Back out "allow passing dynamic shape information in examples dir" (p…

…ytorch#1485)

Summary:
Pull Request resolved: pytorch#1485

D52301732 broke a bunch of OSS CI jobs. Revert it for now.

Reviewed By: larryliu0820

Differential Revision: D52418263

fbshipit-source-id: 1c86682250bb9fb4e99c0984d81e62952a9bc2f1

backends/apple/mps/test/test_mps.py

@@ -80,7 +80,7 @@ def run_model(
 ):
     logging.info(f"Step 1: Retrieving model: {model}...")
     if model_type == MODEL_TYPE.EXIR_DEFAULT_MODEL:
-        m, m_inputs, _ = EagerModelFactory.create_model(*MODEL_NAME_TO_MODEL[model])
+        m, m_inputs = EagerModelFactory.create_model(*MODEL_NAME_TO_MODEL[model])
     elif model_type == MODEL_TYPE.EXIR_TEST_MODEL:
         m, m_inputs = EXIR_MODEL_NAME_TO_MODEL.get(model)()
     elif model_type == MODEL_TYPE.MPS_TEST_MODEL:

examples/apple/coreml/scripts/export_and_delegate.py

@@ -116,7 +116,7 @@ def save_processed_bytes(processed_bytes, model_name, compute_units):
             f"Valid compute units are {compute_units}."
         )
 
-        model, example_inputs, _ = EagerModelFactory.create_model(
+    model, example_inputs = EagerModelFactory.create_model(
         *MODEL_NAME_TO_MODEL[args.model_name]
     )
 

examples/apple/mps/scripts/mps_example.py

@@ -50,7 +50,7 @@
     if args.model_name not in MODEL_NAME_TO_MODEL:
         raise RuntimeError(f"Available models are {list(MODEL_NAME_TO_MODEL.keys())}.")
 
-        model, example_inputs, _ = EagerModelFactory.create_model(
+    model, example_inputs = EagerModelFactory.create_model(
         *MODEL_NAME_TO_MODEL[args.model_name]
     )
 

examples/models/llama2/export_llama.py

@@ -38,7 +38,7 @@ def main() -> None:
 
     args = parser.parse_args()
 
-    model, example_inputs, _ = EagerModelFactory.create_model(
+    model, example_inputs = EagerModelFactory.create_model(
         "llama2", "Llama2Model", checkpoint=args.checkpoint, params=args.params
     )
 

examples/models/model_factory.py

@@ -19,7 +19,7 @@ class EagerModelFactory:
     @staticmethod
     def create_model(
         module_name, model_class_name, **kwargs
-    ) -> Tuple[torch.nn.Module, Any, Any]:
+    ) -> Tuple[torch.nn.Module, Any]:
         """
         Create an instance of a model class that implements EagerModelBase and retrieve related data.
 
@@ -41,14 +41,7 @@ def create_model(
         if hasattr(module, model_class_name):
             model_class = getattr(module, model_class_name)
             model = model_class(**kwargs)
-            if hasattr(model, "get_dynamic_shapes"):
-                return (
-                    model.get_eager_model(),
-                    model.get_example_inputs(),
-                    model.get_dynamic_shapes(),
-                )
-            else:
-                return model.get_eager_model(), model.get_example_inputs(), None
+            return model.get_eager_model(), model.get_example_inputs()
 
         raise ValueError(
             f"Model class '{model_class_name}' not found in module '{module_name}'."

examples/portable/scripts/export.py

@@ -9,9 +9,6 @@
 import argparse
 import logging
 
-from executorch.examples.portable.utils import export_to_edge
-from executorch.exir.capture import EdgeCompileConfig
-
 from executorch.exir.capture._config import ExecutorchBackendConfig
 
 from ...models import MODEL_NAME_TO_MODEL
@@ -47,29 +44,12 @@ def main() -> None:
             f"Available models are {list(MODEL_NAME_TO_MODEL.keys())}."
         )
 
-    model, example_inputs, dynamic_shapes = EagerModelFactory.create_model(
+    model, example_inputs = EagerModelFactory.create_model(
         *MODEL_NAME_TO_MODEL[args.model_name]
     )
 
     config = ExecutorchBackendConfig(extract_constant_segment=args.constant_segment)
-    if (
-        dynamic_shapes is not None
-    ):  # capture_pre_autograd_graph does not work with dynamic shapes
-        edge_manager = export_to_edge(
-            model,
-            example_inputs,
-            dynamic_shapes=dynamic_shapes,
-            edge_compile_config=EdgeCompileConfig(
-                _check_ir_validity=False,
-            ),
-        )
-        prog = edge_manager.to_executorch(
-            ExecutorchBackendConfig(extract_constant_segment=True)
-        )
-    else:
-        prog = export_to_exec_prog(
-            model, example_inputs, dynamic_shapes=dynamic_shapes, backend_config=config
-        )
+    prog = export_to_exec_prog(model, example_inputs, backend_config=config)
     save_pte_program(prog.buffer, args.model_name, args.output_dir)
 
 

examples/portable/scripts/export_and_delegate.py

@@ -59,7 +59,7 @@ def export_composite_module_with_lower_graph():
         "Running the example to export a composite module with lowered graph..."
     )
 
-    m, m_inputs, _ = EagerModelFactory.create_model(*MODEL_NAME_TO_MODEL["add_mul"])
+    m, m_inputs = EagerModelFactory.create_model(*MODEL_NAME_TO_MODEL["add_mul"])
     m_compile_spec = m.get_compile_spec()
 
     # pre-autograd export. eventually this will become torch.export
@@ -168,7 +168,7 @@ def export_and_lower_the_whole_graph():
     """
     logging.info("Running the example to export and lower the whole graph...")
 
