chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,18 @@
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from FlagEmbedding.abc.finetune.embedder import (
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AbsEmbedderDataArguments as DecoderOnlyEmbedderDataArguments,
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AbsEmbedderTrainingArguments as DecoderOnlyEmbedderTrainingArguments,
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)
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from .arguments import DecoderOnlyEmbedderModelArguments
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from .modeling import BiDecoderOnlyEmbedderModel
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from .trainer import DecoderOnlyEmbedderTrainer
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from .runner import DecoderOnlyEmbedderRunner
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__all__ = [
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'DecoderOnlyEmbedderDataArguments',
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'DecoderOnlyEmbedderTrainingArguments',
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'DecoderOnlyEmbedderModelArguments',
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'BiDecoderOnlyEmbedderModel',
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'DecoderOnlyEmbedderTrainer',
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'DecoderOnlyEmbedderRunner',
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]
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@@ -0,0 +1,31 @@
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from transformers import HfArgumentParser
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from FlagEmbedding.finetune.embedder.decoder_only.base import (
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DecoderOnlyEmbedderDataArguments,
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DecoderOnlyEmbedderTrainingArguments,
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DecoderOnlyEmbedderModelArguments,
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DecoderOnlyEmbedderRunner,
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)
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def main():
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parser = HfArgumentParser((
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DecoderOnlyEmbedderModelArguments,
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DecoderOnlyEmbedderDataArguments,
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DecoderOnlyEmbedderTrainingArguments
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))
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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model_args: DecoderOnlyEmbedderModelArguments
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data_args: DecoderOnlyEmbedderDataArguments
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training_args: DecoderOnlyEmbedderTrainingArguments
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runner = DecoderOnlyEmbedderRunner(
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model_args=model_args,
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data_args=data_args,
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training_args=training_args
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)
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runner.run()
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,76 @@
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from typing import Optional, List
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from dataclasses import dataclass, field
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from FlagEmbedding.abc.finetune.embedder import AbsEmbedderModelArguments
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def default_target_modules() -> List[int]:
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return ['v_proj', 'q_proj', 'k_proj', 'gate_proj', 'down_proj', 'o_proj', 'up_proj']
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@dataclass
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class DecoderOnlyEmbedderModelArguments(AbsEmbedderModelArguments):
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"""
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Model argument class for decoder only base model.
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"""
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peft_model_path: str = field(
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default='', metadata={"help": "The peft model checkpoint for initialization."}
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)
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use_lora: bool = field(
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default=True,
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metadata={"help": "If passed, will use LORA (low-rank parameter-efficient training) to train the model."}
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)
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lora_rank: int = field(
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default=64,
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metadata={"help": "The rank of lora."}
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)
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lora_alpha: float = field(
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default=16,
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metadata={"help": "The alpha parameter of lora."}
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)
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lora_dropout: float = field(
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default=0.1,
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metadata={"help": "The dropout rate of lora modules."}
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)
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target_modules: List[str] = field(
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default_factory=default_target_modules,
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metadata={"help": "The target modules to apply LORA."}
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)
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use_flash_attn: bool = field(
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default=False,
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metadata={"help": "If passed, will use flash attention to train the model."}
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)
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use_slow_tokenizer: bool = field(
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default=False,
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metadata={"help": "If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library)."}
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)
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# low_cpu_mem_usage: bool = field(
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# default=False,
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# metadata={"help": "It is an option to create the model as an empty shell,"
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# "then only materialize its parameters when the pretrained weights are loaded."
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# "If passed, LLM loading time and RAM consumption will be benefited."}
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# )
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from_peft: str = field(
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default=None
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)
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modules_to_save: List[str] = field(
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default=None,
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)
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raw_peft: str = field(
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default=None
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)
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additional_special_tokens: Optional[str] = field(
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default=None,
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metadata={"help": "additional special tokens", "nargs": "+"}
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)
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save_merged_lora_model: bool = field(
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default=False,
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metadata={"help": "If passed, will merge the lora modules and save the entire model."}
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)
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only_merge_lora_model: bool = field(
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default=False,
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metadata={"help": "If passed, will only merge the lora modules and save the entire model."}
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)
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@@ -0,0 +1,185 @@
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import os
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import re
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import torch
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import logging
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from transformers import AutoConfig, AutoModel, AutoTokenizer
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from peft import LoraConfig, TaskType, get_peft_model, PeftModel
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from .arguments import DecoderOnlyEmbedderModelArguments
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logger = logging.getLogger(__name__)
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def find_largest_checkpoint(checkpoint_dir):
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"""Find the largest checkpoint from directory.
