mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-12-14 10:56:56 +08:00
support vllm
Former-commit-id: 889f6e910e654d8ec3922c2185042d737ffbf1c3
This commit is contained in:
hiyouga
@@ -1,4 +1,5 @@
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from .base_engine import BaseEngine
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from .chat_model import ChatModel
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__all__ = ["ChatModel"]
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__all__ = ["BaseEngine", "ChatModel"]
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64
src/llmtuner/chat/base_engine.py
Normal file
64
src/llmtuner/chat/base_engine.py
Normal file
@@ -0,0 +1,64 @@
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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from typing import TYPE_CHECKING, Any, AsyncGenerator, Dict, List, Literal, Optional, Sequence, Union
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if TYPE_CHECKING:
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from transformers import PreTrainedModel, PreTrainedTokenizer
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from ..data import Template
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from ..extras.packages import is_vllm_available
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from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
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if is_vllm_available():
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from vllm import AsyncLLMEngine
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@dataclass
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class Response:
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response_text: str
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response_length: int
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prompt_length: int
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finish_reason: Literal["stop", "length"]
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class BaseEngine(ABC):
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model: Union["PreTrainedModel", "AsyncLLMEngine"]
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tokenizer: "PreTrainedTokenizer"
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can_generate: bool
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template: "Template"
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generating_args: Dict[str, Any]
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@abstractmethod
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def __init__(
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self,
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model_args: "ModelArguments",
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data_args: "DataArguments",
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finetuning_args: "FinetuningArguments",
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generating_args: "GeneratingArguments",
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) -> None: ...
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@abstractmethod
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async def chat(
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self,
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messages: Sequence[Dict[str, str]],
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system: Optional[str] = None,
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tools: Optional[str] = None,
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**input_kwargs,
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) -> List["Response"]: ...
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@abstractmethod
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async def stream_chat(
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self,
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messages: Sequence[Dict[str, str]],
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system: Optional[str] = None,
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tools: Optional[str] = None,
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**input_kwargs,
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) -> AsyncGenerator[str, None]: ...
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@abstractmethod
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async def get_scores(
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self,
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batch_input: List[str],
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**input_kwargs,
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) -> List[float]: ...
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@@ -1,124 +1,50 @@
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from dataclasses import dataclass
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from threading import Thread
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from typing import Any, Dict, Generator, List, Literal, Optional, Sequence, Tuple
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import asyncio
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from typing import TYPE_CHECKING, Any, AsyncGenerator, Dict, Generator, List, Optional, Sequence
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import torch
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from transformers import GenerationConfig, TextIteratorStreamer
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from ..data import get_template_and_fix_tokenizer
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from ..extras.misc import get_logits_processor
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from ..hparams import get_infer_args
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from ..model import dispatch_model, load_model_and_tokenizer
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from .hf_engine import HuggingfaceEngine
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from .vllm_engine import VllmEngine
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@dataclass
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class Response:
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response_text: str
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response_length: int
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prompt_length: int
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finish_reason: Literal["stop", "length"]
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if TYPE_CHECKING:
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from .base_engine import BaseEngine, Response
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class ChatModel:
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def __init__(self, args: Optional[Dict[str, Any]] = None) -> None:
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model_args, data_args, finetuning_args, self.generating_args = get_infer_args(args)
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self.can_generate = finetuning_args.stage == "sft"
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self.model, self.tokenizer = load_model_and_tokenizer(
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model_args, finetuning_args, is_trainable=False, add_valuehead=(not self.can_generate)
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)
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self.tokenizer.padding_side = "left" if self.can_generate else "right"
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self.model = dispatch_model(self.model)
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self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
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model_args, data_args, finetuning_args, generating_args = get_infer_args(args)
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if model_args.infer_backend == "hf":
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self.engine: "BaseEngine" = HuggingfaceEngine(model_args, data_args, finetuning_args, generating_args)
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elif model_args.infer_backend == "vllm":
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self.engine: "BaseEngine" = VllmEngine(model_args, data_args, finetuning_args, generating_args)
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else:
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raise NotImplementedError("Unknown backend: {}".format(model_args.infer_backend))
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def _process_args(
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self,
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messages: Sequence[Dict[str, str]],
