class SACAgent(object):
def __init__(self,
device="cpu",
d_word_vec=512,
d_model=256,
limit_dist=0.1,
dropout=0.0,
reparam_noise=1e-6,
**kwargs):
self.device = device
self.actor = Rephraser(d_word_vec=d_word_vec,
d_model=d_model,
limit_dist=limit_dist,
dropout=dropout,
reparam_noise=reparam_noise).to(device)
self.critic = CriticNet(d_word_vec=d_word_vec,
d_model=d_model,
limit_dist=limit_dist,
dropout=dropout,
reparam_noise=reparam_noise).to(device)
self.saver = Saver(save_prefix="{0}.ckpt".format(
os.path.join(kwargs["save_to"], "ACmodel")),
num_max_keeping=kwargs["num_kept_checkpoints"])
self.soft_update_lock = mp.Lock()
# the entropy regularization weight for SAC learning
self.learnable_temperature = kwargs["learnable_temperature"]
self.target_entropy = -d_word_vec # act_dim (d_word_vec) as the expected entropy base
self.log_alpha = torch.tensor(np.log(kwargs["init_temperature"])).to(
self.device)
self.log_alpha.requires_grad = True
# initialize the training mode for the Agent
self.train()
self._init_local_optims(kwargs["rephraser_optimizer_configs"])
# self.load_model() # always reload model if there is any in the path
def to(self, device):
self.actor.to(device)
self.critic.to(device)
self.log_alpha.to(device)
return self
def share_memory(self):
# global model needs to share memory with other threads
self.actor.share_memory()
self.critic.share_memory()
@property
def alpha(self):
return self.log_alpha.exp()
def load_model(self, load_final_path: str = None):
"""
load from path by self.saver
:param load_final_path: final model path dir, final model doesn't have optim_params
:return: training step count int
"""
step = 0
model_collections = Collections()
if load_final_path:
# self.saver.load_latest(
# actor_model=self.actor, critic_model=self.critic
# ) # load from the latest ckpt model
state_dict = torch.load(os.path.join(load_final_path))
self.actor.load_state_dict(state_dict["actor_model"])
self.critic.load_state_dict(state_dict["critic_model"])
else:
self.saver.load_latest(collections=model_collections,
actor_model=self.actor,
critic_model=self.critic,
actor_optim=self.actor_optimizer,
critic_optim=self.critic_optimizer,
actor_scheduler=self.actor_scheduler,
critic_scheduler=self.critic_scheduler)
step = model_collections.get_collection("step", [0])[-1]
return step
def save_model(
self,
step=None,
save_to_final=None): # save model parameters, optims, lr_steps
model_collections = Collections()
if step is not None:
model_collections.add_to_collection("step", step)
self.saver.save(global_step=step,
collections=model_collections,
actor_model=self.actor,
critic_model=self.critic,
actor_optim=self.actor_optimizer,
critic_optim=self.critic_optimizer,
actor_scheduler=self.actor_scheduler,
critic_scheduler=self.critic_scheduler)
else: # only save the model parameters
assert save_to_final is not None, "final model saving dir must be provided"
collection = dict()
collection["actor_model"] = self.actor.state_dict()
collection["critic_model"] = self.critic.state_dict()
torch.save(collection, os.path.join(save_to_final,
"ACmodel.final"))
return
def _init_local_optims(self, rephraser_optimizer_configs):
""" actor, critic, alpha optimizers and lr scheduler if necessary
rephraser_optimizer_configs:
optimizer: "adafactor"
learning_rate: 0.01
grad_clip: -1.0
optimizer_params: ~
schedule_method: rsqrt
scheduler_configs:
d_model: *dim
warmup_steps: 100
"""
# initiate local optimizer
if rephraser_optimizer_configs is None:
self.actor_optimizer = None
self.critic_optimizer = None
self.log_alpha_optimizer = None
# self.actor_icm_optimizer = None
self.actor_scheduler = None
self.critic_scheduler = None
else:
self.actor_optimizer = Optimizer(
name=rephraser_optimizer_configs["optimizer"],
model=self.actor,
lr=rephraser_optimizer_configs["learning_rate"],
grad_clip=rephraser_optimizer_configs["grad_clip"],
optim_args=rephraser_optimizer_configs["optimizer_params"])
self.critic_optimizer = Optimizer(
name=rephraser_optimizer_configs["optimizer"],
model=self.critic,
lr=rephraser_optimizer_configs["learning_rate"],
grad_clip=rephraser_optimizer_configs["grad_clip"],
optim_args=rephraser_optimizer_configs["optimizer_params"])
# hardcoded entropy weight updates and icm updates
self.log_alpha_optimizer = torch.optim.Adam([self.log_alpha],
lr=1e-4,
betas=(0.9, 0.999))
# self.actor_icm_optimizer = torch.optim.Adam(self.actor.icm.parameters(), lr=1e-3, )
# Build scheduler for optimizer if needed
if rephraser_optimizer_configs['schedule_method'] is not None:
if rephraser_optimizer_configs['schedule_method'] == "loss":
self.actor_scheduler = ReduceOnPlateauScheduler(
optimizer=self.actor_optimizer,
**rephraser_optimizer_configs["scheduler_configs"])
self.critic_scheduler = ReduceOnPlateauScheduler(
optimizer=self.critic_optimizer,
**rephraser_optimizer_configs["scheduler_configs"])
elif rephraser_optimizer_configs['schedule_method'] == "noam":
self.actor_scheduler = NoamScheduler(
optimizer=self.actor_optimizer,
**rephraser_optimizer_configs["scheduler_configs"])
self.critic_scheduler = NoamScheduler(
optimizer=self.critic_optimizer,
**rephraser_optimizer_configs["scheduler_configs"])
elif rephraser_optimizer_configs["schedule_method"] == "rsqrt":
self.actor_scheduler = RsqrtScheduler(
optimizer=self.actor_optimizer,
**rephraser_optimizer_configs["scheduler_configs"])
self.critic_scheduler = RsqrtScheduler(
optimizer=self.critic_optimizer,
**rephraser_optimizer_configs["scheduler_configs"])
else:
WARN(
"Unknown scheduler name {0}. Do not use lr_scheduling."
.format(
rephraser_optimizer_configs['schedule_method']))
self.actor_scheduler = None
self.critic_scheduler = None
else:
self.actor_scheduler = None
self.critic_scheduler = None
def sync_from(self, sac_agent):
with sac_agent.soft_update_lock, self.soft_update_lock:
self.actor.sync_from(sac_agent.actor)
self.critic.sync_from(sac_agent.critic)
def train(self, training=True):
# default training is true
self.training = training
self.actor.train(training)
self.critic.train(training)
return self
def update_critic(self,
states,
masks,
rephrase_positions,
actions,
rewards,
survive_and_no_maxs,
target_critic,
update_step,
discount_factor,
summary_writer,
update_trust_region=0.8):
"""
update critic by using a target_critic net (usually a global critic model) and a buffer
SARSA for TD learning
:param states:
:param masks:
:param rephrase_positions:
:param actions: actions
:param rewards: the rewards
:param survive_and_no_maxs: able to rollout next step for TD learning
:param target_critic: provides target value estimation(global model is usually on cpu)
:param discount_factor: for discounted rewards update
:param update_step: learning steps
:param update_trust_region: discount for loss updates
"""
label_emb = slice_by_indices(
states, rephrase_positions,
device=self.device) # next_rephrase_positions to label emb
next_action, log_probs = self.actor.sample_normal(states,
1. - masks,
label_emb,
reparamization=True)
log_probs = log_probs.sum(dim=-1, keepdims=True)
next_states = transition(states, masks, actions, rephrase_positions)
next_rephrase_positions = rephrase_positions + 1
next_label_emb = slice_by_indices(next_states,
next_rephrase_positions,
device=self.device)
# # note that with intrinsic curiosity module, the rewards will add curiosity bonus
# self.actor.icm.eval()
# rephrase_feature = self.actor.preprocess(states, 1.-masks, label_emb)
# next_rephrase_feature = self.actor.preprocess(next_states, 1.-masks, next_label_emb)
# bonus = self.actor.icm.get_surprise_bonus(rephrase_feature, next_rephrase_feature, actions).detach()
# bonus = 0.01 * bonus * survive_and_no_maxs
# print("bonus:", bonus.sum())
# rewards += bonus
# print("rewards:", rewards.squeeze())
# note that log_probs has the same dimension with the action. thus the log_prob of a whole action is the sum along dimensions.
target_critic.eval()
target_V = target_critic(
next_states, 1. - masks, next_label_emb,
next_action) - log_probs * self.alpha.detach()
target_Q = rewards + (
survive_and_no_maxs
) * discount_factor * target_V # we have next states for TD learning rollout
target_Q = target_Q.detach()
# get current Q estimates
current_Q = self.critic(states, 1. - masks, label_emb, actions)
critic_loss = F.mse_loss(current_Q, target_Q)
critic_loss *= update_trust_region
print("critic_loss", critic_loss.sum())
# Optimize the critic
self.critic_optimizer.zero_grad()
if self.critic_scheduler is not None:
self.critic_scheduler.step(global_step=update_step)
critic_loss.backward()
self.critic_optimizer.step()
# logging: entropy/target_entropy ratio, critic_loss,
summary_writer.add_scalar("critic_loss",
scalar_value=critic_loss,
global_step=update_step)
def update_actor_and_alpha(self,
states,
masks,
rephrase_positions,
target_critic,
update_step,
summary_writer,
update_trust_region=0.5):
"""
:param states: tensor states from the buffer samples
:param masks: indicats the valid token positions
:param rephrase_positions: induce the next states by the given states
:param update_trust_region: current annnunciator trust_acc (valid).
served as a trust region for RL updates;
also the weight of rewind or reinforce
trust_acc * rewind_loss + (1-trust_acc) * policy_loss
"""
self.actor.train()
label_emb = slice_by_indices(states,
rephrase_positions,
device=self.device)
actions, log_probs = self.actor.sample_normal(states,
1. - masks,
label_emb,
reparamization=True)
log_probs = log_probs.sum(dim=-1, keepdims=True)
actor_Q = self.critic(states, 1. - masks, label_emb, actions)
policy_loss = (self.alpha.detach() * log_probs - actor_Q).mean()
summary_writer.add_scalar('policy_loss', policy_loss, update_step)
summary_writer.add_scalar('entropy_ratio',
-log_probs.mean() / self.target_entropy,
update_step)
# the policy rewind loss, the rewind is determined by target value estimates (estimated survival + improvements)
# negative means rewind needed.
target_Q = target_critic(states, 1. - masks, label_emb,
actions).detach()
rewind_mask = target_Q.lt(0.).detach().float() # [batch, 1]
next_states = transition(states, masks, actions,
rephrase_positions).detach()
next_label_emb = slice_by_indices(next_states,
rephrase_positions,
device=self.device).detach()
rewind_action, _ = self.actor.forward(next_states, 1. - masks,
next_label_emb)
target_actions = -actions.detach()
rewind_loss = F.mse_loss(
rewind_action * rewind_mask * self.actor.action_range,
target_actions * rewind_mask)
summary_writer.add_scalar('rewind_loss', rewind_loss, update_step)
# the higher trust region means less indicative the perturbations are, policy should focus more on the rewind.
actor_loss = (update_trust_region) * rewind_loss + (
1. - update_trust_region) * policy_loss
## update the intrinsic reward module: action reconstruction and feature prediction mse
# self.actor.icm.train()
# next_states = transition(states, masks, actions, rephrase_positions)
# next_label_emb = slice_by_indices(next_states, rephrase_positions, device=self.device)
# rephrase_feature = self.actor.preprocess(states, 1.0-masks, label_emb)
# next_rephrase_feature = self.actor.preprocess(next_states, 1.0-masks, next_label_emb)
# if update_step<3000:
# # icm updates does not propagate to the policy on the early stage
# icm_loss = self.actor.icm(rephrase_feature.detach(), next_rephrase_feature.detach(), actions)
# else:
# icm_loss = self.actor.icm(rephrase_feature, next_rephrase_feature, actions)
# summary_writer.add_scalar("intrinsic_curiosity_loss", icm_loss, update_step)
# # the 0.1 is the setting by Intrinsic curiosity learning
# actor_loss = actor_loss + icm_loss
# optimize the actor
self.actor_optimizer.zero_grad()
# self.actor_icm_optimizer.zero_grad()
if self.actor_scheduler is not None:
self.actor_scheduler.step(global_step=update_step)
actor_loss.backward()
self.actor_optimizer.step()
# self.actor_icm_optimizer.step()
if self.learnable_temperature:
self.log_alpha_optimizer.zero_grad()
alpha_loss = (self.alpha *
(-log_probs - self.target_entropy).detach()).mean()
summary_writer.add_scalar('alpha_loss', alpha_loss, update_step)
summary_writer.add_scalar('alpha', self.alpha, update_step)
alpha_loss.backward()
self.log_alpha_optimizer.step()
def update_local_net(self,
local_agent_configs,
replay_buffer,
target_critic,
update_step,
discount_factor,
summary_writer,
update_trust_region=1.0):
"""
:param local_agent_configs: provides agent update freq
:param replay_buffer: provides the SARSA listed below
[states, masks, actions, rephrase_positions, rewards, terminal_signals]
states: the embedding as states. [batch, len, emb_dim] float
masks: the indicator of valid token for embedding. [batch, len] float
actions: the action embedding on the position. [batch, emb_dim] float
rephrase_positions: the position to rephrase. [batch, 1] long
rewards: the rewards for the transition. [batch, 1] float
terminal_signals: the terminal signals for the transition. [batch, 1] float
:param target_critic: provides the global critic
:param update_step: for lr scheduler and logging
:param discount_factor: rollout-rewards discount
:param summary_writer: logging
:param update_trust_region: discount for loss updates
"""
learn_batch_size = local_agent_configs["rephraser_learning_batch"]
states, masks, \
actions, rephrase_positions, \
rewards, _, survive_and_no_maxs = replay_buffer.sample(learn_batch_size, device=self.device)
INFO("update local agent critics on device: %s" % self.device)
self.update_critic(states, masks, rephrase_positions, actions, rewards,
survive_and_no_maxs, target_critic, update_step,
discount_factor, summary_writer,
update_trust_region)
if update_step % local_agent_configs["actor_update_freq"] == 0:
INFO("update local agent policy on device: %s" % self.device)
self.update_actor_and_alpha(states, masks, rephrase_positions,
target_critic, update_step,
summary_writer, update_trust_region)
def soft_update_target_net(self, target_SACAgent, tau):
# soft update the target network. first move to CPU, than move back to local
# mind not to update global model while reading and synch local models.
self.to(target_SACAgent.device)
with target_SACAgent.soft_update_lock:
for param, target_param in zip(
self.critic.parameters(),
target_SACAgent.critic.parameters()):
target_param.data.copy_(tau * param.data +
(1 - tau) * target_param.data)
for param, target_param in zip(self.actor.parameters(),
target_SACAgent.actor.parameters()):
target_param.data.copy_(tau * param.data +
(1 - tau) * target_param.data)
self.to(self.device)
def train(FLAGS):
"""
FLAGS:
saveto: str
reload: store_true
config_path: str
pretrain_path: str, default=""
model_name: str
log_path: str
"""
# write log of training to file.
write_log_to_file(
os.path.join(FLAGS.log_path,
"%s.log" % time.strftime("%Y%m%d-%H%M%S")))
GlobalNames.USE_GPU = FLAGS.use_gpu
if GlobalNames.USE_GPU:
CURRENT_DEVICE = "cpu"
else:
CURRENT_DEVICE = "cuda:0"
config_path = os.path.abspath(FLAGS.config_path)
with open(config_path.strip()) as f:
configs = yaml.load(f)
INFO(pretty_configs(configs))
# Add default configs
configs = default_configs(configs)
data_configs = configs['data_configs']
model_configs = configs['model_configs']
optimizer_configs = configs['optimizer_configs']
training_configs = configs['training_configs']
GlobalNames.SEED = training_configs['seed']
set_seed(GlobalNames.SEED)
best_model_prefix = os.path.join(
FLAGS.saveto, FLAGS.model_name + GlobalNames.MY_BEST_MODEL_SUFFIX)
timer = Timer()
# ================================================================================== #
# Load Data
INFO('Loading data...')
