Implemented Runtime Intermediate Output Extraction Based on Corresponding AOT Operators

Summary: This PR adds functionality to extract the last n recorded values as runtime intermediate outputs, where n is the size of the intermediate output for the corresponding AOT operator during the final stage of mapping between AOT and runtime. By doing this, it adds support to delegate because currently, we log all the arguments of the delegate call.

Differential Revision: D77712318

Author: Juntian Liu

devtools/inspector/_inspector_utils.py

@@ -732,6 +732,18 @@ 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 +343,39 @@ 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 extraed 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)