mirror of
https://github.com/BerriAI/litellm.git
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Merge pull request #4337 from spdustin/main
fix: use per-token costs for claude via vertex_ai
This commit is contained in:
Krish Dholakia
GitHub
commit
88c550e3b7
2 changed files with 120 additions and 197 deletions
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@ -101,12 +101,8 @@ 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 = (
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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_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 = 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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@ -122,9 +118,7 @@ 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 (
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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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if model_with_provider in model_cost_ref: # 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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@ -135,6 +129,13 @@ def cost_per_token(
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# see this https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models
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print_verbose(f"Looking up model={model} in model_cost_map")
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if custom_llm_provider == "vertex_ai" and "claude" in model:
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return google_cost_per_token(
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model=model_without_prefix,
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custom_llm_provider=custom_llm_provider,
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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)
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if custom_llm_provider == "vertex_ai":
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return google_cost_per_character(
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model=model_without_prefix,
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@ -153,45 +154,29 @@ 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(
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f"prompt_tokens={prompt_tokens}; completion_tokens={completion_tokens}"
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)
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print_verbose(f"prompt_tokens={prompt_tokens}; completion_tokens={completion_tokens}")
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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 = (
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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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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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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"]
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* response_time_ms
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/ 1000
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model_cost_ref[model]["output_cost_per_second"] * response_time_ms / 1000
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)
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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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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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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 = (
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model_cost_ref[model]["input_cost_per_second"] * response_time_ms / 1000
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)
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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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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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@ -200,57 +185,40 @@ 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 = (
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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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prompt_tokens_cost_usd_dollar = model_cost_ref["ft:gpt-3.5-turbo"]["input_cost_per_token"] * prompt_tokens
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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"]
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* completion_tokens
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model_cost_ref["ft:gpt-3.5-turbo"]["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-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 = (
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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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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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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 = (
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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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prompt_tokens_cost_usd_dollar = model_cost_ref["ft:gpt-4o-2024-05-13"]["input_cost_per_token"] * prompt_tokens
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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"]
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* completion_tokens
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model_cost_ref["ft:gpt-4o-2024-05-13"]["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: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 = (
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model_cost_ref["ft:davinci-002"]["input_cost_per_token"] * prompt_tokens
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)
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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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completion_tokens_cost_usd_dollar = (
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model_cost_ref["ft:davinci-002"]["output_cost_per_token"]
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* completion_tokens