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system: Optional[str] = None,
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tools: Optional[str] = None,
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**input_kwargs,
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) -> Tuple[Dict[str, Any], int]:
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paired_messages = messages + [{"role": "assistant", "content": ""}]
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prompt, _ = self.template.encode_oneturn(
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tokenizer=self.tokenizer, messages=paired_messages, system=system, tools=tools
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)
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prompt_length = len(prompt)
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input_ids = torch.tensor([prompt], device=self.model.device)
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def _get_event_loop():
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try:
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return asyncio.get_running_loop()
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except RuntimeError:
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return asyncio.new_event_loop()
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do_sample = input_kwargs.pop("do_sample", None)
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temperature = input_kwargs.pop("temperature", None)
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top_p = input_kwargs.pop("top_p", None)
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top_k = input_kwargs.pop("top_k", None)
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num_return_sequences = input_kwargs.pop("num_return_sequences", None)
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repetition_penalty = input_kwargs.pop("repetition_penalty", None)
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max_length = input_kwargs.pop("max_length", None)
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max_new_tokens = input_kwargs.pop("max_new_tokens", None)
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generating_args = self.generating_args.to_dict()
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generating_args.update(
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dict(
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do_sample=do_sample if do_sample is not None else generating_args["do_sample"],
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temperature=temperature or generating_args["temperature"],
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top_p=top_p or generating_args["top_p"],
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top_k=top_k or generating_args["top_k"],
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num_return_sequences=num_return_sequences or 1,
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repetition_penalty=repetition_penalty or generating_args["repetition_penalty"],
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eos_token_id=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
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pad_token_id=self.tokenizer.pad_token_id,
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)
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)
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if isinstance(num_return_sequences, int) and num_return_sequences > 1:
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generating_args["do_sample"] = True
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if max_length:
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generating_args.pop("max_new_tokens", None)
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generating_args["max_length"] = max_length
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if max_new_tokens:
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generating_args.pop("max_length", None)
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generating_args["max_new_tokens"] = max_new_tokens
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gen_kwargs = dict(
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inputs=input_ids,
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generation_config=GenerationConfig(**generating_args),
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logits_processor=get_logits_processor(),
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)
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return gen_kwargs, prompt_length
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@torch.inference_mode()
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def chat(
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self,
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messages: Sequence[Dict[str, str]],
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system: Optional[str] = None,
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tools: Optional[str] = None,
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**input_kwargs,
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) -> List[Response]:
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if not self.can_generate:
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raise ValueError("The current model does not support `chat`.")
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) -> List["Response"]:
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loop = self._get_event_loop()
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return loop.run_until_complete(self.achat(messages, system, tools, **input_kwargs))
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gen_kwargs, prompt_length = self._process_args(messages, system, tools, **input_kwargs)
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generate_output = self.model.generate(**gen_kwargs)
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response_ids = generate_output[:, prompt_length:]
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response = self.tokenizer.batch_decode(
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response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
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)
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results = []
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for i in range(len(response)):
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eos_index = (response_ids[i] == self.tokenizer.eos_token_id).nonzero()
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response_length = (eos_index[0].item() + 1) if len(eos_index) else len(response_ids[i])
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results.append(
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Response(
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response_text=response[i],
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response_length=response_length,
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prompt_length=prompt_length,
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finish_reason="stop" if len(eos_index) else "length",
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)
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)
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async def achat(
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self,
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messages: Sequence[Dict[str, str]],
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system: Optional[str] = None,
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tools: Optional[str] = None,
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**input_kwargs,
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) -> List["Response"]:
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return await self.engine.chat(messages, system, tools, **input_kwargs)
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return results
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@torch.inference_mode()
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def stream_chat(
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self,
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messages: Sequence[Dict[str, str]],
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@@ -126,44 +52,35 @@ class ChatModel:
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tools: Optional[str] = None,
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**input_kwargs,
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) -> Generator[str, None, None]:
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if not self.can_generate:
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raise ValueError("The current model does not support `stream_chat`.")
