def testTrain(self):
batch_size = 20
sequence_length = 35
with tf.Graph().as_default(), tf.device(tf.test.gpu_device_name()):
inputs_ph = tf.placeholder(tf.int64, [sequence_length, batch_size],
"inputs")
labels_ph = tf.placeholder(tf.int64, [sequence_length, batch_size],
"labels")
inputs = np.ones(inputs_ph.shape.as_list(), dtype=np.int64)
labels = np.ones(labels_ph.shape.as_list(), dtype=np.int64)
model = rnn_ptb.test_model(tf.test.is_gpu_available())
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
loss = rnn_ptb.loss_fn(model, inputs_ph, labels_ph, training=True)
grads = rnn_ptb.clip_gradients(optimizer.compute_gradients(loss),
0.25)
train_op = optimizer.apply_gradients(grads)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(train_op,
feed_dict={
inputs_ph: inputs,
labels_ph: labels
})
sess.run([train_op, loss],
feed_dict={
inputs_ph: inputs,
labels_ph: labels
})
def _benchmark_train(self, label, model):
num_iters = 100
num_warmup = 10
dataset = tf.data.Dataset.from_tensors(
tf.ones([PTBBenchmark.SEQ_LEN, PTBBenchmark.BATCH_SIZE],
dtype=tf.int64)).repeat(num_iters + num_warmup)
# inputs and labels have the same shape
dataset = tf.data.Dataset.zip((dataset, dataset))
(inputs, labels) = dataset.make_one_shot_iterator().get_next()
with tf.device(tf.test.gpu_device_name()):
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
loss = rnn_ptb.loss_fn(model, inputs, labels, training=True)
grads = rnn_ptb.clip_gradients(optimizer.compute_gradients(loss),
0.25)
train_op = optimizer.apply_gradients(grads)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for _ in range(num_warmup):
sess.run(train_op)
gc.collect()
start = time.time()
for _ in range(num_iters):
sess.run(train_op)
self._report(label, start, num_iters,
tf.test.gpu_device_name(),
PTBBenchmark.BATCH_SIZE)
def testTrain(self):
batch_size = 20
sequence_length = 35
with tf.Graph().as_default(), tf.device(tf.test.gpu_device_name()):
inputs_ph = tf.placeholder(tf.int64, [sequence_length, batch_size],
"inputs")
labels_ph = tf.placeholder(tf.int64, [sequence_length, batch_size],
"labels")
inputs = np.ones(inputs_ph.shape.as_list(), dtype=np.int64)
labels = np.ones(labels_ph.shape.as_list(), dtype=np.int64)
model = rnn_ptb.test_model(tf.test.is_gpu_available())
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
loss = rnn_ptb.loss_fn(model, inputs_ph, labels_ph, training=True)
grads = rnn_ptb.clip_gradients(optimizer.compute_gradients(loss), 0.25)
train_op = optimizer.apply_gradients(grads)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(train_op, feed_dict={inputs_ph: inputs, labels_ph: labels})
sess.run(
[train_op, loss], feed_dict={
inputs_ph: inputs,
labels_ph: labels
})
def _benchmark_train(self, label, model):
num_iters = 100
num_warmup = 10
dataset = tf.data.Dataset.from_tensors(
tf.ones(
[PTBBenchmark.SEQ_LEN, PTBBenchmark.BATCH_SIZE],
dtype=tf.int64)).repeat(num_iters + num_warmup)
# inputs and labels have the same shape
dataset = tf.data.Dataset.zip((dataset, dataset))
(inputs, labels) = tf.compat.v1.data.make_one_shot_iterator(
dataset).get_next()
with tf.device(tf.test.gpu_device_name()):
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1.0)
loss = rnn_ptb.loss_fn(model, inputs, labels, training=True)
grads = rnn_ptb.clip_gradients(optimizer.compute_gradients(loss), 0.25)
train_op = optimizer.apply_gradients(grads)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for _ in range(num_warmup):
sess.run(train_op)
gc.collect()
start = time.time()
for _ in range(num_iters):
sess.run(train_op)
self._report(label, start, num_iters,
tf.test.gpu_device_name(), PTBBenchmark.BATCH_SIZE)
def model_loss(inputs, targets):
return rnn_ptb.loss_fn(model, inputs, targets, training=True)
def model_loss(inputs, targets): return rnn_ptb.loss_fn(model, inputs, targets, training=True)