Add inspector numeric gap calculation between AOT and runtime intermediate outputs

Differential Revision: D76831086

Pull Request resolved: #11855

Author: Juntian Liu

devtools/inspector/TARGETS

@@ -19,6 +19,7 @@ python_library(
         "//executorch/devtools/etrecord:etrecord",
         "//executorch/exir:lib",
         "//executorch/devtools/inspector:intermediate_output_capturer",
+        "//executorch/devtools/inspector/numerical_comparator:lib",
     ],
 )
 

devtools/inspector/_inspector.py

@@ -55,6 +55,7 @@
     inflate_runtime_output,
     is_debug_output,
     is_inference_output_equal,
+    map_runtime_aot_intermediate_outputs,
     ProgramOutput,
     RESERVED_FRAMEWORK_EVENT_NAMES,
     TimeScale,
@@ -63,6 +64,10 @@
 from executorch.devtools.inspector._intermediate_output_capturer import (
     IntermediateOutputCapturer,
 )
+from executorch.devtools.inspector.numerical_comparator import (
+    L1Comparator,
+    MSEComparator,
+)
 from executorch.exir import ExportedProgram
 
 
@@ -1337,3 +1342,50 @@ def get_exported_program(
             if graph is None
             else self._etrecord.graph_map.get(graph)
         )
+
+    def calculate_numeric_gap(self, distance: str = "MSE") -> pd.DataFrame:
+        """
+        Compares logged intermediate outputs from the exported graph (in ETRecord)
+        with runtime outputs (in ETDump) using a user-specific numerical comparator.
+
+        Args:
+            distance: the metrics the inspector will use for gap calculation. Should be one of "MSE", "L1" and "SNR".
+
+        Returns:
+            pd.DataFrame: A DataFrame listing corresponding operator outputs from
+                          both stages and their computed numerical gaps.
+        """
+        if self._aot_intermediate_outputs is None:
+            raise ValueError(
+                "The aot intermediate outputs is required but not populated."
+            )
+        mapping = map_runtime_aot_intermediate_outputs(
+            self._aot_intermediate_outputs, self._get_runtime_intermediate_outputs()
+        )
+        metric = distance.strip().upper()
+        if metric == "MSE":
+            comparator = MSEComparator()
+        elif metric == "L1":
+            comparator = L1Comparator()
+        else:
+            raise ValueError(f"Unsupported distance metric {distance!r}")
+
+        rows = []
+        for (aot_debug_handle, aot_intermediate_output), (
+            runtime_debug_handle,
+            runtime_intermediate_output,
+        ) in mapping.items():
+            if aot_intermediate_output is None or runtime_intermediate_output is None:
+                continue
+            rows.append(
+                {
+                    "aot_debug_handle": aot_debug_handle,
+                    "aot_intermediate_output": aot_intermediate_output,
+                    "runtime_debug_handle": runtime_debug_handle,
+                    "runtime_intermediate_output": runtime_intermediate_output,
+                    "gap": comparator.compare(
+                        aot_intermediate_output, runtime_intermediate_output
+                    ),
+                }
+            )
+        return pd.DataFrame(rows)

devtools/inspector/_inspector_utils.py

@@ -8,6 +8,7 @@
 
 import math
 import sys
+from collections.abc import Sequence
 from dataclasses import dataclass
 from enum import Enum
 from typing import Any, Dict, IO, List, Mapping, Optional, Tuple, TypeAlias, Union
@@ -676,17 +677,25 @@ def map_runtime_aot_intermediate_outputs(
         # Map only if both AOT and runtime data are present.
         if len(aot_list) != 0 and len(runtime_list) != 0:
             # Combine aot debug handles into a single key
-            aot_combined_debug_handle, aot_output = (
+            aot_combined_debug_handle, aot_intermediate_output = (
                 _combine_overlapped_intermediate_outputs(aot_list)
             )
             # Combine runtime debug handles into a single key
-            runtime_combined_debug_handle, runtime_output = (
+            runtime_combined_debug_handle, runtime_intermediate_output = (
                 _combine_overlapped_intermediate_outputs(runtime_list)
             )
+            # List can't be used as a key, so convert to tuple
+            if isinstance(aot_intermediate_output, list):
+                aot_intermediate_output = tuple(aot_intermediate_output)
+            # 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)
             # Create a mapping between runtime and aot
-            aot_runtime_mapping[(aot_combined_debug_handle, aot_output)] = (
+            aot_runtime_mapping[
+                (aot_combined_debug_handle, aot_intermediate_output)
+            ] = (
                 runtime_combined_debug_handle,
-                runtime_output,
+                runtime_intermediate_output,
             )
 