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loop = self._get_event_loop()
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generator = self.astream_chat(messages, system, tools, **input_kwargs)
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while True:
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try:
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yield loop.run_until_complete(generator.__anext__())
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except StopAsyncIteration:
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break
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gen_kwargs, _ = self._process_args(messages, system, tools, **input_kwargs)
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streamer = TextIteratorStreamer(self.tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
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gen_kwargs["streamer"] = streamer
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async def astream_chat(
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self,
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messages: Sequence[Dict[str, str]],
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system: Optional[str] = None,
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tools: Optional[str] = None,
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**input_kwargs,
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) -> AsyncGenerator[str, None]:
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async for new_token in self.engine.stream_chat(messages, system, tools, **input_kwargs):
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yield new_token
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thread = Thread(target=self.model.generate, kwargs=gen_kwargs)
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thread.start()
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def get_scores(
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self,
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batch_input: List[str],
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**input_kwargs,
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) -> List[float]:
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loop = self._get_event_loop()
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return loop.run_until_complete(self.aget_scores(batch_input, **input_kwargs))
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yield from streamer
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@torch.inference_mode()
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def get_scores(self, batch_input: List[str], **input_kwargs) -> List[float]:
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if self.can_generate:
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raise ValueError("Cannot get scores using an auto-regressive model.")
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max_length = input_kwargs.pop("max_length", None)
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device = getattr(self.model.pretrained_model, "device", "cuda")
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inputs = self.tokenizer(
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batch_input,
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padding=True,
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truncation=True,
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max_length=max_length or getattr(self.model.config, "max_position_embeddings", 1024),
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return_tensors="pt",
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add_special_tokens=True,
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).to(device)
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input_ids: torch.Tensor = inputs["input_ids"]
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_, _, values = self.model(**inputs, output_hidden_states=True, return_dict=True)
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if getattr(self.model.config, "model_type", None) == "chatglm":
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values = torch.transpose(values, 0, 1)
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scores = []
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for i in range(input_ids.size(0)):
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end_indexes = (input_ids[i] != self.tokenizer.pad_token_id).nonzero()
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end_index = end_indexes[-1].item() if len(end_indexes) else 0
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scores.append(values[i, end_index].nan_to_num().item())
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return scores
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async def aget_scores(
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self,
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batch_input: List[str],
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**input_kwargs,
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) -> List[float]:
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return await self.engine.get_scores(batch_input, **input_kwargs)
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261
src/llmtuner/chat/hf_engine.py
Normal file
261
src/llmtuner/chat/hf_engine.py
Normal file
@@ -0,0 +1,261 @@
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import asyncio
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import concurrent.futures
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import os
