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from locale import strcoll
from datasets import load_dataset
import numpy as np
import torch
from torch import optim
from torch.nn import functional as F
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from transformers.optimization import Adafactor
from transformers import get_linear_schedule_with_warmup
from tqdm.notebook import tqdm
import random
import sacrebleu
import os
import pandas as pd
from sklearn.model_selection import train_test_split
import torch.multiprocessing as mp
from torch.multiprocessing import Process, Queue
from joblib import Parallel, delayed,parallel_backend
import sys
from functools import partial
import json
import time
import numpy as np
from datetime import datetime
class Config():
def __init__(self,args) -> None:
self.homepath = args.homepath
self.prediction_path = os.path.join(args.homepath,args.prediction_path)
# Use 'google/mt5-small' for non-pro cloab users
self.model_repo = 'google/mt5-base'
self.model_path_dir = args.homepath
self.model_name = f'{args.model_name}.pt'
self.bt_data_dir = os.path.join(args.homepath,args.bt_data_dir)
#Data part
self.parallel_dir= os.path.join(args.homepath,args.parallel_dir)
self.mono_dir= os.path.join(args.homepath,args.mono_dir)
self.log = os.path.join(args.homepath,args.log)
self.mono_data_limit = args.mono_data_limit
self.mono_data_for_noise_limit=args.mono_data_for_noise_limit
#Training params
self.n_epochs = args.n_epochs
self.n_bt_epochs=args.n_bt_epochs
self.batch_size = args.batch_size
self.max_seq_len = args.max_seq_len
self.min_seq_len = args.min_seq_len
self.checkpoint_freq = args.checkpoint_freq
self.lr = 1e-4
self.print_freq = args.print_freq
self.use_multiprocessing = args.use_multiprocessing
self.num_cores = mp.cpu_count()
self.NUM_PRETRAIN = args.num_pretrain_steps
self.NUM_BACKTRANSLATION_TIMES =args.num_backtranslation_steps
self.do_backtranslation=args.do_backtranslation
self.now_on_bt=False
self.bt_time=0
self.using_reconstruction= args.use_reconstruction
self.num_return_sequences_bt=2
self.use_torch_data_parallel = args.use_torch_data_parallel
self.gradient_accumulation_batch = args.gradient_accumulation_batch
self.num_beams = args.num_beams
self.best_loss = 1000
self.best_loss_delta = 0.00000001
self.patience=args.patience
self.L2=0.0000001
self.dropout=args.dropout
self.drop_prob=args.drop_probability
self.num_swaps=args.num_swaps
self.verbose=args.verbose
self.now_on_test=False
#Initialization of state dict which will be saved during training
self.state_dict = {'batch_idx': 0,'epoch':0,'bt_time':self.bt_time,'best_loss':self.best_loss}
self.state_dict_check = {'batch_idx': 0,'epoch':0,'bt_time':self.bt_time,'best_loss':self.best_loss} #this is for tracing training after abrupt end!
self.device = torch.device('cuda' if True and torch.cuda.is_available() else 'cpu')
#We will be leveraging parallel and monolingual data for each of these languages.
#parallel data will be saved in a central 'parallel_data 'folder as 'src'_'tg'_parallel.tsv
#monolingual data will be saved in another folder called 'monolingual_data' as 'lg'_mono.tsv
#Each tsv file is of the form "input", "output"
self.LANG_TOKEN_MAPPING = {
'ig': '<ig>',
'fon': '<fon>',
'en': '<en>',
'fr': '<fr>',
'rw':'<rw>',
'yo':'<yo>',
'xh':'<xh>',
'sw':'<sw>'
}
self.truncation=True
def beautify_time(time):
hr = time//(3600)
mins = (time-(hr*3600))//60
rest = time -(hr*3600) - (mins*60)
#DARIA's implementation!
