,7 +53,13 @@ class TensorOpInfo: } -SKIP_LIFT_OPS = {aten.full_like.default, aten.arange.start_step} +SKIP_LIFT_OPS = { + aten.full_like.default, + aten.arange.start_step, + aten.arange.default, + aten.scalar_tensor.default, + aten.elu.default, +} class LiftConstantScalarOperands(ExportPass): diff --git a/backends/qualcomm/_passes/qnn_pass_manager.py b/backends/qualcomm/_passes/qnn_pass_manager.py index ed3c8eb217e..dda01e4a8a1 100644 --- a/backends/qualcomm/_passes/qnn_pass_manager.py +++ b/backends/qualcomm/_passes/qnn_pass_manager.py @@ -16,6 +16,7 @@ ConvertConv1dToConv2d, ConvertUpsampleBicubicWithBilinear, DecomposeAny, + DecomposeCDist, DecomposeEinsum, DecomposeExpM1, DecomposeLinalgVectorNorm, @@ -32,6 +33,7 @@ RecomposePixelUnshuffle, RecomposeRmsNorm, ReduceDynamicRange, + Remove0DTensor, RemoveRedundancy, ReplaceArangeArgs, ReplaceIndexPutInput, @@ -71,7 +73,7 @@ def get_capture_program_passes(): # If a pass is activated, it will be executed by default. default_passes_and_setting = [ (AnnotateQuantAttrs, True), - (AnnotateStack, False), + (AnnotateStack, True), (AnnotateUnbind, True), (ConvertBmmToMatmul, True), (ConvertConv1dToConv2d, True), @@ -84,6 +86,7 @@ def get_capture_program_passes(): (LayoutTransform, True), (RecomposePixelUnshuffle, True), (RecomposeRmsNorm, False), + (Remove0DTensor, True), (RemoveRedundancy, True), (ReplaceIndexPutInput, True), (TagQuantIO, False), @@ -176,7 +179,23 @@ def transform_for_to_edge_pipeline( return exported_program + # Before quantizer + def transform_for_annotation_pipeline(self, graph_module: GraphModule): + self.add_pass(ReduceDynamicRange()) + self.add_pass(RecomposePixelUnshuffle(quantization_capture=True)) + self.add_pass(ReplaceArangeArgs()) + self.add_pass(DecomposeCDist()) + self.add_pass(DecomposeScaledDotProductAttention()) + self.add_pass(DecomposeSilu()) + self.add_pass(DecomposeEinsum()) + self.add_pass(DecomposeExpM1()) + self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True)) + self.add_pass(ReplaceInfValues()) + self.add_pass(LiftConstantScalarOperands()) + return self._transform(graph_module) + def transform_for_export_pipeline(self, exported_program: ExportedProgram): + self.add_pass(DecomposeCDist()) self.add_pass(DecomposeScaledDotProductAttention()) self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True)) self.add_pass(DecomposeExpM1()) @@ -191,16 +210,3 @@ def transform_for_preprocess_pipeline(self, exported_program: ExportedProgram): self.add_pass(LayoutTransform(exported_program, insert_permute=True)) self.add_pass(FuseConsecutiveTranspose()) return self._transform(exported_program.graph_module) - - def transform_for_annotation_pipeline(self, graph_module: GraphModule): - self.add_pass(ReduceDynamicRange()) - self.add_pass(RecomposePixelUnshuffle(quantization_capture=True)) - self.add_pass(ReplaceArangeArgs()) - self.add_pass(DecomposeScaledDotProductAttention()) - self.add_pass(DecomposeSilu()) - self.add_pass(DecomposeEinsum()) - self.add_pass(DecomposeExpM1()) - self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True)) - self.add_pass(ReplaceInfValues()) - self.add_pass(LiftConstantScalarOperands()) - return self._transform(graph_module) diff --git a/backends/qualcomm/_passes/remove_0d_tensor.py b/backends/qualcomm/_passes/remove_0d_tensor.py new file mode 100644 index 00000000000..1e1d711c2b8 --- /dev/null +++ b/backends/qualcomm/_passes/remove_0d_tensor.py @@ -0,0 +1,36 @@ +# Copyright (c) Qualcomm Innovation Center, Inc. +# All rights reserved +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import torch +from executorch.exir.dialects._ops import ops as exir_ops +from executorch.exir.pass_base import ExportPass, PassResult + + +class Remove0DTensor(ExportPass): + """ + QNN does not allow 0D tensor, we remove the node that will output an 0D tensor. + Before adding operations to the list of nodes to be removed, please ensure that it will not change the logic. + """ + + remove_ops = { + exir_ops.edge.aten.select.int, + exir_ops.edge.aten.select_copy.int, + } + + def __init__(self, quantization_capture=False) -> None: + super().__init__() + + def call(self, graph_module: torch.fx.GraphModule) -> PassResult: + graph = graph_module.graph + for node in graph.nodes: + if node.target in self.remove_ops and len(node.meta["val"].shape) == 0: + for user_n in list(node.users.keys()): + user_n.replace_input_with(node, node.args[0]) + graph.erase_node(node) + + graph.eliminate_dead_code() + graph_module.recompile() + return PassResult(graph_module, True) diff --git a/backends/qualcomm/partition/qnn_partitioner.py b/backends/qualcomm/partition/qnn_partitioner.py index 7b5e72d461d..d9eb188614c 100644 --- a/backends/qualcomm/partition/qnn_partitioner.py +++ b/backends/qualcomm/partition/qnn_partitioner.py @@ -34,7 +34,7 @@ not_supported_operator, to_be_implemented_operator, ) -from .utils import generate_qnn_executorch_option, get_skip_decomp_table +from .utils import filter_fn, generate_qnn_executorch_option, get_skip_decomp_table class QnnOperatorSupport(OperatorSupportBase): @@ -181,5 +181,4 @@ def ops_to_not_decompose( self, ep: ExportedProgram ) -> Tuple[List[torch._ops.OpOverload], Optional[Callable[[torch.fx.Node], bool]]]: do_not_decompose = get_skip_decomp_table() - - return do_not_decompose, None + return (do_not_decompose, filter_fn) diff --git a/backends/qualcomm/partition/utils.py b/backends/qualcomm/partition/utils.py index 6931e35e6e3..816d1ac1d9b 100644 --- a/backends/qualcomm/partition/utils.py +++ b/backends/qualcomm/partition/utils.py @@ -24,6 +24,21 @@ def generate_qnn_executorch_option( return qnn_compile_spec_buffer +# Logic to determine whether to skip decompose and has higher priority than get_skip_decomp_table() +def filter_fn(node: torch.fx.Node) -> bool: + # QNN does not support int32/int64 IO for the following OPs. + potential_i32_i64_io_ops = [ + torch.ops.aten.stack.default, + torch.ops.aten.unbind.int, + ] + if node.target in potential_i32_i64_io_ops and node.meta["val"].dtype in [ + torch.int32, + torch.int64, + ]: + return False + return True + + def get_skip_decomp_table() -> List[torch._ops.OperatorBase]: do_not_decompose = [ torch.ops.aten.adaptive_avg_pool2d.default, @@ -41,7 +56,7 @@ def get_skip_decomp_table() -> List[torch._ops.OperatorBase]: torch.ops.aten.stack.default, torch.ops.aten.upsample_bicubic2d.vec, # This request is ignored because it is in a blocklist. Refer to exir/program/_program.py - # torch.ops.aten.unbind.int, + torch.ops.aten.unbind.int, torch.ops.pt2e_quant.quantize_affine.default, torch.ops.pt2e_quant.dequantize_affine.default, ] diff --git a/backends/qualcomm/quantizer/annotators.py b/backends/qualcomm/quantizer/annotators.py index f59df46b3c1..469a801feeb 100644 --- a/backends/qualcomm/quantizer/annotators.py +++ b/backends/qualcomm/quantizer/annotators.py @@ -97,6 +97,7 @@ def annotate_in_out_obs_sharing_op( QUANT_ANNOTATION_KEY not in input_act.meta or not input_act.meta[QUANT_ANNOTATION_KEY]._annotated or input_act.meta[QUANT_ANNOTATION_KEY].output_qspec is None + or not _is_float_tensor(input_act) ): return @@ -132,9 +133,10 @@ def annotate_single_in_single_out( return input_qspec_map = {} - input_act = node.args[0] - assert isinstance(input_act, Node) - input_qspec_map[input_act] = quantization_config.input_activation + if _is_float_tensor(node.args[0]): + input_act = node.args[0] + assert isinstance(input_act, Node) + input_qspec_map[input_act] = quantization_config.input_activation if _is_float_tensor(node): node.meta[QUANT_ANNOTATION_KEY] = QuantizationAnnotation( @@ -177,7 +179,9 @@ def annotate_binary(node: Node, quantization_config: QuantizationConfig) -> None ) -@register_annotator([torch.ops.aten.add, torch.ops.aten.add.Tensor]) +@register_annotator( + [torch.ops.aten.add, torch.ops.aten.add.Tensor, torch.ops.aten.add_.Tensor] +) def annotate_add(node: Node, quantization_config: QuantizationConfig) -> None: annotate_binary(node, quantization_config) @@ -933,6 +937,11 @@ def annotate_bmm(node: Node, quantization_config: QuantizationConfig) -> None: node.meta["source_fn_stack"] = [(node, torch.bmm)] +@register_annotator([torch.ops.aten.cdist.default]) +def annotate_cdist(node: Node, quantization_config: QuantizationConfig) -> None: + annotate_binary(node, quantization_config) + + @register_annotator( [ torch.ops.aten.conv2d.default, @@ -941,7 +950,7 @@ def annotate_bmm(node: Node, quantization_config: QuantizationConfig) -> None: torch.ops.aten.conv_transpose1d.default, ] ) -def annotate_conv2d(node: Node, quantization_config: QuantizationConfig) -> None: +def annotate_conv(node: Node, quantization_config: QuantizationConfig) -> None: if _is_annotated([node]): return @@ -1118,15 +1127,17 @@ def annotate_cat(node: Node, quantization_config: QuantizationConfig) -> None: input_qspec_map = {} assert isinstance(first_input_node, Node) assert isinstance(node, Node) - input_qspec_map[first_input_node] = quantization_config.input_activation - share_qparams_with_input_act0_qspec = SharedQuantizationSpec( - (first_input_node, node) - ) + if _is_float_tensor(first_input_node): + input_qspec_map[first_input_node] = quantization_config.input_activation + share_qparams_with_input_act0_qspec = SharedQuantizationSpec( + (first_input_node, node) + ) for input_node in input_nodes[1:]: if input_node not in input_qspec_map: assert isinstance(input_node, Node) - input_qspec_map[input_node] = share_qparams_with_input_act0_qspec + if _is_float_tensor(input_node): + input_qspec_map[input_node] = share_qparams_with_input_act0_qspec node.meta[QUANT_ANNOTATION_KEY] = QuantizationAnnotation( input_qspec_map=input_qspec_map, @@ -1140,7 +1151,6 @@ def annotate_unbind(node: Node, quantization_config: QuantizationConfig) -> None # Seems like unbind.int can be either float or int. Only quant when input is float. if _is_annotated([node]) or not _is_float_tensor(node.args[0]): return - input_qspec_map = {} input_act = node.args[0] assert isinstance(input_act, Node) diff --git a/backends/qualcomm/quantizer/custom_annotation.py b/backends/qualcomm/quantizer/custom_annotation.py index 33237f3bebe..bda91609f1c 100644 --- a/backends/qualcomm/quantizer/custom_annotation.py +++ b/backends/qualcomm/quantizer/custom_annotation.py @@ -6,7 +6,10 @@ from typing import Sequence import torch -from executorch.backends.qualcomm.quantizer.annotators import QUANT_ANNOTATION_KEY +from executorch.backends.qualcomm.quantizer.annotators import ( + _is_float_tensor, + QUANT_ANNOTATION_KEY, +) from executorch.backends.qualcomm.quantizer.quantizer import ( get_16a8w_qnn_ptq_config, get_8a8w_qnn_ptq_config, @@ -23,6 +26,38 @@ from torch.fx import Node +def annotate_mimi_decoder(gm: torch.fx.GraphModule): + """ + The 1st transpose conv in mimi decoder is really sensitive to scale/offset in 16a8w, which causes execution failure. + Annotate 1st transpose conv as 8a8w to prevent execution failure. + """ + quantization_config_8a8w = get_8a8w_qnn_ptq_config() + for node in gm.graph.nodes: + if not _is_float_tensor(node): + continue + elif node.target == torch.ops.aten.conv_transpose1d.default: + input_qspec_map = {} + input_act = node.args[0] + assert isinstance(input_act, Node) + input_spec = quantization_config_8a8w.input_activation + input_qspec_map[input_act] = input_spec + + weight = node.args[1] + assert isinstance(weight, Node) + input_qspec_map[weight] = quantization_config_8a8w.weight + + if len(node.args) > 2 and isinstance(node.args[2], Node): + bias = node.args[2] + input_qspec_map[bias] = quantization_config_8a8w.bias + + node.meta[QUANT_ANNOTATION_KEY] = QuantizationAnnotation( + input_qspec_map=input_qspec_map, + output_qspec=quantization_config_8a8w.output_activation, + _annotated=True, + ) + break + + def annotate_linear_16a8w_in_affine_layer(gm: torch.fx.GraphModule) -> None: def annotate_conv2d(node: Node, quantization_config: QuantizationConfig) -> None: input_qspec_map = {} diff --git a/backends/qualcomm/tests/models.py b/backends/qualcomm/tests/models.py index 6570b4befcb..3cba9db39b0 100644 --- a/backends/qualcomm/tests/models.py +++ b/backends/qualcomm/tests/models.py @@ -190,6 +190,14 @@ def forward(self, x, y): return torch.cat((y, y, x, x), axis=2) +class CDist(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x, y): + return torch.cdist(x, y, p=2) + + class Ceil(torch.nn.Module): def __init__(self): super().__init__() @@ -1588,3 +1596,14 @@ def forward(self, x): return torch.nn.functional.softmax( torch.where(x >= 0, 0.1, float("-inf")), dim=-1 ) + + +# Mimi Decoder has 0D tensor which QNN cannot handle. +class ZeroDimTensor(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + input1 = torch.zeros(1) + selected_element = torch.select(input1, 0, 0) + return torch.add(x, selected_element) diff --git a/backends/qualcomm/tests/test_qnn_delegate.py b/backends/qualcomm/tests/test_qnn_delegate.py index 6579dc7c468..57beb201987 100644 --- a/backends/qualcomm/tests/test_qnn_delegate.py +++ b/backends/qualcomm/tests/test_qnn_delegate.py @@ -176,6 +176,14 @@ def test_qnn_backend_cat(self): with self.subTest(i=i): self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_cdist(self): + module = CDist() # noqa: F405 + sample_input = ( + torch.randn(1, 125, 256), + torch.randn(1, 2048, 256), + ) + self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_chunk_single(self): module = Chunk() # noqa: F405 sample_input = (torch.randn(1, 1, 4, 3),) @@ -870,14 +878,14 @@ def test_qnn_backend_where(self): Where(), # noqa: F405 WhereConstant(torch.randn(3, 2), torch.randn(3, 2)), # noqa: F405 WhereConstantOther(), # noqa: F405 - # WhereConstantAll(), # noqa: F405 TODO: constant dtype does not propogate when doing const i64->32, causing where to fail since where does not support int64 output + WhereConstantAll(), # noqa: F405 WhereConstantInf(), # noqa: F405 ] sample_inputs = [ (torch.randn(3, 2), torch.randn(3, 2), torch.randn(3, 2)), (torch.randn(3, 2),), (torch.randn(3, 2),), - # (torch.randn(3, 2),), + (torch.randn(3, 2),), (torch.randn(30, 20),), ] for i, module in enumerate(modules): @@ -1006,6 +1014,11 @@ def test_qnn_backend_view_permute_matmul(self): sample_input = (torch.randn([1, 8, 512]), torch.randn([1, 2, 8, 256])) self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_zero_dim_tensor(self): + module = ZeroDimTensor() # noqa: F405 + sample_input = (torch.randn(1, 256, 125),) + self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_example_models(self): # TODO Fix MobileBertModelExample and TorchVisionViTModel instances = [ @@ -1199,6 +1212,15 @@ def test_qnn_backend_cat(self): module = self.get_qdq_module(module, sample_input) self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_cdist(self): + module = CDist() # noqa: F405 + sample_input = ( + torch.randn(1, 125, 256), + torch.randn(1, 2048, 256), + ) + module = self.get_qdq_module(module, sample_input) + self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_chunk_single(self): module = Chunk() # noqa: F405 sample_input = (torch.randn(1, 1, 4, 3),) @@ -2030,14 +2052,14 @@ def test_qnn_backend_where(self): Where(), # noqa: F405 WhereConstant(torch.randn(3, 2), torch.randn(3, 2)), # noqa: F405 WhereConstantOther(), # noqa: F405 - # WhereConstantAll(), # noqa: F405, TODO: constant dtype does not propogate when doing const i64->32, causing where to fail since where does not support int64 output + WhereConstantAll(), # noqa: F405 WhereConstantInf(), # noqa: F405 ] sample_inputs = [ (torch.randn(3, 2), torch.randn(3, 2), torch.randn(3, 2)), (torch.randn(3, 2),), (torch.randn(3, 2),), - # (torch.randn(3, 2),), + (torch.randn(3, 2),), (torch.randn(30, 20),), ] for i, module in enumerate(modules): @@ -2211,6 +2233,12 @@ def test_qnn_backend_view_permute_matmul(self): module = self.get_qdq_module(module, sample_input) self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_zero_dim_tensor(self): + module = ZeroDimTensor() # noqa: F405 + sample_input = (torch.randn(1, 256, 125),) + module = self.get_qdq_module(module, sample_input) + self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_example_models(self): instances = [ { diff --git a/backends/qualcomm/utils/utils.py b/backends/qualcomm/utils/utils.py index f7b966ee8ea..e0ebc5beebe 100644 --- a/backends/qualcomm/utils/utils.py +++ b/backends/qualcomm/utils/utils.py @@ -16,7 +16,7 @@ import torch -from executorch.backends.qualcomm._passes import AnnotateStack +from executorch.backends.qualcomm._passes import AnnotateStack, AnnotateUnbind from executorch.backends.qualcomm._passes.qnn_pass_manager import QnnPassManager from executorch.backends.qualcomm.builders.node_visitor import ( @@ -304,11 +304,12 @@ def get_decomp_table(passes_job) -> Dict[torch._ops.OperatorBase, Callable]: skip_decompositions = get_skip_decomp_table() # If we want to annotate the decomposed ops, then we should decompose the operation. - if passes_job and passes_job.get(AnnotateStack, False): + if passes_job: skip_decompositions = [ skip_decomp_op for skip_decomp_op in skip_decompositions - if skip_decomp_op not in AnnotateStack.decomp_ops + if skip_decomp_op + not in AnnotateStack.decomp_ops + AnnotateUnbind.decomp_ops ] remove_decompositions(source_decompositions, skip_decompositions) diff --git a/examples/qualcomm/oss_scripts/moshi/mimi.py b/examples/qualcomm/oss_scripts/moshi/mimi.py new file mode 100644 index 00000000000..6b59a71ae64 --- /dev/null +++ b/examples/qualcomm/oss_scripts/moshi/mimi.py @@ -0,0 +1,402 @@ +# Copyright (c) Qualcomm Innovation Center, Inc. +# All rights reserved +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +# import argparse +import io +import json +import os +import random +from multiprocessing.connection import Client + +import numpy as np +import requests + +import sphn +import torch + +import torch.nn as nn +import torchaudio + +from executorch.backends.qualcomm.quantizer.custom_annotation import ( + annotate_mimi_decoder, +) +from executorch.backends.qualcomm.quantizer.quantizer import QuantDtype + +from executorch.examples.qualcomm.utils import ( + build_executorch_binary, + make_output_dir, + make_quantizer, + parse_skip_delegation_node, + setup_common_args_and_variables, + SimpleADB, +) + +from huggingface_hub import hf_hub_download +from moshi.models import loaders + +from torch.ao.quantization.observer import MinMaxObserver + + +def seed_all(seed): + torch.manual_seed(seed) + if torch.cuda.is_available(): + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # for multi-GPU setups + random.seed(seed) + np.random.seed(seed) + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + + +def read_mp3_from_url(url): + response = requests.get(url) + response.raise_for_status() # Ensure request is successful + + # Convert to a file-like object + audio_stream = io.BytesIO(response.content) + + # Load audio using torchaudio + waveform, sample_rate = torchaudio.load(audio_stream, format="mp3") + + return waveform.numpy(), sample_rate + + +def compute_scores(cpu_decode_res: torch.Tensor, htp_decode_res: torch.Tensor): + assert cpu_decode_res.shape == htp_decode_res.shape, "Tensor shapes do not match" + abs_diff = torch.abs(cpu_decode_res - htp_decode_res) + atol = torch.max(abs_diff) + print("Atol: ", atol) + + cpu_decode_res = cpu_decode_res.float() + htp_decode_res = htp_decode_res.float() + error = cpu_decode_res - htp_decode_res + original_power = torch.mean(torch.pow(cpu_decode_res, 2)) + error_power = torch.mean(torch.pow(error, 2)) + sqnr = 10 * torch.log10(original_power / error_power) + print("SQNR: ", sqnr) + + +def test_decoder_with_emb_input(mimi, args): + class MimiDecode(nn.Module): + def __init__(self, mimi: nn.Module): + super().__init__() + self.mimi_model = mimi + + def forward(self, x): + x = x.transpose(1, 2) + x = self.mimi_model.upsample(x) + (emb,) = self.mimi_model.decoder_transformer(x) + emb.transpose(1, 2) + with self.mimi_model._context_for_encoder_decoder: + out = self.mimi_model.decoder(emb) + return out + + emb_input = torch.rand(1, 1, 512, device="cpu") + mimi_decode = MimiDecode(mimi).eval() + cpu_res = mimi_decode(emb_input) + pte_filename = "mimi_decoder_emb_qnn" + + quantizer = make_quantizer( + quant_dtype=QuantDtype.use_16a8w, + per_channel_conv=True, + per_channel_linear=True, + act_observer=MinMaxObserver, + ) + quantizer.add_custom_quant_annotations((annotate_mimi_decoder,)) + + emb_inputs = [(emb_input,)] + build_executorch_binary( + mimi_decode, + emb_inputs[0], + args.model, + f"{args.artifact}/{pte_filename}", + emb_inputs, + custom_quantizer=quantizer, + quant_dtype=QuantDtype.use_16a8w, + shared_buffer=args.shared_buffer, + ) + + adb = SimpleADB( + qnn_sdk=os.getenv("QNN_SDK_ROOT"), + build_path=f"{args.build_folder}", + pte_path=f"{args.artifact}/{pte_filename}.pte", + workspace=f"/data/local/tmp/executorch/{pte_filename}", + device_id=args.device, + host_id=args.host, + soc_model=args.model, + shared_buffer=args.shared_buffer, + ) + adb.push(inputs=emb_inputs, input_list="input_0_0.raw\n") + adb.execute() + + # collect output data + output_data_folder = f"{args.artifact}/outputs" + make_output_dir(output_data_folder) + + adb.pull(output_path=args.artifact) + + emb_predictions = [] + for i in range(len(emb_inputs)): + np_arr = np.fromfile( + os.path.join(output_data_folder, f"output_{i}_0.raw"), dtype=np.float32 + ) + emb_predictions.append(torch.from_numpy(np_arr).view(1, 1, 1920)) + print("Emb input test results") + compute_scores(cpu_res, emb_predictions[0]) + + +def mimi_encode( + mimi, + encode_inputs, + encoder_input_list, + pcm_chunk_size, + skip_node_id_set, + skip_node_op_set, +) -> torch.Tensor: + class MimiEncode(nn.Module): + def __init__(self, mimi: nn.Module): + super().__init__() + self.mimi_model = mimi + + def forward(self, x): + return self.mimi_model.encode(x) + + mimi_encode_model = MimiEncode(mimi) + + pte_filename = "mimi_encoder_qnn" + build_executorch_binary( + mimi_encode_model.eval(), + encode_inputs[0], + args.model, + f"{args.artifact}/{pte_filename}", + encode_inputs, + skip_node_id_set=skip_node_id_set, + skip_node_op_set=skip_node_op_set, + quant_dtype=QuantDtype.use_8a8w, + shared_buffer=args.shared_buffer, + ) + + adb = SimpleADB( + qnn_sdk=os.getenv("QNN_SDK_ROOT"), + build_path=f"{args.build_folder}", + pte_path=f"{args.artifact}/{pte_filename}.pte", + workspace=f"/data/local/tmp/executorch/{pte_filename}", + device_id=args.device, + host_id=args.host, + soc_model=args.model, + shared_buffer=args.shared_buffer, + ) + adb.push(inputs=encode_inputs, input_list=encoder_input_list) + adb.execute() + + # collect output data + output_data_folder = f"{args.artifact}/outputs" + make_output_dir(output_data_folder) + + adb.pull(output_path=args.artifact) + + encoder_predictions = [] + # Num chunks should align with args.chunks_per_batch + num_chunks = encode_inputs[0][0].shape[-1] // pcm_chunk_size + for i in range(len(encode_inputs)): + np_arr = np.fromfile( + os.path.join(output_data_folder, f"output_{i}_0.raw"), dtype=np.int64 + ) + encoder_predictions.append(torch.from_numpy(np_arr).view(1, 8, num_chunks)) + return encoder_predictions + + +def mimi_decode( + mimi, encode_res_list, pcm_chunk_size, skip_node_id_set, skip_node_op_set +) -> torch.Tensor: + class MimiDecode(nn.Module): + def __init__(self, mimi: nn.Module): + super().__init__() + self.mimi_model = mimi + + def forward(self, x): + return self.mimi_model.decode(x) + + mimi_decode_model = MimiDecode(mimi) + decode_inputs, decode_input_list = [], "" + for index, encoder_res in enumerate(encode_res_list): + decode_inputs.append((encoder_res.to(torch.int32),)) + decode_input_list += f"input_{index}_0.raw\n" + + pte_filename = "mimi_decoder_qnn" + + quantizer = make_quantizer( + quant_dtype=QuantDtype.use_16a8w, + per_channel_conv=True, + per_channel_linear=True, + act_observer=MinMaxObserver, + ) + quantizer.add_custom_quant_annotations((annotate_mimi_decoder,)) + + build_executorch_binary( + mimi_decode_model.eval(), + decode_inputs[0], + args.model, + f"{args.artifact}/{pte_filename}", + decode_inputs, + skip_node_id_set=skip_node_id_set, + skip_node_op_set=skip_node_op_set, + custom_quantizer=quantizer, + quant_dtype=QuantDtype.use_16a8w, + shared_buffer=args.shared_buffer, + ) + + adb = SimpleADB( + qnn_sdk=os.getenv("QNN_SDK_ROOT"), + build_path=f"{args.build_folder}", + pte_path=f"{args.artifact}/{pte_filename}.pte", + workspace=f"/data/local/tmp/executorch/{pte_filename}", + device_id=args.device, + host_id=args.host, + soc_model=args.model, + shared_buffer=args.shared_buffer, + ) + adb.push(inputs=decode_inputs, input_list=decode_input_list) + adb.execute() + + # collect output data + output_data_folder = f"{args.artifact}/outputs" + make_output_dir(output_data_folder) + + adb.pull(output_path=args.artifact) + + decoder_predictions = [] + # Num chunks should align with args.chunks_per_batch + num_chunks = decode_inputs[0][0].shape[-1] + shape = num_chunks * pcm_chunk_size + for i in range(len(decode_inputs)): + np_arr = np.fromfile( + os.path.join(output_data_folder, f"output_{i}_0.raw"), dtype=np.float32 + ) + decoder_predictions.append(torch.from_numpy(np_arr).view(1, 1, shape)) + htp_decode_res = torch.cat(decoder_predictions, dim=-1) + + return htp_decode_res + + +def export_mimi(mimi, args, max_duration_sec=10.0): + skip_node_id_set, skip_node_op_set = parse_skip_delegation_node(args) + os.makedirs(args.artifact, exist_ok=True) + + if args.emb_input_test: + test_decoder_with_emb_input(mimi, args) + return + + sample_rate = mimi.sample_rate + url = "https://huggingface.co/lmz/moshi-swift/resolve/main/bria-24khz.mp3" + sample_pcm, sample_sr = read_mp3_from_url(url) + sample_rate = mimi.sample_rate + sample_pcm = torch.tensor(sample_pcm, device="cpu") + max_duration_len = int(sample_rate * max_duration_sec) + if sample_pcm.shape[-1] > max_duration_len: + sample_pcm = sample_pcm[..., :max_duration_len] + sample_pcm = sample_pcm[None].to(device="cpu") + + encoder_inputs, encoder_input_list = [], "" + # 1920 chunk_size = 0.08sec + pcm_chunk_size = int(mimi.sample_rate / mimi.frame_rate) + batch_size = pcm_chunk_size * args.chunks_per_batch + count = 0 + for start_idx in range(0, sample_pcm.shape[-1], batch_size): + end_idx = min(sample_pcm.shape[-1], start_idx + batch_size) + chunk = sample_pcm[..., start_idx:end_idx] + encoder_inputs.append((chunk,)) + encoder_input_list += f"input_{count}_0.raw\n" + count += 1 + + print("streaming encoding...") + cpu_encode_res = mimi.encode(sample_pcm) + htp_encode_res = mimi_encode( + mimi, + encoder_inputs, + encoder_input_list, + pcm_chunk_size, + skip_node_id_set, + skip_node_op_set, + ) + + # Leave it here for now, uncomment this to check htp_encoder with cpu_decoder + # htp_res = torch.cat(htp_encode_res, dim=-1) + # cpu_decode_htp_encode = mimi.decode(htp_res) + # sphn.write_wav("cpu_decode_htp_encode.wav", cpu_decode_htp_encode[0, 0].cpu().numpy(), sample_rate) + + print("streaming decoding...") + cpu_decode_res = mimi.decode(cpu_encode_res) + # TODO: Enable streaming mode, which is the correct way to execute 1 chunk at a time. + # with mimi.streaming(1): + htp_decode_res = mimi_decode( + mimi, htp_encode_res, pcm_chunk_size, skip_node_id_set, skip_node_op_set + ) + compute_scores(cpu_decode_res, htp_decode_res) + + sphn.write_wav( + f"{args.artifact}/cpu_decode_res.wav", + cpu_decode_res[0, 0].cpu().numpy(), + sample_rate, + ) + sphn.write_wav( + f"{args.artifact}/htp_decode_res.wav", + htp_decode_res[0, 0].cpu().numpy(), + sample_rate, + ) + + +def main(args): + seed_all(42424242) + + print("loading mimi") + if args.mimi_weight is None: + args.mimi_weight = hf_hub_download(args.hf_repo, loaders.MIMI_NAME) + mimi = loaders.get_mimi(args.mimi_weight, "cpu") + print("mimi loaded") + + with torch.no_grad(): + export_mimi(mimi, args) + + +if __name__ == "__main__": + + parser = setup_common_args_and_variables() + + parser.add_argument( + "-a", + "--artifact", + help="path for storing generated artifacts by this example. Default ./mimi", + default="./mimi", + type=str, + ) + + parser.add_argument( + "--chunks_per_batch", + help="Number of chunks to process per time. Default is 1 chunk per batch, which equals to 0.08 second", + default=1, + type=int, + ) + + parser.add_argument( + "--emb_input_test", + help="This is just a metrics used to compute accuracy scores, not recommended for general users.", + action="store_true", + default=False, + ) + + parser.add_argument("--mimi-weight", type=str) + parser.add_argument("--hf-repo", type=str, default=loaders.DEFAULT_REPO) + + args = parser.parse_args() + try: + main(args) + except Exception as e: + if args.ip and args.port != -1: + with Client((args.ip, args.port)) as conn: + conn.send(json.dumps({"Error": str(e)})) + else: + raise Exception(e) diff --git a/examples/qualcomm/utils.py b/examples/qualcomm/utils.py index 83e5b9a0442..242170712e1 100755 --- a/examples/qualcomm/utils.py +++ b/examples/qualcomm/utils.py @@ -288,7 +288,7 @@ def build_executorch_binary( skip_node_id_set=None, skip_node_op_set=None, quant_dtype: Optional[QuantDtype] = None, - custom_quantizer=None, + custom_quantizer: Optional[QnnQuantizer] = None, shared_buffer=False, metadata=None, dump_intermediate_outputs=False, @@ -325,8 +325,8 @@ def build_executorch_binary( shared_buffer=shared_buffer, dump_intermediate_outputs=dump_intermediate_outputs, ) - if quant_dtype is not None: - captured_model = torch.export.export(model, inputs, strict=True).module() + if quant_dtype is not None or custom_quantizer is not None: + captured_model = torch.export.export(model, inputs, strict=False).module() if qat_training_data: quantizer = custom_quantizer or make_quantizer( quant_dtype=quant_dtype, is_qat=True From 78d747e33daaaea083c4c8d66dd3fd8a780d5af4 Mon Sep 17 00:00:00 2001 From: Elena Zhelezina Date: Tue, 15 Apr 2025 12:32:07 +0100 Subject: [PATCH 149/244] Arm backend: Fixing typos (#10189) --- examples/arm/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/arm/README.md b/examples/arm/README.md index 0b487ebadb4..a326db70e64 100644 --- a/examples/arm/README.md +++ b/examples/arm/README.md @@ -7,8 +7,8 @@ contains the Cortex-M55 CPU and Ethos-U55 NPU. We will start from a PyTorch model in python, export it, convert it to a `.pte` file - A binary format adopted by ExecuTorch. Then we will take the `.pte` model file and embed that with a baremetal application executor_runner. We will -then take the executor_runner file, which contains not only the `.pte` file but -also necessary software component to run standalone on a baremetal system. +then take the executor_runner file, which contains not only the `.pte` binary but +also necessary software components to run standalone on a baremetal system. Lastly, we will run the executor_runner binary on a Corstone-300 FVP Simulator platform. From b3cea583749209111e0fd084992f8a7415fd39ab Mon Sep 17 00:00:00 2001 From: fumchin Date: Tue, 15 Apr 2025 14:21:50 +0100 Subject: [PATCH 150/244] Arm backend: Add support to ge.Scalar (#10195) This patch converts ge.Scalar to ge.Tensor and adds new scalar test cases for ge. Signed-off-by: Fang-Ching --- backends/arm/_passes/match_arg_ranks_pass.py | 1 + .../replace_scalar_with_tensor_pass.py | 2 + .../arm/operator_support/ethos_u55_support.py | 1 + .../tosa_supported_operators.py | 1 + backends/arm/test/ops/test_ge.py | 133 ++++++++++++------ 5 files changed, 94 insertions(+), 44 deletions(-) diff --git a/backends/arm/_passes/match_arg_ranks_pass.py b/backends/arm/_passes/match_arg_ranks_pass.py index 9f966596de3..3554fc0954c 100644 --- a/backends/arm/_passes/match_arg_ranks_pass.py +++ b/backends/arm/_passes/match_arg_ranks_pass.py @@ -49,6 +49,7 @@ def __init__(self, exported_program): exir_ops.edge.aten.bitwise_left_shift.Tensor, exir_ops.edge.aten.eq.Tensor, exir_ops.edge.aten.gt.Tensor, + exir_ops.edge.aten.ge.Tensor, exir_ops.edge.aten.lt.Tensor, exir_ops.edge.aten.pow.Tensor_Tensor, exir_ops.edge.aten.where.self, diff --git a/backends/arm/_passes/replace_scalar_with_tensor_pass.py b/backends/arm/_passes/replace_scalar_with_tensor_pass.py index f24d53215f5..fed72e664f5 100644 --- a/backends/arm/_passes/replace_scalar_with_tensor_pass.py +++ b/backends/arm/_passes/replace_scalar_with_tensor_pass.py @@ -27,6 +27,7 @@ exir_ops.edge.aten.__lshift__.Scalar: exir_ops.edge.aten.bitwise_left_shift.Tensor, exir_ops.edge.aten.eq.Scalar: exir_ops.edge.aten.eq.Tensor, exir_ops.edge.aten.gt.Scalar: exir_ops.edge.aten.gt.Tensor, + exir_ops.edge.aten.ge.Scalar: exir_ops.edge.aten.ge.Tensor, exir_ops.edge.aten.lt.Scalar: exir_ops.edge.aten.lt.Tensor, torch.ops.aten.add.Scalar: torch.ops.aten.add.Tensor, torch.ops.aten.sub.Scalar: torch.ops.aten.sub.Tensor, @@ -36,6 +37,7 @@ torch.ops.aten.__lshift__.Scalar: torch.ops.aten.bitwise_left_shift.Tensor, torch.ops.aten.eq.Scalar: torch.ops.aten.eq.Tensor, torch.ops.aten.gt.Scalar: torch.ops.aten.gt.Tensor, + torch.ops.aten.ge.Scalar: torch.ops.aten.ge.Tensor, torch.ops.aten.lt.Scalar: torch.ops.aten.lt.Tensor, } diff --git a/backends/arm/operator_support/ethos_u55_support.py b/backends/arm/operator_support/ethos_u55_support.py index b716acd0867..7276e8efffe 100644 --- a/backends/arm/operator_support/ethos_u55_support.py +++ b/backends/arm/operator_support/ethos_u55_support.py @@ -134,6 +134,7 @@ class EthosU55NotSupported(OperatorSupportBase): exir_ops.edge.aten.eq.Tensor, exir_ops.edge.aten.eq.Scalar, exir_ops.edge.aten.ge.Tensor, + exir_ops.edge.aten.ge.Scalar, exir_ops.edge.aten.gt.Tensor, exir_ops.edge.aten.gt.Scalar, exir_ops.edge.aten.le.Tensor, diff --git a/backends/arm/operator_support/tosa_supported_operators.py b/backends/arm/operator_support/tosa_supported_operators.py index 0f500bd759e..3cb30cacc5d 100644 --- a/backends/arm/operator_support/tosa_supported_operators.py +++ b/backends/arm/operator_support/tosa_supported_operators.py @@ -178,6 +178,7 @@ def is_node_supported( exir_ops.edge.aten.full.default, exir_ops.edge.aten.full_like.default, exir_ops.edge.aten.ge.Tensor, + exir_ops.edge.aten.ge.Scalar, exir_ops.edge.aten.gt.Tensor, exir_ops.edge.aten.gt.Scalar, exir_ops.edge.aten.le.Tensor, diff --git a/backends/arm/test/ops/test_ge.py b/backends/arm/test/ops/test_ge.py index a6193f6ea08..7bcd2c923a4 100644 --- a/backends/arm/test/ops/test_ge.py +++ b/backends/arm/test/ops/test_ge.py @@ -5,7 +5,6 @@ from typing import Tuple -import pytest import torch from executorch.backends.arm.test import common @@ -16,13 +15,14 @@ TosaPipelineMI, ) -aten_op = "torch.ops.aten.ge.Tensor" -exir_op = "executorch_exir_dialects_edge__ops_aten_ge_Tensor" - input_t = Tuple[torch.Tensor] class GreaterEqual(torch.nn.Module): + aten_op_tensor = "torch.ops.aten.ge.Tensor" + aten_op_scalar = "torch.ops.aten.ge.Scalar" + exir_op = "executorch_exir_dialects_edge__ops_aten_ge_Tensor" + def __init__(self, input, other): super().