Fix return_bias option in LayerNormLinear and LayerNormMLP (#1569)

* Do not apply bias when apply_bias is False

Signed-off-by: Przemek Tredak <[email protected]>

* Bwd fix for LNMLP and tests

Signed-off-by: Przemek Tredak <[email protected]>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Fix for the dbias calculation

Signed-off-by: Przemek Tredak <[email protected]>

* Improve tests and cleaning the logic

Signed-off-by: Przemek Tredak <[email protected]>

* Tightened test tolerances a little

Signed-off-by: Przemek Tredak <[email protected]>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Revert "Tightened test tolerances a little"

This reverts commit 2e20a92.

Signed-off-by: Przemek Tredak <[email protected]>

* Update tests/pytorch/test_numerics.py

Co-authored-by: Tim Moon <[email protected]>
Signed-off-by: Przemyslaw Tredak <[email protected]>

* Fix the Gelu Aux type

Signed-off-by: Przemek Tredak <[email protected]>

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* Remove use_fc1_bias option

Signed-off-by: Przemek Tredak <[email protected]>

---------

Signed-off-by: Przemek Tredak <[email protected]>
Signed-off-by: Przemyslaw Tredak <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Tim Moon <[email protected]>

Author: 3 people

tests/pytorch/test_numerics.py

@@ -5,7 +5,7 @@
 from collections import OrderedDict
 import math
 import os
-from typing import Dict, List, Optional
+from typing import Dict, List, Tuple, Optional
 import pytest
 import copy
 import random
@@ -331,9 +331,9 @@ def __init__(
         in_features: int,
         out_features: int,
         eps: float,
-        bias: bool = True,
         normalization: str = "LayerNorm",
         zero_centered_gamma: bool = False,
+        bias: bool = True,
     ):
         super().__init__()
         if normalization == "LayerNorm":
@@ -347,7 +347,7 @@ def __init__(
         else:
             raise RuntimeError("Unsupported normalization")
 
-        self.linear = nn.Linear(in_features, out_features)
+        self.linear = nn.Linear(in_features, out_features, bias=bias)
 
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         return self.linear(self.layernorm(x))
@@ -447,6 +447,7 @@ def __init__(
         eps: float = 1e-5,
         activation="gelu",
         normalization: str = "LayerNorm",
+        bias: bool = True,
     ):
         super().__init__()
         if normalization == "LayerNorm":
@@ -462,8 +463,8 @@ def __init__(
             fc1_output_features = ffn_hidden_size
             self.gelu = _supported_act[activation]
 
-        self.fc1 = nn.Linear(hidden_size, fc1_output_features)
-        self.fc2 = nn.Linear(ffn_hidden_size, hidden_size)
+        self.fc1 = nn.Linear(hidden_size, fc1_output_features, bias=bias)
+        self.fc2 = nn.Linear(ffn_hidden_size, hidden_size, bias=bias)
 
     def forward(self, x):
         t = self.gelu(self.fc1(self.ln(x)))
@@ -1039,6 +1040,8 @@ def _test_granular_accuracy(block, bs, dtype, config):
     inp_hidden_states.retain_grad()
 
     out = block(inp_hidden_states)
+    if isinstance(out, (List, Tuple)):
+        out = out[0]
     loss = out.sum()
     loss.backward()
 
@@ -1117,32 +1120,53 @@ def test_dpa_accuracy(dtype, bs, model):
         assert_allclose(te_output, torch_output, atol=5e-2, rtol=1e-2)
 
