def run(**kwargs):
arg_dict.from_dict(kwargs)
args = arg_dict.to_namespace()
# ======================================================================================
# Load Params, Prepare results assets
# ======================================================================================
# os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
# print(args.corpus)
# Experiment parameter summary
res_param_filename = os.path.join(args.out_dir,
"params_{id}.csv".format(id=args.run_id))
with open(res_param_filename, "w") as param_file:
writer = csv.DictWriter(f=param_file, fieldnames=arg_dict.keys())
writer.writeheader()
writer.writerow(arg_dict)
param_file.flush()
# make dir for model checkpoints
if args.save_model:
model_ckpt_dir = os.path.join(args.out_dir,
"model_{id}".format(id=args.run_id))
os.makedirs(model_ckpt_dir, exist_ok=True)
model_path = os.path.join(model_ckpt_dir,
"nnlm_{id}.ckpt".format(id=args.run_id))
# start perplexity file
ppl_header = ["id", "run", "epoch", "step", "lr", "dataset", "perplexity"]
ppl_fname = os.path.join(args.out_dir,
"perplexity_{id}.csv".format(id=args.run_id))
ppl_file = open(ppl_fname, "w")
ppl_writer = csv.DictWriter(f=ppl_file, fieldnames=ppl_header)
ppl_writer.writeheader()
# ======================================================================================
# CORPUS, Vocab and RIs
# ======================================================================================
corpus = h5py.File(os.path.join(args.corpus,
"ptb_{}.hdf5".format(args.ngram_size)),
mode='r')
vocab = marisa_trie.Trie(corpus["vocabulary"])
# generates k-dimensional random indexes with s_active units
all_positive = args.ri_all_positive
ri_generator = Generator(dim=args.k_dim,
num_active=args.s_active,
symmetric=not all_positive)
# pre-gen indices for vocab
# it doesn't matter which ri gets assign to which word since we are pre-generating the indexes
ris = [ri_generator.generate() for i in range(len(vocab))]
ri_tensor = ris_to_sp_tensor_value(ris, dim=args.k_dim)
# ri_tensor = RandomIndexTensor.from_ri_list(ris, args.k_dim, args.s_active)
# ======================================================================================
def data_pipeline(data,
epochs=1,
batch_size=args.batch_size,
shuffle=False):
def chunk_fn(x):
return chunk_it(x, chunk_size=batch_size * 1000)
if epochs > 1:
data = repeat_apply(chunk_fn, data, epochs)
else:
data = chunk_fn(data)
if shuffle:
data = shuffle_it(data, args.shuffle_buffer_size)
data = batch_it(data, size=batch_size, padding=False)
return data
# ======================================================================================
# MODEL
# ======================================================================================
# Activation functions
if args.h_act == "relu":
h_act = tx.relu
h_init = tx.he_normal_init()
elif args.h_act == "tanh":
h_act = tx.tanh
h_init = tx.glorot_uniform()
elif args.h_act == "elu":
h_act = tx.elu
h_init = tx.he_normal_init()
# Parameter Init
if args.embed_init == "normal":
embed_init = tx.random_normal(mean=0., stddev=args.embed_init_val)
elif args.embed_init == "uniform":
embed_init = tx.random_uniform(minval=-args.embed_init_val,
maxval=args.embed_init_val)
if args.logit_init == "normal":
logit_init = tx.random_normal(mean=0., stddev=args.logit_init_val)
elif args.logit_init == "uniform":
logit_init = tx.random_uniform(minval=-args.logit_init_val,
maxval=args.logit_init_val)
if args.f_init == "normal":
f_init = tx.random_normal(mean=0., stddev=args.f_init_val)
elif args.f_init == "uniform":
