def __init__(self, dim, ignore_index=-100, pad_value=0, **kwargs):
super().__init__()
enc_kwargs, dec_kwargs, _ = extract_enc_dec_kwargs(kwargs)
assert (
"return_embedding" not in enc_kwargs
), "you cannot manually set the return embeddings flag for the encoder"
assert ("dim" not in dec_kwargs and "dim" not in enc_kwargs
), "you must set the dim for both encoder and decoder"
enc_kwargs["dim"] = dec_kwargs["dim"] = dim
enc_kwargs["return_embeddings"] = True
dec_kwargs["causal"] = True
enc_kwargs.setdefault("bucket_size", 64)
dec_kwargs.setdefault("bucket_size", enc_kwargs["bucket_size"] * 2)
enc = ReformerLM(**enc_kwargs)
dec = ReformerLM(**dec_kwargs)
self.enc = TrainingWrapper(enc,
ignore_index=ignore_index,
pad_value=pad_value)
self.dec = TrainingWrapper(dec,
ignore_index=ignore_index,
pad_value=pad_value)
def __init__(self, dim, ignore_index=-100, pad_value=0, **kwargs):
super().__init__()
enc_kwargs, dec_kwargs, _ = extract_enc_dec_kwargs(kwargs)
assert 'return_embedding' not in enc_kwargs, 'you cannot manually set the return embeddings flag for the encoder'
assert 'dim' not in dec_kwargs and 'dim' not in enc_kwargs, 'you must set the dim for both encoder and decoder'
enc_kwargs['dim'] = dec_kwargs['dim'] = dim
enc_kwargs['return_embeddings'] = True
enc_kwargs.setdefault('bucket_size', 64)
dec_kwargs.setdefault('bucket_size', enc_kwargs['bucket_size'] * 2)
enc = ReformerLM(**enc_kwargs)
dec = ReformerLM(**dec_kwargs)
self.enc = TrainingWrapper(enc,
ignore_index=ignore_index,
pad_value=pad_value)
self.dec = TrainingWrapper(dec,
ignore_index=ignore_index,
pad_value=pad_value)
def gen(text):
model = ReformerLM(num_tokens=13137,
dim=128,
depth=12,
max_seq_len=4096,
lsh_dropout=0.1,
causal=True,
full_attn_thres=128)
model = TrainingWrapper(model, ignore_index=0, pad_value=0).cpu()
output_dir = "model"
model_cpu_path = os.path.join(output_dir, 'model_cpu.pt')
model.load_state_dict(torch.load(model_cpu_path))
initial = auto_encode(text)
# print(initial)
sample = model.generate(
initial, 10, temperature=1., filter_thres=0.9, eos_token=1
) # assume end token is 1, or omit and it will sample up to 100
# print(sample)
# print(sample.shape) # (1, <=100) token ids
text = tokenizer.convert_ids_to_tokens(sample.tolist()[0])
print(text)
def train_encdec_v1(
input_lang, target_lang, dim, bucket_size, depth, heads, n_hashes,
vir_seq_len, ff_chunks, attn_chunks, mol_seq_len, cmd_args, train_dataset,
test_dataset, output_folder, train_batch_size, epochs, validate_every,
save_every, deepspeed_optimizer, use_full_attn, gradient_accumulation_steps
): #zero_optimization, #unused for now. Use this flag to create IF statement for Zero Compatibility if needed
print('Axial Embedding shape:', compute_axial_position_shape(vir_seq_len))
encoder = ReformerLM(
num_tokens=input_lang.n_words,
dim=dim,
bucket_size=bucket_size,
depth=depth,
heads=heads,
n_hashes=n_hashes,
max_seq_len=vir_seq_len,
ff_chunks=ff_chunks,
attn_chunks=attn_chunks,
weight_tie=True,
weight_tie_embedding=True,
axial_position_emb=True,
axial_position_shape=compute_axial_position_shape(vir_seq_len),
axial_position_dims=(dim // 2, dim // 2),
return_embeddings=True,
use_full_attn=use_full_attn).to(device)
decoder = ReformerLM(
num_tokens=target_lang.n_words,
dim=dim,
bucket_size=bucket_size,
depth=depth,
heads=heads,
n_hashes=n_hashes,
ff_chunks=ff_chunks,
attn_chunks=attn_chunks,
max_seq_len=mol_seq_len,
axial_position_emb=True,
axial_position_shape=compute_axial_position_shape(mol_seq_len),
axial_position_dims=(dim // 2, dim // 2),
weight_tie=True,
weight_tie_embedding=True,
causal=True,
use_full_attn=use_full_attn).to(device)
encoder = TrainingWrapper(encoder, ignore_index=PAD_IDX,
pad_value=PAD_IDX).to(device)
decoder = TrainingWrapper(decoder, ignore_index=PAD_IDX,
pad_value=PAD_IDX).to(device)
encoder_params = filter(lambda p: p.requires_grad, encoder.parameters())
decoder_params = filter(lambda p: p.requires_grad, decoder.parameters())
if deepspeed_optimizer == False:
print('No DeepSpeed optimizer found. Using RangerLars.')
encoder_optimizer = RangerLars(encoder.parameters())
decoder_optimizer = RangerLars(decoder.parameters())
encoder_engine, encoder_optimizer, trainloader, _ = deepspeed.initialize(
args=cmd_args,
model=encoder,
optimizer=encoder_optimizer,
model_parameters=encoder_params,
training_data=train_dataset,
dist_init_required=True)
decoder_engine, decoder_optimizer, testloader, _ = deepspeed.initialize(
args=cmd_args,
model=decoder,
optimizer=decoder_optimizer,
model_parameters=decoder_params,
training_data=test_dataset,
dist_init_required=False)
else:
print('Found optimizer in the DeepSpeed configurations. Using it.')