 
+class TestReturnBiasModule(nn.Module):
+    def __init__(self, mod, **kwargs):
+        super().__init__()
+        self.te_module = mod(**kwargs)
+        self.return_bias = kwargs["return_bias"]
+        self.bias = kwargs["bias"]
+
+    def forward(self, x):
+        if self.return_bias:
+            out, bias = self.te_module(x)
+            if self.bias:
+                out = out + bias
+            return out
+        return self.te_module(x)
+
+
 @pytest.mark.parametrize("dtype", param_types)
 @pytest.mark.parametrize("bs", batch_sizes)
 @pytest.mark.parametrize("model", ["small"])
-def test_linear_accuracy(dtype, bs, model):
+@pytest.mark.parametrize("return_bias", all_boolean)
+@pytest.mark.parametrize("bias", all_boolean)
+def test_linear_accuracy(dtype, bs, model, return_bias, bias):
     config = model_configs[model]
 
-    te_linear = Linear(
-        config.hidden_size,
-        4 * config.hidden_size,
-        bias=True,
+    te_linear = TestReturnBiasModule(
+        Linear,
+        in_features=config.hidden_size,
+        out_features=4 * config.hidden_size,
         params_dtype=dtype,
+        return_bias=return_bias,
+        bias=bias,
         device="cuda",
-    ).eval()
+    )
 
     torch_linear = torch.nn.Linear(
         config.hidden_size,
         4 * config.hidden_size,
-        bias=True,
+        bias=bias,
         device="cuda",
         dtype=dtype,
-    ).eval()
+    )
 
     # Share params
     with torch.no_grad():
-        torch_linear.weight = Parameter(te_linear.weight.clone())
-        torch_linear.bias = Parameter(te_linear.bias.clone())
+        torch_linear.weight = Parameter(te_linear.te_module.weight.clone())
+        if bias:
+            torch_linear.bias = Parameter(te_linear.te_module.bias.clone())
 
     te_outputs = _test_granular_accuracy(te_linear, bs, dtype, config)
     torch_outputs = _test_granular_accuracy(torch_linear, bs, dtype, config)
@@ -1265,41 +1289,51 @@ def test_layernorm_accuracy(dtype, bs, model, eps, zero_centered_gamma):
 @pytest.mark.parametrize("model", ["small"])
 @pytest.mark.parametrize("normalization", all_normalizations)
 @pytest.mark.parametrize("zero_centered_gamma", all_boolean)
-def test_layernorm_linear_accuracy(dtype, bs, model, normalization, zero_centered_gamma):
+@pytest.mark.parametrize("return_bias", all_boolean)
+@pytest.mark.parametrize("bias", all_boolean)
+def test_layernorm_linear_accuracy(
+    dtype, bs, model, normalization, zero_centered_gamma, return_bias, bias
+):
     config = model_configs[model]
 
-    te_ln_linear = LayerNormLinear(
-        config.hidden_size,
-        4 * config.hidden_size,
-        config.eps,
-        bias=True,
+    te_ln_linear = TestReturnBiasModule(
+        LayerNormLinear,
+        in_features=config.hidden_size,
+        out_features=4 * config.hidden_size,
+        eps=config.eps,
         normalization=normalization,
         params_dtype=dtype,
         zero_centered_gamma=zero_centered_gamma,
+        return_bias=return_bias,
+        bias=bias,
         device="cuda",
-    ).eval()
+    )
 
     torch_ln_linear = (
         TorchLayerNormLinear(
             config.hidden_size,
             4 * config.hidden_size,
             config.eps,
-            bias=True,
             normalization=normalization,
             zero_centered_gamma=zero_centered_gamma,
+            bias=bias,
         )
         .to(dtype=dtype)
         .cuda()
-        .eval()
     )
 
