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)
pred.shape
tokenizer.decode(torch.argmax(pred, dim=-1).squeeze(0))
loss_fn = nn.CrossEntropyLoss() #
masked_lm_loss = loss_fn(pred.view(-1, tokenizer.vocab_size), labels.view(-1))
masked_lm_loss
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
total_loss = 0.0
model.train()
model.to(device)
inputs = inputs.to(device)
labels = labels.to(device)
loss = []
optimizer = AdamW(params=model.parameters())
for _ in tqdm(range(100000)):
pred = model(inputs)
mlm_loss = loss_fn(pred.view(-1, tokenizer.vocab_size), labels.view(-1))
total_loss += mlm_loss.item()
loss.append(mlm_loss.item())
mlm_loss.backward()
optimizer.step()
model.zero_grad()
return full_seq
def __len__(self):
return self.data.size(0) // self.seq_len
train_dataset = TextSamplerDataset(data_train, SEQ_LEN)
val_dataset = TextSamplerDataset(data_val, SEQ_LEN)
# setup deepspeed
cmd_args = add_argument()
model_engine, optimizer, trainloader, _ = deepspeed.initialize(
args=cmd_args,
model=model,
model_parameters=model.parameters(),
training_data=train_dataset)
# training
for i, data in enumerate(trainloader):
model_engine.train()
data = data.to(model_engine.local_rank)
loss = model_engine(data, return_loss=True)
model_engine.backward(loss)
model_engine.step()
print(loss.item())
if i % VALIDATE_EVERY == 0:
model.eval()
with torch.no_grad():
return None
if __name__ == '__main__':
dataset = WikiDataset(path='./data/enwiki')
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
tokenizer.max_len = 128
model = ReformerLM(num_tokens=tokenizer.vocab_size,
dim=512,
depth=6,
heads=8,
max_seq_len=tokenizer.max_len,
causal=True)
parameters = filter(lambda p: p.requires_grad, model.parameters())
parser = argparse.ArgumentParser(description='Reformer')
# data
# cuda
parser.add_argument('--with_cuda',
default=False,
action='store_true',
dest='with_cuda',
help='use CPU in case there\'s no GPU support')
parser.add_argument('--use_ema',
default=False,
action='store_true',
dest='use_ema',
help='whether use exponential moving average')
self.data.size(0) - self.seq_len - 1, (1, ))
full_seq = self.data[rand_start:rand_start + self.seq_len + 1].long()
return full_seq.cuda()
def __len__(self):
return self.data.size(0) // self.seq_len
train_dataset = TextSamplerDataset(data_train, SEQ_LEN)
val_dataset = TextSamplerDataset(data_val, SEQ_LEN)
train_loader = cycle(DataLoader(train_dataset, batch_size=BATCH_SIZE))
val_loader = cycle(DataLoader(val_dataset, batch_size=BATCH_SIZE))
# optimizer
optim = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
# training
for i in tqdm.tqdm(range(NUM_BATCHES), mininterval=10., desc='training'):
model.train()
for __ in range(GRADIENT_ACCUMULATE_EVERY):
loss = model(next(train_loader), return_loss=True)
loss.backward()
print(f'training loss: {loss.item()}')
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
optim.step()
optim.zero_grad()
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)
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)