def preprocess(self, data_source_type="small"):
self.logger.info(
f"Loading {data_source_type} data from disk and parse it as a bunch of batches ..."
)
train_dataset, eval_dataset, test_dataset = dataset_generation(
self.context, data_type=data_source_type)
self.logger.info("Build iteral dataset ... ")
self.train_iter = DataLoader(train_dataset,
batch_size=self.nums_batch,
shuffle=True,
collate_fn=input_target_collate_fn)
self.eval_iter = DataLoader(eval_dataset,
batch_size=self.nums_batch,
shuffle=True,
collate_fn=input_target_collate_fn)
self.test_iter = DataLoader(test_dataset,
batch_size=self.nums_batch,
shuffle=True,
collate_fn=custom_collate_fn)
self.logger.info("Build src/tgt Vocabulary ...")
self.src_vocab = train_dataset.src_vocab
self.tgt_vocab = train_dataset.tgt_vocab
self.logger.info("Build transformer model ...")
self.model = build_model(self.context, len(self.src_vocab),
len(self.tgt_vocab))
self.model.cuda() if self.context.is_cuda else None
self.logger.debug(self.model)
def preprocess(self, data_source_type="small"):
self.logger.info(f"Loading {data_source_type} data from disk and parse it as a bunch of batches ...")
train_dataset, eval_dataset, test_dataset = dataset_generation(self.context, data_type=data_source_type)
self.logger.info("Build iteral dataset ... ")
self.train_iter = generated_iter_dataset(self.context, train_dataset, self.nums_batch)
self.eval_iter = generated_iter_dataset(self.context, eval_dataset, self.nums_batch)
self.test_iter = generated_iter_dataset(self.context, test_dataset, self.nums_batch)
self.logger.info("Build src/tgt Vocabulary ...")
self.src_vocab = train_dataset.src_vocab
self.tgt_vocab = train_dataset.tgt_vocab
self.logger.info("Build transformer model ...")
self.model = build_model(self.context, len(self.src_vocab), len(self.tgt_vocab))
# self.model.cuda() if self.context.is_cuda else None
self.logger.debug(self.model)
if i % 50 == 1:
elapsed = time.time() - start
ctx.logger.info("Epoch Step: %d Loss: %f Tokens per Sec: %f", i,
loss / batch.ntokens, tokens / elapsed)
start = time.time()
tokens = 0
return total_loss / total_tokens
if __name__ == "__main__":
# Train the simple copy task.
ctx = Context(desc="Train")
logger = ctx.logger
vocab_size = 11 # V_Size
criterion = LabelSmoothing(size=vocab_size, padding_idx=0, smoothing=0.0)
model = build_model(ctx, vocab_size, vocab_size)
logger.info(model)
model_opt = NoamOpt(
model.src_embed[0].d_model, 1, 400,
torch.optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98),
eps=1e-9))
for epoch in range(100):
logger.debug("Training Epoch %d", epoch)
model.train()
run_epoch(data_gen(vocab_size, 30, 20), model,
SimpleLossCompute(model.generator, criterion, model_opt),
ctx)
model.eval()
run_epoch(data_gen(vocab_size, 30, 5), model,
if __name__ == "__main__":
context = Context(desc="Prediction")
logger = context.logger
logger.info('Constructing dictionaries...')
source_dictionary = IndexDictionary.load(
context.project_processed_dir,
mode='source',
vocabulary_size=context.vocabulary_size)
target_dictionary = IndexDictionary.load(
context.project_processed_dir,
mode='target',
vocabulary_size=context.vocabulary_size)
logger.info('Building model...')
model = build_model(context, source_dictionary.vocabulary_size,
target_dictionary.vocabulary_size)
logger.info("Building Predictor ....")
predictor = Predictor(ctx=context,
m=model,
src_dictionary=source_dictionary,
tgt_dictionary=target_dictionary)
logger.info("Get Predict Result ...")
for index, candidate in enumerate(predictor.predict_one()):
logger.info(f'Candidate {index} : {candidate}')
def run():
ctx = Context("Train_MultiGPU")
logger = ctx.logger
nums_batch = ctx.batch_size
epochs = ctx.epochs
# For data loading.
from torchtext import data, datasets
logger.info(f"Preparing dataset with batch size ... ")
import spacy
# !pip install torchtext spacy
# !python -m spacy download en
# !python -m spacy download de
logger.info("Load en/de data from local ...")
spacy_de = spacy.load('de', path=ctx.project_raw_dir)
spacy_en = spacy.load('en', path=ctx.project_raw_dir)
def tokenize_de(text):
return [tok.text for tok in spacy_de.tokenizer(text)]
# tokenize_en("I am a Chinese") --> ['I', 'am', 'a', 'Chinese']
def tokenize_en(text):
return [tok.text for tok in spacy_en.tokenizer(text)]
# Preparing dataset
logger.info("Build SRC and TGT Fields ...")
