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import os
import sys
import traceback
import uuid
import pytest
from dotenv import load_dotenv
from fastapi import Request
from fastapi.routing import APIRoute
load_dotenv()
import io
import os
import time
import json
# this file is to test litellm/proxy
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system path
import litellm
import asyncio
from typing import Optional
from litellm.types.utils import StandardLoggingPayload, Usage, ModelInfoBase
from litellm.integrations.custom_logger import CustomLogger
class TestCustomLogger(CustomLogger):
def __init__(self):
self.recorded_usage: Optional[Usage] = None
self.standard_logging_payload: Optional[StandardLoggingPayload] = None
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
standard_logging_payload = kwargs.get("standard_logging_object")
self.standard_logging_payload = standard_logging_payload
print(
"standard_logging_payload",
json.dumps(standard_logging_payload, indent=4, default=str),
)
self.recorded_usage = Usage(
prompt_tokens=standard_logging_payload.get("prompt_tokens"),
completion_tokens=standard_logging_payload.get("completion_tokens"),
total_tokens=standard_logging_payload.get("total_tokens"),
)
pass
@pytest.mark.asyncio
async def test_stream_token_counting_gpt_4o():
"""
When stream_options={"include_usage": True} logging callback tracks Usage == Usage from llm API
"""
custom_logger = TestCustomLogger()
litellm.logging_callback_manager.add_litellm_callback(custom_logger)
response = await litellm.acompletion(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello, how are you?" * 100}],
stream=True,
stream_options={"include_usage": True},
)
actual_usage = None
async for chunk in response:
if "usage" in chunk:
actual_usage = chunk["usage"]
print("chunk.usage", json.dumps(chunk["usage"], indent=4, default=str))
pass
await asyncio.sleep(2)
print("\n\n\n\n\n")
print(
"recorded_usage",
json.dumps(custom_logger.recorded_usage, indent=4, default=str),
)
print("\n\n\n\n\n")
assert actual_usage.prompt_tokens == custom_logger.recorded_usage.prompt_tokens
assert (
actual_usage.completion_tokens == custom_logger.recorded_usage.completion_tokens
)
assert actual_usage.total_tokens == custom_logger.recorded_usage.total_tokens
@pytest.mark.asyncio
async def test_stream_token_counting_without_include_usage():
"""
When stream_options={"include_usage": True} is not passed, the usage tracked == usage from llm api chunk
by default, litellm passes `include_usage=True` for OpenAI API
"""
custom_logger = TestCustomLogger()
litellm.logging_callback_manager.add_litellm_callback(custom_logger)
response = await litellm.acompletion(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello, how are you?" * 100}],
stream=True,
)
actual_usage = None
async for chunk in response:
if "usage" in chunk:
actual_usage = chunk["usage"]
print("chunk.usage", json.dumps(chunk["usage"], indent=4, default=str))
pass
await asyncio.sleep(2)
print("\n\n\n\n\n")
print(
"recorded_usage",
json.dumps(custom_logger.recorded_usage, indent=4, default=str),
)
print("\n\n\n\n\n")
assert actual_usage.prompt_tokens == custom_logger.recorded_usage.prompt_tokens
assert (
actual_usage.completion_tokens == custom_logger.recorded_usage.completion_tokens
)
assert actual_usage.total_tokens == custom_logger.recorded_usage.total_tokens
@pytest.mark.asyncio
async def test_stream_token_counting_with_redaction():
"""
When litellm.turn_off_message_logging=True is used, the usage tracked == usage from llm api chunk
"""
litellm.turn_off_message_logging = True
custom_logger = TestCustomLogger()
litellm.logging_callback_manager.add_litellm_callback(custom_logger)
response = await litellm.acompletion(
model="gpt-4o",
messages=[{"role": "user", "content": "Hello, how are you?" * 100}],
stream=True,
)
actual_usage = None
async for chunk in response:
if "usage" in chunk:
actual_usage = chunk["usage"]
print("chunk.usage", json.dumps(chunk["usage"], indent=4, default=str))
pass
await asyncio.sleep(2)
print("\n\n\n\n\n")
print(
"recorded_usage",
json.dumps(custom_logger.recorded_usage, indent=4, default=str),
)
print("\n\n\n\n\n")
assert actual_usage.prompt_tokens == custom_logger.recorded_usage.prompt_tokens
assert (
actual_usage.completion_tokens == custom_logger.recorded_usage.completion_tokens
)
assert actual_usage.total_tokens == custom_logger.recorded_usage.total_tokens
@pytest.mark.asyncio
async def test_stream_token_counting_anthropic_with_include_usage():
""" """
from anthropic import Anthropic
anthropic_client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
litellm._turn_on_debug()
custom_logger = TestCustomLogger()
litellm.logging_callback_manager.add_litellm_callback(custom_logger)
input_text = "Respond in just 1 word. Say ping"
response = await litellm.acompletion(
model="claude-3-5-sonnet-20240620",
messages=[{"role": "user", "content": input_text}],
max_tokens=4096,
stream=True,
)
actual_usage = None
output_text = ""
async for chunk in response:
output_text += chunk["choices"][0]["delta"]["content"] or ""
pass
await asyncio.sleep(1)
print("\n\n\n\n\n")
print(
"recorded_usage",
json.dumps(custom_logger.recorded_usage, indent=4, default=str),
)
print("\n\n\n\n\n")
# print making the same request with anthropic client
anthropic_response = anthropic_client.messages.create(
model="claude-3-5-sonnet-20240620",
max_tokens=4096,
messages=[{"role": "user", "content": input_text}],
stream=True,
)
usage = None
all_anthropic_usage_chunks = []
for chunk in anthropic_response:
print("chunk", json.dumps(chunk, indent=4, default=str))
if hasattr(chunk, "message"):
if chunk.message.usage:
print(
"USAGE BLOCK",
json.dumps(chunk.message.usage, indent=4, default=str),
)
all_anthropic_usage_chunks.append(chunk.message.usage)
elif hasattr(chunk, "usage"):
print("USAGE BLOCK", json.dumps(chunk.usage, indent=4, default=str))
all_anthropic_usage_chunks.append(chunk.usage)
print(
"all_anthropic_usage_chunks",
json.dumps(all_anthropic_usage_chunks, indent=4, default=str),
)
input_tokens_anthropic_api = sum(
[getattr(usage, "input_tokens", 0) or 0 for usage in all_anthropic_usage_chunks]
)
output_tokens_anthropic_api = sum(
[getattr(usage, "output_tokens", 0) or 0 for usage in all_anthropic_usage_chunks]
)
print("input_tokens_anthropic_api", input_tokens_anthropic_api)
print("output_tokens_anthropic_api", output_tokens_anthropic_api)
print("input_tokens_litellm", custom_logger.recorded_usage.prompt_tokens)
print("output_tokens_litellm", custom_logger.recorded_usage.completion_tokens)
## Assert Accuracy of token counting
# input tokens should be exactly the same
assert input_tokens_anthropic_api == custom_logger.recorded_usage.prompt_tokens
# output tokens can have at max abs diff of 10. We can't guarantee the response from two api calls will be exactly the same
assert (
abs(
output_tokens_anthropic_api - custom_logger.recorded_usage.completion_tokens
)
<= 10
)
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