def reset(self, output_dir):
"""Reset the model parameters.
Restores the parameters from the given output_dir if a checkpoint exists,
otherwise randomly initializes them.
Does not re-jit the model.
Args:
output_dir: Output directory.
"""
self._output_dir = output_dir
gfile.makedirs(output_dir)
# Create summary writers and history.
if self._should_write_summaries:
self._train_sw = jaxboard.SummaryWriter(os.path.join(
output_dir, 'train'),
enable=self.is_chief)
self._eval_sw = jaxboard.SummaryWriter(os.path.join(
output_dir, 'eval'),
enable=self.is_chief)
# Reset the train and eval streams.
self._train_stream = self._inputs.train_stream()
# TODO(lukaszkaiser): add an option to evaluate exactly on the full eval
# set by adding a padding and stopping the stream when too large.
self._eval_stream = _repeat_stream(self._inputs.eval_stream)
self._train_eval_stream = _repeat_stream(
self._inputs.train_eval_stream)
# Restore the training state.
state = load_trainer_state(output_dir)
self._step = state.step or 0
history = state.history
self._lr_fn = self._lr_schedule(history)
self._history = history
if state.opt_state:
opt_state = state.opt_state
model_state = state.model_state
else:
opt_state, model_state = self._new_opt_state_and_model_state()
model_state = layers.nested_map(self._maybe_replicate, model_state)
self._opt_state = OptState(
*layers.nested_map(self._maybe_replicate, opt_state))
self._model_state = model_state
if not state.opt_state and self.is_chief:
self._maybe_save_state(keep=False)
self.update_nontrainable_params()
def predict(x, weights, state, rng):
"""Predict function jited and parallelized as requested."""
res, state = backend.combine_devices(model_predict(
backend.reshape_by_device(x, n_devices),
weights,
state,
np.stack(jax_random.split(rng, n_devices))))
return layers.nested_map(lambda y: np.mean(y, axis=0), res), state
def _sizes(x):
"""Get a structure of sizes for a structure of nested arrays."""
def size(x):
try:
return x.size
except Exception: # pylint: disable=broad-except
return 0
return layers.nested_map(size, x)
def _print_n_weights(opt_state, n_devices, step):
"""Print out the number of parameters."""
sizes = _sizes(opt_state.weights)
if n_devices > 1:
unreplicate = lambda x: x[0]
single_weights = layers.nested_map(unreplicate, opt_state.weights)
sizes = _sizes(single_weights)
total_size = _nested_reduce(sum, sizes)
step_log(step, 'Total number of trainable weights: %d' % total_size)
def _print_n_params(opt_state, n_devices, step):
"""Print out the number of parameters."""
sizes = layers.sizes(opt_state.params)
if n_devices > 1:
unreplicate = lambda x: x[0]
single_params = layers.nested_map(unreplicate, opt_state.params)
sizes = layers.sizes(single_params)
total_size = layers.nested_reduce(sizes, sum)
step_log(step, 'Total trainable parameters size: %d' % total_size)
def print_n_weights(self):
"""Prints the total count of trainable weights."""
opt_state = self._opt_state
sizes = _sizes(opt_state.weights)
if self._n_devices > 1:
unreplicate = lambda x: x[0]
single_weights = layers.nested_map(unreplicate, opt_state.weights)
sizes = _sizes(single_weights)
total_size = _nested_reduce(sum, sizes)
self.log_step('Total number of trainable weights: %d' % total_size)
def _save_replicated(opt_state, step, history, model_state, n_devices,
output_dir, keep):
"""Saves trainer state but given a possibly replicated opt_state."""
if n_devices > 1:
first_replica = lambda x: x[0]
opt_state = OptState(*layers.nested_map(first_replica, opt_state))
# This line, while optional, allows JAX to transfer arrays from the device to
# the host in parallel, which is particularly important for cloud TPU.
if backend.get_name() == 'jax':
opt_state = jax.device_get(opt_state)
save_trainer_state(
TrainerState(opt_state=opt_state, step=step, history=history,
model_state=model_state), output_dir, keep=keep)
def _train_step(self, next_train_batch):
"""Run one training step and update self._opt_state."""
