def one_step(self, batch, rng, step=0, learning_rate=None):
"""Updates loss layer weights/state and optimizer slots by running one step.
Args:
batch: Batch of data to use for optimization.
rng: Random number generator to use for running this step.
step: Which step of the training are we running.
learning_rate: Learning rate to use instead of the default one.
Returns:
Tuple (loss, stats) with new values from one step
of training, where stats are current optimizer statistics.
"""
# Update the learning rate if needed.
if learning_rate is not None:
self._opt_params['learning_rate'] = tl.for_n_devices(
learning_rate, self._n_devices)
# batch needs to be split across the local devices -- the difference
# between _for_n_devices and _reshape_by_device is that the latter splits
# the batch dim to batch // n_devices, vs _for_n_devices
# broadcasts/replicates to n_devices dimension.
if self._n_devices > 1:
batch = tl.reshape_by_device(batch, self._n_devices)
# separate rng needs to be created for each device
if self._n_devices > 1:
rng = jnp.stack(fastmath.random.split(rng, self._n_devices))
weights = self._accelerated_loss_layer.weights
state = self._accelerated_loss_layer.state
if logging.vlog_is_on(1) and ((step & step - 1) == 0):
# Prints every power of two, if debugging is enabled.
logging.info('step[%d]', step)
logging.info('opt_params[%s]', self._opt_params)
logging.info('slots[%s]', self._slots)
logging.info('weights[%s]', weights)
logging.info('state[%s]', state)
# NOTE: stats is a replicated dictionary of key to jnp arrays.
(new_weights,
new_slots), new_state, stats = self._accelerated_update_fn(
(weights, self._slots), step, self._opt_params, batch, state, rng)
if logging.vlog_is_on(1) and ((step & step - 1) == 0):
logging.info('updated weights[%s]', new_weights)
logging.info('stats[%s]', stats)
self._accelerated_loss_layer.weights = new_weights
self._accelerated_loss_layer.state = new_state
self._slots = new_slots
self._optimizer.slots = self._unreplicate(self._slots)
return stats['loss'], stats
def one_step(self, batch, rng, step=0, learning_rate=None):
"""Runs one training step, to update model and optimizer parameters.
Args:
batch: Batch of labeled training data.
rng: Single-use random number generator (JAX PRNG key).
step: Training step number.
learning_rate: Learning rate for the optimizer; if None, use optimizer's
default learning rate.
Returns:
Tuple of (loss, optimizer_stats), with the newly computed loss and
updated stats as reported by the optimizer.
"""
if learning_rate is not None:
self._opt_params['learning_rate'] = tl.for_n_devices(
learning_rate, self._n_devices)
# Split the batch across devices (batch_dim --> batch_dim // n_devices)
# and create new rng's 1-1 with devices.
if self._n_devices > 1:
batch = tl.reshape_by_device(batch, self._n_devices)
rng = jnp.stack(fastmath.random.split(rng, self._n_devices))
weights = self._accelerated_model_with_loss.weights
state = self._accelerated_model_with_loss.state
if logging.vlog_is_on(1) and ((step & step - 1) == 0):
# Prints every power of two, if debugging is enabled.
logging.info('step[%d]', step)
logging.info('opt_params[%s]', self._opt_params)
logging.info('slots[%s]', self._slots)
logging.info('weights[%s]', weights)
logging.info('state[%s]', state)
# NOTE: stats is a replicated dictionary of key to jnp arrays.
(new_weights,
new_slots), new_state, stats = self._accelerated_update_fn(
(weights, self._slots), step, self._opt_params, batch, state, rng)
if logging.vlog_is_on(1) and ((step & step - 1) == 0):
logging.info('updated weights[%s]', new_weights)
logging.info('stats[%s]', stats)
self._accelerated_model_with_loss.weights = new_weights
self._accelerated_model_with_loss.state = new_state
self._slots = new_slots
self._optimizer.slots = self._unreplicate(self._slots)
return stats['loss'], stats
def _reshape_by_device(x, n_devices):
"""Reshapes possibly nested x into a shape (n_devices, ...)."""
return tl.reshape_by_device(x, n_devices) # pylint: disable=protected-access
def _reshape_by_device(self, x):
if self.n_devices == 1:
return x
return tl.reshape_by_device(x, self.n_devices)
def one_step(self, batch, rng, step=0, learning_rate=None):
"""Updates layers weights/state and optimizers slots by running one step.
