Fix cast removal bug by adityagoel4512 · Pull Request #17953 · microsoft/onnxruntime

adityagoel4512 · 2023-10-15T03:46:14Z

Description

The RemoveDuplicateCastTransformer fairly naively removed Cast nodes from the graph without considering precision loss when using the same TypeGroup. For instance, F64 -> F32 -> F64 would be optimised out of the graph.

I also noticed that signedness was not accounted for, which is not covered by any existing issue but is a problem. For example doing int -> unsigned int -> int produces very different values for negative inputs and so should not be optimised out

One could argue that we shouldn't be performing such cast elimination at all (at least not in this transformer). The original scope might be well restricted to only eliminating unnecessary casts from the InsertCastTransformer and no others.

Motivation and Context

This should fix #17565, #9915 and #8787.

tianleiwu · 2023-10-15T20:21:25Z

/azp run Linux CPU CI Pipeline, Linux CPU Minimal Build E2E CI Pipeline, Linux GPU CI Pipeline, Linux GPU TensorRT CI Pipeline, Linux OpenVINO CI Pipeline, MacOS CI Pipeline, ONNX Runtime Web CI Pipeline, onnxruntime-binary-size-checks-ci-pipeline, Linux QNN CI Pipeline

tianleiwu · 2023-10-15T20:21:31Z

/azp run Windows CPU CI Pipeline, Windows GPU CI Pipeline, Windows GPU TensorRT CI Pipeline, Windows ARM64 QNN CI Pipeline, orttraining-linux-ci-pipeline, orttraining-linux-gpu-ci-pipeline, orttraining-ortmodule-distributed, ONNX Runtime React Native CI Pipeline, Windows x64 QNN CI Pipeline

azure-pipelines · 2023-10-15T20:22:02Z

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azure-pipelines · 2023-10-15T20:22:08Z

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onnxruntime/core/optimizer/insert_cast_transformer.cc

adityagoel4512 · 2023-10-18T16:50:47Z

@tianleiwu could you retrigger CI please?

yuslepukhin · 2023-10-18T16:56:54Z

/azp run Linux CPU CI Pipeline, Linux CPU Minimal Build E2E CI Pipeline, Linux GPU CI Pipeline, Linux GPU TensorRT CI Pipeline, Linux OpenVINO CI Pipeline, MacOS CI Pipeline, ONNX Runtime Web CI Pipeline, onnxruntime-binary-size-checks-ci-pipeline, Linux QNN CI Pipeline

yuslepukhin · 2023-10-18T16:57:07Z

/azp run Windows CPU CI Pipeline, Windows GPU CI Pipeline, Windows GPU TensorRT CI Pipeline, Windows ARM64 QNN CI Pipeline, orttraining-linux-ci-pipeline, orttraining-linux-gpu-ci-pipeline, orttraining-ortmodule-distributed, ONNX Runtime React Native CI Pipeline, Windows x64 QNN CI Pipeline

azure-pipelines · 2023-10-18T16:57:39Z

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azure-pipelines · 2023-10-18T16:57:45Z

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adityagoel4512 · 2023-10-19T00:03:04Z

@yuslepukhin the Windows_CI / Onnxruntime-TVM CI is also failing on the main branch. Is this PR ready to be merged?

tianleiwu · 2023-10-19T00:25:27Z

This change might have impact on some model. The choice of conservative and aggressive Cast remover might depend on precision requirement of an application. For example, double->float->double might not matter much for some model.

Shall we add some configuration so that user can choose between conservative and aggressive Cast removing?

adityagoel4512 · 2023-10-19T09:24:47Z

I would characterise what was happening as a bug. The point of RemoveDuplicateCastTransformer was really to remove extra casts inserted by InsertCastTransformer, not to be an optimisation pass (see

onnxruntime/onnxruntime/core/optimizer/insert_cast_transformer.cc

Lines 502 to 510 in 35ecce4

    
           // if we had multiple nodes in a row that were converted to fp32 we will have casts around every node. 
        
           // Casts in between converted nodes cancel each other out and can be removed. 
        
           // e.g. 
        
           //      -> NodeA(fp16) -> NodeB(fp16) -> 
        
           // After converting both to fp32 
        
           //      -> CastToFp32 -> NodeA(fp32) -> CastToFp16 -> CastToFp32 -> NodeB(fp32) -> CastToFp16 
        
           // After running duplicate cast removal 
        
           //      -> CastToFp32 -> NodeA(fp32) -> NodeB(fp32) -> CastToFp16 
        
           //

). It runs when optimisations are disabled so should really only be for safe removals in my opinion.

