a6c5e2cd20
### Description
- [x] Rewrite FusedMHARunnerFP16v2 to make it thread-safe.
- [x] Add multi-threading tests
Previously, the kernel parameters params is stored as a member of mha
runner, which means that different threads might change the params at
the same time and impacts the other threads.
For example, if batch_size and seq_len was changed by another thread to
larger values in setup(...), buffer overrun might happen in run(...)
because a kernel could read/write memory out of range of allocated
buffers.
In new implementation, I change the api and remove mutable member
variables to make it thread safe. Below is summary of change:
Before:
```
class FusedMHARunnerFP16v2::mhaImpl {
void setup(int seq_len, int batch_size) {
// change scalar params
}
void run(input, output) {
// change params for input and output pointers
// launch kernel using params
}
Fused_multihead_attention_params_v2 params; // mutable, not thread-safe
}
```
After:
```
class FusedMHARunnerFP16v2::FmhaImpl {
void setup(int seq_len, int batch_size, Fused_multihead_attention_params_v2& params) {
// change params
}
void run(params, input, output) {
// change params with input and output pointers
// launch kernel using params
}
}
```
### Motivation and Context
https://github.com/microsoft/onnxruntime/issues/18854
https://github.com/microsoft/onnxruntime/issues/21413
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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
-
General Information: onnxruntime.ai
-
Usage documentation and tutorials: onnxruntime.ai/docs
-
YouTube video tutorials: youtube.com/@ONNXRuntime
-
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 |  |
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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.