from concurrent.futures import FIRST_COMPLETED, ThreadPoolExecutor, wait
from typing import List, Optional
import litellm
from litellm._logging import print_verbose
from litellm.utils import get_optional_params
from ..llms.vllm.completion import handler as vllm_handler
def batch_completion(
model: str,
# Optional OpenAI params: see https://platform.openai.com/docs/api-reference/chat/create
messages: List = [],
functions: Optional[List] = None,
function_call: Optional[str] = None,
temperature: Optional[float] = None,
top_p: Optional[float] = None,
n: Optional[int] = None,
stream: Optional[bool] = None,
stop=None,
max_tokens: Optional[int] = None,
presence_penalty: Optional[float] = None,
frequency_penalty: Optional[float] = None,
logit_bias: Optional[dict] = None,
user: Optional[str] = None,
deployment_id=None,
request_timeout: Optional[int] = None,
timeout: Optional[int] = 600,
max_workers: Optional[int] = 100,
# Optional liteLLM function params
**kwargs,
):
"""
Batch litellm.completion function for a given model.
Args:
model (str): The model to use for generating completions.
messages (List, optional): List of messages to use as input for generating completions. Defaults to [].
functions (List, optional): List of functions to use as input for generating completions. Defaults to [].
function_call (str, optional): The function call to use as input for generating completions. Defaults to "".
temperature (float, optional): The temperature parameter for generating completions. Defaults to None.
top_p (float, optional): The top-p parameter for generating completions. Defaults to None.
n (int, optional): The number of completions to generate. Defaults to None.
stream (bool, optional): Whether to stream completions or not. Defaults to None.
stop (optional): The stop parameter for generating completions. Defaults to None.
max_tokens (float, optional): The maximum number of tokens to generate. Defaults to None.
presence_penalty (float, optional): The presence penalty for generating completions. Defaults to None.
frequency_penalty (float, optional): The frequency penalty for generating completions. Defaults to None.
logit_bias (dict, optional): The logit bias for generating completions. Defaults to {}.
user (str, optional): The user string for generating completions. Defaults to "".
deployment_id (optional): The deployment ID for generating completions. Defaults to None.
request_timeout (int, optional): The request timeout for generating completions. Defaults to None.
max_workers (int,optional): The maximum number of threads to use for parallel processing.
Returns:
list: A list of completion results.
"""
args = locals()
batch_messages = messages
completions = []
model = model
custom_llm_provider = None
if model.split("/", 1)[0] in litellm.provider_list:
custom_llm_provider = model.split("/", 1)[0]
model = model.split("/", 1)[1]
if custom_llm_provider == "vllm":
optional_params = get_optional_params(
functions=functions,
function_call=function_call,
temperature=temperature,
top_p=top_p,
n=n,
stream=stream or False,
stop=stop,
max_tokens=max_tokens,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
logit_bias=logit_bias,
user=user,
# params to identify the model
model=model,
custom_llm_provider=custom_llm_provider,
)
results = vllm_handler.batch_completions(
model=model,
messages=batch_messages,
custom_prompt_dict=litellm.custom_prompt_dict,
optional_params=optional_params,
)
# all non VLLM models for batch completion models
else:
def chunks(lst, n):
"""Yield successive n-sized chunks from lst."""
for i in range(0, len(lst), n):
yield lst[i : i + n]
with ThreadPoolExecutor(max_workers=max_workers) as executor:
for sub_batch in chunks(batch_messages, 100):
for message_list in sub_batch:
kwargs_modified = args.copy()
kwargs_modified.pop("max_workers")
kwargs_modified["messages"] = message_list
original_kwargs = {}
if "kwargs" in kwargs_modified:
original_kwargs = kwargs_modified.pop("kwargs")
future = executor.submit(
litellm.completion, **kwargs_modified, **original_kwargs
)
completions.append(future)
# Retrieve the results from the futures
# results = [future.result() for future in completions]
# return exceptions if any
results = []
for future in completions:
try:
results.append(future.result())
except Exception as exc:
results.append(exc)
return results
# send one request to multiple models
# return as soon as one of the llms responds
def batch_completion_models(*args, **kwargs):
"""
Send a request to multiple language models concurrently and return the response
as soon as one of the models responds.
Args:
*args: Variable-length positional arguments passed to the completion function.
**kwargs: Additional keyword arguments:
- models (str or list of str): The language models to send requests to.
- Other keyword arguments to be passed to the completion function.
Returns:
str or None: The response from one of the language models, or None if no response is received.
Note:
This function utilizes a ThreadPoolExecutor to parallelize requests to multiple models.
It sends requests concurrently and returns the response from the first model that responds.
"""
if "model" in kwargs:
kwargs.pop("model")
if "models" in kwargs:
models = kwargs["models"]
kwargs.pop("models")
futures = {}
with ThreadPoolExecutor(max_workers=len(models)) as executor:
for model in models:
futures[model] = executor.submit(
litellm.completion, *args, model=model, **kwargs
)
for model, future in sorted(
futures.items(), key=lambda x: models.index(x[0])
):
if future.result() is not None:
return future.result()
elif "deployments" in kwargs:
deployments = kwargs["deployments"]
kwargs.pop("deployments")
kwargs.pop("model_list")
nested_kwargs = kwargs.pop("kwargs", {})
futures = {}
with ThreadPoolExecutor(max_workers=len(deployments)) as executor:
for deployment in deployments:
for key in kwargs.keys():
if (
key not in deployment
): # don't override deployment values e.g. model name, api base, etc.
deployment[key] = kwargs[key]
kwargs = {**deployment, **nested_kwargs}
futures[deployment["model"]] = executor.submit(
litellm.completion, **kwargs
)
while futures:
# wait for the first returned future
print_verbose("\n\n waiting for next result\n\n")
done, _ = wait(futures.values(), return_when=FIRST_COMPLETED)
print_verbose(f"done list\n{done}")
for future in done:
try:
result = future.result()
return result
except Exception:
# if model 1 fails, continue with response from model 2, model3
print_verbose(
"\n\ngot an exception, ignoring, removing from futures"
)
print_verbose(futures)
new_futures = {}
for key, value in futures.items():
if future == value:
print_verbose(f"removing key{key}")
continue
else:
new_futures[key] = value
futures = new_futures
print_verbose(f"new futures{futures}")
continue
print_verbose("\n\ndone looping through futures\n\n")
print_verbose(futures)
return None # If no response is received from any model
def batch_completion_models_all_responses(*args, **kwargs):
"""
Send a request to multiple language models concurrently and return a list of responses
from all models that respond.
Args:
*args: Variable-length positional arguments passed to the completion function.
**kwargs: Additional keyword arguments:
- models (str or list of str): The language models to send requests to.
- Other keyword arguments to be passed to the completion function.
Returns:
list: A list of responses from the language models that responded.
Note:
This function utilizes a ThreadPoolExecutor to parallelize requests to multiple models.
It sends requests concurrently and collects responses from all models that respond.
"""
import concurrent.futures
# ANSI escape codes for colored output
if "model" in kwargs:
kwargs.pop("model")
if "models" in kwargs:
models = kwargs["models"]
kwargs.pop("models")
else:
raise Exception("'models' param not in kwargs")
responses = []
with concurrent.futures.ThreadPoolExecutor(max_workers=len(models)) as executor:
for idx, model in enumerate(models):
future = executor.submit(litellm.completion, *args, model=model, **kwargs)
if future.result() is not None:
responses.append(future.result())
return responses