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Args:
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checkpoint_dir (str): Directory to the checkpoint.
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Returns:
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str: Directory to the checkpoint, None no matching found.
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"""
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checkpoint_pattern = re.compile(r'checkpoint-(\d+)')
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max_number = -1
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max_checkpoint_file = None
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for file in os.listdir(checkpoint_dir):
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match = checkpoint_pattern.search(file)
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if match:
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number = int(match.group(1))
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if number > max_number:
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max_number = number
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max_checkpoint_file = file
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if max_checkpoint_file:
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return os.path.join(checkpoint_dir, max_checkpoint_file)
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else:
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return None
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def get_model(model_args: DecoderOnlyEmbedderModelArguments, output_dir: str, resize: bool, resize_tokens: int):
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"""Get the model.
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Args:
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model_args (DecoderOnlyEmbedderModelArguments): Model arguments instance.
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output_dir (str): Directory to save the model.
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resize (bool): Whether to resize the number of tokens.
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resize_tokens (int): The new token size.
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Returns:
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transformers.PreTrainedModel or PeftModel: The loaded model.
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"""
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if model_args.config_name:
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config = AutoConfig.from_pretrained(
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model_args.config_name,
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token=model_args.token,
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cache_dir=model_args.cache_dir,
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trust_remote_code=model_args.trust_remote_code,
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)
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elif model_args.model_name_or_path:
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config = AutoConfig.from_pretrained(
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model_args.model_name_or_path,
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token=model_args.token,
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cache_dir=model_args.cache_dir,
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trust_remote_code=model_args.trust_remote_code,
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)
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else:
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raise ValueError(
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"You are instantiating a new config instance from scratch. This is not supported by this script."
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)
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config.use_cache = False
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if model_args.model_name_or_path:
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model = AutoModel.from_pretrained(
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model_args.model_name_or_path,
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# torch_dtype=torch.bfloat16,
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attn_implementation = "flash_attention_2" if model_args.use_flash_attn else None,
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token=model_args.token,
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cache_dir=model_args.cache_dir,
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from_tf=bool(".ckpt" in model_args.model_name_or_path),
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config=config,
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trust_remote_code=model_args.trust_remote_code,
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)
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else:
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logger.info("Training new model from scratch")
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model = model_args.from_config(config)
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if model_args.raw_peft is not None:
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model.set_input_embeddings(torch.load(os.path.join(model_args.raw_peft, 'embedding', 'emb.pth')))
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model = PeftModel.from_pretrained(model, model_args.raw_peft)
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model = model.merge_and_unload()
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if resize:
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model.resize_token_embeddings(resize_tokens)
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os.makedirs(os.path.join(output_dir, 'embedding'), exist_ok=True)
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torch.save(model.embed_tokens, os.path.join(output_dir, 'embedding', 'emb.pth'))
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target_modules = model_args.target_modules
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else:
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target_modules = model_args.target_modules
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if 'embed_tokens' in target_modules:
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target_modules.remove('embed_tokens')
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if model_args.from_peft is not None:
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if os.path.exists(os.path.join(model_args.from_peft, 'embedding')):
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model.set_input_embeddings(torch.load(os.path.join(model_args.from_peft, 'embedding', 'emb.pth')))
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torch.save(model.embed_tokens, os.path.join(output_dir, 'embedding', 'emb.pth'))
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model = PeftModel.from_pretrained(model, model_args.from_peft, is_trainable=True)
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model.print_trainable_parameters()
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else:
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if model_args.use_lora:
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peft_config = LoraConfig(
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task_type=TaskType.FEATURE_EXTRACTION,
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inference_mode=False,
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r=model_args.lora_rank,
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target_modules=target_modules,
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modules_to_save=model_args.modules_to_save,
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lora_alpha=model_args.lora_alpha,
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lora_dropout=model_args.lora_dropout
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)
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model = get_peft_model(model, peft_config)
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model.print_trainable_parameters()
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return model
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def save_merged_model(model_args: DecoderOnlyEmbedderModelArguments, output_dir: str):
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"""
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Loads a model with specified configurations, merges it with PEFT layers if available.
