/
base.py
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/
base.py
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import json
from typing import Any, Callable, Dict, List, Optional, Sequence
from llama_index.core.base.llms.types import (
ChatMessage,
ChatResponse,
ChatResponseAsyncGen,
ChatResponseGen,
CompletionResponse,
CompletionResponseAsyncGen,
CompletionResponseGen,
LLMMetadata,
)
from llama_index.core.bridge.pydantic import Field, PrivateAttr
from llama_index.core.callbacks import CallbackManager
from llama_index.core.llms.callbacks import llm_chat_callback, llm_completion_callback
from llama_index.core.base.llms.generic_utils import (
completion_response_to_chat_response,
stream_completion_response_to_chat_response,
)
from llama_index.core.base.llms.generic_utils import (
messages_to_prompt as generic_messages_to_prompt,
)
from llama_index.core.llms.llm import LLM
from llama_index.core.types import BaseOutputParser, PydanticProgramMode
from llama_index.llms.vllm.utils import get_response, post_http_request
class Vllm(LLM):
model: Optional[str] = Field(description="The HuggingFace Model to use.")
temperature: float = Field(description="The temperature to use for sampling.")
tensor_parallel_size: Optional[int] = Field(
default=1,
description="The number of GPUs to use for distributed execution with tensor parallelism.",
)
trust_remote_code: Optional[bool] = Field(
default=True,
description="Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer.",
)
n: int = Field(
default=1,
description="Number of output sequences to return for the given prompt.",
)
best_of: Optional[int] = Field(
default=None,
description="Number of output sequences that are generated from the prompt.",
)
presence_penalty: float = Field(
default=0.0,
description="Float that penalizes new tokens based on whether they appear in the generated text so far.",
)
frequency_penalty: float = Field(
default=0.0,
description="Float that penalizes new tokens based on their frequency in the generated text so far.",
)
top_p: float = Field(
default=1.0,
description="Float that controls the cumulative probability of the top tokens to consider.",
)
top_k: int = Field(
default=-1,
description="Integer that controls the number of top tokens to consider.",
)
use_beam_search: bool = Field(
default=False, description="Whether to use beam search instead of sampling."
)
stop: Optional[List[str]] = Field(
default=None,
description="List of strings that stop the generation when they are generated.",
)
ignore_eos: bool = Field(
default=False,
description="Whether to ignore the EOS token and continue generating tokens after the EOS token is generated.",
)
max_new_tokens: int = Field(
default=512,
description="Maximum number of tokens to generate per output sequence.",
)
logprobs: Optional[int] = Field(
default=None,
description="Number of log probabilities to return per output token.",
)
dtype: str = Field(
default="auto",
description="The data type for the model weights and activations.",
)
download_dir: Optional[str] = Field(
default=None,
description="Directory to download and load the weights. (Default to the default cache dir of huggingface)",
)
vllm_kwargs: Dict[str, Any] = Field(
default_factory=dict,
description="Holds any model parameters valid for `vllm.LLM` call not explicitly specified.",
)
api_url: str = Field(description="The api url for vllm server")
_client: Any = PrivateAttr()
def __init__(
self,
model: str = "facebook/opt-125m",
temperature: float = 1.0,
tensor_parallel_size: int = 1,
trust_remote_code: bool = True,
n: int = 1,
best_of: Optional[int] = None,
presence_penalty: float = 0.0,
frequency_penalty: float = 0.0,
top_p: float = 1.0,
top_k: int = -1,
use_beam_search: bool = False,
stop: Optional[List[str]] = None,
ignore_eos: bool = False,
max_new_tokens: int = 512,
logprobs: Optional[int] = None,
dtype: str = "auto",
download_dir: Optional[str] = None,
vllm_kwargs: Dict[str, Any] = {},
api_url: Optional[str] = "",
callback_manager: Optional[CallbackManager] = None,
system_prompt: Optional[str] = None,
messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
completion_to_prompt: Optional[Callable[[str], str]] = None,
pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
try:
from vllm import LLM as VLLModel
except ImportError:
raise ImportError(
"Could not import vllm python package. "
"Please install it with `pip install vllm`."
