rename package
Former-commit-id: a07ff0c083558cfe6f474d13027642d3052fee08
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hiyouga
201
src/llamafactory/chat/vllm_engine.py
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201
src/llamafactory/chat/vllm_engine.py
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import uuid
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from typing import TYPE_CHECKING, AsyncGenerator, AsyncIterator, Dict, List, Optional, Sequence
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from ..data import get_template_and_fix_tokenizer
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from ..extras.logging import get_logger
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from ..extras.misc import get_device_count, infer_optim_dtype
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from ..extras.packages import is_vllm_available
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from ..model import load_config, load_tokenizer
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from ..model.utils.visual import LlavaMultiModalProjectorForYiVLForVLLM
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from .base_engine import BaseEngine, Response
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if is_vllm_available():
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from vllm import AsyncEngineArgs, AsyncLLMEngine, RequestOutput, SamplingParams
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from vllm.lora.request import LoRARequest
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from vllm.sequence import MultiModalData
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if TYPE_CHECKING:
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import torch
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from numpy.typing import NDArray
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from transformers.image_processing_utils import BaseImageProcessor
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from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments
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logger = get_logger(__name__)
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class VllmEngine(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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config = load_config(model_args) # may download model from ms hub
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infer_dtype = infer_optim_dtype(model_dtype=getattr(config, "torch_dtype", None))
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infer_dtype = str(infer_dtype).split(".")[-1]
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self.can_generate = finetuning_args.stage == "sft"
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tokenizer_module = load_tokenizer(model_args)
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self.tokenizer = tokenizer_module["tokenizer"]
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self.processor = tokenizer_module["processor"]
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self.tokenizer.padding_side = "left"
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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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engine_args = {
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"model": model_args.model_name_or_path,
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"trust_remote_code": True,
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"download_dir": model_args.cache_dir,
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"dtype": infer_dtype,
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"max_model_len": model_args.vllm_maxlen,
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"tensor_parallel_size": get_device_count() or 1,
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"gpu_memory_utilization": model_args.vllm_gpu_util,
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"disable_log_stats": True,
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"disable_log_requests": True,
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"enforce_eager": model_args.vllm_enforce_eager,
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"enable_lora": model_args.adapter_name_or_path is not None,
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}
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if model_args.visual_inputs:
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image_size = config.vision_config.image_size
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patch_size = config.vision_config.patch_size
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self.image_feature_size = (image_size // patch_size) ** 2
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engine_args["image_input_type"] = "pixel_values"
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engine_args["image_token_id"] = self.tokenizer.convert_tokens_to_ids("<image>")
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engine_args["image_input_shape"] = "1,3,{},{}".format(image_size, image_size)
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engine_args["image_feature_size"] = self.image_feature_size
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if getattr(config, "is_yi_vl_derived_model", None):
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# bug in vllm 0.4.2, see: https://github.com/vllm-project/vllm/pull/4828
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import vllm.model_executor.models.llava
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logger.info("Detected Yi-VL model, applying projector patch.")
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vllm.model_executor.models.llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVLForVLLM
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self.model = AsyncLLMEngine.from_engine_args(AsyncEngineArgs(**engine_args))
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if model_args.adapter_name_or_path is not None:
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self.lora_request = LoRARequest("default", 1, model_args.adapter_name_or_path[0])
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else:
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self.lora_request = None
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async def _generate(
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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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image: Optional["NDArray"] = None,
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**input_kwargs,
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) -> AsyncIterator["RequestOutput"]:
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request_id = "chatcmpl-{}".format(uuid.uuid4().hex)
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if self.processor is not None and image is not None and "<image>" not in messages[0]["content"]:
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messages[0]["content"] = "<image>" * self.image_feature_size + messages[0]["content"]
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paired_messages = messages + [{"role": "assistant", "content": ""}]
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prompt_ids, _ = 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_ids)
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use_beam_search = self.generating_args["num_beams"] > 1
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temperature = input_kwargs.pop("temperature", self.generating_args["temperature"])
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top_p = input_kwargs.pop("top_p", self.generating_args["top_p"])
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top_k = input_kwargs.pop("top_k", self.generating_args["top_k"])
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num_return_sequences = input_kwargs.pop("num_return_sequences", 1)
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repetition_penalty = input_kwargs.pop("repetition_penalty", self.generating_args["repetition_penalty"])
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length_penalty = input_kwargs.pop("length_penalty", self.generating_args["length_penalty"])
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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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stop = input_kwargs.pop("stop", None)
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max_tokens = self.generating_args["max_new_tokens"] or self.generating_args["max_length"]
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if max_length:
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max_tokens = max_length - prompt_length if max_length > prompt_length else 1
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if max_new_tokens:
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max_tokens = max_new_tokens
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sampling_params = SamplingParams(
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n=num_return_sequences,
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repetition_penalty=repetition_penalty,
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temperature=temperature,
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top_p=top_p,
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top_k=top_k,
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use_beam_search=use_beam_search,
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length_penalty=length_penalty,
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stop=stop,
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stop_token_ids=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids,
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max_tokens=max_tokens,
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skip_special_tokens=True,
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)
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if self.processor is not None and image is not None:
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image_processor: "BaseImageProcessor" = getattr(self.processor, "image_processor")
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pixel_values: "torch.Tensor" = image_processor(image, return_tensors="pt")["pixel_values"]
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multi_modal_data = MultiModalData(type=MultiModalData.Type.IMAGE, data=pixel_values)
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else:
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multi_modal_data = None
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result_generator = self.model.generate(
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prompt=None,
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sampling_params=sampling_params,
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request_id=request_id,
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prompt_token_ids=prompt_ids,
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lora_request=self.lora_request,
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multi_modal_data=multi_modal_data,
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)
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return result_generator
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async def start(self) -> None:
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pass
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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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image: Optional["NDArray"] = None,
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**input_kwargs,
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) -> List["Response"]:
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final_output = None
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generator = await self._generate(messages, system, tools, image, **input_kwargs)
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async for request_output in generator:
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final_output = request_output
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results = []
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for output in final_output.outputs:
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results.append(
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Response(
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response_text=output.text,
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response_length=len(output.token_ids),
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prompt_length=len(final_output.prompt_token_ids),
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finish_reason=output.finish_reason,
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)
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)
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return results
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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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image: Optional["NDArray"] = None,
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**input_kwargs,
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) -> AsyncGenerator[str, None]:
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generated_text = ""
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generator = await self._generate(messages, system, tools, image, **input_kwargs)
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async for result in generator:
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delta_text = result.outputs[0].text[len(generated_text) :]
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generated_text = result.outputs[0].text
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yield delta_text
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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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raise NotImplementedError("vLLM engine does not support get_scores.")
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