propagate debug handle from edge dialect graph back to exported graph (#12337)

This PR was created by the merge bot to help merge the original PR into
the main branch.
ghstack PR number: #12330 by
@Gasoonjia
^ Please use this as the source of truth for the PR details, comments,
and reviews
ghstack PR base:
https://github.com/pytorch/executorch/tree/gh/gasoonjia/19/base
ghstack PR head:
https://github.com/pytorch/executorch/tree/gh/gasoonjia/19/head
Merge bot PR base:
https://github.com/pytorch/executorch/tree/gh/gasoonjia/18/orig
Merge bot PR head:
https://github.com/pytorch/executorch/tree/gh/gasoonjia/19/orig
@diff-train-skip-merge

---------

Co-authored-by: gasoonjia <[email protected]>

Author: pytorchbot

devtools/inspector/_inspector_utils.py

@@ -35,8 +35,17 @@
 from executorch.devtools.etdump.serialize import deserialize_from_etdump_flatcc
 from executorch.devtools.etrecord import ETRecord
 
+from executorch.exir.debug_handle_utils import (
+    DEBUG_HANDLE_KEY,
+    get_greatest_ancestor_node_identifier,
+)
+
+from executorch.exir.graph_module import bfs_trace_with_node_process
+
 from tabulate import tabulate
 
+from torch.export import ExportedProgram
+
 FORWARD = "forward"
 EDGE_DIALECT_GRAPH_KEY = "edge_dialect_graph_module"
 
@@ -888,3 +897,71 @@ def compare_intermediate_outputs(a: Any, b: Any, comparator) -> List[float]:
     else:
         # Raise an error if one is a sequence and the other is not
         raise ValueError("Both inputs must be sequences or both must be non-sequences.")
+
+
+def propagate_back_debug_handle(
+    exported_program: ExportedProgram,
+    exported_program_graph_id: int,
+    edge_dialect_program: ExportedProgram,
+) -> bool:
+    """
+    Propagate debug handle from edge dialect program back to the exported program while maintain the correctness
+    of operator tracing.
+
+    e.g.
+    export program: op1 -> op2 -> op3
+    edge dialect program: op1_0 -> op3_0 -> op3_1
+    where op1_0 is from op1, op3_0 and op3_1 are from op3, op2 is removed by to_edge pipeline (e.g. RemoveNoopPass).
+
+    Then debug handle of op1 should be same as op1_0, and debug handle of op3 should be same as op3_0 and op3_1.
+    The debug handle of op2 will be a non-existing debug handle in edge dialect program for further skipping.
+
+    Return: True if:
+        a. every debug handle in the edge dialect program has a corresponding node in the exported program
+        b. the exported program is the greatest ancestor of the edge dialect program
+
+    Otherwise, return False.
+    """
+
+    # 1. set up a mapping from debug handle to identifier of export program's node
+    # using edge dialect program nodes' debug handles and from_node info
+    export_graph_node_id_to_debug_handle = {
+        get_greatest_ancestor_node_identifier(node): node.meta[DEBUG_HANDLE_KEY]
+        for node in edge_dialect_program.graph.nodes
+        if node.op not in ("placeholder", "output")
+    }
+
+    # 2. equip debug handle to the exported program's nodes using the mapping
+    # number of nodes in the exported program that have matched entry in export_graph_node_id_to_debug_handle
+    n_matched_node = 0
+
+    # debug handle for the node in the exported program but not in the edge dialect program
+    debug_handle_for_removed_node = (
+        max(export_graph_node_id_to_debug_handle.values()) + 1
+    )
+
+    def _find_n_match_node(node: torch.fx.Node) -> None:
+        nonlocal n_matched_node
+        if node.name in ("output", "placeholder"):
+            return
+        node_id = f"{node.name}.{exported_program_graph_id}"
+        if node_id in export_graph_node_id_to_debug_handle:
+            n_matched_node += 1
+
+    def _equip_debug_handle(node: torch.fx.Node) -> None:
+        if node.name in ("output", "placeholder"):
+            return
+        node_id = f"{node.name}.{exported_program_graph_id}"
+        if node_id in export_graph_node_id_to_debug_handle:
+            node.meta[DEBUG_HANDLE_KEY] = export_graph_node_id_to_debug_handle[node_id]
+        else:
+            node.meta[DEBUG_HANDLE_KEY] = debug_handle_for_removed_node
+
+    bfs_trace_with_node_process(exported_program.graph_module, _find_n_match_node)
+
+    # if any node in the edge dialect program has no corresponding node in the exported program, match failed
+    if n_matched_node != len(export_graph_node_id_to_debug_handle):
+        return False
+
+    bfs_trace_with_node_process(exported_program.graph_module, _equip_debug_handle)
+    return True