If it is desirable for some models perhaps a future PR could implement an explicit cast optimisation pass?

tianleiwu · 2023-10-19T20:26:15Z

If it is desirable for some models perhaps a future PR could implement an explicit cast optimisation pass?
Sounds good to me.

adityagoel4512 · 2023-10-24T12:40:18Z

@tianleiwu @yuslepukhin unless you have any concerns could we merge this?

tianleiwu · 2023-10-24T15:12:16Z

@adityagoel4512, please merge microsoft:main otherwise there is a required pipeline cannot pass.

onnxruntime/core/optimizer/insert_cast_transformer.cc

tianleiwu · 2023-10-27T16:14:31Z

/azp run Linux CPU CI Pipeline, Linux CPU Minimal Build E2E CI Pipeline, Linux GPU CI Pipeline, Linux GPU TensorRT CI Pipeline, Linux OpenVINO CI Pipeline, MacOS CI Pipeline, ONNX Runtime Web CI Pipeline, onnxruntime-binary-size-checks-ci-pipeline, Linux QNN CI Pipeline

tianleiwu · 2023-10-27T16:14:41Z

/azp run Windows CPU CI Pipeline, Windows GPU CI Pipeline, Windows GPU TensorRT CI Pipeline, Windows ARM64 QNN CI Pipeline, orttraining-linux-ci-pipeline, orttraining-linux-gpu-ci-pipeline, orttraining-ortmodule-distributed, ONNX Runtime React Native CI Pipeline, Windows x64 QNN CI Pipeline

azure-pipelines · 2023-10-27T16:15:15Z

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azure-pipelines · 2023-10-27T16:15:23Z

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tianleiwu · 2023-10-27T16:19:27Z

LGTM

…ing-optimisation

tianleiwu · 2023-10-31T17:28:37Z

/azp run Linux CPU CI Pipeline, Linux CPU Minimal Build E2E CI Pipeline, Linux GPU CI Pipeline, Linux GPU TensorRT CI Pipeline, Linux OpenVINO CI Pipeline, MacOS CI Pipeline, ONNX Runtime Web CI Pipeline, onnxruntime-binary-size-checks-ci-pipeline, Linux QNN CI Pipeline

tianleiwu · 2023-10-31T17:28:45Z

/azp run Windows CPU CI Pipeline, Windows GPU CI Pipeline, Windows GPU TensorRT CI Pipeline, Windows ARM64 QNN CI Pipeline, orttraining-linux-ci-pipeline, orttraining-linux-gpu-ci-pipeline, orttraining-ortmodule-distributed, ONNX Runtime React Native CI Pipeline, Windows x64 QNN CI Pipeline