sp = ""
if hr >=1:
sp += '{} hours'.format(hr)
if mins >=1:
sp += ' {} mins'.format(mins)
if rest >=1:
sp += ' {} seconds'.format(rest)
return sp
def word_delete(x,config):
noise=[]
words = x.split(' ')
if len(words) == 1:
return x
for w in words:
a= np.random.choice([0,1], 1, p=[config.drop_prob, 1-config.drop_prob])
if a[0]==1: #It means don't delete
noise.append(w)
#if you end up deleting all words, just return a random word
if len(noise) == 0:
rand_int = random.randint(0, len(words)-1)
return [words[rand_int]]
return ' '.join(noise)
def swap_word(new_words):
random_idx_1 = random.randint(0, len(new_words)-1)
random_idx_2 = random_idx_1
counter = 0
while random_idx_2 == random_idx_1:
random_idx_2 = random.randint(0, len(new_words)-1)
counter += 1
if counter > 3:
return new_words
new_words[random_idx_1], new_words[random_idx_2] = new_words[random_idx_2], new_words[random_idx_1]
return new_words
def random_swap(words, n):
words = words.split()
new_words = words.copy()
for _ in range(n):
new_words = swap_word(new_words)
sentence = ' '.join(new_words)
return sentence
def get_dict(input,target,src,tgt):
inp = [i for i in input]
target_ = [ i for i in target]
s= [src for i in range(len(inp))]
t = [tgt for i in range(len(target_))]
return [{'inputs':inp_,'targets':target__,'src':s_,'tgt':t_} for inp_,target__,s_,t_ in zip(inp,target_,s,t)]
def get_dict_mono(input,src,config):
index = [i for i in range(len(input))]
ids = random.sample(index,config.mono_data_limit)
inp = [input[i] for i in ids]
s= [src for i in range(len(inp))]
data=[]
for lang in config.LANG_TOKEN_MAPPING.keys():
if lang!=src and lang not in ['en','fr']:
data.extend([{'inputs':inp_,'src':s_,'tgt':lang} for inp_,s_ in zip(inp,s)])
return data
def get_dict_mono_noise(input,src,config):
index = [i for i in range(len(input))]
ids = random.sample(index,config.mono_data_for_noise_limit)
inp = [input[i] for i in ids]
noised = [word_delete(random_swap(str(x),config.num_swaps),config) for x in inp]
s= [src for i in range(len(inp))]
data=[]
data.extend([{'inputs':noise_,'targets':inp_,'src':s_,'tgt':s_} for inp_,s_,noise_ in zip(inp,s,noised)])
return data
def compress(input,target,src,tgt):
return {'inputs':input,'targets':target,'src':src,'tgt':tgt}
def make_dataset(config,mode):
if mode!='eval' and mode!='train' and mode!='test':
raise Exception('mode is either train or eval or test!')
else:
files = [f.name for f in os.scandir(config.parallel_dir) ]
files = [f for f in files if f.split('.')[-1]=='tsv' and f.split('.tsv')[0].endswith(mode) and len(f.split('_'))>2 ]
data = [(f_.split('_')[0],f_.split('_')[1],pd.read_csv(os.path.join(config.parallel_dir,f_), sep="\t")) for f_ in files]
dict_ = [get_dict(df['input'],df['target'],src,tgt) for src,tgt,df in data]
return [item for sublist in dict_ for item in sublist]
def get_model_translation(config,model,tokenizer,sentence,tgt):
if config.use_torch_data_parallel:
max_seq_len_ = model.module.config.max_length
else:
max_seq_len_ = model.config.max_length
input_ids = encode_input_str(config,text = sentence,target_lang = tgt,tokenizer = tokenizer,seq_len = max_seq_len_).unsqueeze(0).to(config.device)
if config.use_torch_data_parallel:
out = model.module.generate(input_ids,num_beams=3,do_sample=True, num_return_sequences=config.num_return_sequences_bt,max_length=config.max_seq_len,min_length=config.min_seq_len)
else:
out = model.generate(input_ids,num_beams=3, do_sample=True,num_return_sequences=config.num_return_sequences_bt,max_length=config.max_seq_len,min_length=config.min_seq_len)
out_id = [i for i in range(config.num_return_sequences_bt)]
id_ = random.sample(out_id,1)
return tokenizer.decode(out[id_][0], skip_special_tokens=True)
def do_job(t,id_,tokenizers):
tokenizer = tokenizers[id_ % len(tokenizers)]
#We flip the input as target and vice versa in order to have target-side backtranslation (where source side is synthetic).