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from threading import Thread
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from typing import TYPE_CHECKING, Any, AsyncGenerator, Callable, Dict, List, Optional, Sequence, Tuple
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import torch
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from transformers import GenerationConfig, TextIteratorStreamer
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from ..data import get_template_and_fix_tokenizer
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from ..extras.misc import get_logits_processor
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from ..model import load_model_and_tokenizer
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from .base_engine import BaseEngine, Response
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if TYPE_CHECKING:
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from transformers import PreTrainedModel, PreTrainedTokenizer
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from trl import PreTrainedModelWrapper
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from ..data import Template
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from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
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class HuggingfaceEngine(BaseEngine):
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def __init__(
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self,
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model_args: "ModelArguments",
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data_args: "DataArguments",
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finetuning_args: "FinetuningArguments",
|
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generating_args: "GeneratingArguments",
|
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) -> None:
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self.can_generate = finetuning_args.stage == "sft"
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self.model, self.tokenizer = load_model_and_tokenizer(
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model_args, finetuning_args, is_trainable=False, add_valuehead=(not self.can_generate)
|
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)
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self.tokenizer.padding_side = "left" if self.can_generate else "right"
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self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
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self.generating_args = generating_args.to_dict()
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self._semaphore = asyncio.Semaphore(int(os.environ.get("MAX_CONCURRENT", 1)))
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|
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@staticmethod
|
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def _process_args(
|
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model: "PreTrainedModel",
|
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tokenizer: "PreTrainedTokenizer",
|
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template: "Template",
|
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generating_args: Dict[str, Any],
|
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messages: Sequence[Dict[str, str]],
|
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system: Optional[str] = None,
|
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tools: Optional[str] = None,
|
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input_kwargs: Optional[Dict[str, Any]] = {},
|
||||
) -> Tuple[Dict[str, Any], int]:
|
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paired_messages = messages + [{"role": "assistant", "content": ""}]
|
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prompt_ids, _ = template.encode_oneturn(
|
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tokenizer=tokenizer, messages=paired_messages, system=system, tools=tools
|
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)
|
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prompt_length = len(prompt_ids)
|
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inputs = torch.tensor([prompt_ids], device=model.device)
|
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|
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do_sample = input_kwargs.pop("do_sample", None)
|
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temperature = input_kwargs.pop("temperature", None)
|
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top_p = input_kwargs.pop("top_p", None)
|
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top_k = input_kwargs.pop("top_k", None)
|
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num_return_sequences = input_kwargs.pop("num_return_sequences", None)
|
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repetition_penalty = input_kwargs.pop("repetition_penalty", None)
|
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max_length = input_kwargs.pop("max_length", None)
|
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max_new_tokens = input_kwargs.pop("max_new_tokens", None)
|
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|
||||
generating_args.update(
|
||||
dict(
|
||||
do_sample=do_sample if do_sample is not None else generating_args["do_sample"],
|
||||
temperature=temperature or generating_args["temperature"],
|
||||
top_p=top_p or generating_args["top_p"],
|
||||
