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Revert "[torch][ao] Add customizable loss function to NodeAccuracySummary (#136282)"
This reverts commit f3c54ccf8f.
Reverted https://github.com/pytorch/pytorch/pull/136282 on behalf of https://github.com/huydhn due to This breaks OSS, let revert it and land the revert internally then ([comment](https://github.com/pytorch/pytorch/pull/136282#issuecomment-2364219252))
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2 changed files with 12 additions and 95 deletions
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@ -25,8 +25,8 @@ from torch.testing._internal.common_quantization import TestHelperModules
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from torch.testing._internal.common_utils import IS_WINDOWS, skipIfCrossRef, TestCase
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def _extract_debug_handles(model) -> Dict[str, int]:
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debug_handle_map: Dict[str, int] = {}
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def _extract_debug_handles(model) -> Dict[torch.fx.Node, int]:
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debug_handle_map: Dict[torch.fx.Node, int] = {}
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for node in model.graph.nodes:
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if (
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@ -187,53 +187,3 @@ class TestNumericDebugger(TestCase):
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for node_summary in comparison_results.values():
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if len(node_summary.results) > 0:
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self.assertGreaterEqual(node_summary.results[0].sqnr, 35)
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def test_added_node_gets_unique_id(self) -> None:
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m = TestHelperModules.Conv2dThenConv1d()
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example_inputs = m.example_inputs()
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m = capture_pre_autograd_graph(m, example_inputs)
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assert isinstance(m, torch.fx.GraphModule)
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generate_numeric_debug_handle(m)
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ref_handles = _extract_debug_handles(m)
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ref_counter = Counter(ref_handles.values())
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for k, v in ref_counter.items():
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self.assertEqual(
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v,
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1,
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msg=f"For handle {k}, there were {v} nodes with that handle, but expected only 1",
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)
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# Now that we have unique ids, add a new node into the graph and re-generate
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# to make sure that the new node gets a unique id.
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last_node = next(iter(reversed(m.graph.nodes)))
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with m.graph.inserting_before(last_node):
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arg = last_node.args[0]
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self.assertIsInstance(arg, tuple)
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arg = arg[0]
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# Add a function that only requires a single tensor input.
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n = m.graph.call_function(torch.ops.aten.relu.default, args=(arg,))
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arg.replace_all_uses_with(n, lambda x: x != n)
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m.recompile()
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# Regenerate handles, make sure only the new relu node has a new id, and
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# it doesn't clash with any of the existing ids.
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generate_numeric_debug_handle(m)
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handles_after_modification = _extract_debug_handles(m)
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handles_counter = Counter(handles_after_modification.values())
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for name, handle in ref_handles.items():
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self.assertIn(name, handles_after_modification)
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# Check that handle was unchanged.
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self.assertEqual(handles_after_modification[name], handle)
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# Check that total count was unchanged.
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ref_count = ref_counter[handle]
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after_count = handles_counter[handle]
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self.assertEqual(
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after_count,
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ref_count,
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msg=f"For handle {handle}, there were {after_count} nodes with that handle, but expected only {ref_count}",
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)
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# Check for relu specifically. Avoid hardcoding the handle id since it
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# may change with future node ordering changes.
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self.assertNotEqual(handles_after_modification["relu_default"], 0)
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self.assertEqual(handles_counter[handles_after_modification["relu_default"]], 1)
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@ -1,7 +1,7 @@
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import copy
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import logging
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from dataclasses import dataclass
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from typing import Callable, Dict, List, Optional, Sequence, Tuple
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from typing import Dict, List, Optional, Sequence, Tuple
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import torch
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from torch.ao.ns.fx.utils import compute_sqnr
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@ -19,16 +19,7 @@ def generate_numeric_debug_handle(graph_module: GraphModule) -> None:
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"""Attach numeric_debug_handle_id for all nodes in the model except for placeholder node
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The graph nodes of input model is modified inplace.
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"""
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unique_id = -1
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# Find the max ID that exists in the graph first, in case part of the graph
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# has already been annotated. This way we guarantee there are no duplicate
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# handle IDs.
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for node in graph_module.graph.nodes:
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unique_id = max(
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unique_id, node.meta.get(CUSTOM_KEY, {}).get(NUMERIC_DEBUG_HANDLE_KEY, -1)
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)
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unique_id += 1
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unique_id = 0
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for node in graph_module.graph.nodes:
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if node.op in ["output", "placeholder"]:
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continue
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@ -143,17 +134,6 @@ class QuantizationComparisonResult:
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self.actual.to(dtype=torch.float32), self.ref.to(dtype=torch.float32)
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)
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def loss(
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self, loss_function: Callable[[torch.Tensor, torch.Tensor], torch.Tensor]
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) -> torch.Tensor:
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if self.actual.shape != self.ref.shape:
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raise ValueError(
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f"Cannot compare tensors with different shapes: {self.actual.shape} vs {self.ref.shape}"
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)
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return loss_function(
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self.actual.to(dtype=torch.float32), self.ref.to(dtype=torch.float32)
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)
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def __repr__(self) -> str:
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# Don't include the tensors themselves as they are quite large to print
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# out.
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@ -169,10 +149,6 @@ class QuantizationComparisonResult:
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if not isinstance(self.ref, torch.Tensor):
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raise ValueError(f"`self.ref` value must be a Tensor, got: {self.ref}")
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if self.actual.shape != self.ref.shape:
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raise ValueError(
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f"Cannot compare tensors with different shapes: ref={self.ref.shape} vs actual={self.actual.shape}"
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)
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@dataclass(frozen=True)
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@ -221,8 +197,8 @@ def extract_results_from_loggers(
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def compare_results(
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ref_results: Dict[int, Tuple[Optional[str], object, List[torch.Tensor]]],
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actual_results: Dict[int, Tuple[Optional[str], object, List[torch.Tensor]]],
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ref_results: Dict[int, Tuple[str, object, List[torch.Tensor]]],
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actual_results: Dict[int, Tuple[str, object, List[torch.Tensor]]],
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) -> Dict[int, NodeAccuracySummary]:
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"""Given two dict mapping from `debug_handle_id` (int) to list of tensors
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return a map from `debug_handle_id` to `NodeAccuracySummary` that contains
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@ -244,25 +220,16 @@ def compare_results(
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)
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continue
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actual_name, actual_stack, actual_stats = actual_results[debug_handle]
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try:
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results = [
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QuantizationComparisonResult(actual=a, ref=b)
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for a, b in zip(actual_stats, ref_stats)
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]
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except Exception as e:
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# Add extra information for an exception from QuantizationComparisonResult
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# if the shapes didn't match, to include the handle and the node names.
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raise ValueError(
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f"For numeric_debug_handle={debug_handle} from ref node {ref_name} and actual node {actual_name}"
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) from e
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comparisons[debug_handle] = NodeAccuracySummary(
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handle=debug_handle,
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actual_node_name=actual_name or "",
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actual_node_name=actual_name,
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actual_module_stack=_module_stack_to_str(actual_stack),
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ref_node_name=ref_name or "",
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ref_node_name=ref_name,
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ref_module_stack=_module_stack_to_str(ref_stack),
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results=results,
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results=[
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QuantizationComparisonResult(actual=a, ref=b)
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for a, b in zip(actual_stats, ref_stats)
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],
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)
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return comparisons
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