def load_state(model_path):
state = load_checkpoint_to_cpu(model_path, arg_overrides={})
args = state["args"]
args = recursive_contractuser(args)
args = recursive_expanduser(args)
task = tasks.setup_task(args) # load src/tgt dicts
model = task.build_model(args)
model.load_state_dict(state["model"])
use_cuda = torch.cuda.is_available() and not args['common']['cpu']
if args['common']['fp16'] and use_cuda:
model.half()
if use_cuda:
torch.cuda.empty_cache()
torch.cuda.set_device(torch.cuda.device_count() - 1)
model.cuda()
model.eval()
del state
return args, task, model, use_cuda
def main(model_path, input):
LOGGER.info('Load model from {}'.format(model_path))
state = load_checkpoint_to_cpu(model_path, arg_overrides={})
args = state["args"]
task = tasks.setup_task(args) # load src/tgt dicts
model = task.build_model(args)
model.load_state_dict(state["model"])
use_cuda = torch.cuda.is_available() and not args['common']['cpu']
use_cuda = 0
if use_cuda:
torch.cuda.empty_cache()
torch.cuda.set_device(torch.cuda.device_count() - 1)
model.cuda()
model.eval()
if args['common']['fp16'] and use_cuda:
model.half()
# TODO: source tensor should be handled in corresponding task scripts. here we only use seq2seq pipeline for instance.
intput_ids = task.target_dictionary.encode_string(input, line_tokenizer=None, add_if_not_exist=False)
src_input_ids = intput_ids.long().unsqueeze(dim=0)
sample = {
'net_input': {
'src_tokens': src_input_ids,
},
}
sample = utils.move_to_cuda(sample) if use_cuda else sample
generator = task.sequence_completor
net_output = generator.complete(models=[model], sample=sample)
# from ipdb import set_trace
# set_trace()
pred_prob = torch.softmax(net_output[0][0, -1, :], dim=-1)
topk_prob, topk_idx = pred_prob.topk(k=10, dim=-1)
# remove unk/eos/bos/pad
topk_info = [(round(prob.item(), 6), idx.item()) for prob, idx in zip(topk_prob, topk_idx)][:5]
topk_info = [(task.target_dictionary[idx], prob) for prob, idx in topk_info]
pred_sentence = [
(input[:-1] + [topk_token], topk_prob)
for topk_token, topk_prob in topk_info
]
return topk_info, pred_sentence
def main(model_path, input):
state = load_checkpoint_to_cpu(model_path, arg_overrides={})
args = state["args"]
task = tasks.setup_task(args) # load src/tgt dicts
model = task.build_model(args)
model.load_state_dict(state["model"])
use_cuda = torch.cuda.is_available() and not args['common']['cpu']
if use_cuda:
torch.cuda.empty_cache()
torch.cuda.set_device(torch.cuda.device_count() - 1)
model.cuda()
if args['common']['fp16'] and use_cuda:
model.half()
model.eval()
# TODO: source tensor should be handled in corresponding task scripts. here we only use seq2seq pipeline for instance.
src_input_ids = task.src_dict.encode_line(input,
line_tokenizer=None,
add_if_not_exist=False)
src_input_ids = torch.cat([
src_input_ids[:args['task']['max_source_positions'] - 1],
torch.Tensor([task.src_dict.eos()]).long()
])
padding_size = args['task']['max_source_positions'] - len(src_input_ids)
if padding_size > 0:
src_input_ids = torch.cat([
src_input_ids,
torch.Tensor([task.src_dict.pad()] * padding_size).long()
])
if use_cuda:
src_input_ids = src_input_ids.unsqueeze(dim=0).cuda()
sample = {
'net_input': {
'src_tokens': src_input_ids,
'src_lengths':
torch.LongTensor([s.numel() for s in src_input_ids]),
},
}
sample = utils.move_to_cuda(sample) if use_cuda else sample
generator = task.build_generator(args)
pred_sentence_ids = generator.generate(models=[model], sample=sample)
pred_sentence = task.tgt_dict.string(pred_sentence_ids[0][0]['tokens'])
return pred_sentence
def load_model_ensemble_and_task(filenames,
arg_overrides=None,
task=None,
strict=True,
suffix=''):
from ncc import tasks
ensemble = []
for filename in filenames:
filename = filename.replace(".pt", suffix + ".pt")
if not PathManager.exists(filename):
raise IOError("Model file not found: {}".format(filename))
state = load_checkpoint_to_cpu(filename, arg_overrides)
args = state["args"]
if task is None:
task = tasks.setup_task(args)
# build model for ensemble
model = task.build_model(args)
model.load_state_dict(state["model"], strict=strict, args=args)
ensemble.append(model)
return ensemble, args, task
def main(model_path, input):
LOGGER.info('Load model from {}'.format(model_path))
state = load_checkpoint_to_cpu(model_path, arg_overrides={})
args = state["args"]
args = recursive_contractuser(args, old_cache_name='.ncc')
args = recursive_expanduser(args)
task = tasks.setup_task(args) # load src/tgt dicts
model = task.build_model(args)
model.load_state_dict(state["model"])
use_cuda = torch.cuda.is_available() and not args['common']['cpu']
if use_cuda:
torch.cuda.empty_cache()
torch.cuda.set_device(torch.cuda.device_count() - 1)
model.cuda()
model.eval()
if args['common']['fp16'] and use_cuda:
model.half()
sample = task.encode_input(input)
sample = utils.move_to_cuda(sample) if use_cuda else sample
generator = task.sequence_completor
net_output = generator.complete(models=[model], sample=sample)
out = task.decode_output(net_output)
return out
def single_main(args, init_distributed=False):
assert args['dataset']['max_tokens'] is not None or args['dataset']['max_sentences'] is not None, \
'Must specify batch size either with --max-tokens or --max-sentences'
metrics.reset()
# 0. Initialize CUDA and distributed training
if torch.cuda.is_available() and not args['common']['cpu']:
torch.cuda.set_device(args['distributed_training']['device_id'])
set_seed.set_seed(args['common']['seed'])
if init_distributed:
args['distributed_training'][
'distributed_rank'] = distributed_utils.distributed_init(args)
# Verify checkpoint directory
if distributed_utils.is_master(args):
save_dir = args['checkpoint']['save_dir']
checkpoint_utils.verify_checkpoint_directory(save_dir)
