Add strict=True explicit flag to Executorch invocations to torch.export.export

Differential Revision: D69068797

Pull Request resolved: #8190

Author: gmagogsfm

backends/qualcomm/utils/utils.py

@@ -873,7 +873,6 @@ def generate_multi_graph_program(
     backend_config: ExecutorchBackendConfig = None,
     constant_methods: Optional[Dict[str, Any]] = None,
 ) -> ExecutorchProgramManager:
-
     # compile multiple graphs in qcir into single context binary
     (
         graph_inputs,
@@ -1060,7 +1059,7 @@ def forward(
             outputs_dict[graph_name],
             embedding_quantize,
         )
-        prog = torch.export.export(composite_llama_module, sample_inputs)
+        prog = torch.export.export(composite_llama_module, sample_inputs, strict=True)
         progs_dict[graph_name] = prog
     # leverage ExecutorchProgramManager for generating pte with multi-methods
     edge_prog_mgr = to_edge(

devtools/visualization/visualization_utils_test.py

@@ -84,7 +84,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 def test_visualize_manual_export(server):
     with server():
         model = Linear(20, 30)
-        exported_program = torch.export.export(model, model.get_inputs())
+        exported_program = torch.export.export(model, model.get_inputs(), strict=True)
         visualize(exported_program)
         time.sleep(3.0)
 
@@ -150,7 +150,7 @@ def test_visualize_to_executorch(server):
 def test_visualize_graph(server):
     with server():
         model = Linear(20, 30)
-        exported_program = torch.export.export(model, model.get_inputs())
+        exported_program = torch.export.export(model, model.get_inputs(), strict=True)
         exported_program = to_edge_transform_and_lower(
             exported_program
         ).exported_program()

exir/emit/test/test_emit.py

@@ -265,7 +265,7 @@ def forward(self, x):
 
         m = TestModule()
         example_inputs = (torch.ones(10),)
-        ep = torch.export.export(m, example_inputs)
+        ep = torch.export.export(m, example_inputs, strict=True)
         edge = to_edge(
             ep,
             compile_config=EdgeCompileConfig(

exir/program/test/test_program.py

@@ -343,7 +343,7 @@ def forward(self, a, b, c):
 
             inp = (torch.randn(10), torch.randn(10), torch.tensor(3))
 
-            ep = export(M(), inp)
+            ep = export(M(), inp, strict=True)
             edge = to_edge(ep)
             self.assertTrue(
                 torch.allclose(

extension/llm/modules/test/test_kv_cache.py

@@ -143,6 +143,7 @@ def forward(self, k_val: torch.Tensor, v_val: torch.Tensor):
                     3: torch.export.Dim.STATIC,
                 },
             },
+            strict=True,
         )
         return exported_kv_cache