top_k=top_k or generating_args["top_k"],
|
||||
num_return_sequences=num_return_sequences or 1,
|
||||
repetition_penalty=repetition_penalty or generating_args["repetition_penalty"],
|
||||
eos_token_id=[tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids,
|
||||
pad_token_id=tokenizer.pad_token_id,
|
||||
)
|
||||
)
|
||||
|
||||
if isinstance(num_return_sequences, int) and num_return_sequences > 1:
|
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generating_args["do_sample"] = True
|
||||
|
||||
if max_length:
|
||||
generating_args.pop("max_new_tokens", None)
|
||||
generating_args["max_length"] = max_length
|
||||
|
||||
if max_new_tokens:
|
||||
generating_args.pop("max_length", None)
|
||||
generating_args["max_new_tokens"] = max_new_tokens
|
||||
|
||||
gen_kwargs = dict(
|
||||
inputs=inputs,
|
||||
generation_config=GenerationConfig(**generating_args),
|
||||
logits_processor=get_logits_processor(),
|
||||
)
|
||||
|
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return gen_kwargs, prompt_length
|
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|
||||
@staticmethod
|
||||
@torch.inference_mode()
|
||||
def _chat(
|
||||
model: "PreTrainedModel",
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
template: "Template",
|
||||
generating_args: Dict[str, Any],
|
||||
messages: Sequence[Dict[str, str]],
|
||||
system: Optional[str] = None,
|
||||
tools: Optional[str] = None,
|
||||
input_kwargs: Optional[Dict[str, Any]] = {},
|
||||
) -> List["Response"]:
|
||||
gen_kwargs, prompt_length = HuggingfaceEngine._process_args(
|
||||
model, tokenizer, template, generating_args, messages, system, tools, input_kwargs
|
||||
)
|
||||
generate_output = model.generate(**gen_kwargs)
|
||||
response_ids = generate_output[:, prompt_length:]
|
||||
response = tokenizer.batch_decode(response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
|
||||
results = []
|
||||
for i in range(len(response)):
|
||||
eos_index = (response_ids[i] == tokenizer.eos_token_id).nonzero()
|
||||
response_length = (eos_index[0].item() + 1) if len(eos_index) else len(response_ids[i])
|
||||
results.append(
|
||||
Response(
|
||||
response_text=response[i],
|
||||
response_length=response_length,
|
||||
prompt_length=prompt_length,
|
||||
finish_reason="stop" if len(eos_index) else "length",
|
||||
)
|
||||
)
|
||||
|
||||
return results
|
||||
|
||||
@staticmethod
|
||||
@torch.inference_mode()
|
||||
def _stream_chat(
|
||||
model: "PreTrainedModel",
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
template: "Template",
|
||||
generating_args: Dict[str, Any],
|
||||
messages: Sequence[Dict[str, str]],
|
||||
system: Optional[str] = None,
|
||||
tools: Optional[str] = None,
|
||||
input_kwargs: Optional[Dict[str, Any]] = {},
|
||||
) -> Callable[[], str]:
|
||||
gen_kwargs, _ = HuggingfaceEngine._process_args(
|
||||
model, tokenizer, template, generating_args, messages, system, tools, input_kwargs
|
||||
)
|
||||
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
|
||||
gen_kwargs["streamer"] = streamer
|
||||
thread = Thread(target=model.generate, kwargs=gen_kwargs)
|
||||
thread.start()
|
||||
|
||||
def stream():
|
||||
try:
|
||||
return streamer.__next__()
|
||||
except StopIteration:
|
||||
raise StopAsyncIteration()
|
||||
|
||||
return stream
|
||||
|
||||
@staticmethod
|
||||
@torch.inference_mode()
|
||||
def _get_scores(
|
||||
model: "PreTrainedModelWrapper",
|
||||
tokenizer: "PreTrainedTokenizer",
|
||||
batch_input: List[str],
|
||||
input_kwargs: Optional[Dict[str, Any]] = {},
|
||||
) -> List[float]:
|
||||
max_length = input_kwargs.pop("max_length", None)
|
||||
device = getattr(model.pretrained_model, "device", "cuda")
|
||||
inputs = tokenizer(
|
||||
batch_input,
|
||||
padding=True,
|
||||
truncation=True,
|
||||
max_length=max_length or getattr(model.config, "max_position_embeddings", 1024),
|
||||
return_tensors="pt",
|
||||
add_special_tokens=True,
|
||||
).to(device)
|
||||
|
||||
input_ids: torch.Tensor = inputs["input_ids"]
|
||||
_, _, values = model(**inputs, output_hidden_states=True, return_dict=True)
|
||||
|
||||
if getattr(model.config, "model_type", None) == "chatglm":
|
||||
values = torch.transpose(values, 0, 1)
|
||||
|
||||
scores = []
|
||||
for i in range(input_ids.size(0)):
|
||||
end_indexes = (input_ids[i] != tokenizer.pad_token_id).nonzero()
|
||||
end_index = end_indexes[-1].item() if len(end_indexes) else 0
|
||||
scores.append(values[i, end_index].nan_to_num().item())
|
||||
|
||||
return scores
|
||||
|
||||
async def chat(
|
||||
self,
|
||||
messages: Sequence[Dict[str, str]],
|
||||
system: Optional[str] = None,
|
||||
tools: Optional[str] = None,
|
||||
**input_kwargs,
|
||||
) -> List["Response"]:
|
||||
if not self.can_generate:
|
||||
raise ValueError("The current model does not support `chat`.")
|
||||
|
||||
loop = asyncio.get_running_loop()
|
||||
input_args = (
|
||||
self.model,
|
||||
self.tokenizer,
|
||||
self.template,
|
||||
self.generating_args,
|
||||
messages,
|
||||
system,
|
||||
tools,
|
||||
input_kwargs,
|
||||
)
|
||||
async with self._semaphore:
|
||||
with concurrent.futures.ThreadPoolExecutor() as pool:
|
||||
return await loop.run_in_executor(pool, self._chat, *input_args)
|
||||
|
||||
async def stream_chat(
|
||||
self,
|
||||
messages: Sequence[Dict[str, str]],
|
||||
system: Optional[str] = None,
|
||||
tools: Optional[str] = None,
|
||||
**input_kwargs,
|
||||
) -> AsyncGenerator[str, None]:
|
||||
if not self.can_generate:
|
||||
raise ValueError("The current model does not support `stream_chat`.")
