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### Description MatMulIntegerToFloat is updated to support FP16. The nodes for FP16 Transformation use "Mul" FP16, which is not directly supported by the CPU. For now FP16 transformation is only supported for DML EP. Disabled all FP16 tests on CPU. Tests result without `-use_dml` build flag ``` onnxruntime_test_all.exe --gtest_filter="*MatMulIntegerToFloat*" Note: Google Test filter = *MatMulIntegerToFloat* [==========] Running 8 tests from 4 test suites. [----------] Global test environment set-up. [----------] 1 test from CPU_U8S8_Precision_Tests [ RUN ] CPU_U8S8_Precision_Tests.MatMulIntegerToFloat [ OK ] CPU_U8S8_Precision_Tests.MatMulIntegerToFloat (181 ms) [----------] 1 test from CPU_U8S8_Precision_Tests (181 ms total) [----------] 1 test from GraphTransformationTests [ RUN ] GraphTransformationTests.MatMulIntegerToFloatTest [ OK ] GraphTransformationTests.MatMulIntegerToFloatTest (17 ms) [----------] 1 test from GraphTransformationTests (17 ms total) [----------] 1 test from QDQTransformerTests [ RUN ] QDQTransformerTests.MatMulIntegerToFloat [ OK ] QDQTransformerTests.MatMulIntegerToFloat (656 ms) [----------] 1 test from QDQTransformerTests (656 ms total) [----------] 5 tests from MatMulIntegerToFloat [ RUN ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_U8X8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_U8X8 (195 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_U8X8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_U8X8 (206 ms) [ RUN ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_S8S8 [ OK ] MatMulIntegerToFloat.HasZeroPoint_NoBias_test_S8S8 (107 ms) [ RUN ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_S8S8 [ OK ] MatMulIntegerToFloat.NoZeroPoint_HasBias_test_S8S8 (114 ms) [ RUN ] MatMulIntegerToFloat.MatMulInteger_With_ZeroPoint [ OK ] MatMulIntegerToFloat.MatMulInteger_With_ZeroPoint (227 ms) [----------] 5 tests from MatMulIntegerToFloat (854 ms total) [----------] Global test environment tear-down [==========] 8 tests from 4 test suites ran. (1713 ms total) [ PASSED ] 8 tests. memleakdbg: ----- No memory leaks detected ----- ``` ``` onnxruntime_test_all.exe --gtest_filter="GraphTransformationTests.MatMulIntegerToFloat*" Note: Google Test filter = GraphTransformationTests.MatMulIntegerToFloat* [==========] Running 2 tests from 1 test suite. [----------] Global test environment set-up. [----------] 2 tests from GraphTransformationTests [ RUN ] GraphTransformationTests.MatMulIntegerToFloatTest [ OK ] GraphTransformationTests.MatMulIntegerToFloatTest (13 ms) [ RUN ] GraphTransformationTests.MatMulIntegerToFloat16Test [ OK ] GraphTransformationTests.MatMulIntegerToFloat16Test (4 ms) [----------] 2 tests from GraphTransformationTests (20 ms total) [----------] Global test environment tear-down [==========] 2 tests from 1 test suite ran. (22 ms total) [ PASSED ] 2 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. --> |
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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 documention 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
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Third-party Pipeline Status
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License
This project is licensed under the MIT License.