encoder_engine, encoder_optimizer, trainloader, _ = deepspeed.initialize(
args=cmd_args,
model=encoder,
model_parameters=encoder_params,
training_data=train_dataset,
dist_init_required=True)
decoder_engine, decoder_optimizer, testloader, _ = deepspeed.initialize(
args=cmd_args,
model=decoder,
model_parameters=decoder_params,
training_data=test_dataset,
dist_init_required=False)
SAVE_DIR = os.sep.join([output_folder, 'saved_model'])
os.makedirs(SAVE_DIR, exist_ok=True)
try:
enc_ckp_max = np.max([
int(ckp) for ckp in os.listdir(os.sep.join([SAVE_DIR, 'encoder']))
])
except Exception as e:
print('Exception:', e)
enc_ckp_max = 0
try:
dec_ckp_max = np.max([
int(ckp) for ckp in os.listdir(os.sep.join([SAVE_DIR, 'decoder']))
])
except:
dec_ckp_max = 0
_, encoder_client_sd = encoder_engine.load_checkpoint(
os.sep.join([SAVE_DIR, 'encoder']), enc_ckp_max)
_, decoder_client_sd = decoder_engine.load_checkpoint(
os.sep.join([SAVE_DIR, 'decoder']), dec_ckp_max)
gpus_mini_batch = (train_batch_size // gradient_accumulation_steps
) // torch.cuda.device_count()
print('gpus_mini_batch:', gpus_mini_batch,
'with gradient_accumulation_steps:', gradient_accumulation_steps)
log_file = open(os.sep.join([output_folder, 'training_log.log']), 'a')
log_file.write(
"\n\n\n{}\tStarting new training from chekpoint: Encoder-{} | Decoder-{}\n"
.format(datetime.datetime.now(), enc_ckp_max, dec_ckp_max))
log_file.flush()
for eph in range(epochs):
print('Starting Epoch: {}'.format(eph))
for i, pair in enumerate(tqdm(trainloader)):
tr_step = ((eph * len(trainloader)) + i) + 1
src = pair[0]
trg = pair[1]
encoder_engine.train()
decoder_engine.train()
src = src.to(encoder_engine.local_rank)
trg = trg.to(decoder_engine.local_rank)
enc_keys = encoder_engine(src)
loss = decoder_engine(trg, keys=enc_keys, return_loss=True)
loss.backward()
decoder_engine.step()
encoder_engine.step()
print('Training Loss:', loss.item())
if tr_step % validate_every == 0:
val_loss = []
for pair in tqdm(testloader):
encoder_engine.eval()
decoder_engine.eval()
with torch.no_grad():
ts_src = pair[0]
ts_trg = pair[1]
ts_src = ts_src.to(encoder_engine.local_rank)
ts_trg = ts_trg.to(decoder_engine.local_rank)
enc_keys = encoder_engine(ts_src)
loss = decoder_engine(ts_trg,
keys=enc_keys,
return_loss=True)
val_loss.append(loss.item())
print(
f'\tValidation Loss: AVG: {np.mean(val_loss)}, MEDIAN: {np.median(val_loss)}, STD: {np.std(val_loss)} '
)
log_file.write(
'Step: {}\tTraining Loss:{}\t Validation LOSS: AVG: {}| MEDIAN: {}| STD: {}\n'
.format(i, loss.item(), np.mean(val_loss),
np.median(val_loss), np.std(val_loss)))
else:
log_file.write('Step: {}\tTraining Loss:{}\n'.format(
i, loss.item()))
log_file.flush()
if tr_step % save_every == 0:
print('\tSaving Checkpoint')
enc_ckpt_id = str(enc_ckp_max + tr_step + 1)
dec_ckpt_id = str(dec_ckp_max + tr_step + 1)
encoder_engine.save_checkpoint(
os.sep.join([SAVE_DIR, 'encoder']), enc_ckpt_id)
decoder_engine.save_checkpoint(
os.sep.join([SAVE_DIR, 'decoder']), dec_ckpt_id)
log_file.close()
print('\tSaving Final Checkpoint')
enc_ckpt_id = str(enc_ckp_max + tr_step + 1)
dec_ckpt_id = str(dec_ckp_max + tr_step + 1)
encoder_engine.save_checkpoint(os.sep.join([SAVE_DIR, 'encoder']),
enc_ckpt_id)
decoder_engine.save_checkpoint(os.sep.join([SAVE_DIR, 'decoder']),
dec_ckpt_id)
model = ReformerLM(
dim=512,
depth=6,
max_seq_len=SEQ_LEN,
num_tokens=256,
heads=8,
bucket_size=64,
n_hashes=4,
ff_chunks=10,
lsh_dropout=0.1,
weight_tie=True,
causal=True,
use_full_attn=False # set this to true for comparison with full attention
)
model = TrainingWrapper(model)
model.cuda()
# prepare enwik8 data
with gzip.open('./data/enwik8.gz') as file:
X = np.fromstring(file.read(int(95e6)), dtype=np.uint8)
trX, vaX = np.split(X, [int(90e6)])
data_train, data_val = torch.from_numpy(trX), torch.from_numpy(vaX)
class TextSamplerDataset(Dataset):
def __init__(self, data, seq_len):
super().__init__()
self.data = data
self.seq_len = seq_len
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--device', default='cuda', type=str, required=False, help='设置使用哪些显卡')
# parser.add_argument('--model_config', default='config/model_config_small.json', type=str, required=False,
# help='选择模型参数')
parser.add_argument('--tokenizer_path', default='cache/vocab_small_terry_ai.txt', type=str, required=False, help='选择词库')
parser.add_argument('--raw_data_path', default='data/train.json', type=str, required=False, help='原始训练语料')
parser.add_argument('--tokenized_data_path', default='data/tokenized/', type=str, required=False,
help='tokenized语料存放位置')
parser.add_argument('--raw', action='store_true', help='是否先做tokenize')
parser.add_argument('--epochs', default=5, type=int, required=False, help='训练循环')
parser.add_argument('--batch_size', default=2, type=int, required=False, help='训练batch size')
parser.add_argument('--lr', default=1e-8, type=float, required=False, help='学习率')
parser.add_argument('--warmup_steps', default=2000, type=int, required=False, help='warm up步数')
parser.add_argument('--log_step', default=1, type=int, required=False, help='多少步汇报一次loss')
parser.add_argument('--stride', default=500, type=int, required=False, help=' 向前跨越的长度')
parser.add_argument('--dim', default=1024, type=int, required=False, help='训练时取训练数据的窗口步长单个样本长度')
parser.add_argument('--gradient_accumulation', default=5, type=int, required=False, help='梯度积累')
parser.add_argument('--fp16', action='store_true', help='混合精度')
parser.add_argument('--fp16_opt_level', default='O1', type=str, required=False)
parser.add_argument('--max_grad_norm', default=1.0, type=float, required=False)
parser.add_argument('--num_pieces', default=10, type=int, required=False, help='将训练语料分成多少份')
parser.add_argument('--min_length', default=64, type=int, required=False, help='最短收录文章长度')
parser.add_argument('--output_dir', default='model/', type=str, required=False, help='模型输出路径')
parser.add_argument('--pretrained_model', default='', type=str, required=False, help='模型训练起点路径')
# parser.add_argument('--writer_dir', default='tensorboard_summary/', type=str, required=False, help='Tensorboard路径')
parser.add_argument('--segment', action='store_true', help='中文以词为单位')
parser.add_argument('--bpe_token', action='store_true', help='subword')
# parser.add_argument('--dim', default=1024, type=int, required=False, help='dim')
parser.add_argument('--depth', default=12, type=int, required=False, help='depth')