return {'inputs':get_model_translation(config,model,tokenizer,t['inputs'],t['tgt']),'targets':t['inputs'],'src':t['tgt'],'tgt':t['src']}
#return {'inputs':t['inputs'],'targets':get_model_translation(config,model,tokenizer,t['inputs'],t['tgt']),'src':t['src'],'tgt':t['tgt']}
def do_job_pmap(t):
#tokenizer = tokenizers[id_ % len(tokenizers)]
return {'inputs':t['inputs'],'targets':get_model_translation(config,model,tokenizer,t['inputs'],t['tgt']),'src':t['src'],'tgt':t['tgt']}
def do_job_pool(bt_data,model,id_,tokenizers,config,mono_data):
tokenizer = tokenizers[id_]
if config.verbose:
print(f"Mono data inside job pool: {mono_data}")
sys.stdout.flush()
res = [{'inputs':t['inputs'],'targets':get_model_translation(config,model,tokenizer,t['inputs'],t['tgt']),'src':t['src'],'tgt':t['tgt']} for t in mono_data]
bt_data.put(res)
return None
def mono_data_(config):
#Find and prepare all the mono data in the directory
files_ = [f.name for f in os.scandir(config.mono_dir) ]
files = [f for f in files_ if f.endswith('tsv') and f.split('.tsv')[0].endswith('mono')]
if config.verbose:
print("Generating data for back translation")
print(f"Files found in mono dir: {files}")
data = [(f_.split('_')[0],pd.read_csv(os.path.join(config.mono_dir,f_), sep="\t")) for f_ in files]
dict_ = [get_dict_mono(df['input'],src,config) for src,df in data]
mono_data = [item for sublist in dict_ for item in sublist]
return mono_data
def mono_data_noise(config):
#Find and prepare all the mono data in the directory
files_ = [f.name for f in os.scandir(config.mono_dir) ]
files = [f for f in files_ if f.endswith('tsv') and f.split('.tsv')[0].endswith('mono')]
if config.verbose:
print("Generating data for back translation")
print(f"Files found in mono dir: {files}")
data = [(f_.split('_')[0],pd.read_csv(os.path.join(config.mono_dir,f_), sep="\t")) for f_ in files]
dict_ = [get_dict_mono_noise(df['input'],src,config) for src,df in data]
mono_data = [item for sublist in dict_ for item in sublist]
return mono_data
def get_mono_data(config,model):
mono_data = mono_data_(config)
if config.use_multiprocessing:
if config.verbose:
print(f"Using multiprocessing on {config.num_cores} processes")
if __name__ == "__main__":
ctx = mp.get_context('spawn')
#mp.set_start_method("spawn",force=True)
bt_data = ctx.Queue()
model.share_memory()
num_processes = config.num_cores
NUM_TO_USE = len(mono_data)//num_processes
mini_mono_data = [mono_data[i:i + NUM_TO_USE] for i in range(0, len(mono_data), NUM_TO_USE)]
#print(f"Length of mini mono data {len(mini_mono_data)}. Length of processes: {num_processes}")
assert len(mini_mono_data) == num_processes, "Length of mini mono data and number of processes do not match."
num_processes_range = [i for i in range(num_processes)]
processes = []
for rank,data_ in tqdm(zip(num_processes_range,mini_mono_data)):
p = ctx.Process(target=do_job_pool, args=(bt_data,model,rank,tokenizers_for_parallel,config,data_))
p.start()
if config.verbose:
print(f"Bt data: {bt_data.get()}")
sys.stdout.flush()
processes.append(p)
for p in processes:
p.join()
return bt_data
#output = multiprocessing.Queue()
#multiprocessing.set_start_method("spawn",force=True)
#pool = mp.Pool(processes=config.num_cores)
#bt_data = [pool.apply(do_job, args=(data_,i,tokenizers_for_parallel,)) for i,data_ in enumerate(mono_data)]
'''
# Setup a list of processes that we want to run
processes = [mp.Process(target=do_job, args=(5, output)) for x in range(config.num_cores)]
if __name__ == "__main__":
#pool = mp.Pool(processes=config.num_cores)
with parallel_backend('loky'):
bt_data = Parallel(n_jobs = config.num_cores, require='sharedmem')(delayed(do_job)(data_,i,tokenizers_for_parallel) for i,data_ in enumerate(mono_data))
'''
else:
bt_data = [{'inputs':t['inputs'],'targets':get_model_translation(config,model,tokenizer,t['inputs'],t['tgt']),'src':t['src'],'tgt':t['tgt']} for t in tqdm(mono_data)]
return bt_data
def encode_input_str(config,text, target_lang, tokenizer, seq_len):
target_lang_token = config.LANG_TOKEN_MAPPING[target_lang]
# Tokenize and add special tokens
input_ids = tokenizer.encode(
text = str(target_lang_token) + str(text),
return_tensors = 'pt',
padding = 'max_length',
truncation = config.truncation,
max_length = seq_len)
return input_ids[0]
def encode_target_str(config,text, tokenizer, seq_len):
token_ids = tokenizer.encode(
text = str(text),