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Args:
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model_args (DecoderOnlyEmbedderModelArguments): Model arguments instance.
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output_dir (str): Directory to save the model.
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"""
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if model_args.config_name:
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config = AutoConfig.from_pretrained(
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model_args.config_name,
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token=model_args.token,
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cache_dir=model_args.cache_dir,
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trust_remote_code=model_args.trust_remote_code,
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)
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elif model_args.model_name_or_path:
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config = AutoConfig.from_pretrained(
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model_args.model_name_or_path,
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token=model_args.token,
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cache_dir=model_args.cache_dir,
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trust_remote_code=model_args.trust_remote_code,
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)
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else:
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raise ValueError(
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"You are instantiating a new config instance from scratch. This is not supported by this script."
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)
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config.use_cache = False
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if model_args.model_name_or_path:
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model = AutoModel.from_pretrained(
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model_args.model_name_or_path,
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# torch_dtype=torch.bfloat16,
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attn_implementation = "flash_attention_2" if model_args.use_flash_attn else None,
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token=model_args.token,
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cache_dir=model_args.cache_dir,
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from_tf=bool(".ckpt" in model_args.model_name_or_path),
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config=config,
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trust_remote_code=model_args.trust_remote_code,
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)
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else:
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model = model_args.from_config(config)
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if model_args.raw_peft is not None:
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model.set_input_embeddings(torch.load(os.path.join(model_args.raw_peft, 'embedding', 'emb.pth')))
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model = PeftModel.from_pretrained(model, model_args.raw_peft)
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model = model.merge_and_unload()
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if os.path.exists(os.path.join(output_dir, 'embedding', 'emb.pth')):
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model.set_input_embeddings(torch.load(os.path.join(output_dir, 'embedding', 'emb.pth')))
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# modify the vocab size in the model configuration
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model.config.vocab_size = len(tokenizer)
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try:
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model = PeftModel.from_pretrained(model, output_dir)
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model = model.merge_and_unload()
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except:
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model = PeftModel.from_pretrained(model, find_largest_checkpoint(output_dir))
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model = model.merge_and_unload()
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tokenizer = AutoTokenizer.from_pretrained(output_dir, trust_remote_code=model_args.trust_remote_code)
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tokenizer.save_pretrained(os.path.join(output_dir, 'merged_model'))
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model.save_pretrained(os.path.join(output_dir, 'merged_model'))
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@@ -0,0 +1,207 @@
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import logging
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from typing import List
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import torch
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from transformers import AutoModel, PreTrainedModel, PreTrainedTokenizer
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from FlagEmbedding.abc.finetune.embedder import AbsEmbedderModel
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logger = logging.getLogger(__name__)
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class BiDecoderOnlyEmbedderModel(AbsEmbedderModel):
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"""Embedder model class for decoder only model.
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Args:
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base_model (PreTrainedModel): The base model to train on.
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tokenizer (PreTrainedTokenizer, optional): The tokenizer to use. Defaults to ``None``.
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negatives_cross_device (bool, optional): If True, will compute cross devices negative loss. Defaults to ``False``.
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temperature (float, optional): Temperature to control the scale of scores. Defaults to ``1.0``.
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sub_batch_size (int, optional): Sub-batch size during encoding. If negative, will not split to sub-batch.
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Defaults to ``-1``.
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kd_loss_type (str, optional): Type of knowledge distillation loss. Defaults to ``'kl_div'``.
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use_mrl (bool, optional): Whether to use MRL for training. Defaults to ``False``.
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mrl_dims (List[int], optional): The dimensions of MRL layers. Defaults to ``[]``.
