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fix(cost_calculator.py): infer provider name if not given
Fixes https://github.com/BerriAI/litellm/issues/4452
This commit is contained in:
Krrish Dholakia
parent
01064b4e23
commit
e81fa6ecad
2 changed files with 222 additions and 71 deletions
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@ -101,8 +101,12 @@ def cost_per_token(
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if custom_llm_provider is not None:
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model_with_provider = custom_llm_provider + "/" + model
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if region_name is not None:
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model_with_provider_and_region = f"{custom_llm_provider}/{region_name}/{model}"
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if model_with_provider_and_region in model_cost_ref: # use region based pricing, if it's available
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model_with_provider_and_region = (
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f"{custom_llm_provider}/{region_name}/{model}"
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)
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if (
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model_with_provider_and_region in model_cost_ref
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): # use region based pricing, if it's available
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model_with_provider = model_with_provider_and_region
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else:
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_, custom_llm_provider, _, _ = litellm.get_llm_provider(model=model)
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@ -118,7 +122,9 @@ def cost_per_token(
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Option2. model = "openai/gpt-4" - model = provider/model
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Option3. model = "anthropic.claude-3" - model = model
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"""
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if model_with_provider in model_cost_ref: # Option 2. use model with provider, model = "openai/gpt-4"
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if (
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model_with_provider in model_cost_ref
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): # Option 2. use model with provider, model = "openai/gpt-4"
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model = model_with_provider
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elif model in model_cost_ref: # Option 1. use model passed, model="gpt-4"
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model = model
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@ -154,29 +160,45 @@ def cost_per_token(
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)
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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(f"prompt_tokens={prompt_tokens}; completion_tokens={completion_tokens}")
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print_verbose(
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f"prompt_tokens={prompt_tokens}; completion_tokens={completion_tokens}"
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)
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if (
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model_cost_ref[model].get("input_cost_per_token", None) is not None
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and model_cost_ref[model].get("output_cost_per_token", None) is not None
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):
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## COST PER TOKEN ##
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prompt_tokens_cost_usd_dollar = model_cost_ref[model]["input_cost_per_token"] * prompt_tokens
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completion_tokens_cost_usd_dollar = model_cost_ref[model]["output_cost_per_token"] * completion_tokens
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elif model_cost_ref[model].get("output_cost_per_second", None) is not None and response_time_ms is not None:
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prompt_tokens_cost_usd_dollar = (
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model_cost_ref[model]["input_cost_per_token"] * prompt_tokens
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)
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completion_tokens_cost_usd_dollar = (
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model_cost_ref[model]["output_cost_per_token"] * completion_tokens
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)
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elif (
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model_cost_ref[model].get("output_cost_per_second", None) is not None
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and response_time_ms is not None
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):
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print_verbose(
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f"For model={model} - output_cost_per_second: {model_cost_ref[model].get('output_cost_per_second')}; response time: {response_time_ms}"
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)
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## COST PER SECOND ##
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prompt_tokens_cost_usd_dollar = 0
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completion_tokens_cost_usd_dollar = (
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model_cost_ref[model]["output_cost_per_second"] * response_time_ms / 1000
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model_cost_ref[model]["output_cost_per_second"]
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* response_time_ms
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/ 1000
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)
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elif model_cost_ref[model].get("input_cost_per_second", None) is not None and response_time_ms is not None:
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elif (
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model_cost_ref[model].get("input_cost_per_second", None) is not None
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and response_time_ms is not None
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):
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print_verbose(
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f"For model={model} - input_cost_per_second: {model_cost_ref[model].get('input_cost_per_second')}; response time: {response_time_ms}"
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)
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## COST PER SECOND ##
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prompt_tokens_cost_usd_dollar = model_cost_ref[model]["input_cost_per_second"] * response_time_ms / 1000
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prompt_tokens_cost_usd_dollar = (
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model_cost_ref[model]["input_cost_per_second"] * response_time_ms / 1000
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)
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completion_tokens_cost_usd_dollar = 0.0
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print_verbose(
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f"Returned custom cost for model={model} - prompt_tokens_cost_usd_dollar: {prompt_tokens_cost_usd_dollar}, completion_tokens_cost_usd_dollar: {completion_tokens_cost_usd_dollar}"
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@ -185,40 +207,57 @@ def cost_per_token(
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elif "ft:gpt-3.5-turbo" in model:
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print_verbose(f"Cost Tracking: {model} is an OpenAI FinteTuned LLM")
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# fuzzy match ft:gpt-3.5-turbo:abcd-id-cool-litellm
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prompt_tokens_cost_usd_dollar = model_cost_ref["ft:gpt-3.5-turbo"]["input_cost_per_token"] * prompt_tokens
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prompt_tokens_cost_usd_dollar = (
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model_cost_ref["ft:gpt-3.5-turbo"]["input_cost_per_token"] * prompt_tokens
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)
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completion_tokens_cost_usd_dollar = (
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model_cost_ref["ft:gpt-3.5-turbo"]["output_cost_per_token"] * completion_tokens
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model_cost_ref["ft:gpt-3.5-turbo"]["output_cost_per_token"]
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* completion_tokens
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)
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return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
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elif "ft:gpt-4-0613" in model:
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print_verbose(f"Cost Tracking: {model} is an OpenAI FinteTuned LLM")
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# fuzzy match ft:gpt-4-0613:abcd-id-cool-litellm
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prompt_tokens_cost_usd_dollar = model_cost_ref["ft:gpt-4-0613"]["input_cost_per_token"] * prompt_tokens
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completion_tokens_cost_usd_dollar = model_cost_ref["ft:gpt-4-0613"]["output_cost_per_token"] * completion_tokens
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prompt_tokens_cost_usd_dollar = (
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model_cost_ref["ft:gpt-4-0613"]["input_cost_per_token"] * prompt_tokens
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)
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completion_tokens_cost_usd_dollar = (
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model_cost_ref["ft:gpt-4-0613"]["output_cost_per_token"] * completion_tokens
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)
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return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
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elif "ft:gpt-4o-2024-05-13" in model:
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print_verbose(f"Cost Tracking: {model} is an OpenAI FinteTuned LLM")
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# fuzzy match ft:gpt-4o-2024-05-13:abcd-id-cool-litellm
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prompt_tokens_cost_usd_dollar = model_cost_ref["ft:gpt-4o-2024-05-13"]["input_cost_per_token"] * prompt_tokens
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prompt_tokens_cost_usd_dollar = (
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model_cost_ref["ft:gpt-4o-2024-05-13"]["input_cost_per_token"]
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* prompt_tokens
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)
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completion_tokens_cost_usd_dollar = (
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model_cost_ref["ft:gpt-4o-2024-05-13"]["output_cost_per_token"] * completion_tokens
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model_cost_ref["ft:gpt-4o-2024-05-13"]["output_cost_per_token"]
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* completion_tokens
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)
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return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
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elif "ft:davinci-002" in model:
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print_verbose(f"Cost Tracking: {model} is an OpenAI FinteTuned LLM")
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# fuzzy match ft:davinci-002:abcd-id-cool-litellm
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prompt_tokens_cost_usd_dollar = model_cost_ref["ft:davinci-002"]["input_cost_per_token"] * prompt_tokens
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prompt_tokens_cost_usd_dollar = (
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model_cost_ref["ft:davinci-002"]["input_cost_per_token"] * prompt_tokens
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)
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completion_tokens_cost_usd_dollar = (
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model_cost_ref["ft:davinci-002"]["output_cost_per_token"] * completion_tokens
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model_cost_ref["ft:davinci-002"]["output_cost_per_token"]
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* completion_tokens
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)
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return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
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elif "ft:babbage-002" in model:
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print_verbose(f"Cost Tracking: {model} is an OpenAI FinteTuned LLM")
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# fuzzy match ft:babbage-002:abcd-id-cool-litellm
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prompt_tokens_cost_usd_dollar = model_cost_ref["ft:babbage-002"]["input_cost_per_token"] * prompt_tokens
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prompt_tokens_cost_usd_dollar = (
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model_cost_ref["ft:babbage-002"]["input_cost_per_token"] * prompt_tokens
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)
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completion_tokens_cost_usd_dollar = (
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model_cost_ref["ft:babbage-002"]["output_cost_per_token"] * completion_tokens
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model_cost_ref["ft:babbage-002"]["output_cost_per_token"]
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* completion_tokens
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)
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return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
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elif model in litellm.azure_llms:
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@ -227,17 +266,25 @@ def cost_per_token(
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verbose_logger.debug(
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f"applying cost={model_cost_ref[model]['input_cost_per_token']} for prompt_tokens={prompt_tokens}"
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)
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prompt_tokens_cost_usd_dollar = model_cost_ref[model]["input_cost_per_token"] * prompt_tokens
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prompt_tokens_cost_usd_dollar = (
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model_cost_ref[model]["input_cost_per_token"] * prompt_tokens
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)
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verbose_logger.debug(
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f"applying cost={model_cost_ref[model]['output_cost_per_token']} for completion_tokens={completion_tokens}"
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)
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completion_tokens_cost_usd_dollar = model_cost_ref[model]["output_cost_per_token"] * completion_tokens
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completion_tokens_cost_usd_dollar = (
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model_cost_ref[model]["output_cost_per_token"] * completion_tokens
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)
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return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
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elif model in litellm.azure_embedding_models:
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verbose_logger.debug(f"Cost Tracking: {model} is an Azure Embedding Model")
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model = litellm.azure_embedding_models[model]
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prompt_tokens_cost_usd_dollar = model_cost_ref[model]["input_cost_per_token"] * prompt_tokens
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completion_tokens_cost_usd_dollar = model_cost_ref[model]["output_cost_per_token"] * completion_tokens
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prompt_tokens_cost_usd_dollar = (
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model_cost_ref[model]["input_cost_per_token"] * prompt_tokens
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)
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completion_tokens_cost_usd_dollar = (