Args:
batch: Batch of data to use for optimization.
rng: Random number generator to use for running this step.
step: Which step of the training are we running.
learning_rate: Learning rate to use instead of the default one.
Returns:
Tuple (loss, stats) with new values from one step
of training, where stats are all optimizer statistics.
"""
# Update the learning rate if needed.
if learning_rate is not None:
self._replicated_loss_opt_params['learning_rate'] = tl.for_n_devices(
learning_rate, self._n_devices)
for (std_op, rev_ops) in self._replicated_opt_params:
std_op['learning_rate'] = tl.for_n_devices(
learning_rate, self._n_devices)
for op in rev_ops:
op['learning_rate'] = tl.for_n_devices(
learning_rate, self._n_devices)
# Batch needs to be split across the local devices -- the difference
# between _for_n_devices and _reshape_by_device is that the latter splits
# the batch dim to batch // n_devices, vs _for_n_devices
# broadcasts/replicates to n_devices dimension.
if self._n_devices > 1:
batch = tl.reshape_by_device(batch, self._n_devices)
step = jnp.repeat(step, self._n_devices)
# Create separate rng for each device and layer.
if self._n_devices == 1:
rngs = fastmath.random.split(rng, self._n_layers)
else:
# Splitting by device first to be identical with default trainer.
per_device_rng = fastmath.random.split(rng, self._n_devices)
per_device_rngs = [
fastmath.random.split(r, self._n_layers) for r in per_device_rng]
rngs = [jnp.stack([r[i] for r in per_device_rngs])
for i in range(self._n_layers)]
# Group rngs by layer blocks.
rng_blocks, rng_i = [], 0
for _, rev_layers in self._blocks:
l = len(rev_layers)
rng_blocks.append((rngs[rng_i], rngs[rng_i + 1: rng_i + l + 1]))
rng_i += l + 1
# Run the layers forward upto the loss layer.
stack = batch
block_inputs_states = []
for i, (std_layer, rev_layers) in enumerate(self._blocks):
acc_std_layer_fn, acc_rev_layer_fns = self._accelerated_layer_fns[i]
std_rng, rev_rngs = rng_blocks[i]
# Run the standard layer.
stack, std_inputs, std_state = self._run_forward_standard(
stack, std_layer, acc_std_layer_fn, std_rng)
# Run the reversible layers and collect old and new states.
stack, rev_old_states, rev_new_states = self._run_forward_reversible(
stack, rev_layers, acc_rev_layer_fns, rev_rngs)
block_inputs_states.append(
((std_inputs, std_state), (rev_old_states, rev_new_states)))
# Run the loss layer forward and backward with optimizer update.
loss_state = self._replicate(self._loss_layer.state)
loss_inputs = cb.inputs_from_stack(stack, self._loss_layer.n_in)
loss_stats, grad_stack = self._run_backward_standard(
None, step, self._loss_layer, loss_inputs,
loss_state, self._loss_fbo, rngs[-1], self._loss_opt,
self._replicated_loss_opt_params)
stats = [loss_stats]
# Run the layers backward and run optimizer updates.
for i in range(len(self._blocks) - 1, -1, -1):
std_layer, rev_layers = self._blocks[i]
(std_inputs, std_state), (rev_old_states,
rev_new_states) = block_inputs_states[i]
std_fbo, rev_fbos = self._fbos[i]
std_opt, rev_opts = self._optimizers[i]
std_rng, rev_rngs = rng_blocks[i]
repl_std_opt_params, repl_rev_opts_params = self._replicated_opt_params[i]