__init__() self.input_ = input @@ -31,7 +31,7 @@ def __init__(self, input, other): def forward( self, input_: torch.Tensor, - other_: torch.Tensor, + other_: torch.Tensor | int | float, ): return input_ >= other_ @@ -39,98 +39,143 @@ def get_inputs(self): return (self.input_, self.other_) -op_ge_rank1_ones = GreaterEqual( +op_ge_tensor_rank1_ones = GreaterEqual( torch.ones(5), torch.ones(5), ) -op_ge_rank2_rand = GreaterEqual( +op_ge_tensor_rank2_rand = GreaterEqual( torch.rand(4, 5), torch.rand(1, 5), ) -op_ge_rank3_randn = GreaterEqual( +op_ge_tensor_rank3_randn = GreaterEqual( torch.randn(10, 5, 2), torch.randn(10, 5, 2), ) -op_ge_rank4_randn = GreaterEqual( +op_ge_tensor_rank4_randn = GreaterEqual( torch.randn(3, 2, 2, 2), torch.randn(3, 2, 2, 2), ) -test_data_common = { - "ge_rank1_ones": op_ge_rank1_ones, - "ge_rank2_rand": op_ge_rank2_rand, - "ge_rank3_randn": op_ge_rank3_randn, - "ge_rank4_randn": op_ge_rank4_randn, +op_ge_scalar_rank1_ones = GreaterEqual(torch.ones(5), 1.0) +op_ge_scalar_rank2_rand = GreaterEqual(torch.rand(4, 5), 0.2) +op_ge_scalar_rank3_randn = GreaterEqual(torch.randn(10, 5, 2), -0.1) +op_ge_scalar_rank4_randn = GreaterEqual(torch.randn(3, 2, 2, 2), 0.3) + +test_data_tensor = { + "ge_tensor_rank1_ones": op_ge_tensor_rank1_ones, + "ge_tensor_rank2_rand": op_ge_tensor_rank2_rand, + "ge_tensor_rank3_randn": op_ge_tensor_rank3_randn, + "ge_tensor_rank4_randn": op_ge_tensor_rank4_randn, +} + +test_data_scalar = { + "ge_scalar_rank1_ones": op_ge_scalar_rank1_ones, + "ge_scalar_rank2_rand": op_ge_scalar_rank2_rand, + "ge_scalar_rank3_randn": op_ge_scalar_rank3_randn, + "ge_scalar_rank4_randn": op_ge_scalar_rank4_randn, } -@common.parametrize("test_module", test_data_common) -def test_ge_tosa_MI(test_module): +@common.parametrize("test_module", test_data_tensor) +def test_ge_tensor_tosa_MI(test_module): + pipeline = TosaPipelineMI[input_t]( + test_module, + test_module.get_inputs(), + GreaterEqual.aten_op_tensor, + GreaterEqual.exir_op, + ) + pipeline.run() + + +@common.parametrize("test_module", test_data_scalar) +def test_ge_scalar_tosa_MI(test_module): pipeline = TosaPipelineMI[input_t]( - test_module, test_module.get_inputs(), aten_op, exir_op + test_module, + test_module.get_inputs(), + GreaterEqual.aten_op_scalar, + GreaterEqual.exir_op, ) pipeline.run() -@common.parametrize("test_module", test_data_common) -def test_ge_tosa_BI(test_module): +@common.parametrize("test_module", test_data_tensor) +def test_ge_tensor_tosa_BI(test_module): pipeline = TosaPipelineBI[input_t]( - test_module, test_module.get_inputs(), aten_op, exir_op + test_module, + test_module.get_inputs(), + GreaterEqual.aten_op_tensor, + GreaterEqual.exir_op, ) pipeline.run() -@common.parametrize("test_module", test_data_common) -def test_ge_u55_BI(test_module): - # GREATER_EQUAL is not supported on U55. - pipeline = OpNotSupportedPipeline[input_t]( +@common.parametrize("test_module", test_data_scalar) +def test_ge_scalar_tosa_BI(test_module): + pipeline = TosaPipelineBI[input_t]( test_module, test_module.get_inputs(), - "TOSA-0.80+BI+u55", - {exir_op: 1}, + GreaterEqual.aten_op_tensor, + GreaterEqual.exir_op, ) pipeline.run() -@common.parametrize("test_module", test_data_common) -def test_ge_u85_BI(test_module): - pipeline = EthosU85PipelineBI[input_t]( +@common.parametrize("test_module", test_data_tensor) +@common.XfailIfNoCorstone300 +def test_ge_tensor_u55_BI(test_module): + # GREATER_EQUAL is not supported on U55. + pipeline = OpNotSupportedPipeline[input_t]( test_module, test_module.get_inputs(), - aten_op, - exir_op, - run_on_fvp=False, - use_to_edge_transform_and_lower=True, + "TOSA-0.80+BI+u55", + {GreaterEqual.exir_op: 1}, ) pipeline.run() -@common.parametrize("test_module", test_data_common) -@pytest.mark.skip(reason="The same as test_ge_u55_BI") -def test_ge_u55_BI_on_fvp(test_module): +@common.parametrize("test_module", test_data_scalar) +@common.XfailIfNoCorstone300 +def test_ge_scalar_u55_BI(test_module): # GREATER_EQUAL is not supported on U55. pipeline = OpNotSupportedPipeline[input_t]( test_module, test_module.get_inputs(), "TOSA-0.80+BI+u55", - {exir_op: 1}, + {GreaterEqual.exir_op: 1}, + n_expected_delegates=1, + ) + pipeline.run() + + +@common.parametrize( + "test_module", + test_data_tensor, + xfails={"ge_tensor_rank4_randn": "MLETORCH-847: Boolean eq result unstable on U85"}, +) +@common.XfailIfNoCorstone320 +def test_ge_tensor_u85_BI(test_module): + pipeline = EthosU85PipelineBI[input_t]( + test_module, + test_module.get_inputs(), + GreaterEqual.aten_op_tensor, + GreaterEqual.exir_op, + run_on_fvp=True, ) pipeline.run() @common.parametrize( "test_module", - test_data_common, - xfails={"ge_rank4_randn": "4D fails because boolean Tensors can't be subtracted"}, + test_data_scalar, + xfails={"ge_scalar_rank4_randn": "MLETORCH-847: Boolean eq result unstable on U85"}, ) -@common.SkipIfNoCorstone320 -def test_ge_u85_BI_on_fvp(test_module): +@common.XfailIfNoCorstone320 +def test_ge_scalar_u85_BI(test_module): pipeline = EthosU85PipelineBI[input_t]( test_module, test_module.get_inputs(), - aten_op, - exir_op, + GreaterEqual.aten_op_tensor, + GreaterEqual.exir_op, run_on_fvp=True, - use_to_edge_transform_and_lower=True, ) pipeline.run() From a9a0fdae7ce9efcb52d5495aa6813d3e57948446 Mon Sep 17 00:00:00 2001 From: Ryan OShea <86965113+ArmRyan@users.noreply.github.com> Date: Tue, 15 Apr 2025 16:33:39 +0200 Subject: [PATCH 151/244] Arm Backend: Add New DecomposeSilu pass to arm_pass_manager (#9448) * Adds DecomposeSilu pass * Adds Tests for DecomposeSilu Signed-off-by: Ryan O'Shea --- backends/arm/_passes/__init__.py | 1 + backends/arm/_passes/arm_pass_manager.py | 2 + backends/arm/_passes/decompose_silu_pass.py | 34 ++++++ backends/arm/quantizer/arm_quantizer.py | 4 +- backends/arm/test/ops/test_silu.py | 113 ++++++++++++++++++++ 5 files changed, 152 insertions(+), 2 deletions(-) create mode 100644 backends/arm/_passes/decompose_silu_pass.py create mode 100644 backends/arm/test/ops/test_silu.py diff --git a/backends/arm/_passes/__init__.py b/backends/arm/_passes/__init__.py index 1a1719bf8ae..ddca8ea4a06 100644 --- a/backends/arm/_passes/__init__.py +++ b/backends/arm/_passes/__init__.py @@ -26,6 +26,7 @@ from .decompose_linear_pass import DecomposeLinearPass # noqa from .decompose_meandim_pass import DecomposeMeanDimPass # noqa from .decompose_select import DecomposeSelectPass # noqa +from .decompose_silu_pass import DecomposeSiluPass # noqa from .decompose_softmax_pass import DecomposeSoftmaxPass # noqa from .decompose_softmax_unstable_pass import DecomposeSoftmaxUnstablePass # noqa from .decompose_sqrt_pass import DecomposeSqrtPass # noqa diff --git a/backends/arm/_passes/arm_pass_manager.py b/backends/arm/_passes/arm_pass_manager.py index dd9f8b7bed0..dd4ca7ad7bd 100644 --- a/backends/arm/_passes/arm_pass_manager.py +++ b/backends/arm/_passes/arm_pass_manager.py @@ -31,6 +31,7 @@ DecomposeLinearPass, DecomposeMeanDimPass, DecomposeSelectPass, + DecomposeSiluPass, DecomposeSoftmaxPass, DecomposeSoftmaxUnstablePass, DecomposeSqrtPass, @@ -196,6 +197,7 @@ def transform_for_annotation_pipeline(self, graph_module: GraphModule): self.add_pass(DecomposeDivPass()) self.add_pass(DecomposeLeakyReLUPass()) self.add_pass(DecomposeSqrtPass()) + self.add_pass(DecomposeSiluPass()) if isinstance(self.tosa_spec, Tosa_0_80) and self.tosa_spec.is_U55_subset: # Numerically stable softmax uses amax which is not supported on Ethos-U55 diff --git a/backends/arm/_passes/decompose_silu_pass.py b/backends/arm/_passes/decompose_silu_pass.py new file mode 100644 index 00000000000..68ebb3f4515 --- /dev/null +++ b/backends/arm/_passes/decompose_silu_pass.py @@ -0,0 +1,34 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +# pyre-unsafe + +import torch +from executorch.exir.pass_base import ExportPass + +aten_silu_ops = (torch.ops.aten.silu.default, torch.ops.aten.silu_.default) + + +class DecomposeSiluPass(ExportPass): + """ + This pass decomposes silu into a mul and a sigmoid node. + + Example: + y = silu(a) + Becomes: + x = sigmoid(a) + y = mul(a,x) + """ + + def call_operator(self, op, args, kwargs, meta): + if op not in (aten_silu_ops): + return super().call_operator(op, args, kwargs, meta) + sigmoid_op = torch.ops.aten.sigmoid.default + mul_op = torch.ops.aten.mul.Tensor + + original = args[0] + sigmoid = super().call_operator(sigmoid_op, (original,), {}, meta) + + return super().call_operator(mul_op, (original, sigmoid), {}, meta) diff --git a/backends/arm/quantizer/arm_quantizer.py b/backends/arm/quantizer/arm_quantizer.py index e76ed5fb415..ee08f8e9eec 100644 --- a/backends/arm/quantizer/arm_quantizer.py +++ b/backends/arm/quantizer/arm_quantizer.py @@ -286,10 +286,10 @@ def _annotate_all_static_patterns( quantization_config: Optional[QuantizationConfig], filter_fn: Optional[Callable[[Node], bool]] = None, ) -> GraphModule: - """Loops over all STATIC_OPS and runs the corresponding registred annotator. + """Loops over all STATIC_OPS and runs the corresponding registered annotator. Args: model: The model to annotate statically. - quantization_config: Specifices the QuantizationSpecs for the model's + quantization_config: Specifies the QuantizationSpecs for the model's input activations, output activations, weights and biases. filter_fn: An optional filter function that takes a node and returns whether the node should be annotated. Returns: diff --git a/backends/arm/test/ops/test_silu.py b/backends/arm/test/ops/test_silu.py new file mode 100644 index 00000000000..51748b02450 --- /dev/null +++ b/backends/arm/test/ops/test_silu.py @@ -0,0 +1,113 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + + +from typing import Optional, Tuple + +import torch +from executorch.backends.arm.test import common +from executorch.backends.arm.test.tester.test_pipeline import ( + EthosU55PipelineBI, + EthosU85PipelineBI, + TosaPipelineBI, + TosaPipelineMI, +) + + +input_t = Tuple[torch.Tensor] + + +class Silu(torch.nn.Module): + def forward( + self, + _input: torch.Tensor, + _inplace: Optional[bool] = False, + ): + return torch.nn.SiLU(inplace=_inplace)(_input) + + test_data: list[input_t] = { + "op_silu_rank1_ones": (torch.ones(5),), + "op_silu_rank1_negative_ones": (torch.ones(5) * (-1),), + "op_silu_rank1_rand": (torch.rand(5) * 5,), + "op_silu_rank4_ones": (torch.ones(1, 10, 25, 20),), + "op_silu_rank4_negative_ones": ((-1) * torch.ones(1, 10, 25, 20),), + "op_silu_rank4_large_rand": (200 * torch.rand(1, 10, 25, 20),), + "op_silu_rank4_negative_large_rand": ((-200) * torch.rand(1, 10, 25, 20),), + "op_silu_rank4_large_randn": (200 * torch.randn(1, 10, 25, 20) + 1,), + } + + aten_op_MI = "torch.ops.aten.silu.default" + aten_op_inplace_MI = "torch.ops.aten.silu_.default" + aten_op_BI = ["torch.ops.aten.sigmoid.default", "torch.ops.aten.mul.Tensor"] + + +@common.parametrize("test_data", Silu.test_data) +def test_silu_tosa_MI(test_data: input_t): + silu_data = (test_data[0], False) + pipeline = TosaPipelineMI[input_t](Silu(), silu_data, Silu.aten_op_MI) + pipeline.run() + + +@common.parametrize("test_data", Silu.test_data) +def test_silu_tosa_MI_inplace(test_data: input_t): + silu_data = (test_data[0], True) + pipeline = TosaPipelineMI[input_t](Silu(), silu_data, Silu.aten_op_inplace_MI) + pipeline.run() + + +@common.parametrize("test_data", Silu.test_data) +def test_silu_tosa_BI(test_data: input_t): + silu_data = (test_data[0], False) + pipeline = TosaPipelineBI[input_t](Silu(), silu_data, Silu.aten_op_BI) + pipeline.run() + + +@common.parametrize("test_data", Silu.test_data) +def test_silu_tosa_BI_inplace(test_data: input_t): + silu_data = (test_data[0], True) + pipeline = TosaPipelineBI[input_t](Silu(), silu_data, Silu.aten_op_BI) + pipeline.run() + + +@common.parametrize("test_data", Silu.test_data) +@common.XfailIfNoCorstone300 +def test_silu_u55_BI(test_data: input_t): + silu_data = (test_data[0], False) + pipeline = EthosU55PipelineBI[input_t]( + Silu(), silu_data, Silu.aten_op_BI, run_on_fvp=True + ) + pipeline.run() + + +@common.parametrize("test_data", Silu.test_data) +@common.XfailIfNoCorstone300 +def test_silu_u55_BI_inplace(test_data: input_t): + silu_data = (test_data[0], True) + pipeline = EthosU55PipelineBI[input_t]( + Silu(), silu_data, Silu.aten_op_BI, run_on_fvp=True + ) + pipeline.run() + + +@common.parametrize("test_data", Silu.test_data) +@common.XfailIfNoCorstone320 +def test_silu_u85_BI(test_data: input_t): + silu_data = (test_data[0], False) + pipeline = EthosU85PipelineBI[input_t]( + Silu(), silu_data, Silu.aten_op_BI, run_on_fvp=True + ) + pipeline.run() + + +@common.parametrize("test_data", Silu.test_data) +@common.XfailIfNoCorstone320 +def test_silu_u85_BI_inplace(test_data: input_t): + silu_data = (test_data[0], True) + pipeline = EthosU85PipelineBI[input_t]( + Silu(), silu_data, Silu.aten_op_BI, run_on_fvp=True + ) + pipeline.run() From 532d593b9d8613f725752a5a1e7d5a6f0e69672c Mon Sep 17 00:00:00 2001 From: lucylq Date: Tue, 15 Apr 2025 09:10:38 -0700 Subject: [PATCH 152/244] Update llama cmake for custom ops (#10176) Fix https://github.com/pytorch/executorch/issues/10166 target_link_options_shared_lib has different behavior for macos and other platforms; seems like whole-archive was causing issues. Test Plan `sh .ci/scripts/test_llama_torchao_lowbit.sh` https://github.com/pytorch/executorch/blob/main/examples/models/llama/README.md#running-with-low-bit-kernels --- examples/models/llama/CMakeLists.txt | 3 ++- examples/models/llama/README.md | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/models/llama/CMakeLists.txt b/examples/models/llama/CMakeLists.txt index e6d45424bd4..12385f32d20 100644 --- a/examples/models/llama/CMakeLists.txt +++ b/examples/models/llama/CMakeLists.txt @@ -111,7 +111,8 @@ target_link_options_shared_lib(quantized_ops_lib) list(APPEND link_libraries quantized_kernels quantized_ops_lib) if(EXECUTORCH_BUILD_KERNELS_CUSTOM) - list(APPEND link_libraries $) + target_link_options_shared_lib(custom_ops) + list(APPEND link_libraries custom_ops) endif() if(EXECUTORCH_BUILD_TORCHAO) diff --git a/examples/models/llama/README.md b/examples/models/llama/README.md index 93ef71769b5..aff527c59cf 100644 --- a/examples/models/llama/README.md +++ b/examples/models/llama/README.md @@ -431,7 +431,7 @@ cmake -DPYTHON_EXECUTABLE=python \ -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=ON \ -DEXECUTORCH_BUILD_EXTENSION_MODULE=ON \ -DEXECUTORCH_BUILD_EXTENSION_TENSOR=ON \ - -DEXECUTORCH_BUILD_XNNPACK=ON \ + -DEXECUTORCH_BUILD_XNNPACK=OFF \ -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON \ -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON \ -DEXECUTORCH_BUILD_KERNELS_CUSTOM=ON \ From 935e9ed7509c8fd7ba0f23c211d451fa2fada1be Mon Sep 17 00:00:00 2001 From: JP <46308822+zonglinpeng@users.noreply.github.com> Date: Tue, 15 Apr 2025 09:19:11 -0700 Subject: [PATCH 153/244] port hardtanh and add hardtanh test Differential Revision: D72405686 Pull Request resolved: https://github.com/pytorch/executorch/pull/9914 --- backends/cadence/fusion_g3/operators/targets.bzl | 1 + backends/cadence/utils/facto_util.py | 6 ++++++ 2 files changed, 7 insertions(+) diff --git a/backends/cadence/fusion_g3/operators/targets.bzl b/backends/cadence/fusion_g3/operators/targets.bzl index fffeee0d7b3..b878226fcb1 100644 --- a/backends/cadence/fusion_g3/operators/targets.bzl +++ b/backends/cadence/fusion_g3/operators/targets.bzl @@ -40,6 +40,7 @@ OPERATORS = [ "rsqrt", "sigmoid", "sqrt", + "hardtanh", "tanh", "transpose_copy", "where", diff --git a/backends/cadence/utils/facto_util.py b/backends/cadence/utils/facto_util.py index 52b64dc1581..f38c1cc4154 100644 --- a/backends/cadence/utils/facto_util.py +++ b/backends/cadence/utils/facto_util.py @@ -99,6 +99,7 @@ def apply_scalar_contraints(op_name: str) -> list[ScalarDtype]: match op_name: case "add.Scalar" | "sub.Scalar" | "mul.Scalar" | "div.Scalar": return [ScalarDtype.int] + case _: return [ScalarDtype.float, ScalarDtype.int] @@ -122,6 +123,11 @@ def facto_testcase_gen(op_name: str) -> List[Tuple[List[str], OrderedDict[str, s cp.Size.Le(lambda deps, r, d: 2**2), ] ) + if in_spec.name == "max_val": # hardtanh + spec.inspec[index].deps = [0, 1] + spec.inspec[index].constraints.extend( + [cp.Value.Ge(lambda deps, _: deps[1])] + ) else: spec.inspec[index].constraints.extend( [ From f16cf3eaa81d04a2730bb8cd2dbfab5d66a878a8 Mon Sep 17 00:00:00 2001 From: mcremon-meta <134334895+mcremon-meta@users.noreply.github.com> Date: Tue, 15 Apr 2025 10:23:17 -0700 Subject: [PATCH 154/244] Remove layer norm from the default quantizer, add one that has it Differential Revision: D72941790 Pull Request resolved: https://github.com/pytorch/executorch/pull/10182 --- backends/cadence/aot/quantizer/quantizer.py | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/backends/cadence/aot/quantizer/quantizer.py b/backends/cadence/aot/quantizer/quantizer.py index 69c9518166b..761b2bf8d31 100644 --- a/backends/cadence/aot/quantizer/quantizer.py +++ b/backends/cadence/aot/quantizer/quantizer.py @@ -193,7 +193,6 @@ def get_cadence_default_quantizers() -> List[Quantizer]: CadenceAtenQuantizer(BmmPattern(), qconfig_A8W8), CadenceAtenQuantizer(Conv1dPattern(), qconfig_A8W8sym), CadenceAtenQuantizer(Conv2dPattern(), qconfig_A8W8sym), - CadenceAtenQuantizer(LayerNormPattern(), qconfig_A8W8), CadenceAtenQuantizer(LinearPattern(), qconfig_A8W8), CadenceAtenQuantizer(MatmulPattern(), qconfig_A8W8), CadenceAtenQuantizer(ReluPattern0(), qconfig_A8W8), @@ -236,9 +235,21 @@ def __init__( super().__init__([]) +class CadenceWithLayerNormQuantizer(CadenceQuantizer): + """ + Quantizer including layer norm + """ + + def __init__(self, quantizers: Optional[list[Quantizer]] = None) -> None: + if quantizers is None: + quantizers = get_cadence_default_quantizers() + quantizers.append(CadenceAtenQuantizer(LayerNormPattern(), qconfig_A8W8)) + super().__init__(quantizers) + + class CadenceWakeWordQuantizer(CadenceQuantizer): """ - Quantizer for WakeWord, including add + Quantizer for WakeWord, including add and cat """ def __init__(self, quantizers: Optional[list[Quantizer]] = None) -> None: From be8b7ee6f9dd54aebffd473b525704b2f4f6637c Mon Sep 17 00:00:00 2001 From: jathu Date: Tue, 15 Apr 2025 11:17:55 -0700 Subject: [PATCH 155/244] Update pytorch-labs/tokenizers to 295ee78 (#10161) ### Summary Update tokenizers lib to include https://github.com/pytorch-labs/tokenizers/pull/46. ### Test plan CI --- extension/llm/tokenizers | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/extension/llm/tokenizers b/extension/llm/tokenizers index 71167bf9cf4..295ee78e4b0 160000 --- a/extension/llm/tokenizers +++ b/extension/llm/tokenizers @@ -1 +1 @@ -Subproject commit 71167bf9cf4bed861eb9547d1d77e993fd1004f1 +Subproject commit 295ee78e4b0d99d4527bbe81bc3156341366de11 From 6eec7fbf99dfa078a360f412ef30bf70476992d2 Mon Sep 17 00:00:00 2001 From: Anthony Shoumikhin Date: Tue, 15 Apr 2025 11:32:52 -0700 Subject: [PATCH 156/244] Strip .html suffix from doc links (#10210) --- docs/source/llm/getting-started.md | 2 +- examples/demo-apps/apple_ios/LLaMA/README.md | 2 +- .../apple_ios/LLaMA/docs/delegates/xnnpack_README.md | 4 ++-- examples/llm_manual/README.md | 2 +- examples/models/llama/README.md | 4 ++-- extension/benchmark/apple/Benchmark/README.md | 2 +- 6 files changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/source/llm/getting-started.md b/docs/source/llm/getting-started.md index a6b34adf99b..ea90f346279 100644 --- a/docs/source/llm/getting-started.md +++ b/docs/source/llm/getting-started.md @@ -159,7 +159,7 @@ example_inputs = (torch.randint(0, 100, (1, model.config.block_size), dtype=torc # long as they adhere to the rules specified in the dynamic shape configuration. # Here we set the range of 0th model input's 1st dimension as # [0, model.config.block_size]. -# See https://pytorch.org/executorch/main/concepts.html#dynamic-shapes +# See https://pytorch.org/executorch/main/concepts#dynamic-shapes # for details about creating dynamic shapes. dynamic_shape = ( {1: torch.export.Dim("token_dim", max=model.config.block_size)}, diff --git a/examples/demo-apps/apple_ios/LLaMA/README.md b/examples/demo-apps/apple_ios/LLaMA/README.md index e44b4502cac..c39aa99bb77 100644 --- a/examples/demo-apps/apple_ios/LLaMA/README.md +++ b/examples/demo-apps/apple_ios/LLaMA/README.md @@ -56,7 +56,7 @@ Link your binary with the ExecuTorch runtime and any backends or kernels used by Note: To access logs, link against the Debug build of the ExecuTorch runtime, i.e., the executorch_debug framework. For optimal performance, always link against the Release version of the deliverables (those without the _debug suffix), which have all logging overhead removed. -For more details integrating and Running ExecuTorch on Apple Platforms, checkout this [link](https://pytorch.org/executorch/main/using-executorch-ios.html). +For more details integrating and Running ExecuTorch on Apple Platforms, checkout this [link](https://pytorch.org/executorch/main/using-executorch-ios). ### XCode * Open XCode and select "Open an existing project" to open `examples/demo-apps/apple_ios/LLama`. diff --git a/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md b/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md index 7e5090410a3..54eba9cd790 100644 --- a/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md +++ b/examples/demo-apps/apple_ios/LLaMA/docs/delegates/xnnpack_README.md @@ -163,7 +163,7 @@ If you cannot add the package into your app target (it's greyed out), it might h - More details on integrating and Running ExecuTorch on Apple Platforms, check out the detailed guide [here](https://pytorch.org/executorch/main/using-executorch-ios.html#local-build). + More details on integrating and Running ExecuTorch on Apple Platforms, check out the detailed guide [here](https://pytorch.org/executorch/main/using-executorch-ios#local-build). ### 3. Configure Build Schemes @@ -175,7 +175,7 @@ Navigate to `Product --> Scheme --> Edit Scheme --> Info --> Build Configuration We recommend that you only use the Debug build scheme during development, where you might need to access additional logs. Debug build has logging overhead and will impact inferencing performance, while release build has compiler optimizations enabled and all logging overhead removed. -For more details integrating and Running ExecuTorch on Apple Platforms or building the package locally, checkout this [link](https://pytorch.org/executorch/main/using-executorch-ios.html). +For more details integrating and Running ExecuTorch on Apple Platforms or building the package locally, checkout this [link](https://pytorch.org/executorch/main/using-executorch-ios). ### 4. Build and Run the project diff --git a/examples/llm_manual/README.md b/examples/llm_manual/README.md index e465255fc66..6318bbe7e84 100644 --- a/examples/llm_manual/README.md +++ b/examples/llm_manual/README.md @@ -1,3 +1,3 @@ # LLM Manual -This repository is a storage place for the files that [LLM Manual](https://pytorch.org/executorch/main/llm/getting-started.html) needs. Please refer to the documentation website for more information. +This repository is a storage place for the files that [LLM Manual](https://pytorch.org/executorch/main/llm/getting-started) needs. Please refer to the documentation website for more information. diff --git a/examples/models/llama/README.md b/examples/models/llama/README.md index aff527c59cf..ccd2a82902b 100644 --- a/examples/models/llama/README.md +++ b/examples/models/llama/README.md @@ -379,10 +379,10 @@ adb shell "cd /data/local/tmp/llama && ./llama_main --model_path --t ### iOS -Please refer to [this tutorial](https://pytorch.org/executorch/main/llm/llama-demo-ios.html) to for full instructions on building the iOS LLAMA Demo App. Rename `tokenizer.model` file to `tokenizer.bin` because the demo app looks for the tokenizer file with .bin extension. +Please refer to [this tutorial](https://pytorch.org/executorch/main/llm/llama-demo-ios) to for full instructions on building the iOS LLAMA Demo App. Rename `tokenizer.model` file to `tokenizer.bin` because the demo app looks for the tokenizer file with .bin extension. ### Android -Please refer to [this tutorial](https://pytorch.org/executorch/main/llm/llama-demo-android.html) to for full instructions on building the Android LLAMA Demo App. +Please refer to [this tutorial](https://pytorch.org/executorch/main/llm/llama-demo-android) to for full instructions on building the Android LLAMA Demo App. ## Running with low-bit kernels diff --git a/extension/benchmark/apple/Benchmark/README.md b/extension/benchmark/apple/Benchmark/README.md index a68a9bf8abb..4d8e9374634 100644 --- a/extension/benchmark/apple/Benchmark/README.md +++ b/extension/benchmark/apple/Benchmark/README.md @@ -33,7 +33,7 @@ This command performs a shallow clone to speed up the process. The Benchmark App is configured to use a Swift PM package that provides the prebuilt ExecuTorch frameworks. -By default, the app relies on the package referencing locally built binaries. To ensure it functions correctly, you must first build the frameworks by following the [guide](https://pytorch.org/executorch/main/using-executorch-ios.html#building-from-source). +By default, the app relies on the package referencing locally built binaries. To ensure it functions correctly, you must first build the frameworks by following the [guide](https://pytorch.org/executorch/main/using-executorch-ios#building-from-source). ## Adding Models and Resources From 5b1c2eae4ac49f97a767e662484a483db182320f Mon Sep 17 00:00:00 2001 From: Huy Do Date: Tue, 15 Apr 2025 14:36:31 -0700 Subject: [PATCH 157/244] Experiment with private rooted Pixel 3 devices (#10192) I'm creating a new experimental workflow to run benchmarks on private rooted Pixel 3 devices. We have only 2 devices there though, so I think let's reduce the number of model we want to cover for now. ### Testing https://github.com/pytorch/executorch/actions/runs/14465442714 --- .ci/scripts/gather_benchmark_configs.py | 1 + ...android-perf-private-device-experiment.yml | 62 +++++++++++++++++++ 2 files changed, 63 insertions(+) create mode 100644 .github/workflows/android-perf-private-device-experiment.yml diff --git a/.ci/scripts/gather_benchmark_configs.py b/.ci/scripts/gather_benchmark_configs.py index b2126f84e78..9a4723d7e56 100755 --- a/.ci/scripts/gather_benchmark_configs.py +++ b/.ci/scripts/gather_benchmark_configs.py @@ -23,6 +23,7 @@ "samsung_galaxy_s22": "arn:aws:devicefarm:us-west-2:308535385114:devicepool:02a2cf0f-6d9b-45ee-ba1a-a086587469e6/e59f866a-30aa-4aa1-87b7-4510e5820dfa", "samsung_galaxy_s24": "arn:aws:devicefarm:us-west-2:308535385114:devicepool:02a2cf0f-6d9b-45ee-ba1a-a086587469e6/98f8788c-2e25-4a3c-8bb2-0d1e8897c0db", "google_pixel_8_pro": "arn:aws:devicefarm:us-west-2:308535385114:devicepool:02a2cf0f-6d9b-45ee-ba1a-a086587469e6/d65096ab-900b-4521-be8b-a3619b69236a", + "google_pixel_3_private_rooted": "arn:aws:devicefarm:us-west-2:308535385114:devicepool:02a2cf0f-6d9b-45ee-ba1a-a086587469e6/98d23ca8-ea9e-4fb7-b725-d402017b198d", } # Predefined benchmark configurations diff --git a/.github/workflows/android-perf-private-device-experiment.yml b/.github/workflows/android-perf-private-device-experiment.yml new file mode 100644 index 00000000000..ffbd1e064b1 --- /dev/null +++ b/.github/workflows/android-perf-private-device-experiment.yml @@ -0,0 +1,62 @@ +name: android-perf (private devices) + +on: + schedule: + - cron: 0 0,4,8,12,16,20 * * * + pull_request: + paths: + - .github/workflows/android-perf-private-device-experiment.yml + push: + branches: + - main + paths: + - .github/workflows/android-perf-private-device-experiment.yml + # Note: GitHub has an upper limit of 10 inputs + workflow_dispatch: + inputs: + models: + description: Models to be benchmarked + required: false + type: string + default: mv3,meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8,meta-llama/Llama-3.2-1B-Instruct-QLORA_INT4_EO8 + devices: + description: Target devices to run benchmark + required: false + type: string + default: google_pixel_3_private_rooted + benchmark_configs: + description: The list of configs used the benchmark + required: false + type: string + workflow_call: + inputs: + models: + description: Models to be benchmarked + required: false + type: string + default: mv3,meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8,meta-llama/Llama-3.2-1B-Instruct-QLORA_INT4_EO8 + devices: + description: Target devices to run benchmark + required: false + type: string + default: google_pixel_3_private_rooted + benchmark_configs: + description: The list of configs used the benchmark + required: false + type: string + +concurrency: + group: android-perf-private-devices-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }} + cancel-in-progress: true + +jobs: + android: + uses: ./.github/workflows/android-perf.yml + secrets: inherit + permissions: + id-token: write + contents: read + with: + models: ${{ inputs.models }} + devices: google_pixel_3_private_rooted + benchmark_configs: ${{ inputs.benchmark_configs }} From d22689d9b9430642dbab2a316fc1181df2577e2a Mon Sep 17 00:00:00 2001 From: Huy Do Date: Tue, 15 Apr 2025 14:41:20 -0700 Subject: [PATCH 158/244] Run Android release job on ephemeral runners (#10190) To summary the discussion with @kirklandsign, only repository secrets are accessible in a Nova linux job. However, GitHub warns against using such secrets on persistent self-hosted runners https://docs.github.com/en/actions/security-for-github-actions/security-guides/security-hardening-for-github-actions#hardening-for-self-hosted-runners. In this case, it's important to use a fresh ephemeral runner instead to make sure that nothing on the runner could steal these secrets. ### Testing https://github.com/pytorch/executorch/actions/runs/14465234621 --- .github/workflows/android-release-artifacts.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/android-release-artifacts.yml b/.github/workflows/android-release-artifacts.yml index e40b53a148b..2a35cf4711d 100644 --- a/.github/workflows/android-release-artifacts.yml +++ b/.github/workflows/android-release-artifacts.yml @@ -49,7 +49,8 @@ jobs: contents: read with: secrets-env: EXECUTORCH_MAVEN_SIGNING_KEYID EXECUTORCH_MAVEN_SIGNING_PASSWORD EXECUTORCH_MAVEN_CENTRAL_PASSWORD EXECUTORCH_MAVEN_CENTRAL_USERNAME EXECUTORCH_MAVEN_SIGNING_GPG_KEY_CONTENTS - runner: linux.2xlarge + # As this job has access to Maven credential, run this on a fresh ephemeral runner + runner: ephemeral.linux.2xlarge docker-image: executorch-ubuntu-22.04-clang12-android submodules: 'recursive' ref: ${{ github.sha }} From f52dcd700629a3c4856b461950340ee31521fe3a Mon Sep 17 00:00:00 2001 From: Iliyan Georgiev Date: Tue, 15 Apr 2025 22:58:45 +0100 Subject: [PATCH 159/244] Arm backend: Add support alias_copy operator (#10199) - Add NodeVisitor factory for IDENTITY ops - Add alias_copy support - Add alias_copy tests - Move getitem to new factory Signed-off-by: Iliyan Georgiev --- .../tosa_supported_operators.py | 1 + backends/arm/operators/__init__.py | 2 +- backends/arm/operators/op_get_item.py | 35 -------- backends/arm/operators/ops_identity.py | 47 +++++++++++ .../arm/quantizer/quantization_annotator.py | 7 +- backends/arm/test/ops/test_alias_copy.py | 83 +++++++++++++++++++ 6 files changed, 138 insertions(+), 37 deletions(-) delete mode 100644 backends/arm/operators/op_get_item.py create mode 100644 backends/arm/operators/ops_identity.py create mode 100644 backends/arm/test/ops/test_alias_copy.py diff --git a/backends/arm/operator_support/tosa_supported_operators.py b/backends/arm/operator_support/tosa_supported_operators.py index 3cb30cacc5d..952cfb17cf0 100644 --- a/backends/arm/operator_support/tosa_supported_operators.py +++ b/backends/arm/operator_support/tosa_supported_operators.py @@ -229,6 +229,7 @@ def is_node_supported( exir_ops.edge.aten.__lshift__.Scalar, torch.ops.aten.scalar_tensor.default, exir_ops.edge.aten.gelu.default, + exir_ops.edge.aten.alias_copy.default, ] return supported diff --git a/backends/arm/operators/__init__.py b/backends/arm/operators/__init__.py index 861ed9c7701..da050c5994e 100644 --- a/backends/arm/operators/__init__.py +++ b/backends/arm/operators/__init__.py @@ -22,7 +22,6 @@ op_erf, op_exp, op_ge, - op_get_item, op_gt, op_le, op_log, @@ -51,5 +50,6 @@ op_view, op_where, ops_binary, + ops_identity, ops_unary, ) diff --git a/backends/arm/operators/op_get_item.py b/backends/arm/operators/op_get_item.py deleted file mode 100644 index 0e1192b3bef..00000000000 --- a/backends/arm/operators/op_get_item.py +++ /dev/null @@ -1,35 +0,0 @@ -# Copyright 2023-2025 Arm Limited and/or its affiliates. -# -# This source code is licensed under the BSD-style license found in the -# LICENSE file in the root directory of this source tree. - -# pyre-unsafe -from typing import List - -import torch - -import tosa_tools.v0_80.serializer.tosa_serializer as ts # type: ignore -from executorch.backends.arm.operators.node_visitor import ( - NodeVisitor, - register_node_visitor, -) -from executorch.backends.arm.tosa_mapping import TosaArg - - -@register_node_visitor -class GetItemVisitor(NodeVisitor): - target = "getitem" - - def __init__(self, *args): - super().__init__(*args) - - def define_node( - self, - node: torch.fx.Node, - tosa_graph: ts.TosaSerializer, - inputs: List[TosaArg], - output: TosaArg, - ) -> None: - item_name = inputs[0].name - ## Simply add an identityOp - tosa_graph.addOperator(ts.TosaOp.Op().IDENTITY, [item_name], [output.name]) diff --git a/backends/arm/operators/ops_identity.py b/backends/arm/operators/ops_identity.py new file mode 100644 index 00000000000..0c6527cf336 --- /dev/null +++ b/backends/arm/operators/ops_identity.py @@ -0,0 +1,47 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +# pyre-unsafe + +from typing import List + +import torch +import torch.fx + +import tosa_tools.v0_80.serializer.tosa_serializer as ts + +from executorch.backends.arm.operators.node_visitor import ( + NodeVisitor, + register_node_visitor, +) +from executorch.backends.arm.tosa_mapping import TosaArg + + +def identity_operator_factory(identity_target: str): + """ + Creates and registers NodeVisitors for operators that map directly + to a TOSA IDENTITY op. + """ + + class IdentityOperatorVisitor(NodeVisitor): + target = identity_target + + def define_node( + self, + node: torch.fx.Node, + tosa_graph: ts.TosaSerializer, + inputs: List[TosaArg], + output: TosaArg, + ) -> None: + # Simply add an identityOp + tosa_graph.addOperator( + ts.TosaOp.Op().IDENTITY, [inputs[0].name], [output.name] + ) + + register_node_visitor(IdentityOperatorVisitor) + + +identity_operator_factory("getitem") +identity_operator_factory("aten.alias_copy.default") diff --git a/backends/arm/quantizer/quantization_annotator.py b/backends/arm/quantizer/quantization_annotator.py index 1575d59fd77..5398101fd9a 100644 --- a/backends/arm/quantizer/quantization_annotator.py +++ b/backends/arm/quantizer/quantization_annotator.py @@ -244,6 +244,11 @@ def _match_pattern( operator.getitem, ] +_one_to_one_shared_input_or_input_act_qspec = [ + torch.ops.aten.adaptive_avg_pool2d.default, + torch.ops.aten.alias_copy.default, +] + def get_quant_properties( # noqa: C901 node: Node, gm: torch.fx.GraphModule, quantization_config @@ -332,7 +337,7 @@ def any_or_hardtanh_min_zero(n: Node): _QuantProperty(2, shared_qspec), # type: ignore[arg-type] ] quant_properties.quant_output = _QuantProperty(0, shared_qspec) # type: ignore[arg-type] - elif node.target == torch.ops.aten.adaptive_avg_pool2d.default: + elif node.target in _one_to_one_shared_input_or_input_act_qspec: input_qspec = ( SharedQuantizationSpec(node.args[0]) # type: ignore[arg-type] if arm_quantizer_utils.is_output_annotated(node.args[0]) # type: ignore diff --git a/backends/arm/test/ops/test_alias_copy.py b/backends/arm/test/ops/test_alias_copy.py new file mode 100644 index 00000000000..66fa92bc445 --- /dev/null +++ b/backends/arm/test/ops/test_alias_copy.py @@ -0,0 +1,83 @@ +# Copyright 2025 Arm Limited and/or its affiliates. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Tuple + +import torch +from executorch.backends.arm.test import common +from executorch.backends.arm.test.tester.test_pipeline import ( + EthosU55PipelineBI, + EthosU85PipelineBI, + TosaPipelineBI, + TosaPipelineMI, +) + +input_t1 = Tuple[torch.Tensor] + + +class AliasCopy(torch.nn.Module): + """ + Tests proper handling of alias_copy when used directly. + + alias_copy can also appear from PyTorch/ExecuTorch optimizations + such as `x.transpose(0, 0)`. This is optimized to an alias_copy but + not before dq/q operators are added. + """ + + aten_op = "torch.ops.aten.alias_copy.default" + exir_op = "executorch_exir_dialects_edge__ops_aten_alias_copy_default" + + test_data: dict[input_t1] = { + "1d_ramp": (torch.arange(-16, 16, 0.2),), + "2d_ones": (torch.ones(5, 5),), + "3d_rand": (torch.rand(3, 5, 5),), + "4d_zeros": (torch.zeros(1, 10, 10, 10),), + } + + def __init__(self): + super().__init__() + + def forward(self, x: torch.Tensor): + return torch.alias_copy(x) + + +@common.parametrize("test_data", AliasCopy.test_data) +def test_alias_copy_tosa_MI(test_data: input_t1): + TosaPipelineMI[input_t1]( + AliasCopy(), + test_data, + AliasCopy.aten_op, + AliasCopy.exir_op, + ).run() + + +@common.parametrize("test_data", AliasCopy.test_data) +def test_alias_copy_tosa_BI(test_data: input_t1): + TosaPipelineBI[input_t1]( + AliasCopy(), + test_data, + AliasCopy.aten_op, + AliasCopy.exir_op, + ).run() + + +@common.parametrize("test_data", AliasCopy.test_data) +def test_alias_copy_u55_BI(test_data: input_t1): + EthosU55PipelineBI[input_t1]( + AliasCopy(), + test_data, + AliasCopy.aten_op, + AliasCopy.exir_op, + ).run() + + +@common.parametrize("test_data", AliasCopy.test_data) +def test_alias_copy_u85_BI(test_data: input_t1): + EthosU85PipelineBI[input_t1]( + AliasCopy(), + test_data, + AliasCopy.aten_op, + AliasCopy.exir_op, + ).run() From ce1c7a68e148b441214c34aa7101fe2845bea003 Mon Sep 17 00:00:00 2001 From: Jacob Szwejbka Date: Tue, 15 Apr 2025 15:51:40 -0700 Subject: [PATCH 160/244] Add error message for empty string in filedataloader. Differential Revision: D72981574 Pull Request resolved: https://github.com/pytorch/executorch/pull/10145 --- extension/data_loader/file_data_loader.cpp | 3 +++ extension/data_loader/test/file_data_loader_test.cpp | 6 ++++++ 2 files changed, 9 insertions(+) diff --git a/extension/data_loader/file_data_loader.cpp b/extension/data_loader/file_data_loader.cpp index 1a9ddad259f..503539774a5 100644 --- a/extension/data_loader/file_data_loader.cpp +++ b/extension/data_loader/file_data_loader.cpp @@ -86,6 +86,9 @@ Result FileDataLoader::from( "Alignment %zu is not a power of 2", alignment); + ET_CHECK_OR_RETURN_ERROR( + file_name != nullptr, InvalidArgument, "File name cannot be empty."); + // Use open() instead of fopen() to avoid the layer of buffering that // fopen() does. We will be reading large portions of the file in one shot, // so buffering does not help. diff --git a/extension/data_loader/test/file_data_loader_test.cpp b/extension/data_loader/test/file_data_loader_test.cpp index 1d4f4c16196..7dc872995a5 100644 --- a/extension/data_loader/test/file_data_loader_test.cpp +++ b/extension/data_loader/test/file_data_loader_test.cpp @@ -154,6 +154,12 @@ TEST_P(FileDataLoaderTest, FromMissingFileFails) { EXPECT_NE(fdl.error(), Error::Ok); } +TEST_P(FileDataLoaderTest, FromEmptyFilePathFails) { + // Nullptr should fail + Result fdl = FileDataLoader::from(nullptr); + EXPECT_NE(fdl.error(), Error::Ok); +} + TEST_P(FileDataLoaderTest, BadAlignmentFails) { // Create a temp file; contents don't matter. uint8_t data[256] = {}; From e10883d63e80e66db358284d1cbe6fc2820e30c4 Mon Sep 17 00:00:00 2001 From: Huy Do Date: Tue, 15 Apr 2025 17:32:13 -0700 Subject: [PATCH 161/244] Set the default list of models running on private devices (#10217) I missed this in https://github.com/pytorch/executorch/pull/10192 for the schedule workflow case. So, it wrongly fail back to `llama` ### Testing https://github.com/pytorch/executorch/actions/runs/14481092309. The results shows up on HUD correctly now https://hud.pytorch.org/benchmark/llms?startTime=Wed%2C%2009%20Apr%202025%2000%3A31%3A41%20GMT&stopTime=Wed%2C%2016%20Apr%202025%2000%3A31%3A41%20GMT&granularity=day&lBranch=update-default-private-devices&lCommit=9fa6a49250fdbd96e8f7a5f4765bc6b32b41b6ce&rBranch=update-default-private-devices&rCommit=9fa6a49250fdbd96e8f7a5f4765bc6b32b41b6ce&repoName=pytorch%2Fexecutorch&benchmarkName=&modelName=All%20Models&backendName=All%20Backends&modeName=All%20Modes&dtypeName=All%20DType&deviceName=Google%20Pixel%203%20(Rooted)%20(Android%2012)&archName=All%20Platforms --- .github/workflows/android-perf-private-device-experiment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/android-perf-private-device-experiment.yml b/.github/workflows/android-perf-private-device-experiment.yml index ffbd1e064b1..0108ab119ca 100644 --- a/.github/workflows/android-perf-private-device-experiment.yml +++ b/.github/workflows/android-perf-private-device-experiment.yml @@ -57,6 +57,6 @@ jobs: id-token: write contents: read with: - models: ${{ inputs.models }} + models: ${{ inputs.models || 'mv3,meta-llama/Llama-3.2-1B-Instruct-SpinQuant_INT4_EO8,meta-llama/Llama-3.2-1B-Instruct-QLORA_INT4_EO8' }} devices: google_pixel_3_private_rooted benchmark_configs: ${{ inputs.benchmark_configs }} From 12ac9d16d72cbe9e48b684cc5a58a07647341c57 Mon Sep 17 00:00:00 2001 From: Manuel Candales <42380156+manuelcandales@users.noreply.github.com> Date: Tue, 15 Apr 2025 20:38:59 -0400 Subject: [PATCH 162/244] Complex Support: bmm Differential Revision: D72986238 Pull Request resolved: https://github.com/pytorch/executorch/pull/10197 --- kernels/optimized/cpu/op_bmm.cpp | 37 +++++----- kernels/optimized/cpu/targets.bzl | 1 + kernels/portable/cpu/op_bmm.cpp | 30 +++------ kernels/portable/cpu/util/matmul_ops_util.h | 31 +++++++++ kernels/test/op_bmm_test.cpp | 67 ++++++++++++++++++- .../kernels/portable/op_registration_util.bzl | 1 - 6 files changed, 127 insertions(+), 40 deletions(-) diff --git a/kernels/optimized/cpu/op_bmm.cpp b/kernels/optimized/cpu/op_bmm.cpp index 5e7fa1dd839..11697f9b0de 100644 --- a/kernels/optimized/cpu/op_bmm.cpp +++ b/kernels/optimized/cpu/op_bmm.cpp @@ -6,9 +6,9 @@ * LICENSE file in the root directory of this source tree. */ -#include - #include +#include +#include // Performs a batch matrix-matrix product of matrices stored in input and mat2. @@ -136,33 +136,32 @@ Error resize_out_tensor(const Tensor& self, const Tensor& mat2, Tensor& out) { // bmm.out(Tensor self, Tensor mat2, *, Tensor(a!) out) -> Tensor(a!) Tensor& opt_bmm_out( - KernelRuntimeContext& context, + KernelRuntimeContext& ctx, const Tensor& self, const Tensor& mat2, Tensor& out) { - (void)context; + (void)ctx; ET_KERNEL_CHECK( - context, + ctx, resize_out_tensor(self, mat2, out) == Error::Ok, InvalidArgument, out); ET_KERNEL_CHECK( - context, check_bmm_out_args(self, mat2, out), InvalidArgument, out); - -#define BMM_TENSOR(ctype, dtype) \ - case ScalarType::dtype: \ - bmm_kernel(self, mat2, out); \ - break; - - auto scalar_type = self.scalar_type(); - switch (scalar_type) { - ET_FORALL_REAL_TYPES_AND(Half, BMM_TENSOR) - default: - ET_CHECK_MSG( - false, "Unhandled dtype %" PRId8, static_cast(scalar_type)); + ctx, check_bmm_out_args(self, mat2, out), InvalidArgument, out); + + constexpr auto name = "bmm.out"; + auto self_type = self.scalar_type(); + + if (executorch::runtime::isComplexType(self_type)) { + ET_SWITCH_COMPLEXH_TYPES(self_type, ctx, name, CTYPE, [&]() { + internal::bmm_out_impl(self, mat2, out); + }); + } else { + ET_SWITCH_REALH_TYPES(self_type, ctx, name, CTYPE, [&]() { + bmm_kernel(self, mat2, out); + }); } -#undef BMM_TENSOR return out; } diff --git a/kernels/optimized/cpu/targets.bzl b/kernels/optimized/cpu/targets.bzl index c9da2584f08..bf24e4de49c 100644 --- a/kernels/optimized/cpu/targets.bzl +++ b/kernels/optimized/cpu/targets.bzl @@ -15,6 +15,7 @@ _OPTIMIZED_ATEN_OPS = ( name = "op_bmm", deps = [ "//executorch/kernels/optimized:libblas", + "//executorch/kernels/portable/cpu/util:matmul_ops_util", ], ), op_target( diff --git a/kernels/portable/cpu/op_bmm.cpp b/kernels/portable/cpu/op_bmm.cpp index b9f9d4f2c94..a887cd3c926 100644 --- a/kernels/portable/cpu/op_bmm.cpp +++ b/kernels/portable/cpu/op_bmm.cpp @@ -7,7 +7,6 @@ */ #include -#include #include namespace torch { @@ -37,26 +36,19 @@ Tensor& bmm_out( InvalidArgument, out); - ET_SWITCH_REAL_TYPES_AND( - Half, in.scalar_type(), ctx, "bmm.out", CTYPE, [&]() { - const CTYPE* in_data = in.const_data_ptr(); - const CTYPE* mat2_data = mat2.const_data_ptr(); - CTYPE* out_data = out.mutable_data_ptr(); + constexpr auto name = "bmm.out"; - int64_t batch_size = in.size(0); - int64_t m = in.size(1); - int64_t n = in.size(2); - int64_t p = mat2.size(2); + auto in_type = in.scalar_type(); - for (int i = 0; i < batch_size; ++i) { - const CTYPE* in_data_offset = in_data + i * m * n; - const CTYPE* mat2_data_offset = mat2_data + i * n * p; - CTYPE* out_data_offset = out_data + i * m * p; - - vec_matmul( - out_data_offset, in_data_offset, mat2_data_offset, m, n, p); - } - }); + if (executorch::runtime::isComplexType(in_type)) { + ET_SWITCH_COMPLEXH_TYPES(in_type, ctx, name, CTYPE, [&]() { + internal::bmm_out_impl(in, mat2, out); + }); + } else { + ET_SWITCH_REALH_TYPES(in_type, ctx, name, CTYPE, [&]() { + internal::bmm_out_impl(in, mat2, out); + }); + } return out; } diff --git a/kernels/portable/cpu/util/matmul_ops_util.h b/kernels/portable/cpu/util/matmul_ops_util.h index d2991868e95..2d2799eaa59 100644 --- a/kernels/portable/cpu/util/matmul_ops_util.h +++ b/kernels/portable/cpu/util/matmul_ops_util.h @@ -45,5 +45,36 @@ void get_linear_out_target_size( Tensor::SizesType* out_sizes, size_t* out_ndim); +namespace internal { + +template +void bmm_out_impl(const Tensor& in, const Tensor& mat2, Tensor& out) { + const CTYPE* in_data = in.const_data_ptr(); + const CTYPE* mat2_data = mat2.const_data_ptr(); + CTYPE* out_data = out.mutable_data_ptr(); + + int64_t batch_size = in.size(0); + int64_t m = in.size(1); + int64_t n = in.size(2); + int64_t p = mat2.size(2); + + for (int b = 0; b < batch_size; ++b) { + const CTYPE* in_data_offset = in_data + b * m * n; + const CTYPE* mat2_data_offset = mat2_data + b * n * p; + CTYPE* out_data_offset = out_data + b * m * p; + + for (const auto i : c10::irange(m)) { + for (const auto j : c10::irange(p)) { + CTYPE sum = static_cast(0.0); + for (const auto k : c10::irange(n)) { + sum += in_data_offset[i * n + k] * mat2_data_offset[k * p + j]; + } + out_data_offset[i * p + j] = sum; + } + } + } +} + +} // namespace internal } // namespace executor } // namespace torch diff --git a/kernels/test/op_bmm_test.cpp b/kernels/test/op_bmm_test.cpp index 88671467f46..70a5f37946d 100644 --- a/kernels/test/op_bmm_test.cpp +++ b/kernels/test/op_bmm_test.cpp @@ -43,6 +43,61 @@ class OpBmmOutTest : public OperatorTest { EXPECT_TENSOR_EQ(out, expected); } + + template + void test_complex_dtype() { + TensorFactory tf; + Tensor x = tf.make( + {2, 2, 3}, + {CTYPE(1, 1), + CTYPE(2, 2), + CTYPE(3, 3), + CTYPE(4, 4), + CTYPE(5, 5), + CTYPE(6, 6), + CTYPE(7, 7), + CTYPE(8, 8), + CTYPE(9, 9), + CTYPE(10, 10), + CTYPE(11, 11), + CTYPE(12, 12)}); + Tensor y = tf.make( + {2, 3, 2}, + {CTYPE(2, 1), + CTYPE(4, 2), + CTYPE(6, 3), + CTYPE(8, 4), + CTYPE(10, 5), + CTYPE(12, 6), + CTYPE(14, 7), + CTYPE(16, 8), + CTYPE(18, 9), + CTYPE(20, 10), + CTYPE(22, 11), + CTYPE(24, 12)}); + Tensor out = tf.make( + {2, 2, 2}, + {CTYPE(0, 0), + CTYPE(0, 0), + CTYPE(0, 0), + CTYPE(0, 0), + CTYPE(0, 0), + CTYPE(0, 0), + CTYPE(0, 0), + CTYPE(0, 0)}); + Tensor expected = tf.make( + {2, 2, 2}, + {CTYPE(22, 66), + CTYPE(28, 84), + CTYPE(49, 147), + CTYPE(64, 192), + CTYPE(220, 660), + CTYPE(244, 732), + CTYPE(301, 903), + CTYPE(334, 1002)}); + op_bmm_out(x, y, out); + EXPECT_TENSOR_CLOSE(out, expected); + } }; TEST_F(OpBmmOutTest, OutputDim) { @@ -132,7 +187,7 @@ TEST_F(OpBmmOutTest, OutputDimFloat) { /// A generic smoke test that works for any dtype that supports ones() and /// zeros(). -TEST_F(OpBmmOutTest, AllDtypesSupported) { +TEST_F(OpBmmOutTest, AllRealDtypesSupported) { #define TEST_ENTRY(ctype, dtype) test_dtype(); ET_FORALL_REAL_TYPES(TEST_ENTRY); #undef TEST_ENTRY @@ -141,6 +196,16 @@ TEST_F(OpBmmOutTest, AllDtypesSupported) { // for those types. } +TEST_F(OpBmmOutTest, AllComplexDtypesSupported) { +#define TEST_ENTRY(ctype, dtype) test_complex_dtype(); + if (torch::executor::testing::SupportedFeatures::get()->is_aten) { + ET_FORALL_COMPLEX_TYPES(TEST_ENTRY); + } else { + ET_FORALL_COMPLEXH_TYPES(TEST_ENTRY); + } +#undef TEST_ENTRY +} + TEST_F(OpBmmOutTest, EmptyInputWithEmptyOutTensorPasses) { TensorFactory tf; diff --git a/shim_et/xplat/executorch/kernels/portable/op_registration_util.bzl b/shim_et/xplat/executorch/kernels/portable/op_registration_util.bzl index a1ffdc1eed3..06f9a452935 100644 --- a/shim_et/xplat/executorch/kernels/portable/op_registration_util.bzl +++ b/shim_et/xplat/executorch/kernels/portable/op_registration_util.bzl @@ -372,7 +372,6 @@ ATEN_OPS = ( name = "op_bmm", deps = [ "//executorch/kernels/portable/cpu/util:matmul_ops_util", - ":vec_ops", ], ), op_target( From 06b3feb73b2fd342f364e9a06da8ee66204ba6f0 Mon Sep 17 00:00:00 2001 From: pytorchbot Date: Wed, 16 Apr 2025 03:49:41 -0400 Subject: [PATCH 163/244] [0.6 documentation] Fix Page Developer Tools: Bundled Program (#10222) --- docs/source/bundled-io.md | 111 +++++++------------------------------- 1 file changed, 18 insertions(+), 93 deletions(-) diff --git a/docs/source/bundled-io.md b/docs/source/bundled-io.md index 08961db7769..b2e928877dc 100644 --- a/docs/source/bundled-io.md +++ b/docs/source/bundled-io.md @@ -17,7 +17,7 @@ This stage mainly focuses on the creation of a `BundledProgram` and dumping it o ### Step 1: Create a Model and Emit its ExecuTorch Program. -ExecuTorch Program can be emitted from user's model by using ExecuTorch APIs. Follow the [Generate Sample ExecuTorch program](./getting-started-setup.md) or [Exporting to ExecuTorch tutorial](./tutorials/export-to-executorch-tutorial). +ExecuTorch Program can be emitted from user's model by using ExecuTorch APIs. Follow the [Generate and emit sample ExecuTorch program](./getting-started.md#exporting) or [Exporting to ExecuTorch tutorial](./tutorials/export-to-executorch-tutorial). ### Step 2: Construct `List[MethodTestSuite]` to hold test info @@ -89,7 +89,7 @@ Here is a flow highlighting how to generate a `BundledProgram` given a PyTorch m ```python import torch -from executorch.exir import to_edge +from executorch.exir import to_edge_transform_and_lower from executorch.devtools import BundledProgram from executorch.devtools.bundled_program.config import MethodTestCase, MethodTestSuite @@ -105,8 +105,8 @@ class SampleModel(torch.nn.Module): def __init__(self) -> None: super().__init__() - self.a: torch.Tensor = 3 * torch.ones(2, 2, dtype=torch.int32) - self.b: torch.Tensor = 2 * torch.ones(2, 2, dtype=torch.int32) + self.register_buffer('a', 3 * torch.ones(2, 2, dtype=torch.int32)) + self.register_buffer('b', 2 * torch.ones(2, 2, dtype=torch.int32)) def forward(self, x: torch.Tensor, q: torch.Tensor) -> torch.Tensor: z = x.clone() @@ -136,7 +136,7 @@ method_graph = export( # Emit the traced method into ET Program. -et_program = to_edge(method_graph).to_executorch() +et_program = to_edge_transform_and_lower(method_graph).to_executorch() # Step 2: Construct MethodTestSuite for Each Method @@ -180,7 +180,6 @@ serialized_bundled_program = serialize_from_bundled_program_to_flatbuffer( save_path = "bundled_program.bpte" with open(save_path, "wb") as f: f.write(serialized_bundled_program) - ``` We can also regenerate `BundledProgram` from flatbuffer file if needed: @@ -199,103 +198,29 @@ This stage mainly focuses on executing the model with the bundled inputs and and ### Get ExecuTorch Program Pointer from `BundledProgram` Buffer -We need the pointer to ExecuTorch program to do the execution. To unify the process of loading and executing `BundledProgram` and Program flatbuffer, we create an API: - -:::{dropdown} `get_program_data` - -```{eval-rst} -.. doxygenfunction:: ::executorch::bundled_program::get_program_data -``` -::: - -Here's an example of how to use the `get_program_data` API: -```c++ -// Assume that the user has read the contents of the file into file_data using -// whatever method works best for their application. The file could contain -// either BundledProgram data or Program data. -void* file_data = ...; -size_t file_data_len = ...; - -// If file_data contains a BundledProgram, get_program_data() will return a -// pointer to the Program data embedded inside it. Otherwise it will return -// file_data, which already pointed to Program data. -const void* program_ptr; -size_t program_len; -status = executorch::bundled_program::get_program_data( - file_data, file_data_len, &program_ptr, &program_len); -ET_CHECK_MSG( - status == Error::Ok, - "get_program_data() failed with status 0x%" PRIx32, - status); -``` +We need the pointer to ExecuTorch program to do the execution. To unify the process of loading and executing `BundledProgram` and Program flatbuffer, we create an API for this +`executorch::bundled_program::get_program_data`. Check out an [example usage](https://github.com/pytorch/executorch/blob/release/0.6/examples/devtools/example_runner/example_runner.cpp#L128-L137) of this API. ### Load Bundled Input to Method -To execute the program on the bundled input, we need to load the bundled input into the method. Here we provided an API called `executorch::bundled_program::load_bundled_input`: - -:::{dropdown} `load_bundled_input` - -```{eval-rst} -.. doxygenfunction:: ::executorch::bundled_program::load_bundled_input -``` -::: +To execute the program on the bundled input, we need to load the bundled input into the method. Here we provided an API called `executorch::bundled_program::load_bundled_input`. Check out an [example usage](https://github.com/pytorch/executorch/blob/release/0.6/examples/devtools/example_runner/example_runner.cpp#L253-L259) of this API. ### Verify the Method's Output. -We call `executorch::bundled_program::verify_method_outputs` to verify the method's output with bundled expected outputs. Here's the details of this API: - -:::{dropdown} `verify_method_outputs` - -```{eval-rst} -.. doxygenfunction:: ::executorch::bundled_program::verify_method_outputs -``` -::: - +We call `executorch::bundled_program::verify_method_outputs` to verify the method's output with bundled expected outputs. Check out an [example usage](https://github.com/pytorch/executorch/blob/release/0.6/examples/devtools/example_runner/example_runner.cpp#L300-L311) of this API. ### Runtime Example -Here we provide an example about how to run the bundled program step by step. Most of the code is borrowed from [executor_runner](https://github.com/pytorch/executorch/blob/main/examples/devtools/example_runner/example_runner.cpp), and please review that file if you need more info and context: - -```c++ -// method_name is the name for the method we want to test -// memory_manager is the executor::MemoryManager variable for executor memory allocation. -// program is the ExecuTorch program. -Result method = program->load_method(method_name, &memory_manager); - -ET_CHECK_MSG( - method.ok(), - "load_method() failed with status 0x%" PRIx32, - method.error()); - -// Load testset_idx-th input in the buffer to plan -status = executorch::bundled_program::load_bundled_input( - *method, - program_data.bundled_program_data(), - FLAGS_testset_idx); -ET_CHECK_MSG( - status == Error::Ok, - "load_bundled_input failed with status 0x%" PRIx32, - status); - -// Execute the plan -status = method->execute(); -ET_CHECK_MSG( - status == Error::Ok, - "method->execute() failed with status 0x%" PRIx32, - status); - -// Verify the result. -status = executorch::bundled_program::verify_method_outputs( - *method, - program_data.bundled_program_data(), - FLAGS_testset_idx, - FLAGS_rtol, - FLAGS_atol); -ET_CHECK_MSG( - status == Error::Ok, - "Bundle verification failed with status 0x%" PRIx32, - status); +Please checkout our [example runner](https://github.com/pytorch/executorch/blob/release/0.6/examples/devtools/README.md#bundledprogram) for a bundled program. You could run these commands to test with the BundledProgram binary (`.bpte`) file you generated in the previous step: +```bash +cd executorch + ./examples/devtools/build_example_runner.sh + ./cmake-out/examples/devtools/example_runner --bundled_program_path {your-bpte-file} --output_verification ``` +It is expected to see no output from running the above mentioned snippet. + +For a detailed example of how the runner should be like, please refer to our [example runner](https://github.com/pytorch/executorch/blob/release/0.6/examples/devtools/example_runner/example_runner.cpp). + ## Common Errors Errors will be raised if `List[MethodTestSuites]` doesn't match the `Program`. Here're two common situations: From 94ccfadaa948e94aa998f578db5edc4a2b52762a Mon Sep 17 00:00:00 2001 From: JP <46308822+zonglinpeng@users.noreply.github.com> Date: Wed, 16 Apr 2025 06:44:37 -0700 Subject: [PATCH 164/244] add BoxWithNMSLimit_out to DSP as a custom portable op Differential Revision: D72275947 Pull Request resolved: https://github.com/pytorch/executorch/pull/10157 --- runtime/core/portable_type/c10/c10/targets.bzl | 1 + 1 file changed, 1 insertion(+) diff --git a/runtime/core/portable_type/c10/c10/targets.bzl b/runtime/core/portable_type/c10/c10/targets.bzl index e9728745270..4555d42a567 100644 --- a/runtime/core/portable_type/c10/c10/targets.bzl +++ b/runtime/core/portable_type/c10/c10/targets.bzl @@ -39,6 +39,7 @@ def define_common_targets(): ]), visibility = [ "//executorch/...", + "@EXECUTORCH_CLIENTS", ], deps = select({ "DEFAULT": [], From 7120bc6b207758533b92b124948aed5aa1c7fea6 Mon Sep 17 00:00:00 2001 From: Max Ren <40742183+mcr229@users.noreply.github.com> Date: Wed, 16 Apr 2025 09:11:54 -0700 Subject: [PATCH 165/244] [ET][Testing] Build test_backend_compiler_lib when testing is on (#9953) Adding test_backend_compiler_lib to pybinds when tests are on. This allows us to run some more backend tests in the OSS build. There were a couple of other test files that weren't able to be enabled: ``` --ignore=exir/backend/test/test_backends.py --ignore=exir/backend/test/test_backends_lifted.py --ignore=exir/backend/test/test_partitioner.py ``` This was because they were doing some torch.ops.load_library("buck_path") or there was another backend ExecutorBackend that was required. I'll limit the changes of this PR to just be enablement of the BackendWithCompilerLibDemo backend. --- CMakeLists.txt | 4 ++++ .../inspector/tests/inspector_utils_test.py | 2 +- .../test/test_lowered_backend_module.py | 14 ++----------- pytest.ini | 2 -- runtime/executor/test/CMakeLists.txt | 20 +++++++++++++++++++ setup.py | 1 + tools/cmake/cmake_deps.toml | 14 +++++++++++++ 7 files changed, 42 insertions(+), 15 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 6dbb66afdaa..1ac7de469b8 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -810,6 +810,10 @@ if(EXECUTORCH_BUILD_PYBIND) torch ) + if(EXECUTORCH_BUILD_TESTS) + list(APPEND _dep_libs test_backend_compiler_lib) + endif() + if(EXECUTORCH_BUILD_KERNELS_OPTIMIZED) list(APPEND _dep_libs optimized_native_cpu_ops_lib) else() diff --git a/devtools/inspector/tests/inspector_utils_test.py b/devtools/inspector/tests/inspector_utils_test.py index 5e224415bb6..ee571e365fe 100644 --- a/devtools/inspector/tests/inspector_utils_test.py +++ b/devtools/inspector/tests/inspector_utils_test.py @@ -205,7 +205,7 @@ def test_compare_results(self): self.assertAlmostEqual(calculate_cosine_similarity([a], [b])[0], 1.0) def test_compare_results_uint8(self): - a = torch.randint(0, 255, (4, 4), dtype=torch.uint8) + a = torch.randint(1, 255, (4, 4), dtype=torch.uint8) # Create tensor b which has very close value to tensor a b = a.clone() diff --git a/exir/backend/test/test_lowered_backend_module.py b/exir/backend/test/test_lowered_backend_module.py index dcc5841bc3e..6cdaf92b3d2 100644 --- a/exir/backend/test/test_lowered_backend_module.py +++ b/exir/backend/test/test_lowered_backend_module.py @@ -22,7 +22,6 @@ from executorch.extension.pybindings.portable_lib import ( # @manual _load_for_executorch_from_buffer, ) -from hypothesis import given, settings, strategies as st from torch.export import export @@ -65,7 +64,6 @@ def forward(self, *args): .executorch_program ) - @settings(deadline=500000) def test_emit_lowered_backend_module_end_to_end(self): class SinModule(torch.nn.Module): def __init__(self): @@ -109,11 +107,7 @@ def forward(self, x): torch.allclose(model_outputs[0], expected_res, atol=1e-03, rtol=1e-03) ) - @given( - unlift=st.booleans(), # verify both lifted and unlifted graph - ) - @settings(deadline=500000) - def test_emit_lowered_backend_module(self, unlift): + def test_emit_lowered_backend_module(self): module_list = [ models.Emformer(), models.Repeat(), @@ -166,11 +160,7 @@ def test_emit_lowered_backend_module(self, unlift): _ = lowered_model.buffer() self.validate_lowered_module_program(program) - @given( - unlift=st.booleans(), # verify both lifted and unlifted graph - ) - @settings(deadline=500000) - def test_emit_nested_lowered_backend_module(self, unlift): + def test_emit_nested_lowered_backend_module(self): module_list = [ models.Emformer(), models.Repeat(), diff --git a/pytest.ini b/pytest.ini index cd647c43a1c..8c661aa9ee4 100644 --- a/pytest.ini +++ b/pytest.ini @@ -63,8 +63,6 @@ addopts = --ignore=exir/backend/test/demos --ignore=exir/backend/test/test_backends.py --ignore=exir/backend/test/test_backends_lifted.py - --ignore=exir/backend/test/test_compatibility.py - --ignore=exir/backend/test/test_lowered_backend_module.py --ignore=exir/backend/test/test_partitioner.py --ignore=exir/tests/test_common.py --ignore=exir/tests/test_memory_format_ops_pass_aten.py diff --git a/runtime/executor/test/CMakeLists.txt b/runtime/executor/test/CMakeLists.txt index 2de32c9176a..3003a5d2c74 100644 --- a/runtime/executor/test/CMakeLists.txt +++ b/runtime/executor/test/CMakeLists.txt @@ -152,3 +152,23 @@ target_include_directories( PRIVATE "${CMAKE_INSTALL_PREFIX}/schema/include" "${EXECUTORCH_ROOT}/third-party/flatbuffers/include" ) + +list(TRANSFORM _test_backend_compiler_lib__srcs PREPEND "${EXECUTORCH_ROOT}/") +add_library( + test_backend_compiler_lib + STATIC + ${_test_backend_compiler_lib__srcs} +) + +target_link_libraries( + test_backend_compiler_lib + PUBLIC + executorch_core +) + +target_link_options_shared_lib(test_backend_compiler_lib) + +install( + TARGETS test_backend_compiler_lib + DESTINATION lib +) diff --git a/setup.py b/setup.py index 44fb9a712a3..e29ce76ff7e 100644 --- a/setup.py +++ b/setup.py @@ -718,6 +718,7 @@ def run(self): # enabled. TODO(dbort): Remove this override once this option is # managed by cmake itself. "-DEXECUTORCH_SEPARATE_FLATCC_HOST_PROJECT=OFF", + "-DEXECUTORCH_BUILD_TESTS=ON", ] build_args = [f"-j{self.parallel}"] diff --git a/tools/cmake/cmake_deps.toml b/tools/cmake/cmake_deps.toml index ee810c2bfd5..9913a02c4d5 100644 --- a/tools/cmake/cmake_deps.toml +++ b/tools/cmake/cmake_deps.toml @@ -150,6 +150,20 @@ deps = [ "optimized_cpublas", "portable_kernels", ] + +[targets.test_backend_compiler_lib] +buck_targets = [ + "//runtime/executor/test:test_backend_compiler_lib", +] +filters = [ + ".cpp$", +] +excludes = [ +] +deps = [ + "executorch", + "executorch_core", +] # ---------------------------------- core end ---------------------------------- # ---------------------------------- extension start ---------------------------------- [targets.extension_data_loader] From a5a924700aabe411271bba7ca3446aff2c0592c3 Mon Sep 17 00:00:00 2001 From: Max Ren <40742183+mcr229@users.noreply.github.com> Date: Wed, 16 Apr 2025 09:12:11 -0700 Subject: [PATCH 166/244] [Executorch][to_backend] Introduce preprocess_multimethod (#9823) ### summary We introduce a new preprocess_multimethod. This takes the place of preprocess all in the following discussion: https://github.com/pytorch/executorch/discussions/8230 This is a class method so that we can allow the default implementation to leverage the static preprocess method. The default implementation just applies preprocess on all the exported programs given. ### Testing Testing done in follow up PR https://github.com/pytorch/executorch/pull/9824 --- exir/backend/backend_api.py | 451 +++++++++++++-- exir/backend/backend_details.py | 72 ++- exir/backend/test/TARGETS | 52 ++ .../test/backend_with_preprocess_all_demo.py | 266 +++++++++ .../test/test_to_backend_multi_method.py | 524 ++++++++++++++++++ exir/lowered_backend_module.py | 31 +- 6 files changed, 1320 insertions(+), 76 deletions(-) create mode 100644 exir/backend/test/backend_with_preprocess_all_demo.py create mode 100644 exir/backend/test/test_to_backend_multi_method.py diff --git a/exir/backend/backend_api.py b/exir/backend/backend_api.py index ab2e66f7885..310e5ea9379 100644 --- a/exir/backend/backend_api.py +++ b/exir/backend/backend_api.py @@ -8,8 +8,9 @@ import copy import logging from contextlib import contextmanager, nullcontext +from dataclasses import dataclass from functools import singledispatch -from typing import Generator, List +from typing import Dict, Generator, List, Mapping import torch @@ -36,7 +37,7 @@ update_to_real_program, ) from torch._export.utils import is_buffer, is_lifted_tensor_constant, is_param -from torch.export import ExportedProgram +from torch.export.exported_program import ExportedProgram, InputSpec, OutputSpec @singledispatch @@ -190,6 +191,65 @@ def _get_node_list_with_same_tag( return node_list +def _insert_lowered_submodule( + submodule_program: ExportedProgram, + owning_program: ExportedProgram, + call_submodule_node: torch.fx.Node, + submodule_output_node: torch.fx.Node, + lowered_module: LoweredBackendModule, + is_submodule: bool, + toplevel_input_specs_to_delete: Dict[str, InputSpec], + toplevel_output_specs_to_delete: Dict[str, OutputSpec], +): + owning_graph_module = call_submodule_node.graph.owning_module + # call delegate args should only use user_inputs + call_delegate_args = [] + # Preserve input order as user_inputs + for inp_name in submodule_program.graph_signature.user_inputs: + for inp_node in call_submodule_node.all_input_nodes: + if inp_node.name == inp_name: + call_delegate_args.append(inp_node) + break + + def generate_debug_handle(ep: ExportedProgram) -> int: + """ + Generate a debug handle for the given ExportedProgram. + """ + debug_handle = 0 + for node in ep.graph_module.graph.nodes: + debug_handle = max(debug_handle, node.meta.get("debug_handle", 0)) + return debug_handle + 1 + + # Replace the partitioned submodule with a lowered submodule + # Add call_method node with function "forward" + with owning_graph_module.graph.inserting_before(call_submodule_node): + lowered_name = get_lowered_module_name(owning_graph_module, lowered_module) + lowered_node = owning_graph_module.graph.get_attr(lowered_name) + call_delegate_node = owning_graph_module.graph.call_function( + executorch_call_delegate, + (lowered_node,) + tuple(call_delegate_args), + call_submodule_node.kwargs, + ) + call_delegate_node.meta["debug_handle"] = generate_debug_handle(owning_program) + call_delegate_node.meta["val"] = submodule_output_node.meta["val"] + call_submodule_node.replace_all_uses_with(call_delegate_node) + owning_graph_module.graph.erase_node(call_submodule_node) + + if is_submodule: + assert len(toplevel_input_specs_to_delete) == 0 + assert len(toplevel_output_specs_to_delete) == 0 + elif ( + len(toplevel_input_specs_to_delete) > 0 + or len(toplevel_output_specs_to_delete) > 0 + ): + _unsafe_adjust_original_program( + owning_program, + call_delegate_node, + toplevel_input_specs_to_delete, + toplevel_output_specs_to_delete, + ) + + def _partition_and_lower_one_graph_module( tagged_graph_module: torch.fx.GraphModule, partition_result: PartitionResult, @@ -254,56 +314,16 @@ def _partition_and_lower_one_graph_module( delegation_spec.compile_specs, ) - # call delegate args should only use user_inputs - call_delegate_args = [] - # Preserve input order as user_inputs - for inp_name in submodule_program.graph_signature.user_inputs: - for inp_node in call_module_node.all_input_nodes: - if inp_node.name == inp_name: - call_delegate_args.append(inp_node) - break - - def generate_debug_handle(ep: ExportedProgram) -> int: - """ - Generate a debug handle for the given ExportedProgram. - """ - debug_handle = 0 - for node in ep.graph_module.graph.nodes: - debug_handle = max(debug_handle, node.meta.get("debug_handle", 0)) - return debug_handle + 1 - - # Replace the partitioned submodule with a lowered submodule - # Add call_method node with function "forward" - with tagged_graph_module.graph.inserting_before(call_module_node): - lowered_name = get_lowered_module_name( - tagged_graph_module, lowered_submodule - ) - lowered_node = tagged_graph_module.graph.get_attr(lowered_name) - call_delegate_node = tagged_graph_module.graph.call_function( - executorch_call_delegate, - (lowered_node,) + tuple(call_delegate_args), - call_module_node.kwargs, - ) - call_delegate_node.meta["debug_handle"] = generate_debug_handle( - owning_program - ) - call_delegate_node.meta["val"] = submodule_output_node.meta["val"] - call_module_node.replace_all_uses_with(call_delegate_node) - tagged_graph_module.graph.erase_node(call_module_node) - - if is_submodule: - assert len(toplevel_input_specs_to_delete) == 0 - assert len(toplevel_output_specs_to_delete) == 0 - elif ( - len(toplevel_input_specs_to_delete) > 0 - or len(toplevel_output_specs_to_delete) > 0 - ): - _unsafe_adjust_original_program( - owning_program, - call_delegate_node, - toplevel_input_specs_to_delete, - toplevel_output_specs_to_delete, - ) + _insert_lowered_submodule( + submodule_program, + owning_program, + call_module_node, + submodule_output_node, + lowered_submodule, + is_submodule, + toplevel_input_specs_to_delete, + toplevel_output_specs_to_delete, + ) return tagged_graph_module @@ -417,3 +437,330 @@ def to_backend( constants=tagged_exported_program.constants, verifiers=[tagged_exported_program.verifier], ) + + +def _create_partitions_in_graph_module( + tagged_graph_module: torch.fx.GraphModule, + partition_result: PartitionResult, + owning_program: ExportedProgram, + is_submodule: bool, +) -> Dict[str, List[torch.fx.Node]]: + backend_id_to_submodule_name = {} + for tag, delegation_spec in partition_result.partition_tags.items(): + # Create partition with nodes containing this tag. There should only be + # one contained submodule per tag + node_list = _get_node_list_with_same_tag( + tagged_graph_module, tag, owning_program + ) + + if len(node_list) == 0: + logging.debug(f"Did not find any nodes for tag {tag}") + continue + + logging.debug(f"For tag {tag}, found nodes {node_list}") + # Tag the nodes that are params as buffers, so we can order the submodule as (Parms + Buffers) (User Inputs) + + replace_ctx = ( + tagged_graph_module._set_replace_hook( + owning_program.graph_signature.get_replace_hook() + ) + if not is_submodule + else nullcontext() + ) + with replace_ctx: + submodule, call_module_node = create_submodule_from_nodes( + tagged_graph_module, node_list, tag + ) + + tagged_graph_module_output_node = [ + node for node in tagged_graph_module.graph.nodes if node.op == "output" + ][0] + submodule_output_node = [ + node for node in submodule.graph.nodes if node.op == "output" + ][0] + # Copy the output node meta from the original output node, because + # create_submodule_from_nodes doesn't cover the meta field + submodule_output_node.meta = tagged_graph_module_output_node.meta + logging.debug(f"Partitioned graph module: {tagged_graph_module}") + ( + submodule_program, + toplevel_input_specs_to_delete, + toplevel_output_specs_to_delete, + ) = create_exported_program_from_submodule( + submodule, + owning_program, + tag, + call_module_node, + is_submodule, + ) + call_module_node.meta["backend_id"] = delegation_spec.backend_id + call_module_node.meta["compile_spec"] = delegation_spec.compile_specs + call_module_node.meta["submodule_program"] = submodule_program + call_module_node.meta["toplevel_input_specs_to_delete"] = ( + toplevel_input_specs_to_delete + ) + call_module_node.meta["toplevel_output_specs_to_delete"] = ( + toplevel_output_specs_to_delete + ) + call_module_node.meta["is_submodule"] = is_submodule + call_module_node.meta["submodule_output_node"] = submodule_output_node + + if delegation_spec.backend_id not in backend_id_to_submodule_name: + backend_id_to_submodule_name[delegation_spec.backend_id] = [] + + # The call_module_node created here might not be the same node instance as + # the one in the final graph module. This is because this node might be replaced + # in future edits to the graph. As a result, we just keep track of the node's name + # and at the end we search for this node in our final graph module + backend_id_to_submodule_name[delegation_spec.backend_id].append( + call_module_node.target + ) + + created_submodule_nodes = {key: [] for key in backend_id_to_submodule_name.keys()} + for backend_id, submodule_name in backend_id_to_submodule_name.items(): + for node in tagged_graph_module.graph.nodes: + if node.op == "call_module" and node.target in submodule_name: + created_submodule_nodes[backend_id].append(node) + + # check the number of submodule_names and submodule_nodes are equal + for backend_id in created_submodule_nodes.keys(): + assert len(created_submodule_nodes[backend_id]) == len( + backend_id_to_submodule_name[backend_id] + ) + + return created_submodule_nodes + + +def _create_partitions( + tagged_graph_module: torch.fx.GraphModule, + partition_result: PartitionResult, + owning_program: ExportedProgram, + is_submodule: bool = False, +) -> Dict[str, List[torch.fx.Node]]: + backend_id_to_call_submodules = _create_partitions_in_graph_module( + tagged_graph_module, partition_result, owning_program, is_submodule + ) + + # Recursively partition and lower for submodules + for _, submod, _ in get_control_flow_submodules(tagged_graph_module): + nested_backend_id_to_call_submodules = _create_partitions( + submod, partition_result, owning_program, is_submodule=True + ) + for ( + backend_id, + nested_submodules, + ) in nested_backend_id_to_call_submodules.items(): + if backend_id not in backend_id_to_call_submodules: + backend_id_to_call_submodules[backend_id] = nested_submodules + else: + backend_id_to_call_submodules[backend_id].extend(nested_submodules) + + return backend_id_to_call_submodules + + +def lower_all_submodules_to_backend( + backend_id: str, + method_to_submodules_nodes: Dict[str, List[torch.fx.Node]], + method_to_tagged_edge_program: Dict[str, ExportedProgram], +) -> None: + """ + Lower all submodules nodes given in the method_to_submodule_nodes map to backend_id. + """ + # The created exported program for the submodules are in the call_module node's meta data + # We just map the method_to_submodule_nodes directly to the method_to_partitioned_exported_programs + method_to_partitioned_program = { + method_name: [node.meta["submodule_program"] for node in call_submodule_nodes] + for method_name, call_submodule_nodes in method_to_submodules_nodes.items() + } + method_to_compile_specs = { + method_name: [node.meta["compile_spec"] for node in call_submodule_nodes] + for method_name, call_submodule_nodes in method_to_submodules_nodes.items() + } + backend_found = False + for cls in BackendDetails.