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Diffstat (limited to '.venv/lib/python3.12/site-packages/litellm/llms/vertex_ai/cost_calculator.py')
| -rw-r--r-- | .venv/lib/python3.12/site-packages/litellm/llms/vertex_ai/cost_calculator.py | 242 |
1 files changed, 242 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/vertex_ai/cost_calculator.py b/.venv/lib/python3.12/site-packages/litellm/llms/vertex_ai/cost_calculator.py new file mode 100644 index 00000000..fd238860 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/litellm/llms/vertex_ai/cost_calculator.py @@ -0,0 +1,242 @@ +# What is this? +## Cost calculation for Google AI Studio / Vertex AI models +from typing import Literal, Optional, Tuple, Union + +import litellm +from litellm import verbose_logger +from litellm.litellm_core_utils.llm_cost_calc.utils import _is_above_128k + +""" +Gemini pricing covers: +- token +- image +- audio +- video +""" + +""" +Vertex AI -> character based pricing + +Google AI Studio -> token based pricing +""" + +models_without_dynamic_pricing = ["gemini-1.0-pro", "gemini-pro"] + + +def cost_router( + model: str, + custom_llm_provider: str, + call_type: Union[Literal["embedding", "aembedding"], str], +) -> Literal["cost_per_character", "cost_per_token"]: + """ + Route the cost calc to the right place, based on model/call_type/etc. + + Returns + - str, the specific google cost calc function it should route to. + """ + if custom_llm_provider == "vertex_ai" and ( + "claude" in model + or "llama" in model + or "mistral" in model + or "jamba" in model + or "codestral" in model + ): + return "cost_per_token" + elif custom_llm_provider == "vertex_ai" and ( + call_type == "embedding" or call_type == "aembedding" + ): + return "cost_per_token" + return "cost_per_character" + + +def cost_per_character( + model: str, + custom_llm_provider: str, + prompt_tokens: float, + completion_tokens: float, + prompt_characters: Optional[float] = None, + completion_characters: Optional[float] = None, +) -> Tuple[float, float]: + """ + Calculates the cost per character for a given VertexAI model, input messages, and response object. + + Input: + - model: str, the model name without provider prefix + - custom_llm_provider: str, "vertex_ai-*" + - prompt_characters: float, the number of input characters + - completion_characters: float, the number of output characters + + Returns: + Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd + + Raises: + Exception if model requires >128k pricing, but model cost not mapped + """ + model_info = litellm.get_model_info( + model=model, custom_llm_provider=custom_llm_provider + ) + + ## GET MODEL INFO + model_info = litellm.get_model_info( + model=model, custom_llm_provider=custom_llm_provider + ) + + ## CALCULATE INPUT COST + if prompt_characters is None: + prompt_cost, _ = cost_per_token( + model=model, + custom_llm_provider=custom_llm_provider, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + ) + else: + try: + if ( + _is_above_128k(tokens=prompt_characters * 4) # 1 token = 4 char + and model not in models_without_dynamic_pricing + ): + ## check if character pricing, else default to token pricing + assert ( + "input_cost_per_character_above_128k_tokens" in model_info + and model_info["input_cost_per_character_above_128k_tokens"] + is not None + ), "model info for model={} does not have 'input_cost_per_character_above_128k_tokens'-pricing for > 128k tokens\nmodel_info={}".format( + model, model_info + ) + prompt_cost = ( + prompt_characters + * model_info["input_cost_per_character_above_128k_tokens"] + ) + else: + assert ( + "input_cost_per_character" in model_info + and model_info["input_cost_per_character"] is not None + ), "model info for model={} does not have 'input_cost_per_character'-pricing\nmodel_info={}".format( + model, model_info + ) + prompt_cost = prompt_characters * model_info["input_cost_per_character"] + except Exception as e: + verbose_logger.debug( + "litellm.litellm_core_utils.llm_cost_calc.google.py::cost_per_character(): Exception occured - {}\nDefaulting to None".format( + str(e) + ) + ) + prompt_cost, _ = cost_per_token( + model=model, + custom_llm_provider=custom_llm_provider, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + ) + + ## CALCULATE OUTPUT COST + if completion_characters is None: + _, completion_cost = cost_per_token( + model=model, + custom_llm_provider=custom_llm_provider, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + ) + else: + try: + if ( + _is_above_128k(tokens=completion_characters * 4) # 1 token = 4 char + and model not in models_without_dynamic_pricing + ): + assert ( + "output_cost_per_character_above_128k_tokens" in model_info + and model_info["output_cost_per_character_above_128k_tokens"] + is not None + ), "model info for model={} does not have 'output_cost_per_character_above_128k_tokens' pricing\nmodel_info={}".format( + model, model_info + ) + completion_cost = ( + completion_tokens + * model_info["output_cost_per_character_above_128k_tokens"] + ) + else: + assert ( + "output_cost_per_character" in model_info + and model_info["output_cost_per_character"] is not None + ), "model info for model={} does not have 'output_cost_per_character'-pricing\nmodel_info={}".format( + model, model_info + ) + completion_cost = ( + completion_characters * model_info["output_cost_per_character"] + ) + except Exception as e: + verbose_logger.debug( + "litellm.litellm_core_utils.llm_cost_calc.google.py::cost_per_character(): Exception occured - {}\nDefaulting to None".format( + str(e) + ) + ) + _, completion_cost = cost_per_token( + model=model, + custom_llm_provider=custom_llm_provider, + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens, + ) + + return prompt_cost, completion_cost + + +def cost_per_token( + model: str, + custom_llm_provider: str, + prompt_tokens: float, + completion_tokens: float, +) -> Tuple[float, float]: + """ + Calculates the cost per token for a given model, prompt tokens, and completion tokens. + + Input: + - model: str, the model name without provider prefix + - custom_llm_provider: str, either "vertex_ai-*" or "gemini" + - prompt_tokens: float, the number of input tokens + - completion_tokens: float, the number of output tokens + + Returns: + Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd + + Raises: + Exception if model requires >128k pricing, but model cost not mapped + """ + ## GET MODEL INFO + model_info = litellm.get_model_info( + model=model, custom_llm_provider=custom_llm_provider + ) + + ## CALCULATE INPUT COST + if ( + _is_above_128k(tokens=prompt_tokens) + and model not in models_without_dynamic_pricing + ): + assert ( + "input_cost_per_token_above_128k_tokens" in model_info + and model_info["input_cost_per_token_above_128k_tokens"] is not None + ), "model info for model={} does not have pricing for > 128k tokens\nmodel_info={}".format( + model, model_info + ) + prompt_cost = ( + prompt_tokens * model_info["input_cost_per_token_above_128k_tokens"] + ) + else: + prompt_cost = prompt_tokens * model_info["input_cost_per_token"] + + ## CALCULATE OUTPUT COST + if ( + _is_above_128k(tokens=completion_tokens) + and model not in models_without_dynamic_pricing + ): + assert ( + "output_cost_per_token_above_128k_tokens" in model_info + and model_info["output_cost_per_token_above_128k_tokens"] is not None + ), "model info for model={} does not have pricing for > 128k tokens\nmodel_info={}".format( + model, model_info + ) + completion_cost = ( + completion_tokens * model_info["output_cost_per_token_above_128k_tokens"] + ) + else: + completion_cost = completion_tokens * model_info["output_cost_per_token"] + + return prompt_cost, completion_cost |