Remove args from LLMEdgeManager and misc cleanup (#10288)

Summary:
Decouple `export_llama`'s `argparse.namespace` from `LLMEdgeManager`  + misc cleanup


Test Plan: CI

Reviewed By: iseeyuan

Differential Revision: D73225183

Pulled By: jackzhxng

Author: jackzhxng

examples/models/llama/export_llama_lib.py

@@ -653,7 +653,7 @@ def _prepare_for_llama_export(args) -> LLMEdgeManager:
         _get_source_transforms(
             modelname=args.model,
             dtype_override=dtype_override,
-            checkpoint_dtype=DType.from_torch_dtype(checkpoint_dtype),
+            checkpoint_dtype=DType.from_torch_dtype(checkpoint_dtype),  # type: ignore
             args=args,
         )
     )
@@ -1106,7 +1106,7 @@ def _load_llama_model(
     return LLMEdgeManager(
         model=model,
         modelname=modelname,
-        max_seq_len=model.max_seq_len,
+        max_seq_len=model.max_seq_len,  # type: ignore
         dtype=dtype_override,
         use_kv_cache=use_kv_cache,
         generate_full_logits=generate_full_logits,
@@ -1119,6 +1119,8 @@ def _load_llama_model(
         calibration_seq_length=calibration_seq_length,
         calibration_data=calibration_data,
         tokenizer_path=tokenizer_path,
+        use_legacy_export=args.qnn,
+        save_exported_program=args.export_only,
         verbose=verbose,
         metadata=_load_llama_model_metadata(
             weight_type,
@@ -1139,7 +1141,6 @@ def _load_llama_model(
             model.vocab_size,
             metadata_str,
         ),
-        args=args,
     )
 
 

examples/models/llava/export_llava.py

@@ -92,7 +92,6 @@ def forward(self, input_pos, embeddings):
         use_kv_cache=True,
         example_inputs=(torch.tensor([0], dtype=torch.int64), embeddings),
         dynamic_shapes=dynamic_shapes,
-        args=llava.text_model_args,
     )
 
     dtype_override = DType.fp32
@@ -161,7 +160,6 @@ def forward(self, images):
             use_kv_cache=True,
             example_inputs=(resized,),
             dynamic_shapes=dynamic_shapes,
-            args=None,
         )
         .export()
         .pt2e_quantize([quantizer])

extension/llm/export/builder.py

@@ -13,7 +13,7 @@
 import contextlib
 import logging
 from enum import Enum
-from typing import Any, Callable, Dict, List, Optional
+from typing import Any, Callable, Dict, List, Optional, Tuple
 from unittest.mock import patch
 
 import torch
@@ -81,14 +81,13 @@ class LLMEdgeManager:
 