parser.add_argument('--full_attn_thres', default=1024, type=int, required=False, help='full_attn_thres')
parser.add_argument('--max_seq_len', default=4096, type=int, required=False, help='max_seq_len')
# parser.add_argument('--encoder_json', default="tokenizations/encoder.json", type=str, help="encoder.json")
# parser.add_argument('--vocab_bpe', default="tokenizations/vocab.bpe", type=str, help="vocab.bpe")
args = parser.parse_args()
full_tokenizer=tokenizer_plus(args.tokenizer_path)
config_file=os.path.join(args.output_dir,'config.json')
Config=tkitJson.Config(config_file)
new_conf={'num_tokens':full_tokenizer.vocab_size,
'dim': args.dim, #和窗口长度一样
'depth' : args.depth,
'max_seq_len' : args.max_seq_len,
'lsh_dropout' : 0.1,
'causal' : True,
'full_attn_thres' : args.full_attn_thres,
'stride': args.stride, #滑块长度
}
print("new_conf:",new_conf)
Config.save(new_conf)
#复制词典
shutil.copy(args.tokenizer_path,os.path.join(args.output_dir,'vocab.txt'))
print('args:\n' + args.__repr__())
# if args.segment:
# from tokenizations import tokenization_bert_word_level as tokenization_bert
# else:
# from tokenizations import tokenization_bert
os.environ["CUDA_VISIBLE_DEVICES"] = '0,1,2,3' # 此处设置程序使用哪些显卡
# model_config = transformers.modeling_gpt2.GPT2Config.from_json_file(args.model_config)
# print('config:\n' + model_config.to_json_string())
# dim = model_config.dim
# if args.bpe_token:
# full_tokenizer = get_encoder(args.encoder_json, args.vocab_bpe)
# else:
# full_tokenizer = tokenization_bert.BertTokenizer(vocab_file=args.tokenizer_path)
# full_tokenizer = BertTokenizer.from_pretrained(args.tokenizer_path)
# full_tokenizer.max_len = dim
# if args.device==''
device = 'cuda' if torch.cuda.is_available() else 'cpu'
#强制使用cpu
device = args.device
print('using device:', device)
raw_data_path = args.raw_data_path
tokenized_data_path = args.tokenized_data_path
raw = args.raw # 选择是否从零开始构建数据集
pretrained_model = args.pretrained_model
epochs = args.epochs
batch_size = args.batch_size
lr = args.lr
warmup_steps = args.warmup_steps
log_step = args.log_step
stride = args.stride
dim=args.dim
if stride>= dim:
stride=dim/2-2
gradient_accumulation = args.gradient_accumulation
# fp16 = args.fp16 # 不支持半精度的显卡请勿打开
# fp16_opt_level = args.fp16_opt_level
max_grad_norm = args.max_grad_norm
num_pieces = args.num_pieces
min_length = args.min_length
output_dir = args.output_dir
# tb_writer = SummaryWriter(log_dir=args.writer_dir)
# 加载之前的模型路径
model_path=os.path.join(pretrained_model, 'model.pt')
optimizer_path= os.path.join(pretrained_model, 'optimizer.pt')
scheduler_path=os.path.join(pretrained_model, 'scheduler.pt')
# 设置输出
output_model_path=os.path.join(output_dir, 'model.pt')
output_optimizer_path= os.path.join(output_dir, 'optimizer.pt')
output_scheduler_path=os.path.join(output_dir, 'scheduler.pt')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
if raw:
print('building files')
build_files(data_path=raw_data_path, tokenized_data_path=tokenized_data_path, num_pieces=num_pieces,
full_tokenizer=full_tokenizer, min_length=min_length)
print('files built')
model = ReformerLM(
num_tokens= full_tokenizer.vocab_size,
dim = dim, #窗口长度
depth = args.depth,
max_seq_len = args.max_seq_len,
lsh_dropout = 0.1,
causal = True,
full_attn_thres = args.full_attn_thres
)
# 0 is used for padding and no loss to be calculated on it
if device=='cuda':
model = TrainingWrapper(model, ignore_index = 0, pad_value = 0).to('cuda')
else:
model = TrainingWrapper(model, ignore_index = 0, pad_value = 0)
if os.path.isfile(model_path):
# if so, load them
model.load_state_dict(torch.load(model_path))
else:
# pass
model.train()
weight_decay=0.0
# learning_rate=5e-5
adam_epsilon=1e-8
# warmup_steps=0
max_grad_norm=1.0
max_steps=-1
# gradient_accumulation_steps=10
logging_steps=1000
save_steps=10000
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{
'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
'weight_decay': weight_decay
},
{
'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
'weight_decay': 0.0
}
]
full_len = 0
print('calculating total steps')
for i in tqdm(range(num_pieces)):
with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i), 'r') as f:
full_len += len([int(item) for item in f.read().strip().split()])
total_steps = int(full_len / stride * epochs / batch_size / gradient_accumulation)
print('total steps = {}'.format(total_steps))
# total_steps = len(x_train_text)/gradient_accumulation_steps * num_train_epochs
# t_total=3/1*3
# optimizer = AdamW(model.parameters(), lr=lr, correct_bias=True)
optimizer = AdamW(optimizer_grouped_parameters, lr=lr, eps=adam_epsilon)
scheduler = get_linear_schedule_with_warmup( optimizer=optimizer, num_warmup_steps=warmup_steps,num_training_steps=total_steps)
# # checking if another optimizer/scheduler exists
if os.path.isfile(optimizer_path) and os.path.isfile(scheduler_path):
# if so, load them
optimizer.load_state_dict(torch.load(optimizer_path))
scheduler.load_state_dict(torch.load(scheduler_path))
print("optimizer",optimizer)
loss_fn=nn.CrossEntropyLoss()
print('starting training')
overall_step = 0
running_loss = 0
gradient_accumulation_run=0
for epoch in range(epochs):
print('epoch {}'.format(epoch + 1))
now = datetime.now()
print('time: {}'.format(now))
x = np.linspace(0, num_pieces - 1, num_pieces, dtype=np.int32)
random.shuffle(x)
# piece_num = 0
# model.zero_grad() # reset gradient
# for piece_num, i in tqdm(enumerate( x)):
for piece_num, i in enumerate( x):
with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i), 'r') as f:
line = f.read().strip()
tokens = line.split()
tokens = [int(token) for token in tokens]
# print(len(tokens))
start_point = 0
samples = []
#划窗切割数据
while start_point < len(tokens) - dim:
samples.append(tokens[start_point: start_point + dim])
# print(start_point, start_point + dim)
start_point += stride
if start_point < len(tokens):
samples.append(tokens[len(tokens)-dim:])
# 打乱数据，防止过度拟合
random.shuffle(samples)
for step in range(len(samples) // batch_size): # drop last
# print(step)
# prepare data
batch = samples[step * batch_size: (step + 1) * batch_size]
# batch_labels = []
batch_inputs = []
for ids in batch:
# int_ids_for_labels = [int(x) for x in ids]
int_ids_for_inputs = [int(x) for x in ids]
# batch_labels.append(int_ids_for_labels)
batch_inputs.append(int_ids_for_inputs)