azure-pipelines · 2023-10-31T17:29:18Z

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[![Mend Renovate](https://app.renovatebot.com/images/banner.svg)](https://renovatebot.com) This PR contains the following updates: | Package | Change | Age | Adoption | Passing | Confidence | |---|---|---|---|---|---| | [onnxruntime-web](https://togithub.com/Microsoft/onnxruntime) | [`1.16.3` -> `1.17.1`](https://renovatebot.com/diffs/npm/onnxruntime-web/1.16.3/1.17.1) | [![age](https://developer.mend.io/api/mc/badges/age/npm/onnxruntime-web/1.17.1?slim=true)](https://docs.renovatebot.com/merge-confidence/) | [![adoption](https://developer.mend.io/api/mc/badges/adoption/npm/onnxruntime-web/1.17.1?slim=true)](https://docs.renovatebot.com/merge-confidence/) | [![passing](https://developer.mend.io/api/mc/badges/compatibility/npm/onnxruntime-web/1.16.3/1.17.1?slim=true)](https://docs.renovatebot.com/merge-confidence/) | [![confidence](https://developer.mend.io/api/mc/badges/confidence/npm/onnxruntime-web/1.16.3/1.17.1?slim=true)](https://docs.renovatebot.com/merge-confidence/) | --- ### Release Notes <details> <summary>Microsoft/onnxruntime (onnxruntime-web)</summary> ### [`v1.17.0`](https://togithub.com/microsoft/onnxruntime/releases/tag/v1.17.0): ONNX Runtime v1.17.0 [Compare Source](https://togithub.com/Microsoft/onnxruntime/compare/v1.16.3...v1.17.0) ### Announcements In the next release, we will totally drop support for Windows ARM32. ### General - Added support for new ONNX 1.15 opsets: [IsInf-20](https://togithub.com/onnx/onnx/blob/main/docs/Changelog.md#IsInf-20), [IsNaN-20](https://togithub.com/onnx/onnx/blob/main/docs/Changelog.md#IsNaN-20), [DFT-20](https://togithub.com/onnx/onnx/blob/main/docs/Changelog.md#DFT-20), [ReduceMax-20](https://togithub.com/onnx/onnx/blob/main/docs/Changelog.md#ReduceMax-20), [ReduceMin-20](https://togithub.com/onnx/onnx/blob/main/docs/Changelog.md#reducemin-20), [AffineGrid-20](https://togithub.com/onnx/onnx/blob/main/docs/Changelog.md#AffineGrid-20), [GridSample](https://togithub.com/onnx/onnx/blob/main/docs/Operators.md#GridSample), [ConstantOfShape-20](https://togithub.com/onnx/onnx/blob/main/docs/Changelog.md#ConstantOfShape-20), [RegexFullMatch](https://togithub.com/onnx/onnx/blob/main/docs/Operators.md#RegexFullMatch), [StringConcat](https://togithub.com/onnx/onnx/blob/main/docs/Operators.md#StringConcat), [StringSplit](https://togithub.com/onnx/onnx/blob/main/docs/Operators.md#StringSplit), and [ai.onnx.ml.LabelEncoder-4](https://togithub.com/onnx/onnx/blob/main/docs/Changelog-ml.md#ai.onnx.ml.LabelEncoder-4). - Updated C/C++ libraries: abseil, date, nsync, googletest, wil, mp11, cpuinfo, safeint, and onnx. ### Build System and Packages - Dropped CentOS 7 support. All Linux binaries now require glibc version >=2.28, but users can still build the source code for a lower glibc version. - Added CUDA 12 packages for Python and Nuget. - Added Python 3.12 packages for ONNX Runtime Inference. ONNX Runtime Training Python 3.12 packages cannot be provided at this time since training packages depend on PyTorch, which does not support Python 3.12 yet. - Linux binaries (except those in AMD GPU packages) are built in a more secure way that is compliant with BinSkim's default policy (e.g., the binaries no longer have an executable stack). - Added support for Windows ARM64X for users who build ONNX Runtime from source. No prebuilt package provided yet. - Removed Windows ARM32 binaries from official packages. Users who still need these binaries can build them from source. - Added AMD GPU package with ROCm and MiGraphX (Python + Linux only). - Split ONNX Runtime GPU Nuget package into two packages. - When building the source code for Linux ARM64 or Android, the C/C++ compiler must support BFloat16. Support for Android NDK 24.x has been removed. Please use NDK 25.x or 26.x instead. - Link time code generation (LTCG or LTO) is now disabled by default when building from source. To re-enable it, users can add "--enable_lto" to the build command. All prebuilt binaries are still built with LTO. ### Core - Optimized graph inlining. - Allow custom op to invoke internal thread-pool for parallelism. - Added support for supplying a custom logger at the session level. - Added new logging and tracing of session and execution provider options. - Added new dynamic ETW provider that can trace/diagnose ONNX internals while maintaining great performance. ### Performance - Added 4bit quant support on NVIDIA GPU and ARM64. ### EPs #### TensorRT EP - Added support for direct load of precompiled TensorRT engines and customizable engine prefix. - Added Python support for TensorRT plugins via ORT custom ops. - Fixed concurrent Session::Run bugs. - Updated calls to deprecated TensorRT APIs (e.g., enqueue_v2 → enqueue_v3). - Fixed various memory leak bugs. #### QNN EP - Added support for QNN SDK 2.18. - Added context binary caching and model initialization optimizations. - Added mixed precision (8/16 bit) quantization support. - Add device-level session options (soc_model, htp_arch, device_id), extreme_power_saver for htp_performance_mode, and vtcm_mb settings. - Fixed multi-threaded inference bug. - Fixed various other bugs and added performance improvements. - QNN profiling of the NPU can be enabled dynamically with ETW or write out to CSV. #### OpenVINO EP - Added support for OpenVINO 2023.2. - Added AppendExecutionProvider_OpenVINO_V2 API for supporting new OpenVINO EP options. #### DirectML EP - Updated to [DirectML 1.13.1](https://togithub.com/microsoft/DirectML/blob/master/Releases.md). - Updated operators LpPool-18 and AveragePool-19 with dilations. - Improved Python I/O binding support. - Added RotaryEmbedding. - Added support for fusing subgraphs into DirectML execution plans. - Added new Python API to choose a specific GPU on multi-GPU devices with the DirectML EP. ### Mobile - Added initial support for 4bit quantization on ARM64. - Extended CoreML/NNAPI operator coverage. - Added support for YOLOv8 pose detection pre/post processing. - Added support for macOS in CocoaPods package. ### Web - Added support for external data format. - Added support for I/O bindings. - Added support for training. - Added WebGPU optimizations. - Transitioned WebGPU out of experimental. - Added FP16 support for WebGPU. ### Training #### Large Model Training - Enabled support for QLoRA (with support for BFloat16). - Added symbolic shape support for Triton codegen (see [PR](https://togithub.com/microsoft/onnxruntime/pull/18317)). - Made improvements to recompute optimizer with easy ON/OFF to allow layer-wise recompute (see [PR](https://togithub.com/microsoft/onnxruntime/pull/18566)). - Enabled memory-efficient gradient management. For Mistral, we see ~10GB drop in memory consumption when this feature is ON (see [PR](https://togithub.com/microsoft/onnxruntime/pull/18924)). - Enabled embedding sparsity optimizations. - Added support for Aten efficient attention and Triton Flash Attention (see [PR](https://togithub.com/microsoft/onnxruntime/pull/17959)). - Packages now available for CUDA 11.8 and 12.1. #### On Device Training - On-Device training will now support training on the web. This release focuses on federated learning and developer exploration scenarios. More features coming soon in future releases. ### Extensions - Modified gen_processing_model tokenizer model to output int64, unifying output datatype of all tokenizers. - Implemented support for post-processing of YOLO v8 within the Python extensions package. - Introduced 'fairseq' flag to enhance compatibility with certain Hugging Face tokenizers. - Incorporated 'added_token' attribute into the BPE tokenizer to improve CodeGen tokenizer functionality. - Enhanced the SentencePiece tokenizer by integrating token indices into the output. - Added support for the custom operator implemented with CUDA kernels, including two example operators. - Added more tests on the Hugging Face tokenizer and fixed identified bugs. ### Known Issues - The onnxruntime-training package is not yet available in PyPI but can be accessed in ADO as follows: python -m pip install cerberus flatbuffers h5py numpy>=1.16.6 onnx packaging protobuf sympy setuptools>=41.4.0 pip install -i https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT/pypi/simple/ onnxruntime-training pip install torch-ort python -m torch_ort.configure Installation instructions can also be accessed [here](https://onnxruntime.ai/getting-started). - For models with int4 kernel only: - Crash may occur when int4 is applied on Intel CPUs with hybrid core if the E-cores are disabled in BIOS. 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The `RemoveDuplicateCastTransformer` fairly naively removed Cast nodes from the graph without considering precision loss when using the same `TypeGroup`. For instance, F64 -> F32 -> F64 would be optimised out of the graph. I also noticed that signedness was not accounted for, which is not covered by any existing issue but is a problem. For example doing int -> unsigned int -> int produces very different values for negative inputs and so should not be optimised out One could argue that we shouldn't be performing such cast elimination at all (at least not in this transformer). The original scope might be well restricted to only eliminating unnecessary casts from the `InsertCastTransformer` and no others. ### Motivation and Context This should fix microsoft#17565, ttps://github.com/microsoft/issues/9915 and microsoft#8787.

