PyTorch Out-of-tree Accelerator TestInfra

Welcome to the pytorch-integration-tests repository! 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.

Accelerator Integration Test Results

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	✅

Overview

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.

Key Features

• Automated Integration Tests: Run tests automatically for different devices using GitHub Actions.
• Cross-Device Compatibility: Ensure that PyTorch functions correctly on NPUs, GPUs, and other devices.
• Reusable Workflows: Leverage modular and reusable workflows to streamline the testing process.

Usage

Running Tests

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.

Customizing Workflows

The workflows are designed to be flexible. You can customize the parameters such as the target branch, runner, and loop time by modifying the inputs in the workflow files.

Roadmap

See our roadmap project for more details.

Contributing

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.

Reporting Issues

If you encounter any issues while using the workflows or integrating a device, please report them via the Issues tab.

License

This project is licensed under BSD-3-Clause license. See the LICENSE file for more details.