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ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
27b1dc91ab
### Description DML Implementation for [com.microsoft.MatMulIntegerToFloat](https://github.com/microsoft/onnxruntime/blob/main/docs/ContribOperators.md#com.microsoft.MatMulIntegerToFloat) ``` .\onnxruntime_test_all.exe --gtest_filter="*MatMulIntegerToFloat.*" Note: Google Test filter = *MatMulIntegerToFloat.* [==========] Running 22 tests from 1 test suite. [----------] Global test environment set-up. [----------] 22 tests from MatMulIntegerToFloat [ RUN ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_S8S8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_S8S8 (620 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_S8S8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_S8S8 (497 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_S8S8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_S8S8 (488 ms) [ RUN ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_S8S8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_S8S8 (503 ms) [ RUN ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_U8U8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_U8U8 (495 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_U8U8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_U8U8 (488 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_U8U8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_U8U8 (492 ms) [ RUN ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_U8X8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_U8X8 (502 ms) [ RUN ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_S8U8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_S8U8 (452 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_S8U8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_S8U8 (454 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_S8U8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_S8U8 (446 ms) [ RUN ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_S8U8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_S8U8 (508 ms) [ RUN ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_U8S8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_U8S8 (456 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_U8S8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_U8S8 (455 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_U8S8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_NoBias_test_U8S8 (447 ms) [ RUN ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_U8S8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_HasBias_test_U8S8 (465 ms) [ RUN ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_U8U8 [ OK ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_U8U8 (111 ms) [ RUN ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_U8S8 [ OK ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_U8S8 (115 ms) [ RUN ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_S8S8 [ OK ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_S8S8 (114 ms) [ RUN ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_S8U8 [ OK ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16_S8U8 (110 ms) [ RUN ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16 [ OK ] MatMulIntegerToFloat.MatMulIntegerToFloat_FP16 (112 ms) [ RUN ] MatMulIntegerToFloat.MatMulInteger_With_ZeroPoint [ OK ] MatMulIntegerToFloat.MatMulInteger_With_ZeroPoint (337 ms) [----------] 22 tests from MatMulIntegerToFloat (8679 ms total) [----------] Global test environment tear-down [==========] 22 tests from 1 test suite ran. (8680 ms total) [ PASSED ] 22 tests. memleakdbg: ----- No memory leaks detected ----- ``` ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> * `CalculateMatMulIntegerToFloat` to replace CPU EP run reference * Added more FP32 testcases to isolate all input datatype combinations * Added fixed input to `MatMulIntegerToFloat_FP16*` test cases as for FP16 test cases. * onnxruntime/test/testdata/matmul_integer_to_float.py` is capable of generating FP16 models, but we do not produce any for now |
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ONNX Runtime is a cross-platform inference and training machine-learning accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. Learn more →
ONNX Runtime training can accelerate the model training time on multi-node NVIDIA GPUs for transformer models with a one-line addition for existing PyTorch training scripts. Learn more →
Get Started & Resources
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General Information: onnxruntime.ai
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Usage documentation and tutorials: onnxruntime.ai/docs
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YouTube video tutorials: youtube.com/@ONNXRuntime
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Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Builtin Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
Third-party Pipeline Status
| System | Inference | Training |
|---|---|---|
| Linux |  |
Data/Telemetry
Windows distributions of this project may collect usage data and send it to Microsoft to help improve our products and services. See the privacy statement for more details.
Contributions and Feedback
We welcome contributions! Please see the contribution guidelines.
For feature requests or bug reports, please file a GitHub Issue.
For general discussion or questions, please use GitHub Discussions.
Code of Conduct
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.
License
This project is licensed under the MIT License.