     return aot_runtime_mapping
@@ -698,7 +707,7 @@ def convert_to_float_tensor(input_data: Any) -> torch.Tensor:
     This function handles the following types of input:
     - Scalar (int or float): Converts to a tensor with a single element.
     - Tensor: Converts to a float64 tensor on CPU.
-    - List of Tensors: Stacks the tensors into a single float64 tensor on CPU.
+    - Sequence of Tensors: Stacks the tensors into a single float64 tensor on CPU.
     The resulting tensor is detached, moved to CPU, and cast to torch.float64.
     Parameters:
     input_data (Any): The input data to be converted to a tensor. It can be a scalar,
@@ -709,8 +718,8 @@ def convert_to_float_tensor(input_data: Any) -> torch.Tensor:
     ValueError: If the input_data cannot be converted to a tensor.
     """
     try:
-        # Check if the input is a list of tensors
-        if isinstance(input_data, list):
+        # Check if the input is a Sequence of tensors
+        if isinstance(input_data, Sequence):
             input_tensor = torch.stack([convert_to_float_tensor(a) for a in input_data])
         # Try to convert the input to a tensor
         else:

devtools/inspector/numerical_comparator/TARGETS

@@ -14,7 +14,7 @@ python_library(
     srcs = ["l1_numerical_comparator.py"],
     deps = [
         "//executorch/devtools/inspector/numerical_comparator:numerical_comparator_base",
-        "//executorch/devtools/inspector:lib",
+        "//executorch/devtools/inspector:inspector_utils",
     ],
 )
 
@@ -23,7 +23,7 @@ python_library(
     srcs = ["mse_numerical_comparator.py"],
     deps = [
         "//executorch/devtools/inspector/numerical_comparator:numerical_comparator_base",
-        "//executorch/devtools/inspector:lib",
+        "//executorch/devtools/inspector:inspector_utils",
     ],
 )
 

devtools/inspector/tests/inspector_test.py

@@ -17,6 +17,8 @@
 
 from unittest.mock import patch
 
+import pandas as pd
+
 import torch
 import torch.fx
 
@@ -578,6 +580,75 @@ def test_get_runtime_intermediate_outputs(self):
                 self.assertIn((key,), runtime_outputs)
                 self.assertEqual(len(runtime_outputs[(key,)]), RAW_DATA_SIZE)
 
+    def test_calculate_numeric_gap(self):
+        # Create a context manager to patch functions called by Inspector.__init__
+        with patch.object(
+            _inspector, "parse_etrecord", return_value=None
+        ), patch.object(
+            _inspector, "gen_etdump_object", return_value=None
+        ), patch.object(
+            EventBlock, "_gen_from_etdump"
+        ), patch.object(
+            _inspector, "gen_graphs_from_etrecord"
+        ):
+            # Call the constructor of Inspector
+            inspector_instance = Inspector(
+                etdump_path=ETDUMP_PATH,
+                etrecord=ETRECORD_PATH,
+            )
+
+            aot_intermediate_outputs = {
+                (0,): torch.tensor([1.0, 2.0, 3.0]),
+                (1,): torch.tensor([4.0, 5.0, 6.0]),
+            }
+
+            runtime_intermediate_outputs = {
+                (0,): torch.tensor([2.0, 1.0, 4.0]),
+                (1,): torch.tensor([3.0, 6.0, 5.0]),
+            }
+
+            inspector_instance._aot_intermediate_outputs = aot_intermediate_outputs
+            inspector_instance._get_runtime_intermediate_outputs = (
+                lambda: runtime_intermediate_outputs
+            )
+
+            df = inspector_instance.calculate_numeric_gap(distance="L1")
+            self.assertIsInstance(df, pd.DataFrame)
+            self.assertEqual(len(df), 2)
+            cols = set(df.columns)
+            expected_cols = {
+                "aot_debug_handle",
+                "aot_intermediate_output",
+                "runtime_debug_handle",
+                "runtime_intermediate_output",
+                "gap",
+            }
+            self.assertEqual(cols, expected_cols)
+            founded_aot_debug_handle = set(df["aot_debug_handle"])
+            self.assertEqual(
+                founded_aot_debug_handle, set(aot_intermediate_outputs.keys())
+            )
+            for _, row in df.iterrows():
+                aot_debuh_handle = row["aot_debug_handle"]
+                # aot_intermediate_output should equal aot_intermediate_outputs[h]
+                self.assertTrue(
+                    torch.allclose(
+                        row["aot_intermediate_output"],
+                        aot_intermediate_outputs[aot_debuh_handle],
+                    )
+                )
+                # runtime_debug_hanlde equals aot_debug_handle at this case
+                self.assertEqual(row["runtime_debug_handle"], aot_debuh_handle)
+                # runtime_intermediate_output should equal runtime_intermediate_outputs[h]
+                self.assertTrue(
+                    torch.allclose(
+                        row["runtime_intermediate_output"],
+                        runtime_intermediate_outputs[aot_debuh_handle],
+                    )
+                )
+                # gap should equal 3.0
+                self.assertEqual(row["gap"], 3.0)
+
     def _gen_random_float_list(self) -> List[float]:
         return [random.uniform(0, 10) for _ in range(RAW_DATA_SIZE)]