|
||||
|
||||
loop = asyncio.get_running_loop()
|
||||
input_args = (
|
||||
self.model,
|
||||
self.tokenizer,
|
||||
self.template,
|
||||
self.generating_args,
|
||||
messages,
|
||||
system,
|
||||
tools,
|
||||
input_kwargs,
|
||||
)
|
||||
async with self._semaphore:
|
||||
with concurrent.futures.ThreadPoolExecutor() as pool:
|
||||
stream = self._stream_chat(*input_args)
|
||||
while True:
|
||||
try:
|
||||
yield await loop.run_in_executor(pool, stream)
|
||||
except StopAsyncIteration:
|
||||
break
|
||||
|
||||
async def get_scores(
|
||||
self,
|
||||
batch_input: List[str],
|
||||
**input_kwargs,
|
||||
) -> List[float]:
|
||||
if self.can_generate:
|
||||
raise ValueError("Cannot get scores using an auto-regressive model.")
|
||||
|
||||
loop = asyncio.get_running_loop()
|
||||
input_args = (self.model, self.tokenizer, batch_input, input_kwargs)
|
||||
async with self._semaphore:
|
||||
with concurrent.futures.ThreadPoolExecutor() as pool:
|
||||
return await loop.run_in_executor(pool, self._get_scores, *input_args)
|
||||
144
src/llmtuner/chat/vllm_engine.py
Normal file
144
src/llmtuner/chat/vllm_engine.py
Normal file
@@ -0,0 +1,144 @@
|
||||
import uuid
|
||||
from typing import TYPE_CHECKING, AsyncGenerator, AsyncIterator, Dict, List, Optional, Sequence
|
||||
|
||||
from transformers.utils.versions import require_version
|
||||
|
||||
from ..data import get_template_and_fix_tokenizer
|
||||
from ..extras.misc import get_device_count
|
||||
from ..extras.packages import is_vllm_available
|
||||
from ..model import load_tokenizer
|
||||
from .base_engine import BaseEngine, Response
|
||||
|
||||
|
||||
if is_vllm_available():
|
||||
from vllm import AsyncEngineArgs, AsyncLLMEngine, RequestOutput, SamplingParams
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
|
||||
|
||||
|
||||
class VllmEngine(BaseEngine):
|
||||
def __init__(
|
||||
self,
|
||||
model_args: "ModelArguments",
|
||||
data_args: "DataArguments",
|
||||
finetuning_args: "FinetuningArguments",
|
||||
generating_args: "GeneratingArguments",
|
||||
) -> None:
|
||||
require_version("vllm>=0.3.3", "To fix: pip install vllm>=0.3.3")
|
||||
self.can_generate = finetuning_args.stage == "sft"
|
||||
engine_args = AsyncEngineArgs(
|
||||
model=model_args.model_name_or_path,
|
||||
trust_remote_code=True,
|
||||
max_model_len=model_args.vllm_maxlen,
|
||||
tensor_parallel_size=get_device_count(),
|
||||
disable_log_stats=True,
|
||||
disable_log_requests=True,
|
||||
)
|
||||
self.model = AsyncLLMEngine.from_engine_args(engine_args)
|
||||
self.tokenizer = load_tokenizer(model_args)
|
||||
self.tokenizer.padding_side = "left"
|
||||
self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args.template)
|
||||
self.generating_args = generating_args.to_dict()
|
||||
|
||||
async def _generate(
|
||||
self,
|
||||
messages: Sequence[Dict[str, str]],
|
||||
system: Optional[str] = None,
|
||||
tools: Optional[str] = None,
|
||||
**input_kwargs,
|
||||
) -> AsyncIterator["RequestOutput"]:
|
||||
request_id = "chatcmpl-{}".format(uuid.uuid4().hex)
|
||||
paired_messages = messages + [{"role": "assistant", "content": ""}]
|
||||
prompt_ids, _ = self.template.encode_oneturn(
|
||||
tokenizer=self.tokenizer, messages=paired_messages, system=system, tools=tools
|
||||
)
|
||||
prompt_length = len(prompt_ids)
