def _step(self, samples, labels, first_batch):
self._lr_scheduler()
with tf.GradientTape() as tape:
probs = self._model(samples, training=True)
loss = self._loss_fn(labels, probs)
if self._amp:
loss = self._embedding_optimizer.get_scaled_loss(loss)
embedding_vars, dense_vars = sok.split_embedding_variable_from_others(self._model.trainable_variables)
embedding_grads, dense_grads = tape.gradient(loss, [embedding_vars, dense_vars])
if self._amp:
embedding_grads = self._embedding_optimizer.get_unscaled_gradients(embedding_grads)
dense_grads = self._embedding_optimizer.get_unscaled_gradients(dense_grads)
# embedding_grads = [scale_grad(g, hvd.size()) for g in embedding_grads]
with sok.OptimizerScope(embedding_vars):
self._embedding_optimizer.apply_gradients(zip(embedding_grads, embedding_vars),
experimental_aggregate_gradients=False)
# with tf.control_dependencies(embedding_grads):
dense_grads = [hvd.allreduce(grad, op=hvd.Average, compression=hvd.compression.NoneCompressor) for grad in dense_grads]
self._dense_optimizer.apply_gradients(zip(dense_grads, dense_vars),
experimental_aggregate_gradients=False)
if first_batch:
hvd.broadcast_variables(dense_vars, root_rank=0)
hvd.broadcast_variables(self._dense_optimizer.variables(), root_rank=0)
return loss
def train_step(features, labels, warmup_batch=False):
with tf.GradientTape() as tape:
output_map = model(features)
crossentropy_loss, dice_loss = partial_losses(output_map, labels)
added_losses = tf.add(crossentropy_loss,
dice_loss,
name="total_loss_ref")
loss = added_losses + params.weight_decay * tf.add_n([
tf.nn.l2_loss(v) for v in model.trainable_variables
if 'batch_normalization' not in v.name
])
if params.use_amp:
loss = optimizer.get_scaled_loss(loss)
tape = hvd.DistributedGradientTape(tape)
gradients = tape.gradient(loss, model.trainable_variables)
if params.use_amp:
gradients = optimizer.get_unscaled_gradients(gradients)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
# Note: broadcast should be done after the first gradient step to ensure optimizer
# initialization.
if warmup_batch:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(optimizer.variables(), root_rank=0)
ce_loss(crossentropy_loss)
f1_loss(dice_loss)
return loss
def train_step(images, masks, first_batch=False):
with tf.GradientTape() as tape:
predicted = model(images)
predicted = predicted[:, clip_offset:-
clip_offset, clip_offset:-clip_offset]
masks = masks[:, clip_offset:-
clip_offset, clip_offset:-clip_offset]
loss = bce(masks, predicted)
train_loss_metric.update_state(loss)
tape = hvd.DistributedGradientTape(tape)
gradients = tape.gradient(
loss, model.trainable_variables)
optimizer.apply_gradients(
zip(gradients, model.trainable_variables))
# Horovod: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
#
# Note: broadcast should be done after the first gradient step to ensure optimizer
# initialization.
if first_batch:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(optimizer.variables(), root_rank=0)
return predicted, masks
def train_step(self, data):
"""Perform a single training step."""
x, beta = data
start = time.time()
with tf.GradientTape() as tape:
states, accept_prob, sumlogdet = self((x, beta), training=True)
loss = self.calc_losses(states, accept_prob)
if self.aux_weight > 0:
z = tf.random.normal(x.shape, dtype=x.dtype)
states_, accept_prob_, _ = self((z, beta), training=True)
loss_ = self.calc_losses(states_, accept_prob_)
loss += loss_
if NUM_RANKS > 1:
tape = hvd.DistributedGradientTape(tape)
grads = tape.gradient(loss, self.trainable_variables)
self.optimizer.apply_gradients(zip(grads, self.trainable_variables))
metrics = AttrDict({
'dt': time.time() - start,
'loss': loss,
'accept_prob': accept_prob,
'eps': self.eps,
'beta': states.init.beta,
'sumlogdet': sumlogdet.out,
})
if self.optimizer.iterations == 0 and NUM_RANKS > 1:
hvd.broadcast_variables(self.variables, root_rank=0)
hvd.broadcast_variables(self.optimizer.variables(), root_rank=0)
return states.out.x, metrics
def train_step(inputs_tr, targets_tr, first_batch):
print("Tracing update_step")
print("inputs nodes", inputs_tr.nodes.shape)
print("inputs edges", inputs_tr.edges.shape)
print("input n_node", inputs_tr.n_node.shape)
print(inputs_tr.nodes)
with tf.GradientTape() as tape:
outputs_tr = model(inputs_tr,
num_processing_steps_tr,
is_training=True)
loss_ops_tr = loss_fcn(targets_tr, outputs_tr)
loss_op_tr = tf.math.reduce_sum(loss_ops_tr) / tf.constant(
num_processing_steps_tr, dtype=tf.float32)
