executorch/exir/program/test

backends/apple/coreml/runtime/test/export_stateful_model.py

@@ -47,7 +47,7 @@ def main() -> None:
         torch.randn((1, embedding_dim)),
         torch.tensor([0]),
     )
-    exported_model = export(model, example_inputs)
+    exported_model = export(model, example_inputs, strict=True)
     edge_program_manager = exir.to_edge(exported_model)
     compile_specs = CoreMLBackend.generate_compile_specs(
         compute_precision=ct.precision.FLOAT16,

backends/apple/coreml/test/test_coreml_partitioner.py

@@ -16,7 +16,6 @@
 
 
 class TestCoreMLPartitioner(unittest.TestCase):
-
     # TODO(T182928844): Delegate dim order op to backend.
     edge_compile_config = executorch.exir.EdgeCompileConfig(_skip_dim_order=True)
 
@@ -34,7 +33,7 @@ def forward(self, a, x, b):
         model.eval()
 
         example_inputs = (torch.randn(2, 2), torch.randn(2, 2), torch.randn(2, 2))
-        exir_program_aten = torch.export.export(model, example_inputs)
+        exir_program_aten = torch.export.export(model, example_inputs, strict=True)
 
         edge_program_manager = executorch.exir.to_edge(
             exir_program_aten, compile_config=self.edge_compile_config
@@ -61,7 +60,7 @@ def test_vit_skip_conv(self):
         model.eval()
 
         example_inputs = (torch.randn(1, 3, 224, 224),)
-        exir_program_aten = torch.export.export(model, example_inputs)
+        exir_program_aten = torch.export.export(model, example_inputs, strict=True)
         edge_program_manager = executorch.exir.to_edge(
             exir_program_aten, compile_config=self.edge_compile_config
         )
@@ -106,7 +105,7 @@ def forward(self, q, k_val, input_pos):
         k_val = torch.randn((1, embedding_dim))
         input_pos = torch.tensor([0])
         example_inputs = (q, k_val, input_pos)
-        exir_program_aten = torch.export.export(model, example_inputs)
+        exir_program_aten = torch.export.export(model, example_inputs, strict=True)
 
         compile_specs = CoreMLBackend.generate_compile_specs(
             minimum_deployment_target=ct.target.iOS18

backends/apple/mps/test/test_mps_utils.py

@@ -247,10 +247,7 @@ def lower_module_and_test_output(
             )
 
             executorch_program = to_edge(
-                export(
-                    delegated_program,
-                    sample_inputs,
-                ),
+                export(delegated_program, sample_inputs, strict=True),
                 compile_config=exir.EdgeCompileConfig(
                     _check_ir_validity=False,
                     _skip_dim_order=True,  # TODO(T182928844): Delegate dim order op to backend.

backends/cadence/aot/compiler.py

@@ -176,7 +176,7 @@ def export_program(
     torch._C._set_mkldnn_enabled(False)
 
     # else: capture the model and return it.
-    expo_program = export(model, inputs)
+    expo_program = export(model, inputs, strict=True)
 
     if dump_graphs:
         logging.info("Exported graph:")

backends/example/test_example_delegate.py

@@ -60,7 +60,7 @@ def get_example_inputs():
 
         quantized_gm = m
         exported_program = to_edge(
-            export(quantized_gm, copy.deepcopy(example_inputs)),
+            export(quantized_gm, copy.deepcopy(example_inputs), strict=True),
             compile_config=EDGE_COMPILE_CONFIG,
         )
 