# Run reversible layers backward with optimizer update.
stack, grad_stack, new_stats = self._run_backward_reversible(
stack, grad_stack, step, rev_layers, rev_fbos, rev_old_states,
rev_new_states, rev_rngs, rev_opts, repl_rev_opts_params)
stats.extend(new_stats)
# Run the standard layer forward-and-backward pass and optimizer update.
std_layer_stats, grad_stack = self._run_backward_standard(
grad_stack, step, std_layer, std_inputs, std_state, std_fbo, std_rng,
std_opt, repl_std_opt_params)
stack = cb.outputs_onto_stack( # Put layer inputs on the stack.
std_inputs, stack, std_layer.n_out)
stats.append(std_layer_stats)
# Join stats from different optimizers into one.
joint_stats = {}
for i, stat in enumerate(reversed(stats)):
for k, v in stat.items():
joint_stats[f'layer{i}/' + k] = v
return stats[0]['loss'], joint_stats
def one_step(self, batch, rng, step=0, learning_rate=None):
"""Updates layers weights/state and optimizers slots by running one step.
Args:
batch: Batch of data to use for optimization.
rng: Random number generator to use for running this step.
step: Which step of the training are we running.
learning_rate: Learning rate to use instead of the default one.
Returns:
Tuple (loss, stats) with new values from one step
of training, where stats are all optimizer statistics.
"""
# Update the learning rate if needed.
if learning_rate is not None:
self._replicated_loss_opt_params[
'learning_rate'] = self._replicate_cpu(learning_rate)
for (std_op, rev_ops) in self._replicated_opt_params:
std_op['learning_rate'] = self._replicate_cpu(learning_rate)
for op in rev_ops:
op['learning_rate'] = self._replicate_cpu(learning_rate)
# Batch needs to be split across the local devices -- the difference
# between _for_n_devices and _reshape_by_device is that the latter splits
# the batch dim to batch // n_devices, vs _for_n_devices
# broadcasts/replicates to n_devices dimension.
step_int = step
if self._n_devices > 1:
batch = tl.reshape_by_device(batch, self._n_devices, pure_np=True)
step = np.repeat(step, self._n_devices)
# Create separate rng for each device and layer.
if self._n_devices == 1:
rngs = fastmath.random.split(rng, self._n_layers)
else:
# JIT the function and run it on CPU to avoid memory fragmentation.
rngs = self._jit_per_device_rngs(tl.on_cpu(rng))
# Group rngs by layer blocks.
rng_blocks, rng_i = [], 0
for _, rev_layers in self._blocks:
l = len(rev_layers)
rng_blocks.append((rngs[rng_i], rngs[rng_i + 1:rng_i + l + 1]))
rng_i += l + 1
# Run the layers forward upto the loss layer.
if self._do_free:
self._free_accelerators()
process = psutil.Process(os.getpid())
if isinstance(batch, (list, tuple)):
batch_shapes = [x.shape for x in batch]
else:
batch_shapes = batch.shape
logging.info('running step %d on shapes %s', step_int,
str(batch_shapes))
if step_int % self._n_steps_per_log == 1:
logging.info('run fwd: cpu memory use (MB): %.2f',
process.memory_info().rss / float(1024 * 1024))
stack = batch
block_inputs_states = []
for i, (std_layer, rev_layers) in enumerate(self._blocks):
acc_std_layer_fn, acc_rev_layer_fns = self._accelerated_layer_fns[
i]
std_rng, rev_rngs = rng_blocks[i]
# Run the standard layer.
stack, std_inputs, std_state = self._run_forward_standard(
stack, std_layer, acc_std_layer_fn, std_rng, step_int)
# Run the reversible layers and collect old and new states.
stack, rev_old_states, rev_new_states = self._run_forward_reversible(
stack, rev_layers, acc_rev_layer_fns, rev_rngs, step_int)
block_inputs_states.append(
tl.on_cpu(((std_inputs, std_state), (rev_old_states,
rev_new_states))))