devtools/inspector/tests/inspector_utils_test.py

@@ -10,8 +10,9 @@
 import unittest
 from typing import Dict, Tuple
 
-import torch
+import executorch.exir.tests.models as models
 
+import torch
 from executorch.devtools import generate_etrecord, parse_etrecord
 
 from executorch.devtools.debug_format.base_schema import (
@@ -41,9 +42,13 @@
     map_runtime_aot_intermediate_outputs,
     merge_runtime_overlapping_debug_handles,
     NodeFilter,
+    propagate_back_debug_handle,
     TimeScale,
 )
 from executorch.devtools.inspector.numerical_comparator import L1Comparator
+from executorch.exir import to_edge
+from executorch.exir.debug_handle_utils import DEBUG_HANDLE_KEY
+from torch.export import export
 
 
 class TestInspectorUtils(unittest.TestCase):
@@ -583,6 +588,113 @@ def test_compare_intermediate_outputs_sequence_and_non_sequence(self):
         with self.assertRaises(ValueError):
             compare_intermediate_outputs(a, b, L1Comparator())
 
+    def test_equip_debug_handle_to_export_program_success(self):
+        """Test that propagate_back_debug_handle returns True and properly equips debug handles."""
+        # Create a test model
+        model = models.FeedForwardBlock(5, 10)
+        inputs = (torch.rand(5, 5),)
+
+        # Export the model
+        exported_program = export(model, inputs)
+        export_graph_id = id(exported_program.graph)
+
+        # Convert to edge dialect
+        edge_dialect_program = to_edge(exported_program).exported_program()
+
+        # Call propagate_back_debug_handle
+        result = propagate_back_debug_handle(
+            exported_program, export_graph_id, edge_dialect_program
+        )
+
+        self.assertTrue(result)
+
+        # Check that debug handles are properly equipped in the exported program
+        exported_program_debug_handles = []
+        for node in exported_program.graph.nodes:
+            if node.op not in ("placeholder", "output"):
+                self.assertIn(DEBUG_HANDLE_KEY, node.meta)
+                self.assertIsNotNone(node.meta[DEBUG_HANDLE_KEY])
+                exported_program_debug_handles.append(node.meta[DEBUG_HANDLE_KEY])
+
+        edge_dialect_program_debug_handles = []
+        for node in edge_dialect_program.graph.nodes:
+            if node.op not in ("placeholder", "output"):
+                self.assertIn(DEBUG_HANDLE_KEY, node.meta)
+                self.assertIsNotNone(node.meta[DEBUG_HANDLE_KEY])
+                edge_dialect_program_debug_handles.append(node.meta[DEBUG_HANDLE_KEY])
+
+        # The 0th operator in the exported program (layer_norm) has been decomposed into 0th and 1st ops in edge dialect graph (native_layer_norm and getitem)
+        # So they should have the same debug handle
+        self.assertEqual(
+            exported_program_debug_handles[0], edge_dialect_program_debug_handles[0]
+        )
+        self.assertEqual(
+            exported_program_debug_handles[0], edge_dialect_program_debug_handles[1]
+        )
+
+    def test_equip_debug_handle_to_export_program_failure(self):
+        """Test that propagate_back_debug_handle returns False when there's a mismatch."""
+        # Create a test model
+        model = models.FeedForwardBlock(5, 10)
+        inputs = (torch.rand(5, 5),)
+
+        exported_program = export(model, inputs)
+        edge_dialect_program = to_edge(exported_program).exported_program()
+
+        # Create a different exported program (reexport) to cause mismatch
+        reexported_program = export(model, inputs)
+        reexport_graph_id = id(reexported_program.graph)
+
+        # Call propagate_back_debug_handle with mismatched programs
+        # This should return False because the reexported program has different node identifiers
+        result = propagate_back_debug_handle(
+            reexported_program, reexport_graph_id, edge_dialect_program
+        )
+
+        # Check that it returns False due to mismatch
+        self.assertFalse(result)
+
+    def test_equip_debug_handle_to_export_program_op_to_be_removed_in_to_edge(self):
+        """Test that propagate_back_debug_handle returns True and properly equips debug handles when an op is removed in to_edge"""
+
+        class M(torch.nn.Module):
+            """
+            Simple model with ops that will be removed in to_edge
+            """
+
+            def __init__(self) -> None:
+                super().__init__()
+
+            def forward(self, x: torch.Tensor) -> torch.Tensor:
+                x = x + 1
+                x = x.to(x.dtype)
+                x = x + 1
+                return x
+
+        inputs = (torch.rand(5, 5),)
+        exported_program = torch.export.export(M(), inputs)
+        export_graph_id = id(exported_program.graph)
+        edge_dialect_program = to_edge(exported_program).exported_program()
+
+        self.assertTrue(
+            propagate_back_debug_handle(
+                exported_program, export_graph_id, edge_dialect_program
+            )
+        )
+
+        # only two add ops in the exported program will keep in edge dialect program, so the debug handles for removed op will be three
+        debug_handle_for_removed_node = 3
+
+        for node in exported_program.graph.nodes:
+            if node.name == "add":
+                self.assertEqual(node.meta[DEBUG_HANDLE_KEY], 1)
+            elif node.name == "add_1":
+                self.assertEqual(node.meta[DEBUG_HANDLE_KEY], 2)
+            elif node.op not in ("placeholder", "output"):
+                self.assertEqual(
+                    node.meta[DEBUG_HANDLE_KEY], debug_handle_for_removed_node
+                )
+
 
