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ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
f6c81d8aca
### Introduce padding inspector in ORTModule
In some Transformer-based LLM training recipes, high data sparsity is
observed due to 1). token padding (to max sequence length), 2). labels
contains many ignore_index for calculate loss.
This PR introduces a switch to enable data sparsity inspection, which
1). in short term, can inform training users to use techniques like
dynamic batching to amortize the issue.
2). in medium and longer term, also helps us (training team) to have
better understanding what our training customers' models looks like from
perspective of data sparsity (and potentially motivate us to improve
with runtime).
Here is an example of different data sparsity with same training model
arch, same training input, but with different user models.
**Low Embed Density, High Label Density Case - Sentence Classification**
`
python -m torch.distributed.launch --nproc_per_node=4
examples/onnxruntime/training/text-classification/run_glue.py
--model_name_or_path roberta-large-openai-detector --task_name mnli
--do_train --do_eval --max_seq_length 128 --per_device_train_batch_size
32 --learning_rate 2e-5 --num_train_epochs 3 --overwrite_output_dir
--output_dir ./outputs/ --per_device_eval_batch_size 32 --seed 1137
--fp16 True --ignore_mismatched_sizes True --optim adamw_ort_fused
`
```
>>>Valid token/label density (e.g. valid/total) in passing 10 steps:
| STEP | INPUT TYPE | INPUT NAME | PAD IDX | DENSITY | VALID TOKENS | TOTAL TOKENS | VALID TOKENS/BATCH |
| 60 | EMBED | input_ids | 1 | 35.21 % | 1442 | 4096 | [50, 81, 35, 11, 29, 36, 66, 19, 40, 22, 21, 42, 17, 37, 40, 41, 26, 58, 38, 54, 41, 73, 48, 57, 50, 51, 49, 85, 48, 36, 79, 62] |
| 61 | LABEL | labels | -100 | 100.00 % | 32 | 32 | N/A |
| 62 | EMBED | input_ids | 1 | 30.00 % | 1229 | 4096 | [36, 73, 13, 47, 27, 33, 53, 25, 51, 28, 36, 42, 42, 32, 39, 52, 27, 13, 31, 66, 42, 45, 52, 45, 58, 42, 37, 66, 12, 18, 29, 17] |
| 63 | LABEL | labels | -100 | 100.00 % | 32 | 32 | N/A |
| 64 | EMBED | input_ids | 1 | 26.73 % | 1095 | 4096 | [37, 28, 20, 53, 16, 20, 44, 52, 27, 28, 16, 19, 16, 24, 63, 31, 24, 42, 33, 41, 44, 60, 44, 67, 54, 30, 20, 19, 33, 23, 24, 43] |
| 65 | LABEL | labels | -100 | 100.00 % | 32 | 32 | N/A |
| 66 | EMBED | input_ids | 1 | 30.03 % | 1230 | 4096 | [22, 46, 36, 41, 46, 43, 26, 50, 60, 16, 24, 42, 56, 35, 35, 59, 29, 39, 34, 20, 66, 23, 47, 53, 19, 35, 44, 23, 34, 81, 21, 25] |
| 67 | LABEL | labels | -100 | 100.00 % | 32 | 32 | N/A |
| 68 | EMBED | input_ids | 1 | 31.62 % | 1295 | 4096 | [75, 36, 48, 20, 38, 21, 49, 54, 38, 41, 26, 28, 80, 45, 48, 16, 22, 41, 34, 28, 37, 16, 74, 63, 62, 34, 22, 45, 23, 27, 37, 67] |
| 69 | LABEL | labels | -100 | 100.00 % | 32 | 32 | N/A |
<<<
```
**High Embed Density, Low Label Density Case - masked language model**
`
python -m torch.distributed.launch --nproc_per_node=4
examples/onnxruntime/training/language-modeling/run_mlm.py
--model_name_or_path bert-base-uncased --dataset_name wikitext
--dataset_config_name wikitext-2-raw-v1 --num_train_epochs 10
--per_device_train_batch_size 8 --per_device_eval_batch_size 8
--do_train --do_eval --overwrite_output_dir --output_dir ./outputs/
--seed 1137 --fp16 --report_to none --optim adamw_ort_fused
`
```
>>>Valid token/label density (e.g. valid/total) in passing 10 steps:
| STEP | INPUT TYPE | INPUT NAME | PAD IDX | DENSITY | VALID TOKENS | TOTAL TOKENS | VALID TOKENS/BATCH |
| 710 | EMBED | input_ids | 0 | 100.00 % | 4096 | 4096 | [512, 512, 512, 512, 512, 512, 512, 512] |
| 711 | LABEL | labels | -100 | 13.77 % | 564 | 4096 | N/A |
| 712 | EMBED | input_ids | 0 | 100.00 % | 4096 | 4096 | [512, 512, 512, 512, 512, 512, 512, 512] |
| 713 | LABEL | labels | -100 | 14.48 % | 593 | 4096 | N/A |
| 714 | EMBED | input_ids | 0 | 100.00 % | 4096 | 4096 | [512, 512, 512, 512, 512, 512, 512, 512] |
| 715 | LABEL | labels | -100 | 14.18 % | 581 | 4096 | N/A |
| 716 | EMBED | input_ids | 0 | 100.00 % | 4096 | 4096 | [512, 512, 512, 512, 512, 512, 512, 512] |
| 717 | LABEL | labels | -100 | 14.53 % | 595 | 4096 | N/A |
| 718 | EMBED | input_ids | 0 | 100.00 % | 4096 | 4096 | [512, 512, 512, 512, 512, 512, 512, 512] |
| 719 | LABEL | labels | -100 | 15.31 % | 627 | 4096 | N/A |
<<<
```
#### Next Step
Let's see how we leverage the data sparsity for improvement.
Optimizations on the way around compute optimizer wave 2:
> Loss compute flops reduction.
> Flatten/Unflatten embedding tokens to save compute flops.
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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 documention 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
Build Pipeline Status
| System | Inference | Training |
|---|---|---|
| Windows | ||
| Linux | ||
| Mac | ||
| Android | ||
| iOS | ||
| Web | ||
| Other |
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.