File size: 5,975 Bytes
e3278e4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 |
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
|