adityagoel4512 added 2 commits October 15, 2023 04:27

Fix casting

d1ac106

Fix

36995d9

adityagoel4512 force-pushed the fix-casting-optimisation branch from f806ec4 to 36995d9 Compare October 15, 2023 16:50

Clean diff

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yuslepukhin reviewed Oct 18, 2023

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Define cast_ordering as static constexpr array

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tianleiwu reviewed Oct 24, 2023

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tianleiwu reviewed Oct 24, 2023

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Fix

27587c0

adityagoel4512 force-pushed the fix-casting-optimisation branch from afe4bb4 to 27587c0 Compare October 27, 2023 13:43

tianleiwu approved these changes Oct 27, 2023

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Merge branch 'main' of github.com:microsoft/onnxruntime into fix-cast…

52820ea

…ing-optimisation

tianleiwu merged commit ed41a28 into microsoft:main Oct 31, 2023

adityagoel4512 deleted the fix-casting-optimisation branch January 18, 2024 16:48

tianleiwu mentioned this pull request Feb 6, 2024

onnxruntime 1.17.0 - fp16 model of inswapper causing render issues #19437

Closed

jinhonglu mentioned this pull request Jul 18, 2024

[Performance] Get nan value when I block all the node in fp16 conversion #21345

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tianleiwu mentioned this pull request May 23, 2025

Cast Nodes Fusion #24842

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