# Horovod: add Horovod Distributed GradientTape.
if args.distributed:
tape = hvd.DistributedGradientTape(tape)
gradients = tape.gradient(loss_op_tr, model.trainable_variables)
optimizer.apply(gradients, model.trainable_variables)
# Horovod: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
#
# Note: broadcast should be done after the first gradient step to ensure optimizer
# initialization.
if args.distributed and first_batch:
hvd.broadcast_variables(model.trainable_variables, root_rank=0)
hvd.broadcast_variables(optimizer.variables, root_rank=0)
return loss_op_tr
def train_step(inputs, first_batch):
images, labels = inputs
with tf.GradientTape() as tape:
predictions = model(images, training=True)
loss = loss_func(labels, predictions)
loss += tf.reduce_sum(model.losses)
loss_copy = loss
# Scale the losses
if precision == 'fp16':
loss = loss * tf.cast(loss_scale, loss.dtype)
tape = hvd.DistributedGradientTape(tape)
old_grads = tape.gradient(loss, model.trainable_variables)
# Unscale the grads
if precision == 'fp16':
loss_scale_reciprocal = 1. / loss_scale
grads = [g * tf.cast(loss_scale_reciprocal, g.dtype) if g is not
None else None for g in old_grads]
else:
grads = old_grads
opt.apply_gradients(zip(grads, model.trainable_variables))
train_top1.update_state(labels, predictions)
train_top5.update_state(labels, predictions)
if hvd.size() > 1 and first_batch:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(opt.variables(), root_rank=0)
return loss_copy
def training_step(images, labels, first_batch):
with tf.GradientTape() as tape:
probs = cifar10_model(images, training=True)
loss_value = loss(labels, probs)
# Horovod: add Horovod Distributed GradientTape.
try:
tape = hvd.DistributedGradientTape(tape)
except:
print("no horovod")
grads = tape.gradient(loss_value, cifar10_model.trainable_variables)
opt.apply_gradients(zip(grads, cifar10_model.trainable_variables))
# Horovod: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
#
# Note: broadcast should be done after the first gradient step to ensure optimizer
# initialization.
if first_batch:
try:
hvd.broadcast_variables(cifar10_model.variables, root_rank=0)
hvd.broadcast_variables(opt.variables(), root_rank=0)
except:
print("no horovod")
return loss_value
def train_step(model, opt, loss_func, images, labels, first_batch, batch_size, mixup_alpha=0.0, fp32=False):
images, labels = mixup(batch_size, mixup_alpha, images, labels)
with tf.GradientTape() as tape:
logits = model(images, training=True)
loss_value = loss_func(labels, tf.cast(logits, tf.float32))
loss_value += tf.add_n(model.losses)
if not fp32:
scaled_loss_value = opt.get_scaled_loss(loss_value)
tape = hvd.DistributedGradientTape(tape, compression=hvd.Compression.fp16)
if not fp32:
grads = tape.gradient(scaled_loss_value, model.trainable_variables)
grads = opt.get_unscaled_gradients(grads)
else:
grads = tape.gradient(loss_value, model.trainable_variables)
opt.apply_gradients(zip(grads, model.trainable_variables))
if first_batch:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(opt.variables(), root_rank=0)
probs = layers.Activation('softmax', dtype='float32')(logits)
top_1_pred = tf.squeeze(tf.math.top_k(probs, k=1)[1])
sparse_labels = tf.cast(tf.math.argmax(labels, axis=1), tf.int32)
top_1_accuracy = tf.math.reduce_sum(tf.cast(tf.equal(top_1_pred, sparse_labels), tf.int32))
return loss_value, top_1_accuracy
def train(self, dataset, total_batches=-1):
""" Update the model in 1 epoch """
train_step = self.train_step
if self.hparams.enable_tf_function:
logging.info(
"please be patient, enable tf.function, it takes time ...")
