mirror of
https://github.com/BerriAI/litellm.git
synced 2026-09-05 08:07:05 +00:00
6a0d03914c
* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
248 lines
8.1 KiB
Python
248 lines
8.1 KiB
Python
import os
|
|
import traceback
|
|
from litellm._uuid import uuid
|
|
import pytest
|
|
from dotenv import load_dotenv
|
|
from fastapi import Request
|
|
from fastapi.routing import APIRoute
|
|
|
|
load_dotenv()
|
|
import io
|
|
import time
|
|
import json
|
|
|
|
# this file is to test litellm/proxy
|
|
|
|
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-5.5",
|
|
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-5.5",
|
|
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-5.5",
|
|
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-sonnet-4-5-20250929",
|
|
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-sonnet-4-5-20250929",
|
|
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),
|
|
)
|
|
|
|
# Get the most recent value of input tokens (iterate backwards to find last non-zero value)
|
|
anthropic_api_input_tokens = 0
|
|
for usage in reversed(all_anthropic_usage_chunks):
|
|
if getattr(usage, "input_tokens", 0) > 0:
|
|
anthropic_api_input_tokens = getattr(usage, "input_tokens", 0)
|
|
break
|
|
anthropic_api_output_tokens = 0
|
|
for usage in reversed(all_anthropic_usage_chunks):
|
|
if getattr(usage, "output_tokens", 0) > 0:
|
|
anthropic_api_output_tokens = getattr(usage, "output_tokens", 0)
|
|
break
|
|
print("input_tokens_anthropic_api", anthropic_api_input_tokens)
|
|
print("output_tokens_anthropic_api", anthropic_api_output_tokens)
|
|
|
|
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 anthropic_api_input_tokens == 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(
|
|
anthropic_api_output_tokens - custom_logger.recorded_usage.completion_tokens
|
|
)
|
|
<= 10
|
|
)
|