timer.tic()
# Generate target dictionary
vocab_tgt = Vocabulary(**data_configs["vocabularies"][0])
train_batch_size = training_configs["batch_size"] * max(
1, training_configs["update_cycle"])
train_buffer_size = training_configs["buffer_size"] * max(
1, training_configs["update_cycle"])
train_bitext_dataset = ZipDataset(TextLineDataset(
data_path=data_configs['train_data'][0],
vocabulary=vocab_tgt,
max_len=data_configs['max_len'][0],
),
shuffle=training_configs['shuffle'])
valid_bitext_dataset = ZipDataset(
TextLineDataset(
data_path=data_configs['valid_data'][0],
vocabulary=vocab_tgt,
))
training_iterator = DataIterator(
dataset=train_bitext_dataset,
batch_size=train_batch_size,
use_bucket=training_configs['use_bucket'],
buffer_size=train_buffer_size,
batching_func=training_configs['batching_key'])
valid_iterator = DataIterator(
dataset=valid_bitext_dataset,
batch_size=training_configs['valid_batch_size'],
use_bucket=True,
buffer_size=100000,
numbering=True)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
lrate = optimizer_configs['learning_rate']
is_early_stop = False
# ================================ Begin ======================================== #
# Build Model & Optimizer
# We would do steps below on after another
# 1. build models & criterion
# 2. move models & criterion to gpu if needed
# 3. load pre-trained model if needed
# 4. build optimizer
# 5. build learning rate scheduler if needed
# 6. load checkpoints if needed
# 0. Initial
model_collections = Collections()
checkpoint_saver = Saver(
save_prefix="{0}.ckpt".format(
os.path.join(FLAGS.saveto, FLAGS.model_name)),
num_max_keeping=training_configs['num_kept_checkpoints'])
best_model_saver = Saver(
save_prefix=best_model_prefix,
num_max_keeping=training_configs['num_kept_best_model'])
# 1. Build Model & Criterion
INFO('Building model...')
timer.tic()
lm_model = build_model(n_tgt_vocab=vocab_tgt.max_n_words, **model_configs)
INFO(lm_model)
params_total = sum([p.numel() for n, p in lm_model.named_parameters()])
params_with_embedding = sum([
p.numel() for n, p in lm_model.named_parameters()
if n.find('embedding') == -1
])
INFO('Total parameters: {}'.format(params_total))
INFO('Total parameters (excluding word embeddings): {}'.format(
params_with_embedding))
critic = NMTCriterion(label_smoothing=model_configs['label_smoothing'])
INFO(critic)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# 2. Move to GPU
if GlobalNames.USE_GPU:
lm_model = lm_model.cuda()
critic = critic.cuda()
# 3. Load pretrained model if needed
lm_model.init_parameters(FLAGS.pretrain_path, device=CURRENT_DEVICE)
# 4. Build optimizer
INFO('Building Optimizer...')
optim = Optimizer(name=optimizer_configs['optimizer'],
model=lm_model,
lr=lrate,
grad_clip=optimizer_configs['grad_clip'],
optim_args=optimizer_configs['optimizer_params'])
# 5. Build scheduler for optimizer if needed
if optimizer_configs['schedule_method'] is not None:
if optimizer_configs['schedule_method'] == "loss":
scheduler = ReduceOnPlateauScheduler(
optimizer=optim, **optimizer_configs["scheduler_configs"])
elif optimizer_configs['schedule_method'] == "noam":
scheduler = NoamScheduler(optimizer=optim,
**optimizer_configs['scheduler_configs'])
else:
WARN(
"Unknown scheduler name {0}. Do not use lr_scheduling.".format(
optimizer_configs['schedule_method']))
scheduler = None
else:
scheduler = None
# 6. build moving average
if training_configs['moving_average_method'] is not None:
ma = MovingAverage(
moving_average_method=training_configs['moving_average_method'],
named_params=lm_model.named_parameters(),
alpha=training_configs['moving_average_alpha'])
else:
ma = None
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# Reload from latest checkpoint
if FLAGS.reload:
checkpoint_saver.load_latest(model=lm_model,
optim=optim,
lr_scheduler=scheduler,
collections=model_collections,
ma=ma)
# ================================================================================== #
# Prepare training
eidx = model_collections.get_collection("eidx", [0])[-1]
uidx = model_collections.get_collection("uidx", [0])[-1]
bad_count = model_collections.get_collection("bad_count", [0])[-1]
oom_count = model_collections.get_collection("oom_count", [0])[-1]
summary_writer = SummaryWriter(log_dir=FLAGS.log_path)
cum_samples = 0
cum_words = 0
valid_loss = best_valid_loss = float('inf') # Max Float
saving_files = []
# Timer for computing speed
timer_for_speed = Timer()
timer_for_speed.tic()
INFO('Begin training...')
while True:
summary_writer.add_scalar("Epoch", (eidx + 1), uidx)
# Build iterator and progress bar
training_iter = training_iterator.build_generator()
training_progress_bar = tqdm(desc=' - (Epc {}, Upd {}) '.format(
eidx, uidx),
total=len(training_iterator),
unit="sents")
for batch in training_iter:
uidx += 1
if optimizer_configs[
"schedule_method"] is not None and optimizer_configs[
"schedule_method"] != "loss":
scheduler.step(global_step=uidx)
seqs_y = batch
n_samples_t = len(seqs_y)
n_words_t = sum(len(s) for s in seqs_y)
cum_samples += n_samples_t
cum_words += n_words_t
train_loss = 0.
optim.zero_grad()
try:
# Prepare data
for (seqs_y_t, ) in split_shard(
seqs_y, split_size=training_configs['update_cycle']):
y = prepare_data(seqs_y_t, cuda=GlobalNames.USE_GPU)
loss = compute_forward(
model=lm_model,
critic=critic,
# seqs_x=x,
seqs_y=y,
eval=False,
normalization=n_samples_t,
norm_by_words=training_configs["norm_by_words"])
train_loss += loss / y.size(
1) if not training_configs["norm_by_words"] else loss
optim.step()
except RuntimeError as e:
if 'out of memory' in str(e):
print('| WARNING: ran out of memory, skipping batch')
oom_count += 1
optim.zero_grad()
else:
raise e
if ma is not None and eidx >= training_configs[
'moving_average_start_epoch']:
ma.step()
training_progress_bar.update(n_samples_t)
training_progress_bar.set_description(
' - (Epc {}, Upd {}) '.format(eidx, uidx))
training_progress_bar.set_postfix_str(
'TrainLoss: {:.2f}, ValidLoss(best): {:.2f} ({:.2f})'.format(
train_loss, valid_loss, best_valid_loss))
summary_writer.add_scalar("train_loss",
scalar_value=train_loss,
global_step=uidx)
# ================================================================================== #
# Display some information
if should_trigger_by_steps(
uidx, eidx, every_n_step=training_configs['disp_freq']):
# words per second and sents per second
words_per_sec = cum_words / (timer.toc(return_seconds=True))
sents_per_sec = cum_samples / (timer.toc(return_seconds=True))
lrate = list(optim.get_lrate())[0]
summary_writer.add_scalar("Speed(words/sec)",
scalar_value=words_per_sec,
global_step=uidx)
summary_writer.add_scalar("Speed(sents/sen)",
scalar_value=sents_per_sec,
global_step=uidx)
summary_writer.add_scalar("lrate",
scalar_value=lrate,
global_step=uidx)
summary_writer.add_scalar("oom_count",
scalar_value=oom_count,
global_step=uidx)
# Reset timer
timer.tic()
cum_words = 0
cum_samples = 0
# ================================================================================== #
# Saving checkpoints
if should_trigger_by_steps(
uidx,
eidx,
every_n_step=training_configs['save_freq'],
debug=FLAGS.debug):
model_collections.add_to_collection("uidx", uidx)
model_collections.add_to_collection("eidx", eidx)
model_collections.add_to_collection("bad_count", bad_count)
if not is_early_stop:
checkpoint_saver.save(global_step=uidx,
model=lm_model,
optim=optim,
lr_scheduler=scheduler,
collections=model_collections,
ma=ma)
# ================================================================================== #
# Loss Validation & Learning rate annealing
if should_trigger_by_steps(
global_step=uidx,
n_epoch=eidx,
every_n_step=training_configs['loss_valid_freq'],
debug=FLAGS.debug):
if ma is not None:
origin_state_dict = deepcopy(lm_model.state_dict())
lm_model.load_state_dict(ma.export_ma_params(),
strict=False)
valid_loss = loss_validation(
model=lm_model,
critic=critic,
valid_iterator=valid_iterator,
norm_by_words=training_configs["norm_by_words"])
model_collections.add_to_collection("history_losses",
valid_loss)
min_history_loss = np.array(
model_collections.get_collection("history_losses")).min()
summary_writer.add_scalar("loss", valid_loss, global_step=uidx)
summary_writer.add_scalar("best_loss",
min_history_loss,
global_step=uidx)
if ma is not None:
lm_model.load_state_dict(origin_state_dict)
del origin_state_dict
if optimizer_configs["schedule_method"] == "loss":
scheduler.step(metric=best_valid_loss)
# If model get new best valid loss
if valid_loss < best_valid_loss:
bad_count = 0
if is_early_stop is False:
# 1. save the best model
torch.save(lm_model.state_dict(),
best_model_prefix + ".final")
# 2. record all several best models
best_model_saver.save(global_step=uidx, model=lm_model)
else:
bad_count += 1
# At least one epoch should be traversed
if bad_count >= training_configs[
'early_stop_patience'] and eidx > 0:
is_early_stop = True
WARN("Early Stop!")
best_valid_loss = min_history_loss
summary_writer.add_scalar("bad_count", bad_count, uidx)
INFO("{0} Loss: {1:.2f} lrate: {2:6f} patience: {3}".format(
uidx, valid_loss, lrate, bad_count))
training_progress_bar.close()
eidx += 1
if eidx > training_configs["max_epochs"]:
break
def tune(flags):
"""
flags:
saveto: str
reload: store_true
config_path: str
pretrain_path: str, default=""
model_name: str
log_path: str
"""
# ================================================================================== #
# Initialization for training on different devices
# - CPU/GPU
# - Single/Distributed
Constants.USE_GPU = flags.use_gpu
if flags.multi_gpu:
dist.distributed_init(flags.shared_dir)
world_size = dist.get_world_size()
rank = dist.get_rank()
local_rank = dist.get_local_rank()
else:
world_size = 1
rank = 0
local_rank = 0
if Constants.USE_GPU:
torch.cuda.set_device(local_rank)
Constants.CURRENT_DEVICE = "cuda:{0}".format(local_rank)
else:
Constants.CURRENT_DEVICE = "cpu"
# If not root_rank, close logging
# else write log of training to file.
if rank == 0:
write_log_to_file(
os.path.join(flags.log_path,
"%s.log" % time.strftime("%Y%m%d-%H%M%S")))
else:
close_logging()
# ================================================================================== #
# Parsing configuration files
# - Load default settings
# - Load pre-defined settings
# - Load user-defined settings
configs = prepare_configs(flags.config_path, flags.predefined_config)
data_configs = configs['data_configs']
model_configs = configs['model_configs']
optimizer_configs = configs['optimizer_configs']
training_configs = configs['training_configs']
INFO(pretty_configs(configs))
Constants.SEED = training_configs['seed']
set_seed(Constants.SEED)
timer = Timer()
# ================================================================================== #
# Load Data
INFO('Loading data...')
timer.tic()
# Generate target dictionary
vocab_src = Vocabulary.build_from_file(**data_configs['vocabularies'][0])
vocab_tgt = Vocabulary.build_from_file(**data_configs['vocabularies'][1])
Constants.EOS = vocab_src.eos
Constants.PAD = vocab_src.pad
Constants.BOS = vocab_src.bos
# bt tag dataset
train_bitext_dataset = ZipDataset(
TextLineDataset(data_path=data_configs['train_data'][0],
vocabulary=vocab_src,
max_len=data_configs['max_len'][0],
is_train_dataset=True),
TextLineDataset(data_path=data_configs['train_data'][1],
vocabulary=vocab_tgt,
max_len=data_configs['max_len'][1],
is_train_dataset=True))
training_iterator = DataIterator(
dataset=train_bitext_dataset,
batch_size=training_configs["batch_size"],
use_bucket=training_configs['use_bucket'],
buffer_size=training_configs['buffer_size'],
batching_func=training_configs['batching_key'],
world_size=world_size,
rank=rank)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# ================================ Begin ======================================== #
# Build Model & Optimizer
# We would do steps below on after another
# 1. build models & criterion
# 2. move models & criterion to gpu if needed
# 3. load pre-trained model if needed
# 4. build optimizer
# 5. build learning rate scheduler if needed
# 6. load checkpoints if needed
# 0. Initial
lrate = optimizer_configs['learning_rate']
model_collections = Collections()
checkpoint_saver = Saver(
save_prefix="{0}.ckpt".format(
os.path.join(flags.saveto, flags.model_name)),
num_max_keeping=training_configs['num_kept_checkpoints'])
best_model_prefix = os.path.join(
flags.saveto, flags.model_name + Constants.MY_BEST_MODEL_SUFFIX)
best_model_saver = Saver(
save_prefix=best_model_prefix,
num_max_keeping=training_configs['num_kept_best_model'])
# 1. Build Model & Criterion
INFO('Building model...')
timer.tic()
nmt_model = build_model(n_src_vocab=vocab_src.max_n_words,
n_tgt_vocab=vocab_tgt.max_n_words,
padding_idx=vocab_src.pad,
vocab_src=vocab_src,
vocab_tgt=vocab_tgt,
**model_configs)
INFO(nmt_model)
critic = NMTCriterion(label_smoothing=model_configs['label_smoothing'],
padding_idx=vocab_tgt.pad)
INFO(critic)
# 2. Move to GPU
if Constants.USE_GPU:
nmt_model = nmt_model.cuda()
critic = critic.cuda()
# 3. Load pretrained model if needed
load_pretrained_model(nmt_model,
flags.pretrain_path,
exclude_prefix=flags.pretrain_exclude_prefix,
device=Constants.CURRENT_DEVICE)
# froze_parameters
froze_params(nmt_model, flags.froze_config)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# 4. Build optimizer
INFO('Building Optimizer...')
if not flags.multi_gpu:
optim = Optimizer(name=optimizer_configs['optimizer'],
model=nmt_model,
lr=lrate,
grad_clip=optimizer_configs['grad_clip'],
optim_args=optimizer_configs['optimizer_params'],
update_cycle=training_configs['update_cycle'])
else:
optim = dist.DistributedOptimizer(
name=optimizer_configs['optimizer'],
model=nmt_model,
lr=lrate,
grad_clip=optimizer_configs['grad_clip'],
optim_args=optimizer_configs['optimizer_params'],
device_id=local_rank)
# 5. Build scheduler for optimizer if needed
scheduler = build_scheduler(
schedule_method=optimizer_configs['schedule_method'],
optimizer=optim,
scheduler_configs=optimizer_configs['scheduler_configs'])
# 6. build moving average
if training_configs['moving_average_method'] is not None:
ma = MovingAverage(
moving_average_method=training_configs['moving_average_method'],
named_params=nmt_model.named_parameters(),
alpha=training_configs['moving_average_alpha'])
else:
ma = None
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# Reload from latest checkpoint
if flags.reload:
checkpoint_saver.load_latest(model=nmt_model,
optim=optim,
lr_scheduler=scheduler,
collections=model_collections,
ma=ma,
device=Constants.CURRENT_DEVICE)
# broadcast parameters and optimizer states
if world_size > 1:
INFO("Broadcasting model parameters...")
dist.broadcast_parameters(params=nmt_model.state_dict())
INFO("Broadcasting optimizer states...")
dist.broadcast_optimizer_state(optimizer=optim.optim)
INFO('Done.')
