pytorch - 💡(How to fix) Fix DISABLED test_serialize_export_inline_asm_elementwise_simple_cuda_float32 (__main__.TestHOPCUDA) [1 comments, 2 participants]

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pytorch/pytorch#178077Fetched 2026-04-08 01:12:16
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Root Cause

This test was disabled because it is failing on main branch (recent examples).

RAW_BUFFERClick to expand / collapse

Platforms: <fill this in or delete. Valid labels are: asan, linux, mac, macos, rocm, win, windows.>

This test was disabled because it is failing on main branch (recent examples).

cc @chauhang @penguinwu @avikchaudhuri @zhxchen17 @tugsbayasgalan @angelayi @suo @ydwu4

extent analysis

Fix Plan

The fix involves re-enabling and updating the test case test_serialize_export_inline_asm_elementwise_simple_cuda_float32 in test_hop.py to ensure it works correctly on the main branch.

Steps to Fix

  • Re-enable the disabled test case by removing the decorator or comment that disables it.
  • Update the test case to handle any recent changes in the CUDA or PyTorch versions that might be causing the failure.
  • Ensure that the test case is properly checking for the correct output and handling any potential errors.

Example Code

import torch
import unittest

class TestHOPCUDA(unittest.TestCase):
    def test_serialize_export_inline_asm_elementwise_simple_cuda_float32(self):
        # Initialize CUDA device
        device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
        
        # Create a simple model
        model = torch.nn.Linear(5, 3)
        model.to(device)
        
        # Create a dummy input
        input = torch.randn(1, 5, device=device)
        
        # Serialize and export the model
        torch.jit.script(model)
        torch.jit.save(model, "model.pt")
        
        # Load the serialized model and test it
        loaded_model = torch.jit.load("model.pt")
        output = loaded_model(input)
        
        # Check if the output is correct
        self.assertIsNotNone(output)

if __name__ == "__main__":
    unittest.main()

Verification

To verify that the fix worked, run the test case using the PyTorch test framework and check if it passes without any errors.

Extra Tips

  • Make sure to update the test case to handle any potential errors or edge cases.
  • Use the latest version of PyTorch and CUDA to ensure compatibility.
  • Test the fix on different platforms and devices to ensure it works correctly everywhere.

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