def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of 'train'/'test'/'predict'.
Returns:
Feed values for the model in the given mode.
"""
strategy = model._distribution_strategy
inputs, targets, sample_weights = _get_input_from_iterator(inputs, model)
inputs = distributed_training_utils.flatten_perdevice_values(strategy, inputs)
targets = distributed_training_utils.flatten_perdevice_values(
strategy, targets)
if mode == 'predict':
sample_weights = []
targets = []
else:
sample_weights = [
None for _ in range(len(model.outputs) * strategy.num_replicas_in_sync)
]
ins = inputs + targets + sample_weights
if mode == 'train' and not isinstance(K.learning_phase(), int):
ins += [True]
return ins
def _prepare_feed_values(model, inputs, targets, sample_weights, mode):
"""Prepare feed values to the model execution function.
Arguments:
model: Model to prepare feed values for.
inputs: List or dict of model inputs.
targets: Optional list of model targets.
sample_weights: Optional list of sample weight arrays.
mode: One of 'train'/'test'/'predict'.
Returns:
Feed values for the model in the given mode.
"""
strategy = model._distribution_strategy
inputs, targets, sample_weights = _get_input_from_iterator(inputs, model)
inputs = distributed_training_utils.flatten_perdevice_values(
strategy, inputs)
targets = distributed_training_utils.flatten_perdevice_values(
strategy, targets)
if mode == 'predict':
sample_weights = []
targets = []
else:
sample_weights = [
None
for _ in range(len(model.outputs) * strategy.num_replicas_in_sync)
]
ins = inputs + targets + sample_weights
if mode == 'train' and not isinstance(K.symbolic_learning_phase(), int):
ins += [True]
return ins
def predict_loop(model, iterator, verbose=0, steps=None):
"""Predict loop for predicting with DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
current_strategy = model._distribution_strategy
# TODO(priyag, sourabhbajaj): Remove this when the codepaths are merged.
if current_strategy.__class__.__name__ == 'TPUStrategy':
return _experimental_predict_loop(model, iterator, verbose, steps)
if not model._grouped_model:
clone_model_on_replicas(model, current_strategy)
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs, model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
inputs, _, _ = _get_input_from_iterator(iterator, model)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_replica(
_per_device_predict_function, args=(model._grouped_model, ))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs,
grouped_updates, grouped_session_args)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
distributed_predict_function = K.function(
all_inputs,
all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(current_strategy,
distributed_model,
orig_model_weights)
num_replicas = current_strategy.num_replicas_in_sync
# Since we do not know how many samples we will see, we cannot
# pre-allocate the returned Numpy arrays. Instead, we store one array per
# batch seen and concatenate them upon returning.
unconcatenated_outs = []
assert steps is not None
for step in range(steps):
batch_outs = distributed_predict_function(dataset_inputs)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if step == 0:
# batch_outs gives you the number of model outputs. In the distributed
# case this will be number of model_outputs * num_replicas.
for _ in range(len(model.outputs)):
unconcatenated_outs.append([])
for i in range(len(model.outputs)):
nested_outs = batch_outs[i * num_replicas:i * num_replicas +
num_replicas]
outs = nest.flatten(nested_outs)
unconcatenated_outs[i].extend(outs)
if verbose >= 1:
progbar.update(step + 1)
if len(unconcatenated_outs) == 1:
return np.concatenate(unconcatenated_outs[0], axis=0)
return [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
def fit_loop(model,
iterator,
epochs=100,
verbose=1,
callbacks=None,
val_iterator=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None):
"""Fit loop for training with DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
epochs: Number of times to iterate over the data
verbose: Integer, Verbosity mode, 0, 1 or 2
callbacks: List of callbacks to be called during training
val_iterator: Iterator for validation data.
initial_epoch: Epoch at which to start training
(useful for resuming a previous training run)
steps_per_epoch: Total number of steps (batches of samples)
before declaring one epoch finished and starting the
next epoch. Ignored with the default value of `None`.
validation_steps: Number of steps to run validation for
(only if doing validation from data tensors).
Ignored with the default value of `None`.
Returns:
`History` object.
Raises:
ValueError: in case of invalid arguments.
"""
current_strategy = model._distribution_strategy
# TODO(priyag, sourabhbajaj): Remove this when the codepaths are merged.
if current_strategy.__class__.__name__ == 'TPUStrategy':
return _experimental_fit_loop(model, iterator, epochs, verbose,
callbacks, initial_epoch,
steps_per_epoch, val_iterator,
validation_steps)
if not model._grouped_model:
clone_model_on_replicas(model,
current_strategy,
make_callback_model=True)
def _per_device_fit_function(model):
model._make_fit_function()
return (model._fit_function.inputs, model._fit_function.outputs,
model._fit_function.updates_op,
model._fit_function.session_kwargs)
inputs, targets, sample_weights = _get_input_from_iterator(iterator, model)
with current_strategy.scope():
# Create train ops on each of the devices when we call
# `_per_device_fit_function`.
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_replica(
_per_device_fit_function, args=(model._grouped_model, ))
# Initialize the variables in the replicated model. This is necessary for
# multi-worker training because on some workers, initialization is not
# needed. This method does initialization or waiting for initialization
# according to the context object of distribute coordinator.
distributed_training_utils.init_restore_or_wait_for_variables()
# Unwrap all the per device values returned from `call_for_each_replica`.
# Unwrapping per device values gives you a list of values that can be
# used to construct a new train function that is composed of update ops on
# all the devices over which the model is distributed.
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy,
grouped_inputs,
grouped_outputs,
grouped_updates,
grouped_session_args,
with_loss_tensor=True)