# Calculate the current optimizer parameters.
# TODO(pkozakowski): Optimizer parameters get polluted with model state,
# which doesn't break anything but is weird. Filter it out.
opt_param_updates = layers.nested_map(
lambda x: self._maybe_replicate(np.array(x)),
self.nontrainable_params)
opt_state = self._opt_state
opt_state.opt_params.update(opt_param_updates)
# Run the update.
(weights, slots), self._model_state, self._rngs = self._jit_update_fn(
self._step, opt_state, next_train_batch, self._model_state, self._rngs)
self._model_state = self._map_to_state_dicts(self._state_dicts_update)
self._opt_state = opt_state._replace(weights=weights, slots=slots)
self._step += 1
def save_state(self, keep):
"""Save trainer state given a possibly replicated opt_state."""
opt_state = self._opt_state
if self._n_devices > 1:
first_replica = lambda x: x[0]
opt_state = OptState(*layers.nested_map(first_replica, opt_state))
# This line, while optional, allows JAX to transfer arrays from the device
# to the host in parallel, which is particularly important for cloud TPU.
if backend.get_name() == 'jax':
opt_state = jax.device_get(opt_state)
step, history, model_state = self._step, self._history, self._model_state
output_dir = self._output_dir
pkl_module = utils.get_pickle_module()
weights_file = os.path.join(output_dir, 'model.pkl')
with gfile.GFile(weights_file, 'wb') as f:
pkl_module.dump((tuple(opt_state), step, history, model_state), f)
if keep:
weights_file = os.path.join(output_dir,
'model_{}.pkl'.format(step))
with gfile.GFile(weights_file, 'wb') as f:
pkl_module.dump((tuple(opt_state), step, history, model_state),
f)
log('Model saved to %s' % weights_file, stdout=False)
def __init__(self, model, loss_fn, optimizer, lr_schedule, inputs,
output_dir=None, random_seed=None, n_devices=None,
save_steps=None, should_save_checkpoints=True,
should_write_summaries=True, has_weights=False,
nontrainable_param_map=None, mask_id=None, metrics=None):
if save_steps is None:
save_steps = []
self._save_steps = save_steps
self._should_save_checkpoints = should_save_checkpoints
self._should_write_summaries = should_write_summaries
self._has_weights = has_weights
self._mask_id = mask_id
self._metrics_dict = _METRICS if metrics is None else metrics
loss_fn = loss_fn(has_weights=has_weights, mask_id=mask_id)
device_count = backend.device_count()
n_devices = n_devices or device_count
if backend.get_name() == 'jax':
self._host_id = jax.host_id()
self._host_count = jax.host_count()
else:
self._host_id = 0
self._host_count = 1
# TODO(lukaszkaiser): remove this restriction when possible.
if n_devices != device_count and backend.get_name() == 'jax':
raise ValueError('Jax cannot work yet with n_devices != all devices: '
'%d != %d' % (n_devices, device_count))
self._n_devices = n_devices
# Simple differential seeding of RNG across hosts by host_id and time.
if random_seed is None and self._host_count > 1:
_, random_seed = divmod(int(time.time() * 1e6) +
int(self._host_id * 1e6), 2**32)
rng = get_random_number_generator_and_set_seed(random_seed)
inputs = inputs(n_devices)
self._inputs = inputs
# Initialize the learning rate to a dummy value. It will be set in reset().
opt = optimizer(learning_rate=0.0)
# Setup the model.
model_train = model(mode='train')
model_predict_eval = model(mode='eval')
# Setup state.
rng, init_rng = jax_random.split(rng)
self._rngs = np.stack(jax_random.split(rng, n_devices))
first_shape = inputs.input_shape[0]
# If the inputs are a tuple/list, add [None] (batch) to each element.
if isinstance(first_shape, (list, tuple)):
model_input_shape = tuple(
tuple([None] + list(shape)) for shape in inputs.input_shape)
model_target_shape = tuple(
tuple([None] + list(shape)) for shape in inputs.target_shape)
else: # Otherwise just add [None] to the input shape.
model_input_shape = tuple([None] + list(inputs.input_shape))
model_target_shape = tuple([None] + list(inputs.target_shape))
# Change all None to 1 in input and target shape.
model_input_shape = layers.nested_map(lambda x: x if x else 1,
model_input_shape)
model_target_shape = layers.nested_map(lambda x: x if x else 1,
model_target_shape)
def new_opt_state_and_model_state(input_shape, input_dtype, target_shape,
target_dtype, rng):
"""Returns optimizer and model states suitable for training a model."""