     # Share params
     with torch.no_grad():
-        torch_ln_linear.layernorm.weight = Parameter(te_ln_linear.layer_norm_weight.clone())
+        torch_ln_linear.layernorm.weight = Parameter(
+            te_ln_linear.te_module.layer_norm_weight.clone()
+        )
         if normalization != "RMSNorm":
-            torch_ln_linear.layernorm.bias = Parameter(te_ln_linear.layer_norm_bias.clone())
-        torch_ln_linear.linear.weight = Parameter(te_ln_linear.weight.clone())
-        torch_ln_linear.linear.bias = Parameter(te_ln_linear.bias.clone())
+            torch_ln_linear.layernorm.bias = Parameter(
+                te_ln_linear.te_module.layer_norm_bias.clone()
+            )
+        torch_ln_linear.linear.weight = Parameter(te_ln_linear.te_module.weight.clone())
+        if bias:
+            torch_ln_linear.linear.bias = Parameter(te_ln_linear.te_module.bias.clone())
 
     te_outputs = _test_granular_accuracy(te_ln_linear, bs, dtype, config)
     torch_outputs = _test_granular_accuracy(torch_ln_linear, bs, dtype, config)
@@ -1339,39 +1373,45 @@ def test_layernorm_linear_accuracy(dtype, bs, model, normalization, zero_centere
 @pytest.mark.parametrize("model", ["small"])
 @pytest.mark.parametrize("activation", all_activations)
 @pytest.mark.parametrize("normalization", all_normalizations)
-def test_layernorm_mlp_accuracy(dtype, bs, model, activation, normalization):
+@pytest.mark.parametrize("return_bias", all_boolean)
+@pytest.mark.parametrize("bias", all_boolean)
+def test_layernorm_mlp_accuracy(dtype, bs, model, activation, normalization, return_bias, bias):
     config = model_configs[model]
 
-    te_ln_mlp = LayerNormMLP(
-        config.hidden_size,
-        4 * config.hidden_size,
+    te_ln_mlp = TestReturnBiasModule(
+        LayerNormMLP,
+        hidden_size=config.hidden_size,
+        ffn_hidden_size=4 * config.hidden_size,
         activation=activation,
         normalization=normalization,
         params_dtype=dtype,
+        return_bias=return_bias,
+        bias=bias,
         device="cuda",
-    ).eval()
+    )
 
     torch_ln_mlp = (
         TorchLayerNormMLP(
             config.hidden_size,
             4 * config.hidden_size,
             activation=activation,
             normalization=normalization,
+            bias=bias,
         )
         .to(dtype=dtype)
         .cuda()
-        .eval()
     )
 
     # Share params
     with torch.no_grad():
-        torch_ln_mlp.ln.weight = Parameter(te_ln_mlp.layer_norm_weight.clone())
+        torch_ln_mlp.ln.weight = Parameter(te_ln_mlp.te_module.layer_norm_weight.clone())
         if normalization != "RMSNorm":
-            torch_ln_mlp.ln.bias = Parameter(te_ln_mlp.layer_norm_bias.clone())
-        torch_ln_mlp.fc1.weight = Parameter(te_ln_mlp.fc1_weight.clone())
-        torch_ln_mlp.fc1.bias = Parameter(te_ln_mlp.fc1_bias.clone())
-        torch_ln_mlp.fc2.weight = Parameter(te_ln_mlp.fc2_weight.clone())
-        torch_ln_mlp.fc2.bias = Parameter(te_ln_mlp.fc2_bias.clone())
+            torch_ln_mlp.ln.bias = Parameter(te_ln_mlp.te_module.layer_norm_bias.clone())
+        torch_ln_mlp.fc1.weight = Parameter(te_ln_mlp.te_module.fc1_weight.clone())
+        torch_ln_mlp.fc2.weight = Parameter(te_ln_mlp.te_module.fc2_weight.clone())
+        if bias:
+            torch_ln_mlp.fc1.bias = Parameter(te_ln_mlp.te_module.fc1_bias.clone())
+            torch_ln_mlp.fc2.bias = Parameter(te_ln_mlp.te_module.fc2_bias.clone())
 
     te_outputs = _test_granular_accuracy(te_ln_mlp, bs, dtype, config)
     torch_outputs = _test_granular_accuracy(torch_ln_mlp, bs, dtype, config)

transformer_engine/common/gemm/cublaslt_gemm.cu

@@ -351,6 +351,9 @@ void cublas_gemm(const Tensor *inputA, const Tensor *inputB, Tensor *outputD,
                                                      &pre_gelu_out, sizeof(pre_gelu_out)));
     NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
         operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_LD, &ld_gelumat, sizeof(ld_gelumat)));
+    const cudaDataType_t aux_type = get_cuda_dtype(outputPreGelu->data.dtype);
+    NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(
+        operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE_AUX_DATA_TYPE, &aux_type, sizeof(aux_type)));
   }
 
