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811e03d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | import argparse
import os
import sys
from typing import List
import torch
import transformers
from peft import PeftModel
from peft import (
TaskType,
LoraConfig,
get_peft_model,
get_peft_model_state_dict,
set_peft_model_state_dict,
)
from transformers import LlamaForCausalLM, LlamaTokenizer, LlamaConfig
from utils import *
from collator import Collator
import argparse
from utils import *
from rq_llama import *
parser = argparse.ArgumentParser(description = 'rqllama-finetune')
parser = parse_finetune_args(parser)
args = parser.parse_args()
set_seed(args.seed)
ensure_dir(args.output_dir)
device_map = "auto"
world_size = int(os.environ.get("WORLD_SIZE", 1))
ddp = world_size != 1
local_rank = int(os.environ.get("LOCAL_RANK") or 0)
if local_rank == 0:
print(vars(args))
if ddp:
device_map = {"": local_rank}
train_data, valid_data = load_finetune_datasets(args)
rqllama = LlamaWithRQ.from_pretrained(args.ckpt_path, torch_dtype = torch.float16, low_cpu_mem_usage = True, device_map = device_map)
tokenizer = rqllama.tokenizer
# PeftModelForCausalLM
model = rqllama.model
device = rqllama.device
postfix = '<p-{}>'
new_tokens = []
new_ids = list(range(args.re_index))
for i in new_ids:
new_tokens.append(postfix.format(int(i)))
tokenizer.add_tokens(new_tokens)
if local_rank == 0:
print("token num:", len(rqllama.tokenizer))
print("data num:", len(train_data))
collator = Collator(args, tokenizer)
# Re-index Embedding
new_ids = torch.tensor(new_ids, dtype = torch.float16).reshape(-1,1)
re_index_emb = torch.nn.Linear(1, model.config.hidden_size, dtype = torch.float16).to(device)
new_embeddings = re_index_emb(new_ids.to(device))
# PeftModelForCausalLM -> LlamaForCausalLM -> LlamaModel
model.model.model.embed_tokens.original_module.weight.data = torch.cat([model.model.model.embed_tokens.original_module.weight.data, new_embeddings], dim = 0)
model.model.model.embed_tokens.modules_to_save.default.weight.data = torch.cat([model.model.model.embed_tokens.modules_to_save.default.weight.data, new_embeddings], dim = 0)
new_lm_head = torch.randn(args.re_index, model.config.hidden_size, requires_grad = True).to(device)
# print('new_lm_head:',new_lm_head.requires_grad)
# PeftModelForCausalLM -> LlamaForCausalLM
model.model.lm_head.original_module.weight.data = torch.cat([model.model.lm_head.original_module.weight.data, new_lm_head], dim = 0)
model.model.lm_head.modules_to_save.default.weight.data = torch.cat([model.model.lm_head.modules_to_save.default.weight.data, new_lm_head], dim = 0)
model.config.vocab_size = len(tokenizer)
# print(model.model.model.embed_tokens.original_module.weight.shape)
# print(len(tokenizer))
model.train()
if local_rank == 0:
model.print_trainable_parameters()
trainer = transformers.Trainer(
model = model,
train_dataset = train_data,
eval_dataset = valid_data,
args = transformers.TrainingArguments(
seed = args.seed,
per_device_train_batch_size = args.per_device_batch_size,
per_device_eval_batch_size = args.per_device_batch_size,
gradient_accumulation_steps = args.gradient_accumulation_steps,
warmup_ratio = args.warmup_ratio,
num_train_epochs = args.epochs,
learning_rate = args.learning_rate,
weight_decay = args.weight_decay,
lr_scheduler_type = args.lr_scheduler_type,
fp16 = args.fp16,
bf16 = args.bf16,
logging_steps = args.logging_step,
optim = args.optim,
gradient_checkpointing = True,
evaluation_strategy = args.save_and_eval_strategy,
save_strategy = args.save_and_eval_strategy,
eval_steps = args.save_and_eval_steps,
save_steps = args.save_and_eval_steps,
output_dir = args.output_dir,
save_total_limit = 50,
load_best_model_at_end = True,
deepspeed = args.deepspeed,
ddp_find_unused_parameters = False if ddp else None,
report_to = None,
eval_delay = 1 if args.save_and_eval_strategy=="epoch" else 2000,
dataloader_num_workers = args.dataloader_num_workers,
dataloader_prefetch_factor = args.dataloader_prefetch_factor,
remove_unused_columns = args.remove_unused_columns,
),
tokenizer = tokenizer,
data_collator = collator,
)
model.config.use_cache = False
if torch.__version__ >= "2" and sys.platform != "win32":
model = torch.compile(model)
trainer.train(resume_from_checkpoint = args.resume_from_checkpoint)
trainer.save_state()
trainer.save_model(output_dir = args.output_dir)
if local_rank == 0:
import smtplib
from email.mime.text import MIMEText
mail_host = 'smtp.qq.com'
mail_code = 'ouzplpngooqndjcb'
sender = '1849334588@qq.com'
receiver = 'esperanto1949@foxmail.com'
task = '[A100: finetune tt.llama]'
message = MIMEText('Task {task} Finished'.format(task = task), 'plain', 'utf-8')
message['Subject'] = 'Auto Email'
message['From'] = sender
message['To'] = receiver
server = smtplib.SMTP_SSL("smtp.qq.com", 465)
server.login(sender, mail_code)
server.sendmail(sender, receiver, message.as_string())
server.quit() |