@@ -92,7 +92,7 @@ def test_delegate_mobilenet_v2(self):
 
         quantized_gm = m
         exported_program = to_edge(
-            export(quantized_gm, copy.deepcopy(example_inputs)),
+            export(quantized_gm, copy.deepcopy(example_inputs), strict=True),
             compile_config=EDGE_COMPILE_CONFIG,
         )
 

backends/qualcomm/tests/test_qnn_delegate.py

@@ -1617,7 +1617,7 @@ def test_qnn_backend_multi_contexts_composite(self):
         )
         sample_input = module.get_random_input()
         edge_prog = to_edge(
-            torch.export.export(module, sample_input),
+            torch.export.export(module, sample_input, strict=True),
         )
         update_spill_fill_size(edge_prog.exported_program())
         exec_prog = edge_prog.to_executorch()
@@ -1957,7 +1957,7 @@ def calibrator(gm):
         self.assertEqual(len(exported_progs), 1)
         # lower all graph again, the skipped operators will be left in CPU
         exec_prog = to_edge(
-            torch.export.export(graph_module, sample_input),
+            torch.export.export(graph_module, sample_input, strict=True),
         ).to_executorch()
         self.verify_output(module, sample_input, exec_prog)
 
@@ -2004,7 +2004,7 @@ def calibrator(gm):
         self.assertEqual(len(exported_progs), 2)
         # lower all graph again, the skipped operators will be left in CPU
         exec_prog = exec_prog = to_edge(
-            torch.export.export(graph_module, sample_input),
+            torch.export.export(graph_module, sample_input, strict=True),
         ).to_executorch()
         self.verify_output(module, sample_input, exec_prog)
 
@@ -2041,7 +2041,7 @@ def calibrator(gm):
         self.assertEqual(len(exported_progs), 5)
         # lower all graph again, the skipped operators will be delegated with fp16
         exec_prog = to_edge(
-            torch.export.export(graph_module, sample_input),
+            torch.export.export(graph_module, sample_input, strict=True),
         ).to_executorch()
         self.verify_output(module, sample_input, exec_prog)
 
@@ -2086,7 +2086,7 @@ def test_qnn_backend_multi_contexts_composite(self):
         )
         sample_input = module.get_random_input()
         edge_prog = to_edge(
-            torch.export.export(module, sample_input),
+            torch.export.export(module, sample_input, strict=True),
         )
         update_spill_fill_size(edge_prog.exported_program())
         exec_prog = edge_prog.to_executorch()
@@ -2721,7 +2721,6 @@ def test_ssd300_vgg16(self):
 
 
 class TestExampleQaihubScript(TestQNN):
-
     def required_envs(self, conditions=None) -> bool:
         conditions = [] if conditions is None else conditions
         return all(

backends/qualcomm/tests/utils.py

@@ -385,7 +385,7 @@ def get_qdq_module(
         custom_quant_annotations: Tuple[Callable] = (),
         quant_dtype: QuantDtype = QuantDtype.use_8a8w,
     ) -> torch.fx.GraphModule:
-        m = torch.export.export(module, inputs).module()
+        m = torch.export.export(module, inputs, strict=True).module()
 
         quantizer = QnnQuantizer()
         quantizer.add_custom_quant_annotations(custom_quant_annotations)

backends/qualcomm/utils/utils.py

@@ -337,7 +337,7 @@ def capture_program(
     inputs: Tuple[torch.Tensor],
     custom_pass_config: FrozenSet[str] = frozenset(),
 ) -> exir.ExirExportedProgram:
-    ep = torch.export.export(module, inputs)
+    ep = torch.export.export(module, inputs, strict=True)
     decomposed_ep = ep.run_decompositions(get_decomp_table())
     # We choose call_operator by target in ConvertBinaryOpsWithScalar
     # because it is the same source_fn_stack for MultiheadAttention
@@ -551,7 +551,7 @@ def prepare_subgm(subgm, subgm_name):
 
     fp_node_id_set = fp_node_id_set if fp_node_id_set is not None else set()
     fp_node_op_set = fp_node_op_set if fp_node_op_set is not None else set()
-    graph_module = torch.export.export(nn_module, sample_input).module()
+    graph_module = torch.export.export(nn_module, sample_input, strict=True).module()
     # define node support type
     capability_partitioner = CapabilityBasedPartitioner(
         graph_module,
@@ -664,7 +664,7 @@ def forward(self, *inputs):
                 ).default(inputs)
 