return_tensors = 'pt',
padding = 'max_length',
truncation = config.truncation,
max_length = seq_len)
return token_ids[0]
def format_translation_data(config,sample,tokenizer,seq_len):
# sample is of the form {'inputs':input,'targets':target,'src':src,'tgt':tgt}
# Get the translations for the batch
input_lang = sample['src']
target_lang = sample['tgt']
input_text = sample['inputs']
target_text = sample['targets']
if input_text is None or target_text is None:
return None
input_token_ids = encode_input_str(config,input_text, target_lang, tokenizer, seq_len)
target_token_ids = encode_target_str(config,target_text, tokenizer, seq_len)
return input_token_ids, target_token_ids
def transform_batch(config,batch,tokenizer,max_seq_len):
inputs = []
targets = []
for sample in batch:
formatted_data = format_translation_data(config,sample,tokenizer,max_seq_len)
if formatted_data is None:
continue
input_ids, target_ids = formatted_data
inputs.append(input_ids.unsqueeze(0))
targets.append(target_ids.unsqueeze(0))
batch_input_ids = torch.cat(inputs)
batch_target_ids = torch.cat(targets)
return batch_input_ids, batch_target_ids
def get_data_generator(config,dataset,tokenizer,max_seq_len,batch_size):
random.shuffle(dataset)
for i in range(0, len(dataset), batch_size):
raw_batch = dataset[i:i+batch_size]
yield transform_batch(config,raw_batch, tokenizer,max_seq_len)
def eval_model(config,tokenizer,model, gdataset, max_iters=8):
test_generator = get_data_generator(config,gdataset,tokenizer,config.max_seq_len, config.batch_size)
eval_losses = []
for i, (input_batch, label_batch) in enumerate(test_generator):
input_batch, label_batch = input_batch.to(config.device), label_batch.to(config.device)
model_out = model.forward(
input_ids = input_batch,
labels = label_batch)
if config.use_torch_data_parallel:
loss = torch.mean(model_out.loss)
else:
loss = model_out.loss
eval_losses.append(loss.item())
return np.mean(eval_losses)
def evaluate(config,tokenizer,model,test_dataset,src_lang=None,tgt_lang=None):
if src_lang!=None and tgt_lang!=None:
if config.verbose:
with open(config.log,'a+') as fl:
print(f"Getting evaluation set for source language -> {src_lang} and target language -> {tgt_lang}",file=fl)
data = [t for t in test_dataset if t['src']==src_lang and t['tgt']==tgt_lang]
else:
data= [t for t in test_dataset]
inp = [t['inputs'] for t in data]
truth = [t['targets'] for t in data]
tgt_lang_ = [t['tgt'] for t in data]
seq_len__ = config.max_seq_len
input_tokens = [encode_input_str(config,text = inp[i],target_lang = tgt_lang_[i],tokenizer = tokenizer,seq_len =seq_len__).unsqueeze(0).to(config.device) for i in range(len(inp))]
if config.use_torch_data_parallel:
output = [model.module.generate(input_ids, num_beams=config.num_beams, num_return_sequences=1,max_length=config.max_seq_len,min_length=config.min_seq_len) for input_ids in tqdm(input_tokens)]
else:
output = [model.generate(input_ids, num_beams=config.num_beams, num_return_sequences=1,max_length=config.max_seq_len,min_length=config.min_seq_len) for input_ids in tqdm(input_tokens)]
output = [tokenizer.decode(out[0], skip_special_tokens=True) for out in tqdm(output)]
df= pd.DataFrame({'predictions':output,'truth':truth,'inputs':inp})
if config.now_on_bt and config.using_reconstruction:
filename = f'{src_lang}_{tgt_lang}_bt_{config.bt_time}_rec.tsv'
elif config.now_on_bt:
filename = f'{src_lang}_{tgt_lang}_bt_{config.bt_time}.tsv'
elif config.now_on_test:
filename = f'{src_lang}_{tgt_lang}_TEST.tsv'
else:
filename = f'{src_lang}_{tgt_lang}.tsv'
df.to_csv(os.path.join(config.prediction_path,filename),sep='\t',index=False)
try:
spbleu = sacrebleu.corpus_bleu(output, [truth])
except Exception:
raise Exception(f'There is a problem with {src_lang}_{tgt_lang}. Truth is {truth} \n Input is {inp} ')
return spbleu.score
def do_evaluation(config,tokenizer,model,test_dataset):
LANGS = list(config.LANG_TOKEN_MAPPING.keys())
if config.now_on_bt and config.using_reconstruction:
s=f'---------------------------AFTER BACKTRANSLATION {config.bt_time} with RECONSTRUCTION---------------------------'+'\n'
elif config.now_on_bt:
s=f'---------------------------AFTER BACKTRANSLATION {config.bt_time}---------------------------'+'\n'
elif config.now_on_test:
s=f'---------------------------TESTING EVALUATION---------------------------'+'\n'
else:
s=f'---------------------------EVALUATION ON DEV---------------------------'+'\n'
