def load_model(self):
tf.compat.v1.disable_eager_execution()
# placeholders
self.x = tf.compat.v1.placeholder(tf.int32, shape=[self.batch_size, None])
self.y = tf.compat.v1.placeholder(tf.int32, shape=[self.batch_size, None])
self.mems_i = [tf.compat.v1.placeholder(tf.float32, [self.mem_len, self.batch_size, self.d_model]) for _ in range(self.n_layer)]
# model
self.global_step = tf.compat.v1.train.get_or_create_global_step()
initializer = tf.compat.v1.initializers.random_normal(stddev=0.02, seed=None)
proj_initializer = tf.compat.v1.initializers.random_normal(stddev=0.01, seed=None)
with tf.compat.v1.variable_scope(tf.compat.v1.get_variable_scope()):
xx = tf.transpose(self.x, [1, 0])
yy = tf.transpose(self.y, [1, 0])
loss, self.logits, self.new_mem = modules.transformer(
dec_inp=xx,
target=yy,
mems=self.mems_i,
n_token=self.n_token,
n_layer=self.n_layer,
d_model=self.d_model,
d_embed=self.d_embed,
n_head=self.n_head,
d_head=self.d_head,
d_inner=self.d_ff,
dropout=self.dropout,
dropatt=self.dropout,
initializer=initializer,
proj_initializer=proj_initializer,
is_training=self.is_training,
mem_len=self.mem_len,
cutoffs=[],
div_val=-1,
tie_projs=[],
same_length=False,
clamp_len=-1,
input_perms=None,
target_perms=None,
head_target=None,
untie_r=False,
proj_same_dim=True)
self.avg_loss = tf.reduce_mean(loss)
# vars
all_vars = tf.compat.v1.trainable_variables()
grads = tf.gradients(self.avg_loss, all_vars)
grads_and_vars = list(zip(grads, all_vars))
all_trainable_vars = tf.reduce_sum([tf.reduce_prod(v.shape) for v in tf.compat.v1.trainable_variables()])
# optimizer
decay_lr = tf.compat.v1.train.cosine_decay(
self.learning_rate,
global_step=self.global_step,
decay_steps=400000,
alpha=0.004)
optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=decay_lr)
self.train_op = optimizer.apply_gradients(grads_and_vars, self.global_step)
# saver
self.saver = tf.compat.v1.train.Saver()
config = tf.compat.v1.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
self.sess = tf.compat.v1.Session(config=config)
self.saver.restore(self.sess, self.checkpoint_path)
def load_model(self):
# placeholders
self.x = tf.compat.v1.placeholder(tf.int32,
shape=[self.batch_size, None])
self.y = tf.compat.v1.placeholder(tf.int32,
shape=[self.batch_size, None])
self.mems_i = [
tf.compat.v1.placeholder(
tf.float32, [self.mem_len, self.batch_size, self.d_model])
for _ in range(self.n_layer)
]
# model
initializer = tf.compat.v1.initializers.random_normal(stddev=0.02,
seed=None)
proj_initializer = tf.compat.v1.initializers.random_normal(stddev=0.01,
seed=None)
with tf.compat.v1.variable_scope(tf.compat.v1.get_variable_scope()):
xx = tf.transpose(self.x, [1, 0])
yy = tf.transpose(self.y, [1, 0])
loss, self.logits, self.new_mem = modules.transformer(
dec_inp=xx,
target=yy,
mems=self.mems_i,
n_token=self.n_token,
n_layer=self.n_layer,
d_model=self.d_model,
d_embed=self.d_embed,
n_head=self.n_head,
d_head=self.d_head,
d_inner=self.d_ff,
dropout=0.0,
dropatt=0.0,
initializer=initializer,
proj_initializer=proj_initializer,
is_training=False,
mem_len=self.mem_len,
cutoffs=[],
div_val=-1,
tie_projs=[],
same_length=False,
clamp_len=-1,
input_perms=None,
target_perms=None,
head_target=None,
untie_r=False,
proj_same_dim=True)
# restore
self.saver = tf.compat.v1.train.Saver()
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.compat.v1.Session(config=config)
self.saver.restore(self.sess, self.checkpoint_path)
def test_transformer(self):
in_seq = torch.LongTensor([[1, 2, 3, 4], [8, 7, 6, 5]])
out_seq = torch.LongTensor([[0, 1, 2], [3, 9, 3]])
transformer = Transformer(10, 6, 6, 8, 3, 2, 2, 4)
probs = transformer(in_seq, out_seq)
self.assertEqual(probs.shape, torch.Size([2, 3, 10]))
def load_model(self):
# placeholders
self.x = tf.compat.v1.placeholder(tf.int32, shape=[self.batch_size, None])
self.y = tf.compat.v1.placeholder(tf.int32, shape=[self.batch_size, None])
self.mems_i = [tf.compat.v1.placeholder(tf.float32, [self.mem_len, self.batch_size, self.d_model]) for _ in
range(self.n_layer)]
# model
self.global_step = tf.compat.v1.train.get_or_create_global_step()
initializer = tf.compat.v1.keras.initializers.glorot_normal()
proj_initializer = tf.compat.v1.keras.initializers.glorot_normal()
with tf.compat.v1.variable_scope(tf.compat.v1.get_variable_scope()):
xx = tf.transpose(self.x, [1, 0])
yy = tf.transpose(self.y, [1, 0])
loss, self.logits, self.new_mem = modules.transformer(
dec_inp=xx,
target=yy,
mems=self.mems_i,
n_token=self.n_token,
n_layer=self.n_layer,
d_model=self.d_model,
d_embed=self.d_embed,
n_head=self.n_head,
d_head=self.d_head,
d_inner=self.d_ff,
dropout=self.dropout,
dropatt=self.dropout,
initializer=initializer,
proj_initializer=proj_initializer,
is_training=self.is_training,
mem_len=self.mem_len,
rezero=self.rezero,
cutoffs=[],
div_val=-1,
tie_projs=[],
same_length=False,
clamp_len=-1,
input_perms=None,
target_perms=None,
head_target=None,
untie_r=False,
proj_same_dim=True)
variables = tf.trainable_variables()
grads = tf.gradients(self.loss, variables)
grads_and_vars = list(zip(grads, variables))
self.avg_loss = tf.reduce_mean(loss)
# vars
decay_lr = tf.compat.v1.train.cosine_decay(
self.learning_rate,
global_step=self.global_step,
decay_steps=400000,
alpha=0.004)
optimizer = RAdamOptimizer(decay_lr)
optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(optimizer)
self.train_op = optimizer.apply_gradients(grads_and_vars, self.global_step)
# saver
self.saver = tf.compat.v1.train.Saver()
config = tf.compat.v1.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
config.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
self.sess = tf.compat.v1.Session(config=config)
self.saver.restore(self.sess, self.checkpoint_path)
