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Generalize test_print and test_tensor_descriptor to support different accelerators #801

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Merged
merged 1 commit into from
Oct 3, 2025
Merged

Generalize test_print and test_tensor_descriptor to support different accelerators #801

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8 changes: 4 additions & 4 deletions test/test_print.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,8 +38,8 @@ def run_kernel_and_capture_output(self, kernel_fn, args):
code, result = code_and_output(kernel_fn, args)

# Wait for any device prints to reach the host
if hasattr(result, "device") and result.device.type == "cuda":
torch.cuda.synchronize()
if hasattr(result, "device") and result.device.type == DEVICE.type:
torch.accelerator.synchronize()

# Grab what pytest captured: stdout + stderr
out, err = self._capfd.readouterr()
Expand Down Expand Up @@ -69,8 +69,8 @@ def run_kernel_and_capture_output(self, kernel_fn, args):
code, result = code_and_output(kernel_fn, args)

# Force GPU synchronization to ensure all device prints complete
if hasattr(result, "device") and result.device.type == "cuda":
torch.cuda.synchronize()
if hasattr(result, "device") and result.device.type == DEVICE.type:
torch.accelerator.synchronize()

# Ensure all output is flushed
sys.stdout.flush()
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2 changes: 1 addition & 1 deletion test/test_tensor_descriptor.py
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Expand Up @@ -240,7 +240,7 @@ def matmul(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
y = torch.randn((64, 64), device=DEVICE, dtype=torch.float16)

code, result = code_and_output(matmul, (x, y))
torch.cuda.synchronize()
torch.accelerator.synchronize()
expected = torch.matmul(x, y)
torch.testing.assert_close(result, expected, atol=1e-2, rtol=1e-2)

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