         model = Model()
-        prog = torch.export.export(model, tuple(inputs.values()))
+        prog = torch.export.export(model, tuple(inputs.values()), strict=True)
         # bookkeeping for variables' life cycle
         return {
             "custom_op": custom_op,

backends/vulkan/test/test_vulkan_delegate.py

@@ -112,7 +112,7 @@ def run_test():
             model(*sample_inputs)
 
             program: ExportedProgram = export(
-                model, sample_inputs, dynamic_shapes=dynamic_shapes
+                model, sample_inputs, dynamic_shapes=dynamic_shapes, strict=True
             )
 
             edge_program = to_edge_transform_and_lower(

backends/xnnpack/partition/graphs/sdpa.py

@@ -76,6 +76,7 @@ def forward(
                         v,
                         mask,
                     ),
+                    strict=True,
                 ),
                 compile_config=get_xnnpack_edge_compile_config(),
             )

backends/xnnpack/test/tester/tester.py

@@ -194,7 +194,7 @@ def run(
         inputs: Tuple[torch.Tensor],
     ) -> None:
         self.exported_program = export(
-            artifact, inputs, dynamic_shapes=self.dynamic_shapes
+            artifact, inputs, dynamic_shapes=self.dynamic_shapes, strict=True
         )
 
     @property

build/packaging/smoke_test.py

@@ -65,7 +65,7 @@ def export_linear_model() -> bytes:
 
     # Export the pytorch model and process for ExecuTorch.
     print("Exporting program...")
-    exported_program = export(LinearModel(), example_inputs)
+    exported_program = export(LinearModel(), example_inputs, strict=True)
     print("Lowering to edge...")
     edge_program = to_edge(exported_program)
     print("Creating ExecuTorch program...")

devtools/backend_debug/tests/test_delegation_info.py

@@ -31,7 +31,7 @@ def forward(self, a, x, b):
 
         m = Model()
         inputs = (torch.randn(2, 2), torch.randn(2, 2), torch.randn(2, 2))
-        edge = to_edge(torch.export.export(m, inputs)).to_backend(
+        edge = to_edge(torch.export.export(m, inputs, strict=True)).to_backend(
             AddMulPartitionerDemo()
         )
         delegation_info = get_delegation_info(edge.exported_program().graph_module)

devtools/bundled_program/util/test_util.py

@@ -271,6 +271,7 @@ def get_common_executorch_program() -> (
         m_name: export(
             StatefulWrapperModule(eager_model, getattr(eager_model, m_name)),
             capture_inputs[m_name],
+            strict=True,
         )
         for m_name in eager_model.method_names
     }

devtools/etrecord/tests/etrecord_test.py

@@ -69,7 +69,7 @@ def get_test_model_with_bundled_program(self):
 
     def get_test_model_with_manager(self):
         f = models.BasicSinMax()
-        aten_dialect = export(f, f.get_random_inputs())
+        aten_dialect = export(f, f.get_random_inputs(), strict=True)
         edge_program: EdgeProgramManager = to_edge(
             aten_dialect, compile_config=EdgeCompileConfig(_check_ir_validity=False)
         )

docs/source/tutorials_source/devtools-integration-tutorial.py

@@ -89,10 +89,7 @@ def forward(self, x):
 
 model = Net()
 
-aten_model: ExportedProgram = export(
-    model,
-    (torch.randn(1, 1, 32, 32),),
-)
+aten_model: ExportedProgram = export(model, (torch.randn(1, 1, 32, 32),), strict=True)
 
 edge_program_manager: EdgeProgramManager = to_edge(
     aten_model, compile_config=EdgeCompileConfig(_check_ir_validity=True)
@@ -141,7 +138,7 @@ def forward(self, x):
 