# Run the loss layer forward and backward with optimizer update.
if step_int % self._n_steps_per_log == 1:
logging.info('run loss: cpu memory use (MB): %.2f',
process.memory_info().rss / float(1024 * 1024))
loss_state = self._replicate(self._loss_layer.state)
loss_inputs = cb.inputs_from_stack(stack, self._loss_layer.n_in)
loss_stats, grad_stack = self._run_backward_standard(
None, step, self._loss_layer, loss_inputs, loss_state,
self._loss_fbo, rngs[-1], self._loss_opt,
self._replicated_loss_opt_params)
self._collect_weights(self._loss_layer)
stats = [tl.on_cpu(loss_stats)]
# De-fragment memory.
if self._do_free:
stack, grad_stack = tl.on_cpu(stack), tl.on_cpu(grad_stack)
self._free_accelerators()
# Run the layers backward and run optimizer updates.
if step_int % self._n_steps_per_log == 1:
logging.info('run bwd: cpu memory use (MB): %.2f',
process.memory_info().rss / float(1024 * 1024))
for i in range(len(self._blocks) - 1, -1, -1):
std_layer, rev_layers = self._blocks[i]
(std_inputs, std_state), (rev_old_states,
rev_new_states) = block_inputs_states[i]
std_fbo, rev_fbos = self._fbos[i]
std_opt, rev_opts = self._optimizers[i]
std_rng, rev_rngs = rng_blocks[i]
repl_std_opt_params, repl_rev_opts_params = self._replicated_opt_params[
i]
# Run reversible layers backward with optimizer update.
stack, grad_stack, new_stats = self._run_backward_reversible(
stack, grad_stack, step, rev_layers, rev_fbos, rev_old_states,
rev_new_states, rev_rngs, rev_opts, repl_rev_opts_params)
stats.extend(tl.on_cpu(new_stats))
# Run the standard layer forward-and-backward pass and optimizer update.
std_layer_stats, grad_stack = self._run_backward_standard(
grad_stack, step, std_layer, std_inputs, std_state, std_fbo,
std_rng, std_opt, repl_std_opt_params)
stack = cb.outputs_onto_stack( # Put layer inputs on the stack.
std_inputs, stack, std_layer.n_out)
stats.append(tl.on_cpu(std_layer_stats))
# Collect lazily unreplicated layer weights.
for rev_layer_id in range(self._n_async_layers):
self._collect_weights(rev_layers[rev_layer_id])
self._collect_weights(std_layer)
# Join stats from different optimizers into one.
joint_stats = {}
for i, stat in enumerate(reversed(stats)):
for k, v in stat.items():
joint_stats[f'layer{i}/' + k] = v
return stats[0]['loss'], joint_stats
def one_step(self, batch, rng, step=0, learning_rate=None):
"""Updates layers weights/state and optimizers slots by running one step.
Args:
batch: Batch of data to use for optimization.
rng: Random number generator to use for running this step.
step: Which step of the training are we running.
learning_rate: Learning rate to use instead of the default one.
Returns:
Tuple (loss, stats) with new values from one step
of training, where stats are all optimizer statistics.
"""
# Update the learning rate if needed.
if learning_rate is not None:
for op in self._replicated_opt_params:
op['learning_rate'] = tl.for_n_devices(learning_rate,
self._n_devices)
# Batch needs to be split across the local devices -- the difference
# between _for_n_devices and _reshape_by_device is that the latter splits
# the batch dim to batch // n_devices, vs _for_n_devices
# broadcasts/replicates to n_devices dimension.
if self._n_devices > 1:
batch = tl.reshape_by_device(batch, self._n_devices)
step = jnp.repeat(step, self._n_devices)
# Separate rng needs to be created for each device.
if self._n_devices == 1:
rngs = fastmath.random.split(rng, len(self._reversible_layers) + 2)
else:
# Splitting by device first to be identical with default trainer.
per_device_rng = fastmath.random.split(rng, self._n_devices)
per_device_rngs = [
fastmath.random.split(r,
len(self._reversible_layers) + 2)
for r in per_device_rng
]
rngs = [
jnp.stack([r[i] for r in per_device_rngs])
for i in range(len(self._reversible_layers) + 2)
]
# Run the layers forward upto the loss layer.
stack = batch
# Run the first layer.
first_layer_inputs = _inputs_from_stack(self._first_layer, stack)
first_layer_weights = self._replicate(self._first_layer.weights)
first_layer_state = self._replicate(self._first_layer.state)
outputs, first_layer_new_state = self._accelerated_first_layer_fn(
first_layer_inputs, first_layer_weights, first_layer_state,
rngs[0])
stack = _outputs_onto_stack(self._first_layer, outputs, stack)
# Run the reversible layers and collect old and new states.
old_states, new_states = [], []
for i, layer in enumerate(self._reversible_layers):
weights = self._replicate(
layer.weights) # also copies cpu -> accelerator
state = self._replicate(layer.state)
old_states.append(state)
inputs = _inputs_from_stack(layer, stack)
outputs, new_state = self._accelerated_reversible_layers_fns[i](
inputs, weights, state, rngs[i + 1])
stack = _outputs_onto_stack(layer, outputs, stack)
new_states.append(new_state)
# Run the loss layer forward and backward with optimizer update.
loss_weights = self._replicate(self._loss_layer.weights)
loss_state = self._replicate(self._loss_layer.state)
loss_inputs = _inputs_from_stack(self._loss_layer, stack)
loss_slots = self._replicate(self._optimizers[-1].slots)
new_weights, new_state, new_slots, grad_stack, loss_stats = self._loss_fbo(
loss_inputs, loss_weights, loss_state, loss_slots,
self._replicated_opt_params[-1], rngs[-1], step)
stats = [loss_stats]
self._loss_layer.weights = self._unreplicate(
new_weights) # acceler. -> cpu
self._loss_layer.state = self._unreplicate(new_state)
self._optimizers[-1].slots = self._unreplicate(new_slots)
# Run reversible layers backward with optimizer update.
counter = -1
for layer, reverse_and_fbo, old_state, new_state, rng in reversed(
list(
zip(self._reversible_layers, self._reverse_and_fbos,
old_states, new_states, rngs[1:-1]))):
counter -= 1
# We are running backwards and reversing, so we get *outputs* from stack.
outputs = _inputs_from_stack(layer, stack, layer.n_out)
grads = _inputs_from_stack(layer, grad_stack, layer.n_out)
slots = self._replicate(self._optimizers[counter].slots)
opt_params = self._replicated_opt_params[counter]
weights = self._replicate(layer.weights) # cpu -> accelerator
new_weights, new_slots, inputs, grads, layer_stats = reverse_and_fbo(
outputs, weights, old_state, new_state, slots, opt_params, rng,
step, grads)
layer.weights = self._unreplicate(
new_weights) # accelerator -> cpu
layer.state = self._unreplicate(new_state)
self._optimizers[counter].slots = self._unreplicate(new_slots)
stats.append(layer_stats)
stack = _outputs_onto_stack(layer, inputs, stack, layer.n_out,
layer.n_in)
grad_stack = _outputs_onto_stack(layer, grads, grad_stack,
layer.n_out, layer.n_in)
# Run the first layer forward-and-backward pass and optimizer update.
grads = _inputs_from_stack(self._first_layer, grad_stack,
self._first_layer.n_out)
slots = self._replicate(self._optimizers[0].slots)
new_weights, new_state, new_slots, first_layer_stats = self._first_fbo(
first_layer_inputs, first_layer_weights, first_layer_new_state,
slots, self._replicated_opt_params[0], rngs[0], step, grads)
stats.append(first_layer_stats)
self._first_layer.weights = self._unreplicate(new_weights)
self._first_layer.state = self._unreplicate(new_state)
self._optimizers[0].slots = self._unreplicate(new_slots)
return stats[0]['loss'], stats