__subclasses__(): + if backend_id == cls.__name__: + method_to_preprocess_result: dict[str, List[PreprocessResult]] = ( + cls.preprocess_multimethod( + method_to_partitioned_program, method_to_compile_specs + ) + ) + backend_found = True + + if not backend_found: + raise NotImplementedError(f"Backend {backend_id} was not found.") + + for method_name in method_to_preprocess_result.keys(): + owning_program = method_to_tagged_edge_program[method_name] + list_of_preprocess_results = method_to_preprocess_result[method_name] + list_of_call_submodule_nodes = method_to_submodules_nodes[method_name] + list_of_compile_specs = method_to_compile_specs[method_name] + for preprocess_result, call_submodule_node, compile_spec in zip( + list_of_preprocess_results, + list_of_call_submodule_nodes, + list_of_compile_specs, + ): + submodule_program = call_submodule_node.meta["submodule_program"] + lowered_module = LoweredBackendModule( + edge_program=submodule_program, + backend_id=backend_id, + processed_bytes=preprocess_result.processed_bytes, + compile_specs=compile_spec, + named_data_store_output=preprocess_result.data_store_output, + ) + is_submodule = call_submodule_node.meta["is_submodule"] + toplevel_input_specs_to_delete = call_submodule_node.meta[ + "toplevel_input_specs_to_delete" + ] + toplevel_output_specs_to_delete = call_submodule_node.meta[ + "toplevel_output_specs_to_delete" + ] + submodule_output_node = call_submodule_node.meta["submodule_output_node"] + + _insert_lowered_submodule( + submodule_program, + owning_program, + call_submodule_node, + submodule_output_node, + lowered_module, + is_submodule, + toplevel_input_specs_to_delete, + toplevel_output_specs_to_delete, + ) + + +@dataclass +class MethodProgramsPartitionerSpec: + """ + Since single dispatch for to_backend requires the first argument to be a + valid class, we create the following dataclass spec to hold the dictionaries + mapping the method name to the corresponding program, partitioner + """ + + method_to_edge_program: Mapping[str, ExportedProgram] + method_to_partitioner: Mapping[str, Partitioner] + + +@to_backend.register +def _( + method_edge_program_partitioners: MethodProgramsPartitionerSpec, +) -> Dict[str, ExportedProgram]: + """ + Add overloaded implementations for to_backend: + + :: + + def to_backend( + method_edge_program_partitioners: MethodProgramsPartitionerSpec + ) -> Dict[str, ExportedProgram]: + + Returns a semantically-equivalent dictionary of programs to the programs given as input (represented + as a graph module in Edge dialect), but with portions of the program targeted for + delegation as determined by the partitioner. + + Args: + method_edge_program_partitioners: contains two mappings, + - method_to_edge_program: mapping of method names to their respective programs in Edge dialect. + - method_to_partitioner: mapping of method names to an instance of the partitioner, in charge with tagging + portions of the specified program for delegation. A valid partitioner must return PartitionerResult + including both tagged exported program and partitioner_tag: Dict[str, DelegationSpec], where each key is a tag name and + the nodes with same tag will be fused a one subgraph and delegated to backend specififed in delegation spec. + + + Returns: + ExportedProgram: The input program, with some portions targeted for delegation. + """ + method_to_edge_program = method_edge_program_partitioners.method_to_edge_program + method_to_partitioner = method_edge_program_partitioners.method_to_partitioner + + partitioned_and_lowered_exported_programs = {} + backend_id_to_method_submodules_map = {} + method_to_tagged_exported_program = {} + + for method_name, partitioner_instance in method_to_partitioner.items(): + assert ( + method_name in method_to_edge_program + ), f"Partitioner for method {method_name} is not provided" + edge_program = method_to_edge_program[method_name] + edge_program._validate() + + # Use fake program, with FakeTensors in the state dict, to avoid copying large constant values. + # Fall back to deepcopy if no fake mode is found. TODO(T182910699): Remove this fallback. + try: + fake_edge_program = get_fake_program(edge_program) + except Exception as e: + logging.warning( + f"Error in get_fake_program for graph {edge_program.graph_module}, fallback to deepcopy: {e}" + ) + fake_edge_program = copy.deepcopy(edge_program) + partitioner_result = partitioner_instance(fake_edge_program) + tagged_exported_program = partitioner_result.tagged_exported_program + method_to_tagged_exported_program[method_name] = tagged_exported_program + + # Check that the partitioner did not modify the original graph + if _ENABLE_VALIDATION: + assert is_identical_graph( + tagged_exported_program.graph_module, + edge_program.graph_module, + ), f"The partitioner {partitioner_instance} should not modify the graph module" + else: + logging.warning("Disabled validating the partitioner.") + + assert ( + partitioner_result.partition_tags is not None + ), f"Partitioner {partitioner_instance} needs a `partition_tags` field containing a mapping of tags to delegate spec" + + update_to_real_program(tagged_exported_program, edge_program) + + for tag, _ in partitioner_result.partition_tags.items(): + _maybe_duplicate_constant_nodes(tagged_exported_program, tag) + + backend_id_to_call_submodule_nodes = _create_partitions( + tagged_exported_program.graph_module, + partitioner_result, + tagged_exported_program, + ) + for ( + backend_id, + call_submodule_nodes, + ) in backend_id_to_call_submodule_nodes.items(): + if backend_id not in backend_id_to_method_submodules_map: + backend_id_to_method_submodules_map[backend_id] = {} + backend_id_to_method_submodules_map[backend_id][ + method_name + ] = call_submodule_nodes + + for ( + backend_id, + method_to_submodule_nodes, + ) in backend_id_to_method_submodules_map.items(): + lower_all_submodules_to_backend( + backend_id, + method_to_submodule_nodes, + method_to_tagged_exported_program, + ) + + for method_name in method_to_edge_program.keys(): + if method_name in method_to_tagged_exported_program: + tagged_exported_program = method_to_tagged_exported_program[method_name] + partitioned_and_lowered_exported_programs[method_name] = ExportedProgram( + root=tagged_exported_program.graph_module, + graph=tagged_exported_program.graph_module.graph, + graph_signature=tagged_exported_program.graph_signature, + state_dict=tagged_exported_program.state_dict, + range_constraints=copy.deepcopy( + tagged_exported_program.range_constraints + ), + module_call_graph=copy.deepcopy( + tagged_exported_program.module_call_graph + ), + example_inputs=None, + constants=tagged_exported_program.constants, + verifiers=[tagged_exported_program.verifier], + ) + else: + # this edge program wasn't partitioned, so we can just return it as is + partitioned_and_lowered_exported_programs[method_name] = ( + method_to_edge_program[method_name] + ) + + return partitioned_and_lowered_exported_programs diff --git a/exir/backend/backend_details.py b/exir/backend/backend_details.py index 248d03f2b05..513ae7c64b3 100644 --- a/exir/backend/backend_details.py +++ b/exir/backend/backend_details.py @@ -50,15 +50,6 @@ class BackendDetails(ABC): the decorators, this interface will be static, abstract and all inheritances are enforced to implement this method. - Args: - edge_program: The original exported program. It will not be modified in place. - compile_specs: List of values needed for compilation - - Returns: - PreprocessResult: It wraps the following information: - processed_bytes -> bytes: A compiled blob - a binary that can run the desired program in the backend. - debug_handle_map (Optional[Dict[int, Tuple[int]]]): For profiling purposes, a map from the node_id in the final graph (either EXIR or the user's self-defined IR) - to debug handle id attached in the original exported program. """ @staticmethod @@ -70,6 +61,69 @@ def preprocess( edge_program: ExportedProgram, compile_specs: List[CompileSpec], ) -> PreprocessResult: + """ + Preprocesses an edge program and returns the preprocess result fo the given backend + + Args: + edge_program: The original exported program. It will not be modified in place. + compile_specs: List of values needed for compilation + + Returns: + PreprocessResult: It wraps the following information: + processed_bytes -> bytes: A compiled blob - a binary that can run the desired + program in the backend. + debug_handle_map (Optional[Dict[int, Tuple[int]]]): For profiling purposes, a + map from the node_id in the final graph (either EXIR or the user's self-defined + IR) to debug handle id attached in the original exported program. + """ # Users should return a compiled blob - a binary that can run the desired # program in the backend. pass + + @classmethod + def preprocess_multimethod( + cls, + edge_programs: Dict[str, List[ExportedProgram]], + compile_specs: Dict[str, List[List[CompileSpec]]], + ) -> Dict[str, list[PreprocessResult]]: + """ + Runs preprocess on all partitioned Edge Programs across multiple methods. This allows + backends to share information across partitioned graphs. Backend can serialize shared + data by putting the shared data into the data_store_output of the preprocess results. + This will record the shared data used by that specific partition. + + Default implementation is running the existing preprocess implementation on all + + Args: + edge_programs: Dictionary mapping the method name to a list of all the partitioned + edge_programs from that method to be lowered. + compile_specs: Dictionary mapping the method name to a list of compile_specs. The + list of compile specs maps directly to the list of edge_programs for the + same given method name i.e. edge_program[method_name][i] --> compile_specs[method_name][i] + + Returns: + Dictionary mapping the method name to a list of PreprocessResults. The list of + PreprocessResults maps directly to the list of edge_programs for the same given + method name. i.e. edge_program[method_name][i] --> result[method_name][i] + + + """ + preprocess_results = {} + for method_name, programs in edge_programs.items(): + assert ( + method_name in compile_specs + ), f"Error: missing compile specs for {method_name}" + compile_specs_for_method = compile_specs[method_name] + assert len(compile_specs_for_method) == len( + programs + ), f"Error: method {method_name} has {len(programs)} partitions but only {len(compile_specs_for_method)}" + results_for_method = [] + for program, compile_spec_for_program in zip( + programs, compile_specs_for_method + ): + preprocess_result = cls.preprocess(program, compile_spec_for_program) + results_for_method.append(preprocess_result) + + preprocess_results[method_name] = results_for_method + + return preprocess_results diff --git a/exir/backend/test/TARGETS b/exir/backend/test/TARGETS index f0ba618936d..b7f46076868 100644 --- a/exir/backend/test/TARGETS +++ b/exir/backend/test/TARGETS @@ -189,6 +189,58 @@ python_unittest( ], ) +python_unittest( + name = "test_to_backend_multi_method", + srcs = [ + "test_to_backend_multi_method.py", + ], + preload_deps = [ + "//executorch/kernels/portable:custom_ops_generated_lib", + "//executorch/kernels/quantized:custom_ops_generated_lib", + "//executorch/runtime/executor/test:test_backend_compiler_lib", + ], + deps = [ + ":backend_with_preprocess_all_demo", + "//caffe2:torch", + "//caffe2/functorch:functorch_src", + "//executorch/exir:delegate", + "//executorch/exir:graph_module", + "//executorch/exir:lib", + "//executorch/exir:lowered_backend_module", + "//executorch/exir:print_program", + "//executorch/exir:schema", + "//executorch/exir/backend:backend_api", + "//executorch/exir/backend:compile_spec_schema", + "//executorch/exir/backend:partitioner", + "//executorch/exir/dialects:lib", + "//executorch/extension/pybindings:portable_lib", # @manual + "//executorch/extension/pytree:pylib", + ], +) + +python_library( + name = "backend_with_preprocess_all_demo", + srcs = [ + "backend_with_preprocess_all_demo.py" + ], + deps = [ + "//caffe2:torch", + "//caffe2/functorch:functorch_src", + "//executorch/exir:delegate", + "//executorch/exir:graph_module", + "//executorch/exir:lib", + "//executorch/exir:lowered_backend_module", + "//executorch/exir:print_program", + "//executorch/exir:schema", + "//executorch/exir/backend:backend_api", + "//executorch/exir/backend:compile_spec_schema", + "//executorch/exir/backend:partitioner", + "//executorch/exir/dialects:lib", + "//executorch/extension/pybindings:portable_lib", # @manual + "//executorch/extension/pytree:pylib", + ], +) + python_unittest( name = "test_debug_handle_map", srcs = [ diff --git a/exir/backend/test/backend_with_preprocess_all_demo.py b/exir/backend/test/backend_with_preprocess_all_demo.py new file mode 100644 index 00000000000..ae9a8174be5 --- /dev/null +++ b/exir/backend/test/backend_with_preprocess_all_demo.py @@ -0,0 +1,266 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +from typing import Dict, final, List, Tuple + +import torch + +from executorch.exir.backend.backend_details import BackendDetails, PreprocessResult +from executorch.exir.backend.canonical_partitioners.pattern_op_partitioner import ( + generate_pattern_op_partitions, +) + +from executorch.exir.backend.compile_spec_schema import CompileSpec +from executorch.exir.backend.partitioner import ( + DelegationSpec, + Partitioner, + PartitionResult, +) +from executorch.exir.dialects._ops import ops as exir_ops +from executorch.exir.graph_module import get_control_flow_submodules +from torch.export.exported_program import ExportedProgram +from torch.fx.passes.operator_support import any_chain, OperatorSupportBase + + +def _preprocess_multimethod( + edge_programs: Dict[str, List[ExportedProgram]], + compile_specs: Dict[str, List[List[CompileSpec]]], + supported_ops: List[torch._ops.OpOverload], + backend_name: str, +) -> Dict[str, List[PreprocessResult]]: + """ + Helper function to abstract out the logic to be shared between the two backends: + FirstBackendWithPreprocessAll and SecondBackendWithPreprocessAll. This will be used + in testing for a partitioner which tags different partitions for different backends + to be lowered to + """ + total_number_of_ops = 0 + for edge_program in edge_programs.values(): + for partitioned_program in edge_program: + for node in partitioned_program.graph.nodes: + if node.op == "call_function": + if node.target in supported_ops: + total_number_of_ops += 1 + all_processed_results = {key: [] for key in edge_programs.keys()} + + for method_name, partitioned_programs in edge_programs.items(): + compile_specs_for_method = compile_specs[method_name] + + assert len(compile_specs_for_method) == len(partitioned_programs) + for compile_spec_for_partition, partitioned_program in zip( + compile_specs_for_method, partitioned_programs + ): + debug_handle_map = {} + processed_bytes = f"{backend_name}#{total_number_of_ops}#" + for node in partitioned_program.graph.nodes: + if node.op == "call_function": + if node.target in supported_ops: + op_name = node.target.__name__ + processed_bytes += f"{op_name}:" + original_debug_id = node.meta["debug_handle"] + new_debug_id = original_debug_id + debug_handle_map[new_debug_id] = (original_debug_id,) + else: + raise RuntimeError( + f"{node.op} {node.target.__name__} is not supported in backend {backend_name}" + ) + + processed_bytes += "#" + for cs in compile_spec_for_partition: + processed_bytes += f"{cs.key}:{cs.value};" + + all_processed_results[method_name].append( + PreprocessResult( + processed_bytes=bytes(processed_bytes, encoding="utf8"), + debug_handle_map=debug_handle_map, + ) + ) + + return all_processed_results + + +@final +class FirstBackendWithPreprocessAll(BackendDetails): + """ + Backend used to test the preprocess_multimethod for multi methods lowering. + lowered modules are returned in the format: + FirstBackendWithPreprocessAll##::#;: + + + lowered blobs are not functional, and are purely used for testing purposes + """ + + @staticmethod + def preprocess( + edge_program: ExportedProgram, + compile_specs: List[CompileSpec], + ) -> PreprocessResult: + """ + Not used for testing + """ + return PreprocessResult( + processed_bytes=bytes(b"\x00"), + debug_handle_map={}, + ) + + @staticmethod + def preprocess_multimethod( + edge_programs: Dict[str, List[ExportedProgram]], + compile_specs: Dict[str, List[List[CompileSpec]]], + ) -> Dict[str, list[PreprocessResult]]: + """ + Preprocess all the edge programs in the given dictionary and return a dictionary + of preprocess results. The preprocess result is a tuple of processed bytes and + a map from the node name to the original debug handle. + """ + match_ops = [ + exir_ops.edge.aten.sin.default, + exir_ops.edge.aten.add.Tensor, + ] + + return _preprocess_multimethod( + edge_programs, compile_specs, match_ops, "FirstBackendWithPreprocessAll" + ) + + +@final +class SecondBackendWithPreprocessAll(BackendDetails): + """ + Backend used to test the preprocess_multimethod for multi methods lowering. + lowered modules are returned in the format: + SecondBackendWithPreprocessAll##::#;: + + + lowered blobs are not functional, and are purely used for testing purposes + """ + + @staticmethod + def preprocess( + edge_program: ExportedProgram, + compile_specs: List[CompileSpec], + ) -> PreprocessResult: + """ + Not used for testing + """ + return PreprocessResult( + processed_bytes=bytes(b"\x00"), + debug_handle_map={}, + ) + + @staticmethod + def preprocess_multimethod( + edge_programs: Dict[str, List[ExportedProgram]], + compile_specs: Dict[str, List[List[CompileSpec]]], + ) -> Dict[str, list[PreprocessResult]]: + """ + Preprocess all the edge programs in the given dictionary and return a dictionary + of preprocess results. The preprocess result is a tuple of processed bytes and + a map from the node name to the original debug handle. + """ + match_ops = [ + exir_ops.edge.aten.cos.default, + exir_ops.edge.aten.sub.Tensor, + ] + + return _preprocess_multimethod( + edge_programs, compile_specs, match_ops, "SecondBackendWithPreprocessAll" + ) + + +class AddSinOperatorSupport(OperatorSupportBase): + def is_node_supported(self, submodules, node: torch.fx.Node) -> bool: + return node.op == "call_function" and node.target in [ + exir_ops.edge.aten.add.Tensor, + exir_ops.edge.aten.sin.default, + ] + + +class SubCosOperatorSupport(OperatorSupportBase): + def is_node_supported(self, submodules, node: torch.fx.Node) -> bool: + return node.op == "call_function" and node.target in [ + exir_ops.edge.aten.sub.Tensor, + exir_ops.edge.aten.cos.default, + ] + + +@final +class BackendWithPreprocessAllPartitioner(Partitioner): + """ + Partitioner that partitions for both FirstBackendWithPreprocessAll + and SecondBackendWithPreprocessAll. + + - The partitioner tags all sin and add nodes for delegation to + FirstBackendWithPreprocessAll + - The partitioner tags all cos and sub nodes for delegation to + SecondBackendWithPreprocessAll + """ + + def __init__(self) -> None: + self.add_sin_support = any_chain(AddSinOperatorSupport()) + self.add_sin_backend_id = FirstBackendWithPreprocessAll.__name__ + + self.sub_cos_support = any_chain(SubCosOperatorSupport()) + self.sub_cos_backend_id = SecondBackendWithPreprocessAll.__name__ + + def _partition_graph_module( + self, + graph_module: torch.fx.GraphModule, + id_start=0, + ) -> Tuple[Dict[str, DelegationSpec], int]: + partition_tags: Dict[str, DelegationSpec] = {} + + num_partitions_in_gm = 0 + for op_support, backend_id, tag_prefix in [ + (self.add_sin_support, self.add_sin_backend_id, "first"), + (self.sub_cos_support, self.sub_cos_backend_id, "second"), + ]: + partition_list = generate_pattern_op_partitions( + graph_module, op_support=op_support + ) + num_partitions_in_gm = num_partitions_in_gm + len(partition_list) + for partition in partition_list: + compile_specs = [] + delegation_tag = f"{tag_prefix}_tag{id_start + partition.id}" + for node in partition.nodes: + node.meta["delegation_tag"] = delegation_tag + if ( + node.op == "call_function" + and node.target == exir_ops.edge.aten.add.Tensor + ): + compile_specs.append(CompileSpec("add", bytes(b"\x00"))) + if ( + node.op == "call_function" + and node.target == exir_ops.edge.aten.sin.default + ): + compile_specs.append(CompileSpec("sin", bytes(b"\x01"))) + if ( + node.op == "call_function" + and node.target == exir_ops.edge.aten.sub.Tensor + ): + compile_specs.append(CompileSpec("sub", bytes(b"\x02"))) + if ( + node.op == "call_function" + and node.target == exir_ops.edge.aten.cos.default + ): + compile_specs.append(CompileSpec("cos", bytes(b"\x03"))) + + delegation_spec = DelegationSpec(backend_id, compile_specs) + partition_tags[delegation_tag] = delegation_spec + + start_idx_for_submodules = num_partitions_in_gm + for _, submodule, _ in get_control_flow_submodules(graph_module): + ret_partition_tags, start_idx_for_submodules = self._partition_graph_module( + submodule, id_start=start_idx_for_submodules + ) + partition_tags.update(ret_partition_tags) + + return partition_tags, start_idx_for_submodules + + def partition(self, exported_program: ExportedProgram) -> PartitionResult: + partition_tags, _ = self._partition_graph_module(exported_program.graph_module) + return PartitionResult( + tagged_exported_program=exported_program, partition_tags=partition_tags + ) diff --git a/exir/backend/test/test_to_backend_multi_method.py b/exir/backend/test/test_to_backend_multi_method.py new file mode 100644 index 00000000000..d4f8fccb8f2 --- /dev/null +++ b/exir/backend/test/test_to_backend_multi_method.py @@ -0,0 +1,524 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +import unittest +from typing import Dict, List, Tuple + +import torch + +from executorch.exir import EdgeProgramManager, to_edge +from executorch.exir.backend.backend_api import ( + MethodProgramsPartitionerSpec, + to_backend, +) + +from executorch.exir.backend.canonical_partitioners.all_node_partitioner import ( + AllNodePartitioner, +) +from executorch.exir.backend.compile_spec_schema import CompileSpec +from executorch.exir.backend.partitioner import Partitioner +from executorch.exir.backend.test.backend_with_compiler_demo import ( + BackendWithCompilerDemo, +) + +from executorch.exir.backend.test.backend_with_preprocess_all_demo import ( + BackendWithPreprocessAllPartitioner, +) +from executorch.exir.graph_module import get_control_flow_submodules +from executorch.exir.lowered_backend_module import ( + get_lowered_submodules, + LoweredBackendModule, +) +from executorch.exir.schema import ( + BackendDelegate, + BackendDelegateDataReference, + DataLocation, + Program, +) +from executorch.extension.pybindings.portable_lib import ( # @manual + _load_for_executorch_from_buffer, +) +from torch.export.exported_program import ExportedProgram + +from torch.testing import FileCheck + + +class TestToBackendMultiMethod(unittest.TestCase): + """ + Testing suite used to test multi method to_backend lowering. The test suite uses demo backends + FirstBackendWithPreprocessAll and SecondBackendWithPreprocessAll. + - FirstBackendWithPreprocessAll: supports add + sin + - SecondBackendWithPreprocessAll: supports sub + cos + + Both backends lower exported programs into payloads in the string format: + - (backend_id)#(total_number_ops across methods)#[op_target_name;]#[compile_spec.key:compile_spec.value;] + + We leverage the above expectation to test various lowering across different modules, and ensure + that the right exported programs and compile specs are given when lowering a specifed exported program + + We leverage the demo partitioner BackendWithPreprocessAll which partitions add + sin nodes to + FirstBackendWithPreprocessAll and sub + cos nodes to SecondBackendWithPreprocessAll. This allows + us to test cases in which multiple backends are being lowered. + """ + + def _get_lowered_submodules_across_controlflow( + self, graph_module: torch.fx.GraphModule + ) -> List[Tuple[str, LoweredBackendModule, torch.fx.Node]]: + top_level_submodules = get_lowered_submodules(graph_module) + + for _, submodule, _ in get_control_flow_submodules(graph_module): + top_level_submodules.extend( + self._get_lowered_submodules_across_controlflow(submodule) + ) + + return top_level_submodules + + def check_backend_delegate( + self, + program: Program, + delegate: BackendDelegate, + expected_id: str, + expected_processed: bytes, + ) -> None: + self.assertEqual(delegate.id, expected_id) + processed: BackendDelegateDataReference = delegate.processed + self.assertEqual(processed.location, DataLocation.INLINE) + self.assertLess(processed.index, len(program.backend_delegate_data)) + self.assertEqual( + program.backend_delegate_data[processed.index].data, expected_processed + ) + + def _test( + self, test_set: Dict[str, Tuple[ExportedProgram, Partitioner, List[str]]] + ): + method_to_edge_program = { + method_name: ep for method_name, (ep, _, _) in test_set.items() + } + + method_to_partitioner = { + method_name: partitioner + for method_name, (_, partitioner, _) in test_set.items() + } + + lowered_ep_dict = to_backend( + MethodProgramsPartitionerSpec( + method_to_edge_program, + method_to_partitioner, + ) + ) + + self.assertEqual(len(lowered_ep_dict.keys()), len(test_set.keys())) + for method_name in test_set.keys(): + self.assertTrue(method_name in lowered_ep_dict.keys()) + (_, _, list_of_payload_as_string) = test_set[method_name] + lowered_ep = lowered_ep_dict[method_name] + FileCheck().check_count( + "torch.ops.higher_order.executorch_call_delegate", + len(list_of_payload_as_string), + exactly=True, + ).run(str(lowered_ep)) + lowered_submodules = self._get_lowered_submodules_across_controlflow( + lowered_ep.graph_module + ) + self.assertEqual(len(lowered_submodules), len(list_of_payload_as_string)) + + for idx, (_, lowered_backend_module, _) in enumerate(lowered_submodules): + self.assertEqual( + lowered_backend_module.processed_bytes.decode("utf-8"), + list_of_payload_as_string[idx], + ) + + def test_multi_method_to_backend_single_method(self): + class SinModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + return torch.sin(x) + + edgeir_m = to_edge(torch.export.export(SinModule(), (torch.ones(1),))) + # Payload String: + # [Number of Ops lowered across all methods/partitions]#OpTargetNames#CompileSpecs; + test_set = { + "forward": ( + edgeir_m.exported_program(), + AllNodePartitioner( + "FirstBackendWithPreprocessAll", + [CompileSpec("max_value", bytes([1]))], + ), + [ + "FirstBackendWithPreprocessAll#1#aten.sin.default:#max_value:b'\\x01';" + ], + ) + } + self._test(test_set) + + def test_multi_method_to_backend_two_methods(self): + class SinModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + return torch.sin(x) + + class AddModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + return x + x + + sin_edgeir_m = to_edge(torch.export.export(SinModule(), (torch.ones(1),))) + add_edgeir_m = to_edge(torch.export.export(AddModule(), (torch.ones(1),))) + sin_partitioner = AllNodePartitioner( + "FirstBackendWithPreprocessAll", [CompileSpec("sin", bytes([2]))] + ) + add_partitioner = AllNodePartitioner( + "FirstBackendWithPreprocessAll", [CompileSpec("add", bytes([3]))] + ) + # Payload String: + # [Number of Ops lowered across all methods/partitions]#OpTargetNames#CompileSpecs; + test_set = { + "sin": ( + sin_edgeir_m.exported_program(), + sin_partitioner, + ["FirstBackendWithPreprocessAll#2#aten.sin.default:#sin:b'\\x02';"], + ), + "add": ( + add_edgeir_m.exported_program(), + add_partitioner, + ["FirstBackendWithPreprocessAll#2#aten.add.Tensor:#add:b'\\x03';"], + ), + } + self._test(test_set) + + def test_multi_method_to_backend_two_methods_multiple_partitions(self): + class AddModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + y = x + x + y = y * y + y = y + y + return y + + class SinModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + y = torch.sin(x) + y = y * y + return torch.sin(y) + + add_edgeir_m = to_edge(torch.export.export(AddModule(), (torch.ones(1),))) + sin_edgeir_m = to_edge(torch.export.export(SinModule(), (torch.ones(1),))) + test_set = { + "add": ( + add_edgeir_m.exported_program(), + BackendWithPreprocessAllPartitioner(), + [ + "FirstBackendWithPreprocessAll#4#aten.add.Tensor:#add:b'\\x00';", + "FirstBackendWithPreprocessAll#4#aten.add.Tensor:#add:b'\\x00';", + ], + ), + "sin": ( + sin_edgeir_m.exported_program(), + BackendWithPreprocessAllPartitioner(), + [ + "FirstBackendWithPreprocessAll#4#aten.sin.default:#sin:b'\\x01';", + "FirstBackendWithPreprocessAll#4#aten.sin.default:#sin:b'\\x01';", + ], + ), + } + self._test(test_set) + + def test_multi_method_to_backend_two_methods_different_partitions(self): + class AddSinModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + y = x + x + y = y * y + y = torch.sin(y) + return y + + class SinAddModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + y = torch.sin(x) + y = y * y + return y + y + + add_sin_edgeir_m = to_edge( + torch.export.export(AddSinModule(), (torch.ones(1),)) + ) + sin_add_edgeir_m = to_edge( + torch.export.export(SinAddModule(), (torch.ones(1),)) + ) + test_set = { + "add_sin": ( + add_sin_edgeir_m.exported_program(), + BackendWithPreprocessAllPartitioner(), + [ + "FirstBackendWithPreprocessAll#4#aten.add.Tensor:#add:b'\\x00';", + "FirstBackendWithPreprocessAll#4#aten.sin.default:#sin:b'\\x01';", + ], + ), + "sin_add": ( + sin_add_edgeir_m.exported_program(), + BackendWithPreprocessAllPartitioner(), + [ + "FirstBackendWithPreprocessAll#4#aten.sin.default:#sin:b'\\x01';", + "FirstBackendWithPreprocessAll#4#aten.add.Tensor:#add:b'\\x00';", + ], + ), + } + self._test(test_set) + + def test_multi_method_to_backend_two_methods_different_backends(self): + class AddSinCosSubModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + y = x + x + y = torch.sin(y) + y = torch.cos(y) + y = y - x + return y + + class CosSubAddSinModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + y = torch.cos(x) + y = y - x + y = y + y + y = torch.sin(y) + return y + + first_second_edgeir_m = to_edge( + torch.export.export(AddSinCosSubModule(), (torch.ones(1),)) + ) + second_first_edgeir_m = to_edge( + torch.export.export(CosSubAddSinModule(), (torch.ones(1),)) + ) + test_set = { + "first_second": ( + first_second_edgeir_m.exported_program(), + BackendWithPreprocessAllPartitioner(), + [ + "FirstBackendWithPreprocessAll#4#aten.add.Tensor:aten.sin.default:#add:b'\\x00';sin:b'\\x01';", + "SecondBackendWithPreprocessAll#4#aten.cos.default:aten.sub.Tensor:#cos:b'\\x03';sub:b'\\x02';", + ], + ), + "second_first": ( + second_first_edgeir_m.exported_program(), + BackendWithPreprocessAllPartitioner(), + [ + "SecondBackendWithPreprocessAll#4#aten.cos.default:aten.sub.Tensor:#cos:b'\\x03';sub:b'\\x02';", + "FirstBackendWithPreprocessAll#4#aten.add.Tensor:aten.sin.default:#add:b'\\x00';sin:b'\\x01';", + ], + ), + } + self._test(test_set) + + def test_multi_method_to_backend_control_flow(self): + class SinCosModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def true_fn(self, x): + return torch.sin(x) + + def false_fn(self, x): + return torch.cos(x) + + def forward(self, x): + x = x + x + return torch.cond(x > 0, self.true_fn, self.false_fn, [x]) + + class SinAddModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def true_fn(self, x): + return torch.sin(x) + + def false_fn(self, x): + return x + x + + def forward(self, x): + return torch.cond(x > 0, self.true_fn, self.false_fn, [x]) + + sin_cos_edgeir_m = to_edge( + torch.export.export(SinCosModule(), (torch.ones(1),)) + ) + sin_add_edgeir_m = to_edge( + torch.export.export(SinAddModule(), (torch.ones(1),)) + ) + + test_set = { + "sin_cos": ( + sin_cos_edgeir_m.exported_program(), + BackendWithPreprocessAllPartitioner(), + [ + "FirstBackendWithPreprocessAll#4#aten.add.Tensor:#add:b'\\x00';", + # True Module Partition + "FirstBackendWithPreprocessAll#4#aten.sin.default:#sin:b'\\x01';", + # False Module Partition + "SecondBackendWithPreprocessAll#1#aten.cos.default:#cos:b'\\x03';", + ], + ), + "sin_add": ( + sin_add_edgeir_m.exported_program(), + BackendWithPreprocessAllPartitioner(), + [ + # True Module Partition + "FirstBackendWithPreprocessAll#4#aten.sin.default:#sin:b'\\x01';", + # False Module Partition + "FirstBackendWithPreprocessAll#4#aten.add.Tensor:#add:b'\\x00';", + ], + ), + } + self._test(test_set) + + def test_multi_method_to_backend_not_found(self): + class SinModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + return torch.sin(x) + + class AddModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + return x + x + + sin_edgeir_m = to_edge(torch.export.export(SinModule(), (torch.ones(1),))) + add_edgeir_m = to_edge(torch.export.export(AddModule(), (torch.ones(1),))) + sin_partitioner = AllNodePartitioner( + "Invalid", [CompileSpec("sin", bytes([2]))] + ) + add_partitioner = AllNodePartitioner( + "FirstBackendWithPreprocessAll", [CompileSpec("add", bytes([3]))] + ) + + test_set = { + "sin": ( + sin_edgeir_m.exported_program(), + sin_partitioner, + [], + ), + "add": ( + add_edgeir_m.exported_program(), + add_partitioner, + [], + ), + } + with self.assertRaisesRegex( + NotImplementedError, "Backend Invalid was not found." + ): + self._test(test_set) + + def test_multi_method_end_to_end(self): + """ + Tests multi method lowering end-to-end. Lowers the same Sin Module for two methods + "forward" and "forward_copy". Ensures that the lowered program has two delegates + but only one serialized blob. Ensures that the lowered program runs correctly. + """ + + class SinModule(torch.nn.Module): + def __init__(self): + super().