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f8f0e4e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 | import os
import torch
from trl import SFTTrainer, SFTConfig
from trl.trainer.utils import DataCollatorForCompletionOnlyLM
from accelerate import Accelerator
import random
random.seed(42)
os.environ["WANDB_PROJECT"] = "ma-rlhf"
os.environ["WANDB_RUN_NAME"] = "sft"
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
)
from utils import (
ScriptArguments,
DEFINE_PAD_TOKEN,
create_peft,
formatting_prompt_response_func_batched,
resolve_system_prompt,
)
from data_adapter import load_sft_dataset
parser = HfArgumentParser(ScriptArguments)
train_args: ScriptArguments = parser.parse_args_into_dataclasses(return_remaining_strings=True)[0]
dataset_name = train_args.dataset_name
dataset_sub_name = train_args.dataset_sub_name
dataset_split = train_args.dataset_split
model_name = train_args.model_name
deepspeed_config_name = train_args.deepspeed_config_name
seq_length = train_args.seq_length
batch_size = train_args.batch_size
output_name = train_args.output_name
is_peft = train_args.use_QLora
use_flash_attention_2 = train_args.use_flash_attention_2
dataset_sub_name = None
num_train_epochs = train_args.num_train_epochs
gradient_accumulation_steps = train_args.gradient_accumulation_steps
learning_rate = train_args.learning_rate
default_system_prompt = resolve_system_prompt(train_args.system_prompt)
def create_datasets(dataset_name, dataset_sub_name):
dataset = load_sft_dataset(
dataset_name,
dataset_sub_name=dataset_sub_name,
split=dataset_split,
default_system_prompt=default_system_prompt,
)
return dataset, None
def create_model_tokenizer(name):
# QLoRA
bnb_config = BitsAndBytesConfig(
load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
)
device_map = {"": Accelerator().local_process_index}
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config if is_peft else None,
device_map=device_map,
use_flash_attention_2=use_flash_attention_2, # gpt 2 not support flash attention2
trust_remote_code=True,
torch_dtype=torch.bfloat16,
use_cache=False,
)
model.gradient_checkpointing_enable()
tokenizer = AutoTokenizer.from_pretrained(model_name,
trust_remote_code=True,
padding_side='left',
# model_max_length=1024
)
tokenizer.add_special_tokens({'pad_token': DEFINE_PAD_TOKEN})
model.pad_token_id = tokenizer.pad_token_id
model.pad_token = tokenizer.pad_token
model.config.pad_token_id = model.config.eos_token_id
return model, tokenizer
def create_sft_datasets(datasets, tokenizer, format_func, seq_length=512):
return datasets, None
def create_collator(tokenizer):
'''
ref https://github.com/huggingface/trl/blob/main/tests/test_data_collator_completion_only.py
'''
# instruction_template = "###Question: "
response_template = "###Answer:"
response_template_id = tokenizer.encode(
response_template, add_special_tokens=False
)[1:]
return DataCollatorForCompletionOnlyLM(response_template_id, tokenizer=tokenizer)
def train():
model, tokenizer = create_model_tokenizer(model_name)
datasets, _ = create_datasets(dataset_name, dataset_sub_name)
format_fun = formatting_prompt_response_func_batched
train_datasets, _ = create_sft_datasets(datasets, tokenizer, format_fun, seq_length)
collator = create_collator(tokenizer)
# peft
peft_config = create_peft(is_peft)
training_args = SFTConfig(
output_dir=output_name,
save_strategy='epoch',
logging_steps=1,
num_train_epochs=num_train_epochs,
gradient_checkpointing=True,
bf16=True,
learning_rate=learning_rate,
warmup_ratio=0.1,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
deepspeed=deepspeed_config_name,
report_to='wandb',
lr_scheduler_type='cosine',
max_seq_length=seq_length,
# max_steps=10,
)
trainer = SFTTrainer(
model,
args=training_args,
train_dataset=train_datasets,
peft_config=peft_config,
data_collator=collator,
formatting_func=format_fun,
)
trainer.train()
trainer.save_model(output_name)
if __name__ == "__main__":
train()
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