PathManager.rm(os.path.join(
save_dir, '*.pt')) # this code will remove pre-trained models
# 1. Setup task, e.g., translation, language modeling, etc.
task = tasks.setup_task(args)
# 2. Load valid dataset (we load training data below, based on the latest checkpoint)
task.load_dataset(args['dataset']['valid_subset'], combine=False, epoch=1)
# 3. Build model and criterion
model = task.build_model(args)
criterion = task.build_criterion(args)
LOGGER.info(model)
LOGGER.info('model {}, criterion {}'.format(args['model']['arch'],
criterion.__class__.__name__))
LOGGER.info('num. model params: {} (num. trained: {})'.format(
sum(p.numel() for p in model.parameters()),
sum(p.numel() for p in model.parameters() if p.requires_grad),
))
# 4. Build trainer
trainer = Trainer(args, task, model, criterion)
LOGGER.info('training on {} GPUs'.format(
args['distributed_training']['distributed_world_size']))
LOGGER.info(
'max tokens per GPU = {} and max sentences per GPU = {}'.format(
args['dataset']['max_tokens'],
args['dataset']['max_sentences'],
))
# 5. Load the latest checkpoint if one is available and restore the corresponding train iterator
extra_state, epoch_itr = checkpoint_utils.load_checkpoint(args,
trainer,
combine=False)
# 6. Train until the learning rate gets too small
max_epoch = args['optimization']['max_epoch'] or math.inf
max_update = args['optimization']['max_update'] or math.inf
lr = trainer.get_lr()
train_meter = meters.StopwatchMeter()
train_meter.start()
valid_subsets = args['dataset']['valid_subset'].split(',')
while (lr > args['optimization']['min_lr']
and epoch_itr.next_epoch_idx <= max_epoch
and trainer.get_num_updates() < max_update):
# train for one epoch
train(args, trainer, task, epoch_itr)
if not args['dataset']['disable_validation'] and epoch_itr.epoch % args[
'dataset']['validate_interval'] == 0:
valid_losses = validate(args, trainer, task, epoch_itr,
valid_subsets)
else:
valid_losses = [None]
# only use first validation loss to update the learning rate
lr = trainer.lr_step(epoch_itr.epoch, valid_losses[0])
# save checkpoint
if epoch_itr.epoch % args['checkpoint']['save_interval'] == 0:
checkpoint_utils.save_checkpoint(args, trainer, epoch_itr,
valid_losses[0])
# early stop
if should_stop_early(args, valid_losses[0]):
LOGGER.info(
'early stop since valid performance hasn\'t improved for last {} runs'
.format(args['checkpoint']['patience']))
break
epoch_itr = trainer.get_train_iterator(
epoch_itr.next_epoch_idx,
combine=False, # TODO to be checked
# sharded data: get train iterator for next epoch
load_dataset=(os.pathsep in args['task']['data']),
)
train_meter.stop()
LOGGER.info('done training in {:.1f} seconds'.format(train_meter.sum))
def main(args, out_file=None):
use_cuda = torch.cuda.is_available() and not args['common']['cpu']
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args['dataset']['gen_subset'])
# Set dictionaries
src_dict = task.source_dictionary
tgt_dict = task.target_dictionary
# Load ensemble
LOGGER.info('loading model(s) from {}'.format(args['eval']['path']))
models, _ = checkpoint_utils.load_model_ensemble(
utils.split_paths(args['eval']['path']),
arg_overrides=eval(args['eval']['model_overrides']),
task=task,
)
# Optimize ensemble for generation
for model in models:
model.make_generation_fast_(
beamable_mm_beam_size=None
if args['eval']['no_beamable_mm'] else args['eval']['beam'],
need_attn=args['eval']['print_alignment'],
)
if use_cuda:
device = os.environ.get('CUDA_VISIBALE_DEVICES',
[0])[0] # get first device as default
torch.cuda.set_device(f'cuda:{device}')
model = model.cuda()
if args['common']['fp16'] and use_cuda:
model.half()
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args['dataset']['gen_subset']),
max_tokens=args['dataset']['max_tokens'],
max_sentences=args['eval']['max_sentences'],
max_positions=utils.resolve_max_positions(
task.max_positions(),
*[model.max_positions() for model in models]),
ignore_invalid_inputs=args['dataset']
['skip_invalid_size_inputs_valid_test'],
required_batch_size_multiple=args['dataset']
['required_batch_size_multiple'],
num_shards=args['dataset']['num_shards'],
shard_id=args['dataset']['shard_id'],
num_workers=args['dataset']['num_workers'],
).next_epoch_itr(shuffle=False)
progress = progress_bar.progress_bar(
itr,
log_format=args['common']['log_format'],
log_interval=args['common']['log_interval'],
default_log_format=('tqdm' if not args['common']['no_progress_bar']
else 'none'),
)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(models, args)
sources, hypotheses, references = dict(), dict(), dict()
for sample in progress:
torch.cuda.empty_cache()
sample = move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
gen_timer.start()
hypos = task.inference_step(generator,
models,
sample,
bos_token=tgt_dict.bos())
num_generated_tokens = sum(len(h[0]['tokens'])
for h in hypos) # TODO: warning
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(
sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(sample['target'][i, :],
tgt_dict.pad()).int().cpu()
hypos_tokens = utils.strip_eos(hypos[i][0]['tokens'],
tgt_dict.eos()).int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if src_dict is not None:
src_str = src_dict.string(src_tokens,
args['eval']['remove_bpe'])
else:
src_str = "0"
if has_target:
target_str = tgt_dict.string(target_tokens,
args['eval']['remove_bpe'],
escape_unk=True)
hypo_str = tgt_dict.string(hypos_tokens,
args['eval']['remove_bpe'])
sources[sample_id] = [src_str]
hypotheses[sample_id] = [hypo_str]
references[sample_id] = [target_str]
bleu, rouge_l, meteor = \
summarization_metrics.eval_accuracies(hypotheses, references, filename=out_file, mode='test')
LOGGER.info('BLEU: {:.2f}\t ROUGE-L: {:.2f}\t METEOR: {:.2f}'.format(
bleu, rouge_l, meteor))
def _main(args, output_file):
if args['dataset']['max_tokens'] is None and args['dataset'][
'max_sentences'] is None:
args['dataset']['max_tokens'] = 12000
LOGGER.info(args)
use_cuda = torch.cuda.is_available() and not args['common']['cpu']
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args['dataset']['gen_subset'])
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# Load ensemble
LOGGER.info('loading model(s) from {}'.format(args['eval']['path']))
models, _model_args = checkpoint_utils.load_model_ensemble(
utils.split_paths(args['eval']['path']),
arg_overrides=eval(args['eval']['model_overrides']),
task=task,
)
# Optimize ensemble for generation
for model in models:
model.make_generation_fast_(
beamable_mm_beam_size=None
if args['eval']['no_beamable_mm'] else args['eval']['beam'],
