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[Wip] fix problems introduced by vllm #26016 #3826

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[Wip] fix problems introduced by vllm #26016 #3826

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e83c7dc
[Wip] fix problems introduced by vllm #26016
wxsIcey Oct 28, 2025
820aad0
tiny fix
wxsIcey Oct 28, 2025
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60 changes: 19 additions & 41 deletions vllm_ascend/platform.py
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Original file line number Diff line number Diff line change
Expand Up @@ -143,47 +143,25 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
"Non-MLA LLMs forcibly disable the chunked prefill feature,"
"as the performance of operators supporting this feature "
"functionality is currently suboptimal.")
if vllm_version_is("0.11.0"):
if not model_config.is_multimodal_model and \
structured_outputs_config.backend == "auto" and \
not scheduler_config.send_delta_data and \
not getattr(scheduler_config, "scheduler_delay_factor", 0) > 0 and \
scheduler_config.policy == "fcfs":
ascend_scheduler_config.enabled = True
chunked_prefill_enabled_in_ascend_scheduler = getattr(
ascend_scheduler_config, "enable_chunked_prefill",
False)
if chunked_prefill_enabled_in_ascend_scheduler:
logger.warning(
"Chunked prefill feature is enabled in ascend_scheduler,"
"but note that the operator supporting this feature "
"would lead to performance degradation.")
# In this situation, max_num_batched_tokens would have been rewritten.
# So we must make sure max_num_batched_tokens is not smaller than max_model_len.
if (scheduler_config.max_num_batched_tokens
< scheduler_config.max_model_len and
not chunked_prefill_enabled_in_ascend_scheduler):
scheduler_config.max_num_batched_tokens = scheduler_config.max_model_len
else:
if not model_config.is_multimodal_model and \
structured_outputs_config.backend == "auto" and \
not getattr(scheduler_config, "scheduler_delay_factor", 0) > 0 and \
scheduler_config.policy == "fcfs":
ascend_scheduler_config.enabled = True
chunked_prefill_enabled_in_ascend_scheduler = getattr(
ascend_scheduler_config, "enable_chunked_prefill",
False)
if chunked_prefill_enabled_in_ascend_scheduler:
logger.warning(
"Chunked prefill feature is enabled in ascend_scheduler,"
"but note that the operator supporting this feature "
"would lead to performance degradation.")
# In this situation, max_num_batched_tokens would have been rewritten.
# So we must make sure max_num_batched_tokens is not smaller than max_model_len.
if (scheduler_config.max_num_batched_tokens
< scheduler_config.max_model_len and
not chunked_prefill_enabled_in_ascend_scheduler):
scheduler_config.max_num_batched_tokens = scheduler_config.max_model_len
if not model_config.is_multimodal_model and \
structured_outputs_config.backend == "auto" and \
not getattr(scheduler_config, "send_delta_data", False) and \
not getattr(scheduler_config, "scheduler_delay_factor", 0) > 0 and \
scheduler_config.policy == "fcfs":
ascend_scheduler_config.enabled = True
chunked_prefill_enabled_in_ascend_scheduler = getattr(
ascend_scheduler_config, "enable_chunked_prefill", False)
if chunked_prefill_enabled_in_ascend_scheduler:
logger.warning(
"Chunked prefill feature is enabled in ascend_scheduler,"
"but note that the operator supporting this feature "
"would lead to performance degradation.")
# In this situation, max_num_batched_tokens would have been rewritten.
# So we must make sure max_num_batched_tokens is not smaller than max_model_len.
if (scheduler_config.max_num_batched_tokens
< scheduler_config.max_model_len
and not chunked_prefill_enabled_in_ascend_scheduler):
scheduler_config.max_num_batched_tokens = scheduler_config.max_model_len

kv_cache_dtype = vllm_config.additional_config.get(
"kv_cache_dtype", None)
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5 changes: 2 additions & 3 deletions vllm_ascend/spec_decode/eagle_proposer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -63,9 +63,8 @@ def __init__(self,
== CompilationMode.VLLM_COMPILE
and not self.vllm_config.model_config.enforce_eager)

self.cudagraph_batch_sizes = list(
reversed(
self.vllm_config.compilation_config.cudagraph_capture_sizes))
self.cudagraph_batch_sizes = sorted(
self.vllm_config.compilation_config.cudagraph_capture_sizes)

# persistent buffers for cuda graph
self.input_ids = torch.zeros(
Expand Down
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