for i in range(len(LANGS)):
for j in range(len(LANGS)):
if LANGS[j]!=LANGS[i]:
eval_bleu = evaluate(config,tokenizer,model,test_dataset,src_lang=LANGS[i],tgt_lang=LANGS[j])
a = f'Bleu Score for {LANGS[i]} to {LANGS[j]} -> {eval_bleu} '+'\n'
s+=a
s+='------------------------------------------------------'
with open(os.path.join(config.homepath,'bleu_log.txt'), 'a+') as fl:
print(s,file=fl)
def train(config,n_epochs,optimizer,tokenizer,train_dataset,dev_dataset,n_batches,model,save_with_bt=False):
patience=0
losses = []
for epoch_idx in range(n_epochs):
if epoch_idx>=config.state_dict_check['epoch']+1:
st_time = time.time()
avg_loss=0
# Randomize data order
data_generator = get_data_generator(config,train_dataset,tokenizer,config.max_seq_len, config.batch_size)
optimizer.zero_grad()
for batch_idx, (input_batch, label_batch) in tqdm(enumerate(data_generator), total=n_batches):
if batch_idx >= config.state_dict_check['batch_idx']:
input_batch,label_batch = input_batch.to(config.device),label_batch.to(config.device)
# Forward pass
model_out = model.forward(input_ids = input_batch, labels = label_batch)
# Calculate loss and update weights
if config.use_torch_data_parallel:
loss = torch.mean(model_out.loss)
else:
loss = model_out.loss
losses.append(loss.item())
loss.backward()
#Gradient accumulation
if (batch_idx+1) % config.gradient_accumulation_batch == 0:
optimizer.step()
optimizer.zero_grad()
# Print training update info
if (batch_idx + 1) % config.print_freq == 0:
avg_loss = np.mean(losses)
losses=[]
if config.verbose:
with open(config.log,'a+') as fl:
print('Epoch: {} | Step: {} | Avg. loss: {:.3f}'.format(epoch_idx+1, batch_idx+1, avg_loss),file=fl)
if (batch_idx + 1) % config.checkpoint_freq == 0:
test_loss = eval_model(config,tokenizer,model, dev_dataset)
if config.best_loss-test_loss > config.best_loss_delta:
config.best_loss = test_loss
patience=0
if config.verbose:
with open(config.log,'a+') as fl:
print('Saving model with best test loss of {:.3f}'.format(test_loss),file=fl)
if save_with_bt:
model_name = config.model_name.split('.')[0]+'_bt.pt'
else:
model_name = config.model_name
config.state_dict.update({'batch_idx': batch_idx,'epoch':epoch_idx,'bt_time':config.bt_time-1,'best_loss':config.best_loss})
if config.use_torch_data_parallel:
config.state_dict['model_state_dict']=model.module.state_dict()
torch.save(config.state_dict, os.path.join(config.model_path_dir,model_name))
else:
config.state_dict['model_state_dict']=model.state_dict()
torch.save(config.state_dict, os.path.join(config.model_path_dir,model_name))
else:
if config.verbose:
with open(config.log,'a+') as fl:
print(f'No improvement in loss {test_loss} over best loss {config.best_loss}. Not saving model checkpoint',file=fl)
patience+=1
if patience >= config.patience:
with open(config.log,'a+') as fl:
print("Stopping model training due to early stopping",file=fl)
break
with open(config.log,'a+') as fl:
print('Epoch: {} | Step: {} | Avg. loss: {:.3f} | Time taken: {} | Time: {}'.format(epoch_idx+1, batch_idx+1, avg_loss, beautify_time(time.time()-st_time),datetime.now()),file=fl)
# Do this after epochs to get status of model at end of training----
test_loss = eval_model(config,tokenizer,model, dev_dataset)
if config.best_loss-test_loss > config.best_loss_delta:
config.best_loss = test_loss
patience=0
if config.verbose:
with open(config.log,'a+') as fl:
print('Saving model with best test loss of {:.3f}'.format(test_loss),file=fl)
if save_with_bt:
model_name = config.model_name.split('.')[0]+'_bt.pt'
else:
model_name = config.model_name
config.state_dict.update({'batch_idx': n_batches-1,'epoch':n_epochs-1,'bt_time':config.bt_time-1,'best_loss':config.best_loss})
if config.use_torch_data_parallel:
config.state_dict['model_state_dict']=model.module.state_dict()
torch.save(config.state_dict, os.path.join(config.model_path_dir,model_name))
else:
config.state_dict['model_state_dict']=model.state_dict()
torch.save(config.state_dict, os.path.join(config.model_path_dir,model_name))
else:
if config.verbose:
with open(config.log,'a+') as fl:
print(f'No improvement in loss {test_loss} over best loss {config.best_loss}. Not saving model checkpoint',file=fl)
patience+=1
#---------------------------------------------
def main(args):
if not os.path.exists(args.homepath):
raise Exception(f'HOMEPATH {args.homepath} does not exist!')