need_attn=args['eval']['print_alignment'],
)
if _model_args['common']['fp16']:
model.half()
if use_cuda:
model.cuda()
# Load alignment dictionary for unknown word replacement
# (None if no unknown word replacement, empty if no path to align dictionary)
align_dict = utils.load_align_dict(args['eval']['replace_unk'])
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args['dataset']['gen_subset']),
max_tokens=args['dataset']['max_tokens'],
max_sentences=args['eval']['max_sentences'],
max_positions=utils.resolve_max_positions(
task.max_positions(),
*[model.max_positions() for model in models]),
ignore_invalid_inputs=_model_args['dataset']
['skip_invalid_size_inputs_valid_test'],
required_batch_size_multiple=_model_args['dataset']
['required_batch_size_multiple'],
num_shards=_model_args['dataset']['num_shards'],
shard_id=_model_args['dataset']['shard_id'],
num_workers=_model_args['dataset']['num_workers'],
).next_epoch_itr(shuffle=False)
progress = progress_bar.progress_bar(
itr,
log_format=_model_args['common']['log_format'],
log_interval=_model_args['common']['log_interval'],
default_log_format=('tqdm'
if not _model_args['common']['no_progress_bar']
else 'none'),
)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
num_sentences = 0
has_target = True
wps_meter = TimeMeter()
# for sample in tqdm(progress, total=len(progress)):
sources, hypotheses, references = dict(), dict(), dict()
for sample in progress:
torch.cuda.empty_cache()
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
# prefix_tokens = None
# if args['eval']['prefix_size'] > 0:
# prefix_tokens = sample['target'][:, :args['eval']['prefix_size']]
gen_timer.start()
hypos = task.inference_step(generator, models, sample)
# gen_out = task.sequence_generator.generate(model, sample)
num_generated_tokens = sum(len(h[0]['tokens'])
for h in hypos) # TODO: warning
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(
sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(sample['target'][i, :],
tgt_dict.pad()).int().cpu()
hypos_tokens = utils.strip_eos(hypos[i][0]['tokens'],
tgt_dict.eos()).int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
# if align_dict is not None:
# src_str = task.dataset(args['dataset']['gen_subset']).src.get_original_text(sample_id)
# target_str = task.dataset(args['dataset']['gen_subset']).tgt.get_original_text(sample_id)
# else:
if src_dict is not None:
src_str = src_dict.string(src_tokens,
args['eval']['remove_bpe'])
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens,
args['eval']['remove_bpe'],
escape_unk=True)
# hypo_tokens = tgt_dict.encode_line(hypo_str, add_if_not_exist=True)
hypo_str = tgt_dict.string(hypos_tokens,
args['eval']['remove_bpe'])
sources[sample_id] = [src_str]
hypotheses[sample_id] = [hypo_str]
references[sample_id] = [target_str]
if not args['eval']['quiet']:
if src_dict is not None:
print('S-{}\t{}'.format(sample_id, src_str),
file=output_file)
if has_target:
print('T-{}\t{}'.format(sample_id, target_str),
file=output_file)
print('H-{}\t{}'.format(sample_id, hypo_str), file=output_file)
filename = os.path.join(os.path.dirname(__file__), 'config',
'predict.json')
LOGGER.info('write predicted file at {}'.format(filename))
bleu, rouge_l, meteor = eval_utils.eval_accuracies(hypotheses,
references,
filename=filename,
mode='test')
LOGGER.info('BLEU: {:.2f}\t ROUGE-L: {:.2f}\t METEOR: {:.2f}'.format(
bleu, rouge_l, meteor))
def _main(args, output_file):
if args['dataset']['max_tokens'] is None and args['dataset'][
'max_sentences'] is None:
args['dataset']['max_tokens'] = 12000
use_cuda = torch.cuda.is_available() and not args['common']['cpu']
if use_cuda:
device = os.environ.get('CUDA_VISIBALE_DEVICES',
[0])[0] # get first device as default
torch.cuda.set_device(f'cuda:{device}')
# Load dataset splits
task = tasks.setup_task(args)
# Load ensemble
LOGGER.info('loading model(s) from {}'.format(args['eval']['path']))
models, _model_args = checkpoint_utils.load_model_ensemble(
utils.split_paths(args['eval']['path']),
arg_overrides=eval(args['eval']['model_overrides']),
task=task,
)
# Optimize ensemble for generation
for model in models:
if _model_args['common']['fp16']:
model.half()
if use_cuda:
model.cuda()
sequence_completor = task.build_completor(models, args)
subsets = [
args['dataset']['train_subset'],
args['dataset']['valid_subset'],
args['dataset']['gen_subset'],
]
for subset in subsets:
task.load_dataset(subset, shuffle=False)
task.dataset(subset).shuffle = False
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(subset),
max_tokens=args['dataset']['max_tokens'],
max_sentences=args['eval']['max_sentences_eval'],
max_positions=utils.resolve_max_positions(
task.max_positions(),
*[model.max_positions() for model in models]),
ignore_invalid_inputs=_model_args['dataset']
['skip_invalid_size_inputs_valid_test'],
required_batch_size_multiple=_model_args['dataset']
['required_batch_size_multiple'],
num_shards=_model_args['dataset']['num_shards'],
shard_id=_model_args['dataset']['shard_id'],
num_workers=_model_args['dataset']['num_workers'],
).next_epoch_itr(shuffle=False)
progress = progress_bar.progress_bar(
itr,
log_format=_model_args['common']['log_format'],
log_interval=_model_args['common']['log_interval'],
default_log_format=('tqdm'
if not _model_args['common']['no_progress_bar']
else 'none'),
)
topk = args['kd']['gen_topk']
out_idx, out_prob = [], []
with torch.no_grad():
for sample in progress:
torch.cuda.empty_cache()
sample = move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
net_output = sequence_completor.generate([model],
sample,
prefix_tokens=None)
topk_prob, topk_ids = torch.topk(net_output[0], topk, dim=-1)
# ignore pad
non_padding_mask = sample['net_input'][
'src_tokens'] != task.target_dictionary.pad()
if use_cuda:
topk_prob, topk_ids = topk_prob.cpu(), topk_ids.cpu()
non_padding_mask = non_padding_mask.cpu()
for idx in range(topk_prob.size(0)):
out_idx.append(
topk_ids[idx,
...][non_padding_mask[idx,
...]].view(-1).tolist())
out_prob.append(topk_prob[idx, ...][non_padding_mask[
idx, ...]].view(-1).tolist())
assert len(out_idx) == len(out_prob) == len(task.dataset(subset)), \
Exception(len(out_idx), len(out_prob), len(task.dataset(subset)))
TeacherOutDataset.save_bin(
prefix=os.path.join(args['checkpoint']['save_dir'],
f'{subset}.top{topk}_idx'),
data_list=out_idx,
dtype=np.int32,
)
TeacherOutDataset.save_bin(
prefix=os.path.join(args['checkpoint']['save_dir'],
f'{subset}.top{topk}_prob'),
data_list=out_prob,
dtype=np.float,
)
def main(args, **unused_kwargs):
assert args['eval']['path'] is not None, '--path required for evaluation!'
