fix(utils.py): fix the response object returned when n>1 for stream=true

Fixes https://github.com/BerriAI/litellm/issues/3276
This commit is contained in:
Krrish Dholakia 2024-04-25 13:27:29 -07:00
parent 5ad91e1277
commit 6c5c7cca3d
3 changed files with 310 additions and 55 deletions

View file

@ -1,51 +1,18 @@
environment_variables:
SLACK_WEBHOOK_URL: SQD2/FQHvDuj6Q9/Umyqi+EKLNKKLRCXETX2ncO0xCIQp6EHCKiYD7jPW0+1QdrsQ+pnEzhsfVY2r21SiQV901n/9iyJ2tSnEyWViP7FKQVtTvwutsAqSqbiVHxLHbpjPCu03fhS/idjZrtK7dJLbLBB3RgudjNjHg==
general_settings:
alerting:
- slack
alerting_threshold: 300
database_connection_pool_limit: 100
database_connection_timeout: 60
health_check_interval: 300
proxy_batch_write_at: 10
ui_access_mode: all
litellm_settings:
allowed_fails: 3
failure_callback:
- prometheus
fallbacks:
- gpt-3.5-turbo:
- fake-openai-endpoint
- gpt-4
num_retries: 3
service_callback:
- prometheus_system
success_callback:
- prometheus
model_list: model_list:
- litellm_params: - model_name: text-embedding-3-small
api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/ litellm_params:
api_key: my-fake-key model: text-embedding-3-small
model: openai/my-fake-model - model_name: whisper
model_name: fake-openai-endpoint
- litellm_params:
model: gpt-3.5-turbo
model_name: gpt-3.5-turbo
- model_name: llama-3
litellm_params: litellm_params:
model: replicate/meta/meta-llama-3-8b-instruct model: azure/azure-whisper
router_settings: api_version: 2024-02-15-preview
allowed_fails: 3 api_base: os.environ/AZURE_EUROPE_API_BASE
context_window_fallbacks: null api_key: os.environ/AZURE_EUROPE_API_KEY
cooldown_time: 1 model_info:
fallbacks: mode: audio_transcription
- gpt-3.5-turbo: - litellm_params:
- fake-openai-endpoint model: gpt-4
- gpt-4 model_name: gpt-4
- gpt-3.5-turbo-3:
- fake-openai-endpoint # litellm_settings:
num_retries: 3 # cache: True
retry_after: 0
routing_strategy: simple-shuffle
routing_strategy_args: {}
timeout: 6000

View file

@ -2446,6 +2446,34 @@ class ModelResponseIterator:
return self.model_response return self.model_response
class ModelResponseListIterator:
def __init__(self, model_responses):
self.model_responses = model_responses
self.index = 0
# Sync iterator
def __iter__(self):
return self
def __next__(self):
if self.index >= len(self.model_responses):
raise StopIteration
model_response = self.model_responses[self.index]
self.index += 1
return model_response
# Async iterator
def __aiter__(self):
return self
async def __anext__(self):
if self.index >= len(self.model_responses):
raise StopAsyncIteration
model_response = self.model_responses[self.index]
self.index += 1
return model_response
def test_unit_test_custom_stream_wrapper(): def test_unit_test_custom_stream_wrapper():
""" """
Test if last streaming chunk ends with '?', if the message repeats itself. Test if last streaming chunk ends with '?', if the message repeats itself.
@ -2486,3 +2514,259 @@ def test_unit_test_custom_stream_wrapper():
if "How are you?" in chunk.choices[0].delta.content: if "How are you?" in chunk.choices[0].delta.content:
freq += 1 freq += 1
assert freq == 1 assert freq == 1
chunks = [
{
"id": "chatcmpl-9HzZIMCtVq7CbTmdwEZrktiTeoiYe",
"object": "chat.completion.chunk",
"created": 1714075272,
"model": "gpt-4-0613",
"system_fingerprint": None,
"choices": [
{
"index": 0,
"delta": {"content": "It"},
"logprobs": {
"content": [
{
"token": "It",
"logprob": -1.5952516,
"bytes": [73, 116],
"top_logprobs": [
{
"token": "Brown",
"logprob": -0.7358765,
"bytes": [66, 114, 111, 119, 110],
}
],
}
]
},
"finish_reason": None,
}
],
},
{
"id": "chatcmpl-9HzZIMCtVq7CbTmdwEZrktiTeoiYe",
"object": "chat.completion.chunk",
"created": 1714075272,
"model": "gpt-4-0613",
"system_fingerprint": None,
"choices": [
{
"index": 1,
"delta": {"content": "Brown"},
"logprobs": {
"content": [
{
"token": "Brown",
"logprob": -0.7358765,
"bytes": [66, 114, 111, 119, 110],
"top_logprobs": [
{
"token": "Brown",
"logprob": -0.7358765,
"bytes": [66, 114, 111, 119, 110],
}
],
}
]
},
"finish_reason": None,
}
],
},
{
"id": "chatcmpl-9HzZIMCtVq7CbTmdwEZrktiTeoiYe",
"object": "chat.completion.chunk",
"created": 1714075272,
"model": "gpt-4-0613",
"system_fingerprint": None,
"choices": [
{
"index": 0,
"delta": {"content": "'s"},
"logprobs": {
"content": [
{
"token": "'s",
"logprob": -0.006786893,
"bytes": [39, 115],
"top_logprobs": [
{
"token": "'s",
"logprob": -0.006786893,
"bytes": [39, 115],
}
],
}
]
},
"finish_reason": None,
}
],
},
{
"id": "chatcmpl-9HzZIMCtVq7CbTmdwEZrktiTeoiYe",
"object": "chat.completion.chunk",
"created": 1714075272,
"model": "gpt-4-0613",
"system_fingerprint": None,
"choices": [
{
"index": 0,
"delta": {"content": " impossible"},
"logprobs": {
"content": [
{
"token": " impossible",
"logprob": -0.06528423,
"bytes": [
32,
105,
109,
112,
111,
115,
115,
105,
98,
108,
101,
],
"top_logprobs": [
{
"token": " impossible",
"logprob": -0.06528423,
"bytes": [
32,
105,
109,
112,
111,
115,
115,
105,
98,
108,
101,
],
}
],
}
]
},
"finish_reason": None,
}
],
},
{
"id": "chatcmpl-9HzZIMCtVq7CbTmdwEZrktiTeoiYe",
"object": "chat.completion.chunk",
"created": 1714075272,
"model": "gpt-4-0613",
"system_fingerprint": None,
"choices": [
{
"index": 0,
"delta": {"content": "—even"},
"logprobs": {
"content": [
{
"token": "—even",
"logprob": -9999.0,
"bytes": [226, 128, 148, 101, 118, 101, 110],
"top_logprobs": [
{
"token": " to",
"logprob": -0.12302828,
"bytes": [32, 116, 111],
}
],
}
]
},
"finish_reason": None,
}
],
},
{
"id": "chatcmpl-9HzZIMCtVq7CbTmdwEZrktiTeoiYe",
"object": "chat.completion.chunk",
"created": 1714075272,
"model": "gpt-4-0613",
"system_fingerprint": None,
"choices": [
{"index": 0, "delta": {}, "logprobs": None, "finish_reason": "length"}
],
},
{
"id": "chatcmpl-9HzZIMCtVq7CbTmdwEZrktiTeoiYe",
"object": "chat.completion.chunk",
"created": 1714075272,
"model": "gpt-4-0613",
"system_fingerprint": None,
"choices": [
{"index": 1, "delta": {}, "logprobs": None, "finish_reason": "stop"}
],
},
]
def test_unit_test_custom_stream_wrapper_n():
"""
Test if the translated output maps exactly to the received openai input
Relevant issue: https://github.com/BerriAI/litellm/issues/3276
"""
litellm.set_verbose = False
chunk_list = []
for chunk in chunks:
_chunk = litellm.ModelResponse(**chunk, stream=True)
chunk_list.append(_chunk)
completion_stream = ModelResponseListIterator(model_responses=chunk_list)
response = litellm.CustomStreamWrapper(
completion_stream=completion_stream,
model="gpt-4-0613",
custom_llm_provider="cached_response",
logging_obj=litellm.Logging(
model="gpt-4-0613",
messages=[{"role": "user", "content": "Hey"}],
stream=True,
call_type="completion",
start_time=time.time(),
litellm_call_id="12345",
function_id="1245",
),
)
for idx, chunk in enumerate(response):
chunk_dict = {}
try:
chunk_dict = chunk.model_dump(exclude_none=True)
except:
chunk_dict = chunk.dict(exclude_none=True)
chunk_dict.pop("created")
chunks[idx].pop("created")
if chunks[idx]["system_fingerprint"] is None:
chunks[idx].pop("system_fingerprint", None)
if idx == 0:
for choice in chunk_dict["choices"]:
if "role" in choice["delta"]:
choice["delta"].pop("role")
for choice in chunks[idx]["choices"]:
# ignore finish reason None - since our pydantic object is set to exclude_none = true
if "finish_reason" in choice and choice["finish_reason"] is None:
choice.pop("finish_reason")
if "logprobs" in choice and choice["logprobs"] is None:
choice.pop("logprobs")
assert (
chunk_dict == chunks[idx]
), f"idx={idx} translated chunk = {chunk_dict} != openai chunk = {chunks[idx]}"

View file

@ -19,6 +19,7 @@ from functools import wraps
import datetime, time import datetime, time
import tiktoken import tiktoken
import uuid import uuid
from pydantic import BaseModel
import aiohttp import aiohttp
import textwrap import textwrap
import logging import logging
@ -10120,12 +10121,15 @@ class CustomStreamWrapper:
model_response.id = original_chunk.id model_response.id = original_chunk.id
self.response_id = original_chunk.id self.response_id = original_chunk.id
if len(original_chunk.choices) > 0: if len(original_chunk.choices) > 0:
try: choices = []
delta = dict(original_chunk.choices[0].delta) for idx, choice in enumerate(original_chunk.choices):
print_verbose(f"original delta: {delta}") try:
model_response.choices[0].delta = Delta(**delta) if isinstance(choice, BaseModel):
except Exception as e: choice_json = choice.model_dump()
model_response.choices[0].delta = Delta() choices.append(StreamingChoices(**choice_json))
except Exception as e:
choices.append(StreamingChoices())
model_response.choices = choices
else: else:
return return
model_response.system_fingerprint = ( model_response.system_fingerprint = (