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model_cost_ref["ft:davinci-002"]["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: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 = (
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model_cost_ref["ft:babbage-002"]["input_cost_per_token"] * prompt_tokens
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)
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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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completion_tokens_cost_usd_dollar = (
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model_cost_ref["ft:babbage-002"]["output_cost_per_token"]
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* completion_tokens
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model_cost_ref["ft:babbage-002"]["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_llms:
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@ -259,25 +227,17 @@ 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 = (
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model_cost_ref[model]["input_cost_per_token"] * 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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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 = (
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model_cost_ref[model]["output_cost_per_token"] * 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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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 = (
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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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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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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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@ -301,9 +261,7 @@ 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(
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r"(\d+b)", model_name
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) # catch all decimals like 3b, 70b, etc
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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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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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@ -334,9 +292,7 @@ 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 = (
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0.001400 # assume all calls sent to A100 80GB for now
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)
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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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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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@ -425,13 +381,9 @@ def completion_cost(
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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(
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"completion_tokens", 0
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)
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completion_tokens = completion_response.get("usage", {}).get("completion_tokens", 0)
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total_time = completion_response.get("_response_ms", 0)
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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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verbose_logger.debug(f"completion_response response ms: {completion_response.get('_response_ms')} ")
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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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@ -441,25 +393,15 @@ 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(
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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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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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"size", "1024-x-1024"
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) # openai default
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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 = completion_response._hidden_params.get("optional_params", {}).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(
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"n", 1
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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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else:
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if len(messages) > 0:
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prompt_tokens = token_counter(model=model, messages=messages)
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@ -471,10 +413,7 @@ 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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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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if call_type == CallTypes.image_generation.value or call_type == CallTypes.aimage_generation.value:
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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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@ -492,43 +431,23 @@ 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(
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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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verbose_logger.debug(f"image_gen_model_name_with_quality: {image_gen_model_name_with_quality}")
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if image_gen_model_name in litellm.model_cost:
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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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return litellm.model_cost[image_gen_model_name]["input_cost_per_pixel"] * height * width * n
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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][
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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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litellm.model_cost[image_gen_model_name_with_quality]["input_cost_per_pixel"] * height * width * n