__init__() + + def forward(self, x): + return torch.sin(x) + + sin_edgeir_m = to_edge(torch.export.export(SinModule(), (torch.ones(1),))) + sin_edgeir_m_copy = to_edge(torch.export.export(SinModule(), (torch.ones(1),))) + + method_edge_program = { + "forward": sin_edgeir_m.exported_program(), + "forward_copy": sin_edgeir_m_copy.exported_program(), + } + compile_specs = [CompileSpec("max_value", bytes([1]))] + + method_partitioner = { + "forward": AllNodePartitioner( + BackendWithCompilerDemo.__name__, compile_specs + ), + "forward_copy": AllNodePartitioner( + BackendWithCompilerDemo.__name__, compile_specs + ), + } + + lowered_ep_dict = to_backend( + MethodProgramsPartitionerSpec( + method_edge_program, + method_partitioner, + ) + ) + + new_edge_manager = EdgeProgramManager(lowered_ep_dict) + + exec_prog = new_edge_manager.to_executorch() + + program = exec_prog.executorch_program + # Since the preprocessed bytes are the same, there should only be on copy + self.assertEqual(len(program.backend_delegate_data), 1) + + self.check_backend_delegate( + program=program, + delegate=program.execution_plan[0].delegates[0], + expected_id=BackendWithCompilerDemo.__name__, + expected_processed=b"1version:0#op:demo::aten.sin.default, numel:1, dtype:torch.float322#", + ) + self.check_backend_delegate( + program=program, + delegate=program.execution_plan[1].delegates[0], + expected_id=BackendWithCompilerDemo.__name__, + expected_processed=b"1version:0#op:demo::aten.sin.default, numel:1, dtype:torch.float322#", + ) + + # Check that there are two methods + self.assertEqual(len(program.execution_plan), 2) + + delegate_method_1 = program.execution_plan[0].delegates + delegate_method_2 = program.execution_plan[1].delegates + + # 1 delegate blob for each method + self.assertEqual(len(delegate_method_1), 1) + self.assertEqual(len(delegate_method_2), 1) + + # Delegate Blobs reference the same underlying bytes + delegate_reference1 = delegate_method_1[0].processed + delegate_reference2 = delegate_method_2[0].processed + self.assertEqual(delegate_reference1.index, delegate_reference2.index) + + et_module = _load_for_executorch_from_buffer(exec_prog.buffer) + model_inputs = torch.ones(1) + model_outputs = et_module.run_method("forward", [model_inputs]) + self.assertEqual(model_inputs, torch.ones(1)) + model_outputs_from_copy_method = et_module.run_method( + "forward_copy", [model_inputs] + ) + self.assertEqual(model_inputs, torch.ones(1)) + self.assertEqual(model_outputs, model_outputs_from_copy_method) + self.assertTrue( + torch.allclose( + model_outputs[0], 0.8333 * torch.ones(1), atol=1e-03, rtol=1e-03 + ) + ) diff --git a/exir/lowered_backend_module.py b/exir/lowered_backend_module.py index 6bcc1b2f3d8..78b031a238e 100644 --- a/exir/lowered_backend_module.py +++ b/exir/lowered_backend_module.py @@ -766,15 +766,15 @@ def create_submodule_from_nodes( gm = insert_subgm(gm, sub_gm, orig_inputs, orig_outputs) submodule_node = None for node in gm.graph.nodes: - if node.op == "call_module": - if node.target == submodule_name: - submodule_node = node - else: - raise RuntimeError( - f"The submodule created with nodes {node_list} did not form \ - one fully contained subgraph. Check that these nodes form a \ - fully contained graph. Partitioned graph: {gm.graph}." - ) + if node.op == "call_module" and node.target == submodule_name: + submodule_node = node + + if submodule_node is None: + raise RuntimeError( + f"The submodule created with nodes {node_list} did not form \ + one fully contained subgraph. Check that these nodes form a \ + fully contained graph. Partitioned graph: {gm.graph}." + ) if len(orig_outputs) == 1 and isinstance(orig_outputs[0].meta["val"], FakeTensor): # If the original output is a single tensor, it has been @@ -809,12 +809,13 @@ def create_submodule_from_nodes( for node in gm.graph.nodes: if node.op == "call_module" and node.target == submodule_name: submodule_node = node - elif node.op == "call_module": - raise RuntimeError( - f"The submodule created with nodes {node_list} did not form \ - one fully contained subgraph. Check that these nodes form a \ - fully contained graph. Partitioned graph: {gm.graph}." - ) + + if submodule_node is None: + raise RuntimeError( + f"The submodule created with nodes {node_list} did not form \ + one fully contained subgraph. Check that these nodes form a \ + fully contained graph. Partitioned graph: {gm.graph}." + ) assert ( submodule_node is not None From 947dfab34d5fce65255eda26499515e8c73e45e7 Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Wed, 16 Apr 2025 09:31:17 -0700 Subject: [PATCH 167/244] Android MV2 E2E instrumentation test (#10219) Add the test and setup step. --- .../executorch_android/android_test_setup.sh | 44 ++++++++----- .../org/pytorch/executorch/ModuleE2ETest.java | 62 +++++++++++++++++++ scripts/run_android_emulator.sh | 3 +- 3 files changed, 94 insertions(+), 15 deletions(-) create mode 100644 extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/ModuleE2ETest.java diff --git a/extension/android/executorch_android/android_test_setup.sh b/extension/android/executorch_android/android_test_setup.sh index fff8ba33618..b89b3b4895c 100644 --- a/extension/android/executorch_android/android_test_setup.sh +++ b/extension/android/executorch_android/android_test_setup.sh @@ -13,17 +13,33 @@ fi which "${PYTHON_EXECUTABLE}" BASEDIR=$(dirname "$(realpath $0)") -cp "${BASEDIR}/../../../extension/module/test/resources/add.pte" "${BASEDIR}/src/androidTest/resources" - -pushd "${BASEDIR}/../../../" -curl -C - -Ls "https://huggingface.co/karpathy/tinyllamas/resolve/main/stories110M.pt" --output stories110M.pt -curl -C - -Ls "https://raw.githubusercontent.com/karpathy/llama2.c/master/tokenizer.model" --output tokenizer.model -# Create params.json file -touch params.json -echo '{"dim": 768, "multiple_of": 32, "n_heads": 12, "n_layers": 12, "norm_eps": 1e-05, "vocab_size": 32000}' > params.json -python -m examples.models.llama.export_llama -c stories110M.pt -p params.json -d fp16 -n stories110m_h.pte -kv -python -m pytorch_tokenizers.tools.llama2c.convert -t tokenizer.model -o tokenizer.bin - -cp stories110m_h.pte "${BASEDIR}/src/androidTest/resources/stories.pte" -cp tokenizer.bin "${BASEDIR}/src/androidTest/resources/tokenizer.bin" -popd + +prepare_add() { + cp "${BASEDIR}/../../../extension/module/test/resources/add.pte" "${BASEDIR}/src/androidTest/resources" +} + +prepare_tinyllama() { + pushd "${BASEDIR}/../../../" + curl -C - -Ls "https://huggingface.co/karpathy/tinyllamas/resolve/main/stories15M.pt" --output stories15M.pt + curl -C - -Ls "https://raw.githubusercontent.com/karpathy/llama2.c/master/tokenizer.model" --output tokenizer.model + # Create params.json file + touch params.json + echo '{"dim": 288, "multiple_of": 32, "n_heads": 6, "n_layers": 6, "norm_eps": 1e-05, "vocab_size": 32000}' > params.json + python -m examples.models.llama.export_llama -c stories15M.pt -p params.json -d fp16 -n stories15m_h.pte -kv + python -m pytorch_tokenizers.tools.llama2c.convert -t tokenizer.model -o tokenizer.bin + + cp stories15m_h.pte "${BASEDIR}/src/androidTest/resources/stories.pte" + cp tokenizer.bin "${BASEDIR}/src/androidTest/resources/tokenizer.bin" + popd +} + +prepare_vision() { + pushd "${BASEDIR}/../../../" + python3 -m examples.xnnpack.aot_compiler --model_name "mv2" --delegate + cp mv2*.pte "${BASEDIR}/src/androidTest/resources/" + popd +} + +prepare_add +prepare_tinyllama +prepare_vision diff --git a/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/ModuleE2ETest.java b/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/ModuleE2ETest.java new file mode 100644 index 00000000000..f1338e090ba --- /dev/null +++ b/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/ModuleE2ETest.java @@ -0,0 +1,62 @@ +/* + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + * + * This source code is licensed under the BSD-style license found in the + * LICENSE file in the root directory of this source tree. + */ + +package org.pytorch.executorch; + +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertTrue; +import static org.junit.Assert.assertFalse; +import static org.junit.Assert.assertNotEquals; +import static org.junit.Assert.fail; + +import android.os.Environment; +import androidx.test.rule.GrantPermissionRule; +import android.Manifest; +import android.content.Context; +import org.junit.Test; +import org.junit.Before; +import org.junit.Rule; +import org.junit.runner.RunWith; +import java.io.InputStream; +import java.net.URI; +import java.net.URISyntaxException; +import java.util.concurrent.CountDownLatch; +import java.util.concurrent.atomic.AtomicInteger; +import java.io.IOException; +import java.io.File; +import java.io.FileOutputStream; +import org.junit.runners.JUnit4; +import org.apache.commons.io.FileUtils; +import androidx.test.ext.junit.runners.AndroidJUnit4; +import androidx.test.InstrumentationRegistry; + +/** Unit tests for {@link Module}. */ +@RunWith(AndroidJUnit4.class) +public class ModuleE2ETest { + private static String getTestFilePath(String fileName) { + return InstrumentationRegistry.getInstrumentation().getTargetContext().getExternalCacheDir() + fileName; + } + + @Rule + public GrantPermissionRule mRuntimePermissionRule = GrantPermissionRule.grant(Manifest.permission.READ_EXTERNAL_STORAGE); + + @Test + public void testMv2Fp32() throws IOException, URISyntaxException{ + String filePath = "/mv2_xnnpack_fp32.pte"; + File pteFile = new File(getTestFilePath(filePath)); + InputStream inputStream = getClass().getResourceAsStream(filePath); + FileUtils.copyInputStreamToFile(inputStream, pteFile); + inputStream.close(); + + Module module = Module.load(getTestFilePath(filePath)); + + EValue[] results = module.forward(); + assertTrue(results[0].isTensor()); + } + +} diff --git a/scripts/run_android_emulator.sh b/scripts/run_android_emulator.sh index 52f5fe55bd8..bbf7e070eeb 100755 --- a/scripts/run_android_emulator.sh +++ b/scripts/run_android_emulator.sh @@ -22,7 +22,8 @@ adb uninstall org.pytorch.executorch.test || true adb install -t android-test-debug-androidTest.apk adb logcat -c -adb shell am instrument -w -r -e class org.pytorch.executorch.ModuleInstrumentationTest \ +adb shell am instrument -w -r -e \ + class org.pytorch.executorch.ModuleInstrumentationTest,org.pytorch.executorch.ModuleE2ETest \ org.pytorch.executorch.test/androidx.test.runner.AndroidJUnitRunner >result.txt 2>&1 adb logcat -d > logcat.txt cat logcat.txt From 3793b3658fb7284d81ae7f77af272a2e23b9325e Mon Sep 17 00:00:00 2001 From: DannyYuyang-quic Date: Thu, 17 Apr 2025 01:08:42 +0800 Subject: [PATCH 168/244] Qualcomm AI Engine Direct - OSS models breakage fix (#10191) ### Summary - Fastvit breakage fix - ConvFormer breakage fix - Changed dataset for Edsr due to unavailable dataset link - Add test case for ConvertSquareToPow pass ### Test plan ```bash python ./examples/qualcomm/oss_scripts/fastvit.py -m ${soc} -b build-android -H ${host_id} -s ${device_id} --oss_repo ${Path_to_oss_repo} --pretrained_weight ${Path_to_pretrained_weight} -d ${Path_to_dataset_dir} ``` ```bash python ./examples/qualcomm/oss_scripts/conv_former.py -m ${soc} -b build-android -H ${host_id} -s ${device_id} -d ${Path_to_dataset_dir} ``` cc @cccclai @cbilgin --- backends/qualcomm/_passes/__init__.py | 2 + .../qualcomm/_passes/convert_square_to_pow.py | 38 +++++++++++++++++++ backends/qualcomm/_passes/qnn_pass_manager.py | 2 + backends/qualcomm/tests/models.py | 9 +++++ backends/qualcomm/tests/test_qnn_delegate.py | 13 ++++++- examples/qualcomm/oss_scripts/conv_former.py | 15 ++++++-- examples/qualcomm/oss_scripts/fastvit.py | 6 +-- examples/qualcomm/scripts/edsr.py | 14 ++++++- 8 files changed, 89 insertions(+), 10 deletions(-) create mode 100644 backends/qualcomm/_passes/convert_square_to_pow.py diff --git a/backends/qualcomm/_passes/__init__.py b/backends/qualcomm/_passes/__init__.py index d8e6237ce32..81b86992dee 100644 --- a/backends/qualcomm/_passes/__init__.py +++ b/backends/qualcomm/_passes/__init__.py @@ -9,6 +9,7 @@ from .annotate_unbind import AnnotateUnbind from .convert_bmm_to_matmul import ConvertBmmToMatmul from .convert_conv1d_to_conv2d import ConvertConv1dToConv2d +from .convert_square_to_pow import ConvertSquareToPow from .convert_upsample_bicubic2d import ConvertUpsampleBicubicWithBilinear from .decompose_any import DecomposeAny from .decompose_cdist import DecomposeCDist @@ -42,6 +43,7 @@ AnnotateUnbind, ConvertBmmToMatmul, ConvertConv1dToConv2d, + ConvertSquareToPow, ConvertUpsampleBicubicWithBilinear, DecomposeAny, DecomposeCDist, diff --git a/backends/qualcomm/_passes/convert_square_to_pow.py b/backends/qualcomm/_passes/convert_square_to_pow.py new file mode 100644 index 00000000000..51a74ac5f10 --- /dev/null +++ b/backends/qualcomm/_passes/convert_square_to_pow.py @@ -0,0 +1,38 @@ +# Copyright (c) Qualcomm Innovation Center, Inc. +# All rights reserved +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. +import torch +from executorch.exir.pass_base import ExportPass, PassResult + +from .utils import copy_meta + + +class ConvertSquareToPow(ExportPass): + """ + Convert square to pow with a scalar value of 2. + This allows LiftConstantScalarOperands to lift the scalar into a scalar. + Otherwise, the square op will be converted to pow.tensor_scalar after to_edge. + """ + + def __init__(self) -> None: + super().__init__() + + def call(self, graph_module: torch.fx.GraphModule) -> PassResult: + graph = graph_module.graph + for node in graph.nodes: + if node.target == torch.ops.aten.square.default: + input_node = node.args[0] + with graph_module.graph.inserting_after(input_node): + pow_op = torch.ops.aten.pow.Tensor_Scalar + pow_node = graph.create_node( + "call_function", pow_op, (input_node, 2) + ) + pow_node.meta = copy_meta(node.meta) + for user in node.users.copy(): + user.replace_input_with(node, pow_node) + + graph.eliminate_dead_code() + graph_module.recompile() + return PassResult(graph_module, True) diff --git a/backends/qualcomm/_passes/qnn_pass_manager.py b/backends/qualcomm/_passes/qnn_pass_manager.py index dda01e4a8a1..c98f27db120 100644 --- a/backends/qualcomm/_passes/qnn_pass_manager.py +++ b/backends/qualcomm/_passes/qnn_pass_manager.py @@ -14,6 +14,7 @@ AnnotateUnbind, ConvertBmmToMatmul, ConvertConv1dToConv2d, + ConvertSquareToPow, ConvertUpsampleBicubicWithBilinear, DecomposeAny, DecomposeCDist, @@ -199,6 +200,7 @@ def transform_for_export_pipeline(self, exported_program: ExportedProgram): self.add_pass(DecomposeScaledDotProductAttention()) self.add_pass(DecomposeLinalgVectorNorm(quantization_capture=True)) self.add_pass(DecomposeExpM1()) + self.add_pass(ConvertSquareToPow()) self.add_pass(LiftConstantScalarOperands()) self._transform(exported_program.graph_module) ep = lift_constant_tensor_pass(exported_program) diff --git a/backends/qualcomm/tests/models.py b/backends/qualcomm/tests/models.py index 3cba9db39b0..adf6e256f54 100644 --- a/backends/qualcomm/tests/models.py +++ b/backends/qualcomm/tests/models.py @@ -1436,6 +1436,15 @@ def forward(self, x): return x / torch.sqrt(torch.tensor([64.0])) +class SquaredReLU(torch.nn.Module): + def __init__(self, inplace=False): + super().__init__() + self.relu = torch.nn.ReLU(inplace=inplace) + + def forward(self, x): + return torch.square(self.relu(x)) + + class Squeeze(torch.nn.Module): def __init__(self): super().__init__() diff --git a/backends/qualcomm/tests/test_qnn_delegate.py b/backends/qualcomm/tests/test_qnn_delegate.py index 57beb201987..7cda52f5051 100644 --- a/backends/qualcomm/tests/test_qnn_delegate.py +++ b/backends/qualcomm/tests/test_qnn_delegate.py @@ -843,6 +843,11 @@ def test_qnn_backend_softmax(self): sample_input = (torch.randn([1, 4, 8, 8]),) self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_squared_relu(self): + module = SquaredReLU() # noqa: F405 + sample_input = (torch.randn([2, 5, 1, 3]),) + self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_squeeze(self): module = Squeeze() # noqa: F405 sample_input = (torch.randn([1, 3, 3]),) @@ -2001,6 +2006,12 @@ def test_qnn_backend_softmax(self): module = self.get_qdq_module(module, sample_input) self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_squared_relu(self): + module = SquaredReLU() # noqa: F405 + sample_input = (torch.randn([2, 5, 1, 3]),) + module = self.get_qdq_module(module, sample_input) + self.lower_module_and_test_output(module, sample_input) + def test_qnn_backend_squeeze(self): module = Squeeze() # noqa: F405 sample_input = (torch.randn([1, 3, 3]),) @@ -3642,7 +3653,7 @@ def test_efficientSAM(self): self.skipTest("missing required envs") cmds = [ "python", - f"{self.executorch_root}/examples/qualcomm/oss_scripts/efficientSAM.py", + f"{self.executorch_root}/examples/qualcomm/oss_scripts/efficientSAM/efficientSAM.py", "--dataset", self.image_dataset, "--artifact", diff --git a/examples/qualcomm/oss_scripts/conv_former.py b/examples/qualcomm/oss_scripts/conv_former.py index 76131d659df..8ce16abcc87 100644 --- a/examples/qualcomm/oss_scripts/conv_former.py +++ b/examples/qualcomm/oss_scripts/conv_former.py @@ -12,10 +12,14 @@ import numpy as np import timm import torch -from executorch.backends.qualcomm.quantizer.quantizer import QuantDtype -from executorch.backends.qualcomm.utils.constants import ( - QCOM_PASS_EXPAND_BROADCAST_SHAPE, +from executorch.backends.qualcomm._passes.expand_broadcast_tensor_shape import ( + ExpandBroadcastTensorShape, +) +from executorch.backends.qualcomm._passes.qnn_pass_manager import ( + get_capture_program_passes, ) +from executorch.backends.qualcomm.quantizer.quantizer import QuantDtype +from executorch.backends.qualcomm.utils.constants import QCOM_PASS_ACTIVATE_KEY from executorch.examples.qualcomm.utils import ( build_executorch_binary, get_imagenet_dataset, @@ -55,6 +59,9 @@ def main(args): model = model.eval() + # lower to QNN + passes_job = get_capture_program_passes() + passes_job[ExpandBroadcastTensorShape][QCOM_PASS_ACTIVATE_KEY] = True build_executorch_binary( model, inputs[0], @@ -62,7 +69,7 @@ def main(args): f"{args.artifact}/{pte_filename}", inputs, quant_dtype=QuantDtype.use_8a8w, - custom_pass_config={QCOM_PASS_EXPAND_BROADCAST_SHAPE}, + passes_job=passes_job, ) if args.compile_only: diff --git a/examples/qualcomm/oss_scripts/fastvit.py b/examples/qualcomm/oss_scripts/fastvit.py index b15e7d7267f..ee062735fbd 100644 --- a/examples/qualcomm/oss_scripts/fastvit.py +++ b/examples/qualcomm/oss_scripts/fastvit.py @@ -101,7 +101,7 @@ def main(args): ), ) # rewrite default per-channel ptq config - quantizer.per_channel_quant_config = QuantizationConfig( + quantizer.default_quant_config.per_channel_quant_config = QuantizationConfig( input_activation=act_qspec, output_activation=act_qspec, weight=weight_qspec, @@ -109,8 +109,8 @@ def main(args): ) # rewrite default ptq config - q_config = quantizer.quant_config - quantizer.quant_config = QuantizationConfig( + q_config = quantizer.default_quant_config.quant_config + quantizer.default_quant_config.quant_config = QuantizationConfig( input_activation=act_qspec, output_activation=act_qspec, weight=q_config.weight, diff --git a/examples/qualcomm/scripts/edsr.py b/examples/qualcomm/scripts/edsr.py index fa7efc0c459..a12a5069c3f 100755 --- a/examples/qualcomm/scripts/edsr.py +++ b/examples/qualcomm/scripts/edsr.py @@ -24,7 +24,7 @@ from PIL import Image from torch.utils.data import Dataset -from torchsr.datasets import B100 +from torchsr.datasets import B100, Div2K from torchvision.transforms.functional import to_pil_image, to_tensor @@ -75,6 +75,16 @@ def get_b100( return SrDataset(hr_dir, lr_dir) +def get_Div2K( + dataset_dir: str, +): + hr_dir = f"{dataset_dir}/sr_bm_dataset/DIV2K/DIV2K_valid_HR" + lr_dir = f"{dataset_dir}/sr_bm_dataset/DIV2K/DIV2K_valid_LR_bicubic/X2" + if not os.path.exists(hr_dir) or not os.path.exists(lr_dir): + Div2K(root=f"{dataset_dir}/sr_bm_dataset", scale=2, download=True) + return SrDataset(hr_dir, lr_dir) + + def get_dataset(hr_dir: str, lr_dir: str, default_dataset: str, dataset_dir: str): if not (lr_dir and hr_dir) and not default_dataset: raise RuntimeError( @@ -85,7 +95,7 @@ def get_dataset(hr_dir: str, lr_dir: str, default_dataset: str, dataset_dir: str raise RuntimeError("Either use custom dataset, or use default dataset.") if default_dataset: - return get_b100(dataset_dir) + return get_Div2K(dataset_dir) return SrDataset(hr_dir, lr_dir) From 0554b915256267cec75cc9321b7ebb3fbb5fa769 Mon Sep 17 00:00:00 2001 From: Huy Do Date: Wed, 16 Apr 2025 11:13:31 -0700 Subject: [PATCH 169/244] Increase Android perf test timeout to 4h (#10232) This is to ensure that the benchmark running on the 2 experimental private devices could finish https://github.com/pytorch/executorch/actions/runs/14484430048/job/40628062995. Then we can see how much time the 2 private devices take to finish all the combination to decide how much cool down time we want. --- .github/workflows/android-perf.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/android-perf.yml b/.github/workflows/android-perf.yml index 931bdbfe22d..391eb817384 100644 --- a/.github/workflows/android-perf.yml +++ b/.github/workflows/android-perf.yml @@ -392,7 +392,7 @@ jobs: fail-fast: false with: # Due to scheduling a job may be pushed beyond the default 60m threshold - timeout: 120 + timeout: 240 device-type: android runner: linux.2xlarge test-infra-ref: '' From a68a71d9c29bacc5a875e37d19b4438a86b0a3a9 Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Wed, 16 Apr 2025 11:17:20 -0700 Subject: [PATCH 170/244] Use sccache to accelerate android build (#9587) Android didn't use sccache because it has its own NDK clang. Now use env var to trigger clang from sccache. --- .github/workflows/_android.yml | 4 ++++ .github/workflows/android-perf.yml | 4 ++++ .github/workflows/android-release-artifacts.yml | 4 ++++ 3 files changed, 12 insertions(+) diff --git a/.github/workflows/_android.yml b/.github/workflows/_android.yml index 2a91fb45ffa..630ae2747bf 100644 --- a/.github/workflows/_android.yml +++ b/.github/workflows/_android.yml @@ -22,6 +22,10 @@ jobs: script: | set -eux + # Use sccache for NDK compiler as well + export CMAKE_CXX_COMPILER_LAUNCHER=sccache + export CMAKE_C_COMPILER_LAUNCHER=sccache + # The generic Linux job chooses to use base env, not the one setup by the image CONDA_ENV=$(conda env list --json | jq -r ".envs | .[-1]") conda activate "${CONDA_ENV}" diff --git a/.github/workflows/android-perf.yml b/.github/workflows/android-perf.yml index 391eb817384..5245d2f4f12 100644 --- a/.github/workflows/android-perf.yml +++ b/.github/workflows/android-perf.yml @@ -353,6 +353,10 @@ jobs: script: | set -eux + # Use sccache for NDK compiler as well + export CMAKE_CXX_COMPILER_LAUNCHER=sccache + export CMAKE_C_COMPILER_LAUNCHER=sccache + # The generic Linux job chooses to use base env, not the one setup by the image CONDA_ENV=$(conda env list --json | jq -r ".envs | .[-1]") conda activate "${CONDA_ENV}" diff --git a/.github/workflows/android-release-artifacts.yml b/.github/workflows/android-release-artifacts.yml index 2a35cf4711d..94bca01bea1 100644 --- a/.github/workflows/android-release-artifacts.yml +++ b/.github/workflows/android-release-artifacts.yml @@ -60,6 +60,10 @@ jobs: script: | set -eux + # Use sccache for NDK compiler as well + export CMAKE_CXX_COMPILER_LAUNCHER=sccache + export CMAKE_C_COMPILER_LAUNCHER=sccache + # The generic Linux job chooses to use base env, not the one setup by the image CONDA_ENV=$(conda env list --json | jq -r ".envs | .[-1]") conda activate "${CONDA_ENV}" From dabade965b7aaf00811b0e94c6e070968912a237 Mon Sep 17 00:00:00 2001 From: Hansong <107070759+kirklandsign@users.noreply.github.com> Date: Wed, 16 Apr 2025 11:42:46 -0700 Subject: [PATCH 171/244] Android E2E with real input (#10230) Use MV2, MV3, quantized ResNet50 for validation. --- .../executorch_android/android_test_setup.sh | 4 +- .../org/pytorch/executorch/ModuleE2ETest.java | 52 +++++- .../pytorch/executorch/TensorImageUtils.java | 150 ++++++++++++++++++ .../src/androidTest/resources/banana.jpeg | Bin 0 -> 18063 bytes 4 files changed, 201 insertions(+), 5 deletions(-) create mode 100644 extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/TensorImageUtils.java create mode 100644 extension/android/executorch_android/src/androidTest/resources/banana.jpeg diff --git a/extension/android/executorch_android/android_test_setup.sh b/extension/android/executorch_android/android_test_setup.sh index b89b3b4895c..c1fb2a19386 100644 --- a/extension/android/executorch_android/android_test_setup.sh +++ b/extension/android/executorch_android/android_test_setup.sh @@ -36,7 +36,9 @@ prepare_tinyllama() { prepare_vision() { pushd "${BASEDIR}/../../../" python3 -m examples.xnnpack.aot_compiler --model_name "mv2" --delegate - cp mv2*.pte "${BASEDIR}/src/androidTest/resources/" + python3 -m examples.xnnpack.aot_compiler --model_name "mv3" --delegate + python3 -m examples.xnnpack.aot_compiler --model_name "resnet50" --quantize --delegate + cp mv2*.pte mv3*.pte resnet50*.pte "${BASEDIR}/src/androidTest/resources/" popd } diff --git a/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/ModuleE2ETest.java b/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/ModuleE2ETest.java index f1338e090ba..3a033851be9 100644 --- a/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/ModuleE2ETest.java +++ b/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/ModuleE2ETest.java @@ -14,6 +14,8 @@ import static org.junit.Assert.assertNotEquals; import static org.junit.Assert.fail; +import android.graphics.Bitmap; +import android.graphics.BitmapFactory; import android.os.Environment; import androidx.test.rule.GrantPermissionRule; import android.Manifest; @@ -45,18 +47,60 @@ private static String getTestFilePath(String fileName) { @Rule public GrantPermissionRule mRuntimePermissionRule = GrantPermissionRule.grant(Manifest.permission.READ_EXTERNAL_STORAGE); - @Test - public void testMv2Fp32() throws IOException, URISyntaxException{ - String filePath = "/mv2_xnnpack_fp32.pte"; + static int argmax(float[] array) { + if (array.length == 0) { + throw new IllegalArgumentException("Array cannot be empty"); + } + int maxIndex = 0; + float maxValue = array[0]; + for (int i = 1; i < array.length; i++) { + if (array[i] > maxValue) { + maxValue = array[i]; + maxIndex = i; + } + } + return maxIndex; + } + + public void testClassification(String filePath) throws IOException, URISyntaxException { File pteFile = new File(getTestFilePath(filePath)); InputStream inputStream = getClass().getResourceAsStream(filePath); FileUtils.copyInputStreamToFile(inputStream, pteFile); inputStream.close(); + InputStream imgInputStream = getClass().getResourceAsStream("/banana.jpeg"); + Bitmap bitmap = BitmapFactory.decodeStream(imgInputStream); + bitmap = Bitmap.createScaledBitmap(bitmap, 224, 224, true); + imgInputStream.close(); + + Tensor inputTensor = + TensorImageUtils.bitmapToFloat32Tensor( + bitmap, + TensorImageUtils.TORCHVISION_NORM_MEAN_RGB, + TensorImageUtils.TORCHVISION_NORM_STD_RGB); + Module module = Module.load(getTestFilePath(filePath)); - EValue[] results = module.forward(); + EValue[] results = module.forward(EValue.from(inputTensor)); assertTrue(results[0].isTensor()); + float[] scores = results[0].toTensor().getDataAsFloatArray(); + + int bananaClass = 954; // From ImageNet 1K + assertEquals(bananaClass, argmax(scores)); + } + + @Test + public void testMv2Fp32() throws IOException, URISyntaxException { + testClassification("/mv2_xnnpack_fp32.pte"); } + @Test + public void testMv3Fp32() throws IOException, URISyntaxException { + testClassification("/mv3_xnnpack_fp32.pte"); + } + + @Test + public void testResnet50() throws IOException, URISyntaxException { + testClassification("/resnet50_xnnpack_q8.pte"); + } } diff --git a/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/TensorImageUtils.java b/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/TensorImageUtils.java new file mode 100644 index 00000000000..95434dcb734 --- /dev/null +++ b/extension/android/executorch_android/src/androidTest/java/org/pytorch/executorch/TensorImageUtils.java @@ -0,0 +1,150 @@ +/* + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + * + * This source code is licensed under the BSD-style license found in the + * LICENSE file in the root directory of this source tree. + */ + +package org.pytorch.executorch; + +import android.graphics.Bitmap; +import android.util.Log; +import java.nio.FloatBuffer; +import org.pytorch.executorch.Tensor; + +/** + * Contains utility functions for {@link Tensor} creation from {@link android.graphics.Bitmap} or + * {@link android.media.Image} source. + */ +public final class TensorImageUtils { + + public static float[] TORCHVISION_NORM_MEAN_RGB = new float[] {0.485f, 0.456f, 0.406f}; + public static float[] TORCHVISION_NORM_STD_RGB = new float[] {0.229f, 0.224f, 0.225f}; + + /** + * Creates new {@link Tensor} from full {@link android.graphics.Bitmap}, normalized with specified + * in parameters mean and std. + * + * @param normMeanRGB means for RGB channels normalization, length must equal 3, RGB order + * @param normStdRGB standard deviation for RGB channels normalization, length must equal 3, RGB + * order + */ + public static Tensor bitmapToFloat32Tensor( + final Bitmap bitmap, final float[] normMeanRGB, final float normStdRGB[]) { + checkNormMeanArg(normMeanRGB); + checkNormStdArg(normStdRGB); + + return bitmapToFloat32Tensor( + bitmap, 0, 0, bitmap.getWidth(), bitmap.getHeight(), normMeanRGB, normStdRGB); + } + + /** + * Writes tensor content from specified {@link android.graphics.Bitmap}, normalized with specified + * in parameters mean and std to specified {@link java.nio.FloatBuffer} with specified offset. + * + * @param bitmap {@link android.graphics.Bitmap} as a source for Tensor data + * @param x - x coordinate of top left corner of bitmap's area + * @param y - y coordinate of top left corner of bitmap's area + * @param width - width of bitmap's area + * @param height - height of bitmap's area + * @param normMeanRGB means for RGB channels normalization, length must equal 3, RGB order + * @param normStdRGB standard deviation for RGB channels normalization, length must equal 3, RGB + * order + */ + public static void bitmapToFloatBuffer( + final Bitmap bitmap, + final int x, + final int y, + final int width, + final int height, + final float[] normMeanRGB, + final float[] normStdRGB, + final FloatBuffer outBuffer, + final int outBufferOffset) { + checkOutBufferCapacity(outBuffer, outBufferOffset, width, height); + checkNormMeanArg(normMeanRGB); + checkNormStdArg(normStdRGB); + final int pixelsCount = height * width; + final int[] pixels = new int[pixelsCount]; + bitmap.getPixels(pixels, 0, width, x, y, width, height); + final int offset_g = pixelsCount; + final int offset_b = 2 * pixelsCount; + for (int i = 0; i < 100; i++) { + final int c = pixels[i]; + Log.i("Image", ": " + i + " " + ((c >> 16) & 0xff)); + } + for (int i = 0; i < pixelsCount; i++) { + final int c = pixels[i]; + float r = ((c >> 16) & 0xff) / 255.0f; + float g = ((c >> 8) & 0xff) / 255.0f; + float b = ((c) & 0xff) / 255.0f; + outBuffer.put(outBufferOffset + i, (r - normMeanRGB[0]) / normStdRGB[0]); + outBuffer.put(outBufferOffset + offset_g + i, (g - normMeanRGB[1]) / normStdRGB[1]); + outBuffer.put(outBufferOffset + offset_b + i, (b - normMeanRGB[2]) / normStdRGB[2]); + } + } + + /** + * Creates new {@link Tensor} from specified area of {@link android.graphics.Bitmap}, normalized + * with specified in parameters mean and std. + * + * @param bitmap {@link android.graphics.Bitmap} as a source for Tensor data + * @param x - x coordinate of top left corner of bitmap's area + * @param y - y coordinate of top left corner of bitmap's area + * @param width - width of bitmap's area + * @param height - height of bitmap's area + * @param normMeanRGB means for RGB channels normalization, length must equal 3, RGB order + * @param normStdRGB standard deviation for RGB channels normalization, length must equal 3, RGB + * order + */ + public static Tensor bitmapToFloat32Tensor( + final Bitmap bitmap, + int x, + int y, + int width, + int height, + float[] normMeanRGB, + float[] normStdRGB) { + checkNormMeanArg(normMeanRGB); + checkNormStdArg(normStdRGB); + + final FloatBuffer floatBuffer = Tensor.allocateFloatBuffer(3 * width * height); + bitmapToFloatBuffer(bitmap, x, y, width, height, normMeanRGB, normStdRGB, floatBuffer, 0); + return Tensor.fromBlob(floatBuffer, new long[] {1, 3, height, width}); + } + + private static void checkOutBufferCapacity( + FloatBuffer outBuffer, int outBufferOffset, int tensorWidth, int tensorHeight) { + if (outBufferOffset + 3 * tensorWidth * tensorHeight > outBuffer.capacity()) { + throw new IllegalStateException("Buffer underflow"); + } + } + + private static void checkTensorSize(int tensorWidth, int tensorHeight) { + if (tensorHeight <= 0 || tensorWidth <= 0) { + throw new IllegalArgumentException("tensorHeight and tensorWidth must be positive"); + } + } + + private static void checkRotateCWDegrees(int rotateCWDegrees) { + if (rotateCWDegrees != 0 + && rotateCWDegrees != 90 + && rotateCWDegrees != 180 + && rotateCWDegrees != 270) { + throw new IllegalArgumentException("rotateCWDegrees must be one of 0, 90, 180, 270"); + } + } + + private static void checkNormStdArg(float[] normStdRGB) { + if (normStdRGB.length != 3) { + throw new IllegalArgumentException("normStdRGB length must be 3"); + } + } + + private static void checkNormMeanArg(float[] normMeanRGB) { + if (normMeanRGB.length != 3) { + throw new IllegalArgumentException("normMeanRGB length must be 3"); + } + } +} diff --git a/extension/android/executorch_android/src/androidTest/resources/banana.jpeg b/extension/android/executorch_android/src/androidTest/resources/banana.jpeg new file mode 100644 index 0000000000000000000000000000000000000000..2b237ce3d14c2d663e5233babf4b6b57693bc4c4 GIT binary patch literal 18063 zcmbTd1yo$kwl3N@A;AI!3GNcy-GT-u&;;uQ2^QR;@el~sxJwAG9o*gB-QA_p#xMEL zK6{^c$9rSEd*|v}^^IDyX0LCKxoUOQtbUq(S_R-J$|}eL5D)->$mav_v1*x4)gOZJZo5ISk2YR?k@&EV|<6_n|j87y@YS}UpV_OHu^7I z`;X2?4e4i{!DmcqZ2raM86Q65cSirC-Rys0>u=6~zxS{F_53SS8!gS}8vnVF0^S2G z0FD4Vz&F4bfIEN^pa$Un|Ip|8M_(FX`>gE_aCq)#1~3OWJ@=7(Zux7$^cgDvYyidp 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z1KPf$$flM&V~XalhNn|Xk-4=R^QP|yW8rIW*mJ|Y{{U$Cs`f&6W6Fjk0B4WAyP)gp zeJkI-5qt^oU%~NrK7}Or*Y}_-w^vN1!VW?pRZy#*PjY&i_I^p{kwGAIu6j9c9-Ne+ z4NYp}s?BoBS`zLQnKd5oT4{cQyW2SlNi>_PyA#)^@}`$P4+53PV!