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[Test][BugFix] Fix torch.rand usage in triton penalty test (#6680)

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### What this PR does / why we need it?
This PR fixes a `TypeError` in
`tests/e2e/nightly/single_node/ops/singlecard_ops/triton/test_penality.py`
that was causing nightly test failures. The `torch.rand()` function was
being called with the `device` string as a positional argument, which is
incorrect. This has been corrected to use the `device` keyword argument.

Fixes #

### Does this PR introduce _any_ user-facing change?
No, this change only affects a test file.

### How was this patch tested?
CI is expected to pass with this fix.

- vLLM version: v0.15.0
- vLLM main:
13397841ab

Signed-off-by: whx-sjtu <2952154980@qq.com>
This commit is contained in:
whx
2026-02-11 16:31:49 +08:00
committed by GitHub
parent 0c1cfa2bac
commit bb73478c00
1 changed files with 4 additions and 4 deletions
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8
tests/e2e/nightly/single_node/ops/singlecard_ops/triton/test_penality.py
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@@ -127,22 +127,22 @@ def create_test_data(
repetiton_penalty = torch.ones(num_reqs, device=device, dtype=torch.float32) repetiton_penalty = torch.ones(num_reqs, device=device, dtype=torch.float32)
for i in range(num_reqs): for i in range(num_reqs):
if torch.rand(1) > 0.3: if torch.rand(1) > 0.3:
repetiton_penalty[i] = torch.rand(1, device).item() * 0.8 + 0.6 repetiton_penalty[i] = torch.rand(1, device=device).item() * 0.8 + 0.6
frequency_penalty = torch.zeros(num_reqs, device=device, dtype=torch.float32) frequency_penalty = torch.zeros(num_reqs, device=device, dtype=torch.float32)
for i in range(num_reqs): for i in range(num_reqs):
if torch.rand(1) > 0.5: if torch.rand(1) > 0.5:
frequency_penalty[i] = torch.rand(1, device).item() * 0.2 frequency_penalty[i] = torch.rand(1, device=device).item() * 0.2
presence_penalty = torch.zeros(num_reqs, device=device, dtype=torch.float32) presence_penalty = torch.zeros(num_reqs, device=device, dtype=torch.float32)
for i in range(num_reqs): for i in range(num_reqs):
if torch.rand(1) > 0.5: if torch.rand(1) > 0.5:
presence_penalty[i] = torch.rand(1, device).item() * 0.2 presence_penalty[i] = torch.rand(1, device=device).item() * 0.2
temperature = torch.ones(num_reqs, device=device, dtype=torch.float32) temperature = torch.ones(num_reqs, device=device, dtype=torch.float32)
for i in range(num_reqs): for i in range(num_reqs):
if torch.rand(1) > 0.2: if torch.rand(1) > 0.2:
presence_penalty[i] = torch.rand(1, device).item() * 1.8 + 0.2 presence_penalty[i] = torch.rand(1, device=device).item() * 1.8 + 0.2
idx_mapping = torch.randint(0, num_status, (num_reqs,), device=device, dtype=torch.int32) idx_mapping = torch.randint(0, num_status, (num_reqs,), device=device, dtype=torch.int32)