if torch.cuda.is_available() and not args['common']['cpu']:
torch.cuda.set_device(args['distributed_training']['device_id'])
LOGGER.info(args)
# while evaluation, set fraction_using_func_name = 0, namely, not sample from func_name
args['task']['fraction_using_func_name'] = 0.
use_cuda = torch.cuda.is_available() and not args['common']['cpu']
if use_cuda:
device = os.environ.get('CUDA_VISIBALE_DEVICES',
[0])[0] # get first device as default
torch.cuda.set_device(f'cuda:{device}')
task = tasks.setup_task(args)
# Load ensemble
LOGGER.info('loading model(s) from {}'.format(args['eval']['path']))
models, _model_args = checkpoint_utils.load_model_ensemble(
utils.split_paths(args['eval']['path']),
arg_overrides=eval(args['eval']['model_overrides']),
task=task,
)
for lang in deepcopy(args['dataset']['langs']):
args['dataset']['langs'] = [lang]
# Load dataset splits
LOGGER.info(f'Evaluating {lang} dataset')
task.load_dataset(args['dataset']['gen_subset'])
dataset = task.dataset(args['dataset']['gen_subset'])
# Optimize ensemble for generation and set the source and dest dicts on the model (required by scorer)
for model in models:
model.make_generation_fast_()
if args['common']['fp16']:
model.half()
if use_cuda:
model.cuda()
assert len(models) > 0
LOGGER.info('num. model params: {}'.format(
sum(p.numel() for p in models[0].parameters())))
itr = task.get_batch_iterator(
dataset=dataset,
max_tokens=args['dataset']['max_tokens'] or 36000,
max_sentences=args['eval']['max_sentences'],
max_positions=utils.resolve_max_positions(
*[model.max_positions() for model in models]),
ignore_invalid_inputs=True,
num_shards=args['dataset']['num_shards'],
shard_id=args['dataset']['shard_id'],
num_workers=args['dataset']['num_workers'],
).next_epoch_itr(shuffle=False)
progress = progress_bar.progress_bar(
itr,
log_format=args['common']['log_format'],
log_interval=args['common']['log_interval'],
default_log_format=('tqdm' if not args['common']['no_progress_bar']
else 'none'),
)
code_reprs, query_reprs = [], []
for sample in progress:
if 'net_input' not in sample:
continue
sample = move_to_cuda(sample) if use_cuda else sample
batch_code_reprs, batch_query_reprs = models[0](
**sample['net_input'])
if use_cuda:
batch_code_reprs = batch_code_reprs.cpu().detach()
batch_query_reprs = batch_query_reprs.cpu().detach()
code_reprs.append(batch_code_reprs)
query_reprs.append(batch_query_reprs)
code_reprs = torch.cat(code_reprs, dim=0)
query_reprs = torch.cat(query_reprs, dim=0)
assert code_reprs.shape == query_reprs.shape, (code_reprs.shape,
query_reprs.shape)
eval_size = len(
code_reprs
) if args['eval']['eval_size'] == -1 else args['eval']['eval_size']
k, MRR, topk_idx, topk_prob = 3, [], [], []
for idx in range(len(dataset) // eval_size):
code_emb = code_reprs[idx:idx + eval_size, :]
query_emb = query_reprs[idx:idx + eval_size, :]
if use_cuda:
code_emb = code_emb.cuda()
query_emb = query_emb.cuda()
if args['criterion'] == 'search_cosine':
src_emb_nrom = torch.norm(code_emb, dim=-1,
keepdim=True) + 1e-10
tgt_emb_nrom = torch.norm(query_emb, dim=-1,
keepdim=True) + 1e-10
logits = (query_emb / tgt_emb_nrom) @ (code_emb /
src_emb_nrom).t()
elif args['criterion'] == 'search_softmax':
logits = query_emb @ code_emb.t()
else:
raise NotImplementedError
correct_scores = logits.diag()
compared_scores = logits >= correct_scores.unsqueeze(dim=-1)
mrr = 1 / compared_scores.sum(dim=-1).float()
MRR.extend(mrr.tolist())
if len(dataset) % eval_size:
code_emb = code_reprs[-eval_size:, :]
query_emb = query_reprs[-eval_size:, :]
if use_cuda:
code_emb = code_emb.cuda()
query_emb = query_emb.cuda()
if args['criterion'] == 'search_cosine':
src_emb_nrom = torch.norm(code_emb, dim=-1,
keepdim=True) + 1e-10
tgt_emb_nrom = torch.norm(query_emb, dim=-1,
keepdim=True) + 1e-10
logits = (query_emb / tgt_emb_nrom) @ (code_emb /
src_emb_nrom).t()
elif args['criterion'] == 'search_softmax':
logits = query_emb @ code_emb.t()
else:
raise NotImplementedError
correct_scores = logits.diag()
compared_scores = logits >= correct_scores.unsqueeze(dim=-1)
last_ids = len(code_reprs) % eval_size
mrr = 1 / compared_scores.sum(dim=-1).float()[-last_ids:]
MRR.extend(mrr.tolist())
print('{}, mrr: {:.4f}'.format(lang, np.mean(MRR)))
def main(args, **unused_kwargs):
assert args['eval']['path'] is not None, '--path required for evaluation!'