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sentence_pooling_method (str, optional): Pooling method to get sentence embedding. Defaults to ``'last_token'``.
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normalize_embeddings (bool, optional): If True, normalize the embedding vector. Defaults to ``False``.
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"""
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TRANSFORMER_CLS = AutoModel
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def __init__(
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self,
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base_model: PreTrainedModel,
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tokenizer: PreTrainedTokenizer = None,
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negatives_cross_device: bool = False,
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temperature: float = 1.0,
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sub_batch_size: int = -1,
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kd_loss_type: str = 'kl_div',
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use_mrl: bool = False,
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mrl_dims: List[int] = [],
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sentence_pooling_method: str = 'last_token',
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normalize_embeddings: bool = False,
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):
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super().__init__(
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base_model,
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tokenizer=tokenizer,
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negatives_cross_device=negatives_cross_device,
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temperature=temperature,
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sub_batch_size=sub_batch_size,
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kd_loss_type=kd_loss_type,
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use_mrl=use_mrl,
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mrl_dims=mrl_dims,
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)
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self.sentence_pooling_method = sentence_pooling_method
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self.normalize_embeddings = normalize_embeddings
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self.cross_entropy = torch.nn.CrossEntropyLoss(reduction='mean')
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def encode(self, features):
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"""
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Encode and get the embedding.
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Args:
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features (Union[list, dict]): Features feed to the model.
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Returns:
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Union[torch.Tensor, List[torch.Tensor]]: The embedding vectors or a list of embedding vectors if MRL is used.
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"""
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if features is None:
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return None
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if not isinstance(features, list):
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if self.sub_batch_size is not None and self.sub_batch_size > 0:
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all_p_reps = []
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for i in range(0, len(features['attention_mask']), self.sub_batch_size):
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end_inx = min(i + self.sub_batch_size, len(features['attention_mask']))
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sub_features = {}
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for k, v in features.items():
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sub_features[k] = v[i:end_inx]
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last_hidden_state = self.model(**sub_features, return_dict=True).last_hidden_state
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p_reps = self._sentence_embedding(last_hidden_state, sub_features['attention_mask'])
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all_p_reps.append(p_reps)
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all_p_reps = torch.cat(all_p_reps, 0).contiguous()
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else:
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last_hidden_state = self.model(**features, return_dict=True).last_hidden_state
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all_p_reps = self._sentence_embedding(last_hidden_state, features['attention_mask'])
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else:
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all_p_reps = []
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for sub_features in features:
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last_hidden_state = self.model(**sub_features, return_dict=True).last_hidden_state
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p_reps = self._sentence_embedding(last_hidden_state, sub_features['attention_mask'])
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all_p_reps.append(p_reps)
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all_p_reps = torch.cat(all_p_reps, 0).contiguous()
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if self.use_mrl:
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p_reps_list = []
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ori_dim = all_p_reps.size(-1)
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for dim in self.mrl_dims:
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if dim > ori_dim:
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logger.warning(f"MRL dim {dim} is larger than original dimension {ori_dim}, using original dimension instead.")
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dim = min(dim, ori_dim)
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dim_p_reps = all_p_reps[:, :dim]
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if self.normalize_embeddings:
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dim_p_reps = torch.nn.functional.normalize(dim_p_reps, dim=-1)
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p_reps_list.append(dim_p_reps.contiguous())
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return p_reps_list
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else:
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if self.normalize_embeddings:
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all_p_reps = torch.nn.functional.normalize(all_p_reps, dim=-1)
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return all_p_reps.contiguous()
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def _sentence_embedding(self, last_hidden_state, attention_mask):
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"""Use the pooling method to get the sentence embedding.
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Args:
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last_hidden_state (torch.Tensor): The model output's last hidden state.
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attention_mask (torch.Tensor): Mask out padding tokens during pooling.
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Raises:
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NotImplementedError: Specified pooling method not implemented.
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|
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Returns:
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torch.Tensor: The sentence embeddings.
