119 lines
4.3 KiB
Python
119 lines
4.3 KiB
Python
"""
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Author: LiangSong(sl12160010@gmail.com)
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Date: 2023-04-12 19:12:42
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LastEditors: LiangSong(sl12160010@gmail.com)
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LastEditTime: 2023-05-08 23:39:35
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FilePath: /Open-Llama/train_lm.py
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Description:
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Copyright (c) 2023 by LiangSong(sl12160010@gmail.com), All Rights Reserved.
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"""
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import yaml
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import math
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import logging
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from absl import app
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from absl import flags
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from accelerate import Accelerator
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from torch.utils.data import DataLoader
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from peft import LoraConfig, TaskType, get_peft_model
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from datasets.distributed import split_dataset_by_node
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from transformers import AutoConfig, AutoModelForCausalLM, LlamaTokenizer
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from dataset.dataset import construct_dataset
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from solver.trainer import Trainer
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FLAGS = flags.FLAGS
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flags.DEFINE_string("train_config", None, "Training config path")
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flags.DEFINE_string(
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"model_config", "configs/model_configs/7B.json", "Model config path"
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)
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def main(argv):
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with open(FLAGS.train_config, "r", encoding="utf-8") as fp:
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config = yaml.load(fp, Loader=yaml.FullLoader)
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accelerator = Accelerator(
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gradient_accumulation_steps=config["train"].get(
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"gradient_accumulation_steps", 1
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)
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)
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tokenizer = LlamaTokenizer(
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config["data"]["tokenizer_model_path"],
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pad_token="<pad>",
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add_bos_token=False,
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add_eos_token=True,
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)
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data_config = config["data"]
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if data_config.get("split_by_shard", False):
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train_dataset = construct_dataset(
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data_config, tokenizer, world_size=accelerator.num_processes
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)
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else:
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train_dataset = construct_dataset(data_config, tokenizer)
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train_dataset = split_dataset_by_node(
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train_dataset,
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rank=accelerator.process_index,
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world_size=accelerator.num_processes,
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)
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train_loader = DataLoader(
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train_dataset,
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batch_size=config["train"]["train_batch_size"],
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num_workers=config["train"]["train_num_workers"],
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prefetch_factor=config["train"].get("prefetch_factor", 2),
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pin_memory=True,
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)
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# smaller initializer_range make training more stable
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# add stabel embedding to token embedding
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model_config = AutoConfig.from_pretrained(FLAGS.model_config)
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# Make the vocab size divisible by 16
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# https://huggingface.co/docs/transformers/main_classes/deepspeed#how-to-choose-which-zero-stage-and-offloads-to-use-for-best-performance
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# https://developer.nvidia.com/blog/optimizing-gpu-performance-tensor-cores/
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# vocab_size = math.ceil(tokenizer.vocab_size / 16) * 16
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# logging.warning(
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# "Round vocab_size from {} to {}.".format(tokenizer.vocab_size, vocab_size)
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# )
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vocab_size = tokenizer.vocab_size
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model_config.vocab_size = vocab_size
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model_config.pad_token_id = tokenizer.pad_token_id
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# 使用AutoModel可以在Deepspeed.zero.Init()下正确的生效，而直接使用如OpenLlamaModel不能正确生效，导致浪费大量内存空间
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# https://github.com/huggingface/accelerate/pull/932
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if config["train"]["ckpt"] is not None:
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raw_model = AutoModelForCausalLM.from_pretrained(
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config["train"]["ckpt"], config=model_config
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)
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logging.warning("Loaded ckpt from: {}".format(config["train"]["ckpt"]))
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else:
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raw_model = AutoModelForCausalLM.from_config(model_config)
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# lora
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if config["train"].get("use_lora", False):
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# gradient ckpt bug, https://github.com/huggingface/transformers/issues/23170
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if hasattr(raw_model, "enable_input_require_grads"):
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raw_model.enable_input_require_grads()
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else:
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def make_inputs_require_grad(module, input, output):
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output.requires_grad_(True)
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raw_model.get_input_embeddings().register_forward_hook(
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make_inputs_require_grad
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)
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peft_config = LoraConfig(
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task_type=TaskType.CAUSAL_LM,
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target_modules=["q_proj", "v_proj"],
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inference_mode=False,
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r=1,
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lora_alpha=32,
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lora_dropout=0.1,
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)
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raw_model = get_peft_model(raw_model, peft_config)
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raw_model.print_trainable_parameters()
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if config["train"].get("gradient_checkpointing_enable", False):
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raw_model.gradient_checkpointing_enable()
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trainer = Trainer(config, raw_model, train_loader, tokenizer, accelerator)
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trainer.train()
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if __name__ == "__main__":
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app.run(main)
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