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ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
a3e7da60e7
**Description**: Subgraph-level recompute This PR adds an optional capability trading additional re-computation for better memory efficiency. Specifically, a pre-defined operator list used to iterate the Graph to find some subgraphs for recompute, to reduce some stashed activations whose lifetime across forward and backward pass. When training with ORTModule, by default, the graph transformer will scan the execution graph to find all eligible subgraph to recompute, along with sizes that can save. An example looks like below. If we want to enable some of them to recompute, we can define env variable this way: `export ORTMODULE_ENABLE_MEMORY_ALLEVIATION="Mul+FusedMatMul+Cast+Unsqueeze+Unsqueeze+Cast+Sub+Mul+Add+BiasSoftmaxDropout+Cast+:1:-1,BiasGelu+:1:-1,BitmaskDropout+Cast+:1:-1,FusedMatMul+:1:-1,Cast+:1:-1,Mul+Add+:1:-1,Mul+Sub+:1:-1"` ``` [1,0]<stderr>:2,022-10-12 14:47:39.302,954,530 [W:onnxruntime:, memory_alleviation.cc:595 PrintSummary] [1,0]<stderr>:MemoryAlleviation Summary: [1,0]<stderr>: User config: [1,0]<stderr>: Mul+FusedMatMul+Cast+Unsqueeze+Unsqueeze+Cast+Sub+Mul+Add+BiasSoftmaxDropout+Cast+:1,BiasGelu+:1,BitmaskDropout+Cast+:1,FusedMatMul+:1,Cast+:1,Mul+Add+:1,Mul+Sub+:1 [1,0]<stderr>: ================================= [1,0]<stderr>: Subgraph: BitmaskDropout+ [1,0]<stderr>: AlleviationType: Disabled [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x 1,024 x Frequency:1 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: BiasGelu+ [1,0]<stderr>: AlleviationType: Recompute [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x input_ids_dim1 x 4,096 x Frequency:24 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: Reshape[1,0]<stderr>:+ [1,0]<stderr>: AlleviationType: Disabled [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:labels_dim0 x Frequency:1 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: Unsqueeze+Unsqueeze+Cast+Sub+Mul+Mul+FusedMatMul+Cast+Add+BiasSoftmaxDropout+Cast+ [1,0]<stderr>: AlleviationType: Disabled [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x 16 x input_ids_dim1 x input_ids_dim1 x Frequency:23 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: Mul+FusedMatMul+Cast+Unsqueeze+Unsqueeze+Cast+Sub+Mul+Add+BiasSoftmaxDropout+Cast+ [1,0]<stderr>: AlleviationType: Recompute [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x 16 x input_ids_dim1 x input_ids_dim1 x Frequency:1 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: Mul+Add+ [1,0]<stderr>: AlleviationType: Recompute [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x 16 x input_ids_dim1 x 1 x Frequency:24 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: FusedMatMul+Cast+Add+Reshape+Cast+ [1,0]<stderr>: AlleviationType: Disabled [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x 16 x input_ids_dim1 x 2 x 4 x Frequency:24 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: Mul+Sub+ [1,0]<stderr>: AlleviationType: Recompute [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x 16 x input_ids_dim1 x 1 x Frequency:24 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: Cast+ [1,0]<stderr>: AlleviationType: Recompute [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:1,024 x 1,024 x Frequency:97 [1,0]<stderr>: PatternShape:3 x 1,024 x Frequency:1 [1,0]<stderr>: PatternShape:8 x 64 x Frequency:24 [1,0]<stderr>: PatternShape:1,024 x 4,096 x Frequency:24 [1,0]<stderr>: PatternShape:4,096 x Frequency:24 [1,0]<stderr>: PatternShape:4,096 x 1,024 x Frequency:24 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: Subgraph: FusedMatMul+ [1,0]<stderr>: AlleviationType: Recompute [1,0]<stderr>: Patterns: [1,0]<stderr>: PatternShape:input_ids_dim0 x input_ids_dim1 x 4,096 x Frequency:24 [1,0]<stderr>: -------------------------------- [1,0]<stderr>: ================================= ``` "Type config:" whether recompute is enabled by users. 0 - disable, 1- enable. "Subgraph" means what kind of subgraph will be recomputed, in this case, it is a single node "Gelu", and it will be "Recompute". "Shape && Frequency" means, for this recompute, one tensor of size (batch size, 500) will be saved because it will be recomputed. **Baseline** On a 1P model (DEBERTA V2), sequence length 256, training with 16 A100 GPUs. With latest main branch, we can run batch size 16, and the maximum batch size < 32. So 16 is usually chosen by data scientists. 65% of 40GB memory is used during training. The SamplesPerSec=479.2543353561354.  **With this PR** Gelu is recomputed for saving memory peak, batch size 32 can be run. The 97% of 40GB A100 is used, the SamplesPerSec=562.041593991271 (**1.17X** of baseline).  **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. |
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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
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| WebAssembly |
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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.