# Combine inputs and targets on the stack.
if not isinstance(input_dtype, (list, tuple)):
input_dtype = [input_dtype]
input_shape = [input_shape]
if not isinstance(target_dtype, (list, tuple)):
target_dtype = [target_dtype]
target_shape = [target_shape]
dtypes = list(input_dtype) + list(target_dtype)
shapes = list(input_shape) + list(target_shape)
if self._has_weights:
shapes += list(target_shape)
dtypes += [np.float32 for _ in target_dtype]
input_signature = tuple(ShapeDtype(s, d)
for (s, d) in zip(shapes, dtypes))
# We need to create a new model instance and not reuse `model_train` here,
# because `m.initialize` puts cached parameter values in `m` and hence the
# next call of `m.initialize` will give wrong results.
m = layers.Serial(model(mode='train'), loss_fn)
m._set_rng(rng) # pylint: disable=protected-access
weights, state = m.initialize_once(input_signature)
(slots, opt_params) = opt.tree_init(weights)
return (OptState(weights, slots, opt_params), state)
if _is_jit_init():
# JIT parameter initialization to avoid memory fragmentation
new_opt_state_and_model_state = backend.jit(new_opt_state_and_model_state,
static_argnums=(0, 1, 2, 3))
self._new_opt_state_and_model_state = (
lambda: new_opt_state_and_model_state( # pylint: disable=g-long-lambda
model_input_shape, self._inputs.input_dtype,
model_target_shape, self._inputs.target_dtype, init_rng))
# jit model_predict and update so they're fast
# TODO(lukaszkaiser): the code below creates a layer computing
# multiple metrics from a single model output; re-factor for clarity.
dup_layer = layers.Dup3() if self._has_weights else layers.Dup2()
def lower(layer):
"""Apply layer below the current inputs, targets, and possibly weights."""
if self._has_weights:
# Apply layer below inputs, targets, and loss weights.
return layers.Parallel([], [], [], layer)
else:
# Apply layer below inputs and targets.
return layers.Parallel([], [], layer)
metrics_layer = []
self._metrics = list(sorted(self._metrics_dict.keys()))
for i, m in enumerate(reversed(self._metrics)):
metric = self._metrics_dict[m](has_weights=self._has_weights,
mask_id=self._mask_id)
if i != len(self._metrics) - 1:
metrics_layer.append(dup_layer)
metrics_layer.append(lower(metric))
else:
metrics_layer.append(metric)
# TODO(lukaszkaiser): clean this up once layer API stabilizes.
# For now, we need to initialize metric layers somehow, so here we go.
# We assume that they do not have any parameters, so this is a dummy.
dummy_shapes = ((1, 2), (1,), (1,)) if self._has_weights else ((1, 2), (1,))
dummy_dtypes = [np.float32] * (3 if self._has_weights else 2)
dummy_signature = tuple(ShapeDtype(s, d)
for s, d in zip(dummy_shapes, dummy_dtypes))
metrics_layer = layers.Serial(metrics_layer)
metrics_layer._set_rng(init_rng) # pylint: disable=protected-access
metrics_weights, metrics_state = (
metrics_layer.initialize_once(dummy_signature))
self._metrics_weights = layers.nested_map(self._maybe_replicate,
metrics_weights)
self._metrics_state = layers.nested_map(self._maybe_replicate,
metrics_state)
self._jit_eval = _jit_predict_fn(
model_predict_eval, metrics_layer, n_devices)
self._jit_update_fn = _jit_update_fn(model_train, loss_fn, opt, n_devices)
self._model_train = model_train
self._model_predict_eval = model_predict_eval
self._loss_fn = loss_fn
# TODO(pkozakowski): "Learning rate schedules" are currently able to control
# control all optimizer parameters and model state, so let's rename them
# accordingly.
self._lr_schedule = lr_schedule
if nontrainable_param_map is None:
nontrainable_param_map = {}
self._nontrainable_param_map = nontrainable_param_map
# Those fields will be set in reset().
self._output_dir = None
self._train_sw = None
self._eval_sw = None
self._history = None
self._lr_fn = None
self._opt_state = None
self._step = None
self._model_state = None
if output_dir is not None:
self.reset(output_dir)