# Dataset inputs and targets are also per devices values that need to be
# unwrapped.
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
dataset_targets = distributed_training_utils.flatten_perdevice_values(
current_strategy, targets)
# Create a train function that is composed of all the parameters above.
distributed_fit_function = K.function(all_inputs,
all_outputs,
updates=all_updates,
name='distributed_fit_function',
**all_session_args)
# We need to set sample_weights to None since there are sample weight
# placeholders that are created with default values.
sample_weights = [
None for _ in range(
len(model.outputs) * current_strategy.num_replicas_in_sync)
]
if not isinstance(K.learning_phase(), int):
ins = dataset_inputs + dataset_targets + sample_weights + [1]
else:
ins = dataset_inputs + dataset_targets
do_validation = False
if validation_steps:
do_validation = True
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(current_strategy,
distributed_model,
orig_model_weights)
callbacks = cbks.configure_callbacks(callbacks,
model,
do_validation=do_validation,
val_inputs=None,
val_targets=None,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
verbose=verbose)
out_labels = model.metrics_names or []
callbacks.on_train_begin()
assert steps_per_epoch is not None
for epoch in range(initial_epoch, epochs):
# Reset stateful metrics
for m in model.stateful_metric_functions:
m.reset_states()
callbacks.on_epoch_begin(epoch)
epoch_logs = {}
for step_index in range(steps_per_epoch):
batch_logs = {'batch': step_index, 'size': 1}
callbacks.on_batch_begin(step_index, batch_logs)
try:
outs = distributed_fit_function(ins)
except errors.OutOfRangeError:
logging.warning(
'Your dataset iterator ran out of data; '
'interrupting training. Make sure that your dataset '
'can generate at least `steps_per_epoch * epochs` '
'batches (in this case, %d batches).' %
steps_per_epoch * epochs)
break
if not isinstance(outs, list):
outs = [outs]
for l, o in zip(out_labels, outs):
batch_logs[l] = o
callbacks.on_batch_end(step_index, batch_logs)
if callbacks.model.stop_training:
break
if do_validation:
val_outs = test_loop(model,
val_iterator,
steps=validation_steps,
verbose=0)
if not isinstance(val_outs, list):
val_outs = [val_outs]
# Same labels assumed.
for l, o in zip(out_labels, val_outs):
epoch_logs['val_' + l] = o
callbacks.on_epoch_end(epoch, epoch_logs)
if callbacks.model.stop_training:
break
callbacks.on_train_end()
# Copy the weights back from the replicated model to the original model.
updated_weights = current_strategy.unwrap(
model._grouped_model)[0].get_weights()
model.set_weights(updated_weights)
return model.history
def test_loop(model, iterator, verbose=0, steps=None):
"""Test loop for evaluating with DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the outputs.
"""
current_strategy = model._distribution_strategy
# TODO(priyag, sourabhbajaj): Remove this when the codepaths are merged.
if current_strategy.__class__.__name__ == 'TPUStrategy':
return _experimental_test_loop(model, iterator, verbose, steps)
if not model._grouped_model:
clone_model_on_replicas(model, current_strategy)
def _per_device_eval_function(model):
model._make_eval_function()
return (model._eval_function.inputs, model._eval_function.outputs,
model._eval_function.updates_op,
model._eval_function.session_kwargs)
inputs, targets, sample_weights = _get_input_from_iterator(iterator, model)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_replica(
_per_device_eval_function, args=(model._grouped_model, ))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy,
grouped_inputs,
grouped_outputs,
grouped_updates,
grouped_session_args,
with_loss_tensor=True)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
dataset_targets = distributed_training_utils.flatten_perdevice_values(
current_strategy, targets)
distributed_test_function = K.function(
all_inputs,
all_outputs,
updates=all_updates,
name='distributed_test_function',
**all_session_args)
# We need to set sample_weights to None since there are sample weight
# placeholders that are created with default values.
sample_weights = [
None for _ in range(
len(model.outputs) * current_strategy.num_replicas_in_sync)
]
ins = dataset_inputs + dataset_targets + sample_weights
for m in model.stateful_metric_functions:
m.reset_states()
outs = []
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(current_strategy,
distributed_model,
orig_model_weights)
assert steps is not None
for step in range(steps):
batch_outs = distributed_test_function(ins)
if isinstance(batch_outs, list):
if step == 0:
outs = [0.] * len(batch_outs)
outs[0] += batch_outs[0] # index 0 = 'loss'
outs[1:] = batch_outs[1:]
else:
if step == 0:
outs.append(0.)
outs[0] += batch_outs # index 0 = 'loss'
if verbose >= 1:
progbar.update(step + 1)
outs[0] /= steps # index 0 = 'loss'
if len(outs) == 1:
return outs[0]
return outs
def predict_loop(model, iterator, verbose=0, steps=None):
"""Predict loop for predicting with DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
current_strategy = model._distribution_strategy
# TODO(priyag, sourabhbajaj): Remove this when the codepaths are merged.
if current_strategy.__class__.__name__ == 'TPUStrategy':
return _experimental_predict_loop(model, iterator, verbose, steps)
if not model._grouped_model:
clone_model_on_replicas(model, current_strategy)
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs,
model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
inputs, _, _ = _get_input_from_iterator(iterator, model)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_replica(
_per_device_predict_function, args=(model._grouped_model,))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
distributed_predict_function = K.function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
if not isinstance(K.learning_phase(), int):
ins = dataset_inputs + [0]
else:
ins = dataset_inputs
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(
current_strategy, distributed_model, orig_model_weights)
num_replicas = current_strategy.num_replicas_in_sync
# Since we do not know how many samples we will see, we cannot
# pre-allocate the returned Numpy arrays. Instead, we store one array per
# batch seen and concatenate them upon returning.
unconcatenated_outs = []
assert steps is not None
for step in range(steps):
batch_outs = distributed_predict_function(ins)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if step == 0:
# batch_outs gives you the number of model outputs. In the distributed
# case this will be number of model_outputs * num_replicas.
for _ in range(len(model.outputs)):
unconcatenated_outs.append([])
for i in range(len(model.outputs)):
nested_outs = batch_outs[i * num_replicas:
i * num_replicas + num_replicas]
outs = nest.flatten(nested_outs)
unconcatenated_outs[i].extend(outs)
if verbose >= 1:
progbar.update(step + 1)
if len(unconcatenated_outs) == 1:
return np.concatenate(unconcatenated_outs[0], axis=0)
return [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
def fit_loop(
model,
iterator,
epochs=100,
verbose=1,
callbacks=None,
val_iterator=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None):
"""Fit loop for training with DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
epochs: Number of times to iterate over the data
verbose: Integer, Verbosity mode, 0, 1 or 2
callbacks: List of callbacks to be called during training
val_iterator: Iterator for validation data.
initial_epoch: Epoch at which to start training
(useful for resuming a previous training run)
steps_per_epoch: Total number of steps (batches of samples)
before declaring one epoch finished and starting the
next epoch. Ignored with the default value of `None`.
validation_steps: Number of steps to run validation for
(only if doing validation from data tensors).
Ignored with the default value of `None`.
Returns:
`History` object.
Raises:
ValueError: in case of invalid arguments.
"""
current_strategy = model._distribution_strategy
# TODO(priyag, sourabhbajaj): Remove this when the codepaths are merged.
if current_strategy.__class__.__name__ == 'TPUStrategy':
return _experimental_fit_loop(
model, iterator, epochs, verbose, callbacks, initial_epoch,
steps_per_epoch, val_iterator, validation_steps)
if not model._grouped_model:
clone_model_on_replicas(model, current_strategy, make_callback_model=True)
def _per_device_fit_function(model):
model._make_fit_function()
return (model._fit_function.inputs, model._fit_function.outputs,
model._fit_function.updates_op, model._fit_function.session_kwargs)
inputs, targets, sample_weights = _get_input_from_iterator(iterator, model)
with current_strategy.scope():
# Create train ops on each of the devices when we call
# `_per_device_fit_function`.
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_replica(
_per_device_fit_function, args=(model._grouped_model,))
# Unwrap all the per device values returned from `call_for_each_replica`.
# Unwrapping per device values gives you a list of values that can be
# used to construct a new train function that is composed of update ops on
# all the devices over which the model is distributed.
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs,
grouped_updates, grouped_session_args, with_loss_tensor=True)
# Dataset inputs and targets are also per devices values that need to be
# unwrapped.
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
dataset_targets = distributed_training_utils.flatten_perdevice_values(
current_strategy, targets)
# Create a train function that is composed of all the parameters above.
distributed_fit_function = K.function(
all_inputs,
all_outputs,
updates=all_updates,
name='distributed_fit_function',
**all_session_args)
# We need to set sample_weights to None since there are sample weight
# placeholders that are created with default values.
sample_weights = [None for _ in range(
len(model.outputs) * current_strategy.num_replicas_in_sync)]
if not isinstance(K.learning_phase(), int):
ins = dataset_inputs + dataset_targets + sample_weights + [1]
else:
ins = dataset_inputs + dataset_targets
do_validation = False
if validation_steps:
do_validation = True
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(
current_strategy, distributed_model, orig_model_weights)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=do_validation,
val_inputs=None,
val_targets=None,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
verbose=verbose)
out_labels = model.metrics_names or []
callbacks.on_train_begin()
assert steps_per_epoch is not None
for epoch in range(initial_epoch, epochs):
# Reset stateful metrics
for m in model.stateful_metric_functions:
m.reset_states()
callbacks.on_epoch_begin(epoch)
epoch_logs = {}
for step_index in range(steps_per_epoch):
batch_logs = {'batch': step_index, 'size': 1}
callbacks.on_batch_begin(step_index, batch_logs)
try:
outs = distributed_fit_function(ins)
except errors.OutOfRangeError:
logging.warning('Your dataset iterator ran out of data; '
'interrupting training. Make sure that your dataset '
'can generate at least `steps_per_epoch * epochs` '
'batches (in this case, %d batches).' %
steps_per_epoch * epochs)
break
if not isinstance(outs, list):
outs = [outs]
for l, o in zip(out_labels, outs):
batch_logs[l] = o
callbacks.on_batch_end(step_index, batch_logs)
if callbacks.model.stop_training:
break
if do_validation:
val_outs = test_loop(
model,
val_iterator,
steps=validation_steps,
verbose=0)
if not isinstance(val_outs, list):
val_outs = [val_outs]
# Same labels assumed.
for l, o in zip(out_labels, val_outs):
epoch_logs['val_' + l] = o
callbacks.on_epoch_end(epoch, epoch_logs)
if callbacks.model.stop_training:
break
callbacks.on_train_end()
# Copy the weights back from the replicated model to the original model.
updated_weights = current_strategy.unwrap(
model._grouped_model)[0].get_weights()
model.set_weights(updated_weights)
return model.history
def test_loop(model, iterator, verbose=0, steps=None):
"""Test loop for evaluating with DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the outputs.
