0296bc74c1
### Description
This adds bfloat16 support to the oneDNN ep.
When using the oneDNN ep this enables bfloat16 support for the following
ops:
Exp, Sigmoid, Tanh, Relu, MatMul, Gelu, BiasGelu, Add, Sub,
Mul, Div, Div, Sqrt, Pow, ReduceMean, Abs, Cast, Equal, Exp,
FastGelu, FusedMatMul, Gemm, Greter, GreaterOrEqual, LeakyRelu,
Less, LessOrEqual, LRN, ReduceOps, Reshape, Squeeze, Transpose,
and Unsqueeze.
LayerNorm with some internal casting.
BatchNorm only enabled BFloat16 for input and output, scale and bias
still need fp32 input.
Added bfloat16 unit tests for all of the operators in question. When
possible we reused the already existing unit tests that were added by
CUDA and ROCM eps.
In many of the unit tests an unusual pattern will be seen
#if defined(USE_DNNL)
TEST(Test, bfloat16_test) {
#if defined(USE_DNNL)
// oneDNN ep specific code
#endif
//test code
}
#endif
Although it looks unusual this was purposely done if another ep
implements bfloat16 support for that operator they will be able to
enable the unit test by adding there execution provider to the first
line without needing to edit inside the test.
Example: `#if defined(USE_CUDA) || defined(USE_DNNL)` see the
MatMul_float16 test in matmul_test.cc for and example of how this is
useful.
Additionally two new ISA checks (AVX512_BF16 and AMX-BF16) were added to
the cpuid_info code in. This was important to detecting is bfloat16
operations are supported by the CPU.
### Motivation and Context
This expands the capabilities of the oneDNN execution provider to
support models containing bfloat16 operations.
Signed-off-by: George Nash <george.nash@intel.com>
Signed-off-by: Ruihan-Yin <ruihan.yin@intel.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
General Information: onnxruntime.ai
Usage documention and tutorials: onnxruntime.ai/docs
Companion sample repositories:
- ONNX Runtime Inferencing: microsoft/onnxruntime-inference-examples
- ONNX Runtime Training: microsoft/onnxruntime-training-examples
Build Pipeline Status
| System | CPU | GPU | EPs |
|---|---|---|---|
| Windows | |||
| Linux | |||
| Mac | |||
| Android | |||
| iOS | |||
| WebAssembly |
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.