Tacotron2, yolov3*: benchmark coverage for custom devices. #2230
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Co-authored-by: Eikan Wang <eikan.wang@intel.com>
Co-authored-by: Eikan Wang <eikan.wang@intel.com>
Co-authored-by: Eikan Wang <eikan.wang@intel.com>
EikanWang
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Apr 12, 2024
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@xuzhao9 , @atalman, This PR is a follow-up of Intel GPU-enabling as described in the RFC(pytorch/pytorch#114723). So far, we have landed Intel GPU runtime and Inductor and limited ATen operations. We are evaluating the dynamo benchmarks and have obtained a reasonable accuracy pass rate for HF. However, for TorchBench, we encountered some functionality issues with the Intel GPU as Intel GPU is a new device, and we need to enable it. This PR intends to enable Intel GPU and the fix the functionality issues. |
chuanqi129
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Apr 12, 2024
weishi-deng
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weishi-deng
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The CI failure is pre-existing and is not related to this PR. |
xuzhao9
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Apr 13, 2024
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@xuzhao9 has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
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Works for Roadmap #1293 to increase benchmark coverage.
For these 5 models:tacotron2, yolov3, nvidia_deeprecommender, LearningToPoint and pytorch_CycleGAN_and_pix2pix,
when running on custom devices except for CPU and CUDA(e.g. XPU), it will raise the error as it's hard-coded with CPU/CUDA backends.
In this PR, we accept the device args as a param within the training process and inference process which will cover the model initializing and data transposition for these custom devices.