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[Misc] Replace cuda hard code with current_platform #16983

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Merged
merged 2 commits into from
May 23, 2025
Merged

[Misc] Replace cuda hard code with current_platform #16983

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bee8f3b
remove cuda hard code
shen-shanshan Apr 22, 2025
bb35536
update
shen-shanshan May 14, 2025
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5 changes: 3 additions & 2 deletions vllm/distributed/parallel_state.py
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Expand Up @@ -1221,8 +1221,9 @@ def cleanup_dist_env_and_memory(shutdown_ray: bool = False):
ray.shutdown()
gc.collect()
from vllm.platforms import current_platform
if not current_platform.is_cpu():
torch.cuda.empty_cache()
empty_cache = current_platform.empty_cache
if empty_cache is not None:
empty_cache()
try:
torch._C._host_emptyCache()
except AttributeError:
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5 changes: 4 additions & 1 deletion vllm/forward_context.py
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Expand Up @@ -120,7 +120,10 @@ def set_forward_context(attn_metadata: Any,
# we use synchronous scheduling right now,
# adding a sync point here should not affect
# scheduling of the next batch
torch.cuda.synchronize()
from vllm.platforms import current_platform
synchronize = current_platform.synchronize
if synchronize is not None:
synchronize()
now = time.perf_counter()
# time measurement is in milliseconds
batchsize_forward_time[batchsize].append(
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8 changes: 4 additions & 4 deletions vllm/spec_decode/metrics.py
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Expand Up @@ -126,12 +126,12 @@ def _copy_rejsample_metrics_async(self) -> torch.cuda.Event:
"""Copy rejection/typical-acceptance sampling metrics
(number of accepted tokens, etc) to CPU asynchronously.

Returns a CUDA event recording when the copy is complete.
Returns a device event recording when the copy is complete.
"""
assert self._copy_stream is not None
self._copy_stream.wait_stream(torch.cuda.current_stream())
self._copy_stream.wait_stream(current_platform.current_stream())

with torch.cuda.stream(self._copy_stream):
with current_platform.stream(self._copy_stream):
self._aggregate_num_accepted_tokens.copy_(
self.spec_decode_sampler.num_accepted_tokens,
non_blocking=True)
Expand All @@ -142,7 +142,7 @@ def _copy_rejsample_metrics_async(self) -> torch.cuda.Event:
self._aggregate_num_draft_tokens = (
self.spec_decode_sampler.num_draft_tokens)

aggregate_metrics_ready = torch.cuda.Event()
aggregate_metrics_ready = current_platform.Event()
aggregate_metrics_ready.record(self._copy_stream)

return aggregate_metrics_ready
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