config = Config(args)
if not os.path.exists(config.prediction_path):
os.makedirs(config.prediction_path)
if not os.path.exists(config.bt_data_dir):
os.makedirs(config.bt_data_dir)
"""# Load Tokenizer & Model"""
tokenizer = AutoTokenizer.from_pretrained(config.model_repo)
if config.use_multiprocessing:
tokenizers_for_parallel = [AutoTokenizer.from_pretrained(config.model_repo) for i in range(config.num_cores)]
model = AutoModelForSeq2SeqLM.from_pretrained(config.model_repo)
if not os.path.exists(config.parallel_dir):
raise Exception(f'Directory `{config.parallel_dir}` cannot be empty! It must contain the parallel files')
train_dataset = make_dataset(config,'train')
with open(config.log,'a+') as fl:
print(f"Length of train dataset: {len(train_dataset)}",file=fl)
dev_dataset = make_dataset(config,'eval')
with open(config.log,'a+') as fl:
print(f"Length of dev dataset: {len(dev_dataset)}",file=fl)
"""## Update tokenizer"""
special_tokens_dict = {'additional_special_tokens': list(config.LANG_TOKEN_MAPPING.values())}
tokenizer.add_special_tokens(special_tokens_dict)
if config.use_multiprocessing:
for tk in tokenizers_for_parallel:
tk.add_special_tokens(special_tokens_dict)
model.resize_token_embeddings(len(tokenizer))
"""# Train/Finetune MT5"""
if os.path.exists(os.path.join(config.model_path_dir,config.model_name)):
if config.verbose:
with open(config.log,'a+') as fl:
print("-----------Using model checkpoint-----------",file=fl)
try:
state_dict = torch.load(os.path.join(config.model_path_dir,config.model_name.split('.')[0]+'_bt.pt'))
except Exception:
with open(config.log,'a+') as fl:
print('No mmt_translation_bt.pt present. Default to original mmt_translation.pt',file=fl)
state_dict = torch.load(os.path.join(config.model_path_dir,config.model_name))
# Note to self: Make this beter.
config.state_dict_check['epoch']=state_dict['epoch']
config.state_dict_check['bt_time']=state_dict['bt_time']
config.state_dict_check['best_loss']=state_dict['best_loss']
config.best_loss = config.state_dict_check['best_loss']
config.state_dict_check['batch_idx']=state_dict['batch_idx']
model.load_state_dict(state_dict['model_state_dict'])
#Temp change
config.state_dict_check['epoch']=-1
config.state_dict_check['batch_idx']=0
config.state_dict_check['bt_time']=-1
#Using DataParallel
if config.use_torch_data_parallel:
model = torch.nn.DataParallel(model, device_ids=list(range(torch.cuda.device_count())))
model = model.to(config.device)
#-----
# Optimizer
optimizer = Adafactor(model.parameters(), scale_parameter=False, relative_step=False, warmup_init=False, lr=config.lr)
#Normal training
n_batches = int(np.ceil(len(train_dataset) / config.batch_size))
total_steps = config.n_epochs * n_batches
n_warmup_steps = int(total_steps * 0.01)
#scheduler = get_linear_schedule_with_warmup(optimizer, n_warmup_steps, total_steps)
#scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=config.lr, max_lr=0.001,cycle_momentum=False)
train(config,config.n_epochs,optimizer,tokenizer,train_dataset,dev_dataset,n_batches,model)
if config.verbose:
with open(config.log,'a+') as fl:
print('Evaluaton...',file=fl)
do_evaluation(config,tokenizer,model,dev_dataset)
config.state_dict_check['epoch']=-1
config.state_dict_check['batch_idx']=0
if config.do_backtranslation:
#Backtranslation time
config.now_on_bt=True
with open(config.log,'a+') as fl:
print('---------------Start of Backtranslation---------------',file=fl)
for n_bt in range(config.NUM_BACKTRANSLATION_TIMES):
if n_bt>=config.state_dict_check['bt_time']+1:
with open(config.log,'a+') as fl:
print(f"Backtranslation {n_bt+1} of {config.NUM_BACKTRANSLATION_TIMES}--------------",file=fl)
config.bt_time = n_bt+1
save_bt_file_path = os.path.join(config.bt_data_dir,'bt'+str(n_bt+1)+'.json')
if not os.path.exists(save_bt_file_path):
mono_data = mono_data_(config)
start_time = time.time()
if config.use_multiprocessing:
if config.verbose:
with open(config.log,'a+') as fl:
print(f"Using multiprocessing on {config.num_cores} processes",file=fl)
if __name__ == "__main__":
model.share_memory()
with parallel_backend('loky'):
bt_data = Parallel(n_jobs = config.num_cores, require='sharedmem')(delayed(do_job)(data_,i,tokenizers_for_parallel) for i,data_ in tqdm(enumerate(mono_data)))