if device=='cuda':
batch_inputs = torch.tensor(batch_inputs).long().to("cuda")
# batch_labels = torch.tensor(batch_labels).long().to("cuda")
else:
batch_inputs = torch.tensor(batch_inputs).long()
# batch_labels = torch.tensor(batch_labels).long()
# batch_inputs = torch.tensor(batch_inputs).long().to(device)
# print(batch_labels)
# print(len(batch_inputs))
# print(batch_inputs)
# print(len(batch_inputs))
loss = model(batch_inputs, return_loss = True)
loss = loss/gradient_accumulation
loss.backward()
# print(loss.sum())
if((gradient_accumulation_run+1)%gradient_accumulation)==0:
# optimizer the net
optimizer.step()
scheduler.step() # update parameters of net
optimizer.zero_grad() # update parameters of net
# scheduler.zero_grad() # update parameters of net
# model.zero_grad() # reset gradient
end = datetime.now()
print("epoch:",epoch + 1," piece_num:",piece_num,'/',num_pieces," step:",overall_step+1,'/',total_steps," step完成比例:",(overall_step+1)/total_steps," loss:",loss.item(),'Time',end-now)
overall_step+=1
gradient_accumulation_run=gradient_accumulation_run+1
# scheduler.step()
# model.zero_grad()
# end = datetime.now()
# print("one piece:",end-now," s")
torch.save(model.state_dict(), output_model_path)
torch.save(optimizer.state_dict(), output_optimizer_path)
torch.save(scheduler.state_dict(), output_scheduler_path)
model_cpu_path=os.path.join(output_dir, 'model_cpu.pt')
torch.save(model.cpu().state_dict(), model_cpu_path)
conf = Config.read()
# tokenizer = BertTokenizer.from_pretrained(pretrained_weights)
tokenizer = tokenizer_plus(pretrained_weights)
model = ReformerLM(num_tokens=conf['num_tokens'],
dim=conf['dim'],
depth=conf['depth'],
max_seq_len=conf['max_seq_len'],
lsh_dropout=conf['lsh_dropout'],
causal=conf['causal'],
full_attn_thres=conf['full_attn_thres'])
model_path = os.path.join(output_dir, 'model.pt')
if device == 'cuda':
model = TrainingWrapper(model, ignore_index=0, pad_value=0).cuda()
if os.path.isfile(model_path):
# if so, load them
# print('++++'*20)
model.load_state_dict(torch.load(model_path)).cuda()
else:
model = TrainingWrapper(model, ignore_index=0, pad_value=0).cpu()
# print(model)
# print(model.cpu().state_dict())
# print('++++'*20)
if os.path.isfile(model_path):
# if so, load them
# print('++++'*20)
print("加载模型")
model.load_state_dict(torch.load(model_path))
def main():
cmd_args = add_argument()
path_to_file_tr = cmd_args.path_to_file_tr
path_to_file_ts = cmd_args.path_to_file_ts
min_len_mol = cmd_args.min_len_mol
max_len_mol = cmd_args.max_len_mol
num_examples_tr = cmd_args.num_examples_tr
num_examples_ts = cmd_args.num_examples_ts
train_batch_size = json.load(open(cmd_args.ds_conf))['train_batch_size']
gradient_accumulation_steps = json.load(open(
cmd_args.ds_conf))['gradient_accumulation_steps']
deepspeed_optimizer = True if json.load(open(cmd_args.ds_conf)).get(
'optimizer', None) is not None else False
epochs = cmd_args.epochs
emb_dim = cmd_args.emb_dim
dim = cmd_args.dim
bucket_size = cmd_args.bucket_size
depth = cmd_args.depth
heads = cmd_args.heads
n_hashes = cmd_args.n_hashes
ff_chunks = cmd_args.ff_chunks
attn_chunks = cmd_args.attn_chunks
validate_every = cmd_args.validate_every
save_every = cmd_args.save_every
output_folder = cmd_args.output_folder
use_full_attn = cmd_args.use_full_attn
mrpc_test = cmd_args.mrpc_test
use_deepspeed = cmd_args.use_deepspeed
os.makedirs(output_folder, exist_ok=True)
pickle.dump(cmd_args,
open(os.sep.join([output_folder, 'training_conf.pkl']), 'wb'))
MIN_LENGTH_MOL = min_len_mol
MAX_LENGTH_MOL = max_len_mol # 2048
NUM_EXAMPLES_TR = num_examples_tr # 1024
NUM_EXAMPLES_TS = num_examples_ts # 1024
N_EPOCHS = epochs # 10
VALIDATE_EVERY = validate_every
SAVE_EVERY = save_every
MOL_SEQ_LEN = MAX_LENGTH_MOL # output_lang.max_len if (output_lang.max_len % 2) == 0 else output_lang.max_len + 1 # ??
saved_mol_lang = os.sep.join([output_folder, 'mol_lang.pkl'])
MAX_LENGTH_MOL = cmd_args.max_len_mol
saved_target_lang = os.sep.join([output_folder, 'mol_lang.pkl'])
if mrpc_test:
mol_lang, tr_samples, ts_samples = readMRPC(
molecule_file_tr=path_to_file_tr,
molecule_file_ts=path_to_file_ts,
saved_molecule_lang=saved_target_lang,
num_examples_tr=NUM_EXAMPLES_TR,
num_examples_ts=NUM_EXAMPLES_TS,
min_len_molecule=MIN_LENGTH_MOL,
max_len_molecule=MAX_LENGTH_MOL,
shuffle=True)
else:
mol_lang, tr_samples, ts_samples = readMolecules(
molecule_file_tr=path_to_file_tr,
molecule_file_ts=path_to_file_ts,
saved_molecule_lang=saved_target_lang,
num_examples_tr=NUM_EXAMPLES_TR,
num_examples_ts=NUM_EXAMPLES_TS,
min_len_molecule=MIN_LENGTH_MOL,
max_len_molecule=MAX_LENGTH_MOL,
shuffle=True)
pickle.dump(mol_lang, open(saved_mol_lang, 'wb'))
train_dataset = MolecularSimilarityDataset(
tr_samples, mol_lang, train_batch_size if device == 'cuda' else 1)
test_dataset = MolecularSimilarityDataset(
ts_samples, mol_lang, train_batch_size if device == 'cuda' else 1)
MAX_SEQ_LEN = MOL_SEQ_LEN * 2
print('Axial Embedding shape:', compute_axial_position_shape(MAX_SEQ_LEN))
model = ReformerLM(
num_tokens=mol_lang.n_words,
dim=dim,
bucket_size=bucket_size,
depth=depth,
heads=heads,
n_hashes=n_hashes,
max_seq_len=MAX_SEQ_LEN,
ff_chunks=ff_chunks,
attn_chunks=attn_chunks,
weight_tie=True,
weight_tie_embedding=True,
axial_position_emb=True,
axial_position_shape=compute_axial_position_shape(MAX_SEQ_LEN),
axial_position_dims=(dim // 2, dim // 2),
return_embeddings=True,
use_full_attn=use_full_attn).to(device)
linear_regressor = Linear(512, 2).to(device)
model = TrainingWrapper(model, ignore_index=PAD_IDX,
pad_value=PAD_IDX).to(device)
model_params = filter(lambda p: p.requires_grad, model.parameters())
linear_params = filter(lambda p: p.requires_grad,
linear_regressor.parameters())
SAVE_DIR = os.sep.join([output_folder, 'saved_model'])
os.makedirs(SAVE_DIR, exist_ok=True)
try:
model_ckp_max = np.max(
[int(ckp) for ckp in os.listdir(os.sep.join([SAVE_DIR, 'model']))])
except:
model_ckp_max = 0
gpus_mini_batch = (train_batch_size // gradient_accumulation_steps
) // torch.cuda.device_count()
print('gpus_mini_batch:', gpus_mini_batch,
'with gradient_accumulation_steps:', gradient_accumulation_steps)
log_file = open(os.sep.join([output_folder, 'training_log.log']), 'a')
log_file.write(
"\n\n\n{}\tStarting new training from chekpoint: EncoderDecoder-{}\n".
