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fix(cost_calculator.py): support openai+azure tts calls
This commit is contained in:
Krrish Dholakia
parent
d528b66db0
commit
407639cc7d
6 changed files with 191 additions and 5 deletions
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@ -13,6 +13,7 @@ from litellm.litellm_core_utils.llm_cost_calc.google import (
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from litellm.litellm_core_utils.llm_cost_calc.google import (
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cost_per_token as google_cost_per_token,
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)
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from litellm.litellm_core_utils.llm_cost_calc.utils import _generic_cost_per_character
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from litellm.utils import (
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CallTypes,
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CostPerToken,
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@ -62,6 +63,23 @@ def cost_per_token(
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### CUSTOM PRICING ###
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custom_cost_per_token: Optional[CostPerToken] = None,
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custom_cost_per_second: Optional[float] = None,
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### CALL TYPE ###
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call_type: Literal[
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"embedding",
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"aembedding",
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"completion",
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"acompletion",
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"atext_completion",
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"text_completion",
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"image_generation",
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"aimage_generation",
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"moderation",
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"amoderation",
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"atranscription",
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"transcription",
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"aspeech",
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"speech",
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] = "completion",
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) -> 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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@ -76,6 +94,7 @@ def cost_per_token(
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custom_llm_provider (str): The llm provider to whom the call was made (see init.py for full list)
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custom_cost_per_token: Optional[CostPerToken]: the cost per input + output token for the llm api call.
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custom_cost_per_second: Optional[float]: the cost per second for the llm api call.
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call_type: Optional[str]: the call type
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Returns:
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tuple: A tuple containing the cost in USD dollars for prompt tokens and completion tokens, respectively.
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@ -159,6 +178,27 @@ def cost_per_token(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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)
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elif call_type == "speech" or call_type == "aspeech":
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prompt_cost, completion_cost = _generic_cost_per_character(
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model=model_without_prefix,
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custom_llm_provider=custom_llm_provider,
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prompt_characters=prompt_characters,
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completion_characters=completion_characters,
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custom_prompt_cost=None,
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custom_completion_cost=0,
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)
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if prompt_cost is None or completion_cost is None:
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raise ValueError(
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"cost for tts call is None. prompt_cost={}, completion_cost={}, model={}, custom_llm_provider={}, prompt_characters={}, completion_characters={}".format(
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prompt_cost,
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completion_cost,
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model_without_prefix,
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custom_llm_provider,
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prompt_characters,
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completion_characters,
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)
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)
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return prompt_cost, completion_cost
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elif model in model_cost_ref:
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print_verbose(f"Success: model={model} in model_cost_map")
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print_verbose(
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@ -289,7 +329,7 @@ def cost_per_token(
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return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
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else:
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# if model is not in model_prices_and_context_window.json. Raise an exception-let users know
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error_str = f"Model not in model_prices_and_context_window.json. You passed model={model}. Register pricing for model - https://docs.litellm.ai/docs/proxy/custom_pricing\n"
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error_str = f"Model not in model_prices_and_context_window.json. You passed model={model}, custom_llm_provider={custom_llm_provider}. Register pricing for model - https://docs.litellm.ai/docs/proxy/custom_pricing\n"
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raise litellm.exceptions.NotFoundError( # type: ignore
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message=error_str,
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model=model,
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@ -535,6 +575,11 @@ def completion_cost(
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raise Exception(
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f"Model={image_gen_model_name} not found in completion cost model map"
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)
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elif (
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call_type == CallTypes.speech.value or call_type == CallTypes.aspeech.value
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):
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prompt_characters = litellm.utils._count_characters(text=prompt)
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# Calculate cost based on prompt_tokens, completion_tokens
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if (
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"togethercomputer" in model
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@ -591,6 +636,7 @@ def completion_cost(
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custom_cost_per_token=custom_cost_per_token,
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prompt_characters=prompt_characters,
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completion_characters=completion_characters,
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call_type=call_type,
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)
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_final_cost = prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar
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print_verbose(
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85
litellm/litellm_core_utils/llm_cost_calc/utils.py
Normal file
85
litellm/litellm_core_utils/llm_cost_calc/utils.py
Normal file
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@ -0,0 +1,85 @@
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# What is this?
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## Helper utilities for cost_per_token()
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import traceback
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from typing import List, Literal, Optional, Tuple
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import litellm
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from litellm import verbose_logger
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def _generic_cost_per_character(
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model: str,
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custom_llm_provider: str,
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prompt_characters: float,
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completion_characters: float,
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custom_prompt_cost: Optional[float],
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custom_completion_cost: Optional[float],
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) -> Tuple[Optional[float], Optional[float]]:
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"""
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Generic function to help calculate cost per character.
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"""
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"""
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Calculates the cost per character for a given model, input messages, and response object.
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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, "vertex_ai-*"
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- prompt_characters: float, the number of input characters
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- completion_characters: float, the number of output characters
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Returns:
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Tuple[Optional[float], Optional[float]] - prompt_cost_in_usd, completion_cost_in_usd.
