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ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
cd7112f800
### Description update with ONNX 1.16.0 branch according to https://github.com/microsoft/onnxruntime/blob/main/docs/How_To_Update_ONNX_Dev_Notes.md ONNX 1.16.0 release notes: https://github.com/onnx/onnx/releases/tag/v1.16.0 #### Updated ops for CPU EP: - DequantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block dequantization support - QuantizeLinear(21) - Added int16 and uint16 support + various optimizer tests - Missing int4 and uint4 support - Missing block quantization support - Cast(21) - Missing int4 and uint4 support - CastLike(21) - Missing int4 and uint4 support - ConstantOfShape(21) - Missing int4 and uint4 support - Identity(21) - Missing int4 and uint4 support - If(21) - Missing int4 and uint4 support - Loop(21) - Missing int4 and uint4 support - Reshape(21) - Missing int4 and uint4 support - Scan(21) - Missing int4 and uint4 support - Shape(21) - Missing int4 and uint4 support - Size(21) - Missing int4 and uint4 support - Flatten(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Pad(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Squeeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Transpose(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support - Unsqueeze(21) - Missing float8e4m3fnuz, float8e5m2, float8e5m2fnuz, int4, and uint4 support #### Unimplemented opset 21 features/ops - int4 and uint4 data type - QLinearMatMul(21) - GroupNormalization(21) - ai.onnx.ml.TreeEnsemble(5) ### 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. --> ### Disabled tests #### ORT Training orttraining/orttraining/test/python/orttraining_test_ort_apis_py_bindings.py - test_ort_custom_ops: Potential shape inference bug for custom ops #### Python quantization unit tests test/onnx/python/quantization (shape inference bug) - test_op_conv_transpose.py: test_quantize_conv_transpose_u8u8_fp16 - test_op_conv_transpose.py: test_quantize_conv_transpose_s8s8_fp16 - test_op_gemm.py: test_quantize_qop_gemm_s8s8 - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_same - test_op_gemm.py: test_quantize_qop_gemm_e4m3fn_p3 - test_op_matmul.py: test_quantize_matmul_u8u8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16 - test_op_matmul.py: test_quantize_matmul_s8s8_f16_entropy - test_op_matmul.py: test_quantize_matmul_s8s8_f16_percentile - test_op_matmul.py: test_quantize_matmul_s8s8_f16_distribution - test_op_relu.py: test_quantize_qop_relu_s8s8 #### ONNX tests - test_maxpool_2d_ceil_output_size_reduce_by_one: ONNX 1.16.0 fixed a maxpool output size bug and added this test. Enable this test when [ORT PR](https://github.com/microsoft/onnxruntime/pull/18377) is merged. Refer to original [ONNX PR](https://github.com/onnx/onnx/pull/5741). - test_ai_onnx_ml_tree_ensemble_set_membership_cpu: new unimplemented op ai.onnx.ml.TreeEnsemble - test_ai_onnx_ml_tree_ensemble_single_tree_cpu: same - test_ai_onnx_ml_tree_ensemble_set_membership_cuda: same - test_ai_onnx_ml_tree_ensemble_single_tree_cuda: same - test_cast_INT4_to_FLOAT_cpu: ORT Cast(21) impl doesn't support int4 yet - test_cast_INT4_to_INT8_cpu: same - test_cast_UINT4_to_FLOAT_cpu: same - test_cast_UINT4_to_UINT8_cpu: same - test_cast_INT4_to_FLOAT_cuda - test_cast_INT4_to_INT8_cuda - test_cast_UINT4_to_FLOAT_cuda - test_cast_UINT4_to_UINT8_cuda - test_constantofshape_float_ones_cuda: ConstantOfShape(21) not implemented for cuda - test_constantofshape_int_shape_zero_cuda: same - test_constantofshape_int_zeros_cuda: same - test_flatten_axis0_cuda: Flatten(21) not implemented for cuda - test_flatten_axis1_cuda: same - test_flatten_axis2_cuda: same - test_flatten_axis3_cuda: same - test_flatten_default_axis_cuda: same - test_flatten_negative_axis1_cuda: same - test_flatten_negative_axis2_cuda: same - test_flatten_negative_axis3_cuda: same - test_flatten_negative_axis4_cuda: same - test_qlinearmatmul_2D_int8_float16_cpu: QLinearMatMul(21) for onnx not implemented in ORT yet - test_qlinearmatmul_2D_int8_float32_cpu: same - test_qlinearmatmul_2D_uint8_float16_cpu: same - test_qlinearmatmul_2D_uint8_float32_cpu: same - test_qlinearmatmul_3D_int8_float16_cpu: same - test_qlinearmatmul_3D_int8_float32_cpu: same - test_qlinearmatmul_3D_uint8_float16_cpu: same - test_qlinearmatmul_3D_uint8_float32_cpu: same - test_qlinearmatmul_2D_int8_float16_cuda: same - test_qlinearmatmul_2D_int8_float32_cuda: same - test_qlinearmatmul_2D_uint8_float16_cuda: same - test_qlinearmatmul_2D_uint8_float32_cuda: same - test_qlinearmatmul_3D_int8_float16_cuda: same - test_qlinearmatmul_3D_int8_float32_cuda: same - test_qlinearmatmul_3D_uint8_float16_cuda: same - test_qlinearmatmul_3D_uint8_float32_cuda: same - test_size_cuda: Size(21) not implemented for cuda - test_size_example_cuda: same - test_dequantizelinear_blocked: Missing implementation for block dequant for DequantizeLinear(21) - test_quantizelinear_blocked_asymmetric: Missing implementation for block quant for QuantizeLinear(21) - test_quantizelinear_blocked_symmetric: Missing implementation for block quant for QuantizeLinear(21) --------- Signed-off-by: liqunfu <liqun.fu@microsoft.com> Signed-off-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: Ganesan Ramalingam <grama@microsoft.com> Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: adrianlizarraga <adlizarraga@microsoft.com> |
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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.