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"""
|
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if self.sentence_pooling_method == "cls":
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return last_hidden_state[:, 0]
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elif self.sentence_pooling_method == "mean":
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s = torch.sum(
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last_hidden_state * attention_mask.unsqueeze(-1).float(), dim=1
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)
|
||||
d = attention_mask.sum(dim=1, keepdim=True).float()
|
||||
return s / d
|
||||
elif self.sentence_pooling_method == "last_token":
|
||||
left_padding = attention_mask[:, -1].sum() == attention_mask.shape[0]
|
||||
if left_padding:
|
||||
return last_hidden_state[:, -1]
|
||||
else:
|
||||
sequence_lengths = attention_mask.sum(dim=1) - 1
|
||||
batch_size = last_hidden_state.shape[0]
|
||||
return last_hidden_state[
|
||||
torch.arange(batch_size, device=last_hidden_state.device),
|
||||
sequence_lengths,
|
||||
]
|
||||
else:
|
||||
raise NotImplementedError(f"pooling method {self.sentence_pooling_method} not implemented")
|
||||
|
||||
def compute_score(self, q_reps, p_reps):
|
||||
"""Computes the scores between query and passage representations.
|
||||
|
||||
Args:
|
||||
q_reps (torch.Tensor): Query representations.
|
||||
p_reps (torch.Tensor): Passage representations.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: The computed scores, adjusted by temperature.
|
||||
"""
|
||||
scores = self._compute_similarity(q_reps, p_reps) / self.temperature
|
||||
scores = scores.view(q_reps.size(0), -1)
|
||||
return scores
|
||||
|
||||
def _compute_similarity(self, q_reps, p_reps):
|
||||
"""Computes the similarity between query and passage representations using inner product.
|
||||
|
||||
Args:
|
||||
q_reps (torch.Tensor): Query representations.
|
||||
p_reps (torch.Tensor): Passage representations.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: The computed similarity matrix.
|
||||
"""
|
||||
if len(p_reps.size()) == 2:
|
||||
return torch.matmul(q_reps, p_reps.transpose(0, 1))
|
||||
return torch.matmul(q_reps, p_reps.transpose(-2, -1))
|
||||
|
||||
def compute_loss(self, scores, target):
|
||||
"""Compute the loss using cross entropy.
|
||||
|
||||
Args:
|
||||
scores (torch.Tensor): Computed score.
|
||||
target (torch.Tensor): The target value.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: The computed cross entropy loss.
|
||||
"""
|
||||
return self.cross_entropy(scores, target)
|
||||
|
||||
def gradient_checkpointing_enable(self, **kwargs):
|
||||
"""
|
||||
Activates gradient checkpointing for the current model.
|
||||
"""
|
||||
self.model.gradient_checkpointing_enable(**kwargs)
|
||||
|
||||
def enable_input_require_grads(self, **kwargs):
|
||||
"""
|
||||
Enables the gradients for the input embeddings.
|
||||
"""
|
||||
self.model.enable_input_require_grads(**kwargs)
|
||||
|
||||
def save(self, output_dir: str):
|
||||
"""Save the model to the directory.
|
||||
|
||||
Args:
|
||||
output_dir (str): Directory for saving the model.
|
||||
"""
|
||||
state_dict = self.model.state_dict()
|
||||
state_dict = type(state_dict)(
|
||||
{k: v.clone().cpu()
|
||||
for k,
|
||||
v in state_dict.items()})
|
||||
self.model.save_pretrained(output_dir, state_dict=state_dict)
|
||||
@@ -0,0 +1,134 @@
|
||||
import logging
|
||||
from typing import Tuple
|
||||
from pathlib import Path
|
||||
from transformers import AutoConfig, AutoTokenizer, PreTrainedTokenizer
|
||||
|
||||
from FlagEmbedding.abc.finetune.embedder.AbsArguments import AbsEmbedderDataArguments, AbsEmbedderTrainingArguments
|
||||
from FlagEmbedding.abc.finetune.embedder import AbsEmbedderRunner, AbsEmbedderModel, EmbedderTrainerCallbackForDataRefresh
|
||||
|
||||
from .arguments import DecoderOnlyEmbedderModelArguments
|
||||
from .trainer import DecoderOnlyEmbedderTrainer
|
||||
from .modeling import BiDecoderOnlyEmbedderModel
|
||||
from .load_model import get_model, save_merged_model
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class DecoderOnlyEmbedderRunner(AbsEmbedderRunner):
|
||||
"""Runner class for decoder only embedding model.
