rename package
Former-commit-id: a07ff0c083558cfe6f474d13027642d3052fee08
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hiyouga
299
src/llamafactory/chat/hf_engine.py
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299
src/llamafactory/chat/hf_engine.py
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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, load_tokenizer
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from .base_engine import BaseEngine, Response
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if TYPE_CHECKING:
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from numpy.typing import NDArray
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from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
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from transformers.image_processing_utils import BaseImageProcessor
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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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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" 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.model = load_model(
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self.tokenizer, model_args, finetuning_args, is_trainable=False, add_valuehead=(not self.can_generate)
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) # must after fixing tokenizer to resize vocab
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self.generating_args = generating_args.to_dict()
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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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processor: Optional["ProcessorMixin"],
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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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image: Optional["NDArray"] = None,
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input_kwargs: Optional[Dict[str, Any]] = {},
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) -> Tuple[Dict[str, Any], int]:
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if processor is not None and image is not None and "<image>" not in messages[0]["content"]:
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messages[0]["content"] = "<image>" + messages[0]["content"]
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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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do_sample = input_kwargs.pop("do_sample", generating_args["do_sample"])
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temperature = input_kwargs.pop("temperature", generating_args["temperature"])
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top_p = input_kwargs.pop("top_p", generating_args["top_p"])
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top_k = input_kwargs.pop("top_k", 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", generating_args["repetition_penalty"])
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length_penalty = input_kwargs.pop("length_penalty", 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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if stop is not None:
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raise ValueError("Stop parameter is not supported in Huggingface engine yet.")
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generating_args = generating_args.copy()
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generating_args.update(
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dict(
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do_sample=do_sample,
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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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num_return_sequences=num_return_sequences,
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repetition_penalty=repetition_penalty,
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length_penalty=length_penalty,
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eos_token_id=[tokenizer.eos_token_id] + tokenizer.additional_special_tokens_ids,
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pad_token_id=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 not generating_args["do_sample"]:
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generating_args.pop("temperature", None)
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generating_args.pop("top_p", None)
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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=inputs,
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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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if processor is not None and image is not None:
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image_processor: "BaseImageProcessor" = getattr(processor, "image_processor")
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pixel_values: "torch.Tensor" = image_processor(image, return_tensors="pt")["pixel_values"]
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gen_kwargs["pixel_values"] = pixel_values.to(model.device)
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return gen_kwargs, prompt_length
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@staticmethod
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@torch.inference_mode()
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def _chat(
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model: "PreTrainedModel",
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tokenizer: "PreTrainedTokenizer",
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processor: Optional["ProcessorMixin"],
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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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image: Optional["NDArray"] = None,
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input_kwargs: Optional[Dict[str, Any]] = {},
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) -> List["Response"]:
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gen_kwargs, prompt_length = HuggingfaceEngine._process_args(
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model, tokenizer, processor, template, generating_args, messages, system, tools, image, input_kwargs
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)
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generate_output = model.generate(**gen_kwargs)
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response_ids = generate_output[:, prompt_length:]
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response = tokenizer.batch_decode(response_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
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results = []
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for i in range(len(response)):
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eos_index = (response_ids[i] == 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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return results
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@staticmethod
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@torch.inference_mode()
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def _stream_chat(
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model: "PreTrainedModel",
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tokenizer: "PreTrainedTokenizer",
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processor: Optional["ProcessorMixin"],
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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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image: Optional["NDArray"] = None,
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input_kwargs: Optional[Dict[str, Any]] = {},
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) -> Callable[[], str]:
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gen_kwargs, _ = HuggingfaceEngine._process_args(
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model, tokenizer, processor, template, generating_args, messages, system, tools, image, input_kwargs
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)
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streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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gen_kwargs["streamer"] = streamer
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thread = Thread(target=model.generate, kwargs=gen_kwargs, daemon=True)
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thread.start()
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def stream():
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try:
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return streamer.__next__()
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except StopIteration:
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raise StopAsyncIteration()
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return stream
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@staticmethod
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@torch.inference_mode()
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def _get_scores(
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model: "PreTrainedModelWrapper",
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tokenizer: "PreTrainedTokenizer",
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batch_input: List[str],
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input_kwargs: Optional[Dict[str, Any]] = {},
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) -> List[float]:
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max_length = input_kwargs.pop("max_length", None)
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device = getattr(model.pretrained_model, "device", "cuda")
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inputs = 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(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 = model(**inputs, output_hidden_states=True, return_dict=True)
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if getattr(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] != 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 start(self) -> None:
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self._semaphore = asyncio.Semaphore(int(os.environ.get("MAX_CONCURRENT", 1)))
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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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if not self.can_generate:
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raise ValueError("The current model does not support `chat`.")
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loop = asyncio.get_running_loop()
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input_args = (
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self.model,
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self.tokenizer,
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self.processor,
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self.template,
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self.generating_args,
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messages,
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system,
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tools,
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image,
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input_kwargs,
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)
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async with self._semaphore:
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with concurrent.futures.ThreadPoolExecutor() as pool:
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return await loop.run_in_executor(pool, self._chat, *input_args)
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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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if not self.can_generate:
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raise ValueError("The current model does not support `stream_chat`.")
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loop = asyncio.get_running_loop()
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input_args = (
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self.model,
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self.tokenizer,
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self.processor,
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self.template,
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self.generating_args,
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messages,
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system,
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tools,
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image,
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input_kwargs,
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)
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async with self._semaphore:
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with concurrent.futures.ThreadPoolExecutor() as pool:
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stream = self._stream_chat(*input_args)
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while True:
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try:
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yield await loop.run_in_executor(pool, stream)
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except StopAsyncIteration:
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break
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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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if self.can_generate:
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raise ValueError("Cannot get scores using an auto-regressive model.")
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loop = asyncio.get_running_loop()
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input_args = (self.model, self.tokenizer, batch_input, input_kwargs)
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async with self._semaphore:
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with concurrent.futures.ThreadPoolExecutor() as pool:
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return await loop.run_in_executor(pool, self._get_scores, *input_args)
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