[ONNX] Drop 'aten_graph' arg for 'DynamoExporter'

ghstack-source-id: 6d04ee3f2ee259a6c51cd527c290a357096846f2
Pull Request resolved: #99667

test/onnx/test_fx_to_onnx_with_onnxruntime.py

@@ -488,11 +488,32 @@ def forward(self, x):
         _run_test_with_fx_to_onnx_exporter_and_onnx_runtime(self, Model(), (input,))
 
     @pytorch_test_common.xfail(
-        "RuntimeError: false INTERNAL ASSERT FAILED at "
-        "'/home/titaiwang/pytorch/build/aten/src/ATen/RegisterFunctionalization_0.cpp':3725,"
-        " please report a bug to PyTorch. mutating a non-functional tensor with a "
-        "functional tensor is not allowed. Please ensure that all of your inputs are "
-        "wrapped inside of a functionalize() call."
+        "RuntimeError: Unknown call_function target: aten.var_mean.correction"
+    )
+    @pytorch_test_common.skip_min_ort_version(
+        reason="ORT doesn't support dynamic fx exporter yet making SegFault flaky test",
+        version="1.15",
+        dynamic_only=True,
+    )
+    @skip_if_no_torchvision
+    def test_resnet18(self):
+        model = torchvision.models.resnet18(pretrained=False)
+        dummy_input = torch.randn(1, 3, 224, 224)
+
+        _run_test_with_fx_to_onnx_exporter_and_onnx_runtime(
+            self,
+            model,
+            (dummy_input,),
+        )
+
+    @pytorch_test_common.xfail(
+        "Found unsupported input types on PyTorch Op aten.convolution.default with "
+        "ValueError: Unexpected input argument type is found in node arguments. arg: None;"
+    )
+    @pytorch_test_common.skip_min_ort_version(
+        reason="ORT doesn't support dynamic fx exporter yet making SegFault flaky test",
+        version="1.15",
+        dynamic_only=True,
     )
     @skip_if_no_torchvision
     def test_shufflenet_v2(self):

torch/onnx/_internal/fx/dynamo_exporter.py

@@ -164,10 +164,7 @@ def export(self) -> torch.onnx.ExportOutput:
         # TODO(wechi): There are several symbolic tracing mechanisms to convert
         # nn.Module to FX graph. We should choose the right one after they are
         # matured.
-        # TODO(titaiwang): Set `tracing_mode` according to `self.options.dynamic_shapes`
-        graph_module, graph_guard = torch._dynamo.export(
-            wrapped_model, *args, aten_graph=True, **kwargs
-        )
+        graph_module, graph_guard = torch._dynamo.export(wrapped_model, *args, **kwargs)
         del graph_guard  # Unused
         torch._dynamo.reset()
 

torch/onnx/_internal/fx/fx_exporter.py

@@ -298,22 +298,26 @@ def export_fx_to_onnx(
         fx_module: torch.fx.GraphModule,
         fx_module_args: Sequence[Any],
     ) -> torch.onnx.ExportOutput:
-        # Apply decomposition table to the input graph.
-        module = passes.Decompose(
-            fx_module,
-            self.decomposition_table,
-            enable_dynamic_axes=self.options.dynamic_shapes,
-        ).run(*fx_module_args)
-
         # ONNX does not support views and mutations.
         # Functionalize to get a semantically equivalent graph without mutations.
+        # NOTE: Functionalize must run before decomposition and aten graph lowering.
+        # https://github.com/pytorch/pytorch/issues/99662
         module = passes.Functionalize(
-            module, enable_dynamic_axes=self.options.dynamic_shapes
+            fx_module, enable_dynamic_axes=self.options.dynamic_shapes
         ).run(*fx_module_args)
         # Input mutations are detected and distilled after `Functionalize` pass.
         # Remove them since ONNX inference does not need them.
         module = passes.RemoveInputMutation(module).run(*fx_module_args)
 
+        # Apply decomposition table to the input graph.
+        module = passes.Decompose(
+            module,
+            self.decomposition_table,
+            enable_dynamic_axes=self.options.dynamic_shapes,
+        ).run(*fx_module_args)
+
+        module = passes.ReplaceInplacePostFunctionalization(module).run(*fx_module_args)
+
         # Run ShapeInferenceWithFakeTensor to get static shape of nodes for op_level_debug purposes
         # The pass added nodes with static shape into original node metadata:
         # node.meta["static_shape"]: FakeTensor/int/float/SymInt/SynFloat

torch/onnx/_internal/fx/passes/__init__.py

@@ -1,5 +1,5 @@
 from .decomp import Decompose
-from .functionalization import Functionalize, RemoveInputMutation
+from .functionalization import Functionalize, RemoveInputMutation, ReplaceInplacePostFunctionalization
 from .fx_to_onnxscript import export_fx_to_onnxscript
 from .shape_inference import ShapeInferenceWithFakeTensor
 from .virtualization import MovePlaceholderToFront, ReplaceGetAttrWithPlaceholder
@@ -11,5 +11,6 @@
     "MovePlaceholderToFront",
     "RemoveInputMutation",
     "ReplaceGetAttrWithPlaceholder",
+    "ReplaceInplacePostFunctionalization",
     "ShapeInferenceWithFakeTensor",
 ]

torch/onnx/_internal/fx/passes/functionalization.py

@@ -3,12 +3,15 @@
 import torch
 import torch.func
 import torch.fx
+import torch._ops
 
 from torch.fx.experimental import proxy_tensor
 from torch.onnx._internal import _beartype
-from torch.onnx._internal.fx import _pass
+from torch.onnx._internal.fx import _pass, diagnostics
 from torch.onnx._internal.fx.passes import _utils
 
