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test(test_keys.py): use correct model name for token counting

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Krrish Dholakia 2024-01-23 17:46:14 -08:00
parent 4ca4913468
commit d6844f43c8
3 changed files with 24 additions and 11 deletions
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22
litellm/utils.py
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@ -2938,17 +2938,25 @@ def cost_per_token(
)
return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
elif model_with_provider in model_cost_ref:
print_verbose(f"Looking up model={model_with_provider} in model_cost_map")
verbose_logger.debug(
f"Looking up model={model_with_provider} in model_cost_map"
)
verbose_logger.debug(
f"applying cost={model_cost_ref[model_with_provider]['input_cost_per_token']} for prompt_tokens={prompt_tokens}"
)
prompt_tokens_cost_usd_dollar = (
model_cost_ref[model_with_provider]["input_cost_per_token"] * prompt_tokens
)
verbose_logger.debug(
f"applying cost={model_cost_ref[model_with_provider]['output_cost_per_token']} for completion_tokens={completion_tokens}"
)
completion_tokens_cost_usd_dollar = (
model_cost_ref[model_with_provider]["output_cost_per_token"]
* completion_tokens
)
return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
elif "ft:gpt-3.5-turbo" in model:
print_verbose(f"Cost Tracking: {model} is an OpenAI FinteTuned LLM")
verbose_logger.debug(f"Cost Tracking: {model} is an OpenAI FinteTuned LLM")
# fuzzy match ft:gpt-3.5-turbo:abcd-id-cool-litellm
prompt_tokens_cost_usd_dollar = (
model_cost_ref["ft:gpt-3.5-turbo"]["input_cost_per_token"] * prompt_tokens
@ -2959,17 +2967,23 @@ def cost_per_token(
)
return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
elif model in litellm.azure_llms:
print_verbose(f"Cost Tracking: {model} is an Azure LLM")
verbose_logger.debug(f"Cost Tracking: {model} is an Azure LLM")
model = litellm.azure_llms[model]
verbose_logger.debug(
f"applying cost={model_cost_ref[model]['input_cost_per_token']} for prompt_tokens={prompt_tokens}"
)
prompt_tokens_cost_usd_dollar = (
model_cost_ref[model]["input_cost_per_token"] * prompt_tokens
)
verbose_logger.debug(
f"applying cost={model_cost_ref[model]['output_cost_per_token']} for completion_tokens={completion_tokens}"
)
completion_tokens_cost_usd_dollar = (
model_cost_ref[model]["output_cost_per_token"] * completion_tokens
)
return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
elif model in litellm.azure_embedding_models:
print_verbose(f"Cost Tracking: {model} is an Azure Embedding Model")
verbose_logger.debug(f"Cost Tracking: {model} is an Azure Embedding Model")
model = litellm.azure_embedding_models[model]
prompt_tokens_cost_usd_dollar = (
model_cost_ref[model]["input_cost_per_token"] * prompt_tokens