format(datetime.datetime.now(), model_ckp_max))
log_file.flush()
if use_deepspeed:
if deepspeed_optimizer == False:
print('No DeepSpeed optimizer found. Using RangerLars.')
model_optimizer = RangerLars(model.parameters())
linear_optimizer = RangerLars(linear_regressor.parameters())
model_engine, model_optimizer, trainloader, _ = deepspeed.initialize(
args=cmd_args,
model=model,
optimizer=model_optimizer,
model_parameters=model_params,
training_data=train_dataset)
linear_engine, linear_optimizer, _, _ = deepspeed.initialize(
args=cmd_args,
model=linear_regressor,
optimizer=linear_optimizer,
model_parameters=linear_params)
else:
print('Found optimizer in the DeepSpeed configurations. Using it.')
model_engine, model_optimizer, trainloader, _ = deepspeed.initialize(
args=cmd_args,
model=model,
model_parameters=model_params,
training_data=train_dataset)
linear_engine, linear_optimizer, _, _ = deepspeed.initialize(
args=cmd_args,
model=linear_regressor,
model_parameters=linear_params)
_, model_client_sd = model_engine.load_checkpoint(
os.sep.join([SAVE_DIR, 'model']), model_ckp_max)
testloader = model_engine.deepspeed_io(test_dataset)
######TO DO
for eph in range(epochs):
print('Starting Epoch: {}'.format(eph))
for i, pair in enumerate(tqdm(trainloader)):
tr_step = ((eph * len(trainloader)) + i) + 1
src = pair[0]
trg = pair[1]
pickle.dump(src, open('src.pkl', 'wb'))
pickle.dump(trg, open('trg.pkl', 'wb'))
model_engine.train()
linear_engine.train()
#enc_dec.train()
src = src.to(model_engine.local_rank)
trg = trg.to(linear_engine.local_rank)
print("Sample:", src)
print("Target:", trg)
print("Target Shape:", trg.shape)
print("len Samples:", len(src))
## Need to learn how to use masks correctly
enc_input_mask = torch.tensor(
[[1 if idx != PAD_IDX else 0 for idx in smpl]
for smpl in src]).bool().to(model_engine.local_rank)
# context_mask = torch.tensor([[1 for idx in smpl if idx != PAD_IDX] for smpl in trg]).bool().to(device)
#################
enc_keys = model_engine(
src, return_loss=False, input_mask=enc_input_mask
) #enc_input_mask)#, context_mask=context_mask)
#loss = enc_dec(src, trg, return_loss = True, enc_input_mask = None)#enc_input_mask)#, context_mask=context_mask)
print('enc_keys shape', enc_keys.shape)
#enc_keys_cls = enc_keys[:,0:1,:].to(linear_engine.local_rank)#torch.tensor([s[0] for s in enc_keys]).to(linear_engine.local_rank)
#print('enc_keys_cls shape', enc_keys_cls.shape)
preds = torch.softmax(linear_engine(enc_keys),
dim=1).to(linear_engine.local_rank)
print('preds shape', preds.shape)
#preds = np.array([r[0] for r in results])
#print('Pred:', preds.shape)
loss = F.cross_entropy(preds, trg).to(linear_engine.local_rank)
loss.backward()
model_engine.step()
linear_engine.step()
print('Training Loss:', loss.item())
if tr_step % validate_every == 0:
val_loss = []
for pair in tqdm(
testloader
): #Can't use the testloader or I will mess up with the model assignment and it won't learn during training, need to use normal validation instead of parallel one
model_engine.eval()
linear_engine.eval()
with torch.no_grad():
ts_src = pair[0]
ts_trg = pair[1]
pickle.dump(ts_src, open('ts_src.pkl', 'wb'))
pickle.dump(ts_trg, open('ts_trg.pkl', 'wb'))
ts_src = ts_src.to(model_engine.local_rank)
ts_trg = ts_trg.to(linear_engine.local_rank)
#ts_src = torch.tensor(np.array([pair[0].numpy()])).to(device)
#ts_trg = torch.tensor(np.array([pair[1].numpy()])).to(device)
## Need to learn how to use masks correctly
ts_enc_input_mask = torch.tensor([
[1 if idx != PAD_IDX else 0 for idx in smpl]
for smpl in ts_src
]).bool().to(model_engine.local_rank)
#ts_context_mask = torch.tensor([[1 for idx in smpl if idx != PAD_IDX] for smpl in ts_trg]).bool().to(device)
# loss = model_engine(
# ts_src,
# ts_trg,
# return_loss=True,
# enc_input_mask=ts_enc_input_mask
# ) #ts_enc_input_mask)#, context_mask=ts_context_mask)
# #loss = enc_dec(ts_src, ts_trg, return_loss = True, enc_input_mask = None)
ts_enc_keys = model_engine(
ts_src,
return_loss=False,
input_mask=ts_enc_input_mask)
ts_pred = torch.softmax(
linear_engine(ts_enc_keys),
dim=1).to(linear_engine.local_rank)
loss = F.cross_entropy(ts_pred, ts_trg).to(
linear_engine.local_rank)
val_loss.append(loss.item())
print(
f'\tValidation Loss: AVG: {np.mean(val_loss)}, MEDIAN: {np.median(val_loss)}, STD: {np.std(val_loss)} '
)
log_file.write(
'Step: {}\tTraining Loss:{}\t Validation LOSS: AVG: {}| MEDIAN: {}| STD: {}\n'
.format(i, loss.item(), np.mean(val_loss),
np.median(val_loss), np.std(val_loss)))
else:
log_file.write('Step: {}\tTraining Loss:{}\n'.format(
i, loss.item()))
log_file.flush()
if tr_step % save_every == 0:
print('\tSaving Checkpoint')
model_ckpt_id = str(model_ckp_max + tr_step + 1)
model_engine.save_checkpoint(
os.sep.join([SAVE_DIR, 'model']), model_ckpt_id)
