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add helper functions for token usage calculation

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This commit is contained in:
Krrish Dholakia 2023-08-08 20:47:02 -07:00
parent ef99c616af
commit 39efc57d84
4 changed files with 59 additions and 18 deletions
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55
litellm/utils.py
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@ -131,6 +131,46 @@ def client(original_function):
raise e
return wrapper
####### USAGE CALCULATOR ################
def prompt_token_calculator(model, messages):
# use tiktoken or anthropic's tokenizer depending on the model
text = " ".join(message["content"] for message in messages)
num_tokens = 0
if "claude" in model:
install_and_import('anthropic')
from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
anthropic = Anthropic()
num_tokens = anthropic.count_tokens(text)
else:
num_tokens = len(encoding.encode(text))
return num_tokens
def cost_per_token(model="gpt-3.5-turbo", prompt_tokens = 0, completion_tokens = 0):
## given
prompt_tokens_cost_usd_dollar = 0
completion_tokens_cost_usd_dollar = 0
model_cost_ref = litellm.model_cost
if model in model_cost_ref:
prompt_tokens_cost_usd_dollar = model_cost_ref[model]["input_cost_per_token"] * prompt_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
else:
# calculate average input cost
input_cost_sum = 0
output_cost_sum = 0
model_cost_ref = litellm.model_cost
for model in model_cost_ref:
input_cost_sum += model_cost_ref[model]["input_cost_per_token"]
output_cost_sum += model_cost_ref[model]["output_cost_per_token"]
avg_input_cost = input_cost_sum / len(model_cost_ref.keys())
avg_output_cost = output_cost_sum / len(model_cost_ref.keys())
prompt_tokens_cost_usd_dollar = avg_input_cost * prompt_tokens
completion_tokens_cost_usd_dollar = avg_output_cost * completion_tokens
return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
####### HELPER FUNCTIONS ################
def get_optional_params(
# 12 optional params
@ -367,21 +407,6 @@ def handle_failure(exception, traceback_exception, start_time, end_time, args, k
logging(logger_fn=user_logger_fn, exception=e)
pass
def prompt_token_calculator(model, messages):
# use tiktoken or anthropic's tokenizer depending on the model
text = " ".join(message["content"] for message in messages)
num_tokens = 0
if "claude" in model:
install_and_import('anthropic')
from anthropic import Anthropic, HUMAN_PROMPT, AI_PROMPT
anthropic = Anthropic()
num_tokens = anthropic.count_tokens(text)
else:
num_tokens = len(encoding.encode(text))
return num_tokens
def handle_success(args, kwargs, result, start_time, end_time):
global heliconeLogger, aispendLogger
try: