cfe830b248
### Description
The InsertGatherBeforeSceLoss optimization is enabled when the density
of label padding less than 90%. We need to check the density of the
label padding to decide whether enable the optimization.
Before this pr, we just check the inputs of graph and correlate one with
the SCE node by iterate graph from the SCE node back to one graph input.
This is hard to be general because there may be complicated pattern
between graph input and SCE node.
This pr check padding density by the direct input of SCE module rather
than the input of graph at the first graph execution when exporting onnx
graph.
And if the density < 90%, insert a flag PythonOp after the SCE node as:
```
SoftmaxCrossEntropy
|
PythonOp (func_name: FlagAndPrintDensity) (insert if density < 90%)
|
Following graph
```
When the InsertGatherBeforeSceLoss is invoked, it check if there is the
flag PythonOp(func_name: FlagAndPrintDensity) after the SCE node and if
it is, remove it and do the padding elimination optimization.
If the env of ORTMODULE_PRINT_INPUT_DENSITY is 1, we will print input
density each step by the PythonOp (func_name: FlagAndPrintDensity). In
this case the PythonOp will not be removed.
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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 |  |
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.