if torch.cuda.is_available() and not args['common']['cpu']:
torch.cuda.set_device(args['distributed_training']['device_id'])
LOGGER.info(args)
# while evaluation, set fraction_using_func_name = 0, namely, not sample from func_name
args['task']['fraction_using_func_name'] = 0.
use_cuda = torch.cuda.is_available() and not args['common']['cpu']
task = tasks.setup_task(args)
# Load ensemble
LOGGER.info('loading model(s) from {}'.format(args['eval']['path']))
models, _model_args = checkpoint_utils.load_model_ensemble(
utils.split_paths(args['eval']['path']),
arg_overrides=eval(args['eval']['model_overrides']),
task=task,
)
task = tasks.setup_task(args)
# Load dataset splits
task.load_dataset(args['dataset']['gen_subset'])
dataset = task.dataset(args['dataset']['gen_subset'])
# Optimize ensemble for generation and set the source and dest dicts on the model (required by scorer)
for model in models:
model.make_generation_fast_()
if args['common']['fp16']:
model.half()
if use_cuda:
model.cuda()
assert len(models) > 0
LOGGER.info('num. model params: {}'.format(
sum(p.numel() for p in models[0].parameters())))
itr = task.get_batch_iterator(
dataset=dataset,
max_tokens=args['dataset']['max_tokens'] or 36000,
max_sentences=args['eval']['max_sentences'],
max_positions=utils.resolve_max_positions(
*[model.max_positions() for model in models]),
ignore_invalid_inputs=True,
num_shards=args['dataset']['num_shards'],
shard_id=args['dataset']['shard_id'],
num_workers=args['dataset']['num_workers'],
).next_epoch_itr(shuffle=False)
progress = progress_bar.progress_bar(
itr,
log_format=args['common']['log_format'],
log_interval=args['common']['log_interval'],
default_log_format=('tqdm' if not args['common']['no_progress_bar']
else 'none'),
)
code_reprs, query_reprs = [], []
for sample in progress:
if 'net_input' not in sample:
continue
sample = utils.move_to_cuda(sample) if use_cuda else sample
batch_code_reprs, batch_query_reprs = models[0](**sample['net_input'])
code_reprs.extend(batch_code_reprs.tolist())
query_reprs.extend(batch_query_reprs.tolist())
code_reprs = np.asarray(code_reprs, dtype=np.float32)
query_reprs = np.asarray(query_reprs, dtype=np.float32)
assert code_reprs.shape == query_reprs.shape, (code_reprs.shape,
query_reprs.shape)
eval_size = len(
code_reprs
) if args['eval']['eval_size'] == -1 else args['eval']['eval_size']
k, MRR, topk_idx, topk_prob = 3, [], [], []
for idx in range(len(dataset) // eval_size):
code_emb = torch.from_numpy(code_reprs[idx:idx + eval_size, :]).cuda()
query_emb = torch.from_numpy(query_reprs[idx:idx +
eval_size, :]).cuda()
logits = query_emb @ code_emb.t()
# src_emb_nrom = torch.norm(code_emb, dim=-1, keepdim=True) + 1e-10
# tgt_emb_nrom = torch.norm(query_emb, dim=-1, keepdim=True) + 1e-10
# logits = (query_emb / tgt_emb_nrom) @ (code_emb / src_emb_nrom).t()
correct_scores = logits.diag()
compared_scores = logits >= correct_scores.unsqueeze(dim=-1)
mrr = 1 / compared_scores.sum(dim=-1).float()
MRR.extend(mrr.tolist())
batch_topk_prob, batch_topk_idx = logits.softmax(dim=-1).topk(k)
batch_topk_idx = batch_topk_idx + idx * eval_size
topk_idx.extend(batch_topk_idx.tolist())
topk_prob.extend(batch_topk_prob.tolist())
if len(dataset) % eval_size:
code_emb = torch.from_numpy(code_reprs[-eval_size:, :]).cuda()
query_emb = torch.from_numpy(query_reprs[-eval_size:, :]).cuda()
logits = query_emb @ code_emb.t()
# src_emb_nrom = torch.norm(code_emb, dim=-1, keepdim=True) + 1e-10
# tgt_emb_nrom = torch.norm(query_emb, dim=-1, keepdim=True) + 1e-10
# logits = (query_emb / tgt_emb_nrom) @ (code_emb / src_emb_nrom).t()
correct_scores = logits.diag()
compared_scores = logits >= correct_scores.unsqueeze(dim=-1)
last_ids = len(code_reprs) % eval_size
mrr = 1 / compared_scores.sum(dim=-1).float()[-last_ids:]
MRR.extend(mrr.tolist())
batch_topk_prob, batch_topk_idx = logits[-last_ids:].softmax(
dim=-1).topk(k)
batch_topk_idx = batch_topk_idx + len(code_reprs) - eval_size
topk_idx.extend(batch_topk_idx.tolist())
topk_prob.extend(batch_topk_prob.tolist())
print('mrr: {:.4f}'.format(np.mean(MRR)))
for idx, mrr in enumerate(MRR):
if mrr == 1.0 and topk_prob[idx][0] > 0.8:
print(
np.asarray(topk_idx[idx]) + 1,
[round(porb, 4) for porb in topk_prob[idx]])
def _main(args, output_file):
if args['dataset']['max_tokens'] is None and args['dataset']['max_sentences'] is None:
args['dataset']['max_tokens'] = 12000
LOGGER.info(args)
use_cuda = torch.cuda.is_available() and not args['common']['cpu']
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args['dataset']['gen_subset'])
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# Load ensemble
LOGGER.info('loading model(s) from {}'.format(args['eval']['path']))
models, _model_args = checkpoint_utils.load_model_ensemble(
utils.split_paths(args['eval']['path']),
arg_overrides=eval(args['eval']['model_overrides']),
task=task,
)
# Optimize ensemble for generation
for model in models:
if _model_args['common']['fp16']:
model.half()
if use_cuda:
model.cuda()
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args['dataset']['gen_subset']),
max_tokens=args['dataset']['max_tokens'],
max_sentences=args['dataset']['max_sentences'],
max_positions=utils.resolve_max_positions(
task.max_positions(),
*[model.max_positions() for model in models]
),
ignore_invalid_inputs=_model_args['dataset']['skip_invalid_size_inputs_valid_test'],
required_batch_size_multiple=_model_args['dataset']['required_batch_size_multiple'],
num_shards=_model_args['dataset']['num_shards'],
shard_id=_model_args['dataset']['shard_id'],
num_workers=_model_args['dataset']['num_workers'],