train_step = tf.function(train_step,
input_signature=self.sample_signature)
for batch, samples in enumerate(dataset.take(total_batches)):
# train 1 step
samples = self.model.prepare_samples(samples)
loss, metrics = train_step(samples)
# Horovod: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
#
# Note: broadcast should be done after the first gradient step to ensure optimizer
# initialization.
if batch == 0:
hvd.broadcast_variables(self.model.trainable_variables,
root_rank=0)
hvd.broadcast_variables(self.optimizer.variables(),
root_rank=0)
if batch % self.hparams.log_interval == 0 and hvd.rank() == 0:
logging.info(self.metric_checker(loss, metrics))
self.model.reset_metrics()
def _train_step(inputs, labels, first_batch):
with tf.GradientTape() as tape:
logit, all_vectors = model(inputs, training=True)
replica_loss = _replica_loss(labels, logit)
if args.mixed_precision:
_loss = emb_opt.get_scaled_loss(replica_loss)
else:
_loss = replica_loss
emb_var, other_var = sok.split_embedding_variable_from_others(
model.trainable_variables)
emb_grads, grads = tape.gradient(_loss, [emb_var, other_var])
if args.mixed_precision:
emb_grads = emb_opt.get_unscaled_gradients(emb_grads)
grads = emb_opt.get_unscaled_gradients(grads)
if "plugin" not in args.optimizer:
with sok.OptimizerScope(emb_var):
emb_opt.apply_gradients(zip(emb_grads, emb_var),
experimental_aggregate_gradients=False)
else:
emb_opt.apply_gradients(zip(emb_grads, emb_var),
experimental_aggregate_gradients=False)
with tf.control_dependencies(emb_grads):
grads = [hvd.allreduce(grad) for grad in grads]
dense_opt.apply_gradients(zip(grads, other_var))
if first_batch:
hvd.broadcast_variables(other_var, root_rank=0)
hvd.broadcast_variables(dense_opt.variables(), root_rank=0)
total_loss = hvd.allreduce(replica_loss)
return total_loss, all_vectors
def benchmark_step(first_batch):
# Horovod: (optional) compression algorithm.
compression = hvd.Compression.fp16 if args.fp16_allreduce else hvd.Compression.none
# Horovod: use DistributedGradientTape
with tf.GradientTape() as tape:
probs = model(data, training=True)
loss = tf.losses.sparse_categorical_crossentropy(target, probs)
if args.use_amp:
loss = opt.get_scaled_loss(loss)
# Horovod: add Horovod Distributed GradientTape.
tape = hvd.DistributedGradientTape(tape, compression=compression)
gradients = tape.gradient(loss, model.trainable_variables)
if args.use_amp:
gradients = opt.get_unscaled_gradients(gradients)
opt.apply_gradients(zip(gradients, model.trainable_variables))