+from typing import Dict, Optional
+
 
 class Functionalize(_pass.Transform):
     """Functionalize a GraphModule.
@@ -52,6 +55,9 @@ def fn(a, b):
     For ONNX inference, it is recommended to run ``RemoveInputMutation`` after this pass.
     ``RemoveInputMutation`` removes the "fix up" nodes that were added by ``Functionalize``,
     which are not needed for ONNX inference.
+
+    NOTE: Functionalize must run before decomposition and aten graph lowering.
+    https://github.com/pytorch/pytorch/issues/99662
     """
 
     @_beartype.beartype
@@ -102,3 +108,100 @@ def _run(self, *args) -> torch.fx.GraphModule:
             ):
                 self.module.graph.erase_node(node)
         return self.module
+
+
+class ReplaceInplacePostFunctionalization(_pass.Transform):
+    """
+
+    NOTE: This pass is not needed, if functionalize can be applied on decomposed graph.
+    https://github.com/pytorch/pytorch/issues/99662
+    """
+
+    @_beartype.beartype
+    def _outplace_target(
+        self, inplace_target: torch._ops.OpOverload
+    ) -> Optional[torch._ops.OpOverload]:
+        assert inplace_target.namespace == "aten"
+        outplace_name = inplace_target._schema.name.split("::")[1][:-1]
+        overload_name = inplace_target._overloadname
+
+        opoverloadpacket = getattr(torch.ops.aten, outplace_name)
+        if not isinstance(opoverloadpacket, torch._ops.OpOverloadPacket):
+            return None
+
+        return getattr(opoverloadpacket, overload_name, None)
+
+    @_beartype.beartype
+    def _run(self, *args) -> torch.fx.GraphModule:
+        # Run through reverse nodes and record the first instance of a use
+        # of a given node. This represents the *last* use of the node in the
+        # execution order of the program, which we will use to validate that
+        # the mutated input value is not used after the mutation.
+        node_to_last_use: Dict[torch.fx.Node, torch.fx.Node] = {}
+
+        def register_last_uses(n: torch.fx.Node, user: torch.fx.Node):
+            if n not in node_to_last_use:
+                node_to_last_use[n] = user
+
+        for node in reversed(self.module.graph.nodes):
+            torch.fx.node.map_arg(node.args, lambda n: register_last_uses(n, node))
+            torch.fx.node.map_arg(node.kwargs, lambda n: register_last_uses(n, node))
+
+        for node in self.module.graph.nodes:
+            if node.op != "call_function" or not isinstance(
+                node.target, torch._ops.OpOverload
+            ):
+                continue
+
+            target = node.target
+            mutated_input = node.args[0]
+
+            name_without_overload = target._schema.name
+            is_inplace = name_without_overload.endswith(
+                "_"
+            ) and not name_without_overload.endswith("__")
+            is_aten = target.namespace == "aten"
+
+            if not is_inplace:
+                continue
+
+            if not is_aten:
+                # TODO(bowbao): Turn this into individual diagnostic.
+                diagnostic = diagnostics.export_context().inflight_diagnostic(
+                    rule=diagnostics.rules.fx_pass
+                )
+                diagnostic.level = diagnostics.levels.WARNING
+                diagnostic.with_additional_message(
+                    f"Found non-aten op {target} in graph with inplace naming convention. "
+                    f"Skip replacing this op with outplace version."
+                )
+                continue
+
+            assert isinstance(
+                mutated_input, torch.fx.Node
+            ), f"Expected mutated input to be a torch.fx.Node. Got {type(mutated_input)}"
+
+            if node_to_last_use[mutated_input] != node:
+                # TODO(bowbao): Turn this into individual diagnostic.
+                raise RuntimeError(
+                    f"Found inplace op node {node} that is not the last use of its input. "
+                    f"Its mutated input is later used by {node_to_last_use[mutated_input]}. "
+                    f"Please run RemoveInputMutation pass before ReplaceInplacePostFunctionalization."
+                )
+
+            outplace_target = self._outplace_target(target)
+
+            if outplace_target is None:
+                # TODO(bowbao): Turn this into individual diagnostic.
+                diagnostic = diagnostics.export_context().inflight_diagnostic(
+                    rule=diagnostics.rules.fx_pass
+                )
+                diagnostic.level = diagnostics.levels.WARNING
+                diagnostic.with_additional_message(
+                    f"Failed to find outplace version of {target}. Skip replacing this op."
+                )
+                continue
+
+            node.target = outplace_target
+
+        return self.module