|
||||
|
||||
Args:
|
||||
model_args (DecoderOnlyEmbedderModelArguments): Model arguments instance.
|
||||
data_args (AbsEmbedderDataArguments): Data arguments instance.
|
||||
training_args (AbsEmbedderTrainingArguments): Trainer arguments.
|
||||
"""
|
||||
def __init__(
|
||||
self,
|
||||
model_args: DecoderOnlyEmbedderModelArguments,
|
||||
data_args: AbsEmbedderDataArguments,
|
||||
training_args: AbsEmbedderTrainingArguments
|
||||
):
|
||||
super().__init__(model_args, data_args, training_args)
|
||||
self.model_args: DecoderOnlyEmbedderModelArguments
|
||||
self.data_args: AbsEmbedderDataArguments
|
||||
self.training_args: AbsEmbedderTrainingArguments
|
||||
|
||||
def load_tokenizer_and_model(self) -> Tuple[PreTrainedTokenizer, AbsEmbedderModel]:
|
||||
"""Load tokenizer and model.
|
||||
|
||||
Returns:
|
||||
Tuple[PreTrainedTokenizer, AbsEmbedderModel]: Tokenizer and model instances.
|
||||
"""
|
||||
tokenizer = AutoTokenizer.from_pretrained(
|
||||
self.model_args.tokenizer_name if self.model_args.tokenizer_name else self.model_args.model_name_or_path,
|
||||
token=self.model_args.token,
|
||||
cache_dir=self.model_args.cache_dir,
|
||||
use_fast=self.model_args.use_fast_tokenizer,
|
||||
add_eos_token=True,
|
||||
trust_remote_code=self.model_args.trust_remote_code,
|
||||
)
|
||||
|
||||
if tokenizer.pad_token is None:
|
||||
if tokenizer.unk_token is not None:
|
||||
tokenizer.pad_token = tokenizer.unk_token
|
||||
tokenizer.pad_token_id = tokenizer.unk_token_id
|
||||
else:
|
||||
tokenizer.pad_token = tokenizer.eos_token
|
||||
tokenizer.pad_token_id = tokenizer.eos_token_id
|
||||
tokenizer.padding_side = 'left'
|
||||
|
||||
resize = False
|
||||
if self.model_args.additional_special_tokens is not None:
|
||||
special_tokens_dict = {'additional_special_tokens': self.model_args.additional_special_tokens}
|
||||
add_num = tokenizer.add_special_tokens(special_tokens_dict)
|
||||
if add_num > 0:
|
||||
resize = True
|
||||
logger.info(f"Add {add_num} special tokens to the tokenizer. Special tokens: {self.model_args.additional_special_tokens}")
|
||||
else:
|
||||
logger.warning(f"Special tokens {self.model_args.additional_special_tokens} already exists in the tokenizer.")
|
||||
base_model = get_model(self.model_args, self.training_args.output_dir, resize, len(tokenizer))
|
||||
|
||||
num_labels = 1
|
||||
config = AutoConfig.from_pretrained(
|
||||
self.model_args.config_name if self.model_args.config_name else self.model_args.model_name_or_path,
|
||||
num_labels=num_labels,
|
||||
cache_dir=self.model_args.cache_dir,
|
||||
token=self.model_args.token,
|
||||
trust_remote_code=self.model_args.trust_remote_code,
|
||||
)
|
||||
logger.info('Config: %s', config)
|
||||
|
||||
model = BiDecoderOnlyEmbedderModel(
|
||||
base_model,
|
||||
tokenizer=tokenizer,
|
||||
negatives_cross_device=self.training_args.negatives_cross_device,
|
||||
temperature=self.training_args.temperature,
|
||||
sub_batch_size=self.training_args.sub_batch_size,
|
||||
kd_loss_type=self.training_args.kd_loss_type,
|
||||
use_mrl=self.training_args.use_mrl,
|
||||
mrl_dims=self.training_args.mrl_dims,
|
||||
sentence_pooling_method=self.training_args.sentence_pooling_method,
|
||||
normalize_embeddings=self.training_args.normalize_embeddings
|
||||
)
|
||||
|
||||
if self.training_args.gradient_checkpointing:
|
||||
model.enable_input_require_grads()
|
||||
|
||||
if self.training_args.fix_position_embedding:
|
||||
for k, v in model.named_parameters():
|
||||
if "position_embeddings" in k:
|
||||
logging.info(f"Freeze the parameters for {k}")
|
||||
v.requires_grad = False
|
||||
return tokenizer, model
|
||||
|
||||
def load_trainer(self) -> DecoderOnlyEmbedderTrainer:
|
||||
"""Load the trainer.