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- returns None if not able to calculate cost.
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Raises:
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Exception if 'input_cost_per_character' or 'output_cost_per_character' is missing from model_info
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"""
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args = locals()
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## GET MODEL INFO
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model_info = litellm.get_model_info(
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model=model, custom_llm_provider=custom_llm_provider
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)
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## CALCULATE INPUT COST
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try:
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if custom_prompt_cost is None:
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assert (
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"input_cost_per_character" in model_info
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and model_info["input_cost_per_character"] is not None
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), "model info for model={} does not have 'input_cost_per_character'-pricing\nmodel_info={}".format(
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model, model_info
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)
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custom_prompt_cost = model_info["input_cost_per_character"]
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prompt_cost = prompt_characters * custom_prompt_cost
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except Exception as e:
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verbose_logger.error(
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"litellm.litellm_core_utils.llm_cost_calc.utils.py::cost_per_character(): Exception occured - {}\n{}\nDefaulting to None".format(
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str(e), traceback.format_exc()
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)
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)
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prompt_cost = None
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## CALCULATE OUTPUT COST
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try:
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if custom_completion_cost is None:
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assert (
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"output_cost_per_character" in model_info
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and model_info["output_cost_per_character"] is not None
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), "model info for model={} does not have 'output_cost_per_character'-pricing\nmodel_info={}".format(
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model, model_info
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)
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custom_completion_cost = model_info["output_cost_per_character"]
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completion_cost = completion_characters * custom_completion_cost
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except Exception as e:
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verbose_logger.error(
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"litellm.litellm_core_utils.llm_cost_calc.utils.py::cost_per_character(): Exception occured - {}\n{}\nDefaulting to None".format(
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str(e), traceback.format_exc()
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)
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)
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completion_cost = None
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return prompt_cost, completion_cost
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@ -397,7 +397,27 @@
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"input_cost_per_second": 0,
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"output_cost_per_second": 0.0001,
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"litellm_provider": "openai"
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},
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},
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"tts-1": {
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"mode": "audio_speech",
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"input_cost_per_character": 0.000015,
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"litellm_provider": "openai"
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},
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"tts-1-hd": {
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"mode": "audio_speech",
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"input_cost_per_character": 0.000030,
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"litellm_provider": "openai"
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},
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"azure/tts-1": {
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"mode": "audio_speech",
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"input_cost_per_character": 0.000015,
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"litellm_provider": "azure"
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},
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"azure/tts-1-hd": {
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"mode": "audio_speech",
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"input_cost_per_character": 0.000030,
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"litellm_provider": "azure"
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},
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"azure/whisper-1": {
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"mode": "audio_transcription",
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"input_cost_per_second": 0,
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@ -712,7 +712,6 @@ def test_vertex_ai_claude_completion_cost():
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assert cost == predicted_cost
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@pytest.mark.parametrize("sync_mode", [True, False])
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@pytest.mark.asyncio
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async def test_completion_cost_hidden_params(sync_mode):
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@ -732,6 +731,7 @@ async def test_completion_cost_hidden_params(sync_mode):
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assert "response_cost" in response._hidden_params
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assert isinstance(response._hidden_params["response_cost"], float)
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def test_vertex_ai_gemini_predict_cost():
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model = "gemini-1.5-flash"
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messages = [{"role": "user", "content": "Hey, hows it going???"}]
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@ -739,3 +739,16 @@ def test_vertex_ai_gemini_predict_cost():
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assert predictive_cost > 0
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@pytest.mark.parametrize("model", ["openai/tts-1", "azure/tts-1"])
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def test_completion_cost_tts(model):
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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litellm.model_cost = litellm.get_model_cost_map(url="")
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cost = completion_cost(
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model=model,
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prompt="the quick brown fox jumped over the lazy dogs",
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call_type="speech",
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)
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assert cost > 0
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@ -4705,7 +4705,9 @@ def get_model_info(model: str, custom_llm_provider: Optional[str] = None) -> Mod
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)
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except Exception:
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raise Exception(
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"This model isn't mapped yet. Add it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json"
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"This model isn't mapped yet. model={}, custom_llm_provider={}. Add it here - https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json".format(
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model, custom_llm_provider
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)
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)
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@ -397,7 +397,27 @@
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"input_cost_per_second": 0,
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"output_cost_per_second": 0.0001,
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"litellm_provider": "openai"
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},
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},
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"tts-1": {
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"mode": "audio_speech",
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"input_cost_per_character": 0.000015,
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"litellm_provider": "openai"
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},
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"tts-1-hd": {
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"mode": "audio_speech",
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"input_cost_per_character": 0.000030,
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"litellm_provider": "openai"
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},
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"azure/tts-1": {
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"mode": "audio_speech",
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"input_cost_per_character": 0.000015,
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"litellm_provider": "azure"
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},
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"azure/tts-1-hd": {
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"mode": "audio_speech",
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"input_cost_per_character": 0.000030,
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"litellm_provider": "azure"
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},
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"azure/whisper-1": {
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"mode": "audio_transcription",
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"input_cost_per_second": 0,
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