# ================================================================================== #
# Prepare training
eidx = model_collections.get_collection("eidx", [0])[-1]
uidx = model_collections.get_collection("uidx", [1])[-1]
bad_count = model_collections.get_collection("bad_count", [0])[-1]
oom_count = model_collections.get_collection("oom_count", [0])[-1]
is_early_stop = model_collections.get_collection("is_early_stop", [
False,
])[-1]
train_loss_meter = AverageMeter()
sent_per_sec_meter = TimeMeter()
tok_per_sec_meter = TimeMeter()
update_cycle = training_configs['update_cycle']
grad_denom = 0
train_loss = 0.0
cum_n_words = 0
valid_loss = best_valid_loss = float('inf')
if rank == 0:
summary_writer = SummaryWriter(log_dir=flags.log_path)
else:
summary_writer = None
sent_per_sec_meter.start()
tok_per_sec_meter.start()
INFO('Begin training...')
while True:
if summary_writer is not None:
summary_writer.add_scalar("Epoch", (eidx + 1), uidx)
# Build iterator and progress bar
training_iter = training_iterator.build_generator()
if rank == 0:
training_progress_bar = tqdm(desc=' - (Epc {}, Upd {}) '.format(
eidx, uidx),
total=len(training_iterator),
unit="sents")
else:
training_progress_bar = None
# INFO(Constants.USE_BT)
for batch in training_iter:
# bt attrib data
seqs_x, seqs_y = batch
batch_size = len(seqs_x)
cum_n_words += sum(len(s) for s in seqs_y)
try:
# Prepare data
x, y = prepare_data(seqs_x, seqs_y, cuda=Constants.USE_GPU)
loss = compute_forward(
model=nmt_model,
critic=critic,
seqs_x=x,
seqs_y=y,
eval=False,
normalization=1.0,
norm_by_words=training_configs["norm_by_words"])
update_cycle -= 1
grad_denom += batch_size
train_loss += loss
except RuntimeError as e:
if 'out of memory' in str(e):
print('| WARNING: ran out of memory, skipping batch')
oom_count += 1
else:
raise e
# When update_cycle becomes 0, it means end of one batch. Several things will be done:
# - update parameters
# - reset update_cycle and grad_denom, update uidx
# - learning rate scheduling
# - update moving average
if update_cycle == 0:
# 0. reduce variables
if world_size > 1:
grad_denom = dist.all_reduce_py(grad_denom)
train_loss = dist.all_reduce_py(train_loss)
cum_n_words = dist.all_reduce_py(cum_n_words)
# 1. update parameters
optim.step(denom=grad_denom)
optim.zero_grad()
if training_progress_bar is not None:
training_progress_bar.update(grad_denom)
training_progress_bar.set_description(
' - (Epc {}, Upd {}) '.format(eidx, uidx))
postfix_str = 'TrainLoss: {:.2f}, ValidLoss(best): {:.2f} ({:.2f}), '.format(
train_loss, valid_loss, best_valid_loss)
training_progress_bar.set_postfix_str(postfix_str)
# 2. learning rate scheduling
if scheduler is not None and optimizer_configs[
"schedule_method"] != "loss":
scheduler.step(global_step=uidx)
# 3. update moving average
if ma is not None and eidx >= training_configs[
'moving_average_start_epoch']:
ma.step()
# 4. update meters
train_loss_meter.update(train_loss, grad_denom)
sent_per_sec_meter.update(grad_denom)
tok_per_sec_meter.update(cum_n_words)
# 5. reset accumulated variables, update uidx
update_cycle = training_configs['update_cycle']
grad_denom = 0
uidx += 1
cum_n_words = 0.0
train_loss = 0.0
else:
continue
# ================================================================================== #
# Display some information
if should_trigger_by_steps(
uidx, eidx, every_n_step=training_configs['disp_freq']):
lrate = list(optim.get_lrate())[0]
if summary_writer is not None:
summary_writer.add_scalar(
"Speed(sents/sec)",
scalar_value=sent_per_sec_meter.ave,
global_step=uidx)
summary_writer.add_scalar(
"Speed(words/sec)",
scalar_value=tok_per_sec_meter.ave,
global_step=uidx)
summary_writer.add_scalar(
"train_loss",
scalar_value=train_loss_meter.ave,
global_step=uidx)
summary_writer.add_scalar("lrate",
scalar_value=lrate,
global_step=uidx)
summary_writer.add_scalar("oom_count",
scalar_value=oom_count,
global_step=uidx)
# Reset Meters
sent_per_sec_meter.reset()
tok_per_sec_meter.reset()
train_loss_meter.reset()
# ================================================================================== #
# Saving checkpoints
# if should_trigger_by_steps(uidx, eidx, every_n_step=training_configs['save_freq'], debug=flags.debug):
# model_collections.add_to_collection("uidx", uidx)
# model_collections.add_to_collection("eidx", eidx)
# model_collections.add_to_collection("bad_count", bad_count)
#
# if not is_early_stop:
# if rank == 0:
# checkpoint_saver.save(global_step=uidx,
# model=nmt_model,
# optim=optim,
# lr_scheduler=scheduler,
# collections=model_collections,
# ma=ma)
torch.save(nmt_model.state_dict(), best_model_prefix + ".final")
if training_progress_bar is not None:
training_progress_bar.close()
eidx += 1
if eidx > training_configs["max_epochs"]:
break
def train(rank,
device,
args,
counter,
lock,
attack_configs,
discriminator_configs,
src_vocab,
trg_vocab,
data_set,
global_attacker,
attacker_configs,
optimizer=None,
scheduler=None,
saver=None):
"""
running train process
#1# train the env_discriminator
#2# run attacker AC based on rewards from trained env_discriminator
#3# run training updates attacker AC
#4#
:param rank: (int) the rank of the process (from multiprocess)
:param device: the device of the process
:param counter: python multiprocess variable
:param lock: python multiprocess variable
:param args: global args
:param attack_configs: attack settings
:param discriminator_configs: discriminator settings
:param src_vocab:
:param trg_vocab:
:param data_set: (data_iterator object) provide batched data labels
:param global_attacker: the model to sync from
:param attacker_configs: local attacker settings
:param optimizer: uses shared optimizer for the attacker
use local one if none
:param scheduler: uses shared scheduler for the attacker,
use local one if none
:param saver: model saver
:return:
"""
trust_acc = acc_bound = discriminator_configs["acc_bound"]
converged_bound = discriminator_configs["converged_bound"]
patience = discriminator_configs["patience"]
attacker_model_configs = attacker_configs["attacker_model_configs"]
attacker_optimizer_configs = attacker_configs["attacker_optimizer_configs"]
# this is for multi-processing, GlobalNames can not be direct inherited
GlobalNames.USE_GPU = args.use_gpu
GlobalNames.SEED = attack_configs["seed"]
torch.manual_seed(GlobalNames.SEED + rank)
# initiate local saver and load checkpoint if possible
local_saver = Saver(save_prefix="{0}.local".format(
os.path.join(args.save_to, "train_env%d" % rank, "ACmodel")),
num_max_keeping=attack_configs["num_kept_checkpoints"])
attack_iterator = DataIterator(dataset=data_set,
batch_size=attack_configs["batch_size"],
use_bucket=True,
buffer_size=attack_configs["buffer_size"],
numbering=True)
summary_writer = SummaryWriter(
log_dir=os.path.join(args.save_to, "train_env%d" % rank))
local_attacker = attacker.Attacker(src_vocab.max_n_words,
**attacker_model_configs)
# build optimizer for attacker
if optimizer is None:
optimizer = Optimizer(
name=attacker_optimizer_configs["optimizer"],
model=global_attacker,
lr=attacker_optimizer_configs["learning_rate"],
grad_clip=attacker_optimizer_configs["grad_clip"],
optim_args=attacker_optimizer_configs["optimizer_params"])
# Build scheduler for optimizer if needed
if attacker_optimizer_configs['schedule_method'] is not None:
if attacker_optimizer_configs['schedule_method'] == "loss":
scheduler = ReduceOnPlateauScheduler(
optimizer=optimizer,
**attacker_optimizer_configs["scheduler_configs"])
elif attacker_optimizer_configs['schedule_method'] == "noam":
scheduler = NoamScheduler(
optimizer=optimizer,
**attacker_optimizer_configs['scheduler_configs'])
elif attacker_optimizer_configs["schedule_method"] == "rsqrt":
scheduler = RsqrtScheduler(
optimizer=optimizer,
**attacker_optimizer_configs["scheduler_configs"])
else:
WARN("Unknown scheduler name {0}. Do not use lr_scheduling.".
format(attacker_optimizer_configs['schedule_method']))
scheduler = None
else:
scheduler = None
local_saver.load_latest(model=local_attacker,
optim=optimizer,
lr_scheduler=scheduler)
attacker_iterator = attack_iterator.build_generator()
env = Translate_Env(attack_configs=attack_configs,
discriminator_configs=discriminator_configs,
src_vocab=src_vocab,
trg_vocab=trg_vocab,
data_iterator=attacker_iterator,
save_to=args.save_to,
device=device)
episode_count = 0
episode_length = 0
local_steps = 0 # optimization steps: for learning rate schedules
patience_t = patience
while True: # infinite loop of data set
# we will continue with a new iterator with refreshed environments
# whenever the last iterator breaks with "StopIteration"
attacker_iterator = attack_iterator.build_generator()
env.reset_data_iter(attacker_iterator)
padded_src = env.reset()
padded_src = torch.from_numpy(padded_src)
if device != "cpu":
padded_src = padded_src.to(device)
done = True
discriminator_base_steps = local_steps
while True:
# check for update of discriminator
# if env.acc_validation(local_attacker, use_gpu=True if env.device != "cpu" else False) < 0.55:
if episode_count % attacker_configs["attacker_update_steps"] == 0:
""" stop criterion:
when updates a discriminator, we check for acc. If acc fails acc_bound,
we reset the discriminator and try, until acc reaches the bound with patience.
otherwise the training thread stops
"""
try:
discriminator_base_steps, trust_acc = env.update_discriminator(
local_attacker,
discriminator_base_steps,
min_update_steps=discriminator_configs[
"acc_valid_freq"],
max_update_steps=discriminator_configs[
"discriminator_update_steps"],
accuracy_bound=acc_bound,
summary_writer=summary_writer)
except StopIteration:
INFO("finish one training epoch, reset data_iterator")
break
discriminator_base_steps += 1 # a flag to label the discriminator updates
if trust_acc < converged_bound: # GAN target reached
patience_t -= 1
INFO(
"discriminator reached GAN convergence bound: %d times"
% patience_t)
else: # reset patience if discriminator is refreshed
patience_t = patience
if saver and local_steps % attack_configs["save_freq"] == 0:
local_saver.save(global_step=local_steps,
model=local_attacker,
optim=optimizer,
lr_scheduler=scheduler)
if trust_acc < converged_bound: # and patience_t == patience-1:
# we only save the global params reaching acc_bound
torch.save(global_attacker.state_dict(),
os.path.join(args.save_to, "ACmodel.final"))
# saver.raw_save(model=global_attacker)
if patience_t == 0:
WARN("maximum patience reached. Training Thread should stop")
break
local_attacker.train() # switch back to training mode
# for a initial (reset) attacker from global parameters
if done:
INFO("sync from global model")
local_attacker.load_state_dict(global_attacker.state_dict())
# move the local attacker params back to device after updates
local_attacker = local_attacker.to(device)
values = [] # training critic: network outputs
log_probs = []
rewards = [] # actual rewards
entropies = []
local_steps += 1
# run sequences step of attack
try:
for i in range(args.action_roll_steps):
episode_length += 1
attack_out, critic_out = local_attacker(
padded_src, padded_src[:, env.index - 1:env.index + 2])
logit_attack_out = torch.log(attack_out)
entropy = -(attack_out *
logit_attack_out).sum(dim=-1).mean()
summary_writer.add_scalar("action_entropy",
scalar_value=entropy,
global_step=local_steps)
entropies.append(entropy) # for entropy loss
actions = attack_out.multinomial(num_samples=1).detach()
# only extract the log prob for chosen action (avg over batch)
log_attack_out = logit_attack_out.gather(-1,
actions).mean()
padded_src, reward, terminal_signal = env.step(
actions.squeeze())
done = terminal_signal or episode_length > args.max_episode_lengths
with lock:
counter.value += 1
if done:
episode_length = 0
padded_src = env.reset()
padded_src = torch.from_numpy(padded_src)
if device != "cpu":
padded_src = padded_src.to(device)
values.append(
critic_out.mean()) # list of torch variables (scalar)
log_probs.append(
log_attack_out) # list of torch variables (scalar)
rewards.append(reward) # list of reward variables
if done:
episode_count += 1
break
except StopIteration:
INFO("finish one training epoch, reset data_iterator")
break
R = torch.zeros(1, 1)
gae = torch.zeros(1, 1)
if device != "cpu":
R = R.to(device)
gae = gae.to(device)
if not done: # calculate value loss
value = local_attacker.get_critic(
padded_src, padded_src[:, env.index - 1:env.index + 2])
R = value.mean().detach()
values.append(R)
policy_loss = 0
value_loss = 0
# collect values for training
for i in reversed((range(len(rewards)))):
# value loss and policy loss must be clipped to stabilize training
R = attack_configs["gamma"] * R + rewards[i]
advantage = R - values[i]
value_loss = value_loss + 0.5 * advantage.pow(2)
delta_t = rewards[i] + attack_configs["gamma"] * \
values[i + 1] - values[i]
gae = gae * attack_configs["gamma"] * attack_configs["tau"] + \
delta_t
policy_loss = policy_loss - log_probs[i] * gae.detach() - \
attack_configs["entropy_coef"] * entropies[i]
print("policy_loss", policy_loss)
print("gae", gae)
# update with optimizer
optimizer.zero_grad()
# we decay the loss according to discriminator's accuracy as a trust region constrain
summary_writer.add_scalar("policy_loss",
scalar_value=policy_loss * trust_acc,
global_step=local_steps)
summary_writer.add_scalar("value_loss",
scalar_value=value_loss * trust_acc,
global_step=local_steps)
total_loss = trust_acc * policy_loss + \
trust_acc * attack_configs["value_coef"] * value_loss
total_loss.backward()
if attacker_optimizer_configs[
"schedule_method"] is not None and attacker_optimizer_configs[
"schedule_method"] != "loss":
scheduler.step(global_step=local_steps)
# move the model params to CPU and
# assign local gradients to the global model to update
local_attacker.to("cpu").ensure_shared_grads(global_attacker)
optimizer.step()
print("bingo!")
if patience_t == 0:
INFO("Reach maximum Discriminator patience, Finish")
break
def train(FLAGS):
"""
FLAGS:
saveto: str
reload: store_true
config_path: str
pretrain_path: str, default=""
model_name: str
log_path: str
"""
# ================================================================================== #
# Initialization for training on different devices
# - CPU/GPU
# - Single/Distributed
GlobalNames.USE_GPU = FLAGS.use_gpu
if FLAGS.multi_gpu:
if hvd is None or distributed is None:
ERROR("Distributed training is disable. Please check the installation of Horovod.")
hvd.init()
world_size = hvd.size()
rank = hvd.rank()
local_rank = hvd.local_rank()
else:
world_size = 1
rank = 0
local_rank = 0
if GlobalNames.USE_GPU:
torch.cuda.set_device(local_rank)
CURRENT_DEVICE = "cuda:{0}".format(local_rank)
else:
CURRENT_DEVICE = "cpu"
# If not root_rank, close logging
if rank != 0:
close_logging()
# write log of training to file.
if rank == 0:
write_log_to_file(os.path.join(FLAGS.log_path, "%s.log" % time.strftime("%Y%m%d-%H%M%S")))
# ================================================================================== #
# Parsing configuration files
config_path = os.path.abspath(FLAGS.config_path)
with open(config_path.strip()) as f:
configs = yaml.load(f)
INFO(pretty_configs(configs))
# Add default configs
configs = default_baseline_configs(configs)
data_configs = configs['data_configs']
model_configs = configs['model_configs']
optimizer_configs = configs['optimizer_configs']
training_configs = configs['training_configs']
GlobalNames.SEED = training_configs['seed']
set_seed(GlobalNames.SEED)
timer = Timer()
# ================================================================================== #
# Load Data
INFO('Loading data...')