# Horovod: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
#
# Note: broadcast should be done after the first gradient step to ensure optimizer
# initialization.
if first_batch:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(opt.variables(), root_rank=0)
def train_step(model, inputs, loss, amp, opt, init):
with tf.GradientTape() as tape:
[input_ids, input_mask, segment_ids, label_ids] = inputs
# print(input_ids, input_ids.shape)
outputs = model(
input_ids,
# input_ids=input_ids,
attention_mask=input_mask,
token_type_ids=segment_ids,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=True,
)
loss_value = loss(y_true=label_ids, y_pred=outputs[0])
unscaled_loss = tf.stop_gradient(loss_value)
if amp:
loss_value = opt.get_scaled_loss(loss_value)
tape = hvd.DistributedGradientTape(tape)
gradients = tape.gradient(loss_value, model.trainable_variables)
if amp:
gradients = opt.get_unscaled_gradients(gradients)
opt.apply_gradients(zip(gradients,
model.trainable_variables)) # , clip_norm=1.0)
if init:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(opt.variables(), root_rank=0)
return unscaled_loss, outputs # , tape.gradient(loss_value, model.trainable_variables)
def train_step(model, inputs, loss, amp, opt, init, v2=False, loss_class=None, fp16=False, clip_norm=1.0):
with tf.GradientTape() as tape:
[input_ids, input_mask, segment_ids, start_positions, end_positions, cls_index, p_mask, is_impossible] = inputs
if not v2:
is_impossible = None
start_logits, end_logits, cls_logits = model(input_ids,
attention_mask=input_mask,
token_type_ids=segment_ids,
start_positions=start_positions,
end_positions=end_positions,
cls_index=cls_index,
p_mask=p_mask,
is_impossible=is_impossible,
position_ids=None,
head_mask=None,
inputs_embeds=None,
training=True,
)[0:3]
# If we are on multi-GPU, split add a dimension
if len(start_positions.shape) > 1:
start_positions = tf.squeeze(start_positions, axis=-1, name="squeeze_start_positions")
if len(end_positions.shape) > 1:
end_positions = tf.squeeze(end_positions, axis=-1, name="squeeze_end_positions")
if is_impossible is not None and len(is_impossible.shape) > 1 and v2 and cls_logits is not None:
is_impossible = tf.squeeze(is_impossible, axis=-1, name="squeeze_is_impossible")
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = start_logits.shape[1]
start_positions = tf.clip_by_value(start_positions, 0, ignored_index, name="clip_start_positions")
end_positions = tf.clip_by_value(end_positions, 0, ignored_index, name="clip_end_positions")
start_loss = loss(y_true=start_positions, y_pred=tf.cast(start_logits, tf.float32))
end_loss = loss(y_true=end_positions, y_pred=tf.cast(end_logits, tf.float32))
loss_value = (start_loss + end_loss) / 2
if v2:
cls_loss_value = loss_class(y_true=is_impossible, y_pred=tf.cast(cls_logits, tf.float32))
loss_value += cls_loss_value * 0.5
unscaled_loss = tf.stop_gradient(loss_value)
if amp:
loss_value = opt.get_scaled_loss(loss_value)
tape = hvd.DistributedGradientTape(tape, sparse_as_dense=True,
compression=Compression.fp16 if fp16 else Compression.none)
gradients = tape.gradient(loss_value, model.trainable_variables)
if amp:
gradients = opt.get_unscaled_gradients(gradients)
(gradients, _) = tf.clip_by_global_norm(gradients, clip_norm=clip_norm)
opt.apply_gradients(zip(gradients, model.trainable_variables)) # , clip_norm=1.0)
if init:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(opt.variables(), root_rank=0)
return unscaled_loss # , outputs#, tape.gradient(loss_value, model.trainable_variables)
def main(_):
# Horovod: initialize Horovod.
hvd.init()
# Horovod: pin GPU to be used to process local rank (one GPU per process)
config = tf.ConfigProto()
config.gpu_options.visible_device_list = str(hvd.local_rank())
tf.enable_eager_execution(config=config)
mnist_model = tf.keras.Sequential([
tf.keras.layers.Conv2D(16, [3, 3], activation='relu'),
tf.keras.layers.Conv2D(16, [3, 3], activation='relu'),
tf.keras.layers.GlobalAveragePooling2D(),
tf.keras.layers.Dense(10)
])
# Horovod: adjust learning rate based on number of GPUs.
opt = tf.train.RMSPropOptimizer(0.001 * hvd.size())
(mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data()
dataset = tf.data.Dataset.from_tensor_slices(
(tf.cast(mnist_images[..., tf.newaxis] / 255,
tf.float32), tf.cast(mnist_labels, tf.int64)))
dataset = dataset.shuffle(1000).batch(32)
# Horovod: save checkpoints only on worker 0 to prevent other workers from
checkpoint_dir = './checkpoints'
step_counter = tf.train.get_or_create_global_step()
checkpoint = tf.train.Checkpoint(model=mnist_model,
optimizer=opt,
step_counter=step_counter)
