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perf: Avoid copying inputs_embeds tensors to GPU unless prompt_embeds is enabled (#25739)

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Signed-off-by: Andrew Sansom <andrew@protopia.ai>
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Andrew Sansom 2025-09-26 03:18:09 -05:00 committed by GitHub
parent e84e0735c7
commit d48f4d6daf
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1 changed files with 15 additions and 11 deletions
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26
vllm/v1/worker/gpu_model_runner.py
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@ -836,8 +836,9 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
if self.input_batch.prev_sampled_token_ids is None:
# Normal scheduling case
self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
if self.enable_prompt_embeds:
self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
return
# Async scheduling case, where some decode requests from the previous
@ -863,8 +864,9 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
# If not all requests are decodes from the last iteration,
# We need to copy the input_ids_cpu to the GPU first.
self.input_ids.copy_to_gpu(total_num_scheduled_tokens)
self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
if self.enable_prompt_embeds:
self.inputs_embeds.copy_to_gpu(total_num_scheduled_tokens)
self.is_token_ids.copy_to_gpu(total_num_scheduled_tokens)
if num_commmon_tokens == 0:
# No requests in common with the previous iteration
# So input_ids_cpu will have all the input ids.
@ -878,7 +880,8 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
self.input_batch.prev_sampled_token_ids[:num_commmon_tokens,
0],
non_blocking=True)
self.is_token_ids.gpu[:num_commmon_tokens] = True
if self.enable_prompt_embeds:
self.is_token_ids.gpu[:num_commmon_tokens] = True
return
# Upload the index tensors asynchronously
# so the scatter can be non-blocking.
@ -978,12 +981,13 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
0,
token_indices_tensor,
out=self.input_ids.cpu[:total_num_scheduled_tokens])
is_token_ids = self.input_batch.is_token_ids.flatten()
torch.index_select(
is_token_ids,
0,
token_indices_tensor,
out=self.is_token_ids.cpu[:total_num_scheduled_tokens])
if self.enable_prompt_embeds:
is_token_ids = self.input_batch.is_token_ids.flatten()
torch.index_select(
is_token_ids,
0,
token_indices_tensor,
out=self.is_token_ids.cpu[:total_num_scheduled_tokens])
# Because we did not pre-allocate a massive prompt_embeds CPU tensor on
# the InputBatch, we need to fill in the prompt embeds into the expected