timer.tic()
# Generate target dictionary
vocab_src = Vocabulary(**data_configs["vocabularies"][0])
vocab_tgt = Vocabulary(**data_configs["vocabularies"][1])
actual_buffer_size = training_configs["buffer_size"] * max(1, training_configs["update_cycle"])
train_bitext_dataset = ZipDataset(
TextLineDataset(data_path=data_configs['train_data'][0],
vocabulary=vocab_src,
max_len=data_configs['max_len'][0],
),
TextLineDataset(data_path=data_configs['train_data'][1],
vocabulary=vocab_tgt,
max_len=data_configs['max_len'][1],
)
)
valid_bitext_dataset = ZipDataset(
TextLineDataset(data_path=data_configs['valid_data'][0],
vocabulary=vocab_src,
),
TextLineDataset(data_path=data_configs['valid_data'][1],
vocabulary=vocab_tgt,
)
)
training_iterator = DataIterator(dataset=train_bitext_dataset,
batch_size=training_configs["batch_size"],
use_bucket=training_configs['use_bucket'],
buffer_size=actual_buffer_size,
batching_func=training_configs['batching_key'],
world_size=world_size,
rank=rank)
valid_iterator = DataIterator(dataset=valid_bitext_dataset,
batch_size=training_configs['valid_batch_size'],
use_bucket=True, buffer_size=100000, numbering=True,
world_size=world_size, rank=rank)
bleu_scorer = SacreBLEUScorer(reference_path=data_configs["bleu_valid_reference"],
num_refs=data_configs["num_refs"],
lang_pair=data_configs["lang_pair"],
sacrebleu_args=training_configs["bleu_valid_configs"]['sacrebleu_args'],
postprocess=training_configs["bleu_valid_configs"]['postprocess']
)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
lrate = optimizer_configs['learning_rate']
is_early_stop = False
# ================================ Begin ======================================== #
# Build Model & Optimizer
# We would do steps below on after another
# 1. build models & criterion
# 2. move models & criterion to gpu if needed
# 3. load pre-trained model if needed
# 4. build optimizer
# 5. build learning rate scheduler if needed
# 6. load checkpoints if needed
# 0. Initial
model_collections = Collections()
best_model_prefix = os.path.join(FLAGS.saveto, FLAGS.model_name + GlobalNames.MY_BEST_MODEL_SUFFIX)
checkpoint_saver = Saver(save_prefix="{0}.ckpt".format(os.path.join(FLAGS.saveto, FLAGS.model_name)),
num_max_keeping=training_configs['num_kept_checkpoints']
)
best_model_saver = Saver(save_prefix=best_model_prefix, num_max_keeping=training_configs['num_kept_best_model'])
INFO('Building model...')
timer.tic()
nmt_model = build_model(n_src_vocab=vocab_src.max_n_words,
n_tgt_vocab=vocab_tgt.max_n_words, **model_configs)
INFO(nmt_model)
critic = NMTCriterion(label_smoothing=model_configs['label_smoothing'])
INFO(critic)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# 2. Move to GPU
if GlobalNames.USE_GPU:
nmt_model = nmt_model.cuda()
critic = critic.cuda()
# 3. Load pretrained model if needed
load_pretrained_model(nmt_model, FLAGS.pretrain_path, exclude_prefix=None, device=CURRENT_DEVICE)
# 4. Build optimizer
INFO('Building Optimizer...')
optim = Optimizer(name=optimizer_configs['optimizer'],
model=nmt_model,
lr=lrate,
grad_clip=optimizer_configs['grad_clip'],
optim_args=optimizer_configs['optimizer_params'],
distributed=True if world_size > 1 else False,
update_cycle=training_configs['update_cycle']
)
# 5. Build scheduler for optimizer if needed
if optimizer_configs['schedule_method'] is not None:
if optimizer_configs['schedule_method'] == "loss":
scheduler = ReduceOnPlateauScheduler(optimizer=optim,
**optimizer_configs["scheduler_configs"]
)
elif optimizer_configs['schedule_method'] == "noam":
scheduler = NoamScheduler(optimizer=optim, **optimizer_configs['scheduler_configs'])
else:
WARN("Unknown scheduler name {0}. Do not use lr_scheduling.".format(optimizer_configs['schedule_method']))
scheduler = None
else:
scheduler = None
# 6. build moving average
if training_configs['moving_average_method'] is not None:
ma = MovingAverage(moving_average_method=training_configs['moving_average_method'],
named_params=nmt_model.named_parameters(),
alpha=training_configs['moving_average_alpha'])
else:
ma = None
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# Reload from latest checkpoint
if FLAGS.reload:
checkpoint_saver.load_latest(model=nmt_model, optim=optim, lr_scheduler=scheduler,
collections=model_collections, ma=ma)
# broadcast parameters and optimizer states
if world_size > 1:
hvd.broadcast_parameters(params=nmt_model.state_dict(), root_rank=0)
hvd.broadcast_optimizer_state(optimizer=optim.optim, root_rank=0)
# ================================================================================== #
# Prepare training
eidx = model_collections.get_collection("eidx", [0])[-1]
uidx = model_collections.get_collection("uidx", [1])[-1]
bad_count = model_collections.get_collection("bad_count", [0])[-1]
oom_count = model_collections.get_collection("oom_count", [0])[-1]
cum_n_samples = 0
cum_n_words = 0
best_valid_loss = 1.0 * 1e10 # Max Float
update_cycle = training_configs['update_cycle']
grad_denom = 0
if rank == 0:
summary_writer = SummaryWriter(log_dir=FLAGS.log_path)
else:
summary_writer = None
# Timer for computing speed
timer_for_speed = Timer()
timer_for_speed.tic()
INFO('Begin training...')
while True:
if summary_writer is not None:
summary_writer.add_scalar("Epoch", (eidx + 1), uidx)
# Build iterator and progress bar
training_iter = training_iterator.build_generator()
if rank == 0:
training_progress_bar = tqdm(desc=' - (Epoch %d) ' % eidx,
total=len(training_iterator),
unit="sents"
)
else:
training_progress_bar = None
for batch in training_iter:
seqs_x, seqs_y = batch
batch_size = len(seqs_x)
cum_n_samples += batch_size
cum_n_words += sum(len(s) for s in seqs_y)
try:
# Prepare data
x, y = prepare_data(seqs_x, seqs_y, cuda=GlobalNames.USE_GPU)
loss = compute_forward(model=nmt_model,
critic=critic,
seqs_x=x,
seqs_y=y,
eval=False,
normalization=1.0,
norm_by_words=training_configs["norm_by_words"])
update_cycle -= 1
grad_denom += batch_size
except RuntimeError as e:
if 'out of memory' in str(e):
print('| WARNING: ran out of memory, skipping batch')
oom_count += 1
else:
raise e
# When update_cycle becomes 0, it means end of one batch. Several things will be done:
# - update parameters
# - reset update_cycle and grad_denom
# - update uidx
# - update moving average
if update_cycle == 0:
if world_size > 1:
grad_denom = distributed.all_reduce(grad_denom)
optim.step(denom=grad_denom)
optim.zero_grad()
if training_progress_bar is not None:
training_progress_bar.update(grad_denom)
update_cycle = training_configs['update_cycle']
grad_denom = 0
uidx += 1
if scheduler is None:
pass
elif optimizer_configs["schedule_method"] == "loss":
scheduler.step(metric=best_valid_loss)
else:
scheduler.step(global_step=uidx)
if ma is not None and eidx >= training_configs['moving_average_start_epoch']:
ma.step()
else:
continue
# ================================================================================== #
# Display some information
if should_trigger_by_steps(uidx, eidx, every_n_step=training_configs['disp_freq']):
if world_size > 1:
cum_n_words = sum(distributed.all_gather(cum_n_words))
cum_n_samples = sum(distributed.all_gather(cum_n_samples))
# words per second and sents per second
words_per_sec = cum_n_words / (timer.toc(return_seconds=True))
sents_per_sec = cum_n_samples / (timer.toc(return_seconds=True))
lrate = list(optim.get_lrate())[0]
if summary_writer is not None:
summary_writer.add_scalar("Speed(words/sec)", scalar_value=words_per_sec, global_step=uidx)
summary_writer.add_scalar("Speed(sents/sen)", scalar_value=sents_per_sec, global_step=uidx)
summary_writer.add_scalar("lrate", scalar_value=lrate, global_step=uidx)
summary_writer.add_scalar("oom_count", scalar_value=oom_count, global_step=uidx)
# Reset timer
timer.tic()
cum_n_words = 0
cum_n_samples = 0
# ================================================================================== #
# Loss Validation & Learning rate annealing
if should_trigger_by_steps(global_step=uidx, n_epoch=eidx, every_n_step=training_configs['loss_valid_freq'],
debug=FLAGS.debug):
valid_loss = loss_validation(model=nmt_model,
critic=critic,
valid_iterator=valid_iterator,
rank=rank,
world_size=world_size
)
model_collections.add_to_collection("history_losses", valid_loss)
min_history_loss = np.array(model_collections.get_collection("history_losses")).min()
best_valid_loss = min_history_loss
if summary_writer is not None:
summary_writer.add_scalar("loss", valid_loss, global_step=uidx)
summary_writer.add_scalar("best_loss", min_history_loss, global_step=uidx)
# ================================================================================== #
# BLEU Validation & Early Stop
if should_trigger_by_steps(global_step=uidx, n_epoch=eidx,
every_n_step=training_configs['bleu_valid_freq'],
min_step=training_configs['bleu_valid_warmup'],
debug=FLAGS.debug):
valid_bleu = bleu_validation(uidx=uidx,
valid_iterator=valid_iterator,
batch_size=training_configs["bleu_valid_batch_size"],
model=nmt_model,
bleu_scorer=bleu_scorer,
vocab_tgt=vocab_tgt,
valid_dir=FLAGS.valid_path,
max_steps=training_configs["bleu_valid_configs"]["max_steps"],
beam_size=training_configs["bleu_valid_configs"]["beam_size"],
alpha=training_configs["bleu_valid_configs"]["alpha"],
world_size=world_size,
rank=rank,
)
model_collections.add_to_collection(key="history_bleus", value=valid_bleu)
best_valid_bleu = float(np.array(model_collections.get_collection("history_bleus")).max())
if summary_writer is not None:
summary_writer.add_scalar("bleu", valid_bleu, uidx)
summary_writer.add_scalar("best_bleu", best_valid_bleu, uidx)
# If model get new best valid bleu score
if valid_bleu >= best_valid_bleu:
bad_count = 0
if is_early_stop is False:
if rank == 0:
# 1. save the best model
torch.save(nmt_model.state_dict(), best_model_prefix + ".final")
# 2. record all several best models
best_model_saver.save(global_step=uidx, model=nmt_model, ma=ma)
else:
bad_count += 1
# At least one epoch should be traversed
if bad_count >= training_configs['early_stop_patience'] and eidx > 0:
is_early_stop = True
WARN("Early Stop!")
if summary_writer is not None:
summary_writer.add_scalar("bad_count", bad_count, uidx)
INFO("{0} Loss: {1:.2f} BLEU: {2:.2f} lrate: {3:6f} patience: {4}".format(
uidx, valid_loss, valid_bleu, lrate, bad_count
))
# ================================================================================== #
# Saving checkpoints
if should_trigger_by_steps(uidx, eidx, every_n_step=training_configs['save_freq'], debug=FLAGS.debug):
model_collections.add_to_collection("uidx", uidx)
model_collections.add_to_collection("eidx", eidx)
model_collections.add_to_collection("bad_count", bad_count)
if not is_early_stop:
if rank == 0:
checkpoint_saver.save(global_step=uidx,
model=nmt_model,
optim=optim,
lr_scheduler=scheduler,
collections=model_collections,
ma=ma)
if training_progress_bar is not None:
training_progress_bar.close()
eidx += 1
if eidx > training_configs["max_epochs"]:
break
def train(FLAGS):
"""
FLAGS:
saveto: str
reload: store_true
config_path: str
pretrain_path: str, default=""
model_name: str
log_path: str
"""
# write log of training to file.
write_log_to_file(os.path.join(FLAGS.log_path, "%s.log" % time.strftime("%Y%m%d-%H%M%S")))
GlobalNames.USE_GPU = FLAGS.use_gpu
if GlobalNames.USE_GPU:
CURRENT_DEVICE = "cpu"
else:
CURRENT_DEVICE = "cuda:0"
config_path = os.path.abspath(FLAGS.config_path)
with open(config_path.strip()) as f:
configs = yaml.load(f)
INFO(pretty_configs(configs))
# Add default configs
configs = default_configs(configs)
data_configs = configs['data_configs']
model_configs = configs['model_configs']
optimizer_configs = configs['optimizer_configs']
training_configs = configs['training_configs']
GlobalNames.SEED = training_configs['seed']
set_seed(GlobalNames.SEED)
best_model_prefix = os.path.join(FLAGS.saveto, FLAGS.model_name + GlobalNames.MY_BEST_MODEL_SUFFIX)
timer = Timer()
# ================================================================================== #
# Load Data
INFO('Loading data...')
timer.tic()
# Generate target dictionary
vocab_src = Vocabulary(**data_configs["vocabularies"][0])
vocab_tgt = Vocabulary(**data_configs["vocabularies"][1])
train_batch_size = training_configs["batch_size"] * max(1, training_configs["update_cycle"])
train_buffer_size = training_configs["buffer_size"] * max(1, training_configs["update_cycle"])
train_bitext_dataset = ZipDataset(
TextLineDataset(data_path=data_configs['train_data'][0],
vocabulary=vocab_src,
max_len=data_configs['max_len'][0],
),
TextLineDataset(data_path=data_configs['train_data'][1],
vocabulary=vocab_tgt,
max_len=data_configs['max_len'][1],
),
shuffle=training_configs['shuffle']
)
valid_bitext_dataset = ZipDataset(
TextLineDataset(data_path=data_configs['valid_data'][0],
vocabulary=vocab_src,
),
TextLineDataset(data_path=data_configs['valid_data'][1],
vocabulary=vocab_tgt,
)
)
training_iterator = DataIterator(dataset=train_bitext_dataset,
batch_size=train_batch_size,
use_bucket=training_configs['use_bucket'],
buffer_size=train_buffer_size,
batching_func=training_configs['batching_key'])
valid_iterator = DataIterator(dataset=valid_bitext_dataset,
batch_size=training_configs['valid_batch_size'],
use_bucket=True, buffer_size=100000, numbering=True)
bleu_scorer = SacreBLEUScorer(reference_path=data_configs["bleu_valid_reference"],
num_refs=data_configs["num_refs"],
lang_pair=data_configs["lang_pair"],
sacrebleu_args=training_configs["bleu_valid_configs"]['sacrebleu_args'],
postprocess=training_configs["bleu_valid_configs"]['postprocess']
)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
lrate = optimizer_configs['learning_rate']
is_early_stop = False
# ================================ Begin ======================================== #
# Build Model & Optimizer
# We would do steps below on after another
# 1. build models & criterion
# 2. move models & criterion to gpu if needed
# 3. load pre-trained model if needed
# 4. build optimizer
# 5. build learning rate scheduler if needed
# 6. load checkpoints if needed
# 0. Initial
model_collections = Collections()
checkpoint_saver = Saver(save_prefix="{0}.ckpt".format(os.path.join(FLAGS.saveto, FLAGS.model_name)),
num_max_keeping=training_configs['num_kept_checkpoints']
)
best_model_saver = Saver(save_prefix=best_model_prefix, num_max_keeping=training_configs['num_kept_best_model'])
# 1. Build Model & Criterion
INFO('Building model...')
timer.tic()
nmt_model = build_model(n_src_vocab=vocab_src.max_n_words,
n_tgt_vocab=vocab_tgt.max_n_words, **model_configs)
INFO(nmt_model)
critic = NMTCriterion(label_smoothing=model_configs['label_smoothing'])
INFO(critic)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# 2. Move to GPU
if GlobalNames.USE_GPU:
nmt_model = nmt_model.cuda()
critic = critic.cuda()
# 3. Load pretrained model if needed
load_pretrained_model(nmt_model, FLAGS.pretrain_path, exclude_prefix=None, device=CURRENT_DEVICE)
# 4. Build optimizer
INFO('Building Optimizer...')