).next_epoch_itr(shuffle=False)
progress = progress_bar.progress_bar(
itr,
log_format=_model_args['common']['log_format'],
log_interval=_model_args['common']['log_interval'],
default_log_format=('tqdm' if not _model_args['common']['no_progress_bar'] else 'none'),
)
"""
nohup python -m run.completion.seqrnn.eval > run/completion/seqrnn/case.log 2>&1 &
"""
sequence_completor = task.build_completor([model], args)
for sample in progress:
torch.cuda.empty_cache()
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
non_pad_idx = sample['net_input']['src_tokens'] > task.target_dictionary.pad()
with torch.no_grad():
net_output = sequence_completor.generate([model], sample, prefix_tokens=None)
lprobs = model.get_normalized_probs(net_output, log_probs=True)
# from ipdb import set_trace
# set_trace()
rank = torch.argmax(lprobs, dim=-1)
target = model.get_targets(sample, net_output)
accuracy = 1.0 * ((rank == target) & non_pad_idx).sum(dim=-1) / non_pad_idx.sum(dim=-1)
for idx, (data_idx, acc) in enumerate(zip(sample['id'], accuracy)):
if acc > 0.9:
LOGGER.info(f"{data_idx}: {task.target_dictionary.string(sample['net_input']['src_tokens'][idx, :])}")
def _main(args, output_file):
if args['dataset']['max_tokens'] is None and args['dataset'][
'max_sentences'] is None:
args['dataset']['max_tokens'] = 12000
LOGGER.info(args)
use_cuda = torch.cuda.is_available() and not args['common']['cpu']
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args['dataset']['gen_subset'])
# Set dictionaries
try:
src_dict = getattr(task, 'source_dictionary', None)
except NotImplementedError:
src_dict = None
tgt_dict = task.target_dictionary
# Load ensemble
LOGGER.info('loading model(s) from {}'.format(args['eval']['path']))
models, _model_args = checkpoint_utils.load_model_ensemble(
utils.split_paths(args['eval']['path']),
arg_overrides=eval(args['eval']['model_overrides']),
task=task,
)
# Optimize ensemble for generation
for model in models:
model.make_generation_fast_(
beamable_mm_beam_size=None
if args['eval']['no_beamable_mm'] else args['eval']['beam'],
need_attn=args['eval']['print_alignment'],
)
if _model_args['common']['fp16']:
model.half()
if use_cuda:
model.cuda()
# Load alignment dictionary for unknown word replacement
# (None if no unknown word replacement, empty if no path to align dictionary)
align_dict = utils.load_align_dict(args['eval']['replace_unk'])
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args['dataset']['gen_subset']),
max_tokens=args['dataset']['max_tokens'],
max_sentences=args['eval']['max_sentences'],
max_positions=utils.resolve_max_positions(
task.max_positions(),
*[model.max_positions() for model in models]),
ignore_invalid_inputs=_model_args['dataset']
['skip_invalid_size_inputs_valid_test'],
required_batch_size_multiple=_model_args['dataset']
['required_batch_size_multiple'],
num_shards=_model_args['dataset']['num_shards'],
shard_id=_model_args['dataset']['shard_id'],
num_workers=_model_args['dataset']['num_workers'],
).next_epoch_itr(shuffle=False)
progress = progress_bar.progress_bar(
itr,
log_format=_model_args['common']['log_format'],
log_interval=_model_args['common']['log_interval'],
default_log_format=('tqdm'
if not _model_args['common']['no_progress_bar']
else 'none'),
)
# Initialize generator
gen_timer = StopwatchMeter()
generator = task.build_generator(args)
# Generate and compute BLEU score
scorer = OrderedDict()
if args['eval']['sacrebleu']:
scorer['bleu'] = bleu_scorer.SacrebleuScorer()
elif args['eval']['nltk_bleu']:
scorer['bleu'] = bleu_scorer.NLTKBleuScorer()
else:
scorer['bleu'] = bleu_scorer.Scorer(tgt_dict.pad(), tgt_dict.eos(),
tgt_dict.unk())
# Generate and compute BLEU score
if args['eval']['rouge']:
scorer['rouge'] = rouge_scorer.RougeScorer()
num_sentences = 0
has_target = True
wps_meter = TimeMeter()
# for sample in tqdm(progress, total=len(progress)):
for sample in progress:
torch.cuda.empty_cache()
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
prefix_tokens = None
if args['eval']['prefix_size'] > 0:
prefix_tokens = sample['target'][:, :args['eval']['prefix_size']]
gen_timer.start()
hypos = task.inference_step(generator, models, sample, prefix_tokens)
num_generated_tokens = sum(len(h[0]['tokens']) for h in hypos)
gen_timer.stop(num_generated_tokens)
for i, sample_id in enumerate(sample['id'].tolist()):
has_target = sample['target'] is not None
# Remove padding
src_tokens = utils.strip_pad(
sample['net_input']['src_tokens'][i, :], tgt_dict.pad())
target_tokens = None
if has_target:
target_tokens = utils.strip_pad(sample['target'][i, :],
tgt_dict.pad()).int().cpu()
# Either retrieve the original sentences or regenerate them from tokens.
if align_dict is not None:
src_str = task.dataset(
args['dataset']['gen_subset']).src.get_original_text(
sample_id)
target_str = task.dataset(
args['dataset']['gen_subset']).tgt.get_original_text(
sample_id)
else:
if src_dict is not None:
src_str = src_dict.string(src_tokens,
args['eval']['remove_bpe'])
else:
src_str = ""
if has_target:
target_str = tgt_dict.string(target_tokens,
args['eval']['remove_bpe'],
escape_unk=True)
if not args['eval']['quiet']:
if src_dict is not None:
print('S-{}\t{}'.format(sample_id, src_str),
file=output_file)
if has_target:
print('T-{}\t{}'.format(sample_id, target_str),
file=output_file)
# Process top predictions
for j, hypo in enumerate(hypos[i][:args['eval']['nbest']]):
hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
hypo_tokens=hypo['tokens'].int().cpu(),
src_str=src_str,
alignment=hypo['alignment'],
align_dict=align_dict,
tgt_dict=tgt_dict,
remove_bpe=args['eval']['remove_bpe'],
)
if hypo_str == '.':
