Welcome! This repository is designed to facilitate the integration testing of different accelerators with PyTorch. Our primary focus is to ensure seamless integration and compatibility across various devices by running comprehensive GitHub workflows.
Click here to view the torchbenchmark report
| npu | |
|---|---|
| alexnet | ✅ |
| Background_Matting | ✅ |
| basic_gnn_edgecnn | ✅ |
| basic_gnn_gcn | ✅ |
| basic_gnn_gin | ✅ |
| basic_gnn_sage | ✅ |
| BERT_pytorch | ✅ |
| cm3leon_generate | ✅ |
| dcgan | ✅ |
| demucs | ✅ |
| densenet121 | ✅ |
| detectron2_fasterrcnn_r_101_c4 | ❌ |
| detectron2_fasterrcnn_r_101_dc5 | ❌ |
| detectron2_fasterrcnn_r_101_fpn | ❌ |
| detectron2_fasterrcnn_r_50_c4 | ❌ |
| detectron2_fasterrcnn_r_50_dc5 | ❌ |
| detectron2_fasterrcnn_r_50_fpn | ❌ |
| detectron2_fcos_r_50_fpn | ❌ |
| detectron2_maskrcnn | ❌ |
| detectron2_maskrcnn_r_101_c4 | ❌ |
| detectron2_maskrcnn_r_101_fpn | ❌ |
| detectron2_maskrcnn_r_50_c4 | ❌ |
| detectron2_maskrcnn_r_50_fpn | ❌ |
| dlrm | ✅ |
| doctr_det_predictor | ✅ |
| doctr_reco_predictor | ✅ |
| drq | ✅ |
| fastNLP_Bert | ✅ |
| functorch_dp_cifar10 | ✅ |
| functorch_maml_omniglot | ✅ |
| hf_Albert | ✅ |
| hf_Bart | ✅ |
| hf_Bert | ✅ |
| hf_Bert_large | ✅ |
| hf_BigBird | ✅ |
| hf_clip | ✅ |
| hf_distil_whisper | ✅ |
| hf_DistilBert | ✅ |
| hf_GPT2 | ✅ |
| hf_GPT2_large | ✅ |
| hf_Longformer | ✅ |
| hf_Reformer | ❌ |
| hf_Roberta_base | ✅ |
| hf_T5 | ✅ |
| hf_T5_base | ✅ |
| hf_T5_generate | ✅ |
| hf_T5_large | ✅ |
| hf_Whisper | ✅ |
| LearningToPaint | ✅ |
| lennard_jones | ✅ |
| llama | ✅ |
| llama_v2_7b_16h | ❌ |
| llava | ❌ |
| maml | ✅ |
| maml_omniglot | ✅ |
| microbench_unbacked_tolist_sum | ✅ |
| mnasnet1_0 | ✅ |
| mobilenet_v2 | ✅ |
| mobilenet_v2_quantized_qat | ❌ |
| mobilenet_v3_large | ✅ |
| moco | ❌ |
| moondream | ❌ |
| nanogpt | ✅ |
| nvidia_deeprecommender | ❌ |
| opacus_cifar10 | ✅ |
| phlippe_densenet | ✅ |
| phlippe_resnet | ✅ |
| pyhpc_equation_of_state | ✅ |
| pyhpc_isoneutral_mixing | ✅ |
| pyhpc_turbulent_kinetic_energy | ✅ |
| pytorch_CycleGAN_and_pix2pix | ✅ |
| pytorch_stargan | ✅ |
| pytorch_unet | ✅ |
| resnet152 | ✅ |
| resnet18 | ✅ |
| resnet50 | ✅ |
| resnet50_quantized_qat | ❌ |
| resnext50_32x4d | ✅ |
| sam | ✅ |
| sam_fast | ❌ |
| shufflenet_v2_x1_0 | ✅ |
| simple_gpt | ❌ |
| simple_gpt_tp_manual | ❌ |
| soft_actor_critic | ✅ |
| speech_transformer | ✅ |
| squeezenet1_1 | ✅ |
| stable_diffusion_text_encoder | ❌ |
| stable_diffusion_unet | ❌ |
| Super_SloMo | ✅ |
| tacotron2 | ❌ |
| timm_efficientdet | ❌ |
| timm_efficientnet | ✅ |
| timm_nfnet | ✅ |
| timm_regnet | ✅ |
| timm_resnest | ✅ |
| timm_vision_transformer | ✅ |
| timm_vision_transformer_large | ✅ |
| timm_vovnet | ✅ |
| torch_multimodal_clip | ✅ |
| tts_angular | ✅ |
| vgg16 | ✅ |
| vision_maskrcnn | ❌ |
| yolov3 | ✅ |
This repository contains workflows and scripts that automate the testing process for integrating different hardware devices with PyTorch. The tests aim to validate that PyTorch's device-specific functionalities are working correctly and efficiently across different platforms.
- Automated Integration Tests: Run tests automatically for different devices using GitHub Actions.
- Reusable Workflows: Leverage modular and reusable workflows to streamline the testing process.
To run the integration tests, the repository leverages GitHub Actions. You can trigger the tests by pushing code to the repository or by manually triggering the workflows.
For customize your own workflows, here are some reference configuration in workflow.
We welcome contributions to enhance the integration testing process. Feel free to submit issues, pull requests, or suggestions to help us improve the compatibility and performance of PyTorch on various devices. See the CONTRIBUTING for more details.
If you encounter any issues while using the workflows or integrating a device, please report them via the Issues tab.
This project is licensed under Apache-2.0 license. See the LICENSE file for more details.