     def __init__(
         self,
-        model,
-        modelname,
-        max_seq_len,
-        dtype,
-        use_kv_cache,
-        example_inputs,
+        model: torch.nn.Module,
+        modelname: str,
+        max_seq_len: int,
+        use_kv_cache: bool,
+        example_inputs: Tuple[torch.Tensor, ...],
+        dtype: Optional[DType] = None,
         example_kwarg_inputs: Optional[Dict] = None,
-        args: Optional[Any] = None,
         enable_dynamic_shape: bool = False,
         generate_full_logits: bool = False,
         calibration_tasks: Optional[List[str]] = None,
@@ -99,36 +98,42 @@ def __init__(
         verbose: bool = False,
         metadata: Optional[dict] = None,
         dynamic_shapes: Optional[Any] = None,
+        use_legacy_export: bool = False,
+        save_exported_program: bool = False,
     ):
+        # Store necessary constructor arguments.
         self.model = model
-        # Note: treat this as the source of truth for the result of
-        # torch.export'ing a model. If the overall ExportedProgram is needed,
-        # make sure to re-export this graph module to persist any changes. See
-        # https://github.com/pytorch/pytorch/blob/main/torch/export/exported_program.py#L921
-        self.pre_autograd_graph_module: Optional[torch.nn.Module] = None
         self.modelname = modelname
         self.max_seq_len = max_seq_len
-        self.dtype = dtype
+        self.use_kv_cache = use_kv_cache
         self.example_inputs = example_inputs
+        self.dtype = dtype
         self.example_kwarg_inputs = example_kwarg_inputs
-        self.use_kv_cache = use_kv_cache
-        self.generate_full_logits = generate_full_logits
         self.enable_dynamic_shape = enable_dynamic_shape
-        self.verbose = verbose
-        self.metadata = metadata
-        self.applied_source_transforms = []
-        self.edge_manager: Optional[EdgeProgramManager] = None
-        self.export_program = None
-        self.output_dir = "."
-        self.dynamic_shapes = dynamic_shapes
-        self._saved_pte_filename = None
-        self.args = args
+        self.generate_full_logits = generate_full_logits
         self.calibration_tasks = calibration_tasks
         self.calibration_limit = calibration_limit
         self.calibration_seq_length = calibration_seq_length
         self.calibration_data = calibration_data
         self.tokenizer_path = tokenizer_path
-        self.canonical_passes = [RemoveRedundantTransposes()]
+        self.verbose = verbose
+        self.metadata = metadata
+        self.dynamic_shapes = dynamic_shapes
+        self.use_legacy_export = use_legacy_export
+        self.save_exported_program = save_exported_program
+
+        # Note: treat this as the source of truth for the result of
+        # torch.export'ing a model. If the overall ExportedProgram is needed,
+        # make sure to re-export this graph module to persist any changes. See
+        # https://github.com/pytorch/pytorch/blob/main/torch/export/exported_program.py#L921
+        self.pre_autograd_graph_module: Optional[torch.nn.Module] = None
+        self.edge_manager: Optional[EdgeProgramManager] = None
+        self.canonical_passes = [
+            RemoveRedundantTransposes()
+        ]  # Graph transformations optimizations.
+        self.export_program = None  # Final result of lowering to executorch.
+        self.output_dir = "."
+        self._saved_pte_filename = None
 
     def set_output_dir(self, output_dir: str) -> "LLMEdgeManager":
         """
@@ -167,10 +172,9 @@ def source_transform(
         """
         for transform in transforms:
             self.model = transform(self.model)
-        self.applied_source_transforms.extend(transforms)
 
         if self.verbose:
-            logging.info(f"Applied source transforms: {self.applied_source_transforms}")
+            logging.info(f"Applied source transforms: {transforms}")
         logging.info(f"Model after source transforms: {self.model}")
         return self
 
@@ -209,8 +213,8 @@ def _export(self, module: Optional[torch.nn.Module] = None) -> ExportedProgram:
         # 1. torch.nn.attention.sdpa_kernel([SDPBackend.MATH]) is for bypassing the dynamo error when tracing
         # 2. torch.no_grad() is for getting rid of the dropout (not sure why training ops will show up)
         with torch.nn.attention.sdpa_kernel([SDPBackend.MATH]), torch.no_grad():
-            if hasattr(self.args, "qnn") and self.args.qnn:
-                # TODO: this is temporary, as qnn flow does not work with new, non-functional export IR.
+            if self.use_legacy_export:
+                # TODO: for use cases such as qnn, which does not work with new, non-functional export IR.
                 # See issue: https://github.com/pytorch/executorch/issues/7373
 
                 with patch.object(
@@ -256,8 +260,12 @@ def export(self) -> "LLMEdgeManager":
         # Persisting those changes back to an ExportedProgram will require
         # an additional export().
         self.pre_autograd_graph_module = exported_module.module()
-        if hasattr(self.args, "export_only") and self.args.export_only:
-            torch.export.save(exported_module, self.args.output_name)
+        if self.save_exported_program:
+            export_output = f"{self.modelname}.pt2"
+            logging.info(
+                f"Saving torch.export()/export_for_training() result to {export_output}"
+            )
+            torch.export.save(exported_module, export_output)
         return self
 
     def run_canonical_optimizations(self):
@@ -421,7 +429,7 @@ def export_to_edge(self) -> "LLMEdgeManager":
                 self.export()
 
             override_export_behaviour = contextlib.nullcontext()
-            if hasattr(self.args, "qnn") and self.args.qnn:
+            if self.use_legacy_export:
                 override_export_behaviour = patch.object(
                     torch._utils_internal,
                     "export_training_ir_rollout_check",