f_init = tx.random_uniform(minval=-args.f_init_val,
maxval=args.f_init_val)
# sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
# log_device_placement=True))
# with tf.device('/gpu:{}'.format(args.gpu)):
model = NNLM_NRP(ctx_size=args.ngram_size - 1,
vocab_size=len(vocab),
k_dim=args.k_dim,
s_active=args.s_active,
ri_tensor=ri_tensor,
embed_dim=args.embed_dim,
embed_init=embed_init,
embed_share=args.embed_share,
logit_init=logit_init,
logit_bias=args.logit_bias,
h_dim=args.h_dim,
num_h=args.num_h,
h_activation=h_act,
h_init=h_init,
use_dropout=args.dropout,
keep_prob=args.keep_prob,
embed_dropout=args.embed_dropout,
l2_loss=args.l2_loss,
l2_loss_coef=args.l2_loss_coef,
f_init=f_init)
model_runner = tx.ModelRunner(model)
# sess = tf.Session(config=tf.ConfigProto(
# allow_soft_placement=True, log_device_placement=True))
# model_runner.set_session(sess)
# sess = tf.Session(config=tf.ConfigProto(
# allow_soft_placement=True, log_device_placement=True))
# model_runner.set_session(sess)
# we use an InputParam because we might want to change it during training
lr_param = tx.InputParam(value=args.lr)
if args.optimizer == "sgd":
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=lr_param.tensor)
elif args.optimizer == "adam":
optimizer = tf.train.AdamOptimizer(learning_rate=lr_param.tensor,
beta1=args.optimizer_beta1,
beta2=args.optimizer_beta2,
epsilon=args.optimizer_epsilon)
elif args.optimizer == "ams":
optimizer = tx.AMSGrad(learning_rate=lr_param.tensor,
beta1=args.optimizer_beta1,
beta2=args.optimizer_beta2,
epsilon=args.optimizer_epsilon)
def clip_grad_global(grads):
grads, _ = tf.clip_by_global_norm(grads, 12)
return grads
def clip_grad_local(grad):
return tf.clip_by_norm(grad, args.clip_value)
if args.clip_grads:
if args.clip_local:
clip_fn = clip_grad_local
else:
clip_fn = clip_grad_global
if args.clip_grads:
model_runner.config_optimizer(optimizer,
optimizer_params=lr_param,
gradient_op=clip_fn,
global_gradient_op=not args.clip_local)
else:
model_runner.config_optimizer(optimizer, optimizer_params=lr_param)
# assert(model_runner.session == sess)
# ======================================================================================
# EVALUATION
# ======================================================================================
def eval_model(runner,
dataset_it,
len_dataset=None,
display_progress=False):
if display_progress:
pb = tqdm(total=len_dataset, ncols=60)
batches_processed = 0
sum_loss = 0
for batch in dataset_it:
batch = np.array(batch, dtype=np.int64)
ctx = batch[:, :-1]
target = batch[:, -1:]
mean_loss = runner.eval(ctx, target)
sum_loss += mean_loss
if display_progress:
pb.update(args.batch_size)
batches_processed += 1
if display_progress:
pb.close()
return np.exp(sum_loss / batches_processed)
def evaluation(runner: tx.ModelRunner,
pb,
cur_epoch,
step,
display_progress=False):
pb.write("[Eval Validation]")
val_data = corpus["validation"]
ppl_validation = eval_model(
runner, data_pipeline(val_data, epochs=1, shuffle=False),
len(val_data), display_progress)
res_row = {
"id": args.id,
"run": args.run,
"epoch": cur_epoch,
"step": step,
"lr": lr_param.value,
"dataset": "validation",
"perplexity": ppl_validation
}
ppl_writer.writerow(res_row)
pb.write("Eval Test")
test_data = corpus["test"]
ppl_test = eval_model(
runner, data_pipeline(test_data, epochs=1, shuffle=False),
len(test_data), display_progress)
res_row = {
"id": args.id,
"run": args.run,
"epoch": cur_epoch,
"step": step,
"lr": lr_param.value,
"dataset": "test",
