diff --git a/test/onnx/test_pytorch_onnx_no_runtime.py b/test/onnx/test_pytorch_onnx_no_runtime.py index 5f2ce3fa657a1ee..38564bca4f75470 100644 --- a/test/onnx/test_pytorch_onnx_no_runtime.py +++ b/test/onnx/test_pytorch_onnx_no_runtime.py @@ -32,6 +32,7 @@ def export_to_onnx( mocks: Optional[Iterable] = None, operator_export_type: torch.onnx.OperatorExportTypes = torch.onnx.OperatorExportTypes.ONNX, opset_version: int = GLOBALS.export_onnx_opset_version, + **torch_onnx_export_kwargs, ) -> onnx.ModelProto: """Exports `model(input)` to ONNX and returns it. @@ -44,6 +45,7 @@ def export_to_onnx( mocks: list of mocks to use during export operator_export_type: export type as described by `torch.onnx.export(...operator_export_type,...)` opset_version: ONNX opset version as described by `torch.onnx.export(...opset_version,...)` + torch_onnx_export_kwargs: extra torch.onnx.export kwargs arguments Returns: A valid ONNX model (`onnx.ModelProto`) """ @@ -60,6 +62,7 @@ def export_to_onnx( f, operator_export_type=operator_export_type, opset_version=opset_version, + **torch_onnx_export_kwargs, ) # Validate ONNX graph before returning it @@ -777,6 +780,91 @@ def forward(self, x): model, inputs, f, dynamic_axes={"x": [0, 1]}, input_names=["x"] ) + @common_utils.skipIfNoCaffe2 + def test_caffe2_aten_fallback(self): + class ModelWithAtenNotONNXOp(torch.nn.Module): + def forward(self, x, y): + abcd = x + y + defg = torch.linalg.qr(abcd) + return defg + + x = torch.rand(3, 4) + y = torch.rand(3, 4) + f = io.BytesIO() + torch.onnx.export( + ModelWithAtenNotONNXOp(), + (x, y), + f, + do_constant_folding=False, + operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK, + # support for linalg.qr was added in later op set versions. + opset_version=9, + ) + onnx_model = onnx.load(io.BytesIO(f.getvalue())) + self.assertAtenOp(onnx_model, "linalg_qr") + + @common_utils.skipIfNoCaffe2 + def test_caffe2_onnx_aten(self): + class ModelWithAtenFmod(torch.nn.Module): + def forward(self, x, y): + return torch.fmod(x, y) + + x = torch.randn(3, 4, dtype=torch.float32) + y = torch.randn(3, 4, dtype=torch.float32) + f = io.BytesIO() + torch.onnx.export( + ModelWithAtenFmod(), + (x, y), + f, + do_constant_folding=False, + operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN, + opset_version=10, # or higher + ) + onnx_model = onnx.load(io.BytesIO(f.getvalue())) + assert onnx_model.graph.node[0].op_type == "Mod" + + @common_utils.skipIfCaffe2 + def test_aten_fallback(self): + class ModelWithAtenNotONNXOp(torch.nn.Module): + def forward(self, x, y): + abcd = x + y + defg = torch.linalg.qr(abcd) + return defg + + x = torch.rand(3, 4) + y = torch.rand(3, 4) + f = io.BytesIO() + torch.onnx.export( + ModelWithAtenNotONNXOp(), + (x, y), + f, + do_constant_folding=False, + operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK, + # support for linalg.qr was added in later op set versions. + opset_version=9, + ) + onnx_model = onnx.load(io.BytesIO(f.getvalue())) + self.assertAtenOp(onnx_model, "linalg_qr") + + @common_utils.skipIfCaffe2 + def test_onnx_aten(self): + class ModelWithAtenFmod(torch.nn.Module): + def forward(self, x, y): + return torch.fmod(x, y) + + x = torch.randn(3, 4, dtype=torch.float32) + y = torch.randn(3, 4, dtype=torch.float32) + f = io.BytesIO() + torch.onnx.export( + ModelWithAtenFmod(), + (x, y), + f, + do_constant_folding=False, + operator_export_type=torch.onnx.OperatorExportTypes.ONNX_ATEN, + ) + onnx_model = onnx.load(io.BytesIO(f.getvalue())) + self.assertAtenOp(onnx_model, "fmod", "Tensor") + if __name__ == "__main__": common_utils.run_tests() diff --git a/torch/onnx/utils.py b/torch/onnx/utils.py index 04fc984ded2b921..83482cac0598f03 100644 --- a/torch/onnx/utils.py +++ b/torch/onnx/utils.py @@ -1739,17 +1739,22 @@ def _add_output_to_block(block: _C.Block, value: _C.Value) -> int: @_beartype.beartype def _should_aten_fallback( - name: str, - opset_version: int, - operator_export_type: _C_onnx.OperatorExportTypes, + name: str, opset_version: int, operator_export_type: _C_onnx.OperatorExportTypes ): + # For BUILD_CAFFE2=0 builds, if domain=="aten" and operator_export_type==ONNX_ATEN, + # an aten::ATen operator is created regardless of symbolics existence + # For BUILD_CAFFE2=1, the same applies only if there is no symbolic available + is_exportable_aten_op = registration.registry.is_registered_op(name, opset_version) is_onnx_aten_export = operator_export_type == _C_onnx.OperatorExportTypes.ONNX_ATEN is_aten_fallback_export = ( operator_export_type == _C_onnx.OperatorExportTypes.ONNX_ATEN_FALLBACK ) - return is_onnx_aten_export or ( - not is_exportable_aten_op and is_aten_fallback_export + is_caffe2_build = _C_onnx._CAFFE2_ATEN_FALLBACK + + return name.startswith("aten::") and ( + ((is_onnx_aten_export or is_aten_fallback_export) and not is_caffe2_build) + or (not is_exportable_aten_op and is_aten_fallback_export) ) @@ -1844,6 +1849,21 @@ def _run_symbolic_function( env=env, ) + # Direct ATen export requested + if _should_aten_fallback(ns_op_name, opset_version, operator_export_type): + attrs = { + k + "_" + node.kindOf(k)[0]: symbolic_helper._node_get(node, k) + for k in node.attributeNames() + } + outputs = node.outputsSize() + attrs["outputs"] = outputs + return graph_context.at( + op_name, + *inputs, + overload_name=_get_aten_op_overload_name(node), + **attrs, + ) + try: # Caffe2-specific: Quantized op symbolics are registered for opset 9 only. if symbolic_helper.is_caffe2_aten_fallback() and opset_version == 9: @@ -1861,6 +1881,7 @@ def _run_symbolic_function( if symbolic_function_group is not None: symbolic_fn = symbolic_function_group.get(opset_version) if symbolic_fn is not None: + # TODO Wrap almost identical attrs assignment or comment the difference. attrs = { k: symbolic_helper._node_get(node, k) for k in node.attributeNames() } @@ -1874,18 +1895,6 @@ def _run_symbolic_function( # Clone node to trigger ONNX shape inference return graph_context.op(op_name, *inputs, **attrs, outputs=node.outputsSize()) # type: ignore[attr-defined] - if _should_aten_fallback(ns_op_name, opset_version, operator_export_type): - # Direct ATen export requested - outputs = node.outputsSize() - attrs["outputs"] = outputs - # `overload_name` is set for non-Caffe2 builds only - return graph_context.at( - op_name, - *inputs, - overload_name=_get_aten_op_overload_name(node), - **attrs, - ) - raise errors.UnsupportedOperatorError( domain, op_name,