log_file.close()
print('\tSaving Final Checkpoint')
model_ckpt_id = str(model_ckp_max + tr_step + 1)
model_engine.save_checkpoint(os.sep.join([SAVE_DIR, 'model']),
model_ckpt_id)
else:
#model_optimizer = torch.optim.Adam(model.parameters()) # RangerLars(model.parameters())
#linear_optimizer = torch.optim.Adam(linear_regressor.parameters()) # RangerLars(linear_regressor.parameters())
model_optimizer = torch.optim.Adam(
list(model.parameters()) + list(linear_regressor.parameters())
) #RangerLars(list(model.parameters())+list(linear_regressor.parameters())) #
PATH = os.sep.join(
[SAVE_DIR, 'model',
str(model_ckp_max), 'sts_model.pt'])
if os.path.exists(PATH):
print('********** Found Checkpoint. Loading:', PATH)
checkpoint = torch.load(PATH)
model.load_state_dict(checkpoint['model_state_dict'])
linear_regressor.load_state_dict(checkpoint['linear_state_dict'])
model_optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
trainloader = DataLoader(train_dataset,
batch_size=train_batch_size,
shuffle=False)
testloader = DataLoader(test_dataset,
batch_size=train_batch_size,
shuffle=False)
######TO DO
train_loss_list = []
for eph in range(epochs):
print('Starting Epoch: {}'.format(eph))
for i, pair in enumerate(tqdm(trainloader)):
tr_step = ((eph * len(trainloader)) + i) + 1
src = pair[0]
trg = pair[1]
pickle.dump(src, open('src.pkl', 'wb'))
pickle.dump(trg, open('trg.pkl', 'wb'))
model.train()
linear_regressor.train()
#enc_dec.train()
src = src.to(device)
trg = trg.to(device)
#print("Sample:", src)
#print("Target:", trg)
#print("Target Shape:", trg.shape)
#print("len Samples:", len(src))
## Need to learn how to use masks correctly
enc_input_mask = torch.tensor(
[[1 if idx != PAD_IDX else 0 for idx in smpl]
for smpl in src]).bool().to(device)
# context_mask = torch.tensor([[1 for idx in smpl if idx != PAD_IDX] for smpl in trg]).bool().to(device)
#################
enc_keys = model(
src, return_loss=False, input_mask=enc_input_mask
) #enc_input_mask)#, context_mask=context_mask)
#loss = enc_dec(src, trg, return_loss = True, enc_input_mask = None)#enc_input_mask)#, context_mask=context_mask)
#print('enc_keys shape', enc_keys.shape)
enc_keys_cls = enc_keys[:, 0, :].to(
device
) #torch.tensor([s[0] for s in enc_keys]).to(linear_engine.local_rank)
#print('enc_keys_cls shape', enc_keys_cls.shape)
preds = torch.softmax(linear_regressor(enc_keys_cls),
dim=1).to(device)
#print('preds shape', preds.shape)
#preds = np.array([r[0] for r in results])
#print('Pred:', preds.shape)
loss = F.cross_entropy(preds, trg).to(device)
loss.backward()
model_optimizer.step()
#linear_optimizer.step()
train_loss_list.append(loss.item())
#print('Training Loss:', loss.item())
if tr_step % validate_every == 0:
val_loss = []
ACC_list = []
MCC_list = []
for pair in tqdm(
testloader
): #Can't use the testloader or I will mess up with the model assignment and it won't learn during training, need to use normal validation instead of parallel one
model.eval()
linear_regressor.eval()
with torch.no_grad():
ts_src = pair[0]
ts_trg = pair[1]
pickle.dump(ts_src, open('ts_src.pkl', 'wb'))
pickle.dump(ts_trg, open('ts_trg.pkl', 'wb'))
ts_src = ts_src.to(device)
ts_trg = ts_trg.to(device)
#ts_src = torch.tensor(np.array([pair[0].numpy()])).to(device)
#ts_trg = torch.tensor(np.array([pair[1].numpy()])).to(device)
## Need to learn how to use masks correctly
ts_enc_input_mask = torch.tensor(
[[1 if idx != PAD_IDX else 0 for idx in smpl]
for smpl in ts_src]).bool().to(device)
#ts_context_mask = torch.tensor([[1 for idx in smpl if idx != PAD_IDX] for smpl in ts_trg]).bool().to(device)
# loss = model_engine(
# ts_src,
# ts_trg,
# return_loss=True,
# enc_input_mask=ts_enc_input_mask
# ) #ts_enc_input_mask)#, context_mask=ts_context_mask)
# #loss = enc_dec(ts_src, ts_trg, return_loss = True, enc_input_mask = None)
ts_enc_keys = model(ts_src,
return_loss=False,
input_mask=ts_enc_input_mask)
ts_enc_keys_cls = ts_enc_keys[:, 0, :].to(device)
ts_pred = torch.softmax(
linear_regressor(ts_enc_keys_cls),
dim=1).to(device)
loss = F.cross_entropy(ts_pred, ts_trg).to(device)
ACC, MCC = compute_simple_metrics(ts_pred, ts_trg)
ACC_list.append(ACC)
MCC_list.append(MCC)
val_loss.append(loss.item())
print(
f'\Train Loss: LAST: {train_loss_list[-1]}, AVG: {np.mean(train_loss_list)}, MEDIAN: {np.median(train_loss_list)}, STD: {np.std(train_loss_list)} '
)
print(
f'\tValidation Loss: AVG: {np.mean(val_loss)}, MEDIAN: {np.median(val_loss)}, STD: {np.std(val_loss)} '
)
print(
f'\tValidation ACC: AVG: {np.mean(ACC_list)}, MEDIAN: {np.median(ACC_list)}, STD: {np.std(ACC_list)} '
)
print(
f'\tValidation MCC: AVG: {np.mean(MCC_list)}, MEDIAN: {np.median(MCC_list)}, STD: {np.std(MCC_list)} '
)
log_file.write(