>rS>NaYCuoM%; zO^YTD1`3Ksa%u%2DD|ag7~u0##)p>U6$`HQDtpi~j%qe6azUg}(JYt)02Sk{MJDIw zM$h9+k$`N2^{Ve6Jg+n;-*GuuNf?#d$0De>R3q{gVa|FQs>dBW*Ft mmGmZ@a!v@ys Date: Wed, 16 Apr 2025 11:51:39 -0700 Subject: [PATCH 172/244] Add quantized kernels to executorch_jni_full Differential Revision: D73089140 Pull Request resolved: https://github.com/pytorch/executorch/pull/10223 --- extension/android/jni/BUCK | 1 + 1 file changed, 1 insertion(+) diff --git a/extension/android/jni/BUCK b/extension/android/jni/BUCK index d9ef6d1455e..da2ac49e446 100644 --- a/extension/android/jni/BUCK +++ b/extension/android/jni/BUCK @@ -72,6 +72,7 @@ non_fbcode_target(_kind = fb_android_cxx_library, "//xplat/executorch/extension/module:module_static", "//xplat/executorch/extension/runner_util:inputs_static", "//xplat/executorch/extension/tensor:tensor_static", + "//xplat/executorch/kernels/quantized:generated_lib_static", ], ) From d6624cd26884dce398a7a8ca6da61d90bd2316aa Mon Sep 17 00:00:00 2001 From: pytorchbot Date: Wed, 16 Apr 2025 14:54:47 -0400 Subject: [PATCH 173/244] [ET-VK][ez] Add support for buffer backed qparams in int4 linear + add checks for physical limits when allocating (#10233) ## Context Currently, the groupwise quantized int4 linear op implementation forces the scales and zero tensor to be a `Texture3D`. However, for i.e. transformer models that have a logit linear layer, the image extents required may exceed the maximum image extents available on the device. ## Changes * Add support for the scales and zero tensor being a `Buffer` instead of a `Texture3D` * Add checks when allocating buffers or images for tensors that the requested resource fits within the physical device limits Differential Revision: [D72662176](https://our.internmc.facebook.com/intern/diff/D72662176/) --- .../vulkan/runtime/api/containers/Tensor.cpp | 22 +++++++++++++++++++ ..._linear_weight_transposed_interleaved.glsl | 8 +++---- ..._linear_weight_transposed_interleaved.yaml | 10 +++++---- .../runtime/graph/ops/glsl/q_4w_linear.glsl | 22 ++++++++++++++----- .../runtime/graph/ops/glsl/q_4w_linear.yaml | 9 ++++---- .../ops/impl/QuantizedLinearGroupwiseInt4.cpp | 9 ++++---- backends/vulkan/runtime/vk_api/Adapter.h | 15 ++++++++----- 7 files changed, 66 insertions(+), 29 deletions(-) diff --git a/backends/vulkan/runtime/api/containers/Tensor.cpp b/backends/vulkan/runtime/api/containers/Tensor.cpp index 0712062a37e..62b53f9a76c 100644 --- a/backends/vulkan/runtime/api/containers/Tensor.cpp +++ b/backends/vulkan/runtime/api/containers/Tensor.cpp @@ -260,6 +260,26 @@ vkapi::VulkanImage allocate_image( return vkapi::VulkanImage(); } + // TODO(ssjia): change to always check that the image extents do not exceed + // physical limits. Adding the check now based on `maxImageDimension3D` will + // cause some existing models to break. Anecdotally, on Adreno and + // SwiftShader devices, using 3D textures that exceed `maxImageDimension3D` + // appears to be ok. So we need to figure out if is it undefined behaviour + // or if there's a better way to figure out what the limit is. For now, only + // check during debug build so that we can detect when exceeding physical + // limits could be a potential cause for model outputs to be wrong. In the + // meantime, the threshold for using texture storage can be configured at + // export time. +#ifdef VULKAN_DEBUG + uint32_t max_extent = storage_type == utils::kTexture3D + ? adapter_ptr->max_texture3d_dim() + : adapter_ptr->max_texture2d_dim(); + + VK_CHECK_COND( + image_extents[0] <= max_extent && image_extents[1] <= max_extent && + image_extents[2] <= max_extent); +#endif + VkSampler sampler = adapter_ptr->sampler_cache().retrieve(sampler_props); return adapter_ptr->vma().create_image( @@ -291,6 +311,8 @@ vkapi::VulkanBuffer allocate_buffer( return vkapi::VulkanBuffer(); } + VK_CHECK_COND(numel <= context_ptr->adapter_ptr()->max_buffer_numel()); + return adapter_ptr->vma().create_storage_buffer( element_size(dtype) * numel, allocate_memory); } diff --git a/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.glsl b/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.glsl index c7fcdcc775a..a1309fb9198 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.glsl +++ b/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.glsl @@ -109,8 +109,8 @@ void main() { in_vals[r][0] = get_first(in_val_packed); in_vals[r][1] = get_second(in_val_packed); } else { - in_vals[r][0] = uint8_t(254); - in_vals[r][1] = uint8_t(254); + in_vals[r][0] = uint8_t(0); + in_vals[r][1] = uint8_t(0); } } @@ -131,6 +131,6 @@ void main() { t_qmat2[packed_pos.y * stride + packed_pos.x] = out_tex_1; t_qmat2[(packed_pos.y + 1) * stride + packed_pos.x] = out_tex_2; $else: - imageStore(t_qmat2, ivec3(packed_pos.xy, 0), out_tex_1); - imageStore(t_qmat2, ivec3(packed_pos.x, packed_pos.y + 1, 0), out_tex_2); + imageStore(t_qmat2, packed_pos.xy, out_tex_1); + imageStore(t_qmat2, ivec2(packed_pos.x, packed_pos.y + 1), out_tex_2); } diff --git a/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml b/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml index 168b18fffe4..dffd260b3d8 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml +++ b/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml @@ -6,8 +6,10 @@ pack_int4_linear_weight_transposed_interleaved: parameter_names_with_default_values: - STORAGE: texture3d + STORAGE: texture2d + generate_variant_forall: + STORAGE: + - VALUE: texture2d + - VALUE: buffer shader_variants: - - NAME: pack_int4_linear_weight_transposed_interleaved_texture3d - - NAME: pack_int4_linear_weight_transposed_interleaved_buffer - STORAGE: buffer + - NAME: pack_int4_linear_weight_transposed_interleaved diff --git a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl index 29f2934f957..56261e5a040 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl +++ b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl @@ -21,7 +21,7 @@ layout(std430) buffer; ${layout_declare_tensor(B, "w", "t_out", DTYPE, OUT_STORAGE, is_scalar_array=False)} ${layout_declare_tensor(B, "r", "t_mat1", DTYPE, IN_STORAGE, is_scalar_array=False)} ${layout_declare_tensor(B, "r", "t_qmat2", "uint8", WEIGHT_STORAGE, is_scalar_array=False)} -${layout_declare_tensor(B, "r", "t_qparams", DTYPE, "texture3D")} +${layout_declare_tensor(B, "r", "t_qparams", DTYPE, "buffer", is_scalar_array=False)} layout(push_constant) uniform restrict Block { ivec4 out_sizes; @@ -79,13 +79,23 @@ void main() { $if WEIGHT_STORAGE == "buffer": const int qmat2_stride = qmat2_sizes.x >> 2; + $if PARAMS_STORAGE == "buffer": + const int qparams_y_stride = out_sizes.x >> 2; + const int qparams_z_stride = qparams_y_stride * 2; for (int block_idx = 0; block_idx < num_blocks; ++block_idx) { - scales[0] = texelFetch(t_qparams, ivec3(out_col_texel_idx, 0, block_idx), 0); - zeros[0] = texelFetch(t_qparams, ivec3(out_col_texel_idx, 1, block_idx), 0); + $if PARAMS_STORAGE == "buffer": + scales[0] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx]; + zeros[0] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx + qparams_y_stride]; - scales[1] = texelFetch(t_qparams, ivec3(out_col_texel_idx + 1, 0, block_idx), 0); - zeros[1] = texelFetch(t_qparams, ivec3(out_col_texel_idx + 1, 1, block_idx), 0); + scales[1] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx + 1]; + zeros[1] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx + 1 + qparams_y_stride]; + $else: + scales[0] = texelFetch(t_qparams, ivec3(out_col_texel_idx, 0, block_idx), 0); + zeros[0] = texelFetch(t_qparams, ivec3(out_col_texel_idx, 1, block_idx), 0); + + scales[1] = texelFetch(t_qparams, ivec3(out_col_texel_idx + 1, 0, block_idx), 0); + zeros[1] = texelFetch(t_qparams, ivec3(out_col_texel_idx + 1, 1, block_idx), 0); for (int g_idx = 0; g_idx < group_size; g_idx += 4) { const int k = block_idx * group_size + g_idx; @@ -101,7 +111,7 @@ void main() { $else: const uvec4 packed_weight_tex = texelFetch( t_qmat2, - ivec3(gl_GlobalInvocationID.x, k + comp, 0), + ivec2(gl_GlobalInvocationID.x, k + comp), 0); const uvec4 weight_tex_1 = (packed_weight_tex & 0xF0) >> 4; diff --git a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.yaml b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.yaml index fac9c25c220..b58b5d2cf55 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.yaml +++ b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.yaml @@ -9,12 +9,11 @@ q_4w_linear: DTYPE: float OUT_STORAGE: texture3d IN_STORAGE: texture3d - WEIGHT_STORAGE: texture3d + WEIGHT_STORAGE: texture2d + PARAMS_STORAGE: buffer shader_variants: - - NAME: q_4w_linear_texture3d_texture3d_texture3d_float - - NAME: q_4w_linear_texture3d_buffer_texture3d_float - IN_STORAGE: buffer - - NAME: q_4w_linear_buffer_buffer_texture3d_float + - NAME: q_4w_linear_texture3d_texture3d_texture2d_float + - NAME: q_4w_linear_buffer_buffer_texture2d_float OUT_STORAGE: buffer IN_STORAGE: buffer - NAME: q_4w_linear_buffer_buffer_buffer_float diff --git a/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp b/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp index b795e574291..dc84b945f39 100644 --- a/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp +++ b/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp @@ -83,10 +83,9 @@ ValueRef prepack_int4_linear_weight_transposed_interleaved( const int64_t N = qmat2_orig_sizes.at(ndim - 2); const int64_t N_div2 = N / int64_t(2); - utils::StorageType storage_type = utils::kTexture3D; - utils::uvec3 max_extents = - graph.context()->adapter_ptr()->max_texture_extents(); - if (N_div2 > max_extents[0] * 4 || K > max_extents[1]) { + utils::StorageType storage_type = utils::kTexture2D; + uint32_t max_extent = graph.context()->adapter_ptr()->max_texture2d_dim(); + if (N_div2 > max_extent * 4 || K > max_extent) { storage_type = utils::kBuffer; } @@ -133,7 +132,7 @@ void add_q_4w_linear_node( prepack_int4_linear_weight_transposed_interleaved(graph, mat2_data); ValueRef scales_and_zeros = prepack_standard_hw_transposed( - graph, scales_and_zeros_data, utils::kTexture3D, utils::kWidthPacked); + graph, scales_and_zeros_data, utils::kBuffer, utils::kWidthPacked); std::string kernel_name = "q_4w_linear"; add_storage_type_suffix(kernel_name, graph.storage_type_of(out)); diff --git a/backends/vulkan/runtime/vk_api/Adapter.h b/backends/vulkan/runtime/vk_api/Adapter.h index 68371d3eebf..d73ed1bc0ce 100644 --- a/backends/vulkan/runtime/vk_api/Adapter.h +++ b/backends/vulkan/runtime/vk_api/Adapter.h @@ -211,11 +211,16 @@ class Adapter final { return physical_device_.min_ubo_alignment; } - inline utils::uvec3 max_texture_extents() const { - return { - physical_device_.properties.limits.maxImageDimension1D, - physical_device_.properties.limits.maxImageDimension2D, - physical_device_.properties.limits.maxImageDimension3D}; + inline uint32_t max_texture2d_dim() const { + return physical_device_.properties.limits.maxImageDimension2D; + } + + inline uint32_t max_texture3d_dim() const { + return physical_device_.properties.limits.maxImageDimension3D; + } + + inline uint32_t max_buffer_numel() const { + return physical_device_.properties.limits.maxStorageBufferRange; } // Command Buffer Submission From d47d0bdffabfa5bd3a2ea562b4293794cc6ffbe4 Mon Sep 17 00:00:00 2001 From: pytorchbot Date: Wed, 16 Apr 2025 14:58:24 -0400 Subject: [PATCH 174/244] [ET-VK] Allow int4 linear to execute without 8bit buffer support (#10234) ## Context Some Vulkan devices do not have support for 8-bit buffers, which is currently required to execute the int4 linear compute shader due to the prepacking shader requiring it. This diff bypasses that restriction by introducing a variant of the prepacking shader that does not need 8-bit buffers. ## Changes Introduce a variant of the int4 weight prepacking shader that interprets the tensor data as an array of `uint` instead of `uint8_t`. Each `uint` represents 4 `uint8_t` values. Differential Revision: [D72750897](https://our.internmc.facebook.com/intern/diff/D72750897/) --- backends/vulkan/runtime/gen_vulkan_spv.py | 2 + ..._linear_weight_transposed_interleaved.glsl | 57 ++++++++++++++----- ..._linear_weight_transposed_interleaved.yaml | 11 ++-- .../runtime/graph/ops/glsl/q_4w_linear.glsl | 3 +- .../ops/impl/QuantizedLinearGroupwiseInt4.cpp | 8 +-- .../test/op_tests/linear_weight_int4_test.cpp | 5 -- 6 files changed, 56 insertions(+), 30 deletions(-) diff --git a/backends/vulkan/runtime/gen_vulkan_spv.py b/backends/vulkan/runtime/gen_vulkan_spv.py index a29f7d14964..e52780b6a4d 100644 --- a/backends/vulkan/runtime/gen_vulkan_spv.py +++ b/backends/vulkan/runtime/gen_vulkan_spv.py @@ -125,6 +125,8 @@ def buffer_gvec_type(dtype: str, n: int) -> str: if dtype == "float": return f"vec{n}" + if dtype == "uint": + return f"uvec{n}" elif dtype == "half": return f"f16vec{n}" elif dtype == "int": diff --git a/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.glsl b/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.glsl index a1309fb9198..0079526c248 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.glsl +++ b/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.glsl @@ -10,13 +10,18 @@ #define PRECISION ${PRECISION} -${define_required_extensions("uint8")} -${define_required_extensions("int8")} +$if not NO_INT8_BUFFERS: + ${define_required_extensions("uint8")} +$if STORAGE == "buffer": + ${define_required_extensions("int8")} layout(std430) buffer; ${layout_declare_tensor(B, "w", "t_qmat2", "uint8", STORAGE, is_scalar_array=False)} -${layout_declare_tensor(B, "r", "nchw_4x2", "uint8", "buffer")} +$if NO_INT8_BUFFERS: + ${layout_declare_tensor(B, "r", "nchw_4x2", "uint", "buffer")} +$else: + ${layout_declare_tensor(B, "r", "nchw_4x2", "uint8", "buffer")} layout(push_constant) uniform restrict Block { ivec4 qmat2_sizes; @@ -24,18 +29,33 @@ layout(push_constant) uniform restrict Block { layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in; -uint8_t get_first(const uint8_t packed) { - return uint8_t((packed & 0xF0) >> 4); +$if NO_INT8_BUFFERS: + #define BUF_T uint +$else: + #define BUF_T uint8_t + +$if STORAGE == "buffer": + #define UVEC4_T u8vec4 +$else: + #define UVEC4_T uvec4 + +uint get_first(const BUF_T packed) { + return (packed & 0xF0) >> 4; } -uint8_t get_second(const uint8_t packed) { - return uint8_t(packed & 0x0F); +uint get_second(const BUF_T packed) { + return packed & 0x0F; } -uint8_t combine(const uint8_t first, const uint8_t second) { - return uint8_t(first << 4 | second); +uint combine(const uint first, const uint second) { + return (first << 4 | second); } +$if NO_INT8_BUFFERS: + uint extract_comp(const uint packed4, const uint idx) { + return (packed4 >> (idx * 8)) & 0xFF; + } + /* * This shader packs the weight tensor into a texture. * @@ -102,25 +122,32 @@ void main() { int in_numcols = qmat2_sizes.y; int in_num_int8_cols = qmat2_sizes.y >> 1; - uint8_t in_vals[8][2]; + uint in_vals[8][2]; for (int r = 0; r < 8; ++r) { if (in_row + r < in_numrows) { - uint8_t in_val_packed = nchw_4x2[(in_row + r) * in_num_int8_cols + in_int8_col]; + uint scalar_idx = (in_row + r) * in_num_int8_cols + in_int8_col; + $if NO_INT8_BUFFERS: + BUF_T in_val_packed_texel = nchw_4x2[scalar_idx >> 2]; + const uint packed_idx = scalar_idx % 4; + uint in_val_packed = extract_comp(in_val_packed_texel, packed_idx); + $else: + BUF_T in_val_packed = nchw_4x2[scalar_idx]; + in_vals[r][0] = get_first(in_val_packed); in_vals[r][1] = get_second(in_val_packed); } else { - in_vals[r][0] = uint8_t(0); - in_vals[r][1] = uint8_t(0); + in_vals[r][0] = uint(0); + in_vals[r][1] = uint(0); } } - u8vec4 out_tex_1 = u8vec4( + UVEC4_T out_tex_1 = UVEC4_T( combine(in_vals[0][0], in_vals[4][0]), combine(in_vals[1][0], in_vals[5][0]), combine(in_vals[2][0], in_vals[6][0]), combine(in_vals[3][0], in_vals[7][0])); - u8vec4 out_tex_2 = u8vec4( + UVEC4_T out_tex_2 = UVEC4_T( combine(in_vals[0][1], in_vals[4][1]), combine(in_vals[1][1], in_vals[5][1]), combine(in_vals[2][1], in_vals[6][1]), diff --git a/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml b/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml index dffd260b3d8..57668223f6e 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml +++ b/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml @@ -7,9 +7,10 @@ pack_int4_linear_weight_transposed_interleaved: parameter_names_with_default_values: STORAGE: texture2d - generate_variant_forall: - STORAGE: - - VALUE: texture2d - - VALUE: buffer + NO_INT8_BUFFERS: false shader_variants: - - NAME: pack_int4_linear_weight_transposed_interleaved + - NAME: pack_int4_linear_weight_transposed_interleaved_texture2d + - NAME: pack_int4_linear_weight_transposed_interleaved_buffer + STORAGE: buffer + - NAME: pack_int4_linear_weight_transposed_interleaved_nobitw8buffer_texture2d + NO_INT8_BUFFERS: true \ No newline at end of file diff --git a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl index 56261e5a040..7350af3415c 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl +++ b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl @@ -14,7 +14,8 @@ #define VEC4_T ${buffer_gvec_type(DTYPE, 4)} ${define_required_extensions(DTYPE)} -${define_required_extensions("int8")} +$if WEIGHT_STORAGE == "buffer": + ${define_required_extensions("uint8")} layout(std430) buffer; diff --git a/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp b/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp index dc84b945f39..6bddbd49794 100644 --- a/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp +++ b/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp @@ -22,9 +22,6 @@ void check_q_4w_linear_args( const ValueRef group_size, const ValueRef scales_and_zeros, const ValueRef out) { - VK_CHECK_COND(graph.int16_shader_types_enabled()); - VK_CHECK_COND(graph.int8_buffers_enabled()); - VK_CHECK_COND(graph.val_is_tensor(mat1)); VK_CHECK_COND(graph.val_is_tref(mat2_data)); VK_CHECK_COND(graph.val_is_tref(scales_and_zeros)); @@ -97,7 +94,10 @@ ValueRef prepack_int4_linear_weight_transposed_interleaved( global_wg_size = graph.logical_limits_of(qmat2); global_wg_size[1] = utils::div_up(global_wg_size[1], uint32_t(2)); - std::string kernel_name = "pack_int4_linear_weight_transposed_interleaved"; + std::string kernel_name = + graph.context()->adapter_ptr()->has_full_int8_buffers_support() + ? "pack_int4_linear_weight_transposed_interleaved" + : "pack_int4_linear_weight_transposed_interleaved_nobitw8buffer"; add_storage_type_suffix(kernel_name, storage_type); graph.prepack_nodes().emplace_back(new PrepackNode( diff --git a/backends/vulkan/test/op_tests/linear_weight_int4_test.cpp b/backends/vulkan/test/op_tests/linear_weight_int4_test.cpp index 884b068bc24..b8862b8bb30 100644 --- a/backends/vulkan/test/op_tests/linear_weight_int4_test.cpp +++ b/backends/vulkan/test/op_tests/linear_weight_int4_test.cpp @@ -274,11 +274,6 @@ TEST(VulkanInt4LinearTest, test_reference_impl) { } TEST(VulkanInt4LinearTest, test_vulkan_impl) { - if (!vkcompute::api::context() - ->adapter_ptr() - ->has_full_int8_buffers_support()) { - GTEST_SKIP(); - } test_vulkan_linear_int4( /*B = */ 1, /*M = */ 4, From 49e4f77f4dd489994fee449b9b57900916e01a73 Mon Sep 17 00:00:00 2001 From: pytorchbot Date: Wed, 16 Apr 2025 15:01:48 -0400 Subject: [PATCH 175/244] [ET-VK] Add co-op algorithm for 4 bit weight only quantized linear (#10235) ## Context As title. Add an alternative compute shader for int4 weight-only quantized linear that utilizes a co-operative algorithm. This shader is more performant than standard tiled algorithms for `gemv` cases, i.e. when `mat1` is a vector rather than a matrix. ## Changes * Add the cooperative shader * Use the cooperative shader when the height of `mat1` is 1 Differential Revision: [D73044650](https://our.internmc.facebook.com/intern/diff/D73044650/) --- .../graph/ops/glsl/q_4w_linear_coop.glsl | 199 ++++++++++++++++++ .../graph/ops/glsl/q_4w_linear_coop.yaml | 23 ++ .../ops/impl/QuantizedLinearGroupwiseInt4.cpp | 17 +- .../test/op_tests/linear_weight_int4_test.cpp | 16 +- 4 files changed, 252 insertions(+), 3 deletions(-) create mode 100644 backends/vulkan/runtime/graph/ops/glsl/q_4w_linear_coop.glsl create mode 100644 backends/vulkan/runtime/graph/ops/glsl/q_4w_linear_coop.yaml diff --git a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear_coop.glsl b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear_coop.glsl new file mode 100644 index 00000000000..715f84d3a56 --- /dev/null +++ b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear_coop.glsl @@ -0,0 +1,199 @@ +/* + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + * + * This source code is licensed under the BSD-style license found in the + * LICENSE file in the root directory of this source tree. + */ + +#version 450 core + +#define PRECISION ${PRECISION} + +#define T ${buffer_scalar_type(DTYPE)} +#define VEC4_T ${buffer_gvec_type(DTYPE, 4)} + +#define TILE_ROWS ${TILE_ROWS} + +#define NGROUPS 8 +#define NWORKERS 8 + +${define_required_extensions(DTYPE)} +$if WEIGHT_STORAGE == "buffer": + ${define_required_extensions("uint8")} + +#extension GL_EXT_control_flow_attributes : require + +layout(std430) buffer; + +${layout_declare_tensor(B, "w", "t_out", DTYPE, OUT_STORAGE, is_scalar_array=False)} +${layout_declare_tensor(B, "r", "t_mat1", DTYPE, IN_STORAGE, is_scalar_array=False)} +${layout_declare_tensor(B, "r", "t_qmat2", "uint8", WEIGHT_STORAGE, is_scalar_array=False)} +${layout_declare_tensor(B, "r", "t_qparams", DTYPE, "buffer", is_scalar_array=False)} + +layout(push_constant) uniform restrict Block { + ivec4 out_sizes; + ivec4 mat1_sizes; + ivec4 qmat2_sizes; +}; + +layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in; + +layout(constant_id = 3) const int group_size = 64; + +shared VEC4_T partial_sums[NGROUPS][NWORKERS][TILE_ROWS][2]; + +/* + * This shader computes a linear operator between a floating point input matrix + * x and a weights matrix that is quantized to 4 bits. Please refer to the + * q_4w_linear shader for more details. + * + * This shader implements a co-operative algorithm to compute the output. The + * work group size is {NGROUP, 1, NWORKERS}, and each group of NWORKERS threads + * cooperative to compute TILE_ROWS * 2 output texels. Therefore, + * NGROUP * TILE_ROWS * 2 output texels are computed across one work group. + * + * The threads co-operate by each thread computing a partial reduction along the + * K dimension. To illustrate the computation, consider a scalar variant of the + * algorithm that computes the dot product of 2 vectors. Also assume that + * NWORKERS is 8. + * + * Thread 1 in each group will compute: + * (mat1[0] * mat2[0]) + (mat1[8] * mat2[8]) + (mat1[16] * mat2[16]) + ... + * + * Thread 2 in each group will compute: + * (mat1[1] * mat2[1]) + (mat2[9] * mat2[9]) + (mat1[17] * mat2[17]) + ... + * + * Thread 3 in each group will compute: + * (mat1[2] * mat2[2]) + (mat2[10] * mat2[10]) + (mat1[18] * mat2[18]) + ... + * + * The partial accumulations is structured such that memory accesses in each + * loop iteration can be coalesced. + * + * Then, at the end first thread in each group will accumulate the partial + * accumulations computed by each thread to obtain the final result. + * + * Note that this shader assumes that all tensors are width packed. + */ +void main() { + const uint out_row = gl_GlobalInvocationID.y * TILE_ROWS; + // Each thread writes out 2 texels along the width axis, equivalent to 8 + // scalar elements. Therefore multiply the thread_idx.x by 8. + const uint out_col = gl_GlobalInvocationID.x << 3; + // Similar reasoning to the above, each thread works on 2 texels along the + // width axis so multiply thread_idx.x by 2. + const int out_col_texel_idx = int(gl_GlobalInvocationID.x) << 1; + + const uint gid = gl_LocalInvocationID.x; // group id + const uint wid = gl_LocalInvocationID.z; // worker id + + if (out_col >= out_sizes.x || out_row >= out_sizes.y) { + return; + } + + const int num_blocks = mat1_sizes.x / group_size; + + VEC4_T mat1[TILE_ROWS]; + VEC4_T qmat2[4][2]; + VEC4_T local_sums[TILE_ROWS][2]; + + [[unroll]] for (int r = 0; r < TILE_ROWS; ++r) { + local_sums[r][0] = VEC4_T(0); + local_sums[r][1] = VEC4_T(0); + } + + VEC4_T scales[2]; + VEC4_T zeros[2]; + + $if WEIGHT_STORAGE == "buffer": + const int qmat2_stride = qmat2_sizes.x >> 2; + $if PARAMS_STORAGE == "buffer": + const int qparams_y_stride = out_sizes.x >> 2; + const int qparams_z_stride = qparams_y_stride * 2; + + for (int block_idx = 0; block_idx < num_blocks; ++block_idx) { + $if PARAMS_STORAGE == "buffer": + scales[0] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx]; + zeros[0] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx + qparams_y_stride]; + + scales[1] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx + 1]; + zeros[1] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx + 1 + qparams_y_stride]; + $else: + scales[0] = texelFetch(t_qparams, ivec3(out_col_texel_idx, 0, block_idx), 0); + zeros[0] = texelFetch(t_qparams, ivec3(out_col_texel_idx, 1, block_idx), 0); + + scales[1] = texelFetch(t_qparams, ivec3(out_col_texel_idx + 1, 0, block_idx), 0); + zeros[1] = texelFetch(t_qparams, ivec3(out_col_texel_idx + 1, 1, block_idx), 0); + + for (uint g_idx = 4 * wid; g_idx < group_size; g_idx += (4 * NWORKERS)) { + const uint k = block_idx * group_size + g_idx; + + // Preload B + [[unroll]] for (int r = 0; r < 4; ++r) { + $if WEIGHT_STORAGE == "buffer": + const u8vec4 packed_weight_tex = t_qmat2[(k + r) * qmat2_stride + gl_GlobalInvocationID.x]; + $else: + const uvec4 packed_weight_tex = texelFetch( + t_qmat2, + ivec2(gl_GlobalInvocationID.x, k + r), + 0); + + qmat2[r][0] = (VEC4_T((packed_weight_tex & 0xF0) >> 4) - 8.0) * scales[0] + zeros[0]; + qmat2[r][1] = (VEC4_T(packed_weight_tex & 0x0F) - 8.0) * scales[1] + zeros[1]; + } + + // Preload A + [[unroll]] for (int r = 0; r < TILE_ROWS; ++r) { + $if IN_STORAGE == "buffer": + mat1[r] = t_mat1[((out_row + r) * mat1_sizes.x + k) >> 2]; + $else: + mat1[r] = texelFetch(t_mat1, ivec3(k >> 2, out_row + r, 0), 0); + } + + // Accumulate local output tile + [[unroll]] for (int r = 0; r < TILE_ROWS; ++r) { + local_sums[r][0] += mat1[r].x * qmat2[0][0] + + mat1[r].y * qmat2[1][0] + + mat1[r].z * qmat2[2][0] + + mat1[r].w * qmat2[3][0]; + + local_sums[r][1] += mat1[r].x * qmat2[0][1] + + mat1[r].y * qmat2[1][1] + + mat1[r].z * qmat2[2][1] + + mat1[r].w * qmat2[3][1]; + } + } + } + + [[unroll]] for (int r = 0; r < TILE_ROWS; ++r) { + partial_sums[gid][wid][r][0] = local_sums[r][0]; + partial_sums[gid][wid][r][1] = local_sums[r][1]; + } + + memoryBarrierShared(); + barrier(); + + if (wid != 0) { + return; + } + + VEC4_T sums[TILE_ROWS][2]; + + for (int r = 0; r < TILE_ROWS; ++r) { + sums[r][0] = VEC4_T(0); + sums[r][1] = VEC4_T(0); + [[unroll]] for (int worker = 0; worker < NWORKERS; ++ worker) { + sums[r][0] += partial_sums[gid][worker][r][0]; + sums[r][1] += partial_sums[gid][worker][r][1]; + } + } + + [[unroll]] for (int r = 0; r < TILE_ROWS; ++r) { + $if OUT_STORAGE == "buffer": + t_out[((out_row + r) * out_sizes.x + out_col) >> 2] = sums[r][0]; + t_out[((out_row + r) * out_sizes.x + out_col + 4) >> 2] = sums[r][1]; + $else: + imageStore(t_out, ivec3(out_col_texel_idx, out_row + r, 0), sums[r][0]); + imageStore(t_out, ivec3(out_col_texel_idx + 1, out_row + r, 0), sums[r][1]); + } +} diff --git a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear_coop.yaml b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear_coop.yaml new file mode 100644 index 00000000000..504cc4ab3b1 --- /dev/null +++ b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear_coop.yaml @@ -0,0 +1,23 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# +# This source code is licensed under the BSD-style license found in the +# LICENSE file in the root directory of this source tree. + +q_4w_linear_coop: + parameter_names_with_default_values: + DTYPE: float + OUT_STORAGE: texture3d + IN_STORAGE: texture3d + WEIGHT_STORAGE: texture2d + PARAMS_STORAGE: buffer + TILE_ROWS: 1 + shader_variants: + - NAME: q_4w_linear_coop_texture3d_texture3d_texture2d_float + - NAME: q_4w_linear_coop_buffer_buffer_texture2d_float + OUT_STORAGE: buffer + IN_STORAGE: buffer + - NAME: q_4w_linear_coop_buffer_buffer_buffer_float + OUT_STORAGE: buffer + IN_STORAGE: buffer + WEIGHT_STORAGE: buffer diff --git a/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp b/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp index 6bddbd49794..3740c2d49c9 100644 --- a/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp +++ b/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp @@ -128,6 +128,15 @@ void add_q_4w_linear_node( check_q_4w_linear_args( graph, mat1, mat2_data, group_size, scales_and_zeros_data, out); + const uint32_t group_size_val = graph.extract_scalar(group_size); + + bool use_coop_algorithm = false; + // Apply the coop algorithm for gemv cases, i.e. mat1 is a vector as opposed + // to a matrix. + if (graph.size_at(-2, mat1) == 1) { + use_coop_algorithm = true; + } + ValueRef mat2 = prepack_int4_linear_weight_transposed_interleaved(graph, mat2_data); @@ -135,17 +144,21 @@ void add_q_4w_linear_node( graph, scales_and_zeros_data, utils::kBuffer, utils::kWidthPacked); std::string kernel_name = "q_4w_linear"; + if (use_coop_algorithm) { + kernel_name += "_coop"; + } add_storage_type_suffix(kernel_name, graph.storage_type_of(out)); add_storage_type_suffix(kernel_name, graph.storage_type_of(mat1)); add_storage_type_suffix(kernel_name, graph.storage_type_of(mat2)); add_dtype_suffix(kernel_name, graph.dtype_of(out)); - const uint32_t group_size_val = graph.extract_scalar(group_size); - utils::uvec3 global_wg_size = graph.logical_limits_of(out); global_wg_size[0] = utils::div_up(global_wg_size[0], uint32_t(2)); utils::uvec3 local_wg_size = graph.create_local_wg_size(global_wg_size); + if (use_coop_algorithm) { + local_wg_size = {8, 1, 8}; + } graph.execute_nodes().emplace_back(new DispatchNode( graph, diff --git a/backends/vulkan/test/op_tests/linear_weight_int4_test.cpp b/backends/vulkan/test/op_tests/linear_weight_int4_test.cpp index b8862b8bb30..e617f5b5249 100644 --- a/backends/vulkan/test/op_tests/linear_weight_int4_test.cpp +++ b/backends/vulkan/test/op_tests/linear_weight_int4_test.cpp @@ -273,10 +273,24 @@ TEST(VulkanInt4LinearTest, test_reference_impl) { /*N = */ 32); } -TEST(VulkanInt4LinearTest, test_vulkan_impl) { +TEST(VulkanInt4LinearTest, test_vulkan_impl_small_m) { test_vulkan_linear_int4( /*B = */ 1, /*M = */ 4, /*K = */ 128, /*N = */ 32); + + test_vulkan_linear_int4( + /*B = */ 1, + /*M = */ 1, + /*K = */ 256, + /*N = */ 256); +} + +TEST(VulkanInt4LinearTest, test_vulkan_impl_gemm) { + test_vulkan_linear_int4( + /*B = */ 1, + /*M = */ 256, + /*K = */ 256, + /*N = */ 256); } From bc537e9125b5cf15812fbe570eb648290f97a8f8 Mon Sep 17 00:00:00 2001 From: Chun-I Tsai Date: Thu, 17 Apr 2025 03:03:54 +0800 Subject: [PATCH 176/244] Qualcomm AI Engine Direct - Add block quantization to llama (#10225) - Add CLI argument to use block quantization for llama Co-authored-by: Chun-I Tsai --- examples/qualcomm/oss_scripts/llama/llama.py | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/examples/qualcomm/oss_scripts/llama/llama.py b/examples/qualcomm/oss_scripts/llama/llama.py index 7e1b6872882..375edf9fb6c 100755 --- a/examples/qualcomm/oss_scripts/llama/llama.py +++ b/examples/qualcomm/oss_scripts/llama/llama.py @@ -390,6 +390,14 @@ def quantize(self, quant_dtype, args, tokenizer, custom_annotations=()): fx_graph_module = torch.export.export( self.llama_graph_module, self.inputs, strict=True ).module() + + if QuantDtype == QuantDtype.use_16a4w_block: + conv_nodes = [ + n for n in fx_graph_module.graph.nodes if "conv" in n.name + ] + block_size_map = {n.name: (1, 64, 1, 1) for n in conv_nodes} + quantizer.set_block_size_map(block_size_map) + fx_graph_module = prepare_pt2e(fx_graph_module, quantizer) logging.info("Quantizing the model...") @@ -574,13 +582,14 @@ def permute(w, heads): fixed_point_type["kv_type"] = torch.uint8 if args.ptq == "8a8w": fixed_point_type["io_type"] = torch.uint8 - elif args.ptq == "16a4w": + elif args.ptq in ("16a4w", "16a4w_block"): fixed_point_type["io_type"] = torch.uint16 else: assert args.ptq in [ "8a8w", "16a4w", - ], f"No support for quant type {args.ptq}. Support 8a8w and 16a4w." + "16a4w_block", + ], f"No support for quant type {args.ptq}. Support 8a8w, 16a4w and 16a4w_block." quant_dtype = getattr(QuantDtype, f"use_{args.ptq}") assert args.tokenizer_model is not None, "Need tokenizer model for calibration" @@ -954,7 +963,7 @@ def _build_parser(): parser.add_argument( "-P", "--ptq", - help="If specified, will do PTQ quantization. default is 16bits activation and 4bits weight. Support 8a8w and 16a4w.", + help="If specified, will do PTQ quantization. default is 16bits activation and 4bits weight. Support 8a8w, 16a4w and 16a4w_block.", type=str, ) From 21c37aa1936833fe3db5addb10fba56e2b4f0f2d Mon Sep 17 00:00:00 2001 From: pytorchbot Date: Wed, 16 Apr 2025 15:13:54 -0400 Subject: [PATCH 177/244] [ET-VK] Use performant tiled algorithm for 4 bit weight only quantized linear (#10236) ## Context As title. Update the default compute shader for weight-only quantized int4 linear to use a tiled algorithm, which should boost performance for `gemm` cases, i.e. where `mat1` is a matrix. ## Changes * Changed `q_4w_linear` name to `q_4w_linear_tiled` name * Update the compute shader to use tiled algorithm Using a value of 3 for `TILE_ROWS`; I expect to add variants which switch between different output tile configurations. Differential Revision: [D73044649](https://our.internmc.facebook.com/intern/diff/D73044649/) --- ..._linear_weight_transposed_interleaved.yaml | 2 +- .../graph/ops/glsl/q_4w_linear_tiled.glsl | 161 ++++++++++++++++++ ..._4w_linear.yaml => q_4w_linear_tiled.yaml} | 9 +- .../ops/impl/QuantizedLinearGroupwiseInt4.cpp | 6 +- 4 files changed, 172 insertions(+), 6 deletions(-) create mode 100644 backends/vulkan/runtime/graph/ops/glsl/q_4w_linear_tiled.glsl rename backends/vulkan/runtime/graph/ops/glsl/{q_4w_linear.yaml => q_4w_linear_tiled.yaml} (70%) diff --git a/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml b/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml index 57668223f6e..145f4301f14 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml +++ b/backends/vulkan/runtime/graph/ops/glsl/pack_int4_linear_weight_transposed_interleaved.yaml @@ -13,4 +13,4 @@ pack_int4_linear_weight_transposed_interleaved: - NAME: pack_int4_linear_weight_transposed_interleaved_buffer STORAGE: buffer - NAME: pack_int4_linear_weight_transposed_interleaved_nobitw8buffer_texture2d - NO_INT8_BUFFERS: true \ No newline at end of file + NO_INT8_BUFFERS: true diff --git a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear_tiled.glsl b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear_tiled.glsl new file mode 100644 index 00000000000..64d0991e489 --- /dev/null +++ b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear_tiled.glsl @@ -0,0 +1,161 @@ +/* + * Copyright (c) Meta Platforms, Inc. and affiliates. + * All rights reserved. + * + * This source code is licensed under the BSD-style license found in the + * LICENSE file in