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Diffstat (limited to '.venv/lib/python3.12/site-packages/litellm/batch_completion/main.py')
| -rw-r--r-- | .venv/lib/python3.12/site-packages/litellm/batch_completion/main.py | 253 |
1 files changed, 253 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/litellm/batch_completion/main.py b/.venv/lib/python3.12/site-packages/litellm/batch_completion/main.py new file mode 100644 index 00000000..7100fb00 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/litellm/batch_completion/main.py @@ -0,0 +1,253 @@ +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 |