'Step: {}\tTraining Loss:{}\t Validation LOSS: AVG: {}| MEDIAN: {}| STD: {}\n'
.format(i, loss.item(), np.mean(val_loss),
np.median(val_loss), np.std(val_loss)))
else:
log_file.write('Step: {}\tTraining Loss:{}\n'.format(
i, loss.item()))
log_file.flush()
if tr_step % save_every == 0:
print('\tSaving Checkpoint')
model_ckpt_id = str(model_ckp_max + tr_step + 1)
#model_engine.save_checkpoint(os.sep.join([SAVE_DIR, 'model']),
# model_ckpt_id)
PATH = os.sep.join([
SAVE_DIR, 'model',
str(model_ckpt_id), 'sts_model.pt'
])
os.makedirs(os.sep.join(PATH.split(os.sep)[:-1]),
exist_ok=True)
torch.save(
{
'step': tr_step,
'model_state_dict': model.state_dict(),
'linear_state_dict': linear_regressor.state_dict(),
'optimizer_state_dict':
model_optimizer.state_dict(),
}, PATH)
log_file.close()
print('\tSaving Final Checkpoint')
model_ckpt_id = str(model_ckp_max + tr_step + 1)
#model_engine.save_checkpoint(os.sep.join([SAVE_DIR, 'model']),
# model_ckpt_id)
PATH = os.sep.join(
[SAVE_DIR, 'model',
str(model_ckpt_id), 'sts_model.pt'])
os.makedirs(os.sep.join(PATH.split(os.sep)[:-1]), exist_ok=True)
torch.save(
{
'step': tr_step,
'model_state_dict': model.state_dict(),
'linear_state_dict': linear_regressor.state_dict(),
'optimizer_state_dict': model_optimizer.state_dict(),
}, PATH)
# 加载albert
path = "model/albert_tiny/"
albert_model, full_tokenizer = load_albert(path)
# outputs = albert_model(batch_inputs)
model = ReformerLM(num_tokens=20000,
dim=1024,
depth=12,
max_seq_len=4096,
lsh_dropout=0.1,
causal=True,
full_attn_thres=1024)
# 0 is used for padding and no loss to be calculated on it
model = TrainingWrapper(model, ignore_index=0, pad_value=0)
# the wrapper can handle evenly packed sequences
x_train = randint(0, 20000, (3, 357))
# or if you have a list of uneven sequences, it will be padded for you
x_train = [
randint(0, 20000, (120, )),
randint(0, 20000, (253, )),
randint(0, 20000, (846, ))
]
# when training, set return_loss equal to True
model.train()
loss = model(x_train, return_loss=True)
loss.backward()
def test_encdec_v1(input_lang, target_lang, dim, bucket_size, depth, heads,
n_hashes, vir_seq_len, ff_chunks, attn_chunks, mol_seq_len,
cmd_args, train_dataset, test_dataset, output_folder,
train_batch_size, epochs, validate_every, save_every,
checkpoint_id, deepspeed_optimizer, use_full_attn,
gradient_accumulation_steps, filter_thres):
results = {
'generated_seq': [],
'generated_mol': [],
'target_mol': [],
'input_genome': []
}
encoder = ReformerLM(
num_tokens=input_lang.n_words,
dim=dim,
bucket_size=bucket_size,
depth=depth,
heads=heads,
n_hashes=n_hashes,
max_seq_len=vir_seq_len,
ff_chunks=ff_chunks,
attn_chunks=attn_chunks,
weight_tie=True,
weight_tie_embedding=True,
axial_position_emb=True,
axial_position_shape=compute_axial_position_shape(vir_seq_len),
axial_position_dims=(dim // 2, dim // 2),
return_embeddings=True,
use_full_attn=use_full_attn).to(device)
decoder = ReformerLM(
num_tokens=target_lang.n_words,
dim=dim,
bucket_size=bucket_size,
depth=depth,
heads=heads,
n_hashes=n_hashes,
ff_chunks=ff_chunks,
attn_chunks=attn_chunks,
max_seq_len=mol_seq_len,
axial_position_emb=True,
axial_position_shape=compute_axial_position_shape(mol_seq_len),
axial_position_dims=(dim // 2, dim // 2),
weight_tie=True,
weight_tie_embedding=True,
causal=True,
use_full_attn=use_full_attn).to(device)
SAVE_DIR = os.sep.join([output_folder, 'saved_model'])
if checkpoint_id:
enc_ckp_max = checkpoint_id
dec_ckp_max = checkpoint_id
else:
try:
enc_ckp_max = np.max([
int(ckp)
for ckp in os.listdir(os.sep.join([SAVE_DIR, 'encoder']))
])
except Exception as e:
print('Exception:', e)
enc_ckp_max = 0
try:
dec_ckp_max = np.max([
int(ckp)
for ckp in os.listdir(os.sep.join([SAVE_DIR, 'decoder']))
])
except:
dec_ckp_max = 0
encoder = TrainingWrapper(encoder, ignore_index=PAD_IDX,
pad_value=PAD_IDX).to(device)
decoder = TrainingWrapper(decoder, ignore_index=PAD_IDX,
pad_value=PAD_IDX).to(device)
'''
encoder_params = filter(lambda p: p.requires_grad, encoder.parameters())
decoder_params = filter(lambda p: p.requires_grad, decoder.parameters())
if deepspeed_optimizer == False:
print('No DeepSpeed optimizer found. Using RangerLars.')
encoder_optimizer = RangerLars(encoder.parameters())
decoder_optimizer = RangerLars(decoder.parameters())
encoder_engine, encoder_optimizer, trainloader, _ = deepspeed.initialize(
args=cmd_args,
model=encoder,
optimizer=encoder_optimizer,
model_parameters=encoder_params,
training_data=train_dataset,
dist_init_required=True
)
decoder_engine, decoder_optimizer, testloader, _ = deepspeed.initialize(
args=cmd_args,
model=decoder,
optimizer=decoder_optimizer,
model_parameters=decoder_params,
training_data=test_dataset,
dist_init_required=False
)
else:
print('Found optimizer in the DeepSpeed configurations. Using it.')