else:
bt_data = [{'inputs':get_model_translation(config,model,tokenizer,t['inputs'],t['tgt']),'targets':t['inputs'],'src':t['tgt'],'tgt':t['src']} for t in tqdm(mono_data)]
with open(config.log,'a+') as fl:
print(f'Time taken for backtranslation of data: {beautify_time(time.time()-start_time)}',file=fl)
with open(save_bt_file_path,'w') as fp:
json.dump(bt_data,fp)
else:
with open(save_bt_file_path,'r') as f:
bt_data = json.load(f)
with open(config.log,'a+') as fl:
print('-'*15+'Printing 5 random BT Data'+'-'*15,file=fl)
ids_print = random.sample([i for i in range(len(bt_data))],5)
with open(config.log,'a+') as fl:
for ids_print_ in ids_print:
print(bt_data[ids_print_],file=fl)
augmented_dataset = train_dataset + bt_data + mono_data_noise(config) #mono_data_noise adds denoising objective
random.shuffle(augmented_dataset)
with open(config.log,'a+') as fl:
print(f'New length of dataset: {len(augmented_dataset)}',file=fl)
n_batches = int(np.ceil(len(augmented_dataset) / config.batch_size))
total_steps = config.n_bt_epochs * n_batches
n_warmup_steps = int(total_steps * 0.01)
#scheduler = get_linear_schedule_with_warmup(optimizer, n_warmup_steps, total_steps)
#scheduler = torch.optim.lr_scheduler.CyclicLR(optimizer, base_lr=config.lr, max_lr=0.001,cycle_momentum=False)
train(config,config.n_bt_epochs,optimizer,tokenizer,augmented_dataset,dev_dataset,n_batches,model,save_with_bt=True)
if config.verbose:
with open(config.log,'a+') as fl:
print('Evaluaton...',file=fl)
do_evaluation(config,tokenizer,model,dev_dataset)
config.state_dict_check['epoch']=-1
config.state_dict_check['batch_idx']=0
with open(config.log,'a+') as fl:
print('---------------End of Backtranslation---------------',file=fl)
with open(config.log,'a+') as fl:
print('---------------End of Training---------------',file=fl)
config.now_on_bt=False
config.now_on_test=True
with open(config.log,'a+') as fl:
print('Evaluating on test set',file=fl)
test_dataset = make_dataset(config,'test')
with open(config.log,'a+') as fl:
print(f"Length of test dataset: {len(test_dataset)}",file=fl)
do_evaluation(config,tokenizer,model,test_dataset)
with open(config.log,'a+') as fl:
print("ALL DONE",file=fl)
def load_params(args: dict) -> dict:
"""
Load the parameters passed to `translate`
"""
#if not os.path.exists(args['checkpoint']):
# raise Exception(f'Checkpoint file does not exist')
params = {}
model_repo = 'google/mt5-base'
LANG_TOKEN_MAPPING = {
'ig': '<ig>',
'fon': '<fon>',
'en': '<en>',
'fr': '<fr>',
'rw':'<rw>',
'yo':'<yo>',
'xh':'<xh>',
'sw':'<sw>'
}
tokenizer = AutoTokenizer.from_pretrained(model_repo)
model = AutoModelForSeq2SeqLM.from_pretrained(model_repo)
"""## Update tokenizer"""
special_tokens_dict = {'additional_special_tokens': list(LANG_TOKEN_MAPPING.values())}
tokenizer.add_special_tokens(special_tokens_dict)
model.resize_token_embeddings(len(tokenizer))
state_dict = torch.load(args['checkpoint'],map_location=args['device'])
model.load_state_dict(state_dict['model_state_dict'])
model = model.to(args['device'])
#Load the model, load the tokenizer, max and min seq len
params['model'] = model
params['device'] = args['device']
params['max_seq_len'] = args['max_seq_len'] if 'max_seq_len' in args else 50
params['min_seq_len'] = args['min_seq_len'] if 'min_seq_len' in args else 2
params['tokenizer'] = tokenizer
params['num_beams'] = args['num_beams'] if 'num_beams' in args else 4
params['lang_token'] = LANG_TOKEN_MAPPING
params['truncation'] = args['truncation'] if 'truncation' in args else True
return params
def encode_input_str_translate(params,text, target_lang, tokenizer, seq_len):
target_lang_token = params['lang_token'][target_lang]
# Tokenize and add special tokens
input_ids = tokenizer.encode(
text = str(target_lang_token) + str(text),
return_tensors = 'pt',
padding = 'max_length',
truncation = params['truncation'] ,
max_length = seq_len)
return input_ids[0]
def translate(
params: dict,
sentence: str,
source_lang: str,
target_lang: str
) -> str:
"""
Given a sentence and its source and target sentences, this translates the sentence
to the given target sentence.