optim = Optimizer(name=optimizer_configs['optimizer'],
model=nmt_model,
lr=lrate,
grad_clip=optimizer_configs['grad_clip'],
optim_args=optimizer_configs['optimizer_params']
)
# 5. Build scheduler for optimizer if needed
if optimizer_configs['schedule_method'] is not None:
if optimizer_configs['schedule_method'] == "loss":
scheduler = ReduceOnPlateauScheduler(optimizer=optim,
**optimizer_configs["scheduler_configs"]
)
elif optimizer_configs['schedule_method'] == "noam":
scheduler = NoamScheduler(optimizer=optim, **optimizer_configs['scheduler_configs'])
else:
WARN("Unknown scheduler name {0}. Do not use lr_scheduling.".format(optimizer_configs['schedule_method']))
scheduler = None
else:
scheduler = None
# 6. build EMA
if training_configs['ema_decay'] > 0.0:
ema = ExponentialMovingAverage(named_params=nmt_model.named_parameters(), decay=training_configs['ema_decay'])
else:
ema = None
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# Reload from latest checkpoint
if FLAGS.reload:
checkpoint_saver.load_latest(model=nmt_model, optim=optim, lr_scheduler=scheduler,
collections=model_collections)
# ================================================================================== #
# Prepare training
eidx = model_collections.get_collection("eidx", [0])[-1]
uidx = model_collections.get_collection("uidx", [0])[-1]
bad_count = model_collections.get_collection("bad_count", [0])[-1]
summary_writer = SummaryWriter(log_dir=FLAGS.log_path)
cum_samples = 0
cum_words = 0
best_valid_loss = 1.0 * 1e10 # Max Float
saving_files = []
# Timer for computing speed
timer_for_speed = Timer()
timer_for_speed.tic()
INFO('Begin training...')
while True:
summary_writer.add_scalar("Epoch", (eidx + 1), uidx)
# Build iterator and progress bar
training_iter = training_iterator.build_generator()
training_progress_bar = tqdm(desc=' - (Epoch %d) ' % eidx,
total=len(training_iterator),
unit="sents"
)
for batch in training_iter:
uidx += 1
if scheduler is None:
pass
elif optimizer_configs["schedule_method"] == "loss":
scheduler.step(metric=best_valid_loss)
else:
scheduler.step(global_step=uidx)
seqs_x, seqs_y = batch
n_samples_t = len(seqs_x)
n_words_t = sum(len(s) for s in seqs_y)
cum_samples += n_samples_t
cum_words += n_words_t
training_progress_bar.update(n_samples_t)
optim.zero_grad()
# Prepare data
for seqs_x_t, seqs_y_t in split_shard(seqs_x, seqs_y, split_size=training_configs['update_cycle']):
x, y = prepare_data(seqs_x_t, seqs_y_t, cuda=GlobalNames.USE_GPU)
loss = compute_forward(model=nmt_model,
critic=critic,
seqs_x=x,
seqs_y=y,
eval=False,
normalization=n_samples_t,
norm_by_words=training_configs["norm_by_words"])
optim.step()
if ema is not None:
ema.step()
# ================================================================================== #
# Display some information
if should_trigger_by_steps(uidx, eidx, every_n_step=training_configs['disp_freq']):
# words per second and sents per second
words_per_sec = cum_words / (timer.toc(return_seconds=True))
sents_per_sec = cum_samples / (timer.toc(return_seconds=True))
lrate = list(optim.get_lrate())[0]
summary_writer.add_scalar("Speed(words/sec)", scalar_value=words_per_sec, global_step=uidx)
summary_writer.add_scalar("Speed(sents/sen)", scalar_value=sents_per_sec, global_step=uidx)
summary_writer.add_scalar("lrate", scalar_value=lrate, global_step=uidx)
# Reset timer
timer.tic()
cum_words = 0
cum_samples = 0
# ================================================================================== #
# Saving checkpoints
if should_trigger_by_steps(uidx, eidx, every_n_step=training_configs['save_freq'], debug=FLAGS.debug):
model_collections.add_to_collection("uidx", uidx)
model_collections.add_to_collection("eidx", eidx)
model_collections.add_to_collection("bad_count", bad_count)
if not is_early_stop:
checkpoint_saver.save(global_step=uidx, model=nmt_model, optim=optim, lr_scheduler=scheduler,
collections=model_collections, ema=ema)
# ================================================================================== #
# Loss Validation & Learning rate annealing
if should_trigger_by_steps(global_step=uidx, n_epoch=eidx, every_n_step=training_configs['loss_valid_freq'],
debug=FLAGS.debug):
if ema is not None:
origin_state_dict = deepcopy(nmt_model.state_dict())
nmt_model.load_state_dict(ema.state_dict(), strict=False)
valid_loss = loss_validation(model=nmt_model,
critic=critic,
valid_iterator=valid_iterator,
)
model_collections.add_to_collection("history_losses", valid_loss)
min_history_loss = np.array(model_collections.get_collection("history_losses")).min()
summary_writer.add_scalar("loss", valid_loss, global_step=uidx)
summary_writer.add_scalar("best_loss", min_history_loss, global_step=uidx)
best_valid_loss = min_history_loss
if ema is not None:
nmt_model.load_state_dict(origin_state_dict)
del origin_state_dict
# ================================================================================== #
# BLEU Validation & Early Stop
if should_trigger_by_steps(global_step=uidx, n_epoch=eidx,
every_n_step=training_configs['bleu_valid_freq'],
min_step=training_configs['bleu_valid_warmup'],
debug=FLAGS.debug):
if ema is not None:
origin_state_dict = deepcopy(nmt_model.state_dict())
nmt_model.load_state_dict(ema.state_dict(), strict=False)
valid_bleu = bleu_validation(uidx=uidx,
valid_iterator=valid_iterator,
batch_size=training_configs["bleu_valid_batch_size"],
model=nmt_model,
bleu_scorer=bleu_scorer,
vocab_tgt=vocab_tgt,
valid_dir=FLAGS.valid_path,
max_steps=training_configs["bleu_valid_configs"]["max_steps"],
beam_size=training_configs["bleu_valid_configs"]["beam_size"],
alpha=training_configs["bleu_valid_configs"]["alpha"]
)
model_collections.add_to_collection(key="history_bleus", value=valid_bleu)
best_valid_bleu = float(np.array(model_collections.get_collection("history_bleus")).max())
summary_writer.add_scalar("bleu", valid_bleu, uidx)
summary_writer.add_scalar("best_bleu", best_valid_bleu, uidx)
# If model get new best valid bleu score
if valid_bleu >= best_valid_bleu:
bad_count = 0
if is_early_stop is False:
# 1. save the best model
torch.save(nmt_model.state_dict(), best_model_prefix + ".final")
# 2. record all several best models
best_model_saver.save(global_step=uidx, model=nmt_model)
else:
bad_count += 1
# At least one epoch should be traversed
if bad_count >= training_configs['early_stop_patience'] and eidx > 0:
is_early_stop = True
WARN("Early Stop!")
summary_writer.add_scalar("bad_count", bad_count, uidx)
if ema is not None:
nmt_model.load_state_dict(origin_state_dict)
del origin_state_dict
INFO("{0} Loss: {1:.2f} BLEU: {2:.2f} lrate: {3:6f} patience: {4}".format(
uidx, valid_loss, valid_bleu, lrate, bad_count
))
training_progress_bar.close()
eidx += 1
if eidx > training_configs["max_epochs"]:
break
class Translate_Env(object):
"""
wrap translate environment for multiple agents
env needs parallel data to evaluate final bleu improvement
stores the states as [current src embeddings, index], yields rewards at each step
environment yields rewards based on scorer and finally by sentence-level BLEU
:return: translation multiple sentences and return changed bleu
"""
def __init__(self, reinforce_configs,
annunciator_configs,
src_vocab, trg_vocab,
data_iterator,
save_to,
device="cpu",
):
"""
initiate translation environments, needs a Scorer and translator
:param reinforce_configs: attack configures dictionary
:param annunciator_configs: discriminator or scorer configs(provide survive signals)
:param save_to: path to save the model
:param data_iterator: use to provide data for environment initiate
the directory of the src sentences
:param device: (string) devices to allocate variables("cpu", "cuda:*")
default as cpu
"""
# environment devices
self.device = device
self.data_iterator = data_iterator
scorer_model_configs = annunciator_configs["scorer_model_configs"]
# discriminator_model_configs = annunciator_configs["discriminator_model_configs"]
annunciator_optim_configs = annunciator_configs["annunciator_optimizer_configs"]
victim_config_path = reinforce_configs["victim_configs"]
victim_model_path = reinforce_configs["victim_model"]
with open(victim_config_path.strip()) as v_f:
INFO("env open victim configs at %s" % victim_config_path)
victim_configs = yaml.load(v_f, Loader=yaml.FullLoader)
# to extract the embedding as representation
# *vocab and *emb will provide psudo-reinforced embedding to train annunciator
self.src_vocab = src_vocab
self.trg_vocab = trg_vocab
# translation model for BLEU(take src_embs as inputs) and corresponding embedding layers
self.src_emb, self.trg_emb, self.translate_model = build_translate_model(
victim_configs, victim_model_path,
vocab_src=self.src_vocab, vocab_trg=self.trg_vocab,
device=self.device)
self.max_roll_out_step = victim_configs["data_configs"]["max_len"][0]
self.src_emb.eval() # source language embeddings
self.trg_emb.eval() # target language embeddings
self.translate_model.eval()
# the epsilon range used for action space when perturbation
_, _, self.limit_dist = load_or_extract_near_vocab(
config_path=victim_config_path, model_path=victim_model_path,
init_perturb_rate=reinforce_configs["init_perturb_rate"],
save_to=os.path.join(save_to, "near_vocab"),
save_to_full=os.path.join(save_to, "full_near_vocab"),
top_reserve=12, emit_as_id=True)
#########################################################
# scorer(an Annunciator object) provides intrinsic step rewards
self.annunciator = TransScorer(
victim_configs, victim_model_path, self.trg_emb,
**scorer_model_configs)
self.annunciator.to(self.device)
# # discriminator(an Annunciator object) provides intrisic step rewards and terminal signal
# self.discriminator = TransDiscriminator(
# victim_configs, victim_model_path,
# **discriminator_model_configs)
# self.discriminator.to(self.device)
# Annunciator update configs
self.acc_bound = annunciator_configs["acc_bound"]
self.mse_bound = annunciator_configs["mse_bound"]
self.min_update_steps = annunciator_configs["valid_freq"]
self.max_update_steps = annunciator_configs["annunciator_update_steps"]
# the optimizer and schedule used for Annunciator update.
self.optim_A = Optimizer(
name=annunciator_optim_configs["optimizer"],
model=self.annunciator,
lr=annunciator_optim_configs["learning_rate"],
grad_clip=annunciator_optim_configs["grad_clip"],
optim_args=annunciator_optim_configs["optimizer_params"])
self.scheduler_A = None # default as None
if annunciator_optim_configs['schedule_method'] is not None:
if annunciator_optim_configs['schedule_method'] == "loss":
self.scheduler_A = ReduceOnPlateauScheduler(optimizer=self.optim_A,
**annunciator_optim_configs["scheduler_configs"])
elif annunciator_optim_configs['schedule_method'] == "noam":
self.scheduler_A = NoamScheduler(optimizer=self.optim_A,
**annunciator_optim_configs['scheduler_configs'])
elif annunciator_optim_configs["schedule_method"] == "rsqrt":
self.scheduler_A = RsqrtScheduler(optimizer=self.optim_A,
**annunciator_optim_configs["scheduler_configs"])
else:
WARN("Unknown scheduler name {0}. Do not use lr_scheduling.".format(
annunciator_optim_configs['schedule_method']))
self.criterion_A = nn.CrossEntropyLoss()
############################################################
self.adversarial = reinforce_configs["adversarial"] # adversarial or reinforce as learning objects
self.r_s_weight = reinforce_configs["r_s_weight"]
self.r_i_weight = reinforce_configs["r_i_weight"]
def _init_state(self, rephraser=None):
"""
initiate batched sentences / origin_bleu / index (start from first label, no BOS/EOS)
the initial state of the environment. (applied on the env's device)
:return: env states (the src, index)
"""
self.index = 1 # step index for perturbation
self.origin_bleu = [] # saving origin BLEU
batch = next(self.data_iterator)
assert len(batch) == 3, "must be provided with line index (check for data_iterator)"
# training, parallel trg is provided for evaluation (src grouped by similar length)
_, seqs_x, self.seqs_y = batch
self.sent_len = [len(x) for x in seqs_x]
self.survival_signals = np.array([1] * len(seqs_x)) # the survival signals, 1 when true.
# for reinforce inputs(embedding level).
padded_src, padded_trg = self.prepare_data(
seqs_x=seqs_x, seqs_y=self.seqs_y)
self.x_emb = self.src_emb(padded_src).detach() # float
self.y_emb = self.trg_emb(padded_trg).detach()
self.x_pad_indicator = padded_src.detach().eq(PAD) # byte indicating PAD tokens
self.y_pad_indicator = padded_trg.detach().eq(PAD)
# randomly choose half of the sequence and perturbed by given agent
# for self learning (rephraser can be on the cpu())
if rephraser is not None:
# self.x_emb, mask_to_UNK = rephraser.random_seq_perturb(
# self.x_emb, self.x_pad_indicator,
# half_mode=True, rand_act=False, enable_UNK=False)
# self.x_emb = self.x_emb.to(self.device)
# mask_to_UNK = mask_to_UNK.to(self.device)
# # print("x_emb shape:", self.x_emb.shape, "mask_to_UNK shape:", mask_to_UNK.shape)
# self.x_emb = self.x_emb*(1.-mask_to_UNK.float().unsqueeze(dim=2)) + \
# self.src_emb((UNK * mask_to_UNK).long())
# self.x_emb = self.x_emb.detach()
_, self.x_emb, _ = rephraser.random_seq_perturb(
self.x_emb, self.x_pad_indicator, half_mode=True,
rand_act=False)
self.x_emb = self.x_emb.detach()
# print(self.x_mask.shape, self.x_emb.shape)
self.origin_result = self.translate()
# calculate BLEU scores for the top candidate
for index, sent_t in enumerate(self.seqs_y):
bleu_t = bleu.sentence_bleu(references=[sent_t],
hypothesis=self.origin_result[index],
emulate_multibleu=True)
self.origin_bleu.append(bleu_t)
INFO("initialize env on: %s"%self.x_emb.device)
return self.x_emb.cpu().numpy()
def get_src_vocab(self):
return self.src_vocab
def reset(self, rephraser=None):
"""
when the steps are exhausted.
:param rephraser: rephraser is default None for no self-improving learning
:return: reset environments' embedding
"""
return self._init_state(rephraser)
def reset_data_iter(self, data_iter): # reset data iterator with provided iterator
self.data_iterator = data_iter
return
def reset_annunciator(self):
# a backup, would be deprecated
self.annunciator.reset()
def prepare_A_data(self, agent,
seqs_x, seqs_y,
batch_first=True,
half_mode=True,
rand_act=True):
"""
use the current rephraser to generate data for Annunciator training
perturbation will be applied to a random sequence step.
(perturb all the former steps as the origin_emb, and perturb one more step as
the perturbed_emb)
such process will rephrase the entire batch.
