Tiny skip_sample adjust (#11225)
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@@ -237,7 +237,7 @@ class TpModelWorker:
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self,
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model_worker_batch: ModelWorkerBatch,
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launch_done: Optional[threading.Event] = None,
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skip_sample: bool = False,
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is_verify: bool = False,
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) -> ForwardBatchOutput:
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# update the consumer index of hicache to the running batch
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self.set_hicache_consumer(model_worker_batch.hicache_consumer_index)
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@@ -259,19 +259,16 @@ class TpModelWorker:
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if launch_done is not None:
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launch_done.set()
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if skip_sample:
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next_token_ids = None
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# For prefill-only requests, we still need to compute logprobs even when sampling is skipped
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if (
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model_worker_batch.is_prefill_only
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and model_worker_batch.return_logprob
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):
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# Compute logprobs without full sampling
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self.model_runner.compute_logprobs_only(
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logits_output, model_worker_batch
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)
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else:
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skip_sample = is_verify or model_worker_batch.is_prefill_only
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next_token_ids = None
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if not skip_sample:
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next_token_ids = self.model_runner.sample(logits_output, forward_batch)
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elif model_worker_batch.return_logprob and not is_verify:
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# NOTE: Compute logprobs without full sampling
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self.model_runner.compute_logprobs_only(
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logits_output, model_worker_batch
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)
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return ForwardBatchOutput(
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logits_output=logits_output,
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