 def gen_mock_operator_graph_with_expected_map() -> (
     Tuple[OperatorGraph, Dict[int, OperatorNode]]

exir/TARGETS

@@ -277,3 +277,11 @@ python_library(
         "fbsource//third-party/pypi/typing-extensions:typing-extensions",
     ],
 )
+
+python_library(
+    name = "debug_handle_utils",
+    srcs = ["debug_handle_utils.py"],
+    deps = [
+        "//caffe2:torch",
+    ],
+)

exir/debug_handle_utils.py

@@ -0,0 +1,27 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+from torch.fx import Node
+
+FROM_NODE_KEY = "from_node"
+DEBUG_HANDLE_KEY = "debug_handle"
+
+
+def get_greatest_ancestor_node_identifier(node: Node) -> str:
+    """Get the identifier of the greatest ancestor node of the given node.
+
+    The identifier is the concatenation of the node name and graph id of the
+    greatest ancestor node, where the graph id is the unique id for every graph
+    module in the export flow and node name is unique within the same graph module.
+    """
+
+    node_source = node.meta[FROM_NODE_KEY]
+    node_source = node_source[-1]
+
+    while len(node_source.from_node) > 0:
+        node_source = node_source.from_node[-1]
+
+    return f"{node_source.name}.{str(node_source.graph_id)}"

exir/passes/TARGETS

@@ -342,6 +342,7 @@ python_library(
     ],
     deps = [
         "//caffe2:torch",
+        "//executorch/exir:debug_handle_utils",
         "//executorch/exir:graph_module",
         "//executorch/exir:pass_base",
     ],

exir/passes/debug_handle_generator_pass.py

@@ -6,6 +6,11 @@
 
 from typing import Dict
 
+from executorch.exir.debug_handle_utils import (
+    DEBUG_HANDLE_KEY,
+    FROM_NODE_KEY,
+    get_greatest_ancestor_node_identifier,
+)
 from executorch.exir.graph_module import bfs_trace_with_node_process
 from executorch.exir.pass_base import ExportPass
 from torch.export import ExportedProgram
@@ -21,27 +26,8 @@ def call(self, graph_module: GraphModule) -> PassResult:
         greatest ancestor node in the export flow.
         """
 
-        FROM_NODE_KEY = "from_node"
-        DEBUG_HANDLE_KEY = "debug_handle"
-
         source_node_id_to_debug_handle: Dict[str, int] = {}
 
-        def _get_greatest_ancestor_node_identifier(node: Node) -> str:
-            """Get the identifier of the greatest ancestor node of the given node.
-
-            The identifier is the concatenation of the node name and graph id of the
-            greatest ancestor node, where the graph id is the unique id for every graph
-            module in the export flow and node name is unique within the same graph module.
-            """
-
-            node_source = node.meta[FROM_NODE_KEY]
-            node_source = node_source[-1]
-
-            while len(node_source.from_node) > 0:
-                node_source = node_source.from_node[-1]
-
-            return node_source.name + str(node_source.graph_id)
-
         def _extract_debug_handles_from_node(node: Node) -> None:
             """
             Generate a debug handle based on node's oldest ancestor node's name
@@ -56,7 +42,7 @@ def _extract_debug_handles_from_node(node: Node) -> None:
                 FROM_NODE_KEY in node.meta
             ), f"Node {node} does not have meta key {FROM_NODE_KEY}"
 
-            greatest_ancestor_node_id = _get_greatest_ancestor_node_identifier(node)
+            greatest_ancestor_node_id = get_greatest_ancestor_node_identifier(node)
 
             debug_handle = (
                 len(source_node_id_to_debug_handle) + 1