)
if model != "":
self._client = VLLModel(
model=model,
tensor_parallel_size=tensor_parallel_size,
trust_remote_code=trust_remote_code,
dtype=dtype,
download_dir=download_dir,
**vllm_kwargs
)
else:
self._client = None
callback_manager = callback_manager or CallbackManager([])
super().__init__(
model=model,
temperature=temperature,
n=n,
best_of=best_of,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
top_p=top_p,
top_k=top_k,
use_beam_search=use_beam_search,
stop=stop,
ignore_eos=ignore_eos,
max_new_tokens=max_new_tokens,
logprobs=logprobs,
dtype=dtype,
download_dir=download_dir,
vllm_kwargs=vllm_kwargs,
api_url=api_url,
system_prompt=system_prompt,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
pydantic_program_mode=pydantic_program_mode,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
return "Vllm"
@property
def metadata(self) -> LLMMetadata:
return LLMMetadata(model_name=self.model)
@property
def _model_kwargs(self) -> Dict[str, Any]:
base_kwargs = {
"temperature": self.temperature,
"max_tokens": self.max_new_tokens,
"n": self.n,
"frequency_penalty": self.frequency_penalty,
"presence_penalty": self.presence_penalty,
"use_beam_search": self.use_beam_search,
"best_of": self.best_of,
"ignore_eos": self.ignore_eos,
"stop": self.stop,
"logprobs": self.logprobs,
"top_k": self.top_k,
"top_p": self.top_p,
}
return {**base_kwargs}
def __del__(self) -> None:
import torch
if torch.cuda.is_available():
from vllm.model_executor.parallel_utils.parallel_state import (
destroy_model_parallel,
)
destroy_model_parallel()
del self._client
torch.cuda.synchronize()
def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
return {
**self._model_kwargs,
**kwargs,
}
@llm_chat_callback()
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
kwargs = kwargs if kwargs else {}
prompt = self.messages_to_prompt(messages)
completion_response = self.complete(prompt, **kwargs)
return completion_response_to_chat_response(completion_response)
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
kwargs = kwargs if kwargs else {}
params = {**self._model_kwargs, **kwargs}
from vllm import SamplingParams
# build sampling parameters
sampling_params = SamplingParams(**params)
outputs = self._client.generate([prompt], sampling_params)
return CompletionResponse(text=outputs[0].outputs[0].text)
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
raise (ValueError("Not Implemented"))
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
raise (ValueError("Not Implemented"))
@llm_chat_callback()
async def achat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
kwargs = kwargs if kwargs else {}
return self.chat(messages, **kwargs)
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
kwargs = kwargs if kwargs else {}
return self.complete(prompt, **kwargs)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
raise (ValueError("Not Implemented"))
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
raise (ValueError("Not Implemented"))
class VllmServer(Vllm):
def __init__(
self,
model: str = "facebook/opt-125m",
api_url: str = "http://localhost:8000",
temperature: float = 1.0,
tensor_parallel_size: Optional[int] = 1,
trust_remote_code: Optional[bool] = True,
n: int = 1,
best_of: Optional[int] = None,
presence_penalty: float = 0.0,
frequency_penalty: float = 0.0,
top_p: float = 1.0,
top_k: int = -1,
use_beam_search: bool = False,
stop: Optional[List[str]] = None,
ignore_eos: bool = False,
max_new_tokens: int = 512,
logprobs: Optional[int] = None,
dtype: str = "auto",
download_dir: Optional[str] = None,
messages_to_prompt: Optional[Callable] = None,
completion_to_prompt: Optional[Callable] = None,
vllm_kwargs: Dict[str, Any] = {},
callback_manager: Optional[CallbackManager] = None,
output_parser: Optional[BaseOutputParser] = None,
) -> None:
self._client = None
messages_to_prompt = messages_to_prompt or generic_messages_to_prompt
completion_to_prompt = completion_to_prompt or (lambda x: x)
callback_manager = callback_manager or CallbackManager([])
model = ""
super().__init__(
model=model,
temperature=temperature,
n=n,
best_of=best_of,
presence_penalty=presence_penalty,
frequency_penalty=frequency_penalty,
top_p=top_p,
top_k=top_k,
use_beam_search=use_beam_search,
stop=stop,
ignore_eos=ignore_eos,
max_new_tokens=max_new_tokens,
logprobs=logprobs,
dtype=dtype,
download_dir=download_dir,
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
vllm_kwargs=vllm_kwargs,
api_url=api_url,
callback_manager=callback_manager,
output_parser=output_parser,
)
@classmethod
def class_name(cls) -> str:
return "VllmServer"
@llm_completion_callback()
def complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> List[CompletionResponse]:
kwargs = kwargs if kwargs else {}
params = {**self._model_kwargs, **kwargs}
from vllm import SamplingParams
# build sampling parameters
sampling_params = SamplingParams(**params).__dict__
sampling_params["prompt"] = prompt
response = post_http_request(self.api_url, sampling_params, stream=False)
output = get_response(response)
return CompletionResponse(text=output[0])
@llm_completion_callback()
def stream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
kwargs = kwargs if kwargs else {}
params = {**self._model_kwargs, **kwargs}
from vllm import SamplingParams
# build sampling parameters
sampling_params = SamplingParams(**params).__dict__
sampling_params["prompt"] = prompt
response = post_http_request(self.api_url, sampling_params, stream=True)
def gen() -> CompletionResponseGen:
response_str = ""
for chunk in response.iter_lines(
chunk_size=8192, decode_unicode=False, delimiter=b"\0"
):
if chunk:
data = json.loads(chunk.decode("utf-8"))
response_str += data["text"][0]
yield CompletionResponse(text=response_str, delta=data["text"][0])
return gen()
@llm_completion_callback()
async def acomplete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
kwargs = kwargs if kwargs else {}
return self.complete(prompt, **kwargs)
@llm_completion_callback()
async def astream_complete(
self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
kwargs = kwargs if kwargs else {}
params = {**self._model_kwargs, **kwargs}
from vllm import SamplingParams
# build sampling parameters
sampling_params = SamplingParams(**params).__dict__
sampling_params["prompt"] = prompt
async def gen() -> CompletionResponseAsyncGen:
for message in self.stream_complete(prompt, **kwargs):
yield message
return gen()
@llm_chat_callback()
def stream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
prompt = self.messages_to_prompt(messages)
completion_response = self.stream_complete(prompt, **kwargs)
return stream_completion_response_to_chat_response(completion_response)
@llm_chat_callback()
async def astream_chat(
self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseAsyncGen:
return self.stream_chat(messages, **kwargs)