|
||||
|
||||
Returns:
|
||||
DecoderOnlyEmbedderTrainer: Loaded trainer instance.
|
||||
"""
|
||||
trainer = DecoderOnlyEmbedderTrainer(
|
||||
model=self.model,
|
||||
args=self.training_args,
|
||||
train_dataset=self.train_dataset,
|
||||
data_collator=self.data_collator,
|
||||
processing_class=self.tokenizer
|
||||
)
|
||||
if self.data_args.same_dataset_within_batch:
|
||||
trainer.add_callback(EmbedderTrainerCallbackForDataRefresh(self.train_dataset))
|
||||
return trainer
|
||||
|
||||
def run(self):
|
||||
"""
|
||||
Run the finetune.
|
||||
"""
|
||||
if not self.model_args.only_merge_lora_model:
|
||||
Path(self.training_args.output_dir).mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Training
|
||||
self.trainer.train(resume_from_checkpoint=self.training_args.resume_from_checkpoint)
|
||||
self.trainer.save_model()
|
||||
|
||||
# save merged model
|
||||
if self.model_args.save_merged_lora_model and self.training_args.process_index == 0:
|
||||
save_merged_model(self.model_args, self.training_args.output_dir)
|
||||
@@ -0,0 +1,45 @@
|
||||
import os
|
||||
import torch
|
||||
import logging
|
||||
from typing import Optional
|
||||
|
||||
from FlagEmbedding.abc.finetune.embedder import AbsEmbedderTrainer
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class DecoderOnlyEmbedderTrainer(AbsEmbedderTrainer):
|
||||
"""
|
||||
Trainer class for base encoder models.
|
||||
"""
|
||||
def _save(self, output_dir: Optional[str] = None, state_dict=None):
|
||||
"""Save the model to directory.
|
||||
|
||||
Args:
|
||||
output_dir (Optional[str], optional): Output directory to save the model. Defaults to ``None``.
|
||||
|
||||
Raises:
|
||||
NotImplementedError
|
||||
"""
|
||||
output_dir = output_dir if output_dir is not None else self.args.output_dir
|
||||
os.makedirs(output_dir, exist_ok=True)
|
||||
logger.info("Saving model checkpoint to %s", output_dir)
|
||||
# Save a trained model and configuration using `save_pretrained()`.
|
||||
# They can then be reloaded using `from_pretrained()`
|
||||
if not hasattr(self.model, 'save'):
|
||||
raise NotImplementedError(
|
||||
f'MODEL {self.model.__class__.__name__} '
|
||||
f'does not support save interface')
|
||||
else:
|
||||
self.model.save(output_dir)
|
||||
|
||||
if self.tokenizer is not None and self.is_world_process_zero():
|
||||
self.tokenizer.save_pretrained(output_dir)
|
||||
|
||||
torch.save(self.args, os.path.join(output_dir, "training_args.bin"))
|
||||
|
||||
# save the checkpoint for sentence-transformers library
|
||||
# if self.is_world_process_zero():
|
||||
# save_ckpt_for_sentence_transformers(output_dir,
|
||||
# pooling_mode=self.args.sentence_pooling_method,
|
||||
# normlized=self.args.normlized)
|
||||
Reference in New Issue
Block a user