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import time # type: ignore
from typing import Callable
import httpx
from litellm.litellm_core_utils.prompt_templates.factory import (
custom_prompt,
prompt_factory,
)
from litellm.utils import ModelResponse, Usage
llm = None
class VLLMError(Exception):
def __init__(self, status_code, message):
self.status_code = status_code
self.message = message
self.request = httpx.Request(method="POST", url="http://0.0.0.0:8000")
self.response = httpx.Response(status_code=status_code, request=self.request)
super().__init__(
self.message
) # Call the base class constructor with the parameters it needs
# check if vllm is installed
def validate_environment(model: str):
global llm
try:
from vllm import LLM, SamplingParams # type: ignore
if llm is None:
llm = LLM(model=model)
return llm, SamplingParams
except Exception as e:
raise VLLMError(status_code=0, message=str(e))
def completion(
model: str,
messages: list,
model_response: ModelResponse,
print_verbose: Callable,
encoding,
logging_obj,
optional_params: dict,
custom_prompt_dict={},
litellm_params=None,
logger_fn=None,
):
global llm
try:
llm, SamplingParams = validate_environment(model=model)
except Exception as e:
raise VLLMError(status_code=0, message=str(e))
sampling_params = SamplingParams(**optional_params)
if model in custom_prompt_dict:
# check if the model has a registered custom prompt
model_prompt_details = custom_prompt_dict[model]
prompt = custom_prompt(
role_dict=model_prompt_details["roles"],
initial_prompt_value=model_prompt_details["initial_prompt_value"],
final_prompt_value=model_prompt_details["final_prompt_value"],
messages=messages,
)
else:
prompt = prompt_factory(model=model, messages=messages)
## LOGGING
logging_obj.pre_call(
input=prompt,
api_key="",
additional_args={"complete_input_dict": sampling_params},
)
if llm:
outputs = llm.generate(prompt, sampling_params)
else:
raise VLLMError(
status_code=0, message="Need to pass in a model name to initialize vllm"
)
## COMPLETION CALL
if "stream" in optional_params and optional_params["stream"] is True:
return iter(outputs)
else:
## LOGGING
logging_obj.post_call(
input=prompt,
api_key="",
original_response=outputs,
additional_args={"complete_input_dict": sampling_params},
)
print_verbose(f"raw model_response: {outputs}")
## RESPONSE OBJECT
model_response.choices[0].message.content = outputs[0].outputs[0].text # type: ignore
## CALCULATING USAGE
prompt_tokens = len(outputs[0].prompt_token_ids)
completion_tokens = len(outputs[0].outputs[0].token_ids)
model_response.created = int(time.time())
model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
setattr(model_response, "usage", usage)
return model_response
def batch_completions(
model: str, messages: list, optional_params=None, custom_prompt_dict={}
):
"""
Example usage:
import litellm
import os
from litellm import batch_completion
responses = batch_completion(
model="vllm/facebook/opt-125m",
messages = [
[
{
"role": "user",
"content": "good morning? "
}
],
[
{
"role": "user",
"content": "what's the time? "
}
]
]
)
"""
try:
llm, SamplingParams = validate_environment(model=model)
except Exception as e:
error_str = str(e)
raise VLLMError(status_code=0, message=error_str)
sampling_params = SamplingParams(**optional_params)
prompts = []
if model in custom_prompt_dict:
# check if the model has a registered custom prompt
model_prompt_details = custom_prompt_dict[model]
for message in messages:
prompt = custom_prompt(
role_dict=model_prompt_details["roles"],
initial_prompt_value=model_prompt_details["initial_prompt_value"],
final_prompt_value=model_prompt_details["final_prompt_value"],
messages=message,
)
prompts.append(prompt)
else:
for message in messages:
prompt = prompt_factory(model=model, messages=message)
prompts.append(prompt)
if llm:
outputs = llm.generate(prompts, sampling_params)
else:
raise VLLMError(
status_code=0, message="Need to pass in a model name to initialize vllm"
)
final_outputs = []
for output in outputs:
model_response = ModelResponse()
## RESPONSE OBJECT
model_response.choices[0].message.content = output.outputs[0].text # type: ignore
## CALCULATING USAGE
prompt_tokens = len(output.prompt_token_ids)
completion_tokens = len(output.outputs[0].token_ids)
model_response.created = int(time.time())
model_response.model = model
usage = Usage(
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
)
setattr(model_response, "usage", usage)
final_outputs.append(model_response)
return final_outputs
def embedding():
# logic for parsing in - calling - parsing out model embedding calls
pass
|