# Horovod: adjust number of steps based on number of GPUs.
for (batch, (images,
labels)) in enumerate(dataset.take(20000 // hvd.size())):
with tf.GradientTape() as tape:
logits = mnist_model(images, training=True)
loss_value = tf.losses.sparse_softmax_cross_entropy(labels, logits)
# Horovod: broadcast initial variable states
# from rank 0 to all other processes. This is necessary to ensure consistent
# initialization of all workers when training is started with random weights
# or restored from a checkpoint.
if batch == 0:
hvd.broadcast_variables(0, mnist_model.variables)
# Horovod: add Horovod Distributed GradientTape.
tape = hvd.DistributedGradientTape(tape)
grads = tape.gradient(loss_value, mnist_model.variables)
opt.apply_gradients(zip(grads, mnist_model.variables),
global_step=tf.train.get_or_create_global_step())
if batch % 10 == 0 and hvd.local_rank() == 0:
print('Step #%d\tLoss: %.6f' % (batch, loss_value))
if hvd.rank() == 0:
checkpoint.save(checkpoint_dir)
def train_first_step(inputs):
images, labels = inputs
with tf.GradientTape() as tape:
probs = model(images, training=True)
loss_value = loss(labels, probs)
tape = hvd_tf.DistributedGradientTape(tape, compression=compression)
grads = tape.gradient(loss_value, model.trainable_variables)
opt.apply_gradients(zip(grads, model.trainable_variables))
hvd_tf.broadcast_variables(model.variables, root_rank=0)
hvd_tf.broadcast_variables(opt.variables(), root_rank=0)
def train_step(model, opt, loss_func, images, labels, first_batch):
with tf.GradientTape() as tape:
probs = model(images, training=True)
loss_value = loss_func(labels, probs)
tape = hvd.DistributedGradientTape(tape, compression=hvd.Compression.fp16)
grads = tape.gradient(loss_value, model.trainable_variables)
opt.apply_gradients(zip(grads, model.trainable_variables))
if first_batch:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(opt.variables(), root_rank=0)
return loss_value
def initialize(self, io_only=False):
tf_trainer.initialize(self, io_only)
# Here, we broadcast parameters from rank 0.
# If the model was restored, this is correct. If not,
# This syncs everythign up.
# print(bcast)
hvd.broadcast_variables(self._net.variables, root_rank=0)
hvd.broadcast_variables(self._opt.variables(), root_rank=0)
def train_one_step(config,
model,
optimizer,
features,
accumulator,
first_step,
take_step,
clip_norm=1.0):
#Forward and Backward pass
with tf.GradientTape() as tape:
total_loss, eval_fn_inputs = model(features, is_training=True)
unscaled_loss = tf.stop_gradient(total_loss)
if config.amp:
total_loss = optimizer.get_scaled_loss(total_loss)
#Backpropogate gradients
#tape = hvd.DistributedGradientTape(
# tape, sparse_as_dense=True,
# compression=Compression.fp16 if config.amp and config.fp16_compression else Compression.none)
gradients = tape.gradient(total_loss, model.trainable_variables)
#Get unscaled gradients if AMP
if config.amp:
gradients = optimizer.get_unscaled_gradients(gradients)
#Accumulate gradients
accumulator(gradients)
#Need to call apply_gradients on very first step irrespective of gradient accumulation
#This is required for the optimizer to build it's states
if first_step or take_step:
#All reduce and Clip the accumulated gradients
allreduced_accumulated_gradients = [
