Fix bug where source transform passes untransformed model to next stage

backends/apple/coreml/test/test_coreml_recipes.py

@@ -166,7 +166,7 @@ def forward(self, x):
                     session, example_inputs, atol=1e-3
                 )
                 self._compare_eager_unquantized_model_outputs(
-                    session, model, example_inputs
+                    session, model, example_inputs, sqnr_threshold=15
                 )
 
     def test_int4_weight_only_per_group_validation(self):

backends/xnnpack/test/recipes/test_xnnpack_recipes.py

@@ -154,7 +154,7 @@ def forward(self, x) -> torch.Tensor:
             ),
             ExportRecipe.get_recipe(
                 XNNPackRecipeType.TORCHAO_INT8_DYNAMIC_ACT_INT4_WEIGHT_PER_TENSOR,
-                group_size=8,
+                group_size=32,
             ),
         ]
 

export/stages.py

@@ -332,7 +332,7 @@ def run(self, artifact: PipelineArtifact) -> None:
         self._transformed_models = copy.deepcopy(artifact.data)
 
         # Apply torchao quantize_ to each model
-        for _, model in artifact.data.items():
+        for _, model in self._transformed_models.items():
             # pyre-ignore
             if len(self._quantization_recipe.ao_quantization_configs) > 1:
                 raise ValueError(

export/tests/test_export_stages.py

@@ -300,11 +300,26 @@ def test_run_with_ao_quantization_configs(
         artifact = PipelineArtifact(data=models_dict, context={})
         stage.run(artifact)
 
-        # Verify quantize_ was called with the model and config
-        mock_quantize.assert_called_once_with(self.model, mock_config, mock_filter_fn)
+        # Verify quantize_ was called once (with the copied model, not the original)
+        self.assertEqual(mock_quantize.call_count, 1)
+        # Verify the config and filter_fn arguments are correct
+        call_args = mock_quantize.call_args[0]
+        self.assertNotEqual(self.model, call_args[0])
+        self.assertEqual(call_args[1], mock_config)
+        self.assertEqual(call_args[2], mock_filter_fn)
 
-        # Verify unwrap_tensor_subclass was called with the model
-        mock_unwrap.assert_called_once_with(self.model)
+        # Verify unwrap_tensor_subclass was called once (with the copied model)
+        self.assertEqual(mock_unwrap.call_count, 1)
+
+        # Verify that the original models_dict is unchanged
+        self.assertEqual(models_dict, {"forward": self.model})
+
+        # Verify that the result artifact data contains valid models
+        result_artifact = stage.get_artifacts()
+        self.assertIn("forward", result_artifact.data)
+        self.assertIsNotNone(result_artifact.data["forward"])
+        # verify the result model is NOT the same object as the original
+        self.assertIsNot(result_artifact.data["forward"], self.model)
 
 
 class TestQuantizeStage(unittest.TestCase):
@@ -398,6 +413,10 @@ def test_run_with_quantizers(
         self.assertIn("forward", result_artifact.data)
         self.assertEqual(result_artifact.data["forward"], mock_quantized_model)
 
+        # Verify that the original model in the input artifact is unchanged
+        self.assertEqual(artifact.data["forward"], self.model)
+        self.assertIsNot(result_artifact.data["forward"], self.model)
+
     def test_run_empty_example_inputs(self) -> None:
         """Test error when example inputs list is empty."""
         mock_quantizer = Mock()