"""
current_strategy = model._distribution_strategy
# TODO(priyag, sourabhbajaj): Remove this when the codepaths are merged.
if current_strategy.__class__.__name__ == 'TPUStrategy':
return _experimental_test_loop(model, iterator, verbose, steps)
if not model._grouped_model:
clone_model_on_replicas(model, current_strategy)
def _per_device_eval_function(model):
model._make_eval_function()
return (model._eval_function.inputs, model._eval_function.outputs,
model._eval_function.updates_op,
model._eval_function.session_kwargs)
inputs, targets, sample_weights = _get_input_from_iterator(iterator, model)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_replica(
_per_device_eval_function, args=(model._grouped_model,))
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args, with_loss_tensor=True)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
dataset_targets = distributed_training_utils.flatten_perdevice_values(
current_strategy, targets)
distributed_test_function = K.function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_test_function',
**all_session_args)
# We need to set sample_weights to None since there are sample weight
# placeholders that are created with default values.
sample_weights = [None for _ in range(
len(model.outputs) * current_strategy.num_replicas_in_sync)]
if not isinstance(K.learning_phase(), int):
ins = dataset_inputs + dataset_targets + sample_weights + [0]
else:
ins = dataset_inputs + dataset_targets
for m in model.stateful_metric_functions:
m.reset_states()
outs = []
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(
current_strategy, distributed_model, orig_model_weights)
assert steps is not None
for step in range(steps):
batch_outs = distributed_test_function(ins)
if isinstance(batch_outs, list):
if step == 0:
outs = [0.] * len(batch_outs)
outs[0] += batch_outs[0] # index 0 = 'loss'
outs[1:] = batch_outs[1:]
else:
if step == 0:
outs.append(0.)
outs[0] += batch_outs # index 0 = 'loss'
if verbose >= 1:
progbar.update(step + 1)
outs[0] /= steps # index 0 = 'loss'
if len(outs) == 1:
return outs[0]
return outs
def predict_loop(model, iterator, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
current_strategy = model._distribution_strategy
clone_model_on_towers(model, current_strategy)
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs, model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
inputs, _ = _get_input_from_iterator(iterator, model)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_tower(
_per_device_predict_function, model._grouped_model)
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs,
grouped_updates, grouped_session_args)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
distributed_predict_function = K.Function(
all_inputs,
all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = dataset_inputs + [0]
else:
ins = dataset_inputs
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(current_strategy,
distributed_model,
orig_model_weights)
if steps is not None:
# Since we do not know how many samples we will see, we cannot pre-allocate
# the returned Numpy arrays. Instead, we store one array per batch seen
# and concatenate them upon returning.
unconcatenated_outs = []
for step in range(steps):
batch_outs = distributed_predict_function(ins)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if step == 0:
for _ in batch_outs:
unconcatenated_outs.append([])
for i, batch_out in enumerate(batch_outs):
unconcatenated_outs[i].append(batch_out)
if verbose == 1:
progbar.update(step + 1)
if len(unconcatenated_outs) == 1:
return np.concatenate(unconcatenated_outs[0], axis=0)
return [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
def test_loop(model, iterator, verbose=0, steps=None):
"""evaluate method to validate a model that uses DistributionStrategy.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
"""
current_strategy = model._distribution_strategy
clone_model_on_towers(model, current_strategy)
def _per_device_test_function(model):
model._make_test_function()
return (model.test_function.inputs, model.test_function.outputs,
model.test_function.updates_op,
model.test_function.session_kwargs)
inputs, targets = _get_input_from_iterator(iterator, model)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_tower(
_per_device_test_function, model._grouped_model)
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy,
grouped_inputs,
grouped_outputs,
grouped_updates,
grouped_session_args,
with_loss_tensor=True)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
dataset_targets = distributed_training_utils.flatten_perdevice_values(
current_strategy, targets)
distributed_test_function = K.Function(all_inputs,
all_outputs,
updates=all_updates,
name='distributed_test_function',
**all_session_args)
# We need to set sample_weights to None since there are sample weight
# placeholders that are created with default values.
sample_weights = [
None for _ in range(len(model.outputs) * current_strategy.num_towers)
]
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = dataset_inputs + dataset_targets + sample_weights + [0]
else:
ins = dataset_inputs + dataset_targets
outs = []
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(current_strategy,
distributed_model,
orig_model_weights)
if steps is not None:
for step in range(steps):
batch_outs = distributed_test_function(ins)
batch_outs = _aggregate_metrics_across_towers(
current_strategy.num_towers, model.metrics_names, batch_outs)
if isinstance(batch_outs, list):
if step == 0:
for _ in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
outs[i] += batch_out
else:
if step == 0:
outs.append(0.)
outs[0] += batch_outs
if verbose == 1:
progbar.update(step + 1)
for i in range(len(outs)):
outs[i] /= steps
if len(outs) == 1:
return outs[0]
return outs
def experimental_tpu_predict_loop(model,
dataset,
verbose=0,
steps=None,
callbacks=None):
"""Predict loop for predicting with TPU DistributionStrategy.
Arguments:
model: Keras Model instance.
dataset: Dataset for input data.
verbose: Integer, Verbosity mode 0 or 1.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
callbacks: List of callbacks to be called during training
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
mode = ModeKeys.PREDICT
steps = training_utils.infer_steps_for_dataset(dataset,
steps,
steps_name='steps')
dataset_fully_shaped = (
distributed_training_utils.is_dataset_shape_fully_defined(dataset))
padding_handler = None
if not dataset_fully_shaped:
# TODO(hongjunchoi): Investigate whether operations from
# PartialBatchPaddingHandler are unnecessarily pruned out
# during graph optimization.
padding_handler = padding_util.PartialBatchPaddingHandler(
model._feed_output_shapes)
batch_size, _, prefetch_buffer = input_lib._get_dataset_attributes(
dataset)
padding_handler.padded_batch_size = batch_size
padding_handler.padding_mask = dataset.reduce(
padding_handler.padding_mask, padding_handler.update_mask)
dataset = dataset.map(padding_handler.pad_batch)
dataset = dataset.apply(batching.unbatch())
# Upon this point, it is guaranteed that the dataset does not
# have partial batches. Thus, we set `drop_remainder=True` to
# get static shape information about the elements in the dataset.
dataset = dataset.batch(batch_size, drop_remainder=True)
if prefetch_buffer is not None:
dataset = dataset.prefetch(prefetch_buffer)
current_strategy = model._distribution_strategy
iterator = distributed_training_utils.get_iterator(dataset,
current_strategy)
scope = distributed_training_utils.distributed_scope(
strategy=current_strategy, learning_phase=0)
scope.__enter__()
def _predict_step_fn(inputs):
"""A fn that returns output of single prediction step."""