None if g is None else hvd.allreduce(
g / tf.cast(config.gradient_accumulation_steps, g.dtype),
compression=Compression.fp16 if config.amp
and config.fp16_compression else Compression.none)
for g in accumulator.gradients
]
(clipped_accumulated_gradients,
_) = tf.clip_by_global_norm(allreduced_accumulated_gradients,
clip_norm=clip_norm)
#Weight update
optimizer.apply_gradients(
zip(clipped_accumulated_gradients, model.trainable_variables))
accumulator.reset()
#brodcast model weights after first train step
if first_step:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(optimizer.variables(), root_rank=0)
return unscaled_loss, eval_fn_inputs
def train_step(images, labels, first_batch):
gradients, predictions, loss = get_grads(images, labels, first_batch)
gradients = [hvd.allreduce(g.reduce_mean()) for g in gradients]
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
if first_batch:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(optimizer.variables(), root_rank=0)
train_loss(loss.reduce_mean())
train_accuracy(labels, predictions.merge())
return loss.reduce_mean()
def train_step(self, first_epoch):
epoch_global_norm = tf.TensorArray(
tf.float32,
size=self.params['dataloader']["number_of_elements"],
dynamic_size=False,
clear_after_read=False,
)
epoch_loss_avg = tf.TensorArray(
tf.float32,
size=self.params['dataloader']["number_of_elements"],
dynamic_size=False,
clear_after_read=False,
)
for element in self.train_dataset.enumerate():
index = tf.dtypes.cast(element[0], tf.int32)
set = element[1]
shape = [
self.params['dataloader']['batch_size'], self.pixel_num,
self.params['dataloader']['tomographic_bin_number']
]
kappa_data = tf.boolean_mask(tf.transpose(set[0], perm=[0, 2, 1]),
self.bool_mask,
axis=1)
kappa_data = tf.ensure_shape(kappa_data, shape)
labels = set[1]
# Add noise
noise = tf.ensure_shape(self._make_noise(), shape)
kappa_data = tf.math.add(kappa_data, noise)
# Optimize the model
with tf.GradientTape() as tape:
loss_object = tf.keras.losses.MeanAbsoluteError()
y_ = self.model.__call__(kappa_data, training=True)
loss_value = loss_object(y_true=labels, y_pred=y_)
if self.params['training']['distributed']:
tape = hvd.DistributedGradientTape(tape)
grads = tape.gradient(loss_value, self.model.trainable_variables)
self.optimizer.apply_gradients(
zip(grads, self.model.trainable_variables))
if self.params['training'][
'distributed'] and index == 0 and first_epoch:
hvd.broadcast_variables(self.model.variables, root_rank=0)
hvd.broadcast_variables(self.optimizer.variables(),
root_rank=0)
epoch_loss_avg = epoch_loss_avg.write(index, loss_value)
epoch_global_norm = epoch_global_norm.write(
index, tf.linalg.global_norm(grads))
return epoch_loss_avg.stack(), epoch_global_norm.stack()
def join_and_broadcast(self):
hvd.join()
if not self.args.benchmark:
hvd.broadcast_variables(self.model.linear_model.variables, root_rank=0)
hvd.broadcast_variables(self.model.dnn_model.variables, root_rank=0)
hvd.broadcast_variables(self.wide_optimizer.variables(), root_rank=0)
hvd.broadcast_variables(self.deep_optimizer.variables(), root_rank=0)
def train_GAN(_batch_size, _training_epochs, global_size):
tf.keras.mixed_precision.set_global_policy("mixed_float16")
generator = Generator()
random_input = numpy.random.uniform(-1, 1, [1, 100]).astype(numpy.float16)