# rouge cannot handle hypo'.'
continue
if not args['eval']['quiet']:
score = hypo['score'] / math.log(2) # convert to base 2
print('H-{}\t{}\t{}'.format(sample_id, score, hypo_str),
file=output_file)
print(
'P-{}\t{}'.format(
sample_id,
' '.join(
map(
lambda x: '{:.4f}'.format(x),
# convert from base e to base 2
hypo['positional_scores'].div_(math.log(2)
).tolist(),
))),
file=output_file)
if args['eval']['print_alignment']:
print('A-{}\t{}'.format(
sample_id, ' '.join([
'{}-{}'.format(src_idx, tgt_idx)
for src_idx, tgt_idx in alignment
])),
file=output_file)
if args['eval']['print_step']:
print('I-{}\t{}'.format(sample_id, hypo['steps']),
file=output_file)
# if getattr(args, 'retain_iter_history', False):
if args['eval']['retain_iter_history']:
for step, h in enumerate(hypo['history']):
_, h_str, _ = utils.post_process_prediction(
hypo_tokens=h['tokens'].int().cpu(),
src_str=src_str,
alignment=None,
align_dict=None,
tgt_dict=tgt_dict,
remove_bpe=None,
)
print('E-{}_{}\t{}'.format(sample_id, step, h_str),
file=output_file)
# Score only the top hypothesis
if has_target and j == 0:
# print('Ref>> {}'.format(target_str), file=output_file)
# print('Hyp>> {}'.format(hypo_str), file=output_file)
if align_dict is not None or args['eval'][
'remove_bpe'] is not None:
# Convert back to tokens for evaluation with unk replacement and/or without BPE
target_tokens = tgt_dict.encode_line(
target_str, add_if_not_exist=True)
for metric in scorer:
if hasattr(scorer[metric], 'add_string'):
scorer[metric].add_string(target_str, hypo_str)
else:
scorer[metric].add(target_tokens, hypo_tokens)
wps_meter.update(num_generated_tokens)
progress.log({'wps': round(wps_meter.avg)})
num_sentences += sample['nsentences']
LOGGER.info('NOTE: hypothesis and token scores are output in base 2')
LOGGER.info(
'Translated {} sentences ({} tokens) in {:.1f}s ({:.2f} sentences/s, {:.2f} tokens/s)'
.format(num_sentences, gen_timer.n, gen_timer.sum,
num_sentences / gen_timer.sum, 1. / gen_timer.avg))
if has_target:
LOGGER.info('Generate {} with beam={}: {}'.format(
args['dataset']['gen_subset'], args['eval']['beam'], {
'\n{}:\n{}'.format(str.upper(metric), value.score())
for metric, value in scorer.items()
}))
return scorer
def _main(args, output_file):
if args['dataset']['max_tokens'] is None and args['dataset'][
'max_sentences'] is None:
args['dataset']['max_tokens'] = 12000
use_cuda = torch.cuda.is_available() and not args['common']['cpu']
if use_cuda:
device = os.environ.get('CUDA_VISIBALE_DEVICES',
[0])[0] # get first device as default
torch.cuda.set_device(f'cuda:{device}')
# Load dataset splits
task = tasks.setup_task(args)
task.load_dataset(args['dataset']['gen_subset'], shuffle=False)
# Load ensemble
LOGGER.info('loading model(s) from {}'.format(args['eval']['path']))
models, _model_args = checkpoint_utils.load_model_ensemble(
utils.split_paths(args['eval']['path']),
arg_overrides=eval(args['eval']['model_overrides']),
task=task,
)
# Optimize ensemble for generation
for model in models:
if _model_args['common']['fp16']:
model.half()
if use_cuda:
model.cuda()
# Load dataset (possibly sharded)
itr = task.get_batch_iterator(
dataset=task.dataset(args['dataset']['gen_subset']),
max_tokens=args['dataset']['max_tokens'],
max_sentences=args['eval']['max_sentences_eval'],
max_positions=utils.resolve_max_positions(
task.max_positions(),
*[model.max_positions() for model in models]),
ignore_invalid_inputs=_model_args['dataset']
['skip_invalid_size_inputs_valid_test'],
required_batch_size_multiple=_model_args['dataset']
['required_batch_size_multiple'],
num_shards=_model_args['dataset']['num_shards'],
shard_id=_model_args['dataset']['shard_id'],
num_workers=_model_args['dataset']['num_workers'],
).next_epoch_itr(shuffle=False)
progress = progress_bar.progress_bar(
itr,
log_format=_model_args['common']['log_format'],
log_interval=_model_args['common']['log_interval'],
default_log_format=('tqdm'
if not _model_args['common']['no_progress_bar']
else 'none'),
)
sequence_completor = task.build_completor([model], args)
accuracy = {'all': 0.}
mrr = {'all': 0.}
sample_num = {'all': 0.}
if task.dataset('test').attrs is not None:
for attr in task.dataset('test').attrs:
accuracy[attr] = 0.
mrr[attr] = 0.
sample_num[attr] = 0
def _eval(lprobs, target, idx, num):
with torch.no_grad():
lprobs = lprobs[idx]
target = target[idx]
accuracy = (torch.argmax(lprobs,
dim=-1) == target).sum().float().item()
# Ref: Code Prediction by Feeding Trees to Transformers
# With this practical perspective and for ease of computation, we only consider ranki ≤ 10 for each
# location i (all ranki > 10 will have a score of 0).
ranks = (lprobs >= lprobs[:,
target].diag().unsqueeze(dim=-1)).sum(-1)
mrr = 1. / ranks
mrr[ranks > 10] = 0.