BOS_WORD = '<s>'
EOS_WORD = '</s>'
BLANK_WORD = "<blank>"
SRC = data.Field(tokenize=tokenize_de, pad_token=BLANK_WORD)
TGT = data.Field(tokenize=tokenize_en, init_token=BOS_WORD,
eos_token=EOS_WORD, pad_token=BLANK_WORD)
logger.info("Split datasets into train, val and test using SRC/TGT fileds ...")
MAX_LEN = 150
# Spilt dataset in root path into train, val, and test dataset
train, val, test = datasets.IWSLT.splits(
exts=('.de', '.en'), # A tuple containing the extension to path for each language.
fields=(SRC, TGT), # A tuple containing the fields that will be used for data in each language.
root=ctx.project_raw_dir, # Root dataset storage directory.
filter_pred=lambda x: len(vars(x)['src']) <= MAX_LEN and len(vars(x)['trg']) <= MAX_LEN)
logger.info("Build vocabularies for src and tgt ...")
MIN_FREQ = 2
SRC.build_vocab(train.src, min_freq=MIN_FREQ)
TGT.build_vocab(train.trg, min_freq=MIN_FREQ)
# GPUs to use
devices = ctx.device_id # [0, 1, 2, 3]
pad_idx = TGT.vocab.stoi["<blank>"]
logger.info("Build Model ...")
model = build_model(ctx, len(SRC.vocab), len(TGT.vocab))
model.cuda() if ctx.is_cuda else None
# Print out log info for debug ...
logger.info(model)
criterion = LabelSmoothing(size=len(TGT.vocab), padding_idx=pad_idx, smoothing=0.1)
criterion.cuda() if ctx.is_cuda else None
logger.info("Generating Training and Validating Batch datasets ...")
train_iter = MyIterator(train, batch_size=nums_batch, device=ctx.device,
repeat=False, sort_key=lambda x: (len(x.src), len(x.trg)),
batch_size_fn=batch_size_fn, train=True)
logger.info(f"Trainning Dataset: epoch[{epochs}], iterations[{train_iter.iterations}], batch size [{nums_batch}]")
valid_iter = MyIterator(val, batch_size=nums_batch, device=ctx.device,
repeat=False, sort_key=lambda x: (len(x.src), len(x.trg)),
batch_size_fn=batch_size_fn, train=False)
logger.info(f"Validate Dataset: epoch[{epochs}], iterations[{valid_iter.iterations}], batch size [{nums_batch}]")
if ctx.is_gpu_parallel:
# Using multiple GPU resource to train ...
model_parallel = nn.DataParallel(model, device_ids=devices)
loss_func = MultiGPULossCompute
elif ctx.is_cuda:
# Using Single GPU resource to train ...
model_parallel = model
loss_func = SimpleLossCompute
else:
# Using Single CPU resource to train ...
model_parallel = model
loss_func = SimpleLossCompute
logger.info("Training Process is begining ...")
# Training or load model from checkpoint
if True:
model_opt = NoamOpt(model_size = model.src_embed[0].d_model,
factor = 1,
warmup = 2000,
optimizer = torch.optim.Adam(model.parameters(),
lr=0, betas=(0.9, 0.98), eps=1e-9))
for epoch in range(epochs):
# Set model in train
model_parallel.train()
run_epoch((rebatch(pad_idx, b) for b in train_iter),
model_parallel,
loss_func(model.generator, criterion, devices, opt=model_opt),
ctx)
# Evaluation Model
model_parallel.eval()
# Get loss
loss = run_epoch((rebatch(pad_idx, b) for b in valid_iter),
model_parallel,
loss_func(model.generator, criterion, devices, opt=None),
ctx)
logger.info("The loss is %d", loss)
else:
model = torch.load("iwslt.pt")
logger.info("Training is over and Evaluate Model ...")