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)
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else:
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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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raise Exception(f"Model={image_gen_model_name} not found in completion cost model map")
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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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or "together_ai" in model
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or custom_llm_provider == "together_ai"
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):
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if "togethercomputer" in model or "together_ai" in model or custom_llm_provider == "together_ai":
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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 (
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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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elif (model in litellm.replicate_models or "replicate" in model) 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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|
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@ -545,21 +464,15 @@ def completion_cost(
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):
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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(
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data={"messages": messages}, call_type="completion"
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)
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prompt_string = litellm.utils.get_formatted_prompt(data={"messages": messages}, call_type="completion")
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else:
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prompt_string = ""
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prompt_characters = litellm.utils._count_characters(text=prompt_string)
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completion_string = litellm.utils.get_response_string(
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response_obj=completion_response
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)
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completion_string = litellm.utils.get_response_string(response_obj=completion_response)
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completion_characters = litellm.utils._count_characters(
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text=completion_string
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)
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completion_characters = litellm.utils._count_characters(text=completion_string)
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(
|
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prompt_tokens_cost_usd_dollar,
|
||||
|
|
@ -631,9 +544,7 @@ 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
|
||||
|
|
@ -645,7 +556,5 @@ 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
|
||||
|
|
|
|||
|
|
@ -4,9 +4,7 @@ import traceback
|
|||
|
||||
import litellm.cost_calculator
|
||||
|
||||
sys.path.insert(
|
||||
0, os.path.abspath("../..")
|
||||
) # Adds the parent directory to the system path
|
||||
sys.path.insert(0, os.path.abspath("../..")) # Adds the parent directory to the system path
|
||||
import asyncio
|
||||
import time
|
||||
from typing import Optional
|
||||
|
|
@ -169,15 +167,11 @@ def test_cost_ft_gpt_35():
|
|||
input_cost = model_cost["ft:gpt-3.5-turbo"]["input_cost_per_token"]
|
||||
output_cost = model_cost["ft:gpt-3.5-turbo"]["output_cost_per_token"]
|
||||
print(input_cost, output_cost)
|
||||
expected_cost = (input_cost * resp.usage.prompt_tokens) + (
|
||||
output_cost * resp.usage.completion_tokens
|
||||
)
|
||||
expected_cost = (input_cost * resp.usage.prompt_tokens) + (output_cost * resp.usage.completion_tokens)
|
||||
print("\n Excpected cost", expected_cost)
|
||||
assert cost == expected_cost
|
||||
except Exception as e:
|
||||
pytest.fail(
|
||||
f"Cost Calc failed for ft:gpt-3.5. Expected {expected_cost}, Calculated cost {cost}"
|
||||
)
|
||||
pytest.fail(f"Cost Calc failed for ft:gpt-3.5. Expected {expected_cost}, Calculated cost {cost}")
|
||||
|
||||
|
||||
# test_cost_ft_gpt_35()
|
||||
|
|
@ -206,21 +200,15 @@ def test_cost_azure_gpt_35():
|
|||
usage=Usage(prompt_tokens=21, completion_tokens=17, total_tokens=38),
|
||||
)
|
||||
|
||||
cost = litellm.completion_cost(
|
||||
completion_response=resp, model="azure/gpt-35-turbo"
|
||||
)
|
||||
cost = litellm.completion_cost(completion_response=resp, model="azure/gpt-35-turbo")
|
||||
print("\n Calculated Cost for azure/gpt-3.5-turbo", cost)
|
||||
input_cost = model_cost["azure/gpt-35-turbo"]["input_cost_per_token"]
|
||||
output_cost = model_cost["azure/gpt-35-turbo"]["output_cost_per_token"]
|
||||
expected_cost = (input_cost * resp.usage.prompt_tokens) + (
|
||||
output_cost * resp.usage.completion_tokens
|
||||
)
|
||||
expected_cost = (input_cost * resp.usage.prompt_tokens) + (output_cost * resp.usage.completion_tokens)
|
||||
print("\n Excpected cost", expected_cost)
|
||||
assert cost == expected_cost
|
||||
except Exception as e:
|
||||
pytest.fail(
|
||||
f"Cost Calc failed for azure/gpt-3.5-turbo. Expected {expected_cost}, Calculated cost {cost}"
|
||||
)
|
||||
pytest.fail(f"Cost Calc failed for azure/gpt-3.5-turbo. Expected {expected_cost}, Calculated cost {cost}")
|
||||
|
||||
|
||||
# test_cost_azure_gpt_35()
|
||||
|
|
@ -251,9 +239,7 @@ def test_cost_azure_embedding():
|
|||
assert cost == expected_cost
|
||||
|
||||
except Exception as e:
|
||||
pytest.fail(
|
||||
f"Cost Calc failed for azure/gpt-3.5-turbo. Expected {expected_cost}, Calculated cost {cost}"
|
||||
)
|
||||
pytest.fail(f"Cost Calc failed for azure/gpt-3.5-turbo. Expected {expected_cost}, Calculated cost {cost}")
|
||||
|
||||
|
||||
# test_cost_azure_embedding()
|
||||
|
|
@ -329,9 +315,7 @@ def test_cost_bedrock_pricing_actual_calls():
|
|||
litellm.set_verbose = True
|
||||
model = "anthropic.claude-instant-v1"
|
||||
messages = [{"role": "user", "content": "Hey, how's it going?"}]
