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import os |
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import json |
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from enum import Enum |
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import requests |
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import time, httpx |
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from typing import Callable, Any |
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from litellm.utils import ModelResponse, Usage |
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from .prompt_templates.factory import prompt_factory, custom_prompt |
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llm = None |
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class VLLMError(Exception): |
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def __init__(self, status_code, message): |
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self.status_code = status_code |
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self.message = message |
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self.request = httpx.Request(method="POST", url="http://0.0.0.0:8000") |
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self.response = httpx.Response(status_code=status_code, request=self.request) |
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super().__init__( |
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self.message |
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) |
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def validate_environment(model: str): |
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global llm |
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try: |
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from vllm import LLM, SamplingParams |
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if llm is None: |
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llm = LLM(model=model) |
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return llm, SamplingParams |
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except Exception as e: |
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raise VLLMError(status_code=0, message=str(e)) |
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def completion( |
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model: str, |
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messages: list, |
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model_response: ModelResponse, |
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print_verbose: Callable, |
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encoding, |
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logging_obj, |
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custom_prompt_dict={}, |
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optional_params=None, |
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litellm_params=None, |
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logger_fn=None, |
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): |
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global llm |
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try: |
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llm, SamplingParams = validate_environment(model=model) |
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except Exception as e: |
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raise VLLMError(status_code=0, message=str(e)) |
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sampling_params = SamplingParams(**optional_params) |
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if model in custom_prompt_dict: |
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model_prompt_details = custom_prompt_dict[model] |
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prompt = custom_prompt( |
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role_dict=model_prompt_details["roles"], |
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initial_prompt_value=model_prompt_details["initial_prompt_value"], |
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final_prompt_value=model_prompt_details["final_prompt_value"], |
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messages=messages, |
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) |
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else: |
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prompt = prompt_factory(model=model, messages=messages) |
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logging_obj.pre_call( |
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input=prompt, |
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api_key="", |
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additional_args={"complete_input_dict": sampling_params}, |
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) |
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if llm: |
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outputs = llm.generate(prompt, sampling_params) |
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else: |
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raise VLLMError( |
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status_code=0, message="Need to pass in a model name to initialize vllm" |
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) |
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if "stream" in optional_params and optional_params["stream"] == True: |
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return iter(outputs) |
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else: |
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logging_obj.post_call( |
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input=prompt, |
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api_key="", |
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original_response=outputs, |
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additional_args={"complete_input_dict": sampling_params}, |
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) |
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print_verbose(f"raw model_response: {outputs}") |
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model_response["choices"][0]["message"]["content"] = outputs[0].outputs[0].text |
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prompt_tokens = len(outputs[0].prompt_token_ids) |
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completion_tokens = len(outputs[0].outputs[0].token_ids) |
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model_response["created"] = int(time.time()) |
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model_response["model"] = model |
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usage = Usage( |
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prompt_tokens=prompt_tokens, |
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completion_tokens=completion_tokens, |
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total_tokens=prompt_tokens + completion_tokens, |
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) |
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model_response.usage = usage |
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return model_response |
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def batch_completions( |
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model: str, messages: list, optional_params=None, custom_prompt_dict={} |
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): |
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""" |
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Example usage: |
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import litellm |
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import os |
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from litellm import batch_completion |
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responses = batch_completion( |
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model="vllm/facebook/opt-125m", |
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messages = [ |
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[ |
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{ |
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"role": "user", |
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"content": "good morning? " |
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} |
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], |
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[ |
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{ |
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"role": "user", |
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"content": "what's the time? " |
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} |
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] |
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] |
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) |
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""" |
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try: |
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llm, SamplingParams = validate_environment(model=model) |
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except Exception as e: |
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error_str = str(e) |
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if "data parallel group is already initialized" in error_str: |
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pass |
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else: |
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raise VLLMError(status_code=0, message=error_str) |
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sampling_params = SamplingParams(**optional_params) |
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prompts = [] |
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if model in custom_prompt_dict: |
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model_prompt_details = custom_prompt_dict[model] |
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for message in messages: |
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prompt = custom_prompt( |
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role_dict=model_prompt_details["roles"], |
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initial_prompt_value=model_prompt_details["initial_prompt_value"], |
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final_prompt_value=model_prompt_details["final_prompt_value"], |
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messages=message, |
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) |
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prompts.append(prompt) |
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else: |
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for message in messages: |
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prompt = prompt_factory(model=model, messages=message) |
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prompts.append(prompt) |
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if llm: |
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outputs = llm.generate(prompts, sampling_params) |
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else: |
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raise VLLMError( |
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status_code=0, message="Need to pass in a model name to initialize vllm" |
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) |
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final_outputs = [] |
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for output in outputs: |
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model_response = ModelResponse() |
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model_response["choices"][0]["message"]["content"] = output.outputs[0].text |
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prompt_tokens = len(output.prompt_token_ids) |
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completion_tokens = len(output.outputs[0].token_ids) |
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model_response["created"] = int(time.time()) |
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model_response["model"] = model |
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usage = Usage( |
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prompt_tokens=prompt_tokens, |
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completion_tokens=completion_tokens, |
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total_tokens=prompt_tokens + completion_tokens, |
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) |
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model_response.usage = usage |
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final_outputs.append(model_response) |
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return final_outputs |
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def embedding(): |
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pass |
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