generated_image = generator(random_input)
discriminator = Discriminator()
classification = discriminator(generated_image)
models = {"generator": generator, "discriminator": discriminator}
opts = {
"generator": tf.keras.optimizers.Adam(0.001),
"discriminator": tf.keras.optimizers.RMSprop(0.0001)
}
if global_size != 1:
hvd.broadcast_variables(generator.variables, root_rank=0)
hvd.broadcast_variables(discriminator.variables, root_rank=0)
hvd.broadcast_variables(opts['generator'].variables(), root_rank=0)
hvd.broadcast_variables(opts['discriminator'].variables(), root_rank=0)
train_loop(_batch_size, _training_epochs, models, opts, global_size)
def train_step(x, y, first_batch):
with tf.GradientTape(persistent=True) as tape:
y_pred = model(x, training=True)
loss = compiled_loss(y, y_pred)
linear_loss = wide_optimizer.get_scaled_loss(
loss) if args.amp else loss
deep_loss = deep_optimizer.get_scaled_loss(
loss) if args.amp else loss
if not args.cpu:
tape = hvd.DistributedGradientTape(tape)
for metric in metrics:
metric.update_state(y, y_pred)
linear_vars = model.linear_model.trainable_variables
dnn_vars = model.dnn_model.trainable_variables
linear_grads = tape.gradient(linear_loss, linear_vars)
dnn_grads = tape.gradient(deep_loss, dnn_vars)
if args.amp:
linear_grads = wide_optimizer.get_unscaled_gradients(linear_grads)
dnn_grads = deep_optimizer.get_unscaled_gradients(dnn_grads)
wide_optimizer.apply_gradients(zip(linear_grads, linear_vars))
deep_optimizer.apply_gradients(zip(dnn_grads, dnn_vars))
if first_batch and not args.cpu:
hvd.broadcast_variables(model.linear_model.variables, root_rank=0)
hvd.broadcast_variables(model.dnn_model.variables, root_rank=0)
hvd.broadcast_variables(wide_optimizer.variables(), root_rank=0)
hvd.broadcast_variables(deep_optimizer.variables(), root_rank=0)
return loss
def __call__(self, x, y):
with tf.GradientTape(persistent=True) as tape:
y_pred = self.model(x, training=True)
loss = self.compiled_loss(y, y_pred)
linear_loss = (
self.wide_optimizer.get_scaled_loss(loss) if self.args.amp else loss
)
deep_loss = (
self.deep_optimizer.get_scaled_loss(loss) if self.args.amp else loss
)
if not self.args.cpu:
tape = hvd.DistributedGradientTape(tape, sparse_as_dense=True)
linear_vars = self.model.linear_model.trainable_variables
dnn_vars = self.model.dnn_model.trainable_variables
linear_grads = tape.gradient(linear_loss, linear_vars)
dnn_grads = tape.gradient(deep_loss, dnn_vars)
if self.args.amp:
linear_grads = self.wide_optimizer.get_unscaled_gradients(linear_grads)
dnn_grads = self.deep_optimizer.get_unscaled_gradients(dnn_grads)
self.wide_optimizer.apply_gradients(zip(linear_grads, linear_vars))
self.deep_optimizer.apply_gradients(zip(dnn_grads, dnn_vars))
if self.current_step_var == 0:
hvd.broadcast_variables(self.model.linear_model.variables, root_rank=0)
hvd.broadcast_variables(self.model.dnn_model.variables, root_rank=0)
hvd.broadcast_variables(self.wide_optimizer.variables(), root_rank=0)
hvd.broadcast_variables(self.deep_optimizer.variables(), root_rank=0)
return loss
def train_one_step(model, opt, x, y, step, loss_func, compression, opts):
preprocess = PreProcess(opts)
with tf.GradientTape(persistent=True) as tape:
logits = model(x, training=True)
loss = loss_func(y, logits)
