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This describes an issue where the torch.compile() function exhibits unexpected behavior when used within Hugging Face Spaces and Endpoint API (dedicated environments). Specifically, this bug manifests when attempting to compile PyTorch models, leading to inconsistent performance, unpredictable errors, or failed compilations. The behavior deviates from the expected results when using torch.compile() in standard environments, making it challenging to debug and optimize models effectively.
• Unexpected compilation errors: Models that compile successfully in standard environments may fail when using Hugging Face Spaces or Endpoint API.
• Performance inconsistencies: Compiled models may show unstable inference speeds or inaccurate optimizations.
• Environmental dependence: The issue is specific to Hugging Face's dedicated environments, making it difficult to replicate elsewhere.
• Error anz۰۰rz: The errors are often non-deterministic, making troubleshooting cumbersome.
To observe or utilize this behavior, follow these steps:
torch.compile() on your model or a part of your code.model = MyModel()
model = torch.compile(model)
1. What causes this strange behavior?
This issue likely stems from environment-specific configurations or compatibility problems between PyTorch and Hugging Face's dedicated environments.
2. How can I report this bug?
You can report the issue on Hugging Face's forums or PyTorch's GitHub repository with detailed reproduction steps and error logs.
3. Does this affect CPU-only environments?
While the primary reports involve GPU environments, similar behavior has been observed in CPU-only setups, though it is less common.