(distribution_strategy_context.get_replica_context().merge_call(
_build_model, args=(model, mode, inputs)))
(_, outputs, updates, _) = (_per_device_execution_function(
distributed_training_utils.get_distributed_model(model, mode),
mode))
with ops.control_dependencies([updates]):
return outputs
# TODO(hongjunchoi): When numpy array is passed as an input to `predict()`
# use numpy arrays directly to avoid cumulating unnecessary input pipeline
# ops.
predict_input_data = iterator.get_next()
per_replica_outputs = current_strategy.experimental_run_v2(
_predict_step_fn, args=(predict_input_data, ))
output_tensors = distributed_training_utils.flatten_perdevice_values(
current_strategy, per_replica_outputs)
if verbose == 1:
progbar = Progbar(target=steps)
if model._compile_distribution:
distributed_training_utils._copy_weights_to_distributed_model(
model, mode)
distributed_training_utils._reset_metrics(model)
callbacks = cbks.configure_callbacks(callbacks,
model,
do_validation=False,
epochs=1,
steps_per_epoch=steps,
verbose=verbose,
count_mode='steps',
mode=mode)
callbacks._call_begin_hook(mode)
# Since we do not know how many samples we will see, we cannot pre-allocate
# the returned Numpy arrays. Instead, we store one array per batch seen
# and concatenate them upon returning.
num_model_outputs = len(model.output_names)
unconcatenated_outs = [[] for _ in range(num_model_outputs)]
if steps is not None:
target_steps = steps
else:
target_steps = np.inf
current_step = 0
while current_step < target_steps:
batch_logs = {'batch': current_step, 'size': 1}
callbacks._call_batch_hook(mode, 'begin', current_step, batch_logs)
try:
predict_ops = control_flow_ops.group(output_tensors)
_, batch_outs = K.batch_get_value([predict_ops, output_tensors])
except errors.OutOfRangeError:
if steps is not None:
warning_msg = 'Make sure that your dataset can generate at least '
'`steps` batches (in this case, {} batches).'.format(steps)
else:
warning_msg = 'Number of steps ran: {} steps'.format(
current_step)
logging.warning('Your dataset iterator ran out of data; '
'interrupting evaluation. ' + warning_msg)
break
# TODO(priyag): maybe need to unwrap the outputs first for MirroredStrategy.
for i in range(num_model_outputs):
output_start_index = i * current_strategy.num_replicas_in_sync
output_end_index = (output_start_index +
current_strategy.num_replicas_in_sync)
single_model_output = batch_outs[
output_start_index:output_end_index]
unconcatenated_outs[i].extend(single_model_output)
batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
callbacks._call_batch_hook(mode, 'end', current_step, batch_logs)
if verbose >= 1:
progbar.update(current_step + 1)
current_step += 1
callbacks._call_end_hook(mode)
scope.__exit__(None, None, None)
if len(unconcatenated_outs) == 1:
prediction_result = np.concatenate(unconcatenated_outs[0], axis=0)
else:
prediction_result = [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
if padding_handler:
prediction_result = padding_handler.apply_mask(prediction_result)
return prediction_result
def fit_loop(model,
iterator,
epochs=100,
verbose=1,
callbacks=None,
val_iterator=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None):
"""fit function when using DistributionStrategy for training.
Arguments:
model: Keras Model instance.
iterator: Iterator for input data.
epochs: Number of times to iterate over the data
verbose: Verbosity mode, 0, 1 or 2
callbacks: List of callbacks to be called during training
val_iterator: Iterator for validation data.
initial_epoch: Epoch at which to start training
(useful for resuming a previous training run)
steps_per_epoch: Total number of steps (batches of samples)
before declaring one epoch finished and starting the
next epoch. Ignored with the default value of `None`.
validation_steps: Number of steps to run validation for
(only if doing validation from data tensors).
Ignored with the default value of `None`.
Returns:
`History` object.
Raises:
ValueError: in case of invalid arguments.
"""
current_strategy = model._distribution_strategy
def _per_device_train_function(model):
model._make_train_function()
return (model.train_function.inputs, model.train_function.outputs,
model.train_function.updates_op,
model.train_function.session_kwargs)
inputs, targets = _get_input_from_iterator(iterator, model)
with current_strategy.scope():
# Create train ops on each of the devices when we call
# `_per_device_train_function`.
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_tower(
_per_device_train_function, model._grouped_model)
# Unwrap all the per device values returned from `call_for_each_tower`.
# Unwrapping per device values gives you a list of values that can be
# used to construct a new train function that is composed of update ops on
# all the devices over which the model is distributed.
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy,
grouped_inputs,
grouped_outputs,
grouped_updates,
grouped_session_args,
with_loss_tensor=True)
# Dataset inputs and targets are also per devices values that need to be
# unwrapped.
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
dataset_targets = distributed_training_utils.flatten_perdevice_values(
current_strategy, targets)
# Create a train function that is composed of all the parameters above.
distributed_train_function = K.Function(all_inputs,
all_outputs,
updates=all_updates,
name='distributed_train_function',
**all_session_args)
# We need to set sample_weights to None since there are sample weight
# placeholders that are created with default values.
sample_weights = [
None for _ in range(len(model.outputs) * current_strategy.num_towers)
]
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = dataset_inputs + dataset_targets + sample_weights + [1]
else:
ins = dataset_inputs + dataset_targets
do_validation = False
if validation_steps:
do_validation = True
if steps_per_epoch is None:
raise ValueError('Can only use `validation_steps` '
'when doing step-wise '
'training, i.e. `steps_per_epoch` '
'must be set.')