 # Step 1: ExecuTorch Program Export
 m_name = "forward"
-method_graphs = {m_name: export(model, (torch.randn(1, 1, 32, 32),))}
+method_graphs = {m_name: export(model, (torch.randn(1, 1, 32, 32),), strict=True)}
 
 # Step 2: Construct Method Test Suites
 inputs = [[torch.randn(1, 1, 32, 32)] for _ in range(2)]

docs/source/tutorials_source/export-to-executorch-tutorial.py

@@ -66,7 +66,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 
 
 example_args = (torch.randn(1, 3, 256, 256),)
-aten_dialect: ExportedProgram = export(SimpleConv(), example_args)
+aten_dialect: ExportedProgram = export(SimpleConv(), example_args, strict=True)
 print(aten_dialect)
 
 ######################################################################
@@ -101,7 +101,7 @@ def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
 
 
 example_args = (torch.randn(3, 3), torch.randn(3, 3))
-aten_dialect: ExportedProgram = export(Basic(), example_args)
+aten_dialect: ExportedProgram = export(Basic(), example_args, strict=True)
 
 # Works correctly
 print(aten_dialect.module()(torch.ones(3, 3), torch.ones(3, 3)))
@@ -131,7 +131,7 @@ def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
 dim1_x = Dim("dim1_x", min=1, max=10)
 dynamic_shapes = {"x": {1: dim1_x}, "y": {1: dim1_x}}
 aten_dialect: ExportedProgram = export(
-    Basic(), example_args, dynamic_shapes=dynamic_shapes
+    Basic(), example_args, dynamic_shapes=dynamic_shapes, strict=True
 )
 print(aten_dialect)
 
@@ -213,7 +213,7 @@ def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
 print("Quantized Graph")
 print(converted_graph)
 
-aten_dialect: ExportedProgram = export(converted_graph, example_args)
+aten_dialect: ExportedProgram = export(converted_graph, example_args, strict=True)
 print("ATen Dialect Graph")
 print(aten_dialect)
 
@@ -243,7 +243,7 @@ def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
 from executorch.exir import EdgeProgramManager, to_edge
 
 example_args = (torch.randn(1, 3, 256, 256),)
-aten_dialect: ExportedProgram = export(SimpleConv(), example_args)
+aten_dialect: ExportedProgram = export(SimpleConv(), example_args, strict=True)
 
 edge_program: EdgeProgramManager = to_edge(aten_dialect)
 print("Edge Dialect Graph")
@@ -267,10 +267,10 @@ def forward(self, x):
 
 
 encode_args = (torch.randn(1, 10),)
-aten_encode: ExportedProgram = export(Encode(), encode_args)
+aten_encode: ExportedProgram = export(Encode(), encode_args, strict=True)
 
 decode_args = (torch.randn(1, 5),)
-aten_decode: ExportedProgram = export(Decode(), decode_args)
+aten_decode: ExportedProgram = export(Decode(), decode_args, strict=True)
 
 edge_program: EdgeProgramManager = to_edge(
     {"encode": aten_encode, "decode": aten_decode}
@@ -291,7 +291,7 @@ def forward(self, x):
 # rather than the ``torch.ops.aten`` namespace.
 
 example_args = (torch.randn(1, 3, 256, 256),)
-aten_dialect: ExportedProgram = export(SimpleConv(), example_args)
+aten_dialect: ExportedProgram = export(SimpleConv(), example_args, strict=True)
 edge_program: EdgeProgramManager = to_edge(aten_dialect)
 print("Edge Dialect Graph")
 print(edge_program.exported_program())
@@ -357,7 +357,7 @@ def forward(self, x):
 
 # Export and lower the module to Edge Dialect
 example_args = (torch.ones(1),)
-aten_dialect: ExportedProgram = export(LowerableModule(), example_args)
+aten_dialect: ExportedProgram = export(LowerableModule(), example_args, strict=True)
 edge_program: EdgeProgramManager = to_edge(aten_dialect)
 to_be_lowered_module = edge_program.exported_program()
 