for i, batch in enumerate(valid_iter):
src = batch.src.transpose(0, 1)[:1]
src_mask = (src != SRC.vocab.stoi["<blank>"]).unsqueeze(-2)
out = greedy_decode(model, src, src_mask,
max_len=60, start_symbol=TGT.vocab.stoi["<s>"])
print("Translation:", end="\t")
for i in range(1, out.size(1)):
sym = TGT.vocab.itos[out[0, i]]
if sym == "</s>":
break
print(sym, end=" ")
print()
print("Target:", end="\t")
for i in range(1, batch.trg.size(0)):
sym = TGT.vocab.itos[batch.trg.data[i, 0]]
if sym == "</s>": break
print(sym, end=" ")
print()
break
if __name__ == "__main__":
context = Context(desc="Prediction")
logger = context.logger
data_source_type = "small"
logger.info(
f"Loading {data_source_type} data from disk and parse it as a bunch of batches ..."
)
train_dataset, eval_dataset, test_dataset = dataset_generation(
context, data_type=data_source_type)
logger.info('Constructing dictionaries...')
source_dictionary = train_dataset.src_vocab
target_dictionary = train_dataset.tgt_vocab
logger.info('Building model...')
model = build_model(context, len(source_dictionary),
len(target_dictionary))
logger.info("Building Predictor ....")
predictor = Predictor(ctx=context,
m=model,
src_dictionary=source_dictionary,
tgt_dictionary=target_dictionary)
logger.info("Get Predict Result ...")
for index, candidate in enumerate(predictor.predict()):
logger.info(f'Candidate {index} : {candidate}')
def run_trainer_standalone(ctx):
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
logger = ctx.logger
run_name = ("d_model={d_model}-"
"layers_count={layers_count}-"
"heads_count={heads_count}-"
"pe={positional_encoding}-"
"optimizer={optimizer}-"
"{timestamp}").format(**ctx.config,
timestamp=datetime.now().strftime(
"%Y_%m_%d_%H_%M_%S"))
logger.info(f'Run name : {run_name}')
logger.info('Constructing dictionaries...')
source_dictionary = IndexDictionary.load(
ctx.project_processed_dir,
mode='source',
vocabulary_size=ctx.vocabulary_size)
logger.info(
f'Source dictionary vocabulary Size: {source_dictionary.vocabulary_size} tokens'
)
target_dictionary = IndexDictionary.load(
ctx.project_processed_dir,
mode='target',
vocabulary_size=ctx.vocabulary_size)
logger.info(
f'Target dictionary vocabulary Size: {target_dictionary.vocabulary_size} tokens'
)
logger.info('Building model...')
model = build_model(ctx, source_dictionary.vocabulary_size,
target_dictionary.vocabulary_size)
logger.info(model)
logger.info(
'Encoder : {parameters_count} parameters'.format(parameters_count=sum(
[p.nelement() for p in model.encoder.parameters()])))
logger.info(
'Decoder : {parameters_count} parameters'.format(parameters_count=sum(
[p.nelement() for p in model.decoder.parameters()])))
logger.info('Total : {parameters_count} parameters'.format(
parameters_count=sum([p.nelement() for p in model.parameters()])))
logger.info('Loading datasets...')
train_dataset = IndexedInputTargetTranslationDataset(ctx=ctx,
phase='train')
val_dataset = IndexedInputTargetTranslationDataset(ctx=ctx, phase='val')
train_dataloader = DataLoader(train_dataset,
batch_size=ctx.batch_size,
shuffle=True,
collate_fn=input_target_collate_fn)
val_dataloader = DataLoader(val_dataset,
batch_size=ctx.batch_size,
collate_fn=input_target_collate_fn)
if ctx.label_smoothing > 0.0:
loss_function = LabelSmoothingLoss(
ctx=ctx,
label_smoothing=ctx.label_smoothing,
vocabulary_size=target_dictionary.vocabulary_size)
else:
loss_function = TokenCrossEntropyLoss()
accuracy_function = AccuracyMetric()
if ctx.optimizer == 'Noam':
optimizer = NoamOptimizer(model.parameters(), d_model=ctx.d_model)
elif ctx.optimizer == 'Adam':
optimizer = Adam(model.parameters(), lr=ctx.lr)
else:
raise NotImplementedError()
logger.info('Start training...')
trainer = TransformerTrainer(model=model,
train_dataloader=train_dataloader,
val_dataloader=val_dataloader,
loss_function=loss_function,
metric_function=accuracy_function,
optimizer=optimizer,
run_name=run_name,
ctx=ctx)
trainer.run(ctx.epochs)
return trainer