"""
if source_lang!='' and target_lang!='':
inp = [sentence]
input_tokens = [encode_input_str_translate(params,text = inp[i],target_lang = target_lang,tokenizer = params['tokenizer'],seq_len =params['max_seq_len']).unsqueeze(0).to(params['device']) for i in range(len(inp))]
output = [params['model'].generate(input_ids, num_beams=params['num_beams'], num_return_sequences=1,max_length=params['max_seq_len'],min_length=params['min_seq_len']) for input_ids in input_tokens]
output = [params['tokenizer'].decode(out[0], skip_special_tokens=True) for out in tqdm(output)]
return output[0]
else:
return ''
if __name__=="__main__":
from argparse import ArgumentParser
import json
import os
parser = ArgumentParser('MMTArica Experiments')
parser.add_argument('-homepath', type=str, default=os.getcwd(),
help="Homepath directory. Where all experiments are saved and all \
necessary files/folders are saved. (default: current working directory)")
parser.add_argument('--prediction_path', type=str, default='./predictions',
help='directory path to save predictions (default: %(default)s)')
parser.add_argument('--model_name', type=str, default='mmt_translation',
help='Name of model (default: %(default)s)')
parser.add_argument('--bt_data_dir', type=str, default='btData',
help='Directory to save back-translation files (default: %(default)s)')
parser.add_argument('--parallel_dir', type=str, default='parallel',
help='name of directory where parallel corpora is saved')
parser.add_argument('--mono_dir', type=str, default='mono',
help='name of directory where monolingual files are saved (default: %(default)s)')
parser.add_argument('--log', type=str, default='train.log',
help='name of file to log experiments (default: %(default)s)')
parser.add_argument('--mono_data_limit', type=int, default=300,
help='limit of monolingual sentences to use for training (default: %(default)s)')
parser.add_argument('--mono_data_for_noise_limit', type=int, default=50,
help='limit of monolingual sentences to use for noise (default: %(default)s)')
parser.add_argument('--n_epochs', type=int, default=10,
help='number of training epochs (default: %(default)s)')
parser.add_argument('--n_bt_epochs', type=int, default=3,
help='number of backtranslation epochs (default: %(default)s)')
parser.add_argument('--batch_size', type=int, default=64,
help='batch size (default: %(default)s)')
parser.add_argument('--max_seq_len', type=int, default=50,
help='maximum length of sentence. All sentences beyond this length will be skipped. (default: %(default)s)')
parser.add_argument('--min_seq_len', type=int, default=2,
help='mnimum length of sentence. All sentences beyond this length will be skipped. (default: %(default)s)')
parser.add_argument('--checkpoint_freq', type=int, default=10_000,
help='maximum length of sentence. All sentences beyond this length will be skipped. (default: %(default)s)')
parser.add_argument('--lr', type=int, default=1e-4,
help='learning rate. (default: %(default)s)')
parser.add_argument('--print_freq', type=int, default=5_000,
help='frequency at which to print to log. (default: %(default)s)')
parser.add_argument('--use_multiprocessing', type=bool, default=False,
help='whether or not to use multiprocessing. (default: %(default)s)')
parser.add_argument('--num_pretrain_steps', type=int, default=20,
help='number of pretrain steps. (default: %(default)s)')
parser.add_argument('--num_backtranslation_steps', type=int, default=5,
help='number of pretrain steps. (default: %(default)s)')
parser.add_argument('--do_backtranslation', type=bool, default=True,
help='whether or not to do backtranslation during training. (default: %(default)s)')
parser.add_argument('--use_reconstruction', type=bool, default=True,
help='whether or not to use reconstruction during training. (default: %(default)s)')
parser.add_argument('--use_torch_data_parallel', type=bool, default=False,
help='whether or not to use torch data parallelism. (default: %(default)s)')
parser.add_argument('--gradient_accumulation_batch', type=int, default=4096//64,
help='batch size for gradient accumulation. (default: %(default)s)')
parser.add_argument('--num_beams', type=int, default=4,
help='number of beams to use for inference. (default: %(default)s)')
parser.add_argument('--patience', type=int, default=15_000_000,
help='patience for early stopping. (default: %(default)s)')
parser.add_argument('--drop_probability', type=float, default=0.2,
help='drop probability for reconstruction. (default: %(default)s)')
parser.add_argument('--dropout', type=float, default=0.1,
help='dropout probability. (default: %(default)s)')
parser.add_argument('--num_swaps', type=int, default=3,
help='number of word swaps to perform during reconstruction. (default: %(default)s)')
parser.add_argument('--verbose', type=bool, default=True,
help='whether or not to print information during experiments. (default: %(default)s)')
args = parser.parse_args()
main(args)