# Copyright 2024 the LlamaFactory team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import uuid from typing import TYPE_CHECKING, Any, AsyncGenerator, AsyncIterator, Dict, List, Optional, Sequence, Union from typing_extensions import override from ..data import get_template_and_fix_tokenizer from ..extras.constants import IMAGE_PLACEHOLDER from ..extras.logging import get_logger from ..extras.misc import get_device_count from ..extras.packages import is_pillow_available, is_vllm_available from ..model import load_config, load_tokenizer from ..model.model_utils.quantization import QuantizationMethod from ..model.model_utils.visual import LlavaMultiModalProjectorForYiVLForVLLM from .base_engine import BaseEngine, Response if is_pillow_available(): from PIL import Image from PIL.Image import Image as ImageObject if is_vllm_available(): from vllm import AsyncEngineArgs, AsyncLLMEngine, RequestOutput, SamplingParams from vllm.lora.request import LoRARequest if TYPE_CHECKING: from ..data.mm_plugin import ImageInput, VideoInput from ..hparams import DataArguments, FinetuningArguments, GeneratingArguments, ModelArguments logger = get_logger(__name__) class VllmEngine(BaseEngine): def __init__( self, model_args: "ModelArguments", data_args: "DataArguments", finetuning_args: "FinetuningArguments", generating_args: "GeneratingArguments", ) -> None: config = load_config(model_args) # may download model from ms hub if getattr(config, "quantization_config", None): # gptq models should use float16 quantization_config: Dict[str, Any] = getattr(config, "quantization_config", None) quant_method = quantization_config.get("quant_method", "") if quant_method == QuantizationMethod.GPTQ and model_args.infer_dtype == "auto": model_args.infer_dtype = "float16" self.can_generate = finetuning_args.stage == "sft" tokenizer_module = load_tokenizer(model_args) self.tokenizer = tokenizer_module["tokenizer"] self.processor = tokenizer_module["processor"] self.tokenizer.padding_side = "left" self.template = get_template_and_fix_tokenizer(self.tokenizer, data_args) self.generating_args = generating_args.to_dict() engine_args = { "model": model_args.model_name_or_path, "trust_remote_code": True, "download_dir": model_args.cache_dir, "dtype": model_args.infer_dtype, "max_model_len": model_args.vllm_maxlen, "tensor_parallel_size": get_device_count() or 1, "gpu_memory_utilization": model_args.vllm_gpu_util, "disable_log_stats": True, "disable_log_requests": True, "enforce_eager": model_args.vllm_enforce_eager, "enable_lora": model_args.adapter_name_or_path is not None, "max_lora_rank": model_args.vllm_max_lora_rank, } if getattr(config, "is_yi_vl_derived_model", None): import vllm.model_executor.models.llava logger.info("Detected Yi-VL model, applying projector patch.") vllm.model_executor.models.llava.LlavaMultiModalProjector = LlavaMultiModalProjectorForYiVLForVLLM self.model = AsyncLLMEngine.from_engine_args(AsyncEngineArgs(**engine_args)) if model_args.adapter_name_or_path is not None: self.lora_request = LoRARequest("default", 1, model_args.adapter_name_or_path[0]) else: self.lora_request = None async def _generate( self, messages: Sequence[Dict[str, str]], system: Optional[str] = None, tools: Optional[str] = None, image: Optional["ImageInput"] = None, video: Optional["VideoInput"] = None, **input_kwargs, ) -> AsyncIterator["RequestOutput"]: request_id = "chatcmpl-{}".format(uuid.uuid4().hex) if image is not None: if IMAGE_PLACEHOLDER not in messages[0]["content"]: messages[0]["content"] = IMAGE_PLACEHOLDER + messages[0]["content"] paired_messages = messages + [{"role": "assistant", "content": ""}] system = system or self.generating_args["default_system"] prompt_ids, _ = self.template.encode_oneturn(self.tokenizer, paired_messages, system, tools) prompt_length = len(prompt_ids) use_beam_search: bool = self.generating_args["num_beams"] > 1 temperature: Optional[float] = input_kwargs.pop("temperature", None) top_p: Optional[float] = input_kwargs.pop("top_p", None) top_k: Optional[float] = input_kwargs.pop("top_k", None) num_return_sequences: int = input_kwargs.pop("num_return_sequences", 1) repetition_penalty: Optional[float] = input_kwargs.pop("repetition_penalty", None) length_penalty: Optional[float] = input_kwargs.pop("length_penalty", None) max_length: Optional[int] = input_kwargs.pop("max_length", None) max_new_tokens: Optional[int] = input_kwargs.pop("max_new_tokens", None) stop: Optional[Union[str, List[str]]] = input_kwargs.pop("stop", None) if "max_new_tokens" in self.generating_args: max_tokens = self.generating_args["max_new_tokens"] elif "max_length" in self.generating_args: if self.generating_args["max_length"] > prompt_length: max_tokens = self.generating_args["max_length"] - prompt_length else: max_tokens = 1 if max_length: max_tokens = max_length - prompt_length if max_length > prompt_length else 1 if max_new_tokens: max_tokens = max_new_tokens sampling_params = SamplingParams( n=num_return_sequences, repetition_penalty=( repetition_penalty if repetition_penalty is not None else self.generating_args["repetition_penalty"] ) or 1.0, # repetition_penalty must > 0 temperature=temperature if temperature is not None else self.generating_args["temperature"], top_p=(top_p if top_p is not None else self.generating_args["top_p"]) or 1.0, # top_p must > 0 top_k=top_k if top_k is not None else self.generating_args["top_k"], use_beam_search=use_beam_search, length_penalty=length_penalty if length_penalty is not None else self.generating_args["length_penalty"], stop=stop, stop_token_ids=[self.tokenizer.eos_token_id] + self.tokenizer.additional_special_tokens_ids, max_tokens=max_tokens, skip_special_tokens=True, ) if image is not None: # add image features if not isinstance(image, (str, ImageObject)): raise ValueError("Expected image input is a path or PIL.Image, but got {}.".format(type(image))) if isinstance(image, str): image = Image.open(image).convert("RGB") multi_modal_data = {"image": image} else: multi_modal_data = None result_generator = self.model.generate( inputs={"prompt_token_ids": prompt_ids, "multi_modal_data": multi_modal_data}, sampling_params=sampling_params, request_id=request_id, lora_request=self.lora_request, ) return result_generator @override async def chat( self, messages: Sequence[Dict[str, str]], system: Optional[str] = None, tools: Optional[str] = None, image: Optional["ImageInput"] = None, video: Optional["VideoInput"] = None, **input_kwargs, ) -> List["Response"]: final_output = None generator = await self._generate(messages, system, tools, image, video, **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 @override async def stream_chat( self, messages: Sequence[Dict[str, str]], system: Optional[str] = None, tools: Optional[str] = None, image: Optional["ImageInput"] = None, video: Optional["VideoInput"] = None, **input_kwargs, ) -> AsyncGenerator[str, None]: generated_text = "" generator = await self._generate(messages, system, tools, image, video, **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 @override async def get_scores( self, batch_input: List[str], **input_kwargs, ) -> List[float]: raise NotImplementedError("vLLM engine does not support get_scores.")