encoder_engine, encoder_optimizer, trainloader, _ = deepspeed.initialize(args=cmd_args, model=encoder, model_parameters=encoder_params, training_data=train_dataset, dist_init_required=True)
decoder_engine, decoder_optimizer, testloader, _ = deepspeed.initialize(args=cmd_args, model=decoder, model_parameters=decoder_params, training_data=test_dataset, dist_init_required=False)
_, encoder_client_sd = encoder_engine.load_checkpoint(os.sep.join([SAVE_DIR,'encoder']), enc_ckp_max)
_, decoder_client_sd = decoder_engine.load_checkpoint(os.sep.join([SAVE_DIR,'decoder']), dec_ckp_max)
gpus_mini_batch = (train_batch_size// gradient_accumulation_steps) // torch.cuda.device_count()
print('gpus_mini_batch:', gpus_mini_batch, 'with gradient_accumulation_steps:', gradient_accumulation_steps)
for pair in tqdm(testloader):
encoder_engine.eval()
decoder_engine.eval()
encoder.eval()
decoder.eval()
with torch.no_grad():
ts_src = pair[0]
ts_trg = pair[1]
input_genome = [[input_lang.index2word[gen_idx.item()] for gen_idx in smpl] for smpl in pair[0]]
target_mol = [[target_lang.index2word[mol_idx.item()] for mol_idx in smpl] for smpl in pair[1]]
ts_src = ts_src.to(encoder_engine.local_rank) #ts_src.to(device) #
ts_trg = ts_trg.to(decoder_engine.local_rank) #ts_trg.to(device) #
print('ts_src.shape', ts_src.shape)
print('ts_src.shape', ts_trg.shape)
enc_keys = encoder(ts_src) #encoder_engine(ts_src)
yi = torch.tensor([[SOS_token] for _ in range(gpus_mini_batch)]).long().to(decoder_engine.local_rank) #to(device) #
#sample = decoder_engine.generate(yi, mol_seq_len, filter_logits_fn=top_p, filter_thres=0.95, keys=enc_keys, eos_token = EOS_token)
sample = decoder.generate(yi, mol_seq_len, filter_logits_fn=top_p, filter_thres=0.95, keys=enc_keys, eos_token = EOS_token)
actual_mol = []
for mol_seq in sample.cpu().numpy():
for mol_idx in mol_seq:
actual_mol.append(target_lang.index2word[mol_idx])
print('Generated Seq:', sample)
print('Generated Mol:', actual_mol)
print('Real Mol:', target_mol[:target_mol.index(target_lang.index2word[EOS_token])])
results['generated_seq'].append(sample)
results['generated_mol'].append(actual_mol)
results['target_mol'].append(target_mol)
results['input_genome'].append(input_genome)
print('Saving Test Results..')
pickle.dump(results, open(os.sep.join([output_folder,'test_results.pkl']), 'wb'))
'''
encoder_checkpoint = os.sep.join([
output_folder, 'saved_model', 'encoder', enc_ckp_max,
'mp_rank_00_model_states.pt'
])
decoder_checkpoint = os.sep.join([
output_folder, 'saved_model', 'decoder', dec_ckp_max,
'mp_rank_00_model_states.pt'
])
encoder.load_state_dict(
torch.load(encoder_checkpoint,
map_location=torch.device(device))['module'])
decoder.load_state_dict(
torch.load(decoder_checkpoint,
map_location=torch.device(device))['module'])
real_batch_size = train_batch_size // gradient_accumulation_steps
test_loader = DataLoader(dataset=test_dataset,
batch_size=real_batch_size,
shuffle=True)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
# dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs
encoder = nn.DataParallel(encoder)
decoder = nn.DataParallel(decoder)
encoder.to(device)
decoder.to(device)
for pair in tqdm(test_loader):
encoder.eval()
decoder.eval()
with torch.no_grad():
ts_src = torch.tensor(np.array([pair[0].numpy()])).to(device)
ts_trg = torch.tensor(np.array([pair[1].numpy()])).to(device)
input_genome = [
input_lang.index2word[gen_idx.item()] for gen_idx in pair[0]
]
target_mol = [
target_lang.index2word[mol_idx.item()] for mol_idx in pair[1]
]
enc_keys = encoder(ts_src)
yi = torch.tensor([[SOS_token]]).long().to(device)
sample = decoder.generate(yi,
mol_seq_len,
filter_logits_fn=top_p,
filter_thres=filter_thres,
keys=enc_keys,
eos_token=EOS_token)
actual_mol = []
for mol_seq in sample.cpu().numpy():
for mol_idx in mol_seq:
actual_mol.append(target_lang.index2word[mol_idx])
print('Generated Seq:', sample)
print('Generated Mol:', actual_mol)
print(
'Real Mol:',
target_mol[:target_mol.index(target_lang.
index2word[EOS_token])])
results['generated_seq'].append(sample)
results['generated_mol'].append(actual_mol)
results['target_mol'].append(target_mol)
results['input_genome'].append(input_genome)
print('Saving Test Results..')
pickle.dump(results,
open(os.sep.join([output_folder, 'test_results.pkl']), 'wb'))
'''
def train(device='cpu',
output_dir='model',
epochs=5,
save_step=5,
batch_size=4):
model = ReformerLM(num_tokens=13137,
dim=128,
depth=12,
max_seq_len=4096,
lsh_dropout=0.1,
causal=True,
full_attn_thres=128)
model = TrainingWrapper(model, ignore_index=0, pad_value=0).to(device)
# output_dir="model"
model_cpu_path = os.path.join(output_dir, 'model_cpu.pt')
try:
model.load_state_dict(torch.load(model_cpu_path))
except:
pass
model.train()
optimizer = AdamW(params=model.parameters())
optimizer_path = os.path.join(output_dir, 'optimizer.pt')
try:
optimizer.load_state_dict(torch.load(optimizer_path))
except:
pass
print(optimizer)
total_loss = 0.0
# batch_size=4
loss = []
data = []
for it in get_data("data/train.json", tokenizer):
data.append(it)
# data=data[:1000]
loss_fn = nn.CrossEntropyLoss() # -100 index = padding token
for n in tqdm(range(epochs)):
# print(n)
random.shuffle(data)
inputs = []
labels = []
for i, it in enumerate(data):
# print("it",it)
inputs.append(it['keywords'])
labels.append(it['text'])
if i % batch_size == 0 and i != 0:
# print(it)
inputs_batch = torch.tensor(inputs).long().to(device)
labels_batch = torch.tensor(labels).long().to(device)
# print(inputs_batch)
inputs = []
labels = []
# inputs = torch.tensor(it['keywords']).long()
# labels = torch.tensor(it['text']).long()
# print("inputs",inputs)
pred = model(inputs_batch)
mlm_loss = loss_fn(pred.view(-1, tokenizer.vocab_size),
labels_batch.view(-1))
total_loss += mlm_loss.item()
loss.append(mlm_loss.item())
print('loss', mlm_loss.item())
mlm_loss.backward()
optimizer.step()
model.zero_grad()
# output_dir="model"
if i % save_step == 0 and i != 0:
model_cpu_path = os.path.join(output_dir, 'model_cpu.pt')
optimizer_path = os.path.join(output_dir, 'optimizer.pt')
torch.save(model.state_dict(), model_cpu_path)
torch.save(optimizer.state_dict(), optimizer_path)
model_cpu_path = os.path.join(output_dir, 'model_cpu.pt')
optimizer_path = os.path.join(output_dir, 'optimizer.pt')
torch.save(model.state_dict(), model_cpu_path)
torch.save(optimizer.state_dict(), optimizer_path)