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run_training.py
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run_training.py
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#coding=utf-8
import torch.fx
from tqdm import tqdm
from transformers import AutoTokenizer
import torch
from mindmerger_tools.utils import save_model, set_seed
from mindmerger_tools.read_datasets import *
import argparse
import ast
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
import json
import deepspeed
from mindmerger_tools.input_features import *
from modeling_mindmerger import MindMerger
import os
from mindmerger_tools.deepspeed_config import get_train_ds_config
from evaluation import evaluate_ppl
def main(args):
llm_path = args.llm_path
mt_path = args.mt_path
train_num = args.train_num
stage_name = args.stage_name
task = args.task
augmentation = args.augmentation
save_name = args.save_name
result_path_base = f'./results/{save_name}/{task}/{stage_name}/'
output_model_path_base = f'./outputs/{save_name}/{task}/{stage_name}/'
if stage_name == 'mapping':
if 'math' in task:
languages = ['Bengali', 'Thai', 'Swahili', 'Japanese', 'Chinese', 'German', 'French', 'Russian',
'Spanish']
train_set = read_lego(train_num, languages)
elif 'csqa' in task:
languages = ['Urdu', 'Hindi', 'Swahili', 'Japanese', 'Vietnamese', 'Polish', 'Chinese',
'Flemish', 'Russian', 'Italian', 'German', 'Portuguese', 'French', 'Spanish', 'Arabic']
train_set = read_lego(train_num, languages)
else:
languages = ['Swahili', 'Urdu', 'Hindi', 'Thai', 'Arabic', 'Turkish', 'Greek',
'Vietnamese', 'Chinese', 'Russian', 'Bulgarian', 'German', 'French', 'Spanish']
train_set = read_lego(train_num, languages)
task = 'translation'
else:
if 'math' in task:
train_set = read_math_train(train_num)
elif 'csqa' in task:
train_set = read_x_csqa_train()
else:
train_set = read_xnli_train()
dev_set = train_set[:args.dev_size]
train_set = train_set[args.dev_size:]
train_set = MathDataset(train_set, task)
dev_set = MathDataset(dev_set, task)
lr = args.lr
epoch_num = args.epoch_num
max_seq_len = args.max_seq_len
max_gen_len = args.max_gen_len
train_batch_size = args.train_batch_size
eval_batch_size = args.eval_batch_size
train_micro_batch_size_per_gpu = args.train_micro_batch_size_per_gpu
gpu_num = torch.cuda.device_count()
gradient_accumulation = train_batch_size // (train_micro_batch_size_per_gpu * gpu_num)
assert train_micro_batch_size_per_gpu * gpu_num * gradient_accumulation == train_batch_size
ds_config = get_train_ds_config(train_batch_size, train_micro_batch_size_per_gpu, lr, gradient_accumulation)
os.makedirs(output_model_path_base, exist_ok=True)
os.makedirs(result_path_base, exist_ok=True)
tokenizer_m2m = AutoTokenizer.from_pretrained(mt_path)
tokenizer_llm = AutoTokenizer.from_pretrained(llm_path, use_fast=True)
tokenizer_llm.pad_token = tokenizer_llm.eos_token
tokenizer_llm.padding_side = "left"
# tokenizer_llm.pad_token = "[PAD]"
print(json.dumps({
'llm_path': llm_path,
'mt_path': mt_path,
'lr': lr,
'epoch_num': epoch_num,
'gradient_accumulation': gradient_accumulation,
'train_set:': len(train_set),
'dev_set:': len(dev_set),
'max_seq_len': max_seq_len,
'max_gen_len': max_gen_len,
'train_batch_size': train_batch_size,
'result_path': result_path_base,
'output_model_path': output_model_path_base,
}, indent=2))
if stage_name != 'mapping' and args.init_checkpoint is None:
args.init_checkpoint = f'./outputs/{save_name}/{task}/mapping/pytorch_model.bin'
model = MindMerger(mt_path, llm_path, max_gen_len,
tokenizer_llm.bos_token_id,
tokenizer_llm.pad_token_id)
if args.init_checkpoint is not None:
init_checkpoint = args.init_checkpoint
checkpoint = torch.load(init_checkpoint, map_location='cpu')
model_dict = checkpoint['model_state_dict']
model.mapping.load_state_dict(model_dict, False)
print('mapping layer init from:', init_checkpoint)
parameters = filter(lambda p: p.requires_grad, model.parameters())
model, optimizer, _, __ = deepspeed.initialize(
config=ds_config,
model=model,
model_parameters=parameters,
training_data=None)
train_sampler = DistributedSampler(train_set)
dev_sampler = SequentialSampler(dev_set)
train_set = torch.utils.data.DataLoader(
dataset=train_set,
batch_size=train_micro_batch_size_per_gpu,
sampler=train_sampler,
)
dev_set = torch.utils.data.DataLoader(
dataset=dev_set,
batch_size=eval_batch_size,
shuffle=False,
sampler=dev_sampler,
num_workers=1,
drop_last=False)
global_rank = torch.distributed.get_rank()
# best_perplexity = 1000000000
best_perplexity = evaluate_ppl(model, dev_set, tokenizer_llm, tokenizer_m2m,