:param agent: prepare the data for scorer training (actor and critic)
:param seqs_x: list of sources
:param seqs_y: list of targets
:param batch_first: first dimension of seqs be batch
:param rand_act: sample the actions based on rephraser outputs
:return: origin_x_emb, perturbed_x_emb, y_emb, x_mask, y_mask, flags
"""
def _np_pad_batch_2D(samples, pad, batch_first=True):
# pack seqs into tensor with pads
batch_size = len(samples)
sizes = [len(s) for s in samples]
max_size = max(sizes)
x_np = np.full((batch_size, max_size), fill_value=pad, dtype='int64')
for ii in range(batch_size):
x_np[ii, :sizes[ii]] = samples[ii]
if batch_first is False:
x_np = np.transpose(x_np, [1, 0])
x = torch.tensor(x_np).to(self.device)
return x
seqs_x = list(map(lambda s: [BOS] + s + [EOS], seqs_x))
x = _np_pad_batch_2D(samples=seqs_x, pad=PAD,
batch_first=batch_first)
x_emb = self.src_emb(x).detach().to(self.device)
x_pad_indicator = x.detach().eq(PAD).to(self.device)
# # mere actor rollout
# origin_x_emb, perturbed_x_emb, flags = rephraser.random_seq_perturb(
# x_emb, x_pad_indicator,
# half_mode=True, rand_act=rand_act)
# actor rollout w/ critic's restriction
with torch.no_grad():
agent.actor.eval()
agent.critic.eval()
batch_size, max_seq_len = x_pad_indicator.shape
perturbed_x_emb = x_emb.detach().clone()
x_mask = 1 - x_pad_indicator.int()
for t in range(1, max_seq_len-1):
former_emb = perturbed_x_emb
input_emb = former_emb[:, t-1:t+2, :]
if rand_act:
actions, _ = agent.actor.sample_normal(
x_emb=former_emb, x_pad_indicator=x_pad_indicator,
label_emb=input_emb, reparamization=False)
else:
mu, _ = agent.actor.forward(
x_emb=former_emb, x_pad_indicator=x_pad_indicator,
label_emb=input_emb)
actions = mu * agent.actor.action_range
# actions shape [batch, emb_dim]
critique = agent.critic(
x_emb=former_emb, x_pad_indicator=x_pad_indicator,
label_emb=input_emb, action=actions)
# actions_masks shape [batch, 1]
actions_mask = critique.gt(0).int() * x_mask[:, t].unsqueeze(dim=1)
# mask unnecessary actions
perturbed_x_emb[:,t,:] += actions * actions_mask
flags = x_emb.new_ones(batch_size)
if half_mode:
flags = torch.bernoulli(0.5 * flags).to(x_emb.device)
perturbed_x_emb = perturbed_x_emb * flags.unsqueeze(dim=1). unsqueeze(dim=2) \
+ x_emb * (1-flags).unsqueeze(dim=1).unsqueeze(dim=2)
origin_x_emb = x_emb
seqs_y = list(map(lambda s: [BOS] + s + [EOS], seqs_y))
y = _np_pad_batch_2D(seqs_y, pad=PAD,
batch_first=batch_first)
y_emb = self.trg_emb(y).detach().to(self.device)
y_pad_indicator = y.detach().eq(PAD).to(self.device)
perturbed_x_emb.detach().to(self.device)
return origin_x_emb, perturbed_x_emb, y_emb, x_pad_indicator, y_pad_indicator, flags.long()
def prepare_data(self, seqs_x, seqs_y=None, batch_first=True):
"""
prepare the batched, padded data with BOS and EOS for translation.
used in initialization.
Returns: padded data matrices (batch_size, max_seq_len)
"""
def _np_pad_batch_2D(samples, pad, batch_first=True):
batch_size = len(samples)
sizes = [len(s) for s in samples]
max_size = max(sizes)
x_np = np.full((batch_size, max_size), fill_value=pad, dtype='int64')
for ii in range(batch_size):
x_np[ii, :sizes[ii]] = samples[ii]
if batch_first is False:
x_np = np.transpose(x_np, [1, 0])
x = torch.tensor(x_np).to(self.device)
return x
seqs_x = list(map(lambda s: [BOS] + s + [EOS], seqs_x))
x = _np_pad_batch_2D(samples=seqs_x, pad=PAD,
batch_first=batch_first)
if seqs_y is None:
return x
seqs_y = list(map(lambda s: [BOS] + s + [EOS], seqs_y))
y = _np_pad_batch_2D(seqs_y, pad=PAD,
batch_first=batch_first)
return x, y
def ratio_validation(self, agent, overall_contrast=True):
"""
validate the mse of the environments scorer for the given rephraser
used for checkpoints and other checks
:param rephraser generates the data for validation.
:return: the mse of the current scorer in environment.
"""
# set victim encoder and scorer to evaluation mode
self.annunciator.eval()
# for i in range(5):
try:
batch = next(self.data_iterator)
except StopIteration:
batch = next(self.data_iterator)
seq_nums, seqs_x, seqs_y = batch
origin_x_emb, perturbed_x_emb, y_emb, x_mask, y_mask, flags = self.prepare_A_data(
agent, seqs_x, seqs_y, half_mode=False, rand_act=False)
origin_density_score = self.annunciator.get_density_score(
origin_x_emb, x_mask, seqs_y)
perturbed_density_score = self.annunciator.get_density_score(
perturbed_x_emb, x_mask, seqs_y)
density_score = origin_density_score/(origin_density_score+perturbed_density_score)
if overall_contrast:
return density_score.mean().item()
else:
return perturbed_density_score.mean().item()
def acc_validation(self, agent):
"""
validate the acc of the environments discriminator by a given rephraser
used for checkpoints
:param agent generates data for validation
:return the accuracy of the discriminator to evaluation mode
"""
self.annunciator.eval()
acc = 0
sample_count = 0
for i in range(5):
try:
batch = next(self.data_iterator)
except StopIteration:
batch = next(self.data_iterator)
seq_nums, seqs_x, seqs_y = batch
origin_x_emb, perturbed_x_emb, y_emb, x_pad_indicator, y_pad_indicator, flags = \
self.prepare_A_data(agent, seqs_x, seqs_y, half_mode=True)
with torch.no_grad():
preds = self.annunciator(perturbed_x_emb, x_pad_indicator,
y_emb, y_pad_indicator).argmax(dim=-1)
acc += torch.eq(preds, flags).sum()
sample_count += preds.shape[0]
acc = acc.float() / sample_count
return acc.item()
# def compute_P_forward(self,
# origin_x_emb, perturbed_x_emb, x_mask,
# evaluate=False):
# """
# process the victim encoder embedding and get CE loss
# :param origin_x_emb: float tensor, input embeddings of input tokens
# :param perturbed_x_emb: float tensor, perturbed inputs embeddings
# :param x_mask: byte tensor, mask of the input tokens
# :return: loss value
# """
# if not evaluate:
# # set components to training mode(dropout layers)
# self.scorer.train()
# with torch.enable_grad():
# loss = self.scorer(origin_x_emb, perturbed_x_emb, x_mask).mean()
# torch.autograd.backward(loss)
# return loss.item()
# else:
# # set components to evaluation mode(dropout layers)
# self.scorer.eval()
# with torch.enable_grad():
# loss = self.scorer(origin_x_emb, perturbed_x_emb, x_mask).mean()
# return loss.item()
def compute_A_forward(self, x_emb, y_emb, x_pad_indicator, y_pad_indicator, gold_flags,
evaluate=False):
"""get loss according to criterion
:param gold_flags=1 if perturbed, otherwise 0
:param evaluate: False during training mode
:return loss value
"""
if not evaluate:
# set components to training mode(dropout layers)
self.annunciator.train()
self.criterion_A.train()
with torch.enable_grad():
class_probs = self.annunciator(
x_emb, x_pad_indicator,
y_emb, y_pad_indicator)
loss = self.criterion_A(class_probs, gold_flags)
torch.autograd.backward(loss)
return loss.item()
else:
# set components to evaluation mode(dropout layers)
self.annunciator.eval()
self.criterion_A.eval()
with torch.no_grad():
class_probs = self.annunciator(
x_emb, x_pad_indicator,
y_emb, y_pad_indicator)
loss = self.criterion_A(class_probs, gold_flags)
return loss.item()
def update_annunciator(self,
agent,
base_steps=0,
min_update_steps=1,
max_update_steps=300,
accuracy_bound=0.8,
overall_update_weight=0.5,
summary_writer=None):
"""
update discriminator using given rephraser
:param agent: AC agent to generate training data for discriminator
:param base_steps: used for saving
:param min_update_steps: (integer) minimum update steps,
also the discriminator evaluate steps
:param max_update_steps: (integer) maximum update steps
:param accuracy_bound: (float) update until accuracy reaches the bound
(or max_update_steps)
:param summary_writer: used to log discriminator learning information
:return: steps and test accuracy as trust region
"""
INFO("update annunciator")
self.optim_A.zero_grad()
agent.to(self.device)
step = 0
while True:
try:
batch = next(self.data_iterator)
except StopIteration:
batch = next(self.data_iterator)
# update the discriminator
step += 1
if self.scheduler_A is not None:
# override learning rate in self.optim_D
self.scheduler_A.step(global_step=step)
_, seqs_x, seqs_y = batch # returned tensor type of the data
try:
x_emb, perturbed_x_emb, y_emb, x_pad_indicator, y_pad_indicator, flags = \
self.prepare_A_data(agent, seqs_x, seqs_y, half_mode=False, rand_act=True)
loss = self.annunciator(x_emb, perturbed_x_emb, x_pad_indicator, seqs_y, overall_update_weight)
# for name, p in self.annunciator.named_parameters():
# if "weight" in name:
# loss += torch.norm(p, 2) # with l2-norm against overfitting
torch.autograd.backward(loss)
self.optim_A.step()
print("annunciator loss:", loss)
except RuntimeError as e:
if "out of memory" in str(e):
print("WARNING: out of memory, skipping batch")
self.optim_A.zero_grad()
else:
raise e
# valid for accuracy / check for break (if any)
if step % min_update_steps == 0:
perturbed_density = self.ratio_validation(agent, overall_contrast=False)
overall_density = self.ratio_validation(agent)
if summary_writer is not None:
summary_writer.add_scalar("a_contrast_ratio", scalar_value=overall_density, global_step=base_steps+step)
summary_writer.add_scalar("a_ratio_src", scalar_value=perturbed_density, global_step=base_steps+step)
print("overall density: %2f" % overall_density)
if accuracy_bound and overall_density > accuracy_bound:
INFO("annunciator reached training bound, updated")
return base_steps+step, overall_density
if step > max_update_steps:
overall_density = self.ratio_validation(agent)
perturbed_density = self.ratio_validation(agent, overall_contrast=False)
print("overall density: %2f" % overall_density)
INFO("Reach maximum annunciator update. Finished.")
return base_steps+step, overall_density # stop updates
def translate(self, x_emb=None, x_mask=None):
"""
translate by given embedding
:param src_emb: if None, translate embeddings stored in the environments
:param src_mask: input mask paired with embedding
:return: list of translation results
"""
if x_emb is None: # original translation with original embedding
x_emb = self.x_emb
x_mask = self.x_pad_indicator
with torch.no_grad():
perturbed_results = beam_search(
self.translate_model,
beam_size=5, max_steps=150,
src_embs=x_emb, src_mask=x_mask,
alpha=-1.0)
perturbed_results = perturbed_results.cpu().numpy().tolist()
# only use the top result from the result
result = []
for sent in perturbed_results:
sent = [wid for wid in sent[0] if wid != PAD]
result.append(sent)
return result
def get_state(self):
"""
retrieve states for the learning
:return: the states of the environment
"""
states = self.x_emb # current sen embeddings, [batch_size, len, emb_dim]
masks = 1. - self.x_pad_indicator.float() # indicates valid tokens [batch, max_len]
rephrase_positions = torch.tensor(np.array([self.index] * masks.shape[0])).unsqueeze(dim=-1).long() # current state positions [batch, 1]
survival_signals = torch.tensor(self.survival_signals).unsqueeze(dim=-1).float() # [batch_size, 1]
return states, masks, rephrase_positions, survival_signals
def step(self, action):
"""
step update for the environment: finally update self.index
this is defined as inference of the environments
states are returned in np.array
:param action: tensor.variable as action input(in shape [batch, dim])
on current index for step updates
:return: updated states/ rewards/ terminal signal from the environments
reward (list of float), terminal_signal (list of boolean)
"""
with torch.no_grad():
self.annunciator.eval()
batch_size, _ = action.shape
batched_rewards = [0.] * batch_size
if self.device != "cpu" and not action.is_cuda:
WARN("mismatching action for gpu_env, move actions to %s"%self.device)
action = action.to(self.device)
# extract the step mask for actions and rewards
inputs_mask = 1. - self.x_pad_indicator.float()
inputs_mask = inputs_mask[:, self.index] # slice at current step(index), mask of [batch]
inputs_mask *= inputs_mask.new_tensor(self.survival_signals) # mask those terminated
# update current src embedding with action
origin_emb = self.x_emb.clone().detach()
# update embedding; cancel modification on PAD
self.x_emb[:, self.index, :] += (action * inputs_mask.unsqueeze(dim=1)) # actions on PAD is masked
# update survival_signals, which later determines whether rewards are valid for return
# 1. mask survival by step and sent-len
step_reward_mask = [int(self.index <= i) for i in self.sent_len]
# 2. get batched sentence matching for survival signals on the current src state
# # as the reward process (probs on ``survival'' as rewards)
d_probs = self.annunciator.get_density_score(self.x_emb, self.x_pad_indicator, self.seqs_y)
# print("dprobs:",d_probs)
signals = d_probs.detach().lt(0.5).long().cpu().numpy().tolist() # 1 as terminate
# print("signals:", signals)
if 1 in step_reward_mask: # rollout reaches the sents length
# 0 as survive, 1 as terminate
probs = d_probs.detach().cpu().numpy()
discriminate_index = d_probs.detach().lt(0.5).long()
survival_mask = (1 - discriminate_index).cpu().numpy()
survival_value = probs * survival_mask
terminate_punishment = probs * discriminate_index.cpu().numpy()
# looping for survival signals and step rewards
origin_survival_signals = self.survival_signals.copy()
for i in range(batch_size):
# update survivals signals
self.survival_signals[i] = self.survival_signals[i] * (1-signals[i]) * step_reward_mask[i]
if self.survival_signals[i]:
batched_rewards[i] += survival_value[i] * self.r_s_weight
elif origin_survival_signals[i]:
# punish once the survival signal flips
batched_rewards[i] -= terminate_punishment[i] * self.r_i_weight
else: # all dead, no need to calculate other rewards, it's ok to waste some samples
return self.x_emb.cpu().numpy(), np.array(batched_rewards), self.survival_signals
# additional episodic reward for surviving sequences (w/ finished sentence at current step)
bleu_mask = [int(self.index == i) for i in self.sent_len]
bleu_mask = [bleu_mask[i]*self.survival_signals[i] for i in range(batch_size)]
if 1 in bleu_mask:
# check for the finished line and mask out the others
perturbed_results = self.translate(self.x_emb, self.x_pad_indicator)
episodic_rewards = []
for i, sent in enumerate(self.seqs_y):
if bleu_mask[i] == 1:
degraded_value = (self.origin_bleu[i]-bleu.sentence_bleu(
references=[sent],
hypothesis=perturbed_results[i],
emulate_multibleu=True
))
if self.adversarial: # relative degradation
if self.origin_bleu[i] == 0:
relative_degraded_bleu = 0
else:
relative_degraded_bleu = degraded_value/self.origin_bleu[i]
episodic_rewards.append(relative_degraded_bleu)
else: # absolute improvement
print("bleu variation:", self.origin_bleu[i],-degraded_value)
episodic_rewards.append(-degraded_value)
else:
episodic_rewards.append(0.0)
# append additional episodic rewards
batched_rewards = [batched_rewards[i]+episodic_rewards[i]*self.r_i_weight
for i in range(batch_size)]
# update sequences' pointer for rephrasing
self.index += 1
return self.x_emb.cpu().numpy(), np.array(batched_rewards), self.survival_signals
def train(flags):
"""
flags:
saveto: str
reload: store_true
config_path: str
pretrain_path: str, default=""
model_name: str
log_path: str
"""
# ================================================================================== #
# Initialization for training on different devices
# - CPU/GPU
# - Single/Distributed
Constants.USE_GPU = flags.use_gpu
if flags.multi_gpu:
dist.distributed_init(flags.shared_dir)
world_size = dist.get_world_size()
rank = dist.get_rank()
local_rank = dist.get_local_rank()
else:
world_size = 1
rank = 0
local_rank = 0
if Constants.USE_GPU:
torch.cuda.set_device(local_rank)
Constants.CURRENT_DEVICE = "cuda:{0}".format(local_rank)
else:
Constants.CURRENT_DEVICE = "cpu"
# If not root_rank, close logging
# else write log of training to file.
if rank == 0:
write_log_to_file(
os.path.join(flags.log_path,
"%s.log" % time.strftime("%Y%m%d-%H%M%S")))
else:
close_logging()
# ================================================================================== #
# Parsing configuration files
# - Load default settings
# - Load pre-defined settings
# - Load user-defined settings
configs = prepare_configs(flags.config_path, flags.predefined_config)
data_configs = configs['data_configs']
model_configs = configs['model_configs']
optimizer_configs = configs['optimizer_configs']
training_configs = configs['training_configs']
INFO(pretty_configs(configs))
# use odc
if training_configs['use_odc'] is True:
ave_best_k = check_odc_config(training_configs)
else:
ave_best_k = 0
Constants.SEED = training_configs['seed']
set_seed(Constants.SEED)
timer = Timer()
# ================================================================================== #
# Load Data
INFO('Loading data...')
