[6410139] Fix ONNX AutoCast for large external initializers - #2317
[6410139] Fix ONNX AutoCast for large external initializers#2317ajrasane wants to merge 3 commits into
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Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
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Included review availability: Your plan provides up to 12 included reviews per hour; 9 remain after this review. 📝 WalkthroughWalkthroughONNX AutoCast now handles external initializers larger than 2 GiB. It loads external data after type inference, resolves model-relative paths during sanitization, centralizes protobuf-size detection, duplicates shared initializers through protobuf processing, and manages temporary files during reference execution. ChangesONNX external-data handling
Estimated code review effort: 4 (Complex) | ~45 minutes Merge Risk: 🔵 Low · up to The primary file-based conversion path preserves and resolves large external initializers correctly, but direct in-memory callers may still fail when reference-only tensors use relative external-data paths because their source directory is not available. The PR is mergeable with explicit owner awareness or follow-up to document or enforce this caller contract. Sequence Diagram(s)sequenceDiagram
participant convert
participant GraphSanitizer
participant PrecisionConverter
participant ReferenceRunner
participant ONNXRuntime
convert->>GraphSanitizer: sanitize with source ONNX path
convert->>convert: load external data after type inference
convert->>PrecisionConverter: convert without repeated sanitization
PrecisionConverter-->>ReferenceRunner: provide modified model
ReferenceRunner->>ONNXRuntime: create memory or file-backed session
ONNXRuntime-->>ReferenceRunner: run inference
ReferenceRunner->>ReferenceRunner: clean temporary model data
🚥 Pre-merge checks | ✅ 5 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (5 passed)
Full details: Security Anti-PatternsExplanation PASS. The full PR diff (base 411d072 through HEAD) adds no
✨ Finishing Touches 💡 1📝 Generate docstrings 💡
🧪 Generate unit tests (beta)
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## main #2317 +/- ##
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+ Coverage 78.69% 78.72% +0.02%
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+ Misses 13075 13073 -2
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Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
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Found a correctness regression in the new direct GraphProto initializer duplication: a shared initializer that is also a top-level graph output is deleted even though the graph output still refers to it, producing an invalid/disconnected output. The new duplication coverage should include this case. Also, the newly expanded function-local trt_utils import should follow the project's module-level import convention unless there is a documented reason to defer it.
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Inline comments:
In `@modelopt/onnx/autocast/graphsanitizer.py`:
- Around line 127-131: Move the get_custom_layers, infer_types_shapes_tensorrt,
and set_trt_plugin_domain imports out of GraphSanitizer.find_custom_nodes and
place them at module scope, preserving their existing usage.
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📒 Files selected for processing (10)
CHANGELOG.rstmodelopt/onnx/autocast/convert.pymodelopt/onnx/autocast/graphsanitizer.pymodelopt/onnx/autocast/precisionconverter.pymodelopt/onnx/autocast/referencerunner.pymodelopt/onnx/utils.pytests/unit/onnx/autocast/test_autocast.pytests/unit/onnx/autocast/test_graphsanitizer.pytests/unit/onnx/autocast/test_referencerunner.pytests/unit/onnx/test_onnx_utils.py
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Signed-off-by: ajrasane <131806219+ajrasane@users.noreply.github.com>
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Re-review complete. The prior correctness issue is addressed: shared initializers exposed as top-level graph outputs are retained, node consumers are rewired to duplicates, and a checker-backed regression covers the case. The prior minor import-convention issue is also resolved by moving the TensorRT helpers to module scope. The external-data/file-backed paths and cleanup behavior have focused test coverage, and I found no new blocking issues.
Complex PR: 1 existing test file modified or removed. Looping in a human for approval.
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What does this PR do?
Type of change: Bug fix
Fix ONNX AutoCast for models whose external initializers exceed the in-memory protobuf limit.
GraphProto, preserving external-data metadata without reading tensor bytes.PrecisionConverterandconvert_to_f16()callers.No CLI flags, dependencies, or public return types change.
Usage
python -m modelopt.onnx.autocast \ --onnx_path model.onnx \ --output_path model_bf16.onnx \ --low_precision_type bf16Testing
onnx.checker.check_model(..., full_check=True); outputs expected to remain external-data-backed did so.Before your PR is "Ready for review"
Make sure you read and follow Contributor guidelines and your commits are signed (
git commit -s -S).Make sure you read and follow the Security Best Practices.
CONTRIBUTING.md: N/ASummary by CodeRabbit