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Refactor spec decoding metrics calculation into separate TokenizerManager utility function (#11586)

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This commit is contained in:
Scott Lee
2025-10-14 05:45:49 -07:00
committed by GitHub
parent 5a33c3aae7
commit 817e46f412
1 changed files with 38 additions and 31 deletions
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69
python/sglang/srt/managers/tokenizer_manager.py
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@@ -1394,37 +1394,7 @@ class TokenizerManager(TokenizerCommunicatorMixin):
state.finished = recv_obj.finished_reasons[i] is not None state.finished = recv_obj.finished_reasons[i] is not None
if state.finished: if state.finished:
if self.server_args.speculative_algorithm: if self.server_args.speculative_algorithm:
meta_info["spec_verify_ct"] = recv_obj.spec_verify_ct[i] self._calculate_spec_decoding_metrics(meta_info, recv_obj, i)
if (
recv_obj.spec_verify_ct[i] > 0
and self.server_args.speculative_num_steps is not None
and not isinstance(recv_obj, BatchEmbeddingOutput)
and hasattr(recv_obj, "spec_accepted_tokens")
# Checks that `spec_accepted_tokens[i]` will exist.
and len(recv_obj.spec_accepted_tokens) > i
):
total_draft_tokens = (
recv_obj.spec_verify_ct[i]
* self.server_args.speculative_num_steps
)
accepted_tokens = recv_obj.spec_accepted_tokens[i]
# Calculate per-request acceptance rate and average acceptance length.
if total_draft_tokens > 0:
# Calculate acceptance rate: accepted / (steps * lookahead)
meta_info["spec_accept_rate"] = (
accepted_tokens / total_draft_tokens
)
meta_info["spec_accept_length"] = (
recv_obj.completion_tokens[i]
/ recv_obj.spec_verify_ct[i]
)
else:
meta_info["spec_accept_rate"] = 0.0
meta_info["spec_accept_length"] = 0
else:
meta_info["spec_accept_rate"] = 0.0
meta_info["spec_accept_length"] = 0
state.finished_time = time.time() state.finished_time = time.time()
meta_info["e2e_latency"] = state.finished_time - state.created_time meta_info["e2e_latency"] = state.finished_time - state.created_time
@@ -1572,6 +1542,43 @@ class TokenizerManager(TokenizerCommunicatorMixin):
ret.append(None) ret.append(None)
return ret return ret
def _calculate_spec_decoding_metrics(
self,
meta_info: Dict[str, Any],
recv_obj: Union[
BatchStrOutput,
BatchEmbeddingOutput,
BatchMultimodalOutput,
BatchTokenIDOutput,
],
i: int,
) -> None:
"""Calculate speculative decoding metrics, such as acceptance rate and acceptance length metrics."""
meta_info["spec_accept_rate"] = 0.0
meta_info["spec_accept_length"] = 0
meta_info["spec_verify_ct"] = recv_obj.spec_verify_ct[i]
if (
recv_obj.spec_verify_ct[i] > 0
and self.server_args.speculative_num_steps is not None
and not isinstance(recv_obj, BatchEmbeddingOutput)
and hasattr(recv_obj, "spec_accepted_tokens")
# Checks that `spec_accepted_tokens[i]` will exist.
and len(recv_obj.spec_accepted_tokens) > i
):
total_draft_tokens = (
recv_obj.spec_verify_ct[i] * self.server_args.speculative_num_steps
)
accepted_tokens = recv_obj.spec_accepted_tokens[i]
# Calculate per-request acceptance rate and average acceptance length.
if total_draft_tokens > 0:
# Calculate acceptance rate: accepted / (steps * lookahead)
meta_info["spec_accept_rate"] = accepted_tokens / total_draft_tokens
meta_info["spec_accept_length"] = (
recv_obj.completion_tokens[i] / recv_obj.spec_verify_ct[i]
)
def collect_metrics(self, state: ReqState, recv_obj: BatchStrOutput, i: int): def collect_metrics(self, state: ReqState, recv_obj: BatchStrOutput, i: int):
completion_tokens = ( completion_tokens = (
recv_obj.completion_tokens[i] recv_obj.completion_tokens[i]