"perplexity": ppl_test
}
ppl_writer.writerow(res_row)
ppl_file.flush()
pb.write("valid. ppl = {} \n test ppl {}".format(
ppl_validation, ppl_test))
return ppl_validation
# ======================================================================================
# TRAINING LOOP
# ======================================================================================
# preparing evaluation steps
# I use ceil because I make sure we have padded batches at the end
epoch_step = 0
global_step = 0
current_epoch = 0
patience = 0
cfg = tf.ConfigProto()
cfg.gpu_options.allow_growth = True
sess = tf.Session(config=cfg)
model_runner.set_session(sess)
model_runner.init_vars()
training_dset = corpus["training"]
progress = tqdm(total=len(training_dset) * args.epochs)
training_data = data_pipeline(training_dset,
epochs=args.epochs,
shuffle=True)
evals = []
try:
for ngram_batch in training_data:
epoch = progress.n // len(training_dset) + 1
# Start New Epoch
if epoch != current_epoch:
current_epoch = epoch
epoch_step = 0
progress.write("epoch: {}".format(current_epoch))
# Eval Time
if epoch_step == 0:
current_eval = evaluation(model_runner, progress, epoch,
global_step)
evals.append(current_eval)
if global_step > 0:
if args.early_stop:
if evals[-2] - evals[-1] < args.eval_threshold:
if patience >= 3:
progress.write("early stop")
break
patience += 1
else:
patience = 0
# lr decay only at the start of each epoch
if args.lr_decay and len(evals) > 0:
if evals[-2] - evals[-1] < args.eval_threshold:
lr_param.value = max(
lr_param.value * args.lr_decay_rate,
args.lr_decay_threshold)
progress.write("lr changed to {}".format(
lr_param.value))
# ================================================
# TRAIN MODEL
# ================================================
ngram_batch = np.array(ngram_batch, dtype=np.int64)
ctx_ids = ngram_batch[:, :-1]
word_ids = ngram_batch[:, -1:]
model_runner.train(ctx_ids, word_ids)
progress.update(args.batch_size)
epoch_step += 1
global_step += 1
# if not early stop, evaluate last state of the model
if not args.early_stop or patience < 3:
evaluation(model_runner, progress, epoch, epoch_step)
ppl_file.close()
if args.save_model:
model_runner.save_model(model_name=model_path,
step=global_step,
write_state=False)
model_runner.close_session()
progress.close()
tf.reset_default_graph()
except Exception as e:
traceback.print_exc()
os.remove(ppl_file.name)
os.remove(param_file.name)
raise e
use_hidden=args.use_hidden,
h_dim=args.h_dim,
h_activation=h_act,
h_init=h_init,
h_to_f_init=h_to_f_init,
use_dropout=args.dropout,
embed_dropout=args.embed_dropout,
keep_prob=args.keep_prob,
l2_loss=args.l2_loss,
l2_loss_coef=args.l2_loss_coef,
use_nce=False)
model_runner = tx.ModelRunner(model)
# we use an InputParam because we might want to change it during training
lr_param = tx.InputParam(value=args.lr)
if args.optimizer == "sgd":
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=lr_param.tensor)
elif args.optimizer == "adam":
optimizer = tf.train.AdamOptimizer(learning_rate=lr_param.tensor,
beta1=args.optimizer_beta1,
beta2=args.optimizer_beta2,
epsilon=args.optimizer_epsilon)
elif args.optimizer == "ams":
optimizer = tx.AMSGrad(learning_rate=lr_param.tensor,
beta1=args.optimizer_beta1,
beta2=args.optimizer_beta2,
epsilon=args.optimizer_epsilon)
def test_nce_nrp(self):
vocab_size = 1000
k = 500
s = 8
embed_size = 128
nce_samples = 10
noise_ratio = 0.1
use_nce = True
vocab = [str(i) for i in range(vocab_size)]
generator = Generator(k, s)
sign_index = TrieSignIndex(generator,
vocabulary=vocab,