timer.tic()
# Generate target dictionary
vocab_src = Vocabulary.build_from_file(**data_configs['vocabularies'][0])
vocab_tgt = Vocabulary.build_from_file(**data_configs['vocabularies'][1])
Constants.EOS = vocab_src.eos
Constants.PAD = vocab_src.pad
Constants.BOS = vocab_src.bos
train_bitext_dataset = ZipDataset(
TextLineDataset(data_path=data_configs['train_data'][0],
vocabulary=vocab_src,
max_len=data_configs['max_len'][0],
is_train_dataset=True),
TextLineDataset(data_path=data_configs['train_data'][1],
vocabulary=vocab_tgt,
max_len=data_configs['max_len'][1],
is_train_dataset=True))
valid_bitext_dataset = ZipDataset(
TextLineDataset(
data_path=data_configs['valid_data'][0],
vocabulary=vocab_src,
is_train_dataset=False,
),
TextLineDataset(data_path=data_configs['valid_data'][1],
vocabulary=vocab_tgt,
is_train_dataset=False))
training_iterator = DataIterator(
dataset=train_bitext_dataset,
batch_size=training_configs["batch_size"],
use_bucket=training_configs['use_bucket'],
buffer_size=training_configs['buffer_size'],
batching_func=training_configs['batching_key'],
world_size=world_size,
rank=rank)
valid_iterator = DataIterator(
dataset=valid_bitext_dataset,
batch_size=training_configs['valid_batch_size'],
use_bucket=True,
buffer_size=100000,
numbering=True,
world_size=world_size,
rank=rank)
bleu_scorer = SacreBLEUScorer(
reference_path=data_configs["bleu_valid_reference"],
num_refs=data_configs["num_refs"],
lang_pair=data_configs["lang_pair"],
sacrebleu_args=training_configs["bleu_valid_configs"]
['sacrebleu_args'],
postprocess=training_configs["bleu_valid_configs"]['postprocess'])
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# ================================ Begin ======================================== #
# Build Model & Optimizer
# We would do steps below on after another
# 1. build models & criterion
# 2. move models & criterion to gpu if needed
# 3. load pre-trained model if needed
# 4. build optimizer
# 5. build learning rate scheduler if needed
# 6. load checkpoints if needed
# 0. Initial
lrate = optimizer_configs['learning_rate']
model_collections = Collections()
checkpoint_saver = Saver(
save_prefix="{0}.ckpt".format(
os.path.join(flags.saveto, flags.model_name)),
num_max_keeping=training_configs['num_kept_checkpoints'])
best_model_prefix = os.path.join(
flags.saveto, flags.model_name + Constants.MY_BEST_MODEL_SUFFIX)
best_k_saver = BestKSaver(
save_prefix="{0}.best_k_ckpt".format(
os.path.join(flags.saveto, flags.model_name)),
num_max_keeping=training_configs['num_kept_best_k_checkpoints'])
# 1. Build Model & Criterion
INFO('Building model...')
timer.tic()
nmt_model = build_model(n_src_vocab=vocab_src.max_n_words,
n_tgt_vocab=vocab_tgt.max_n_words,
padding_idx=vocab_src.pad,
vocab_src=vocab_src,
**model_configs)
INFO(nmt_model)
# build teacher model
teacher_model, teacher_model_path = get_teacher_model(
training_configs, model_configs, vocab_src, vocab_tgt, flags)
# build critic
critic = CombinationCriterion(model_configs['loss_configs'],
padding_idx=vocab_tgt.pad,
teacher=teacher_model)
# INFO(critic)
critic.INFO()
# 2. Move to GPU
if Constants.USE_GPU:
nmt_model = nmt_model.cuda()
critic = critic.cuda()
# 3. Load pretrained model if needed
load_pretrained_model(nmt_model,
flags.pretrain_path,
exclude_prefix=None,
device=Constants.CURRENT_DEVICE)
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# 4. Build optimizer
INFO('Building Optimizer...')
if not flags.multi_gpu:
optim = Optimizer(name=optimizer_configs['optimizer'],
model=nmt_model,
lr=lrate,
grad_clip=optimizer_configs['grad_clip'],
optim_args=optimizer_configs['optimizer_params'],
update_cycle=training_configs['update_cycle'])
else:
optim = dist.DistributedOptimizer(
name=optimizer_configs['optimizer'],
model=nmt_model,
lr=lrate,
grad_clip=optimizer_configs['grad_clip'],
optim_args=optimizer_configs['optimizer_params'],
device_id=local_rank)
# 5. Build scheduler for optimizer if needed
scheduler = build_scheduler(
schedule_method=optimizer_configs['schedule_method'],
optimizer=optim,
scheduler_configs=optimizer_configs['scheduler_configs'])
# 6. build moving average
ma = build_ma(training_configs, nmt_model.named_parameters())
INFO('Done. Elapsed time {0}'.format(timer.toc()))
# Reload from latest checkpoint
if flags.reload:
checkpoint_saver.load_latest(model=nmt_model,
optim=optim,
lr_scheduler=scheduler,
collections=model_collections,
ma=ma,
device=Constants.CURRENT_DEVICE)
# broadcast parameters and optimizer states
if world_size > 1:
INFO("Broadcasting model parameters...")
dist.broadcast_parameters(params=nmt_model.state_dict())
INFO("Broadcasting optimizer states...")
dist.broadcast_optimizer_state(optimizer=optim.optim)
INFO('Done.')
# ================================================================================== #
# Prepare training
eidx = model_collections.get_collection("eidx", [0])[-1]
uidx = model_collections.get_collection("uidx", [1])[-1]
bad_count = model_collections.get_collection("bad_count", [0])[-1]
oom_count = model_collections.get_collection("oom_count", [0])[-1]
is_early_stop = model_collections.get_collection("is_early_stop", [
False,
])[-1]
teacher_patience = model_collections.get_collection(
"teacher_patience", [training_configs['teacher_patience']])[-1]
train_loss_meter = AverageMeter()
train_loss_dict_meter = AverageMeterDict(critic.get_critic_name())
sent_per_sec_meter = TimeMeter()
tok_per_sec_meter = TimeMeter()
update_cycle = training_configs['update_cycle']
grad_denom = 0
train_loss = 0.0
cum_n_words = 0
train_loss_dict = dict()
valid_loss = best_valid_loss = float('inf')
if rank == 0:
summary_writer = SummaryWriter(log_dir=flags.log_path)
else:
summary_writer = None
sent_per_sec_meter.start()
tok_per_sec_meter.start()
INFO('Begin training...')
while True:
if summary_writer is not None:
summary_writer.add_scalar("Epoch", (eidx + 1), uidx)
# Build iterator and progress bar
training_iter = training_iterator.build_generator()
if rank == 0:
training_progress_bar = tqdm(desc=' - (Epc {}, Upd {}) '.format(
eidx, uidx),
total=len(training_iterator),
unit="sents")
else:
training_progress_bar = None
for batch in training_iter:
seqs_x, seqs_y = batch
batch_size = len(seqs_x)
cum_n_words += sum(len(s) for s in seqs_y)
try:
# Prepare data
x, y = prepare_data(seqs_x, seqs_y, cuda=Constants.USE_GPU)
loss, loss_dict = compute_forward(
model=nmt_model,
critic=critic,
seqs_x=x,
seqs_y=y,
eval=False,
normalization=1.0,
norm_by_words=training_configs["norm_by_words"])
update_cycle -= 1
grad_denom += batch_size
train_loss += loss
train_loss_dict = add_dict_value(train_loss_dict, loss_dict)
except RuntimeError as e:
if 'out of memory' in str(e):
print('| WARNING: ran out of memory, skipping batch')
oom_count += 1
else:
raise e
# When update_cycle becomes 0, it means end of one batch. Several things will be done:
# - update parameters
# - reset update_cycle and grad_denom, update uidx
# - learning rate scheduling
# - update moving average
if update_cycle == 0:
# 0. reduce variables
if world_size > 1:
grad_denom = dist.all_reduce_py(grad_denom)
train_loss = dist.all_reduce_py(train_loss)
train_loss_dict = dist.all_reduce_py(train_loss_dict)
cum_n_words = dist.all_reduce_py(cum_n_words)
# 1. update parameters
optim.step(denom=grad_denom)
optim.zero_grad()
if training_progress_bar is not None:
training_progress_bar.update(grad_denom)
training_progress_bar.set_description(
' - (Epc {}, Upd {}) '.format(eidx, uidx))
postfix_str = 'TrainLoss: {:.2f}, ValidLoss(best): {:.2f} ({:.2f}), '.format(
train_loss, valid_loss, best_valid_loss)
for critic_name, loss_value in train_loss_dict.items():
postfix_str += (critic_name +
': {:.2f}, ').format(loss_value)
training_progress_bar.set_postfix_str(postfix_str)
# 2. learning rate scheduling
if scheduler is not None and optimizer_configs[
"schedule_method"] != "loss":
scheduler.step(global_step=uidx)
# 3. update moving average
if ma is not None and eidx >= training_configs[
'moving_average_start_epoch']:
ma.step()
# 4. update meters
train_loss_meter.update(train_loss, grad_denom)
train_loss_dict_meter.update(train_loss_dict, grad_denom)
sent_per_sec_meter.update(grad_denom)
tok_per_sec_meter.update(cum_n_words)
# 5. reset accumulated variables, update uidx
update_cycle = training_configs['update_cycle']
grad_denom = 0
uidx += 1
cum_n_words = 0.0
train_loss = 0.0
train_loss_dict = dict()
else:
continue
# ================================================================================== #
# Display some information
if should_trigger_by_steps(
uidx, eidx, every_n_step=training_configs['disp_freq']):
lrate = list(optim.get_lrate())[0]
if summary_writer is not None:
summary_writer.add_scalar(
"Speed(sents/sec)",
scalar_value=sent_per_sec_meter.ave,
global_step=uidx)
summary_writer.add_scalar(
"Speed(words/sec)",
scalar_value=tok_per_sec_meter.ave,
global_step=uidx)
summary_writer.add_scalar(
"train_loss",
scalar_value=train_loss_meter.ave,
global_step=uidx)
# add loss for every critic
if flags.display_loss_detail:
combination_loss = train_loss_dict_meter.value
for key, value in combination_loss.items():
summary_writer.add_scalar(key,
scalar_value=value,
global_step=uidx)
summary_writer.add_scalar("lrate",
scalar_value=lrate,
global_step=uidx)
summary_writer.add_scalar("oom_count",
scalar_value=oom_count,
global_step=uidx)
# Reset Meters
sent_per_sec_meter.reset()
tok_per_sec_meter.reset()
train_loss_meter.reset()
train_loss_dict_meter.reset()
# ================================================================================== #
# Loss Validation & Learning rate annealing
if should_trigger_by_steps(
global_step=uidx,
n_epoch=eidx,
every_n_step=training_configs['loss_valid_freq'],
debug=flags.debug):
with cache_parameters(nmt_model):
valid_loss, valid_loss_dict = loss_evaluation(
model=nmt_model,
critic=critic,
valid_iterator=valid_iterator,
rank=rank,
world_size=world_size)
if scheduler is not None and optimizer_configs[
"schedule_method"] == "loss":
scheduler.step(metric=valid_loss)
model_collections.add_to_collection("history_losses",
valid_loss)
min_history_loss = np.array(
model_collections.get_collection("history_losses")).min()
best_valid_loss = min_history_loss
if summary_writer is not None:
summary_writer.add_scalar("loss",
valid_loss,
global_step=uidx)
summary_writer.add_scalar("best_loss",
min_history_loss,
global_step=uidx)
# ================================================================================== #
# BLEU Validation & Early Stop
if should_trigger_by_steps(
global_step=uidx,
n_epoch=eidx,
every_n_step=training_configs['bleu_valid_freq'],
min_step=training_configs['bleu_valid_warmup'],
debug=flags.debug):
with cache_parameters(nmt_model):
valid_bleu = bleu_evaluation(
uidx=uidx,
valid_iterator=valid_iterator,
batch_size=training_configs["bleu_valid_batch_size"],
model=nmt_model,
bleu_scorer=bleu_scorer,
vocab_src=vocab_src,
vocab_tgt=vocab_tgt,
valid_dir=flags.valid_path,
max_steps=training_configs["bleu_valid_configs"]
["max_steps"],
beam_size=training_configs["bleu_valid_configs"]
["beam_size"],
alpha=training_configs["bleu_valid_configs"]["alpha"],
world_size=world_size,
rank=rank,
)
model_collections.add_to_collection(key="history_bleus",
value=valid_bleu)
best_valid_bleu = float(
np.array(model_collections.get_collection(
"history_bleus")).max())
if summary_writer is not None:
summary_writer.add_scalar("bleu", valid_bleu, uidx)
summary_writer.add_scalar("best_bleu", best_valid_bleu,
uidx)
# If model get new best valid bleu score
if valid_bleu >= best_valid_bleu:
bad_count = 0
if is_early_stop is False:
if rank == 0:
# 1. save the best model
torch.save(nmt_model.state_dict(),
best_model_prefix + ".final")
else:
bad_count += 1
# At least one epoch should be traversed
if bad_count >= training_configs[
'early_stop_patience'] and eidx > 0:
is_early_stop = True
WARN("Early Stop!")