mrr = mrr.sum().float().item()
return accuracy, mrr, num
for sample in progress:
torch.cuda.empty_cache()
sample = utils.move_to_cuda(sample) if use_cuda else sample
if 'net_input' not in sample:
continue
with torch.no_grad():
net_output = sequence_completor.generate([model],
sample,
prefix_tokens=None)
# lprobs = model.get_normalized_probs(net_output, log_probs=True)
lprobs = torch.softmax(net_output[0], dim=-1)
lprobs = lprobs.view(-1, lprobs.size(-1))
target = model.get_targets(sample, net_output).view(-1)
# all
# ignore pad and unk
idx = sample['net_input']['src_tokens'].view(
-1) != task.target_dictionary.pad()
idx[sample['target'].view(-1) == task.target_dictionary.unk()] = 0
# ignore overlapping tokens
max_len = sample['target'].size(-1)
for i, ext_i in enumerate(sample['extends']):
idx[i * max_len:i * max_len + ext_i] = 0
batch_acc, batch_mrr, batch_num = _eval(lprobs,
target,
idx,
num=idx.sum().item())
accuracy['all'] += batch_acc
mrr['all'] += batch_mrr
sample_num['all'] += batch_num
# other attrs
if sample['attr_masks'] is not None:
for attr, attr_idx in sample['attr_masks'].items():
# pick out attr_idx who are not unk/pad
attr_idx = attr_idx[idx[attr_idx].tolist()]
if len(attr_idx) > 0:
batch_acc, batch_mrr, batch_num = _eval(
lprobs, target, attr_idx, num=attr_idx.size)
accuracy[attr] += batch_acc
mrr[attr] += batch_mrr
sample_num[attr] += batch_num
for attr in accuracy.keys():
avg_acc = round(accuracy[attr] /
sample_num[attr], 6) if sample_num[attr] > 0. else None
avg_mrr = round(mrr[attr] /
sample_num[attr], 6) if sample_num[attr] > 0. else None
print('[{}] tokens, accuracy: {}, MRR: {}'.format(
attr, avg_acc, avg_mrr))
def single_main(args, init_distributed=False):
assert args['dataset']['max_tokens'] is not None or args['dataset']['max_sentences'] is not None, \
'Must specify batch size either with --max-tokens or --max-sentences'
metrics.reset()
# 0. Initialize CUDA and distributed training
if torch.cuda.is_available() and not args['common']['cpu']:
torch.cuda.set_device(args['distributed_training']['device_id'])
random.seed(args['common']['seed'])
np.random.seed(args['common']['seed'])
torch.manual_seed(args['common']['seed'])
torch.cuda.manual_seed(args['common']['seed'])
if init_distributed:
args['distributed_training'][
'distributed_rank'] = distributed_utils.distributed_init(args)
# Verify checkpoint directory
if distributed_utils.is_master(args):
save_dir = args['checkpoint']['save_dir']
checkpoint_utils.verify_checkpoint_directory(save_dir)
remove_files(save_dir,
'pt') # this code will remove pre-trained models
# 1. Setup task, e.g., translation, language modeling, etc.
task = tasks.setup_task(args)
# 2. Load valid dataset (we load training data below, based on the latest checkpoint)
# calculate accuracy for decay learning rate
task.load_dataset(args['dataset']['valid_subset'], combine=False, epoch=1)
# # compute meteor to select model
# task.load_dataset(args['dataset']['dev_subset'], combine=False, epoch=1)
# # load dev/ref.txt
# dev_refs = load_refs(os.path.join(args['task']['data'], args['dataset']['dev_ref_subset']))
# 3. Build model and criterion
model = task.build_model(args)
criterion = task.build_criterion(args)
LOGGER.info(model)
LOGGER.info('model {}, criterion {}'.format(args['model']['arch'],
criterion.__class__.__name__))
LOGGER.info('num. model params: {} (num. trained: {})'.format(
sum(p.numel() for p in model.parameters()),
sum(p.numel() for p in model.parameters() if p.requires_grad),
))
# 4. Build trainer
trainer = Trainer(args, task, model, criterion)
LOGGER.info('training on {} GPUs'.format(
args['distributed_training']['distributed_world_size']))
LOGGER.info(
'max tokens per GPU = {} and max sentences per GPU = {}'.format(
args['dataset']['max_tokens'],
args['dataset']['max_sentences'],
))
# 5. Load the latest checkpoint if one is available and restore the corresponding train iterator
extra_state, epoch_itr = checkpoint_utils.load_checkpoint(args,
trainer,
combine=False)
# 6. Train until the learning rate gets too small
max_epoch = args['optimization']['max_epoch'] or math.inf
max_update = args['optimization']['max_update'] or math.inf
lr = trainer.get_lr()
train_meter = meters.StopwatchMeter()
train_meter.start()
valid_subsets = args['dataset']['valid_subset'].split(',')
dev_subsets = args['dataset']['dev_subset'].split(',')
valid_accs_after_60e = []
while (lr > args['optimization']['min_lr']
and epoch_itr.next_epoch_idx <= max_epoch
and trainer.get_num_updates() < max_update):
# train for one epoch
train(args, trainer, task, epoch_itr)
if not args['dataset']['disable_validation'] and epoch_itr.epoch % args[
'dataset']['validate_interval'] == 0:
valid_acc, dev_prf = validate(args,
trainer,
task,
epoch_itr,
valid_subsets,
dev_subsets,
dev_refs=None)
else:
valid_acc, dev_prf = None, None
# if epoch_itr.next_epoch_idx > 61 and valid_acc < valid_accs_after_60e[-1]:
# """
# We start with a learning rate of 0.5 and start
# decaying it by a factor of 0.8 after 60 epochs if
# accuracy on the validation set goes down, and
# terminate training when the learning rate goes
# below 0.001.
# """
# lr = trainer.set_lr(lr * trainer.args['optimization']['lr_shrink'])
#
# if epoch_itr.epoch >= 60:
# valid_accs_after_60e.append(valid_acc)
# if len(valid_accs_after_60e) > 10 and valid_accs_after_60e[-5] >= valid_acc:
# lr = trainer.set_lr(lr * trainer.args['optimization']['lr_shrink'])
# valid_accs_after_60e.append(valid_acc)
if len(valid_accs_after_60e
) > 10 and valid_accs_after_60e[-5] >= valid_acc:
lr = trainer.set_lr(lr * trainer.args['optimization']['lr_shrink'])
# eval on dev and dev.ref data
# save checkpoint
if epoch_itr.epoch % args['checkpoint']['save_interval'] == 0:
checkpoint_utils.save_checkpoint(args, trainer, epoch_itr,
valid_acc)
epoch_itr = trainer.get_train_iterator(
epoch_itr.next_epoch_idx,
combine=False, # TODO to be checked
# sharded data: get train iterator for next epoch
load_dataset=(os.pathsep in args['task']['data']),
)
train_meter.stop()
LOGGER.info('done training in {:.1f} seconds'.format(train_meter.sum))