-    m, m_inputs, _ = EagerModelFactory.create_model(*MODEL_NAME_TO_MODEL["add_mul"])
+    m, m_inputs = EagerModelFactory.create_model(*MODEL_NAME_TO_MODEL["add_mul"])
     m_compile_spec = m.get_compile_spec()
 
     m_inputs = m.get_example_inputs()

examples/portable/test/test_export.py

@@ -62,7 +62,7 @@ def validate_tensor_allclose(eager_output, executorch_output, rtol=1e-5, atol=1e
         return result
 
     def test_mv3_export_to_executorch(self):
-        eager_model, example_inputs, _ = EagerModelFactory.create_model(
+        eager_model, example_inputs = EagerModelFactory.create_model(
             *MODEL_NAME_TO_MODEL["mv3"]
         )
         eager_model = eager_model.eval()
@@ -75,7 +75,7 @@ def test_mv3_export_to_executorch(self):
         )
 
     def test_mv2_export_to_executorch(self):
-        eager_model, example_inputs, _ = EagerModelFactory.create_model(
+        eager_model, example_inputs = EagerModelFactory.create_model(
             *MODEL_NAME_TO_MODEL["mv2"]
         )
         eager_model = eager_model.eval()
@@ -85,7 +85,7 @@ def test_mv2_export_to_executorch(self):
         )
 
     def test_vit_export_to_executorch(self):
-        eager_model, example_inputs, _ = EagerModelFactory.create_model(
+        eager_model, example_inputs = EagerModelFactory.create_model(
             *MODEL_NAME_TO_MODEL["vit"]
         )
         eager_model = eager_model.eval()
@@ -95,7 +95,7 @@ def test_vit_export_to_executorch(self):
         )
 
     def test_w2l_export_to_executorch(self):
-        eager_model, example_inputs, _ = EagerModelFactory.create_model(
+        eager_model, example_inputs = EagerModelFactory.create_model(
             *MODEL_NAME_TO_MODEL["w2l"]
         )
         eager_model = eager_model.eval()
@@ -105,7 +105,7 @@ def test_w2l_export_to_executorch(self):
         )
 
     def test_ic3_export_to_executorch(self):
-        eager_model, example_inputs, _ = EagerModelFactory.create_model(
+        eager_model, example_inputs = EagerModelFactory.create_model(
             *MODEL_NAME_TO_MODEL["ic3"]
         )
         eager_model = eager_model.eval()
@@ -115,7 +115,7 @@ def test_ic3_export_to_executorch(self):
         )
 
     def test_resnet18_export_to_executorch(self):
-        eager_model, example_inputs, _ = EagerModelFactory.create_model(
+        eager_model, example_inputs = EagerModelFactory.create_model(
             *MODEL_NAME_TO_MODEL["resnet18"]
         )
         eager_model = eager_model.eval()
@@ -125,7 +125,7 @@ def test_resnet18_export_to_executorch(self):
         )
 
     def test_resnet50_export_to_executorch(self):
-        eager_model, example_inputs, _ = EagerModelFactory.create_model(
+        eager_model, example_inputs = EagerModelFactory.create_model(
             *MODEL_NAME_TO_MODEL["resnet50"]
         )
         eager_model = eager_model.eval()
@@ -135,7 +135,7 @@ def test_resnet50_export_to_executorch(self):
         )
 
     def test_dl3_export_to_executorch(self):
-        eager_model, example_inputs, _ = EagerModelFactory.create_model(
+        eager_model, example_inputs = EagerModelFactory.create_model(
             *MODEL_NAME_TO_MODEL["dl3"]
         )
         eager_model = eager_model.eval()

examples/qualcomm/scripts/export_example.py

@@ -35,7 +35,7 @@
             f"Available models are {list(MODEL_NAME_TO_MODEL.keys())}."
         )
 
-        model, example_inputs, _ = EagerModelFactory.create_model(
+    model, example_inputs = EagerModelFactory.create_model(
         *MODEL_NAME_TO_MODEL[args.model_name]
     )
 

examples/sdk/scripts/export_bundled_program.py

@@ -145,7 +145,7 @@ def main() -> None:
             f"Available models are {list(MODEL_NAME_TO_MODEL.keys())}."
         )
 
-    model, example_inputs, _ = EagerModelFactory.create_model(
+    model, example_inputs = EagerModelFactory.create_model(
         *MODEL_NAME_TO_MODEL[args.model_name]
     )
 

examples/sdk/scripts/gen_sample_etrecord.py

@@ -72,7 +72,7 @@ def main() -> None:
             f"Available models are {list(MODEL_NAME_TO_MODEL.keys())}."
         )
 
-    model, example_inputs, _ = EagerModelFactory.create_model(
+    model, example_inputs = EagerModelFactory.create_model(
         *MODEL_NAME_TO_MODEL[args.model_name]
     )
 

examples/xnnpack/aot_compiler.py

@@ -65,7 +65,7 @@
             f"Available models are {list(MODEL_NAME_TO_OPTIONS.keys())}."
         )
 
-    model, example_inputs, _ = EagerModelFactory.create_model(
+    model, example_inputs = EagerModelFactory.create_model(
         *MODEL_NAME_TO_MODEL[args.model_name]
     )
 

examples/xnnpack/quantization/example.py

@@ -160,7 +160,7 @@ def main() -> None:
         )
 
     start = time.perf_counter()
-    model, example_inputs, _ = EagerModelFactory.create_model(
+    model, example_inputs = EagerModelFactory.create_model(
         *MODEL_NAME_TO_MODEL[args.model_name]
     )
     end = time.perf_counter()