Implemented Runtime Intermediate Output Extraction Based on Corresponding AOT Operators

Differential Revision: D77712318

Pull Request resolved: #12212

Author: Juntian777

devtools/inspector/_inspector_utils.py

@@ -732,6 +732,19 @@ def map_runtime_aot_intermediate_outputs(
             # runtime follow the same format as aot, so it's safe to convert to tuple
             if isinstance(runtime_intermediate_output, list):
                 runtime_intermediate_output = tuple(runtime_intermediate_output)
+
+            # Currently, runtime_intermediate_output logs all delegate call arguments.
+            # Process here to extract only the outputs.
+            if isinstance(aot_intermediate_output, tuple):
+                # If both are sequences, slice runtime_intermediate_output to match the length of aot_intermediate_output
+                if isinstance(runtime_intermediate_output, tuple):
+                    runtime_intermediate_output = runtime_intermediate_output[
+                        -len(aot_intermediate_output) :
+                    ]
+            # If aot_intermediate_output is not a sequence but runtime_intermediate_output is, get the last element
+            elif isinstance(runtime_intermediate_output, tuple):
+                runtime_intermediate_output = runtime_intermediate_output[-1]
+
             # Create a mapping between runtime and aot
             aot_runtime_mapping[
                 (aot_combined_debug_handle, aot_intermediate_output)

devtools/inspector/tests/inspector_test.py

@@ -571,15 +571,15 @@ def test_get_runtime_intermediate_outputs_and_op_names(self):
             self.assertIn((4,), runtime_outputs)
             self.assertIn((4,), op_names)
             self.assertTrue(
-                torch.equal(runtime_outputs[(4,)][0], torch.tensor([4.0, 5.0, 6.0]))
+                torch.allclose(runtime_outputs[(4,)][0], torch.tensor([4.0, 5.0, 6.0]))
             )
             self.assertEqual(op_names[(4,)], "op_3")
 
             # Check that keys (5,) to (8,) are in the dictionary and have values of the correct size
             for key in range(5, 9):
                 self.assertIn((key,), runtime_outputs)
                 self.assertIn((key,), op_names)
-                self.assertEqual(len(runtime_outputs[(key,)]), RAW_DATA_SIZE)
+                self.assertEqual(runtime_outputs[(key,)][0].size(0), RAW_DATA_SIZE)
                 self.assertEqual(op_names[(key,)], f"op_{key-1}")
 
     def test_calculate_numeric_gap(self):
@@ -659,7 +659,7 @@ def _gen_random_float_list(self) -> List[float]:
     def _gen_random_runtime_output(
         self,
     ) -> List[Union[None, List[torch.Tensor], bool, float, int, str, torch.Tensor]]:
-        return list(torch.randn(RAW_DATA_SIZE))
+        return [torch.randn(RAW_DATA_SIZE)]
 
     def _gen_random_events(self) -> List[Event]:
         events = []

devtools/inspector/tests/inspector_utils_test.py

@@ -343,6 +343,60 @@ def test_map_runtime_aot_intermediate_outputs_complex_chain(self):
         expected = {((1, 2, 3, 4, 5, 6), 300): ((2, 3, 4, 5, 6, 7), 350)}
         self.assertEqual(actual, expected)
 
+    def test_map_runtime_aot_intermediate_outputs_delegated(self):
+        # Currently, runtime_intermediate_output logs all delegate call arguments
+        # Test that the map function correctly extracted out the delegated outputs
+        aot_intermediate_outputs = {
+            (1, 2): torch.tensor([4, 5]),
+            (3, 4): torch.tensor([10, 11, 12]),
+            (5, 6): torch.tensor([13, 14, 15, 16, 17]),
+        }
+        runtime_intermediate_outputs = {
+            (1, 2): [torch.tensor([1, 2, 3]), torch.tensor([4, 5])],
+            (3, 4): [
+                torch.tensor([6, 7, 8, 9]),
+                torch.tensor(1),
+                torch.tensor([10, 11, 12]),
+            ],
+            (5, 6): [
+                torch.tensor([1]),
+                torch.tensor([2]),
+                torch.tensor([13, 14, 15, 16, 17]),
+            ],
+        }
+        actual = map_runtime_aot_intermediate_outputs(
+            aot_intermediate_outputs, runtime_intermediate_outputs
+        )
+        expected = {
+            ((1, 2), torch.tensor([4, 5])): ((1, 2), torch.tensor([4, 5])),
+            ((3, 4), torch.tensor([10, 11, 12])): ((3, 4), torch.tensor([10, 11, 12])),
+            ((5, 6), torch.tensor([13, 14, 15, 16, 17])): (
+                (5, 6),
+                torch.tensor([13, 14, 15, 16, 17]),
+            ),
+        }
+        self.assertEqual(len(actual), len(expected))
+
+        for (exp_aot_key, exp_aot_value), (
+            exp_runtime_key,
+            exp_runtime_value,
+        ) in expected.items():
+            found = False
+            for (act_aot_key, act_aot_value), (
+                act_runtime_key,
+                act_runtime_value,
+            ) in actual.items():
+                if exp_aot_key == act_aot_key and torch.allclose(
+                    exp_aot_value, act_aot_value
+                ):
+                    found = True
+                    self.assertEqual(exp_runtime_key, act_runtime_key)
+                    self.assertTrue(
+                        torch.allclose(exp_runtime_value, act_runtime_value)
+                    )
+                    break
+            self.assertTrue(found)
+
     def test_convert_input_to_tensor_convertible_inputs(self):
         # Scalar -> tensor
         actual_output1 = convert_to_float_tensor(5)