|
||||
response = litellm.completion(
|
||||
model=model, messages=messages, mock_response="hello cool one"
|
||||
)
|
||||
response = litellm.completion(model=model, messages=messages, mock_response="hello cool one")
|
||||
|
||||
print("response", response)
|
||||
cost = litellm.completion_cost(
|
||||
|
|
@ -361,8 +345,7 @@ def test_whisper_openai():
|
|||
print(f"cost: {cost}")
|
||||
print(f"whisper dict: {litellm.model_cost['whisper-1']}")
|
||||
expected_cost = round(
|
||||
litellm.model_cost["whisper-1"]["output_cost_per_second"]
|
||||
* _total_time_in_seconds,
|
||||
litellm.model_cost["whisper-1"]["output_cost_per_second"] * _total_time_in_seconds,
|
||||
5,
|
||||
)
|
||||
assert cost == expected_cost
|
||||
|
|
@ -382,15 +365,12 @@ def test_whisper_azure():
|
|||
_total_time_in_seconds = 3
|
||||
|
||||
transcription._response_ms = _total_time_in_seconds * 1000
|
||||
cost = litellm.completion_cost(
|
||||
model="azure/azure-whisper", completion_response=transcription
|
||||
)
|
||||
cost = litellm.completion_cost(model="azure/azure-whisper", completion_response=transcription)
|
||||
|
||||
print(f"cost: {cost}")
|
||||
print(f"whisper dict: {litellm.model_cost['whisper-1']}")
|
||||
expected_cost = round(
|
||||
litellm.model_cost["whisper-1"]["output_cost_per_second"]
|
||||
* _total_time_in_seconds,
|
||||
litellm.model_cost["whisper-1"]["output_cost_per_second"] * _total_time_in_seconds,
|
||||
5,
|
||||
)
|
||||
assert cost == expected_cost
|
||||
|
|
@ -421,9 +401,7 @@ def test_dalle_3_azure_cost_tracking():
|
|||
response.usage = {"prompt_tokens": 0, "completion_tokens": 0, "total_tokens": 0}
|
||||
response._hidden_params = {"model": "dall-e-3", "model_id": None}
|
||||
print(f"response hidden params: {response._hidden_params}")
|
||||
cost = litellm.completion_cost(
|
||||
completion_response=response, call_type="image_generation"
|
||||
)
|
||||
cost = litellm.completion_cost(completion_response=response, call_type="image_generation")
|
||||
assert cost > 0
|
||||
|
||||
|
||||
|
|
@ -455,9 +433,7 @@ def test_replicate_llama3_cost_tracking():
|
|||
model="replicate/meta/meta-llama-3-8b-instruct",
|
||||
object="chat.completion",
|
||||
system_fingerprint=None,
|
||||
usage=litellm.utils.Usage(
|
||||
prompt_tokens=48, completion_tokens=31, total_tokens=79
|
||||
),
|
||||
usage=litellm.utils.Usage(prompt_tokens=48, completion_tokens=31, total_tokens=79),
|
||||
)
|
||||
cost = litellm.completion_cost(
|
||||
completion_response=response,
|
||||
|
|
@ -467,14 +443,8 @@ def test_replicate_llama3_cost_tracking():
|
|||
print(f"cost: {cost}")
|
||||
cost = round(cost, 5)
|
||||
expected_cost = round(
|
||||
litellm.model_cost["replicate/meta/meta-llama-3-8b-instruct"][
|
||||
"input_cost_per_token"
|
||||
]
|
||||
* 48
|
||||
+ litellm.model_cost["replicate/meta/meta-llama-3-8b-instruct"][
|
||||
"output_cost_per_token"
|
||||
]
|
||||
* 31,
|
||||
litellm.model_cost["replicate/meta/meta-llama-3-8b-instruct"]["input_cost_per_token"] * 48
|
||||
+ litellm.model_cost["replicate/meta/meta-llama-3-8b-instruct"]["output_cost_per_token"] * 31,
|
||||
5,
|
||||
)
|
||||
assert cost == expected_cost
|
||||
|
|
@ -568,9 +538,7 @@ def test_together_ai_qwen_completion_cost():
|
|||
"custom_cost_per_second": None,
|
||||
}
|
||||
|
||||
response = litellm.cost_calculator.get_model_params_and_category(
|
||||
model_name="qwen/Qwen2-72B-Instruct"
|
||||
)
|
||||
response = litellm.cost_calculator.get_model_params_and_category(model_name="qwen/Qwen2-72B-Instruct")
|
||||
|
||||
assert response == "together-ai-41.1b-80b"
|
||||
|
||||
|
|
@ -608,12 +576,8 @@ def test_gemini_completion_cost(above_128k, provider):
|
|||
), "model info for model={} does not have pricing for > 128k tokens\nmodel_info={}".format(
|
||||
model_name, model_info
|
||||
)
|
||||
input_cost = (
|
||||
prompt_tokens * model_info["input_cost_per_token_above_128k_tokens"]
|
||||
)
|
||||
output_cost = (
|
||||
output_tokens * model_info["output_cost_per_token_above_128k_tokens"]
|
||||
)
|
||||
input_cost = prompt_tokens * model_info["input_cost_per_token_above_128k_tokens"]
|
||||
output_cost = output_tokens * model_info["output_cost_per_token_above_128k_tokens"]
|
||||
else:
|
||||
input_cost = prompt_tokens * model_info["input_cost_per_token"]
|
||||
output_cost = output_tokens * model_info["output_cost_per_token"]
|
||||
|
|
@ -660,3 +624,53 @@ def test_vertex_ai_completion_cost():
|
|||
assert round(expected_input_cost, 6) == round(calculated_input_cost, 6)
|
||||
print("expected_input_cost: {}".format(expected_input_cost))
|
||||
print("calculated_input_cost: {}".format(calculated_input_cost))
|
||||
|
||||
|
||||
def test_vertex_ai_claude_completion_cost():
|
||||
from litellm import Choices, Message, ModelResponse
|
||||
from litellm.utils import Usage
|
||||
|
||||
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
|
||||
litellm.model_cost = litellm.get_model_cost_map(url="")
|
||||
|
||||
litellm.set_verbose = True
|
||||
input_tokens = litellm.token_counter(
|
||||
model="vertex_ai/claude-3-sonnet@20240229",
|
||||
messages=[{"role": "user", "content": "Hey, how's it going?"}],
|
||||
)
|
||||
print(f"input_tokens: {input_tokens}")
|
||||
output_tokens = litellm.token_counter(
|
||||
model="vertex_ai/claude-3-sonnet@20240229",
|
||||
text="It's all going well",
|
||||
count_response_tokens=True,
|
||||
)
|
||||
print(f"output_tokens: {output_tokens}")
|
||||
response = ModelResponse(
|
||||
id="chatcmpl-e41836bb-bb8b-4df2-8e70-8f3e160155ac",
|
||||
choices=[
|
||||
Choices(
|
||||
finish_reason=None,
|
||||
index=0,
|
||||
message=Message(
|
||||
content="It's all going well",
|
||||
role="assistant",
|
||||
),
|
||||
)
|
||||
],
|
||||
created=1700775391,
|
||||
model="vertex_ai/claude-3-sonnet@20240229",
|
||||
object="chat.completion",
|
||||
system_fingerprint=None,
|
||||
usage=Usage(
|
||||
prompt_tokens=input_tokens,
|
||||
completion_tokens=output_tokens,
|
||||
total_tokens=input_tokens + output_tokens,
|
||||
),
|
||||
)
|
||||
cost = litellm.completion_cost(
|
||||
model="vertex_ai/claude-3-sonnet@20240229",
|
||||
completion_response=response,
|
||||
messages=[{"role": "user", "content": "Hey, how's it going?"}],
|
||||
)
|
||||
predicted_cost = input_tokens * 0.000003 + 0.000015 * output_tokens
|
||||
assert cost == predicted_cost
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue