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sglang/python/sglang/srt/entrypoints/openai/usage_processor.py
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4.5 KiB
Python

from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional, final
from sglang.srt.entrypoints.openai.protocol import PromptTokensDetails, UsageInfo
@final
class UsageProcessor:
"""Stateless helpers that turn raw token counts into a UsageInfo."""
@staticmethod
def _details_if_cached(count: int) -> Optional[PromptTokensDetails]:
"""Return PromptTokensDetails only when count > 0 (keeps JSON slim)."""
return PromptTokensDetails(cached_tokens=count) if count > 0 else None
@staticmethod
def calculate_response_usage(
responses: List[Dict[str, Any]],
n_choices: int = 1,
enable_cache_report: bool = False,
image_tokens: int = 0,
audio_tokens: int = 0,
video_tokens: int = 0,
) -> UsageInfo:
completion_tokens = sum(
r["meta_info"].get("completion_tokens", 0) for r in responses
)
prompt_tokens = sum(
responses[i]["meta_info"].get("prompt_tokens", 0)
for i in range(0, len(responses), n_choices)
)
# some API don't have reasoning_tokens semantics
reasoning_tokens = sum(
r["meta_info"].get("reasoning_tokens", 0) for r in responses
)
cached_details = None
if enable_cache_report:
cached_total = sum(
responses[i]["meta_info"].get("cached_tokens", 0)
for i in range(0, len(responses), n_choices)
)
cached_details = UsageProcessor._details_if_cached(cached_total)
return UsageProcessor.calculate_token_usage(
prompt_tokens=prompt_tokens,
reasoning_tokens=reasoning_tokens,
completion_tokens=completion_tokens,
cached_tokens=cached_details,
image_tokens=image_tokens,
audio_tokens=audio_tokens,
video_tokens=video_tokens,
)
@staticmethod
def calculate_streaming_usage(
prompt_tokens: Mapping[int, int],
reasoning_tokens: Mapping[int, int],
completion_tokens: Mapping[int, int],
cached_tokens: Mapping[int, int],
n_choices: int,
enable_cache_report: bool = False,
image_tokens: int = 0,
audio_tokens: int = 0,
video_tokens: int = 0,
) -> UsageInfo:
# index % n_choices == 0 marks the first choice of a prompt
total_prompt_tokens = sum(
tok for idx, tok in prompt_tokens.items() if idx % n_choices == 0
)
total_reasoning_tokens = sum(reasoning_tokens.values())
total_completion_tokens = sum(completion_tokens.values())
cached_details = (
UsageProcessor._details_if_cached(
sum(tok for idx, tok in cached_tokens.items() if idx % n_choices == 0)
)
if enable_cache_report
else None
)
return UsageProcessor.calculate_token_usage(
prompt_tokens=total_prompt_tokens,
reasoning_tokens=total_reasoning_tokens,
completion_tokens=total_completion_tokens,
cached_tokens=cached_details,
image_tokens=image_tokens,
audio_tokens=audio_tokens,
video_tokens=video_tokens,
)
@staticmethod
def calculate_token_usage(
prompt_tokens: int,
completion_tokens: int,
reasoning_tokens: Optional[int] = 0,
cached_tokens: Optional[PromptTokensDetails] = None,
image_tokens: int = 0,
audio_tokens: int = 0,
video_tokens: int = 0,
) -> UsageInfo:
"""Calculate token usage information"""
# `cached_tokens` is already a PromptTokensDetails (or None) carrying the
# cached count. Attach multimodal counts to the same object, creating one
# only when there is something to report so plain-text requests keep
# prompt_tokens_details=None (backward compatible).
details = cached_tokens
if image_tokens or audio_tokens or video_tokens:
if details is None:
details = PromptTokensDetails()
if image_tokens:
details.image_tokens = image_tokens
if audio_tokens:
details.audio_tokens = audio_tokens
if video_tokens:
details.video_tokens = video_tokens
return UsageInfo(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
prompt_tokens_details=details,
reasoning_tokens=reasoning_tokens,
)