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Add MiSS, an evolution of Bone, from https://arxiv.org/abs/2409.15371. MiSS will replace Bone, which is now deprecated. A script to convert Bone checkpoints to MiSS checkpoints is included.
108 lines
3.9 KiB
Python
108 lines
3.9 KiB
Python
# Copyright 2025-present the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from dataclasses import dataclass, field
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from typing import Literal, Optional
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import torch
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from datasets import load_dataset
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from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
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from trl import SFTConfig, SFTTrainer
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from peft import MissConfig, get_peft_model
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@dataclass
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class ScriptArguments(SFTConfig):
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# model configs
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base_model_name_or_path: Optional[str] = field(
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default=None, metadata={"help": "The name or path of the fp32/16 base model."}
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)
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bits: str = field(default="bf16", metadata={"help": "(`['bf16', 'fp16', fp32]`)"})
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init_weights: Literal[True, "bat"] = field(
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default=True,
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metadata={
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"help": (
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"True -> MiSS efficience and balance; `bat` -> Bat, `mini` -> smaller MiSS efficience and balance"
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),
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},
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)
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miss_r: int = field(default=16)
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merge_and_save: bool = field(default=False)
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# dataset configs
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data_path: str = field(default="imdb", metadata={"help": "Path to the training data."})
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dataset_split: str = field(default="train[:1%]", metadata={"help": "(`['train', 'test', 'eval']`):"})
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dataset_field: list[str] = field(default=None, metadata={"help": "Fields of dataset input and output."})
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parser = HfArgumentParser(ScriptArguments)
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script_args = parser.parse_args_into_dataclasses()[0]
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print(script_args)
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print(f"Load pre-processed residual model in {script_args.bits} bits.")
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if script_args.bits in ["nf4", "fp4", "int8"]:
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print("MiSS currently does not support quantization.")
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elif script_args.base_model_name_or_path is not None:
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print(f"No available pre-processed model, manually initialize a MiSS using {script_args.base_model_name_or_path}.")
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model = AutoModelForCausalLM.from_pretrained(
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script_args.base_model_name_or_path,
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torch_dtype=(
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torch.float16
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if script_args.bits == "fp16"
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else (torch.bfloat16 if script_args.bits == "bf16" else torch.float32)
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),
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained(script_args.base_model_name_or_path)
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tokenizer.pad_token_id = tokenizer.eos_token_id
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miss_config = MissConfig(
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r=script_args.miss_r,
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target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"],
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bias="none",
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task_type="CAUSAL_LM",
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init_weights=script_args.init_weights,
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)
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peft_model = get_peft_model(model, miss_config)
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print(peft_model)
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peft_model.print_trainable_parameters()
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print(f"Training MiSS with trl on the {script_args.data_path}[{script_args.dataset_split}] dataset.")
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dataset = load_dataset(script_args.data_path, split=script_args.dataset_split)
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dataset = dataset.map(
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lambda example: {
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"text": f"### USER: {example[script_args.dataset_field[0]]}\n### ASSISTANT: {example[script_args.dataset_field[1]]}"
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}
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)
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trainer = SFTTrainer(
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model=peft_model,
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args=script_args,
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train_dataset=dataset,
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tokenizer=tokenizer,
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)
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trainer.train()
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trainer.save_state()
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peft_model.save_pretrained(
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os.path.join(script_args.output_dir, "miss_ft"),
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)
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if script_args.merge_and_save:
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model = peft_model.merge_and_unload()
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model.save_pretrained(os.path.join(script_args.output_dir, "miss_merged"))
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tokenizer.save_pretrained(os.path.join(script_args.output_dir, "miss_merged"))
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