   NVTE_CHECK_CUBLAS(cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE,

transformer_engine/pytorch/csrc/extensions/gemm.cpp

@@ -36,6 +36,10 @@ namespace transformer_engine::pytorch {
 
 namespace detail {
 
+bool is_low_precision(const DType type) {
+  return type == DType::kFloat8E4M3 || type == DType::kFloat8E5M2;
+}
+
 std::vector<size_t> getGemmOutputShape(const NVTEShape& A_shape, const bool transa,
                                        const NVTEShape& B_shape, const bool transb) {
   // Flatten outer dims to get 2D matrices
@@ -96,6 +100,9 @@ std::vector<py::object> gemm(py::handle A, bool transa, py::handle B, bool trans
   TensorWrapper A_tensor = makeTransformerEngineTensor(A, none);
   TensorWrapper B_tensor = makeTransformerEngineTensor(B, none);
 
+  const bool low_precision =
+      detail::is_low_precision(A_tensor.dtype()) || detail::is_low_precision(B_tensor.dtype());
+
   // Check tensor dimensions
   const auto& A_shape = A_tensor.shape();
   const auto& B_shape = B_tensor.shape();
@@ -137,7 +144,7 @@ std::vector<py::object> gemm(py::handle A, bool transa, py::handle B, bool trans
 
   // Activation input tensor
   MaybeTensor pre_gelu_out = std::nullopt;
-  DType gelu_type = bias_type;
+  DType gelu_type = low_precision ? bias_type : D_tensor.dtype();
   if (gelu) {
     if (!grad) {
       auto dtype = GetATenDType(gelu_type);

transformer_engine/pytorch/module/layernorm_linear.py

@@ -79,7 +79,6 @@ def forward(
         ln_bias: Union[torch.Tensor, None],
         weight: torch.Tensor,
         bias: torch.Tensor,
-        use_bias: bool,
         eps: float,
         is_first_microbatch: Union[bool, None],
         fp8: bool,
@@ -422,7 +421,7 @@ def forward(
             ctx.fuse_wgrad_accumulation = fuse_wgrad_accumulation
             ctx.cpu_offloading = cpu_offloading
             ctx.is_first_microbatch = is_first_microbatch
-            ctx.use_bias = use_bias
+            ctx.use_bias = bias is not None
             ctx.sequence_parallel = sequence_parallel
             ctx.tensor_parallel = tensor_parallel
             ctx.inp_shape = inp_shape
@@ -756,10 +755,6 @@ def backward(
                     # TODO (pgadzinski) - deallocate transpose only  # pylint: disable=fixme
                     clear_tensor_data(ln_out_total)
 
-            # Don't return grad bias if not needed
-            if not ctx.use_bias:
-                grad_bias = None
-
             # Synchronize tensor parallel communication
             if ln_out_total_work is not None:
                 ln_out_total_work.wait()
@@ -841,7 +836,6 @@ def backward(
             dbeta,
             wgrad,
             grad_bias,
-            None,  # use_bias
             None,  # eps
             None,  # is_first_microbatch
             None,  # fp8
@@ -1344,8 +1338,7 @@ def forward(
                 self.layer_norm_weight,
                 self.layer_norm_bias,
                 weight_tensor,
-                bias_tensor,
-                self.apply_bias and not self.gemm_bias_unfused_add,
+                bias_tensor if self.apply_bias and not self.gemm_bias_unfused_add else None,
                 self.eps,
                 is_first_microbatch,
                 self.fp8,