|
||||
|
||||
temperature = input_kwargs.pop("temperature", None)
|
||||
top_p = input_kwargs.pop("top_p", None)
|
||||
top_k = input_kwargs.pop("top_k", None)
|
||||
num_return_sequences = input_kwargs.pop("num_return_sequences", None)
|
||||
repetition_penalty = input_kwargs.pop("repetition_penalty", None)
|
||||
max_length = input_kwargs.pop("max_length", None)
|
||||
max_new_tokens = input_kwargs.pop("max_new_tokens", None)
|
||||
|
||||
generating_args = self.generating_args.copy()
|
||||
generating_args.update(
|
||||
dict(
|
||||
temperature=temperature or generating_args["temperature"],
|
||||
top_p=top_p or generating_args["top_p"],
|
||||
top_k=top_k or generating_args["top_k"],
|
||||
num_return_sequences=num_return_sequences or 1,
|
||||
repetition_penalty=repetition_penalty or generating_args["repetition_penalty"],
|
||||
)
|
||||
)
|
||||
|
||||
if max_length:
|
||||
generating_args["max_new_tokens"] = max_length - prompt_length
|
||||
|
||||
if max_new_tokens:
|
||||
generating_args["max_new_tokens"] = max_new_tokens
|
||||
|
||||
sampling_params = SamplingParams(
|
||||
n=generating_args["num_return_sequences"],
|
||||
repetition_penalty=generating_args["repetition_penalty"],
|
||||
temperature=generating_args["temperature"],
|
||||
top_p=generating_args["top_p"],
|
||||
top_k=generating_args["top_k"],
|
||||
use_beam_search=generating_args["num_beams"] > 1,
|
||||
length_penalty=generating_args["length_penalty"],
|
||||
stop_token_ids=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
|
||||
max_tokens=generating_args["max_new_tokens"],
|
||||
skip_special_tokens=True,
|
||||
)
|
||||
result_generator = self.model.generate(
|
||||
prompt=None, sampling_params=sampling_params, request_id=request_id, prompt_token_ids=prompt_ids
|
||||
)
|
||||
return result_generator
|
||||
|
||||
async def chat(
|
||||
self,
|
||||
messages: Sequence[Dict[str, str]],
|
||||
system: Optional[str] = None,
|
||||
tools: Optional[str] = None,
|
||||
**input_kwargs,
|
||||
) -> List["Response"]:
|
||||
final_output = None
|
||||
generator = await self._generate(messages, system, tools, **input_kwargs)
|
||||
async for request_output in generator:
|
||||
final_output = request_output
|
||||
|
||||
results = []
|
||||
for output in final_output.outputs:
|
||||
results.append(
|
||||
Response(
|
||||
response_text=output.text,
|
||||
response_length=len(output.token_ids),
|
||||
prompt_length=len(final_output.prompt_token_ids),
|
||||
finish_reason=output.finish_reason,
|
||||
)
|
||||
)
|
||||
|
||||
return results
|
||||
|
||||
async def stream_chat(
|
||||
self,
|
||||
messages: Sequence[Dict[str, str]],
|
||||
system: Optional[str] = None,
|
||||
tools: Optional[str] = None,
|
||||
**input_kwargs,
|
||||
) -> AsyncGenerator[str, None]:
|
||||
generated_text = ""
|
||||
generator = await self._generate(messages, system, tools, **input_kwargs)
|
||||
async for result in generator:
|
||||
delta_text = result.outputs[0].text[len(generated_text) :]
|
||||
generated_text = result.outputs[0].text
|
||||
yield delta_text
|
||||
|
||||
async def get_scores(
|
||||
self,
|
||||
batch_input: List[str],
|
||||
**input_kwargs,
|
||||
) -> List[float]:
|
||||
raise NotImplementedError("vLLM engine does not support get_scores.")
|
||||
Reference in New Issue
Block a user