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[Refactor] Replace hardcoded CUDA device selection with platform-agnostic API - #4496
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hsliuustc0106 merged 2 commits intoJun 23, 2026
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…stic API
Replace all `torch.device("cuda" if torch.cuda.is_available() else "cpu")`
and similar patterns with `current_omni_platform.get_torch_device()`
This enables the same code to work on non-CUDA platforms (XPU, NPU) without
per-model changes, using the existing platform abstraction layer.
Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: Nick Cao <ncao@redhat.com>
NickCao
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ZeldaHuang,
gcanlin,
linyueqian,
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tzhouam and
yuanheng-zhao
as code owners
June 16, 2026 19:39
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| @@ -198,7 +199,7 @@ def _ensure_s3_model(cls): | |||
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Good move to use platform-agnostic device selection
| @@ -57,7 +58,7 @@ def init_device(self): | |||
| f"be less than or equal to the number of visible devices " | |||
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Consistent platform-agnostic device selection - good
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hsliuustc0106
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Posting one inline finding from review.
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cc @gcanlin |
hsliuustc0106
enabled auto-merge (squash)
June 22, 2026 14:50
Fall back to torch.device("cpu") instead of raising
NotImplementedError, so helper paths that used to run on
CPU keep working when no accelerator platform is resolved.
Co-Authored-By: Claude <noreply@anthropic.com>
Signed-off-by: Nick Cao <ncao@redhat.com>
auto-merge was automatically disabled
June 22, 2026 14:56
Head branch was pushed to by a user without write access
Joshna-Medisetty
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Jul 29, 2026
Replace hardcoded torch.cuda.* calls in the async output builder with the torch.accelerator.* / generic torch.Event / torch.Stream equivalents that the repo already mandates (pyproject banned-api; PRs vllm-project#3365, vllm-project#4496). This unblocks the AR model-runner warm-up path on XPU (and other non-CUDA accelerators) without relying on the XPU torch.cuda shim. Signed-off-by: Joshna Medisetty <joshna.medisetty@intel.com>
khairulkabir1661
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to khairulkabir1661/vllm-omni
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Sep 25, 2026
…stic API (vllm-project#4496) Signed-off-by: Nick Cao <ncao@redhat.com>
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Purpose
Replace all
torch.device("cuda" if torch.cuda.is_available() else "cpu")and similar patterns withcurrent_omni_platform.get_torch_device()This enables the same code to work on non-CUDA platforms (XPU, NPU) without per-model changes, using the existing platform abstraction layer.
Test Plan
N/A, behavior remains the same as long as
torch.cuda.is_available()returns true (since we don't support cpu platform, it's supposed to be always true)vLLM Version:
vLLM-Omni Commit:
Test Result
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