@@ -423,7 +423,7 @@ def forward(self, x):
 
 
 example_args = (torch.ones(1),)
-aten_dialect: ExportedProgram = export(ComposedModule(), example_args)
+aten_dialect: ExportedProgram = export(ComposedModule(), example_args, strict=True)
 edge_program: EdgeProgramManager = to_edge(aten_dialect)
 exported_program = edge_program.exported_program()
 print("Edge Dialect graph")
@@ -461,7 +461,7 @@ def forward(self, a, x, b):
 
 
 example_args = (torch.randn(2, 2), torch.randn(2, 2), torch.randn(2, 2))
-aten_dialect: ExportedProgram = export(Foo(), example_args)
+aten_dialect: ExportedProgram = export(Foo(), example_args, strict=True)
 edge_program: EdgeProgramManager = to_edge(aten_dialect)
 exported_program = edge_program.exported_program()
 print("Edge Dialect graph")
@@ -495,7 +495,7 @@ def forward(self, a, x, b):
 
 
 example_args = (torch.randn(2, 2), torch.randn(2, 2), torch.randn(2, 2))
-aten_dialect: ExportedProgram = export(Foo(), example_args)
+aten_dialect: ExportedProgram = export(Foo(), example_args, strict=True)
 edge_program: EdgeProgramManager = to_edge(aten_dialect)
 exported_program = edge_program.exported_program()
 delegated_program = edge_program.to_backend(AddMulPartitionerDemo())
@@ -577,7 +577,9 @@ def forward(self, x):
 pre_autograd_aten_dialect = export_for_training(M(), example_args).module()
 # Optionally do quantization:
 # pre_autograd_aten_dialect = convert_pt2e(prepare_pt2e(pre_autograd_aten_dialect, CustomBackendQuantizer))
-aten_dialect: ExportedProgram = export(pre_autograd_aten_dialect, example_args)
+aten_dialect: ExportedProgram = export(
+    pre_autograd_aten_dialect, example_args, strict=True
+)
 edge_program: exir.EdgeProgramManager = exir.to_edge(aten_dialect)
 # Optionally do delegation:
 # edge_program = edge_program.to_backend(CustomBackendPartitioner)

examples/apple/coreml/scripts/export.py

@@ -88,7 +88,9 @@ def partition_module_to_coreml(module):
 
 def lower_module_to_coreml(module, compile_specs, example_inputs):
     module = module.eval()
-    edge = to_edge(export(module, example_inputs), compile_config=_EDGE_COMPILE_CONFIG)
+    edge = to_edge(
+        export(module, example_inputs, strict=True), compile_config=_EDGE_COMPILE_CONFIG
+    )
     # All of the subsequent calls on the edge_dialect_graph generated above (such as delegation or
     # to_executorch()) are done in place and the graph is also modified in place. For debugging purposes
     # we would like to keep a copy of the original edge dialect graph and hence we create a deepcopy of
@@ -107,7 +109,8 @@ def lower_module_to_coreml(module, compile_specs, example_inputs):
 def export_lowered_module_to_executorch_program(lowered_module, example_inputs):
     lowered_module(*example_inputs)
     exec_prog = to_edge(
-        export(lowered_module, example_inputs), compile_config=_EDGE_COMPILE_CONFIG
+        export(lowered_module, example_inputs, strict=True),
+        compile_config=_EDGE_COMPILE_CONFIG,
     ).to_executorch(config=exir.ExecutorchBackendConfig(extract_delegate_segments=True))
 
     return exec_prog
@@ -170,7 +173,7 @@ def main():
 
     if args.use_partitioner:
         model.eval()
-        exir_program_aten = torch.export.export(model, example_inputs)
+        exir_program_aten = torch.export.export(model, example_inputs, strict=True)
 
         edge_program_manager = exir.to_edge(exir_program_aten)
         edge_copy = copy.deepcopy(edge_program_manager)