f68dfcd888
### Description
Currently there are 2 memory latency bound hotspots in the
DecoderMaskedMultiheadAttention kernel in terms of reading from global
memory - one reading K values and the other reading V values.
The current logic to read them both is something like this -
for(int i=0; i<all_time_steps; ++i) {
auto data_in_register = load_chunk_from_global_memory(i);
do_compute(data_in_register);
}
This incurs a data read stall as data needs to be fetched into the
registers before compute can begin and the compute instruction incurs a
data read stall and this also does not fully utilize the memory
bandwidth of A100. The above logic can be re-written by doing some
manual loop unrolling so that more data read is triggered "in flight".
Unroll factor: 4
for(int i=0; i<all_time_steps; i+=4) {
auto data_in_register_0 = load_chunk_from_global_memory(i);
// Do bounds check for the following
auto data_in_registers_1 = load_chunk_from_global_memory(i+1);
auto data_in_register_2 = load_chunk_from_global_memory(i+2);
auto data_in_register_3 = load_chunk_from_global_memory(i+3);
do_compute(data_in_register_0);
// Do bounds check for the following
do_compute(data_in_register_1);
do_compute(data_in_register_2);
do_compute(data_in_register_3);
}
The idea is that the memory read latency is hidden by instructions being
issued for subsequent data reads. See here for more details -
https://forums.developer.nvidia.com/t/global-memory-access-synchronous-or-asynchronous-read-write/3256/4
Kernel clock cycles, latency, and memory bandwidth usage before:
<img width="1210" alt="image"
src="https://github.com/microsoft/onnxruntime/assets/9969784/7a1f41f9-fdaa-47b3-b629-996d7b5eef17">
Kernel clock cycles, latency, and memory bandwidth usage after:
<img width="1205" alt="image"
src="https://github.com/microsoft/onnxruntime/assets/9969784/c76b2d2f-43e3-43c9-a710-b5fae76f69b6">
As can be seen, the kernel latency is better by >30% and memory
throughput is better by >14%.
We have a 1P customer using the Whisper model (sampling using
BeamSearch) and the E2E perf for a representative production input is >
6.5%
Whisper E2E Latency for sample input before (on A100):
<img width="194" alt="image"
src="https://github.com/microsoft/onnxruntime/assets/9969784/84ef59f5-84f2-4277-b9f8-b04c27336642">
Whisper E2E Latency for sample input after (on A100):
<img width="191" alt="image"
src="https://github.com/microsoft/onnxruntime/assets/9969784/ca9fe5d3-f726-403e-b27c-be4ee07e0625">
This feature of loading more data in flight may not always yield gains
and it will be workload dependent. For now, keeping the feature turned
OFF by default. It can be turned ON by the user when needed.
### Motivation and Context
Improve BeamSearch performance on CUDA EP
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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.