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model_cost_ref[model]["output_cost_per_token"] * completion_tokens
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)
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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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@ -261,7 +308,9 @@ def get_model_params_and_category(model_name) -> str:
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import re
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model_name = model_name.lower()
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re_params_match = re.search(r"(\d+b)", model_name) # catch all decimals like 3b, 70b, etc
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re_params_match = re.search(
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r"(\d+b)", model_name
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) # catch all decimals like 3b, 70b, etc
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category = None
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if re_params_match is not None:
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params_match = str(re_params_match.group(1))
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@ -292,7 +341,9 @@ def get_model_params_and_category(model_name) -> str:
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def get_replicate_completion_pricing(completion_response=None, total_time=0.0):
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# see https://replicate.com/pricing
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# for all litellm currently supported LLMs, almost all requests go to a100_80gb
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a100_80gb_price_per_second_public = 0.001400 # assume all calls sent to A100 80GB for now
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a100_80gb_price_per_second_public = (
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0.001400 # assume all calls sent to A100 80GB for now
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)
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if total_time == 0.0: # total time is in ms
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start_time = completion_response["created"]
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end_time = getattr(completion_response, "ended", time.time())
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@ -377,13 +428,16 @@ def completion_cost(
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prompt_characters = 0
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completion_tokens = 0
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completion_characters = 0
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custom_llm_provider = None
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if completion_response is not None:
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# get input/output tokens from completion_response
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prompt_tokens = completion_response.get("usage", {}).get("prompt_tokens", 0)
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completion_tokens = completion_response.get("usage", {}).get("completion_tokens", 0)
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completion_tokens = completion_response.get("usage", {}).get(
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"completion_tokens", 0
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)
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total_time = completion_response.get("_response_ms", 0)
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verbose_logger.debug(f"completion_response response ms: {completion_response.get('_response_ms')} ")
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verbose_logger.debug(
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f"completion_response response ms: {completion_response.get('_response_ms')} "
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)
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model = model or completion_response.get(
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"model", None
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) # check if user passed an override for model, if it's none check completion_response['model']
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@ -393,16 +447,30 @@ def completion_cost(
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and len(completion_response._hidden_params["model"]) > 0
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):
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model = completion_response._hidden_params.get("model", model)
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custom_llm_provider = completion_response._hidden_params.get("custom_llm_provider", "")
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region_name = completion_response._hidden_params.get("region_name", region_name)
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size = completion_response._hidden_params.get("optional_params", {}).get(
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custom_llm_provider = completion_response._hidden_params.get(
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"custom_llm_provider", ""
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)
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region_name = completion_response._hidden_params.get(
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"region_name", region_name
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)
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size = completion_response._hidden_params.get(
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"optional_params", {}
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).get(
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"size", "1024-x-1024"
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) # openai default
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quality = completion_response._hidden_params.get("optional_params", {}).get(
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quality = completion_response._hidden_params.get(
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"optional_params", {}
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).get(
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"quality", "standard"
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) # openai default
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n = completion_response._hidden_params.get("optional_params", {}).get("n", 1) # openai default
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n = completion_response._hidden_params.get("optional_params", {}).get(
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"n", 1
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) # openai default
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else:
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if model is None:
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raise ValueError(
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f"Model is None and does not exist in passed completion_response. Passed completion_response={completion_response}, model={model}"
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)
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if len(messages) > 0:
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prompt_tokens = token_counter(model=model, messages=messages)
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elif len(prompt) > 0:
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@ -413,7 +481,19 @@ def completion_cost(
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f"Model is None and does not exist in passed completion_response. Passed completion_response={completion_response}, model={model}"
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)
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if call_type == CallTypes.image_generation.value or call_type == CallTypes.aimage_generation.value:
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if custom_llm_provider is None:
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try:
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_, custom_llm_provider, _, _ = litellm.get_llm_provider(model=model)
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except Exception as e:
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verbose_logger.error(
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"litellm.cost_calculator.py::completion_cost() - Error inferring custom_llm_provider - {}".format(