# scaled_loss = opt.get_scaled_loss(loss)
# Horovod: add Horovod Distributed GradientTape.
tape = hvd.DistributedGradientTape(tape, compression=compression,
op=hvd.Average) # ,device_sparse='/gpu:2', device_dense='/gpu:2')
# scaled_gradients = tape.gradient(scaled_loss, model.trainable_variables)
# grads = opt.get_unscaled_gradients(scaled_gradients)
if opts.lr_scheduler == 'constant':
lr = opts.base_lr
elif opts.lr_scheduler == 'cosine':
lr = cosine_decay_with_warmup(global_step=step,
learning_rate_base=opts.base_lr,
total_steps=opts.steps_per_epoch // 2,
warmup_learning_rate=opts.warmup_learning_rate,
warmup_steps=2 * hvd.size())
elif opts.lr_scheduler == 'cyclic':
lr = cyclic_learning_rate(global_step=step,
base_lr=opts.min_lr,
max_lr=opts.max_lr,
step_size=opts.step_size,
gamma=opts.gamma)
else:
raise NotImplementedError('Unsupported learning rate scheduling type')
tf.keras.backend.set_value(opt.lr, lr)
grads = tape.gradient(loss, model.trainable_variables)
opt.apply_gradients(zip(grads, model.trainable_variables))
if step == 0:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(opt.variables(), root_rank=0)
if not opts.evaluate:
lr = cosine_decay_with_warmup(global_step=step,
learning_rate_base=0.001,
warmup_learning_rate=0.00001,
total_steps=opts.steps_per_epoch // 1,
warmup_steps=2*hvd.size())
opt = tf.keras.optimizers.SGD(learning_rate=lr*hvd.size(), momentum=0.9, nesterov=True)
grads = tape.gradient(loss, model.trainable_variables)
opt.apply_gradients(zip(grads, model.trainable_variables))
if step == 0:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(opt.variables(), root_rank=0)
pred = tf.argmax(logits, axis=-1)
del tape
return loss, pred, opt
def train_step(self, y_sketch_gt, y_sketch_teacher, x_image, first_step):
with tf.GradientTape() as tape:
params = self.forward(y_sketch_teacher, x_image, training=True)[:-1]
total_loss, pen_loss, offset_loss, pixel_loss, kl_loss = self.compute_loss(params, y_sketch_gt, x_image)
if self._distributed:
tape = hvd.DistributedGradientTape(tape)
grads = tape.gradient(total_loss, self._encoder.trainable_variables + self._decoder.trainable_variables)
self._optimizer.apply_gradients(zip(grads, self._encoder.trainable_variables + self._decoder.trainable_variables))
if self._distributed and first_step:
hvd.broadcast_variables(self._encoder.trainable_variables + self._decoder.trainable_variables, root_rank=0)
hvd.broadcast_variables(self._optimizer.variables(), root_rank=0)
return total_loss, pen_loss, offset_loss, pixel_loss, kl_loss
def on_batch_end(self, batch, logs=None):
if self.broadcast_done:
return
with tf.device(self.device):
if hvd._executing_eagerly() and hasattr(self.model, 'variables'):
# TensorFlow 2.0 or TensorFlow eager
hvd.broadcast_variables(self.model.variables,
root_rank=self.root_rank)
hvd.broadcast_variables(self.model.optimizer.variables(),
root_rank=self.root_rank)
else:
bcast_op = hvd.broadcast_global_variables(self.root_rank)
self.backend.get_session().run(bcast_op)
self.broadcast_done = True
def benchmark_step(first_batch):
with tf.GradientTape() as tape:
probs = model(data, training=True)
loss = tf.losses.categorical_crossentropy(target, probs)
gradients = tape.gradient(loss, model.trainable_variables)
opt.apply_gradients(zip(gradients, model.trainable_variables))
# Horovod: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
#
# Note: broadcast should be done after the first gradient step to ensure optimizer
# initialization.
if first_batch:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(opt.variables(), root_rank=0)
def benchmark_step(dataset_inputs, first_batch=False):
x_input, y_label = dataset_inputs
y_label = tf.reshape(y_label, (y_label[0], 1))
with tf.GradientTape() as tape:
prediction = model(x_input, training=True)
loss = tf.losses.sparse_categorical_crossentropy(y_label, prediction)
# Horovod: add Horovod Distributed GradientTape for reduction:=============#
tape = hvd.DistributedGradientTape(tape)
gradients = tape.gradient(loss, model.trainable_variables)
opt.apply_gradients(zip(gradients, model.trainable_variables))
# Horovod: broadcast initial variable states from rank 0 to all other
# processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
# Note: broadcast should be done after the first gradient step to ensure
# optimizer initialization.
if first_batch:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(opt.variables(), root_rank=0)
def train_step(images, labels, first_batch):
gradients, loss, predictions = get_grads(images, labels)
# Rubik: Accumulate the gradients across microbatches
# Horovod: Allreduce the accumulated gradients
gradients = [hvd.allreduce(g.accumulate()) for g in gradients]
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
# Horovod: Broadcast the variables after first batch
if first_batch:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(optimizer.variables(), root_rank=0)
# Rubik: Average the loss across microbatches
train_loss(loss.reduce_mean())
# Rubik: Merge predictions across microbatches
train_accuracy(labels, predictions.merge())
return loss.reduce_mean()