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(current_strategy,
distributed_model,
orig_model_weights)
callbacks = cbks.configure_callbacks(callbacks,
model,
do_validation=do_validation,
val_inputs=None,
val_targets=None,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
verbose=verbose)
out_labels = model.metrics_names or []
callbacks.on_train_begin()
for epoch in range(initial_epoch, epochs):
callbacks.on_epoch_begin(epoch)
if steps_per_epoch is not None:
epoch_logs = {}
for step_index in range(steps_per_epoch):
batch_logs = {'batch': step_index, 'size': 1}
callbacks.on_batch_begin(step_index, batch_logs)
try:
outs = distributed_train_function(ins)
except errors.OutOfRangeError:
logging.warning(
'Your dataset iterator ran out of data; '
'interrupting training. Make sure that your dataset '
'can generate at least `steps_per_epoch * epochs` '
'batches (in this case, %d batches).' %
steps_per_epoch * epochs)
break
if not isinstance(outs, list):
outs = [outs]
outs = _aggregate_metrics_across_towers(
current_strategy.num_towers, out_labels, outs)
for l, o in zip(out_labels, outs):
batch_logs[l] = o
callbacks.on_batch_end(step_index, batch_logs)
if callbacks.model.stop_training:
break
if do_validation:
val_outs = test_loop(model,
val_iterator,
steps=validation_steps,
verbose=0)
if not isinstance(val_outs, list):
val_outs = [val_outs]
# Same labels assumed.
for l, o in zip(out_labels, val_outs):
epoch_logs['val_' + l] = o
callbacks.on_epoch_end(epoch, epoch_logs)
if callbacks.model.stop_training:
break
callbacks.on_train_end()
# Copy the weights back from the replicated model to the original model.
with current_strategy.scope():
updated_weights = current_strategy.unwrap(
model._grouped_model)[0].get_weights()
model.set_weights(updated_weights)
return model.history
def fit_loop(
model,
inputs,
targets,
epochs=100,
verbose=1,
callbacks=None,
val_inputs=None,
val_targets=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None):
"""fit function when using DistributionStrategy for training.
Arguments:
model: Keras Model instance.
inputs: List of input arrays.
targets: List of target arrays.
epochs: Number of times to iterate over the data
verbose: Verbosity mode, 0, 1 or 2
callbacks: List of callbacks to be called during training
val_inputs: List of input arrays.
val_targets: List of target arrays.
initial_epoch: Epoch at which to start training
(useful for resuming a previous training run)
steps_per_epoch: Total number of steps (batches of samples)
before declaring one epoch finished and starting the
next epoch. Ignored with the default value of `None`.
validation_steps: Number of steps to run validation for
(only if doing validation from data tensors).
Ignored with the default value of `None`.
Returns:
`History` object.
Raises:
ValueError: in case of invalid arguments.
"""
current_strategy = model._distribution_strategy
def _per_device_train_function(model):
model._make_train_function()
return (model.train_function.inputs,
model.train_function.outputs,
model.train_function.updates_op,
model.train_function.session_kwargs)
with current_strategy.scope():
# Create train ops on each of the devices when we call
# `_per_device_train_function`.
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_tower(
_per_device_train_function, model._grouped_model)
# Unwrap all the per device values returned from `call_for_each_tower`.
# Unwrapping per device values gives you a list of values that can be
# used to construct a new train function that is composed of update ops on
# all the devices over which the model is distributed.
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs,
grouped_updates, grouped_session_args, with_loss_tensor=True)
# Dataset inputs and targets are also per devices values that need to be
# unwrapped.
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
dataset_targets = distributed_training_utils.flatten_perdevice_values(
current_strategy, targets)
# Create a train function that is composed of all the parameters above.
distributed_train_function = K.Function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_train_function',
**all_session_args)
# We need to set sample_weights to None since there are sample weight
# placeholders that are created with default values.
sample_weights = [None for _ in range(len(model.outputs) *
current_strategy.num_towers)]
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = dataset_inputs + dataset_targets + sample_weights + [1]
else:
ins = dataset_inputs + dataset_targets
do_validation = False
if validation_steps:
do_validation = True
if steps_per_epoch is None:
raise ValueError('Can only use `validation_steps` '
'when doing step-wise '
'training, i.e. `steps_per_epoch` '
'must be set.')
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(
current_strategy, distributed_model, orig_model_weights)
callbacks = cbks.configure_callbacks(
callbacks,
model,
do_validation=do_validation,
val_inputs=None,
val_targets=None,
epochs=epochs,
steps_per_epoch=steps_per_epoch,
verbose=verbose)
out_labels = model.metrics_names or []
callbacks.on_train_begin()
for epoch in range(initial_epoch, epochs):
callbacks.on_epoch_begin(epoch)
if steps_per_epoch is not None:
epoch_logs = {}
for step_index in range(steps_per_epoch):
batch_logs = {'batch': step_index, 'size': 1}
callbacks.on_batch_begin(step_index, batch_logs)
try:
outs = distributed_train_function(ins)
except errors.OutOfRangeError:
logging.warning('Your dataset iterator ran out of data; '
'interrupting training. Make sure that your dataset '
'can generate at least `steps_per_epoch * epochs` '
'batches (in this case, %d batches).' %
steps_per_epoch * epochs)
break
if not isinstance(outs, list):
outs = [outs]
outs = _aggregate_metrics_across_towers(
len(current_strategy._devices), out_labels, outs)
for l, o in zip(out_labels, outs):
batch_logs[l] = o
callbacks.on_batch_end(step_index, batch_logs)
if callbacks.model.stop_training:
break
if do_validation:
val_outs = test_loop(
model,
val_inputs,
val_targets,
steps=validation_steps,
verbose=0)
if not isinstance(val_outs, list):
val_outs = [val_outs]
# Same labels assumed.
for l, o in zip(out_labels, val_outs):
epoch_logs['val_' + l] = o
callbacks.on_epoch_end(epoch, epoch_logs)
if callbacks.model.stop_training:
break
callbacks.on_train_end()
# Copy the weights back from the replicated model to the original model.
with current_strategy.scope():
updated_weights = current_strategy.unwrap(
model._grouped_model)[0].get_weights()
model.set_weights(updated_weights)
return model.history
def predict_loop(model, inputs, verbose=0, steps=None):
"""Abstract method to loop over some data in batches.