exit(0)
if rank == 0:
best_k_saver.save(global_step=uidx,
metric=valid_bleu,
model=nmt_model,
optim=optim,
lr_scheduler=scheduler,
collections=model_collections,
ma=ma)
# ODC
if training_configs['use_odc'] is True:
if valid_bleu >= best_valid_bleu:
pass
# choose method to generate teachers from checkpoints
# - best
# - ave_k_best
# - ma
if training_configs['teacher_choice'] == 'ma':
teacher_params = ma.export_ma_params()
elif training_configs['teacher_choice'] == 'best':
teacher_params = nmt_model.state_dict()
elif "ave_best" in training_configs['teacher_choice']:
if best_k_saver.num_saved >= ave_best_k:
teacher_params = average_checkpoints(
best_k_saver.get_all_ckpt_path()
[-ave_best_k:])
else:
teacher_params = nmt_model.state_dict()
else:
raise ValueError(
"can not support teacher choice %s" %
training_configs['teacher_choice'])
torch.save(teacher_params, teacher_model_path)
del teacher_params
teacher_patience = 0
critic.set_use_KD(False)
else:
teacher_patience += 1
if teacher_patience >= training_configs[
'teacher_refresh_warmup']:
teacher_params = torch.load(
teacher_model_path,
map_location=Constants.CURRENT_DEVICE)
teacher_model.load_state_dict(teacher_params,
strict=False)
del teacher_params
critic.reset_teacher(teacher_model)
critic.set_use_KD(True)
if summary_writer is not None:
summary_writer.add_scalar("bad_count", bad_count, uidx)
info_str = "{0} Loss: {1:.2f} BLEU: {2:.2f} lrate: {3:6f} patience: {4} ".format(
uidx, valid_loss, valid_bleu, lrate, bad_count)
for key, value in valid_loss_dict.items():
info_str += (key + ': {0:.2f} '.format(value))
INFO(info_str)
# ================================================================================== #
# Saving checkpoints
if should_trigger_by_steps(
uidx,
eidx,
every_n_step=training_configs['save_freq'],
debug=flags.debug):
model_collections.add_to_collection("uidx", uidx)
model_collections.add_to_collection("eidx", eidx)
model_collections.add_to_collection("bad_count", bad_count)
model_collections.add_to_collection("teacher_patience",
teacher_patience)
if not is_early_stop:
if rank == 0:
checkpoint_saver.save(global_step=uidx,
model=nmt_model,
optim=optim,
lr_scheduler=scheduler,
collections=model_collections,
ma=ma)
if training_progress_bar is not None:
training_progress_bar.close()
eidx += 1
if eidx > training_configs["max_epochs"]:
break
class Translate_Env(object):
"""
wrap translate environment for multiple agents
env needs parallel data to evaluate bleu_degredation
state of the env is defined as the batched src labels and current target index
environment yields rewards based on discriminator and finally by sentence-level BLEU
:return: translation multiple sentences and return changed bleu
"""
def __init__(
self,
attack_configs,
discriminator_configs,
src_vocab,
trg_vocab,
data_iterator,
save_to,
device="cpu",
):
"""
initiate translation environments, needs a discriminator and translator
:param attack_configs: attack configures dictionary
:param save_to: discriminator models
:param data_iterator: use to provide data for environment initiate
the directory of the src sentences
:param device: (string) devices to allocate variables("cpu", "cuda:*")
default as cpu
"""
self.data_iterator = data_iterator
discriminator_model_configs = discriminator_configs[
"discriminator_model_configs"]
discriminator_optim_configs = discriminator_configs[
"discriminator_optimizer_configs"]
self.victim_config_path = attack_configs["victim_configs"]
self.victim_model_path = attack_configs["victim_model"]
# determine devices
self.device = device
with open(self.victim_config_path.strip()) as v_f:
print("open victim configs...%s" % self.victim_config_path)
victim_configs = yaml.load(v_f)
self.src_vocab = src_vocab
self.trg_vocab = trg_vocab
self.translate_model = build_translate_model(victim_configs,
self.victim_model_path,
vocab_src=self.src_vocab,
vocab_trg=self.trg_vocab,
device=self.device)
self.translate_model.eval()
self.w2p, self.w2vocab = load_or_extract_near_vocab(
config_path=self.victim_config_path,
model_path=self.victim_model_path,
init_perturb_rate=attack_configs["init_perturb_rate"],
save_to=os.path.join(save_to, "near_vocab"),
save_to_full=os.path.join(save_to, "full_near_vocab"),
top_reserve=12,
emit_as_id=True)
#########################################################
# to update discriminator
# discriminator_data_configs = attack_configs["discriminator_data_configs"]
self.discriminator = TransDiscriminator(
n_src_words=self.src_vocab.max_n_words,
n_trg_words=self.trg_vocab.max_n_words,
**discriminator_model_configs)
self.discriminator.to(self.device)
load_embedding(self.discriminator,
model_path=self.victim_model_path,
device=self.device)
self.optim_D = Optimizer(
name=discriminator_optim_configs["optimizer"],
model=self.discriminator,
lr=discriminator_optim_configs["learning_rate"],
grad_clip=discriminator_optim_configs["grad_clip"],
optim_args=discriminator_optim_configs["optimizer_params"])
self.criterion_D = nn.CrossEntropyLoss(
) # used in discriminator updates
self.scheduler_D = None # default as None
if discriminator_optim_configs['schedule_method'] is not None:
if discriminator_optim_configs['schedule_method'] == "loss":
self.scheduler_D = ReduceOnPlateauScheduler(
optimizer=self.optim_D,
**discriminator_optim_configs["scheduler_configs"])
elif discriminator_optim_configs['schedule_method'] == "noam":
self.scheduler_D = NoamScheduler(
optimizer=self.optim_D,
**discriminator_optim_configs['scheduler_configs'])
elif discriminator_optim_configs["schedule_method"] == "rsqrt":
self.scheduler_D = RsqrtScheduler(
optimizer=self.optim_D,
**discriminator_optim_configs["scheduler_configs"])
else:
WARN("Unknown scheduler name {0}. Do not use lr_scheduling.".
format(discriminator_optim_configs['schedule_method']))
############################################################
self._init_state()
self.adversarial = attack_configs[
"adversarial"] # adversarial sample or reinforced samples
self.r_s_weight = attack_configs["r_s_weight"]
self.r_d_weight = attack_configs["r_d_weight"]
def _init_state(self):
"""
initiate batched sentences / origin_bleu / index (start from first label, no BOS/EOS)
the initial state of the environment
:return: env states (the src, index)
"""
self.index = 1
self.origin_bleu = []
batch = next(self.data_iterator)
assert len(
batch
) == 3, "must be provided with line index (check for data_iterator)"
# training, parallel trg is provided
_, seqs_x, self.seqs_y = batch
self.sent_len = [len(x) for x in seqs_x] # for terminal signals
self.terminal_signal = [0] * len(seqs_x) # for terminal signals
self.padded_src, self.padded_trg = self.prepare_data(
seqs_x=seqs_x, seqs_y=self.seqs_y)
self.origin_result = self.translate()
# calculate BLEU scores for the top candidate
for index, sent_t in enumerate(self.seqs_y):
bleu_t = bleu.sentence_bleu(references=[sent_t],
hypothesis=self.origin_result[index],
emulate_multibleu=True)
self.origin_bleu.append(bleu_t)
return self.padded_src.cpu().numpy()
def get_src_vocab(self):
return self.src_vocab
def reset(self):
return self._init_state()
def reset_data_iter(
self, data_iter): # reset data iterator with provided iterator
self.data_iterator = data_iter
return
def reset_discriminator(self):
self.discriminator.reset()
load_embedding(self.discriminator,
model_path=self.victim_model_path,
device=self.device)
def prepare_D_data(self, attacker, seqs_x, seqs_y, batch_first=True):
"""
using global_attacker to generate training data for discriminator
:param attacker: prepare the data
:param seqs_x: list of sources
:param seqs_y: corresponding targets
:param batch_first: first dimension of seqs be batch
:param device: cpu or cuda*
:return: perturbed seqsx, seqsy, flags
"""
def _np_pad_batch_2D(samples, pad, batch_first=True):
# pack seqs into tensor with pads
batch_size = len(samples)
sizes = [len(s) for s in samples]
max_size = max(sizes)
x_np = np.full((batch_size, max_size),
fill_value=pad,
dtype='int64')
for ii in range(batch_size):
x_np[ii, :sizes[ii]] = samples[ii]
if batch_first is False:
x_np = np.transpose(x_np, [1, 0])
x = torch.tensor(x_np).to(self.device)
return x
seqs_x = list(map(lambda s: [BOS] + s + [EOS], seqs_x))
x = _np_pad_batch_2D(samples=seqs_x, pad=PAD, batch_first=batch_first)
# training mode attack: randomly choose half of the seqs to attack
attacker.eval()
x, flags = attacker.seq_attack(x, self.w2vocab, training_mode=True)
seqs_y = list(map(lambda s: [BOS] + s + [EOS], seqs_y))
y = _np_pad_batch_2D(seqs_y, pad=PAD, batch_first=batch_first)
flags.to(self.device)
# # print trace
# flag_list = flags.cpu().numpy().tolist()
# x_list = x.cpu().numpy().tolist()
# for i in range(len(flag_list)):
# if flag_list[i]==1:
# print(self.src_vocab.ids2sent(seqs_x[i]))
# print(self.src_vocab.ids2sent(x_list[i]))
# print(self.trg_vocab.ids2sent(seqs_y[i]))
return x, y, flags
def prepare_data(self, seqs_x, seqs_y=None, batch_first=True):
"""
Args:
eval ('bool'): indicator for eval/infer.
Returns: padded data matrices
"""
def _np_pad_batch_2D(samples, pad, batch_first=True):
batch_size = len(samples)
sizes = [len(s) for s in samples]
max_size = max(sizes)
x_np = np.full((batch_size, max_size),
fill_value=pad,
dtype='int64')
for ii in range(batch_size):
x_np[ii, :sizes[ii]] = samples[ii]
if batch_first is False:
x_np = np.transpose(x_np, [1, 0])
x = torch.tensor(x_np).to(self.device)
return x
seqs_x = list(map(lambda s: [BOS] + s + [EOS], seqs_x))
x = _np_pad_batch_2D(samples=seqs_x, pad=PAD, batch_first=batch_first)
if seqs_y is None:
return x
seqs_y = list(map(lambda s: [BOS] + s + [EOS], seqs_y))
y = _np_pad_batch_2D(seqs_y, pad=PAD, batch_first=batch_first)
return x, y
def acc_validation(self, attacker):
self.discriminator.eval()
acc = 0
sample_count = 0
for i in range(5):
try:
batch = next(self.data_iterator)
except StopIteration:
batch = next(self.data_iterator)
seq_nums, seqs_x, seqs_y = batch
x, y, flags = self.prepare_D_data(attacker, seqs_x, seqs_y)
# set components to evaluation mode
self.discriminator.eval()
with torch.no_grad():
preds = self.discriminator(x, y).argmax(dim=-1)
acc += torch.eq(preds, flags).sum()
sample_count += preds.size(0)
acc = acc.float() / sample_count
return acc.item()
def compute_D_forward(self, seqs_x, seqs_y, gold_flags, evaluate=False):
"""
get loss according to criterion
:param: gold_flags=1 if perturbed, otherwise 0
:return: loss value
"""
if not evaluate:
# set components to training mode(dropout layers)
self.discriminator.train()
self.criterion_D.train()
with torch.enable_grad():
class_probs = self.discriminator(seqs_x, seqs_y)
loss = self.criterion_D(class_probs, gold_flags)
torch.autograd.backward(loss)
return loss.item()
else:
# set components to evaluation mode(dropout layers)
self.discriminator.eval()
self.criterion_D.eval()
with torch.no_grad():
class_probs = self.discriminator(seqs_x, seqs_y)
loss = self.criterion_D(class_probs, gold_flags)
return loss.item()
def update_discriminator(self,
attacker_model,
base_steps=0,
min_update_steps=20,
max_update_steps=300,
accuracy_bound=0.8,
summary_writer=None):
"""
update discriminator
:param attacker_model: attacker to generate training data for discriminator
:param base_steps: used for saving
:param min_update_steps: (integer) minimum update steps,
also the discriminator evaluate steps
:param max_update_steps: (integer) maximum update steps
:param accuracy_bound: (float) update until accuracy reaches the bound
(or max_update_steps)
:param summary_writer: used to log discriminator learning information
:return: steps and test accuracy as trust region
"""
INFO("update discriminator")
self.optim_D.zero_grad()
attacker_model = attacker_model.to(self.device)
step = 0
while True:
try:
batch = next(self.data_iterator)
except StopIteration:
batch = next(self.data_iterator)
# update the discriminator
step += 1
if self.scheduler_D is not None:
# override learning rate in self.optim_D
self.scheduler_D.step(global_step=step)
_, seqs_x, seqs_y = batch # returned tensor type of the data
try:
x, y, flags = self.prepare_D_data(attacker_model, seqs_x,
seqs_y)
loss = self.compute_D_forward(seqs_x=x,
seqs_y=y,
gold_flags=flags)
self.optim_D.step()
print("discriminator loss:", loss)
except RuntimeError as e:
if "out of memory" in str(e):
print("WARNING: out of memory, skipping batch")
self.optim_D.zero_grad()
else:
raise e
# valid for accuracy / check for break (if any)
if step % min_update_steps == 0:
acc = self.acc_validation(attacker_model)
print("discriminator acc: %2f" % acc)
summary_writer.add_scalar("discriminator",
scalar_value=acc,
global_step=base_steps + step)
if accuracy_bound and acc > accuracy_bound:
INFO("discriminator reached training acc bound, updated.")
return base_steps + step, acc
if step > max_update_steps:
acc = self.acc_validation(attacker_model)
print("discriminator acc: %2f" % acc)
INFO("Reach maximum discriminator update. Finished.")
return base_steps + step, acc # stop updates
def translate(self, inputs=None):
"""
translate the self.perturbed_src
:param inputs: if None, translate perturbed sequences stored in the environments
:return: list of translation results
"""
if inputs is None:
inputs = self.padded_src
with torch.no_grad():
print(inputs.device)
perturbed_results = beam_search(
self.translate_model,
beam_size=5,
max_steps=150,
src_seqs=inputs,
alpha=-1.0,
)
perturbed_results = perturbed_results.cpu().numpy().tolist()
# only use the top result from the result
result = []
for sent in perturbed_results:
sent = [wid for wid in sent[0] if wid != PAD]
result.append(sent)
return result
def step(self, actions):
"""
step update for the environment: finally update self.index
this is defined as inference of the environments
:param actions: whether to perturb (action distribution vector
in shape [batch, 1])on current index
* result of torch.argmax(actor_output_distribution, dim=-1)
test: actions = actor_output_distribution.argmax(dim=-1)
or train: actions = actor.output_distribution.multinomial(dim=-1)
can be on cpu or cuda.
:return: updated states/ rewards/ terminal signal from the environments
reward (float), terminal_signal (boolean)
"""
with torch.no_grad():
terminal = False # default is not terminated
batch_size = actions.shape[0]
reward = 0
inputs = self.padded_src[:, self.index]
inputs_mask = ~inputs.eq(PAD)
target_of_step = []
# modification on sequences (state)
for batch_index in range(batch_size):
word_id = inputs[batch_index]
target_word_id = self.w2vocab[word_id.item()][np.random.choice(
len(self.w2vocab[word_id.item()]), 1)[0]]
target_of_step += [target_word_id]
if self.device != "cpu" and not actions.is_cuda:
actions = actions.to(self.device)
actions *= inputs_mask # PAD is neglect
# override the state src with random choice from candidates
self.padded_src[:, self.index] *= (1 - actions)
adjustification_ = torch.tensor(target_of_step)
adjustification_ = adjustification_.to(self.device)
self.padded_src[:, self.index] += adjustification_ * actions
# update sequences' pointer
self.index += 1
""" run discriminator check for terminal signals, update local terminal list
True: all sentences in the batch is defined as false by self.discriminator
False: otherwise
"""
# get discriminator distribution on the current src state
discriminate_out = self.discriminator(self.padded_src,
self.padded_trg)
self.terminal_signal = self.terminal_signal or discriminate_out.detach(
).argmax(dim=-1).cpu().numpy().tolist()
signal = (1 - discriminate_out.argmax(dim=-1)).sum().item()
if signal == 0 or self.index == self.padded_src.shape[1] - 1:
terminal = True # no need to further explore or reached EOS for all src
""" collect rewards on the current state
"""
# calculate intermediate survival rewards
if not terminal:
# survival rewards for survived objects
distribution, discriminate_index = discriminate_out.max(dim=-1)
distribution = distribution.detach().cpu().numpy()
discriminate_index = (1 - discriminate_index).cpu().numpy()
survival_value = distribution * discriminate_index * (
1 - np.array(self.terminal_signal))
reward += survival_value.sum() * self.r_s_weight
else: # only penalty for overall intermediate termination
reward = -1 * batch_size
# only check for finished relative BLEU degradation when survival on the last label
if self.index == self.padded_src.shape[1] - 1:
# re-tokenize ignore the original UNK for victim model
inputs = self.padded_src.cpu().numpy().tolist()
new_inputs = []
for indices in inputs:
# remove EOS, BOS, PAD
new_line = [
word_id for word_id in indices
if word_id not in [EOS, BOS, PAD]
]
new_line = self.src_vocab.ids2sent(new_line)
if not hasattr(self.src_vocab.tokenizer, "bpe"):
new_line = new_line.strip().split()
else:
new_token = []
for w in new_line.strip().split():
if w != self.src_vocab.id2token(UNK):
new_token.append(
self.src_vocab.tokenizer.bpe.segment_word(
w))
else:
new_token.append([w])
new_line = sum(new_token, [])
new_line = [self.src_vocab.token2id(t) for t in new_line]
new_inputs.append(new_line)
# translate calculate padded_src
perturbed_result = self.translate(
self.prepare_data(seqs_x=new_inputs, ))
# calculate final BLEU degredation:
episodic_rewards = []
for i, sent in enumerate(self.seqs_y):
# sentence is still surviving
if self.index >= self.sent_len[
i] - 1 and self.terminal_signal[i] == 0:
if self.origin_bleu[i] == 0:
# here we want to minimize noise from original bad cases
relative_degraded_value = 0
else:
relative_degraded_value = (
self.origin_bleu[i] - bleu.sentence_bleu(
references=[sent],
hypothesis=perturbed_result[i],
emulate_multibleu=True))
# print(relative_degraded_value, self.origin_bleu[i])
relative_degraded_value /= self.origin_bleu[i]
if self.adversarial:
episodic_rewards.append(relative_degraded_value)
else:
episodic_rewards.append(-relative_degraded_value)
else:
episodic_rewards.append(0.0)
reward += sum(episodic_rewards) * self.r_d_weight
reward = reward / batch_size
return self.padded_src.cpu().numpy(), reward, terminal,