pregen_indexes=True)
ris = [
sign_index.get_ri(sign_index.get_sign(i))
for i in range(len(sign_index))
]
# ris = [generator.generate() for _ in range(vocab_size)]
ri_tensor = ris_to_sp_tensor_value(ri_seq=ris,
dim=k,
all_positive=False)
ri_tensor_input = tx.SparseInput(n_units=k, value=ri_tensor)
if use_nce:
label_inputs = tx.SparseInput(k, name="target_random_indices")
else:
label_inputs = [
tx.Input(1, dtype=tf.int64, name="ids"),
tx.InputParam(dtype=tf.int32,
value=vocab_size,
name="vocab_size")
]
eval_label_inputs = [
tx.Input(1, dtype=tf.int64, name="ids_eval"),
tx.InputParam(dtype=tf.int32, value=vocab_size, name="vocab_size")
]
model = NRP(
run_inputs=tx.SparseInput(n_units=k, name="random_index_inputs"),
label_inputs=label_inputs,
eval_label_input=eval_label_inputs,
ctx_size=2,
# vocab_size=vocab_size,
k_dim=k,
ri_tensor_input=ri_tensor_input, # current dictionary state
embed_dim=embed_size,
h_dim=128,
num_h=1,
h_activation=tx.relu,
use_dropout=True,
embed_dropout=True,
keep_prob=0.70,
use_nce=use_nce,
nce_samples=nce_samples,
nce_noise_amount=noise_ratio,
noise_input=tx.SparseInput(k, name="noise"))
tf.summary.histogram("embeddings", model.embeddings.weights)
for h in model.h_layers:
tf.summary.histogram("h", h.linear.weights)
# model.eval_tensors.append(model.train_loss_tensors[0])
runner = tx.ModelRunner(model)
runner.set_log_dir("/tmp")
runner.log_graph()
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
# options = None
runner.set_session(runtime_stats=True, run_options=options)
# options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
# runner.config_optimizer(tf.train.GradientDescentOptimizer(learning_rate=0.005))#,
# SGD with 0.025
# lr = tx.InputParam(init_value=0.0002)
lr = tx.InputParam(value=0.025)
# runner.config_optimizer(tf.train.AdamOptimizer(learning_rate=lr.tensor, beta1=0.9), params=lr,
runner.config_optimizer(
tf.train.GradientDescentOptimizer(learning_rate=lr.tensor),
optimizer_params=lr,
global_gradient_op=False,
# gradient_op=lambda grad: tf.clip_by_global_norm(grad, 10.0)[0])
gradient_op=lambda grad: tf.clip_by_norm(grad, 1.0))
data = np.array([[0, 2], [5, 7], [9, 8], [3, 4], [1, 9], [12, 8]])
labels = np.array([[32], [56], [12], [2], [5], [23]])
ppl_curve = []
n = 256
batch_size = 128
dataset = np.column_stack((data, labels))
# print(dataset)
dataset = views.repeat_it([dataset], n)
dataset = views.flatten_it(dataset)
# shuffle 5 at a time
dataset = views.shuffle_it(dataset, 6)
dataset = views.batch_it(dataset, batch_size)
# print(np.array(list(dataset)))
# d = list(views.take_it(1, views.shuffle_it(d, 4)))[0]
data_stream = dataset
for data_stream in tqdm(data_stream, total=n * 5 / batch_size):
sample = np.array(data_stream)
ctx = sample[:, :-1]
ctx.flatten()
ctx = ctx.flatten()
ctx_ris = [sign_index.get_ri(sign_index.get_sign(i)) for i in ctx]
ctx_ris = ris_to_sp_tensor_value(
ctx_ris,
dim=sign_index.feature_dim(),
all_positive=not sign_index.generator.symmetric)
lbl_ids = sample[:, -1:]
lbl = lbl_ids.flatten()
if use_nce:
lbl_ris = [
sign_index.get_ri(sign_index.get_sign(i)) for i in lbl
]
lbl_ris = ris_to_sp_tensor_value(
lbl_ris,
dim=sign_index.feature_dim(),
all_positive=not sign_index.generator.symmetric)
noise = generate_noise(k_dim=k,
batch_size=lbl_ris.dense_shape[0] *
nce_samples,
ratio=noise_ratio)
runner.train(ctx_ris, [lbl_ris, noise],
output_loss=True,
write_summaries=True)
else:
runner.train(model_input_data=ctx_ris,
loss_input_data=lbl_ids,
output_loss=True,
write_summaries=True)
runner.close_session()