Arguments:
model: Keras Model instance.
inputs: list of tensors to be fed to `f`.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring `_predict_loop` finished.
Ignored with the default value of `None`.
Returns:
Array of predictions (if the model has a single output)
or list of arrays of predictions
(if the model has multiple outputs).
"""
current_strategy = model._distribution_strategy
def _per_device_predict_function(model):
model._make_predict_function()
return (model.predict_function.inputs,
model.predict_function.outputs,
model.predict_function.updates_op,
model.predict_function.session_kwargs)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_tower(
_per_device_predict_function, model._grouped_model)
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
distributed_predict_function = K.Function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_predict_function',
**all_session_args)
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = dataset_inputs + [0]
else:
ins = dataset_inputs
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(
current_strategy, distributed_model, orig_model_weights)
if steps is not None:
# Since we do not know how many samples we will see, we cannot pre-allocate
# the returned Numpy arrays. Instead, we store one array per batch seen
# and concatenate them upon returning.
unconcatenated_outs = []
for step in range(steps):
batch_outs = distributed_predict_function(ins)
if not isinstance(batch_outs, list):
batch_outs = [batch_outs]
if step == 0:
for _ in batch_outs:
unconcatenated_outs.append([])
for i, batch_out in enumerate(batch_outs):
unconcatenated_outs[i].append(batch_out)
if verbose == 1:
progbar.update(step + 1)
if len(unconcatenated_outs) == 1:
return np.concatenate(unconcatenated_outs[0], axis=0)
return [
np.concatenate(unconcatenated_outs[i], axis=0)
for i in range(len(unconcatenated_outs))
]
def test_loop(model, inputs, targets, verbose=0, steps=None):
"""evaluate method to validate a model that uses DistributionStrategy.
Arguments:
model: Keras Model instance.
inputs: List of input arrays.
targets: List of target arrays.
verbose: verbosity mode.
steps: Total number of steps (batches of samples)
before declaring predictions finished.
Ignored with the default value of `None`.
Returns:
Scalar loss (if the model has a single output and no metrics)
or list of scalars (if the model has multiple outputs
and/or metrics). The attribute `model.metrics_names` will give you
the display labels for the scalar outputs.
"""
current_strategy = model._distribution_strategy
def _per_device_test_function(model):
model._make_test_function()
return (model.test_function.inputs,
model.test_function.outputs,
model.test_function.updates_op,
model.test_function.session_kwargs)
with current_strategy.scope():
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_tower(
_per_device_test_function, model._grouped_model)
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy, grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args, with_loss_tensor=True)
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
dataset_targets = distributed_training_utils.flatten_perdevice_values(
current_strategy, targets)
distributed_test_function = K.Function(
all_inputs, all_outputs,
updates=all_updates,
name='distributed_test_function',
**all_session_args)
# We need to set sample_weights to None since there are sample weight
# placeholders that are created with default values.
sample_weights = [None for _ in range(len(model.outputs) *
current_strategy.num_towers)]
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = dataset_inputs + dataset_targets + sample_weights + [0]
else:
ins = dataset_inputs + dataset_targets
outs = []
if verbose == 1:
progbar = Progbar(target=steps)
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(
current_strategy, distributed_model, orig_model_weights)
if steps is not None:
for step in range(steps):
batch_outs = distributed_test_function(ins)
batch_outs = _aggregate_metrics_across_towers(
len(current_strategy._devices), model.metrics_names, batch_outs)
if isinstance(batch_outs, list):
if step == 0:
for _ in enumerate(batch_outs):
outs.append(0.)
for i, batch_out in enumerate(batch_outs):
outs[i] += batch_out
else:
if step == 0:
outs.append(0.)
outs[0] += batch_outs
if verbose == 1:
progbar.update(step + 1)
for i in range(len(outs)):
outs[i] /= steps
if len(outs) == 1:
return outs[0]
return outs
def fit_loop(model,
inputs,
targets,
epochs=100,
verbose=1,
callbacks=None,
val_inputs=None,
val_targets=None,
callback_metrics=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None):
"""fit function when using DistributionStrategy for training.
Arguments:
model: Keras Model instance.
inputs: List of input arrays.
targets: List of target arrays.
epochs: Number of times to iterate over the data
verbose: Verbosity mode, 0, 1 or 2
callbacks: List of callbacks to be called during training
val_inputs: List of input arrays.
val_targets: List of target arrays.
callback_metrics: List of strings, the display names of the metrics
passed to the callbacks. They should be the
concatenation of list the display names of the outputs of
`f` and the list of display names of the outputs of `f_val`.
initial_epoch: Epoch at which to start training
(useful for resuming a previous training run)
steps_per_epoch: Total number of steps (batches of samples)
before declaring one epoch finished and starting the
next epoch. Ignored with the default value of `None`.
validation_steps: Number of steps to run validation for
(only if doing validation from data tensors).
Ignored with the default value of `None`.
Returns:
`History` object.