the root directory of this source tree. + */ + +#version 450 core + +#define PRECISION ${PRECISION} + +#define T ${buffer_scalar_type(DTYPE)} +#define VEC4_T ${buffer_gvec_type(DTYPE, 4)} + +#define TILE_ROWS ${TILE_ROWS} + +${define_required_extensions(DTYPE)} +$if WEIGHT_STORAGE == "buffer": + ${define_required_extensions("uint8")} + +#extension GL_EXT_control_flow_attributes : require + +layout(std430) buffer; + +${layout_declare_tensor(B, "w", "t_out", DTYPE, OUT_STORAGE, is_scalar_array=False)} +${layout_declare_tensor(B, "r", "t_mat1", DTYPE, IN_STORAGE, is_scalar_array=False)} +${layout_declare_tensor(B, "r", "t_qmat2", "uint8", WEIGHT_STORAGE, is_scalar_array=False)} +${layout_declare_tensor(B, "r", "t_qparams", DTYPE, "buffer", is_scalar_array=False)} + +layout(push_constant) uniform restrict Block { + ivec4 out_sizes; + ivec4 mat1_sizes; + ivec4 qmat2_sizes; +}; + +layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in; + +layout(constant_id = 3) const int group_size = 64; + +/* + * This shader computes a linear operator between a floating point input matrix + * x and a weights matrix that is quantized to 4 bits. + * + * The (W, H, C) shape of each tensor is: + * - x: (K, M) + * - weights: (N / 2, K) + * - The weights tensor has a data type of `uint8`. Each element in the tensor + * contains 2 4-bit values packed into a uint8. + * - See the pack_int4_linear_weight_transposed_interleave shader to see more + * details on how the weight tensor is stored. + * - qparams: (2, N, number_of_groups) + * - This tensor contains the scales and zeros quantization parameters for the + * weights tensor. The weight tensor is quantized group-wise, which means + * that every `group_size` elements along the K dimension of the weights + * tensor has independent quantization parameters. Along the width dim, the + * first value contains the scale for the group and the second value + * contains the zero point for the group. + * + * Each thread computes a tile of TILE_ROWS * 2 texels of the output tensor. + * + * Note that this shader assumes that all tensors are width packed. + */ +void main() { + const uint out_row = gl_GlobalInvocationID.y * TILE_ROWS; + // Each thread writes out 2 texels along the width axis, equivalent to 8 + // scalar elements. Therefore multiply the thread_idx.x by 8. + const uint out_col = gl_GlobalInvocationID.x << 3; + // Similar reasoning to the above, each thread works on 2 texels along the + // width axis so multiply thread_idx.x by 2. + const int out_col_texel_idx = int(gl_GlobalInvocationID.x) << 1; + + if (out_col >= out_sizes.x || out_row >= out_sizes.y) { + return; + } + + const int num_blocks = mat1_sizes.x / group_size; + + VEC4_T mat1[TILE_ROWS]; + VEC4_T qmat2[4][2]; + VEC4_T sums[TILE_ROWS][2]; + + [[unroll]] for (int r = 0; r < TILE_ROWS; ++r) { + sums[r][0] = VEC4_T(0); + sums[r][1] = VEC4_T(0); + } + + VEC4_T scales[2]; + VEC4_T zeros[2]; + + $if WEIGHT_STORAGE == "buffer": + const int qmat2_stride = qmat2_sizes.x >> 2; + $if PARAMS_STORAGE == "buffer": + const int qparams_y_stride = out_sizes.x >> 2; + const int qparams_z_stride = qparams_y_stride * 2; + + for (int block_idx = 0; block_idx < num_blocks; ++block_idx) { + $if PARAMS_STORAGE == "buffer": + scales[0] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx]; + zeros[0] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx + qparams_y_stride]; + + scales[1] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx + 1]; + zeros[1] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx + 1 + qparams_y_stride]; + $else: + scales[0] = texelFetch(t_qparams, ivec3(out_col_texel_idx, 0, block_idx), 0); + zeros[0] = texelFetch(t_qparams, ivec3(out_col_texel_idx, 1, block_idx), 0); + + scales[1] = texelFetch(t_qparams, ivec3(out_col_texel_idx + 1, 0, block_idx), 0); + zeros[1] = texelFetch(t_qparams, ivec3(out_col_texel_idx + 1, 1, block_idx), 0); + + for (int g_idx = 0; g_idx < group_size; g_idx += 4) { + const int k = block_idx * group_size + g_idx; + + // Preload B + [[unroll]] for (int r = 0; r < 4; ++r) { + $if WEIGHT_STORAGE == "buffer": + const u8vec4 packed_weight_tex = t_qmat2[(k + r) * qmat2_stride + gl_GlobalInvocationID.x]; + $else: + const uvec4 packed_weight_tex = texelFetch( + t_qmat2, + ivec2(gl_GlobalInvocationID.x, k + r), + 0); + + qmat2[r][0] = (VEC4_T((packed_weight_tex & 0xF0) >> 4) - 8.0) * scales[0] + zeros[0]; + qmat2[r][1] = (VEC4_T(packed_weight_tex & 0x0F) - 8.0) * scales[1] + zeros[1]; + } + + // Preload A + [[unroll]] for (int r = 0; r < TILE_ROWS; ++r) { + $if IN_STORAGE == "buffer": + mat1[r] = t_mat1[((out_row + r) * mat1_sizes.x + k) >> 2]; + $else: + mat1[r] = texelFetch(t_mat1, ivec3(k >> 2, out_row + r, 0), 0); + } + + // Accumulate output tile + [[unroll]] for (int r = 0; r < TILE_ROWS; ++r) { + sums[r][0] += mat1[r].x * qmat2[0][0] + + mat1[r].y * qmat2[1][0] + + mat1[r].z * qmat2[2][0] + + mat1[r].w * qmat2[3][0]; + + sums[r][1] += mat1[r].x * qmat2[0][1] + + mat1[r].y * qmat2[1][1] + + mat1[r].z * qmat2[2][1] + + mat1[r].w * qmat2[3][1]; + } + } + } + + [[unroll]] for (int r = 0; r < TILE_ROWS; ++r) { + $if OUT_STORAGE == "buffer": + if (out_row + r < out_sizes.y) { + t_out[((out_row + r) * out_sizes.x + out_col) >> 2] = sums[r][0]; + t_out[((out_row + r) * out_sizes.x + out_col + 4) >> 2] = sums[r][1]; + } + $else: + imageStore(t_out, ivec3(out_col_texel_idx, out_row + r, 0), sums[r][0]); + imageStore(t_out, ivec3(out_col_texel_idx + 1, out_row + r, 0), sums[r][1]); + } +} diff --git a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.yaml b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear_tiled.yaml similarity index 70% rename from backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.yaml rename to backends/vulkan/runtime/graph/ops/glsl/q_4w_linear_tiled.yaml index b58b5d2cf55..865a46629df 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.yaml +++ b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear_tiled.yaml @@ -4,19 +4,20 @@ # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. -q_4w_linear: +q_4w_linear_tiled: parameter_names_with_default_values: DTYPE: float OUT_STORAGE: texture3d IN_STORAGE: texture3d WEIGHT_STORAGE: texture2d PARAMS_STORAGE: buffer + TILE_ROWS: 3 shader_variants: - - NAME: q_4w_linear_texture3d_texture3d_texture2d_float - - NAME: q_4w_linear_buffer_buffer_texture2d_float + - NAME: q_4w_linear_tiled_texture3d_texture3d_texture2d_float + - NAME: q_4w_linear_tiled_buffer_buffer_texture2d_float OUT_STORAGE: buffer IN_STORAGE: buffer - - NAME: q_4w_linear_buffer_buffer_buffer_float + - NAME: q_4w_linear_tiled_buffer_buffer_buffer_float OUT_STORAGE: buffer IN_STORAGE: buffer WEIGHT_STORAGE: buffer diff --git a/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp b/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp index 3740c2d49c9..4b33dd9b806 100644 --- a/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp +++ b/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearGroupwiseInt4.cpp @@ -146,6 +146,8 @@ void add_q_4w_linear_node( std::string kernel_name = "q_4w_linear"; if (use_coop_algorithm) { kernel_name += "_coop"; + } else { + kernel_name += "_tiled"; } add_storage_type_suffix(kernel_name, graph.storage_type_of(out)); add_storage_type_suffix(kernel_name, graph.storage_type_of(mat1)); @@ -154,10 +156,12 @@ void add_q_4w_linear_node( utils::uvec3 global_wg_size = graph.logical_limits_of(out); global_wg_size[0] = utils::div_up(global_wg_size[0], uint32_t(2)); - utils::uvec3 local_wg_size = graph.create_local_wg_size(global_wg_size); + if (use_coop_algorithm) { local_wg_size = {8, 1, 8}; + } else { + global_wg_size[1] = utils::div_up(global_wg_size[1], uint32_t(3)); } graph.execute_nodes().emplace_back(new DispatchNode( From d6e4c18b701c7863cff48a33949c81dca0877916 Mon Sep 17 00:00:00 2001 From: Manuel Candales <42380156+manuelcandales@users.noreply.github.com> Date: Wed, 16 Apr 2025 16:05:29 -0400 Subject: [PATCH 178/244] clean up complex tests Differential Revision: D72197507 Pull Request resolved: https://github.com/pytorch/executorch/pull/10213 --- kernels/test/op_abs_test.cpp | 7 +---- kernels/test/op_diagonal_copy_test.cpp | 37 ++++++++++---------------- 2 files changed, 15 insertions(+), 29 deletions(-) diff --git a/kernels/test/op_abs_test.cpp b/kernels/test/op_abs_test.cpp index 0d022d0a839..eb4a808b321 100644 --- a/kernels/test/op_abs_test.cpp +++ b/kernels/test/op_abs_test.cpp @@ -44,12 +44,7 @@ class OpAbsTest : public OperatorTest { TensorFactory tf; constexpr auto REAL_DTYPE = executorch::runtime::toRealValueType(DTYPE); TensorFactory tf_out; - using REAL_CTYPE = - typename executorch::runtime::ScalarTypeToCppType::type; - Tensor in = tf.make( - {1, 2}, - {CTYPE{REAL_CTYPE(3), REAL_CTYPE(4)}, - CTYPE{REAL_CTYPE(5), REAL_CTYPE(12)}}); + Tensor in = tf.make({1, 2}, {CTYPE(3, 4), CTYPE(5, 12)}); Tensor out = tf_out.zeros({1, 2}); Tensor expected = tf_out.make({1, 2}, {5, 13}); Tensor ret = op_abs_out(in, out); diff --git a/kernels/test/op_diagonal_copy_test.cpp b/kernels/test/op_diagonal_copy_test.cpp index 35d19bf661e..080b0d70645 100644 --- a/kernels/test/op_diagonal_copy_test.cpp +++ b/kernels/test/op_diagonal_copy_test.cpp @@ -54,31 +54,22 @@ class OpDiagonalCopyOutTest : public ::testing::Test { template void run_2d_complex_dtype() { TensorFactory tf; - constexpr auto REAL_DTYPE = executorch::runtime::toRealValueType(DTYPE); - using REAL_CTYPE = - typename executorch::runtime::ScalarTypeToCppType::type; Tensor input = tf.make( {3, 4}, - {CTYPE{REAL_CTYPE(1), REAL_CTYPE(1)}, - CTYPE{REAL_CTYPE(2), REAL_CTYPE(2)}, - CTYPE{REAL_CTYPE(3), REAL_CTYPE(3)}, - CTYPE{REAL_CTYPE(4), REAL_CTYPE(4)}, - CTYPE{REAL_CTYPE(5), REAL_CTYPE(5)}, - CTYPE{REAL_CTYPE(6), REAL_CTYPE(6)}, - CTYPE{REAL_CTYPE(7), REAL_CTYPE(7)}, - CTYPE{REAL_CTYPE(8), REAL_CTYPE(8)}, - CTYPE{REAL_CTYPE(9), REAL_CTYPE(9)}, - CTYPE{REAL_CTYPE(10), REAL_CTYPE(10)}, - CTYPE{REAL_CTYPE(11), REAL_CTYPE(11)}, - CTYPE{REAL_CTYPE(12), REAL_CTYPE(12)}}); - Tensor out = tf.make( - {2}, - {CTYPE{REAL_CTYPE(0), REAL_CTYPE(0)}, - CTYPE{REAL_CTYPE(0), REAL_CTYPE(0)}}); - Tensor out_expected = tf.make( - {2}, - {CTYPE{REAL_CTYPE(5), REAL_CTYPE(5)}, - CTYPE{REAL_CTYPE(10), REAL_CTYPE(10)}}); + {CTYPE(1, 1), + CTYPE(2, 2), + CTYPE(3, 3), + CTYPE(4, 4), + CTYPE(5, 5), + CTYPE(6, 6), + CTYPE(7, 7), + CTYPE(8, 8), + CTYPE(9, 9), + CTYPE(10, 10), + CTYPE(11, 11), + CTYPE(12, 12)}); + Tensor out = tf.make({2}, {CTYPE(0, 0), CTYPE(0, 0)}); + Tensor out_expected = tf.make({2}, {CTYPE(5, 5), CTYPE(10, 10)}); op_diagonal_copy_out(input, 1, 1, 0, out); EXPECT_TENSOR_CLOSE(out, out_expected); } From 5589ab4384355a8545eff98f5a4fe9b1db20919e Mon Sep 17 00:00:00 2001 From: Sicheng Stephen Jia Date: Wed, 16 Apr 2025 16:13:19 -0400 Subject: [PATCH 179/244] [ET-VK] Manual sync to fbsource (#10238) Contains commits squashed from https://github.com/pytorch/executorch/pull/10117 PR stack. Differential Revision: [D72866962](https://our.internmc.facebook.com/intern/diff/D72866962/) Differential Revision: [D72862490](https://our.internmc.facebook.com/intern/diff/D72862490/) Differential Revision: [D72581293](https://our.internmc.facebook.com/intern/diff/D72581293/) Differential Revision: [D72430290](https://our.internmc.facebook.com/intern/diff/D72430290/) @diff-train-skip-merge Co-authored-by: Vivek Trivedi <5340687+trivedivivek@users.noreply.github.com> --- .../graph/ops/glsl/native_layer_norm.glsl | 234 ++++++++++++------ .../runtime/graph/ops/glsl/permute.glsl | 14 +- .../runtime/graph/ops/glsl/q_4w_linear.glsl | 133 ---------- .../runtime/graph/ops/glsl/q_8w_linear.glsl | 54 ++-- .../graph/ops/impl/NativeLayerNorm.cpp | 15 +- .../graph/ops/impl/QuantizedLinearInt8.cpp | 58 ++--- 6 files changed, 228 insertions(+), 280 deletions(-) delete mode 100644 backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl diff --git a/backends/vulkan/runtime/graph/ops/glsl/native_layer_norm.glsl b/backends/vulkan/runtime/graph/ops/glsl/native_layer_norm.glsl index d6c94661ace..cdbb85da4a7 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/native_layer_norm.glsl +++ b/backends/vulkan/runtime/graph/ops/glsl/native_layer_norm.glsl @@ -43,106 +43,190 @@ ${layout_declare_spec_const(C, "int", "out_layout", "DEFAULT_LAYOUT")} const lowp ivec4 out_axis_map = unhash_axis_map(out_layout); const lowp int out_packed_dim = unhash_packed_dim(out_layout); -void main() { - const ivec3 lpos = ivec3(gl_GlobalInvocationID); +#define SHARED_MEMORY_FACTOR 2 +#define MAX_WORKGROUP_SIZE 64 + +#define offset_pos_index(index) ((index) + ((index) >> 2)) + +shared VEC4_T shared_input[offset_pos_index(MAX_WORKGROUP_SIZE * SHARED_MEMORY_FACTOR)]; + +// function to reduce input data in workgroup's x dimension +void reduce_input(const int width_stride, const int shared_idx_offset) { + // wait for all shared memory writes to finish + memoryBarrierShared(); + barrier(); + + // loop log(width_stride) times + for (int current_stride = 1, index = int(gl_LocalInvocationID.x << 1); current_stride < width_stride; current_stride *= 2, index <<= 1) { + // if the index at this thread is within the width stride + if (index < width_stride) { + const int local_shared_idx = shared_idx_offset + index; + // add the value at current stride to this thread's value + shared_input[offset_pos_index(local_shared_idx)] += shared_input[offset_pos_index(local_shared_idx + current_stride)]; + } - if (any(greaterThanEqual(lpos, out_limits))) { - return; + memoryBarrierShared(); + barrier(); } +} +void main() { + const ivec3 lpos = ivec3(gl_GlobalInvocationID); const int width = int(sizes.x); + ivec3 in_pos = lpos_to_pos(lpos, in_axis_map); + + // width batch read stride + const int width_stride = int(gl_WorkGroupSize.x) * SHARED_MEMORY_FACTOR; + + // local memory starting offset for this thread + const int shared_idx_offset = width_stride * int(gl_WorkGroupSize.y * gl_LocalInvocationID.z + gl_LocalInvocationID.y); + + // local memory index for this thread + const int shared_idx = shared_idx_offset + int(gl_LocalInvocationID.x); + + // if packed dimension width if (in_packed_dim != W_DIM) { VEC4_T mean = VEC4_T(0); - VEC4_T delta = VEC4_T(0); - VEC4_T delta2 = VEC4_T(0); - VEC4_T M2 = VEC4_T(0); - - // Use Welford's online algorithm to compute mean and variance in one pass - // https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_online_algorithm - ivec3 in_pos = lpos_to_pos(lpos, in_axis_map); - for (int w = 0; w < width; ++w) { - in_pos[in_axis_map.x] = w; - VEC4_T v = load_texel(t_in, in_pos); - delta = v - mean; - mean += delta / (w + 1); - delta2 = v - mean; - M2 += delta * delta2; + VEC4_T var = VEC4_T(0); + + // Loop over the width in stride increments + for (int width_offset = 0; width_offset < width; width_offset += width_stride) { + // Read input in shared memory + for (int si = 0; si < SHARED_MEMORY_FACTOR; si++) { + in_pos[in_axis_map.x] = width_offset + int(gl_LocalInvocationID.x + si * gl_WorkGroupSize.x); + + VEC4_T in_val = VEC4_T(0); + if (all(lessThan(in_pos, out_limits))) { + in_val = load_texel(t_in, in_pos); + } + shared_input[offset_pos_index(shared_idx + si * gl_WorkGroupSize.x)] = in_val; + } + + reduce_input(width_stride, shared_idx_offset); + mean += shared_input[offset_pos_index(shared_idx_offset)]; + } + + mean /= width; + + // Loop over the width in stride increments + for (int width_offset = 0; width_offset < width; width_offset += width_stride) { + // Read input in shared memory + for (int si = 0; si < SHARED_MEMORY_FACTOR; si++) { + in_pos[in_axis_map.x] = width_offset + int(gl_LocalInvocationID.x + si * gl_WorkGroupSize.x); + + VEC4_T in_val = mean; + if (all(lessThan(in_pos, out_limits))) { + in_val = load_texel(t_in, in_pos); + } + + const VEC4_T delta = in_val - mean; + shared_input[offset_pos_index(shared_idx + si * gl_WorkGroupSize.x)] = delta * delta; + } + + reduce_input(width_stride, shared_idx_offset); + var += shared_input[offset_pos_index(shared_idx_offset)]; } - VEC4_T var = M2 / width; + var /= width; + VEC4_T rstd = pow(var + epsilon, VEC4_T(-0.5)); VEC4_T offset = -rstd * mean; - for (int w = 0; w < width; ++w) { - in_pos[in_axis_map.x] = w; - VEC4_T v = load_texel(t_in, in_pos); - // broadcasting - VEC4_T weight = load_texel(t_weight, ivec3(w, 0, 0)).xxxx; - VEC4_T bias = load_texel(t_bias, ivec3(w, 0, 0)).xxxx; - VEC4_T outtex = (v * rstd + offset) * weight + bias; - write_texel_lpos(t_out, ivec3(w, lpos.y, lpos.z), outtex, out_axis_map); + VEC4_T v = load_texel(t_in, lpos); + VEC4_T weight = load_texel(t_weight, ivec3(lpos.x, 0, 0)).xxxx; + VEC4_T bias = load_texel(t_bias, ivec3(lpos.x, 0, 0)).xxxx; + VEC4_T outtex = (v * rstd + offset) * weight + bias; + if (all(lessThan(lpos, out_limits))) { + write_texel_lpos(t_out, ivec3(lpos.x, lpos.y, lpos.z), outtex, out_axis_map); } - write_texel(t_mean, lpos, mean); - write_texel(t_rstd, lpos, rstd); + if (gl_GlobalInvocationID.x == 0) { + write_texel(t_mean, lpos, mean); + write_texel(t_rstd, lpos, rstd); + } } else { - const int packed_width = divup4(width); - + const int last_packed_width_index = divup4(width) - 1; T mean = T(0); - T delta = T(0); - T delta2 = T(0); - T M2 = T(0); - // Use Welford's online algorithm to compute mean and variance in one pass - // https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_online_algorithm - ivec3 in_pos = lpos_to_pos(lpos, in_axis_map); - T width_counter = T(1); - - const bool has_unaligned_width = (width & 0x3) != 0; - const int fully_packed_4_comp_count = packed_width - mix(0, 1, has_unaligned_width); - - // iterate through texels that are fully packed ie. has 4 components - for (int w = 0; w < fully_packed_4_comp_count; ++w) { - in_pos[in_axis_map.x] = w; - VEC4_T v = load_texel(t_in, in_pos); - for (int i=0; i<4; i++) { - delta = v[i] - mean; - mean += delta / width_counter; - delta2 = v[i] - mean; - M2 += delta * delta2; - width_counter++; + T var = T(0); + const int remain = width & 3; + + const int in_pos_x_limit = out_limits[in_axis_map.x]; + + // Loop over the width in stride increments + for (int width_offset = 0; width_offset <= last_packed_width_index; width_offset += width_stride) { + // Read input in shared memory + for (int si = 0; si < SHARED_MEMORY_FACTOR; si++) { + const int in_pos_x = width_offset + int(gl_LocalInvocationID.x + si * gl_WorkGroupSize.x); + in_pos[in_axis_map.x] = in_pos_x; + + VEC4_T in_val = VEC4_T(0); + if (in_pos_x < in_pos_x_limit) { + in_val = load_texel(t_in, in_pos); + } + + if (in_pos_x == last_packed_width_index && remain != 0) { + const int remain_inv = 4 - remain; + in_val.y = mix(in_val.y, T(0), remain_inv > 2); + in_val.z = mix(in_val.z, T(0), remain_inv > 1); + in_val.w = mix(in_val.w, T(0), remain_inv > 0); + } + + shared_input[offset_pos_index(shared_idx + si * gl_WorkGroupSize.x)] = in_val; } + + reduce_input(width_stride, shared_idx_offset); + const VEC4_T val = shared_input[offset_pos_index(shared_idx_offset)]; + mean += val.x + val.y + val.z + val.w; } - // handle last texel if its not 4 aligned - if (has_unaligned_width) { - in_pos[in_axis_map.x] = fully_packed_4_comp_count; - const int remaining_width = width & 0x3; - - VEC4_T v = load_texel(t_in, in_pos); - for (int i=0; i 2); + in_val.z = mix(in_val.z, mean.x, remain_inv > 1); + in_val.w = mix(in_val.w, mean.x, remain_inv > 0); + } + + const VEC4_T delta = in_val - mean; + const VEC4_T delta2 = delta * delta; + shared_input[offset_pos_index(shared_idx + si * gl_WorkGroupSize.x)] = delta2; } + + reduce_input(width_stride, shared_idx_offset); + const VEC4_T val = shared_input[offset_pos_index(shared_idx_offset)]; + var += val.x + val.y + val.z + val.w; } - T var = M2 / (width_counter - 1); - T rstd = inversesqrt(var + epsilon); + var /= width; + + T rstd = pow(var + epsilon, T(-0.5)); T offset = -rstd * mean; - for (int w = 0; w < packed_width; ++w) { - in_pos[in_axis_map.x] = w; - VEC4_T v = load_texel(t_in, in_pos); - VEC4_T weight = load_texel(t_weight, ivec3(w, 0, 0)); - VEC4_T bias = load_texel(t_bias, ivec3(w, 0, 0)); - VEC4_T outtex = (v * rstd + offset) * weight + bias; - write_texel_lpos(t_out, ivec3(w, lpos.y, lpos.z), outtex, out_axis_map); + VEC4_T v = load_texel(t_in, lpos); + VEC4_T weight = load_texel(t_weight, ivec3(lpos.x, 0, 0)); + VEC4_T bias = load_texel(t_bias, ivec3(lpos.x, 0, 0)); + VEC4_T outtex = (v * rstd + offset) * weight + bias; + if (all(lessThan(lpos, out_limits))) { + write_texel_lpos(t_out, ivec3(lpos.x, lpos.y, lpos.z), outtex, out_axis_map); } - write_texel(t_mean, lpos, VEC4_T(mean)); - write_texel(t_rstd, lpos, VEC4_T(rstd)); + if (gl_GlobalInvocationID.x == 0) { + write_texel(t_mean, lpos, VEC4_T(mean)); + write_texel(t_rstd, lpos, VEC4_T(rstd)); + } } } diff --git a/backends/vulkan/runtime/graph/ops/glsl/permute.glsl b/backends/vulkan/runtime/graph/ops/glsl/permute.glsl index 8a8703becd9..716c42e8ede 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/permute.glsl +++ b/backends/vulkan/runtime/graph/ops/glsl/permute.glsl @@ -31,6 +31,8 @@ layout(push_constant) uniform PRECISION restrict Block { layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in; layout(constant_id = 3) const int packed_dim = C_DIM; +#extension GL_EXT_control_flow_attributes : require + void main() { ivec3 pos = ivec3(gl_GlobalInvocationID); @@ -54,11 +56,16 @@ void main() { in_bchw_pos[out_ndims[2]] = pos.y; in_bchw_pos[out_ndims[3]] = pos.x; - for (int j = 0; j < 4; ++j) { + const int in_packed_dim_size = in_sizes[3 - out_ndims[in_packed_dim_bchw_index]]; + + [[unroll]] for (int j = 0, bchw_index = in_bchw_pos[out_ndims[in_packed_dim_bchw_index]]; j < 4; ++j, ++bchw_index) { // terminate the loop if trying to access input texture out of bounds - if (any(greaterThanEqual(in_bchw_pos.wzyx, in_sizes.xyzw))) { + if (bchw_index >= in_packed_dim_size) { break; } + // go to position in the input, that is mapped to the packed dim in the output + in_bchw_pos[out_ndims[in_packed_dim_bchw_index]] = bchw_index; + ivec3 fetch_pos; fetch_pos.xy = in_bchw_pos.wz; @@ -74,9 +81,6 @@ void main() { // fetch input texel VEC4_T inval = VEC4_T(load_texel(t_in, fetch_pos)); outval[j] = inval[in_packed_dim_lane_index]; - - // go to next position in the input, that is mapped to the packed dim in the output - in_bchw_pos[out_ndims[in_packed_dim_bchw_index]]++; } pos[packed_dim] = int(gl_GlobalInvocationID[packed_dim]); diff --git a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl b/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl deleted file mode 100644 index 7350af3415c..00000000000 --- a/backends/vulkan/runtime/graph/ops/glsl/q_4w_linear.glsl +++ /dev/null @@ -1,133 +0,0 @@ -/* - * Copyright (c) Meta Platforms, Inc. and affiliates. - * All rights reserved. - * - * This source code is licensed under the BSD-style license found in the - * LICENSE file in the root directory of this source tree. - */ - -#version 450 core - -#define PRECISION ${PRECISION} - -#define T ${buffer_scalar_type(DTYPE)} -#define VEC4_T ${buffer_gvec_type(DTYPE, 4)} - -${define_required_extensions(DTYPE)} -$if WEIGHT_STORAGE == "buffer": - ${define_required_extensions("uint8")} - -layout(std430) buffer; - -${layout_declare_tensor(B, "w", "t_out", DTYPE, OUT_STORAGE, is_scalar_array=False)} -${layout_declare_tensor(B, "r", "t_mat1", DTYPE, IN_STORAGE, is_scalar_array=False)} -${layout_declare_tensor(B, "r", "t_qmat2", "uint8", WEIGHT_STORAGE, is_scalar_array=False)} -${layout_declare_tensor(B, "r", "t_qparams", DTYPE, "buffer", is_scalar_array=False)} - -layout(push_constant) uniform restrict Block { - ivec4 out_sizes; - ivec4 mat1_sizes; - ivec4 qmat2_sizes; -}; - -layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in; - -layout(constant_id = 3) const int group_size = 64; - -/* - * This shader computes a linear operator between a floating point input matrix - * x and a weights matrix that is quantized to 4 bits. - * - * The (W, H, C) shape of each tensor is: - * - x: (K, M) - * - weights: (N / 2, K) - * - The weights tensor has a data type of `uint8`. Each element in the tensor - * contains 2 4-bit values packed into a uint8. - * - See the pack_int4_linear_weight_transposed_interleave shader to see more - * details on how the weight tensor is stored. - * - qparams: (2, N, number_of_groups) - * - This tensor contains the scales and zeros quantization parameters for the - * weights tensor. The weight tensor is quantized group-wise, which means - * that every `group_size` elements along the K dimension of the weights - * tensor has independent quantization parameters. Along the width dim, the - * first value contains the scale for the group and the second value - * contains the zero point for the group. - * - * Note that this shader assumes that all tensors are width packed. - */ -void main() { - const uint out_row = gl_GlobalInvocationID.y; - // Each thread writes out 2 texels along the width axis, equivalent to 8 - // scalar elements. Therefore multiply the thread_idx.x by 8. - const uint out_col = gl_GlobalInvocationID.x << 3; - // Similar reasoning to the above, each thread works on 2 texels along the - // width axis so multiply thread_idx.x by 2. - const int out_col_texel_idx = int(gl_GlobalInvocationID.x) << 1; - - if (out_col >= out_sizes.x || out_row >= out_sizes.y) { - return; - } - - const int num_blocks = mat1_sizes.x / group_size; - - VEC4_T sums[2]; - - sums[0] = VEC4_T(0); - sums[1] = VEC4_T(0); - - VEC4_T scales[2]; - VEC4_T zeros[2]; - - $if WEIGHT_STORAGE == "buffer": - const int qmat2_stride = qmat2_sizes.x >> 2; - $if PARAMS_STORAGE == "buffer": - const int qparams_y_stride = out_sizes.x >> 2; - const int qparams_z_stride = qparams_y_stride * 2; - - for (int block_idx = 0; block_idx < num_blocks; ++block_idx) { - $if PARAMS_STORAGE == "buffer": - scales[0] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx]; - zeros[0] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx + qparams_y_stride]; - - scales[1] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx + 1]; - zeros[1] = t_qparams[block_idx * qparams_z_stride + out_col_texel_idx + 1 + qparams_y_stride]; - $else: - scales[0] = texelFetch(t_qparams, ivec3(out_col_texel_idx, 0, block_idx), 0); - zeros[0] = texelFetch(t_qparams, ivec3(out_col_texel_idx, 1, block_idx), 0); - - scales[1] = texelFetch(t_qparams, ivec3(out_col_texel_idx + 1, 0, block_idx), 0); - zeros[1] = texelFetch(t_qparams, ivec3(out_col_texel_idx + 1, 1, block_idx), 0); - - for (int g_idx = 0; g_idx < group_size; g_idx += 4) { - const int k = block_idx * group_size + g_idx; - - $if IN_STORAGE == "buffer": - const VEC4_T mat1_tex = t_mat1[(out_row * mat1_sizes.x + k) >> 2]; - $else: - const VEC4_T mat1_tex = texelFetch(t_mat1, ivec3(k >> 2, out_row, 0), 0); - - for (int comp = 0; comp < 4; ++comp) { - $if WEIGHT_STORAGE == "buffer": - const u8vec4 packed_weight_tex = t_qmat2[(k + comp) * qmat2_stride + gl_GlobalInvocationID.x]; - $else: - const uvec4 packed_weight_tex = texelFetch( - t_qmat2, - ivec2(gl_GlobalInvocationID.x, k + comp), - 0); - - const uvec4 weight_tex_1 = (packed_weight_tex & 0xF0) >> 4; - const uvec4 weight_tex_2 = packed_weight_tex & 0x0F; - - sums[0] += mat1_tex[comp] * ((vec4(weight_tex_1) - 8.0) * scales[0] + zeros[0]); - sums[1] += mat1_tex[comp] * ((vec4(weight_tex_2) - 8.0) * scales[1] + zeros[1]); - } - } - } - - $if OUT_STORAGE == "buffer": - t_out[(out_row * out_sizes.x + out_col) >> 2] = sums[0]; - t_out[(out_row * out_sizes.x + out_col + 4) >> 2] = sums[1]; - $else: - imageStore(t_out, ivec3(out_col_texel_idx, out_row, 0), sums[0]); - imageStore(t_out, ivec3(out_col_texel_idx + 1, out_row, 0), sums[1]); -} diff --git a/backends/vulkan/runtime/graph/ops/glsl/q_8w_linear.glsl b/backends/vulkan/runtime/graph/ops/glsl/q_8w_linear.glsl index 228e2e8f870..dfb5f1f2f9c 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/q_8w_linear.glsl +++ b/backends/vulkan/runtime/graph/ops/glsl/q_8w_linear.glsl @@ -29,16 +29,20 @@ ${layout_declare_tensor(2, "r", "t_qmat2", "int8", STORAGE)} ${layout_declare_tensor(3, "r", "t_scales", DTYPE, STORAGE)} $if STORAGE == "buffer": - ${layout_declare_ubo(4, "ivec4", "out_sizes")} - ${layout_declare_ubo(5, "ivec4", "out_strides")} - ${layout_declare_ubo(6, "int", "out_numel")} - ${layout_declare_ubo(7, "ivec4", "mat1_sizes")} - ${layout_declare_ubo(8, "ivec4", "mat1_strides")} - ${layout_declare_ubo(9, "ivec4", "qmat2_strides")} - ${layout_declare_ubo(10, "ivec4", "scales_strides")} + layout(push_constant) uniform restrict Block { + ivec4 out_sizes; + ivec4 out_strides; + ivec4 mat1_sizes; + ivec4 mat1_strides; + ivec4 qmat2_strides; + ivec4 scales_strides; + int out_numel; + }; $else: - ${layout_declare_ubo(4, "ivec3", "out_limits")} - ${layout_declare_ubo(5, "ivec4", "mat1_sizes")} + layout(push_constant) uniform restrict Block { + ivec3 out_limits; + ivec4 mat1_sizes; + }; layout(local_size_x_id = 0, local_size_y_id = 1, local_size_z_id = 2) in; @@ -83,42 +87,40 @@ void main() { #else // USING_TEXTURE -#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require - void main() { - const u16vec2 out_pos = u16vec2( - gl_GlobalInvocationID.x, - gl_GlobalInvocationID.y); + const ivec2 out_pos = ivec2( + gl_GlobalInvocationID.x % out_limits.x, + gl_GlobalInvocationID.x / out_limits.x); - if (out_pos.x >= out_limits.x || out_pos.y >= out_limits.y) { + if (out_pos.y >= out_limits.y) { return; } - const uint16_t qmat2_pos_x = out_pos.x; + const int qmat2_pos_x = out_pos.x; VEC4_T outtex = VEC4_T(0); - const VEC4_T scales = load_texel(t_scales, u16vec3(out_pos.x, 0, 0)); + const VEC4_T scales = load_texel(t_scales, ivec3(out_pos.x, 0, 0)); VEC4_T mat1_tex; VEC4_T mat2_tex[4]; for ( - uint16_t i = uint16_t(0), x = uint16_t(0); - i < uint16_t(mat1_sizes.x); - i += uint16_t(4), x++) + int i = 0, x = 0; + i < mat1_sizes.x; + i += 4, x++) { - mat1_tex = load_texel(t_mat1, u16vec3(x, out_pos.y, 0)); + mat1_tex = load_texel(t_mat1, ivec3(x, out_pos.y, 0)); - mat2_tex[0] = load_texel(t_qmat2, u16vec3(out_pos.x, i, 0)); - mat2_tex[1] = load_texel(t_qmat2, u16vec3(out_pos.x, i + uint16_t(1), 0)); - mat2_tex[2] = load_texel(t_qmat2, u16vec3(out_pos.x, i + uint16_t(2), 0)); - mat2_tex[3] = load_texel(t_qmat2, u16vec3(out_pos.x, i + uint16_t(3), 0)); + mat2_tex[0] = load_texel(t_qmat2, ivec3(out_pos.x, i, 0)); + mat2_tex[1] = load_texel(t_qmat2, ivec3(out_pos.x, i + 1, 0)); + mat2_tex[2] = load_texel(t_qmat2, ivec3(out_pos.x, i + 2, 0)); + mat2_tex[3] = load_texel(t_qmat2, ivec3(out_pos.x, i + 3, 0)); outtex += mat1_tex.x * mat2_tex[0] + mat1_tex.y * mat2_tex[1] + mat1_tex.z * mat2_tex[2] + mat1_tex.w * mat2_tex[3]; } outtex *= scales; - write_texel(t_out, u16vec3(out_pos, 0), outtex); + write_texel(t_out, ivec3(out_pos, 0), outtex); } #endif diff --git a/backends/vulkan/runtime/graph/ops/impl/NativeLayerNorm.cpp b/backends/vulkan/runtime/graph/ops/impl/NativeLayerNorm.cpp index 7aa98e52654..f2e8eff763a 100644 --- a/backends/vulkan/runtime/graph/ops/impl/NativeLayerNorm.cpp +++ b/backends/vulkan/runtime/graph/ops/impl/NativeLayerNorm.cpp @@ -83,8 +83,19 @@ void add_native_layer_norm_node( std::vector in_sizes = t_input->sizes(); - utils::uvec3 global_size = t_mean->logical_limits(); - utils::uvec3 local_size = adaptive_work_group_size(global_size); + utils::uvec3 global_size = t_out->logical_limits(); + utils::uvec3 local_size; + + // Since the shader sets shared memory scale factor > 1, if dispatch is + // greater than maximum WG size. Setting WG size in X axis to max WG size, + // would allow best thread utilization. + if (global_size[0] > 64) { + local_size = {64, 1, 1}; + } else { + // If thread size in X axis is smaller or equal to maximum WG size, we can + // let the function decide the best WG size. + local_size = graph.create_local_wg_size(global_size); + } std::string kernel_name("native_layer_norm"); kernel_name.reserve(kShaderNameReserve); diff --git a/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearInt8.cpp b/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearInt8.cpp index 49085ff4e06..5054b2e5e9c 100644 --- a/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearInt8.cpp +++ b/backends/vulkan/runtime/graph/ops/impl/QuantizedLinearInt8.cpp @@ -98,47 +98,25 @@ void add_q_8w_linear_node( add_dtype_suffix(kernel_name, graph.dtype_of(out_W_packed)); add_storage_type_suffix(kernel_name, graph.storage_type_of(out_W_packed)); - vkapi::ParamsBindList ubos({}); + std::vector pcs; if (graph.is_buffer_storage(out_W_packed)) { - ubos.append( - {graph.sizes_ubo(out_W_packed), - graph.strides_ubo(out_W_packed), - graph.numel_ubo(out_W_packed), - graph.sizes_ubo(mat1_W_packed), - graph.strides_ubo(mat1), - graph.strides_ubo(q_mat2), - graph.strides_ubo(scales)}); + pcs = { + graph.sizes_pc_of(out_W_packed), + graph.strides_pc_of(out_W_packed), + graph.sizes_pc_of(mat1_W_packed), + graph.strides_pc_of(mat1), + graph.strides_pc_of(q_mat2), + graph.strides_pc_of(scales), + graph.numel_pc_of(out_W_packed)}; } else { - ubos.append( - {graph.logical_limits_ubo(out_W_packed), - graph.sizes_ubo(mat1_W_packed)}); + pcs = { + graph.logical_limits_pc_of(out_W_packed), + graph.sizes_pc_of(mat1_W_packed)}; } - utils::uvec3 global_wg; - if (graph.is_buffer_storage(out)) { - global_wg = {static_cast(graph.numel_of(out_W_packed)), 1, 1}; - } else { - global_wg = graph.logical_limits_of(out_W_packed); - } - - utils::uvec3 local_wg{8, 8, 1}; - int32_t out_W = graph.size_at(-1, out_W_packed); - - if (graph.is_buffer_storage(out_W_packed)) { - local_wg[0] = 64; - local_wg[1] = 1; - local_wg[2] = 1; - } else { - if (out_W % 8 != 0) { - if (out_W % 4 == 0) { - local_wg[0] = 4; - local_wg[1] = 16; - } else { - local_wg[0] = 2; - local_wg[1] = 32; - } - } - } + const utils::uvec3 global_wg = { + static_cast(graph.numel_of(out_W_packed)), 1, 1}; + const utils::uvec3 local_wg{64, 1, 1}; graph.execute_nodes().emplace_back(new DispatchNode( graph, @@ -149,11 +127,13 @@ void add_q_8w_linear_node( {{out_W_packed, vkapi::MemoryAccessType::WRITE}, {{mat1_W_packed, q_mat2, scales}, vkapi::MemoryAccessType::READ}}, // Shader params buffers - ubos, + {}, // Specialization Constants {}, // Resizing Logic - resize_q_8w_linear_node)); + resize_q_8w_linear_node, + {}, + pcs)); if (!graph.is_buffer_storage(out) && graph.packed_dim_of(out) != WHCN::kWidthDim) { viewFn(graph, {out_W_packed, graph.add_none(), out}); From 73be01fe34d14b30045ef9f5c699697763c2ef9f Mon Sep 17 00:00:00 2001 From: Gregory Comer Date: Wed, 16 Apr 2025 13:41:26 -0700 Subject: [PATCH 180/244] Add redirects for relocated docs (#10221) ### Summary We've moved a number of doc pages in 0.6. This PR adds the sphinx-reredirects extension, which adds a sphinx feature to define redirected doc pages. I've used this to declare redirects for key relocated doc pages to preserve SEO and incoming external links. Solves #8728. ### Test plan I've locally built and served the docs and navigated to each of the redirected URLs. I confirmed that all redirect as expected. cc @mergennachin @byjlw --- .ci/docker/requirements-ci.txt | 1 + docs/source/conf.py | 19 +++++++++++++++++++ 2 files changed, 20 insertions(+) diff --git a/.ci/docker/requirements-ci.txt b/.ci/docker/requirements-ci.txt index b93b7b0283c..ff0277c2056 100644 --- a/.ci/docker/requirements-ci.txt +++ b/.ci/docker/requirements-ci.txt @@ -17,6 +17,7 @@ parameterized==0.9.0 # Doc build requirements, same as https://github.com/pytorch/pytorch/blob/main/.ci/docker/requirements-docs.txt sphinx==5.3.0 +sphinx-reredirects==0.1.4 sphinx-gallery==0.14.0 breathe==4.34.0 exhale==0.2.3 diff --git a/docs/source/conf.py b/docs/source/conf.py index a3c8baacb05..023c64149ec 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -62,6 +62,7 @@ "myst_parser", "sphinx_design", "sphinx_gallery.gen_gallery", + "sphinx_reredirects", ] if not FBCODE: @@ -195,6 +196,24 @@ "torch": ("https://pytorch.org/docs/stable/", None), } +# Redirects for moved pages +redirects = { + "getting-started-setup": "getting-started.html", + "export-overview": "using-executorch-export.html", + "runtime-build-and-cross-compilation": "using-executorch-building-from-source.html", + "tutorials/export-to-executorch-tutorial": "../using-executorch-export.html", + "running-a-model-cpp-tutorial": "using-executorch-cpp.html", + "build-run-vulkan": "backends-vulkan.html", + "executorch-arm-delegate-tutorial": "backends-arm-ethos-u.html", + "build-run-coreml": "backends-coreml.html", + "build-run-mediatek-backend": "backends-mediatek.html", + "build-run-mps": "backends-mps.html", + "build-run-qualcomm-ai-engine-direct-backend": "backends-qualcomm.html", + "build-run-xtensa": "backends-cadence.html", + "apple-runtime": "using-executorch-ios.html", + "tutorials/devtools-integration-tutorial": "../using-executorch-troubleshooting.html", +} + # Custom directives defintions to create cards on main landing page from custom_directives import ( # type: ignore[import-not-found] From 625d47a17141b5c27f6131f817f72070ea20c9cb Mon Sep 17 00:00:00 2001 From: Sicheng Stephen Jia Date: Wed, 16 Apr 2025 17:02:25 -0400 Subject: [PATCH 181/244] [ET-VK] Manual sync native layer norm (#10242) As title. The automated squash in https://github.com/pytorch/executorch/pull/10238 did not sync the changes for the native layer norm shader for some reason. --- .../graph/ops/glsl/native_layer_norm.glsl | 331 +++++++++++------- 1 file changed, 208 insertions(+), 123 deletions(-) diff --git a/backends/vulkan/runtime/graph/ops/glsl/native_layer_norm.glsl b/backends/vulkan/runtime/graph/ops/glsl/native_layer_norm.glsl index cdbb85da4a7..c3e53cbfc3b 100644 --- a/backends/vulkan/runtime/graph/ops/glsl/native_layer_norm.glsl +++ b/backends/vulkan/runtime/graph/ops/glsl/native_layer_norm.glsl @@ -43,14 +43,71 @@ ${layout_declare_spec_const(C, "int", "out_layout", "DEFAULT_LAYOUT")} const lowp ivec4 out_axis_map = unhash_axis_map(out_layout); const lowp int out_packed_dim = unhash_packed_dim(out_layout); -#define SHARED_MEMORY_FACTOR 2 #define MAX_WORKGROUP_SIZE 64 +// Shared memory factor increases shared memory allocation by a scale that should either be 1 or a power of 2. +// +// Increasing factor allows more data to be stored in shared memory and increase thread utilization during reduction. +// Why? Because when performing reduction, the number of active threads becomes half in each iteration. +// Increasing scaling factor increases the thread occupancy and hence utilize the GPU better. +// eg. +// If local thread size in x dimension is 32, and SHARED_MEMORY_FACTOR is 1, 32 elements will be loaded into shared memory. +// First iteration of reduce will have 16 threads sum up 32 elements. +// Second iteration will have 8 threads sum up 16 elements from previous iteration and so on. +// So thread utilization starts at 50%. +// +// By contrast if local thread size in x dimension is 32, and SHARED_MEMORY_FACTOR is 2, 64 elements will be loaded into shared memory. +// First iteration of reduce will have 32 threads sum up 64 elements. +// Second iteration will have 32 threads sum up 16 elements from previous iteration and so on. +// Thus thread utilization starts at 100%. +#define SHARED_MEMORY_FACTOR 2 + #define offset_pos_index(index) ((index) + ((index) >> 2)) shared VEC4_T shared_input[offset_pos_index(MAX_WORKGROUP_SIZE * SHARED_MEMORY_FACTOR)]; -// function to reduce input data in workgroup's x dimension +// Function to reduce input data in workgroup's x dimension +// +// The implementation resembles reduction as depicted below +// | 10 | 1 | 8 | 1 | 0 | 2 | 3 | 5 | 2 | 3 | 2 | 7 | 0 | 11 | 0 | 2 | current_stride -> 1 +// | / | / | / | / | / | / | / | / +// | / | / | / | / | / | / | / | / +// | / | / | / | / | / | / | / | / +// | 11 | 1 | 9 | 1 | 2 | 2 | 8 | 5 | 5 | 3 | 9 | 7 | 11 | 11 | 2 | 2 | current_stride -> 2 +// | / | / | / | / +// | / | / | / | / +// | / | / | / | / +// | 20 | 1 | 9 | 1 | 10 | 2 | 8 | 5 |14 | 3 | 9 | 7 |13 | 11 | 2 | 2 | current_stride -> 4 +// | / | / +// | / | / +// | / | / +// | / | / +// | / | / +// | 30 | 1 | 9 | 1 | 10 | 2 | 8 | 5 |27 | 3 | 9 | 7 |13 | 11 | 2 | 2 | current_stride -> 8 +// | / +// | / +// | / +// | / +// | / +// | / +// | / +// | / +// | / +// | 57 | 1 | 9 | 1 | 10 | 2 | 8 | 5 |27 | 3 | 9 | 7 |13 | 11 | 2 | 2 | current_stride = -> 16 +// +// Threads access shared index in following pattern +// Thread | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | current_stride -> 1 +// Shared Index | 0 | 2 | 4 | 6 | 8 | 10 | 12 | 14 | X | X | X | X | X | X | X | X | index *= 1 +// +// Thread | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | current_stride -> 2 +// Shared Index | 0 | 4 | 8 | 12 | X | X | X | X | X | X | X | X | X | X | X | X | index *= 2 +// +// Thread | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | current_stride -> 4 +// Shared Index | 0 | 8 | X | X | X | X | X | X | X | X | X | X | X | X | X | X | index *= 4 +// +// Thread | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | current_stride -> 8 +// Shared Index | 0 | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | index *= 8 + void reduce_input(const int width_stride, const int shared_idx_offset) { // wait for all shared memory writes to finish memoryBarrierShared(); @@ -70,10 +127,9 @@ void reduce_input(const int width_stride, const int shared_idx_offset) { } } -void main() { +void reduce_non_packed_dim() { const ivec3 lpos = ivec3(gl_GlobalInvocationID); const int width = int(sizes.x); - ivec3 in_pos = lpos_to_pos(lpos, in_axis_map); // width batch read stride @@ -85,148 +141,177 @@ void main() { // local memory index for this thread const int shared_idx = shared_idx_offset + int(gl_LocalInvocationID.x); - // if packed dimension width - if (in_packed_dim != W_DIM) { - VEC4_T mean = VEC4_T(0); - VEC4_T var = VEC4_T(0); - - // Loop over the width in stride increments - for (int width_offset = 0; width_offset < width; width_offset += width_stride) { - // Read input in shared memory - for (int si = 0; si < SHARED_MEMORY_FACTOR; si++) { - in_pos[in_axis_map.x] = width_offset + int(gl_LocalInvocationID.x + si * gl_WorkGroupSize.x); - - VEC4_T in_val = VEC4_T(0); - if (all(lessThan(in_pos, out_limits))) { - in_val = load_texel(t_in, in_pos); - } - shared_input[offset_pos_index(shared_idx + si * gl_WorkGroupSize.x)] = in_val; - } + VEC4_T mean = VEC4_T(0); + VEC4_T var = VEC4_T(0); - reduce_input(width_stride, shared_idx_offset); - mean += shared_input[offset_pos_index(shared_idx_offset)]; + // Loop over the width in stride increments + for (int width_offset = 0; width_offset < width; width_offset += width_stride) { + // Read input in shared memory + for (int si = 0; si < SHARED_MEMORY_FACTOR; si++) { + in_pos[in_axis_map.x] = width_offset + int(gl_LocalInvocationID.x + si * gl_WorkGroupSize.x); + + VEC4_T in_val = VEC4_T(0); + if (all(lessThan(in_pos, out_limits))) { + in_val = load_texel(t_in, in_pos); + } + shared_input[offset_pos_index(shared_idx + si * gl_WorkGroupSize.x)] = in_val; } - mean /= width; + reduce_input(width_stride, shared_idx_offset); + mean += shared_input[offset_pos_index(shared_idx_offset)]; + } + + mean /= width; - // Loop over the width in stride increments - for (int width_offset = 0; width_offset < width; width_offset += width_stride) { - // Read input in shared memory - for (int si = 0; si < SHARED_MEMORY_FACTOR; si++) { - in_pos[in_axis_map.x] = width_offset + int(gl_LocalInvocationID.x + si * gl_WorkGroupSize.x); + memoryBarrierShared(); + barrier(); - VEC4_T in_val = mean; - if (all(lessThan(in_pos, out_limits))) { - in_val = load_texel(t_in, in_pos); - } + // Loop over the width in stride increments + for (int width_offset = 0; width_offset < width; width_offset += width_stride) { + // Read input in shared memory + for (int si = 0; si < SHARED_MEMORY_FACTOR; si++) { + in_pos[in_axis_map.x] = width_offset + int(gl_LocalInvocationID.x + si * gl_WorkGroupSize.x); - const VEC4_T delta = in_val - mean; - shared_input[offset_pos_index(shared_idx + si * gl_WorkGroupSize.x)] = delta * delta; + VEC4_T in_val = mean; + if (all(lessThan(in_pos, out_limits))) { + in_val = load_texel(t_in, in_pos); } - reduce_input(width_stride, shared_idx_offset); - var += shared_input[offset_pos_index(shared_idx_offset)]; + const VEC4_T delta = in_val - mean; + shared_input[offset_pos_index(shared_idx + si * gl_WorkGroupSize.x)] = delta * delta; } - var /= width; + reduce_input(width_stride, shared_idx_offset); + var += shared_input[offset_pos_index(shared_idx_offset)]; + } - VEC4_T rstd = pow(var + epsilon, VEC4_T(-0.5)); - VEC4_T offset = -rstd * mean; + var /= width; - VEC4_T v = load_texel(t_in, lpos); - VEC4_T weight = load_texel(t_weight, ivec3(lpos.x, 0, 0)).xxxx; - VEC4_T bias = load_texel(t_bias, ivec3(lpos.x, 0, 0)).xxxx; - VEC4_T outtex = (v * rstd + offset) * weight + bias; - if (all(lessThan(lpos, out_limits))) { - write_texel_lpos(t_out, ivec3(lpos.x, lpos.y, lpos.z), outtex, out_axis_map); - } + VEC4_T rstd = pow(var + epsilon, VEC4_T(-0.5)); + VEC4_T offset = -rstd * mean; - if (gl_GlobalInvocationID.x == 0) { - write_texel(t_mean, lpos, mean); - write_texel(t_rstd, lpos, rstd); - } - } else { - const int last_packed_width_index = divup4(width) - 1; - T mean = T(0); - T var = T(0); - const int remain = width & 3; - - const int in_pos_x_limit = out_limits[in_axis_map.x]; - - // Loop over the width in stride increments - for (int width_offset = 0; width_offset <= last_packed_width_index; width_offset += width_stride) { - // Read input in shared memory - for (int si = 0; si < SHARED_MEMORY_FACTOR; si++) { - const int in_pos_x = width_offset + int(gl_LocalInvocationID.x + si * gl_WorkGroupSize.x); - in_pos[in_axis_map.x] = in_pos_x; - - VEC4_T in_val = VEC4_T(0); - if (in_pos_x < in_pos_x_limit) { - in_val = load_texel(t_in, in_pos); - } - - if (in_pos_x == last_packed_width_index && remain != 0) { - const int remain_inv = 4 - remain; - in_val.y = mix(in_val.y, T(0), remain_inv > 2); - in_val.z = mix(in_val.z, T(0), remain_inv > 1); - in_val.w = mix(in_val.w, T(0), remain_inv > 0); - } - - shared_input[offset_pos_index(shared_idx + si * gl_WorkGroupSize.x)] = in_val; + VEC4_T v = load_texel(t_in, lpos); + VEC4_T weight = load_texel(t_weight, ivec3(lpos.x, 0, 0)).xxxx; + VEC4_T bias = load_texel(t_bias, ivec3(lpos.x, 0, 0)).xxxx; + VEC4_T outtex = (v * rstd + offset) * weight + bias; + + if (all(lessThan(lpos, out_limits))) { + write_texel_lpos(t_out, lpos, outtex, out_axis_map); + } + + if (gl_GlobalInvocationID.x == 0) { + write_texel(t_mean, lpos, mean); + write_texel(t_rstd, lpos, rstd); + } +} + +void reduce_packed_dim() { + const ivec3 lpos = ivec3(gl_GlobalInvocationID); + const int width = int(sizes.x); + ivec3 in_pos = lpos_to_pos(lpos, in_axis_map); + + // width batch read stride + const int width_stride = int(gl_WorkGroupSize.x) * SHARED_MEMORY_FACTOR; + + // local memory starting offset for this thread + const int shared_idx_offset = width_stride * int(gl_WorkGroupSize.y * gl_LocalInvocationID.z + gl_LocalInvocationID.y); + + // local memory index for this thread + const int shared_idx = shared_idx_offset + int(gl_LocalInvocationID.x); + + const int last_packed_width_index = divup4(width) - 1; + T mean = T(0); + T var = T(0); + const int remain = width & 3; + + const int in_pos_x_limit = out_limits[in_axis_map.x]; + + // Loop over the width in stride increments + for (int width_offset = 0; width_offset <= last_packed_width_index; width_offset += width_stride) { + // Read input in shared memory + for (int si = 0; si < SHARED_MEMORY_FACTOR; si++) { + const int in_pos_x = width_offset + int(gl_LocalInvocationID.x + si * gl_WorkGroupSize.x); + in_pos[in_axis_map.x] = in_pos_x; + + VEC4_T in_val = VEC4_T(0); + if (in_pos_x < in_pos_x_limit) { + in_val = load_texel(t_in, in_pos); } - reduce_input(width_stride, shared_idx_offset); - const VEC4_T val = shared_input[offset_pos_index(shared_idx_offset)]; - mean += val.x + val.y + val.z + val.w; + if (in_pos_x == last_packed_width_index && remain != 0) { + const int remain_inv = 4 - remain; + in_val.y = mix(in_val.y, T(0), remain_inv > 2); + in_val.z = mix(in_val.z, T(0), remain_inv > 1); + in_val.w = mix(in_val.w, T(0), remain_inv > 0); + } + + shared_input[offset_pos_index(shared_idx + si * gl_WorkGroupSize.x)] = in_val; } - mean /= width; - - // Loop over the width in stride increments - for (int width_offset = 0; width_offset <= last_packed_width_index; width_offset += width_stride) { - // Read input in shared memory - for (int si = 0; si < SHARED_MEMORY_FACTOR; si++) { - const int in_pos_x = width_offset + int(gl_LocalInvocationID.x + si * gl_WorkGroupSize.x); - in_pos[in_axis_map.x] = in_pos_x; - - VEC4_T in_val = VEC4_T(mean); - if (in_pos_x < in_pos_x_limit) { - in_val = load_texel(t_in, in_pos); - } - - if (in_pos_x == last_packed_width_index && remain != 0) { - const int remain_inv = 4 - remain; - in_val.y = mix(in_val.y, mean.x, remain_inv > 2); - in_val.z = mix(in_val.z, mean.x, remain_inv > 1); - in_val.w = mix(in_val.w, mean.x, remain_inv > 0); - } - - const VEC4_T delta = in_val - mean; - const VEC4_T delta2 = delta * delta; - shared_input[offset_pos_index(shared_idx + si * gl_WorkGroupSize.x)] = delta2; + reduce_input(width_stride, shared_idx_offset); + const VEC4_T val = shared_input[offset_pos_index(shared_idx_offset)]; + mean += val.x + val.y + val.z + val.w; + } + + mean /= width; + + memoryBarrierShared(); + barrier(); + + // Loop over the width in stride increments + for (int width_offset = 0; width_offset <= last_packed_width_index; width_offset += width_stride) { + // Read input in shared memory + for (int si = 0; si < SHARED_MEMORY_FACTOR; si++) { + const int in_pos_x = width_offset + int(gl_LocalInvocationID.x + si * gl_WorkGroupSize.x); + in_pos[in_axis_map.x] = in_pos_x; + + VEC4_T in_val = VEC4_T(mean); + if (in_pos_x < in_pos_x_limit) { + in_val = load_texel(t_in, in_pos); + } + + if (in_pos_x == last_packed_width_index && remain != 0) { + const int remain_inv = 4 - remain; + in_val.y = mix(in_val.y, mean.x, remain_inv > 2); + in_val.z = mix(in_val.z, mean.x, remain_inv > 1); + in_val.w = mix(in_val.w, mean.x, remain_inv > 0); } - reduce_input(width_stride, shared_idx_offset); - const VEC4_T val = shared_input[offset_pos_index(shared_idx_offset)]; - var += val.x + val.y + val.z + val.w; + const VEC4_T delta = in_val - mean; + const VEC4_T delta2 = delta * delta; + shared_input[offset_pos_index(shared_idx + si * gl_WorkGroupSize.x)] = delta2; } - var /= width; + reduce_input(width_stride, shared_idx_offset); + const VEC4_T val = shared_input[offset_pos_index(shared_idx_offset)]; + var += val.x + val.y + val.z + val.w; + } + + var /= width; - T rstd = pow(var + epsilon, T(-0.5)); - T offset = -rstd * mean; + T rstd = pow(var + epsilon, T(-0.5)); + T offset = -rstd * mean; - VEC4_T v = load_texel(t_in, lpos); - VEC4_T weight = load_texel(t_weight, ivec3(lpos.x, 0, 0)); - VEC4_T bias = load_texel(t_bias, ivec3(lpos.x, 0, 0)); - VEC4_T outtex = (v * rstd + offset) * weight + bias; - if (all(lessThan(lpos, out_limits))) { - write_texel_lpos(t_out, ivec3(lpos.x, lpos.y, lpos.z), outtex, out_axis_map); - } + VEC4_T v = load_texel(t_in, lpos); + VEC4_T weight = load_texel(t_weight, ivec3(lpos.x, 0, 0)); + VEC4_T bias = load_texel(t_bias, ivec3(lpos.x, 0, 0)); + VEC4_T outtex = (v * rstd + offset) * weight + bias; - if (gl_GlobalInvocationID.x == 0) { - write_texel(t_mean, lpos, VEC4_T(mean)); - write_texel(t_rstd, lpos, VEC4_T(rstd)); - } + if (all(lessThan(lpos, out_limits))) { + write_texel_lpos(t_out, lpos, outtex, out_axis_map); + } + + if (gl_GlobalInvocationID.x == 0) { + write_texel(t_mean, lpos, VEC4_T(mean)); + write_texel(t_rstd, lpos, VEC4_T(rstd)); + } +} + +void main() { + // if packed dimension width + if (in_packed_dim != W_DIM) { + reduce_non_packed_dim(); + } else { + reduce_packed_dim(); } } From 9dd1dded8202cc5f4beb2f733ffb07c1b829bd2c Mon Sep 17 00:00:00 2001 From: Max Ren <40742183+mcr229@users.noreply.github.com> Date: Wed, 16 Apr 2025 16:05:25 -0700 Subject: [PATCH 182/244] forward fix preprocess multimethod Differential Revision: D73134377 Pull Request resolved: https://github.com/pytorch/executorch/pull/10239 --- exir/backend/test/TARGETS | 1 + 1 file changed, 1 insertion(+) diff --git a/exir/backend/test/TARGETS b/exir/backend/test/TARGETS index b7f46076868..5b12d673f7c 100644 --- a/exir/backend/test/TARGETS +++ b/exir/backend/test/TARGETS @@ -203,6 +203,7 @@ python_unittest( ":backend_with_preprocess_all_demo", "//caffe2:torch", "//caffe2/functorch:functorch_src", + "//executorch/exir/backend/test:backend_with_compiler_demo", "//executorch/exir:delegate", "//executorch/exir:graph_module", "//executorch/exir:lib", From e43cc9b622269642fa3379ce2e332dbf78ed1823 Mon Sep 17 00:00:00 2001 From: Scott Roy <161522778+metascroy@users.noreply.github.com> Date: Wed, 16 Apr 2025 16:22:56 -0700 Subject: [PATCH 183/244] Add split_with_sizes to block list Differential Revision: D73129825 Pull Request resolved: https://github.com/pytorch/executorch/pull/10244 --- exir/program/_program.py | 1 + 1 file changed, 1 insertion(+) diff --git a/exir/program/_program.py b/exir/program/_program.py index da5ca06c927..687852234dc 100644 --- a/exir/program/_program.py +++ b/exir/program/_program.py @@ -1027,6 +1027,7 @@ def keep(op): torch.ops.aten.item.default, torch.ops.aten._local_scalar_dense.default, torch.ops.aten.unbind.int, + torch.ops.aten.split_with_sizes.default, ]: logging.warn( f"Op {op} was requested for preservation by partitioner. This request is ignored because it is in a blocklist." From f0ed485d00cac8f16ae1eb9da0c21bcd936e1225 Mon Sep 17 00:00:00 2001 From: Eli Amesefe Date: Wed, 16 Apr 2025 16:30:25 -0700 Subject: [PATCH 184/244] Buckify Sigmoid test Differential Revision: D72940392 Pull Request resolved: https://github.com/pytorch/executorch/pull/10224 --- backends/arm/test/ops/test_sigmoid.py | 24 +++++++++++++++++++----- backends/arm/test/targets.bzl | 7 +++++-- 2 files changed, 24 insertions(+), 7 deletions(-) diff --git a/backends/arm/test/ops/test_sigmoid.py b/backends/arm/test/ops/test_sigmoid.py index b3411475e24..43b4abd2039 100644 --- a/backends/arm/test/ops/test_sigmoid.py +++ b/backends/arm/test/ops/test_sigmoid.py @@ -9,8 +9,10 @@ from typing import Tuple +import pytest + import torch -from executorch.backends.arm.test import common +from executorch.backends.arm.test import common, conftest from executorch.backends.arm.test.tester.arm_tester import ArmTester from executorch.exir.backend.compile_spec_schema import CompileSpec from parameterized import parameterized @@ -63,7 +65,7 @@ def forward(self, x, y): def _test_sigmoid_tosa_MI_pipeline( self, module: torch.nn.Module, test_data: Tuple[torch.tensor] ): - ( + tester = ( ArmTester( module, example_inputs=test_data, @@ -77,11 +79,13 @@ def _test_sigmoid_tosa_MI_pipeline( .check_not(["executorch_exir_dialects_edge__ops_aten_sigmoid_default"]) .check_count({"torch.ops.higher_order.executorch_call_delegate": 1}) .to_executorch() - .run_method_and_compare_outputs(inputs=test_data) ) + if conftest.is_option_enabled("tosa_ref_model"): + tester.run_method_and_compare_outputs(inputs=test_data) + def _test_sigmoid_tosa_BI_pipeline(self, module: torch.nn.Module, test_data: Tuple): - ( + tester = ( ArmTester( module, example_inputs=test_data, @@ -96,9 +100,11 @@ def _test_sigmoid_tosa_BI_pipeline(self, module: torch.nn.Module, test_data: Tup .check_not(["executorch_exir_dialects_edge__ops_aten_sigmoid_default"]) .check_count({"torch.ops.higher_order.executorch_call_delegate": 1}) .to_executorch() - .run_method_and_compare_outputs(inputs=test_data) ) + if conftest.is_option_enabled("tosa_ref_model"): + tester.run_method_and_compare_outputs(inputs=test_data) + def _test_sigmoid_tosa_ethos_BI_pipeline( self, compile_spec: list[CompileSpec], @@ -137,6 +143,7 @@ def _test_sigmoid_tosa_u85_BI_pipeline( ) @parameterized.expand(test_data_suite) + @pytest.mark.tosa_ref_model def test_sigmoid_tosa_MI( self, test_name: str, @@ -145,26 +152,33 @@ def test_sigmoid_tosa_MI( self._test_sigmoid_tosa_MI_pipeline(self.Sigmoid(), (test_data,)) @parameterized.expand(test_data_suite) + @pytest.mark.tosa_ref_model def test_sigmoid_tosa_BI(self, test_name: str, test_data: torch.Tensor): self._test_sigmoid_tosa_BI_pipeline(self.Sigmoid(), (test_data,)) + @pytest.mark.tosa_ref_model def test_add_sigmoid_tosa_MI(self): self._test_sigmoid_tosa_MI_pipeline(self.AddSigmoid(), (test_data_suite[0][1],)) + @pytest.mark.tosa_ref_model def test_add_sigmoid_tosa_BI(self): self._test_sigmoid_tosa_BI_pipeline(self.AddSigmoid(), (test_data_suite[5][1],)) + @pytest.mark.tosa_ref_model def test_sigmoid_add_tosa_MI(self): self._test_sigmoid_tosa_MI_pipeline(self.SigmoidAdd(), (test_data_suite[0][1],)) + @pytest.mark.tosa_ref_model def test_sigmoid_add_tosa_BI(self): self._test_sigmoid_tosa_BI_pipeline(self.SigmoidAdd(), (test_data_suite[0][1],)) + @pytest.mark.tosa_ref_model def test_sigmoid_add_sigmoid_tosa_MI(self): self._test_sigmoid_tosa_MI_pipeline( self.SigmoidAddSigmoid(), (test_data_suite[4][1], test_data_suite[3][1]) ) + @pytest.mark.tosa_ref_model def test_sigmoid_add_sigmoid_tosa_BI(self): self._test_sigmoid_tosa_BI_pipeline( self.SigmoidAddSigmoid(), (test_data_suite[4][1], test_data_suite[3][1]) diff --git a/backends/arm/test/targets.bzl b/backends/arm/test/targets.bzl index 4eece953f2d..1e137e0a675 100644 --- a/backends/arm/test/targets.bzl +++ b/backends/arm/test/targets.bzl @@ -12,8 +12,11 @@ def define_arm_tests(): test_files.remove("passes/test_ioquantization_pass.py") # Operators - test_files += ["ops/test_linear.py"] - test_files += ["ops/test_slice.py"] + test_files += [ + "ops/test_linear.py", + "ops/test_slice.py", + "ops/test_sigmoid.py", + ] TESTS = {} From 0e8bb30f3fc8e6e12a6055294af27194fddc87c4 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Sun, 20 Apr 2025 09:52:00 -0700 Subject: [PATCH 185/244] Add unit tests for dynamic quant sequential and parallel convs --- backends/xnnpack/test/ops/test_conv2d.py | 69 +++++++++++++++++++----- 1 file changed, 55 insertions(+), 14 deletions(-) diff --git a/backends/xnnpack/test/ops/test_conv2d.py b/backends/xnnpack/test/ops/test_conv2d.py index 0c456ebc21a..4d1b387b8ec 100644 --- a/backends/xnnpack/test/ops/test_conv2d.py +++ b/backends/xnnpack/test/ops/test_conv2d.py @@ -173,12 +173,10 @@ def get_inputs(self): return (torch.randn(2, 2, 4, 4),) -class Conv2dDynamicQuant(torch.nn.Module): +class Conv2dDQ(torch.nn.Module): def __init__(self): super().__init__() - self.conv = torch.nn.Conv2d(3, 10, 3) - self.conv.weight.requires_grad = False - self.conv.bias.requires_grad = False + self.conv = torch.nn.Conv2d(in_channels=3, out_channels=10, kernel_size=3) def forward(self, x): return self.conv(x) @@ -187,6 +185,43 @@ def get_inputs(self): return (torch.randn(1, 3, 8, 8),) +class Conv2dDQSeq(torch.nn.Module): + def __init__(self): + super().__init__() + self.first = torch.nn.Conv2d( + in_channels=3, out_channels=8, kernel_size=3, padding=1 + ) + self.second = torch.nn.Conv2d( + in_channels=8, out_channels=10, kernel_size=3, padding=1 + ) + + def forward(self, x): + y = self.first(x) + return self.second(y) + + def get_inputs(self): + return (torch.randn(1, 3, 8, 8),) + + +class Conv2dDQParallel(torch.nn.Module): + def __init__(self): + super().__init__() + self.first = torch.nn.Conv2d( + in_channels=3, out_channels=8, kernel_size=3, padding=1 + ) + self.second = torch.nn.Conv2d( + in_channels=3, out_channels=10, kernel_size=3, padding=1 + ) + + def forward(self, x): + first = self.first(x) + second = self.second(x) + return first, second + + def get_inputs(self): + return (torch.randn(1, 3, 8, 8),) + + class TestConv2d(unittest.TestCase): def setUp(self): torch._dynamo.reset() @@ -244,8 +279,8 @@ def _test( def _test_dq( self, m: torch.nn.Module, - inputs, - dynamic_shapes, + conv_count=1, + dynamic_shapes=None, ): quant_config = get_symmetric_quantization_config( is_per_channel=True, @@ -257,14 +292,16 @@ def _test_dq( per_op_mode=True, ) - tester = Tester(m, inputs, dynamic_shapes=dynamic_shapes) + tester = Tester(m, m.get_inputs(), dynamic_shapes=dynamic_shapes) tester.quantize(Quantize(quantization_config=quant_config)) tester.export() tester.check(["torch.ops.quantized_decomposed.choose_qparams"]) tester.to_edge_transform_and_lower( ToEdgeTransformAndLower([DynamicallyQuantizedPartitioner]) ) - tester.check_count({"torch.ops.higher_order.executorch_call_delegate": 1}) + tester.check_count( + {"torch.ops.higher_order.executorch_call_delegate": conv_count} + ) tester.check_not(["executorch_exir_dialects_edge__ops_aten_conv2d_default"]) tester.to_executorch() tester.serialize() @@ -748,9 +785,13 @@ def forward(self, x): ) def test_dq_conv2d(self) -> None: - model = Conv2dDynamicQuant() - self._test_dq( - model, - model.get_inputs(), - dynamic_shapes=None, - ) + model = Conv2dDQ() + self._test_dq(model) + + def test_dq_conv2d_seq(self) -> None: + model = Conv2dDQSeq() + self._test_dq(model, conv_count=2) + + def test_dq_conv2d_parallel(self) -> None: + model = Conv2dDQParallel() + self._test_dq(model, conv_count=2) From 85692ee14c3c49d2bf54d20ce4152b4920123504 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Sun, 20 Apr 2025 09:55:36 -0700 Subject: [PATCH 186/244] Add unit test for dynamic quant conv2d with channels-last permute --- .../test_channels_last_tagged_reshape.py | 43 ++++++++++++++++++- 1 file changed, 42 insertions(+), 1 deletion(-) diff --git a/backends/xnnpack/test/passes/test_channels_last_tagged_reshape.py b/backends/xnnpack/test/passes/test_channels_last_tagged_reshape.py index 6d60f9d76b5..a00209f4ea6 100644 --- a/backends/xnnpack/test/passes/test_channels_last_tagged_reshape.py +++ b/backends/xnnpack/test/passes/test_channels_last_tagged_reshape.py @@ -10,10 +10,13 @@ from executorch.backends.xnnpack._passes.channels_last_tagged_reshape_pass import ( ChannelsLastTaggedReshapePass, ) +from executorch.backends.xnnpack.quantizer.xnnpack_quantizer import ( + get_symmetric_quantization_config, +) from executorch.backends.xnnpack.test.test_xnnpack_utils_classes import ( OpSequencesAddConv2d, ) -from executorch.backends.xnnpack.test.tester import RunPasses, Tester +from executorch.backends.xnnpack.test.tester import Quantize, RunPasses, Tester class TestChannelsLastTaggedReshapePass(unittest.TestCase): @@ -35,6 +38,10 @@ def setUp(self): dequant_name = "executorch_exir_dialects_edge__ops_quantized_decomposed_dequantize_per_tensor_default" conv_name = "executorch_exir_dialects_edge__ops_aten_convolution_default" relu_name = "executorch_exir_dialects_edge__ops_aten_relu_default" + choose_qparams_name = ( + "executorch_exir_dialects_edge__ops_quantized_decomposed_choose_qparams_tensor" + ) + dynamic_quant_name = "executorch_exir_dialects_edge__ops_quantized_decomposed_quantize_per_tensor_tensor" def test_fp32_channels_last_tagged_reshape_pass(self): for module, num_reshape in self.modules.items(): @@ -179,3 +186,37 @@ def test_fp32_channels_last_tagged_reshape_pass_conv_bn_hardtanh_mean_seq(self): ) .run_method_and_compare_outputs() ) + + class Conv2dDynamicQuant(torch.nn.Module): + def __init__(self): + super().__init__() + self.conv = torch.nn.Conv2d(3, 10, 3) + + def forward(self, x): + return self.conv(x) + + def test_dq_conv2d_channels_last_tagged_reshape_pass(self) -> None: + ( + Tester(self.Conv2dDynamicQuant().eval(), (torch.randn(1, 3, 8, 8),)) + .quantize( + Quantize( + quantization_config=get_symmetric_quantization_config( + is_dynamic=True + ) + ) + ) + .export() + .to_edge() + .run_passes(self.PassStage) + .check( + [ + self.to_copy_name, + self.choose_qparams_name, + self.dynamic_quant_name, + self.dequant_name, + self.conv_name, + self.to_copy_name, + ] + ) + .run_method_and_compare_outputs() + ) From 877e31b45a7ed042122c83ca9538d9f4c3a25d12 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Sun, 20 Apr 2025 09:57:54 -0700 Subject: [PATCH 187/244] Add check to determine if node feeds into conv and set non-batch dims accordingly --- backends/xnnpack/operators/quant_params.py | 24 +++++++++++++++------- 1 file changed, 17 insertions(+), 7 deletions(-) diff --git a/backends/xnnpack/operators/quant_params.py b/backends/xnnpack/operators/quant_params.py index ddb9db865f3..fbee1d192cf 100644 --- a/backends/xnnpack/operators/quant_params.py +++ b/backends/xnnpack/operators/quant_params.py @@ -141,17 +141,27 @@ def quantize_tensor(self, tensor: torch.Tensor) -> torch.Tensor: tensor, self.scale, self.zp, self.qmin, self.qmax, self.dtype ) + # Temporary helper until non-batch dimensions can be inferred + # Detects if a node feeds into a conv op by checking all downstream users + @staticmethod + def _feeds_into_conv(node: torch.fx.Node) -> bool: + users_list = [node] + + while users_list: + current_user = users_list.pop() + if "convolution" in str(current_user.target): + return True + users_list.extend(current_user.users) + + return False + @classmethod def _from_dynamic_input_node(cls, quant_node: torch.fx.Node) -> QuantParams: q_input = quant_node.args[0] # fp32 input assert isinstance(q_input, torch.fx.Node) - num_nonbatch_dims = 1 - - # Compute non-batch dimensions (shape length - 1), defaulting to 1 - q_input_val = q_input.meta.get("val", None) - q_input_shape = getattr(q_input_val, "shape", None) - if q_input_shape is not None: - num_nonbatch_dims = max(len(q_input_shape) - 1, 1) + # TODO - materialize this from the quant_node scale count and val shape + # Set non-batch dims to 3 if node feeds into conv (only 2D is supported), otherwise set to 1 for linear + num_nonbatch_dims = 3 if cls._feeds_into_conv(quant_node) else 1 return cls( per_channel=False, # True is not valid From 206cf786f26511ff3026be66a2f7e6f96f960e35 Mon Sep 17 00:00:00 2001 From: Zuby Afzal Date: Sun, 20 Apr 2025 10:01:43 -0700 Subject: [PATCH 188/244] Add depthwise conv checks for dynamic quant --- .../xnnpack/partition/config/gemm_configs.py | 34 ++++++++++++++----- .../quantizer/xnnpack_quantizer_utils.py | 18 +++++++++- 2 files changed, 43 insertions(+), 9 deletions(-) diff --git a/backends/xnnpack/partition/config/gemm_configs.py b/backends/xnnpack/partition/config/gemm_configs.py index a05bf623e05..b779f2e6204 100644 --- a/backends/xnnpack/partition/config/gemm_configs.py +++ b/backends/xnnpack/partition/config/gemm_configs.py @@ -9,6 +9,7 @@ from typing import cast, List, Optional, Tuple import torch +from executorch.backends.transforms import get_shape from executorch.backends.xnnpack.operators.quant_params import QuantParams from executorch.backends.xnnpack.partition.config.xnnpack_config import ( ConfigPrecisionType, @@ -358,18 +359,35 @@ def check_constraints(self, node: torch.fx.Node, ep: ExportedProgram) -> bool: why(node, "Only support 1D + 2D Conv") return False # Only support 1D + 2D Conv - precision = self._detect_precision(node) - if precision == ConfigPrecisionType.DYNAMIC_QUANT and len(conv_stride) != 2: - why(node, "Only support 2D Conv for dynamic quantization") - return False - kernel_node = get_input_node(node, 1) + kernel_shape = get_shape(kernel_node) weight_quant_params = QuantParams.from_weights(kernel_node, ep) - - is_transpose = node.args[6] groups = cast(int, node.args[8]) + is_transpose = node.args[6] + + if is_transpose: + group_input_channels = int(kernel_shape[0] / groups) + group_output_channels = kernel_shape[1] + else: + group_input_channels = kernel_shape[1] + group_output_channels = int(kernel_shape[0] / groups) + + is_depthwise = ( + group_input_channels == 1 + and group_output_channels % group_input_channels == 0 + ) + + # XNNPACK does not support dynamic quantization convs that are not 2D or are depthwise + if self._detect_precision(node) == ConfigPrecisionType.DYNAMIC_QUANT and ( + len(conv_stride) != 2 or is_depthwise + ): + why( + node, + "XNNPACK only supports standard 2D convolutions for dynamic quantization", + ) + return False - # XNNPack does not support non-zero output padding in transposed + # XNNPACK does not support non-zero output padding in transposed # convolutions. if is_transpose and any( out_pad != 0 for out_pad in cast(List[int], node.args[7]) diff --git a/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py b/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py index 92eff3d0d68..91cb816b795 100644 --- a/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py +++ b/backends/xnnpack/quantizer/xnnpack_quantizer_utils.py @@ -323,7 +323,7 @@ def _do_annotate_conv( assert isinstance(weight, Node) input_qspec_map[weight] = get_weight_qspec(quantization_config) - # Only annotate dynamically quantized conv if it's 2D + # Only annotate dynamically quantized conv if it's 2D and not depthwise if ( quantization_config and quantization_config.input_activation @@ -336,6 +336,22 @@ def _do_annotate_conv( if weight_shape is not None and len(weight_shape) != 4: continue + # Default to 1 since groups is not available in the node + groups = 1 + if is_conv_transpose: + group_input_channels = int(weight_shape[0] / groups) + group_output_channels = weight_shape[1] + else: + group_input_channels = weight_shape[1] + group_output_channels = int(weight_shape[0] / groups) + + # Skip if depthwise + if ( + group_input_channels == 1 + and group_output_channels % group_input_channels == 0 + ): + continue + # adding weight node to the partition as well partition = [conv_node, conv_node.args[1]] From de1926dd906a8ec1a0bc7e018c114c27f5cbcbf6 Mon Sep 17 00:00:00 2001 From: derekxu Date: Wed, 16 Apr 2025 17:13:02 -0700 Subject: [PATCH 189/244] Fix timespec_get not compatiable for AOSP OS Android N14 Differential Revision: D73136434 Pull Request resolved: https://github.com/pytorch/executorch/pull/10240 --- extension/llm/runner/util.h | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/extension/llm/runner/util.h b/extension/llm/runner/util.h index f7b48433cfb..0cb2463d163 100644 --- a/extension/llm/runner/util.h +++ b/extension/llm/runner/util.h @@ -68,9 +68,9 @@ ET_EXPERIMENTAL void inline safe_printf(const char* piece) { ET_EXPERIMENTAL long inline time_in_ms() { // return time in milliseconds, for benchmarking the model speed struct timespec time; - // The `timespec_get` function is only available on Android API levels - // 29 or later. -#if defined(__ANDROID_API__) && __ANDROID_API__ < 29 + // The `timespec_get` function is for windows time access. Some AOSP OS does + // not have timespec_get support. +#if defined(__ANDROID_API__) clock_gettime(CLOCK_REALTIME, &time); #else timespec_get(&time, TIME_UTC); From db701965d049f07188c277d9bd31b8f72f58fb86 Mon Sep 17 00:00:00 2001 From: Gasoonjia Date: Thu, 17 Apr 2025 00:43:31 -0700 Subject: [PATCH 190/244] reset devtool webpage tutorial Differential Revision: D73167751 Pull Request resolved: https://github.com/pytorch/executorch/pull/10254 --- docs/source/conf.py | 1 - 1 file changed, 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 023c64149ec..c33b5be27a8 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -211,7 +211,6 @@ "build-run-qualcomm-ai-engine-direct-backend": "backends-qualcomm.html", "build-run-xtensa": "backends-cadence.html", "apple-runtime": "using-executorch-ios.html", - "tutorials/devtools-integration-tutorial": "../using-executorch-troubleshooting.html", } # Custom directives defintions to create cards on main landing page From 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