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4351c77253
* fix(vertex_and_google_ai_studio_gemini.py): log gemini audio tokens in usage object enables accurate cost tracking * refactor(vertex_ai/cost_calculator.py): refactor 128k+ token cost calculation to only run if model info has it Google has moved away from this for gemini-2.0 models * refactor(vertex_ai/cost_calculator.py): migrate to usage object for more flexible data passthrough * fix(llm_cost_calc/utils.py): support audio token cost tracking in generic cost per token enables vertex ai cost tracking to work with audio tokens * fix(llm_cost_calc/utils.py): default to total prompt tokens if text tokens field not set * refactor(llm_cost_calc/utils.py): move openai cost tracking to generic cost per token more consistent behaviour across providers * test: add unit test for gemini audio token cost calculation * ci: bump ci config * test: fix test
122 lines
4.6 KiB
Python
122 lines
4.6 KiB
Python
"""
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Helper util for handling openai-specific cost calculation
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- e.g.: prompt caching
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"""
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from typing import Literal, Optional, Tuple
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from litellm._logging import verbose_logger
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from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token
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from litellm.types.utils import CallTypes, Usage
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from litellm.utils import get_model_info
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def cost_router(call_type: CallTypes) -> Literal["cost_per_token", "cost_per_second"]:
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if call_type == CallTypes.atranscription or call_type == CallTypes.transcription:
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return "cost_per_second"
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else:
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return "cost_per_token"
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def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]:
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"""
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Calculates the cost per token for a given model, prompt tokens, and completion tokens.
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Input:
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- model: str, the model name without provider prefix
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- usage: LiteLLM Usage block, containing anthropic caching information
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Returns:
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Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
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"""
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## CALCULATE INPUT COST
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return generic_cost_per_token(
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model=model, usage=usage, custom_llm_provider="openai"
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)
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# ### Non-cached text tokens
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# non_cached_text_tokens = usage.prompt_tokens
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# cached_tokens: Optional[int] = None
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# if usage.prompt_tokens_details and usage.prompt_tokens_details.cached_tokens:
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# cached_tokens = usage.prompt_tokens_details.cached_tokens
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# non_cached_text_tokens = non_cached_text_tokens - cached_tokens
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# prompt_cost: float = non_cached_text_tokens * model_info["input_cost_per_token"]
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# ## Prompt Caching cost calculation
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# if model_info.get("cache_read_input_token_cost") is not None and cached_tokens:
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# # Note: We read ._cache_read_input_tokens from the Usage - since cost_calculator.py standardizes the cache read tokens on usage._cache_read_input_tokens
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# prompt_cost += cached_tokens * (
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# model_info.get("cache_read_input_token_cost", 0) or 0
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# )
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# _audio_tokens: Optional[int] = (
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# usage.prompt_tokens_details.audio_tokens
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# if usage.prompt_tokens_details is not None
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# else None
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# )
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# _audio_cost_per_token: Optional[float] = model_info.get(
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# "input_cost_per_audio_token"
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# )
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# if _audio_tokens is not None and _audio_cost_per_token is not None:
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# audio_cost: float = _audio_tokens * _audio_cost_per_token
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# prompt_cost += audio_cost
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# ## CALCULATE OUTPUT COST
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# completion_cost: float = (
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# usage["completion_tokens"] * model_info["output_cost_per_token"]
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# )
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# _output_cost_per_audio_token: Optional[float] = model_info.get(
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# "output_cost_per_audio_token"
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# )
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# _output_audio_tokens: Optional[int] = (
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# usage.completion_tokens_details.audio_tokens
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# if usage.completion_tokens_details is not None
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# else None
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# )
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# if _output_cost_per_audio_token is not None and _output_audio_tokens is not None:
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# audio_cost = _output_audio_tokens * _output_cost_per_audio_token
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# completion_cost += audio_cost
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# return prompt_cost, completion_cost
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def cost_per_second(
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model: str, custom_llm_provider: Optional[str], duration: float = 0.0
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) -> Tuple[float, float]:
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"""
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Calculates the cost per second for a given model, prompt tokens, and completion tokens.
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Input:
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- model: str, the model name without provider prefix
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- custom_llm_provider: str, the custom llm provider
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- duration: float, the duration of the response in seconds
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Returns:
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Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
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"""
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## GET MODEL INFO
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model_info = get_model_info(
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model=model, custom_llm_provider=custom_llm_provider or "openai"
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)
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prompt_cost = 0.0
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completion_cost = 0.0
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## Speech / Audio cost calculation
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if (
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"output_cost_per_second" in model_info
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and model_info["output_cost_per_second"] is not None
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):
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verbose_logger.debug(
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f"For model={model} - output_cost_per_second: {model_info.get('output_cost_per_second')}; duration: {duration}"
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)
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## COST PER SECOND ##
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completion_cost = model_info["output_cost_per_second"] * duration
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elif (
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"input_cost_per_second" in model_info
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and model_info["input_cost_per_second"] is not None
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):
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verbose_logger.debug(
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f"For model={model} - input_cost_per_second: {model_info.get('input_cost_per_second')}; duration: {duration}"
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)
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## COST PER SECOND ##
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prompt_cost = model_info["input_cost_per_second"] * duration
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completion_cost = 0.0
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return prompt_cost, completion_cost
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