Raises:
ValueError: in case of invalid arguments.
"""
current_strategy = model._distribution_strategy
def _per_device_train_function(model):
model._make_train_function()
return (model.train_function.inputs, model.train_function.outputs,
model.train_function.updates_op,
model.train_function.session_kwargs)
with current_strategy.scope():
# Create train ops on each of the devices when we call
# `_per_device_train_function`.
(grouped_inputs, grouped_outputs, grouped_updates,
grouped_session_args) = current_strategy.call_for_each_tower(
_per_device_train_function, model._grouped_model)
# Unwrap all the per device values returned from `call_for_each_tower`.
# Unwrapping per device values gives you a list of values that can be
# used to construct a new train function that is composed of update ops on
# all the devices over which the model is distributed.
(all_inputs, all_outputs, all_updates,
all_session_args) = distributed_training_utils.unwrap_values(
current_strategy,
grouped_inputs,
grouped_outputs,
grouped_updates,
grouped_session_args,
with_loss_tensor=True)
# Dataset inputs and targets are also per devices values that need to be
# unwrapped.
dataset_inputs = distributed_training_utils.flatten_perdevice_values(
current_strategy, inputs)
dataset_targets = distributed_training_utils.flatten_perdevice_values(
current_strategy, targets)
# Create a train function that is composed of all the parameters above.
distributed_train_function = K.Function(all_inputs,
all_outputs,
updates=all_updates,
name='distributed_train_function',
**all_session_args)
# We need to set sample_weights to None since there are sample weight
# placeholders that are created with default values.
sample_weights = [
None for _ in range(len(model.outputs) * current_strategy.num_towers)
]
if model.uses_learning_phase and not isinstance(K.learning_phase(), int):
ins = dataset_inputs + dataset_targets + sample_weights + [1]
else:
ins = dataset_inputs + dataset_targets
do_validation = False
if validation_steps:
do_validation = True
if steps_per_epoch is None:
raise ValueError('Can only use `validation_steps` '
'when doing step-wise '
'training, i.e. `steps_per_epoch` '
'must be set.')
out_labels = model.metrics_names
if do_validation:
callback_metrics = copy.copy(out_labels) + [
'val_' + n for n in out_labels
]
else:
callback_metrics = copy.copy(out_labels)
model.history = cbks.History()
all_callbacks = [
cbks.BaseLogger(stateful_metrics=model.stateful_metric_names)
]
if verbose:
# We assume that `steps_per_epoch` is always set since we have to use
# Datasets.
count_mode = 'steps'
all_callbacks.append(
cbks.ProgbarLogger(count_mode,
stateful_metrics=model.stateful_metric_names))
all_callbacks += (callbacks or []) + [model.history]
callbacks = cbks.CallbackList(all_callbacks)
out_labels = out_labels or []
# We set the callback model to an instance of the `DistributedModel` that we
# create in the `compile` call. The `DistributedModel` is initialized with
# the first replicated model. We need to set the callback model to a
# DistributedModel to allow us to override saving and loading weights when
# we checkpoint the model during training.
callback_model = model._replicated_model
callbacks.set_model(callback_model)
callbacks.set_params({
'epochs': epochs,
'steps': steps_per_epoch,
'samples': None,
'verbose': verbose,
'do_validation': do_validation,
'metrics': callback_metrics or [],
})
callbacks.on_train_begin()
callback_model.stop_training = False
out_labels = out_labels or []
# Copy the weights from the original model to each of the replicated models.
orig_model_weights = model.get_weights()
with current_strategy.scope():
distributed_model = current_strategy.unwrap(model._grouped_model)[0]
distributed_training_utils.set_weights(current_strategy,
distributed_model,
orig_model_weights)
for epoch in range(initial_epoch, epochs):
callbacks.on_epoch_begin(epoch)
if steps_per_epoch is not None:
epoch_logs = {}
for step_index in range(steps_per_epoch):
batch_logs = {}
batch_logs['batch'] = step_index
batch_logs['size'] = 1
callbacks.on_batch_begin(step_index, batch_logs)
try:
outs = distributed_train_function(ins)
except errors.OutOfRangeError:
logging.warning(
'Your dataset iterator ran out of data; '
'interrupting training. Make sure that your dataset '
'can generate at least `steps_per_epoch * epochs` '
'batches (in this case, %d batches).' %
steps_per_epoch * epochs)
break
if not isinstance(outs, list):
outs = [outs]
# TODO(anjalisridhar): Temporary workaround for aggregating metrics
# across towers. Replace with the new metrics module eventually.
merged_output = []
# The first output is the total loss.
merged_output.append(outs[0])
current_index = 1
num_devices = len(current_strategy._devices)
# Each label in `out_labels` corresponds to one set of metrics. The
# number of metric values corresponds to the number of devices. We
# currently take the mean of the values.
for _ in out_labels[1:]:
m = np.mean(outs[current_index:current_index +
num_devices])
merged_output.append(m)
current_index += num_devices
for l, o in zip(out_labels, outs):
batch_logs[l] = o
callbacks.on_batch_end(step_index, batch_logs)
if callback_model.stop_training:
break
if do_validation:
val_outs = test_loop(model,
val_inputs,
val_targets,
steps=validation_steps,
verbose=0)
if not isinstance(val_outs, list):
val_outs = [val_outs]
# Same labels assumed.
for l, o in zip(out_labels, val_outs):
epoch_logs['val_' + l] = o
callbacks.on_epoch_end(epoch, epoch_logs)
if callback_model.stop_training:
break
callbacks.on_train_end()
# Copy the weights back from the replicated model to the original model.
with current_strategy.scope():
updated_weights = current_strategy.unwrap(
model._grouped_model)[0].get_weights()
model.set_weights(updated_weights)
return model.history