max_seq_len, max_gen_len, langs_map, augmentation)
eval_step = 2000
for epoch in range(epoch_num):
model.train()
tr_loss, nb_tr_steps = 0, 0
step_count = 0
step_trange = tqdm(train_set)
for train_step in step_trange:
sources = train_step['source']
prompts = train_step['prompt']
targets = train_step['target']
source_languages = train_step['source_language']
input_ids_m2m, attention_mask_m2m = mt_input_features(sources, tokenizer_m2m,
max_seq_len, source_languages,
langs_map)
add_bos_token = False
add_eos_token = True
labels, mask_label = llm_input_features(targets, tokenizer_llm,
max_gen_len, add_bos_token, add_eos_token)
input_ids_prompt, mask_prompt = None, None
if augmentation:
add_bos_token = False
add_eos_token = False
input_ids_prompt, mask_prompt = llm_input_features(prompts, tokenizer_llm,
max_gen_len, add_bos_token,
add_eos_token)
loss = model(input_ids_m2m, attention_mask_m2m,
input_ids_prompt=input_ids_prompt, mask_prompt=mask_prompt,
labels=labels, mask_label=mask_label)
loss = loss.mean()
tr_loss += loss.item()
nb_tr_steps += 1
model.backward(loss)
model.step()
loss_show = ' Epoch:' + str(epoch) + " loss:" + str(round(tr_loss / nb_tr_steps, 4)) #+ f" lr:{'%.2E' % scheduler.get_last_lr()[0]}"
step_trange.set_postfix_str(loss_show)
if step_count % eval_step == 0 and step_count > 0:
perplexity = evaluate_ppl(model, dev_set, tokenizer_llm, tokenizer_m2m,
max_seq_len, max_gen_len, langs_map, augmentation)
print('ppl:', perplexity)
if global_rank == 0 and perplexity < best_perplexity:
best_perplexity = perplexity
save_model(output_model_path_base, model.mapping)
print('save new best')
step_count += 1
perplexity = evaluate_ppl(model, dev_set, tokenizer_llm, tokenizer_m2m,
max_seq_len, max_gen_len, langs_map, augmentation)
print('ppl:', perplexity)
if global_rank == 0 and perplexity < best_perplexity:
best_perplexity = perplexity
save_model(output_model_path_base, model.mapping)
print('save new best')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--llm_path",
type=str,
default='../LLMs/Llama-2-7b-hf/'
)
parser.add_argument(
"--mt_path",
type=str,
default='../LLMs/mt5-xl/'
)
parser.add_argument(
"--save_name",
type=str,
default='MindMerger'
)
parser.add_argument(
"--task",
type=str,
default='translation'
)
parser.add_argument(
"--stage_name",
type=str,
default='mapping'
)
parser.add_argument(
"--lr",
type=float,
default=2e-5
)
parser.add_argument(
"--epoch_num",
type=int,
default=3
)
parser.add_argument(
"--train_num",
type=int,
default=100000
)
parser.add_argument(
"--train_batch_size",
type=int,
default=24
)
parser.add_argument(
"--train_micro_batch_size_per_gpu",
type=int,
default=1
)
parser.add_argument(
"--eval_batch_size",
type=int,
default=2
)
parser.add_argument(
"--max_seq_len",
type=int,
default=512
)
parser.add_argument(
"--max_gen_len",
type=int,
default=512
)
parser.add_argument(
"--dev_size",
type=int,
default=3000
)
parser.add_argument(
"--init_checkpoint",
type=str,
default=None,
)
parser.add_argument(
"--gpu",
type=str,
default='0'
)
parser.add_argument(
"--local_rank",
type=int,
default=0
)
parser.add_argument(
"--augmentation",
type=ast.literal_eval,
default=False
)
parser = deepspeed.add_config_arguments(parser)
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
os.environ["TOKENIZERS_PARALLELISM"] = "false"
set_seed(0)
langs = ['Thai', 'Swahili', 'Bengali', 'Chinese', 'German', 'Spanish', 'French', 'Japanese', 'Russian', 'English']
langs_map_flores = {'Swahili': 'swh', 'Benli': 'ben', 'English': 'eng', 'Thai': 'tha', 'Chinese': 'zho_simpl',
'German': 'deu', 'Spanish': 'spa', 'French': 'fra', 'Japanese': 'jpn', 'Russian': 'rus', }
langs_map_m2m = {'English': 'en', 'Swahili': 'sw', 'Chinese': 'zh', 'Bengali': 'bn',
'German': 'de', 'Spanish': 'es', 'French': 'fr', 'Japanese': 'ja',
'Russian': 'ru', 'Thai': 'th', 'Greek': 'el', 'Telugu': 'te',
'Arabic': 'ar', 'Bulgarian': 'bg', 'Croatian': 'hr', 'Hungarian': 'hu',
'Italian': 'it', 'Lithuanian': 'lt', 'Macedonian': 'mk', 'Polish': 'pl',
'Portuguese': 'pt', 'Albanian': 'sq', 'Serbian': 'sr', 'Turkish': 'tr',
'Vietnamese': 'vi', 'Hindi': 'hi', 'Flemish': 'nl', 'Urdu': 'ur'}
langs_map_nllb = {
'English': 'eng_Latn', 'Swahili': 'swh_Latn', 'Chinese': 'zho_Hans', 'Bengali': 'ben_Beng',
'German': 'deu_Latn', 'Spanish': 'spa_Latn', 'French': 'fra_Latn', 'Japanese': 'jpn_Jpan',
'Russian': 'rus_Cyrl', 'Thai': 'tha_Thai'
}
if 'nllb' in args.mt_path:
langs_map = langs_map_nllb
else:
langs_map = langs_map_m2m
main(args)