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str(e)
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)
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)
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if (
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call_type == CallTypes.image_generation.value
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or call_type == CallTypes.aimage_generation.value
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):
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### IMAGE GENERATION COST CALCULATION ###
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if custom_llm_provider == "vertex_ai":
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# https://cloud.google.com/vertex-ai/generative-ai/pricing
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@ -431,23 +511,43 @@ def completion_cost(
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height = int(size[0]) # if it's 1024-x-1024 vs. 1024x1024
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width = int(size[1])
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verbose_logger.debug(f"image_gen_model_name: {image_gen_model_name}")
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verbose_logger.debug(f"image_gen_model_name_with_quality: {image_gen_model_name_with_quality}")
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verbose_logger.debug(
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f"image_gen_model_name_with_quality: {image_gen_model_name_with_quality}"
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)
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if image_gen_model_name in litellm.model_cost:
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return litellm.model_cost[image_gen_model_name]["input_cost_per_pixel"] * height * width * n
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return (
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litellm.model_cost[image_gen_model_name]["input_cost_per_pixel"]
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* height
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* width
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* n
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)
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elif image_gen_model_name_with_quality in litellm.model_cost:
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return (
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litellm.model_cost[image_gen_model_name_with_quality]["input_cost_per_pixel"] * height * width * n
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litellm.model_cost[image_gen_model_name_with_quality][
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"input_cost_per_pixel"
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]
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* height
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* width
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* n
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)
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else:
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raise Exception(f"Model={image_gen_model_name} not found in completion cost model map")
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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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# Calculate cost based on prompt_tokens, completion_tokens
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if "togethercomputer" in model or "together_ai" in model or custom_llm_provider == "together_ai":
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if (
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"togethercomputer" in model
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or "together_ai" in model
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or custom_llm_provider == "together_ai"
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):
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# together ai prices based on size of llm
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# get_model_params_and_category takes a model name and returns the category of LLM size it is in model_prices_and_context_window.json
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model = get_model_params_and_category(model)
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# replicate llms are calculate based on time for request running
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# see https://replicate.com/pricing
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elif (model in litellm.replicate_models or "replicate" in model) and model not in litellm.model_cost:
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elif (
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model in litellm.replicate_models or "replicate" in model
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) and model not in litellm.model_cost:
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# for unmapped replicate model, default to replicate's time tracking logic
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return get_replicate_completion_pricing(completion_response, total_time)
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@ -456,23 +556,26 @@ def completion_cost(
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f"Model is None and does not exist in passed completion_response. Passed completion_response={completion_response}, model={model}"
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)
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if (
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custom_llm_provider is not None
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and custom_llm_provider == "vertex_ai"
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and completion_response is not None
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and isinstance(completion_response, ModelResponse)
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):
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if custom_llm_provider is not None and custom_llm_provider == "vertex_ai":
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# Calculate the prompt characters + response characters
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if len("messages") > 0:
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prompt_string = litellm.utils.get_formatted_prompt(data={"messages": messages}, call_type="completion")
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prompt_string = litellm.utils.get_formatted_prompt(
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data={"messages": messages}, call_type="completion"
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)
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else:
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prompt_string = ""
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prompt_characters = litellm.utils._count_characters(text=prompt_string)
|
||||
if completion_response is not None and isinstance(
|
||||
completion_response, ModelResponse
|
||||
):
|
||||
completion_string = litellm.utils.get_response_string(
|
||||
response_obj=completion_response
|
||||
)
|
||||
|
||||
completion_string = litellm.utils.get_response_string(response_obj=completion_response)
|
||||
|
||||
completion_characters = litellm.utils._count_characters(text=completion_string)
|
||||
completion_characters = litellm.utils._count_characters(
|
||||
text=completion_string
|
||||
)
|
||||
|
||||
(
|
||||
prompt_tokens_cost_usd_dollar,
|
||||
|
|
@ -544,7 +647,9 @@ def response_cost_calculator(
|
|||
)
|
||||
else:
|
||||
if (
|
||||
model in litellm.model_cost and custom_pricing is not None and custom_llm_provider is True
|
||||
model in litellm.model_cost
|
||||
and custom_pricing is not None
|
||||
and custom_llm_provider is True
|
||||
): # override defaults if custom pricing is set
|
||||
base_model = model
|
||||
# base_model defaults to None if not set on model_info
|
||||
|
|
@ -556,5 +661,7 @@ def response_cost_calculator(
|
|||
)
|
||||
return response_cost
|
||||
except litellm.NotFoundError as e:
|
||||
print_verbose(f"Model={model} for LLM Provider={custom_llm_provider} not found in completion cost map.")
|
||||
print_verbose(
|
||||
f"Model={model} for LLM Provider={custom_llm_provider} not found in completion cost map."
|
||||
)
|
||||
return None
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue