def _run_value_model(self, observations, dist_inputs):
if dist_inputs is None:
dist_inputs = jnp.zeros(observations.shape[:2] +
(self._policy_dist.n_inputs, ))
actions = None
if self._q_value:
dist_inputs = jnp.broadcast_to(
dist_inputs, (self._q_value_n_samples, ) + dist_inputs.shape)
# Swapping the n_samples and batch_size axes, so the input is split
# between accelerators along the batch_size axis.
dist_inputs = jnp.swapaxes(dist_inputs, 0, 1)
actions = self._policy_dist.sample(dist_inputs)
log_probs = self._policy_dist.log_prob(dist_inputs, actions)
obs = observations
obs = jnp.reshape(obs, [obs.shape[0], 1] + list(obs.shape[1:]))
inputs = (obs, actions)
else:
log_probs = None
inputs = (observations, )
n_devices = math.device_count()
weights = tl.for_n_devices(self._value_eval_model.weights, n_devices)
state = tl.for_n_devices(self._value_eval_model.state, n_devices)
rng = self._value_eval_model.rng
values, _ = self._value_eval_jit(inputs, weights, state, rng)
values *= self._value_network_scale
values = jnp.squeeze(values,
axis=-1) # Remove the singleton depth dim.
return (values, actions, log_probs)
def _run_value_model(self, obs):
"""Runs value model."""
n_devices = fastmath.device_count()
weights = tl.for_n_devices(self._value_eval_model.weights, n_devices)
state = tl.for_n_devices(self._value_eval_model.state, n_devices)
rng = self._value_eval_model.rng
# TODO(henrykm): the line below fails on TPU with the error
# ValueError: Number of devices (8) does not evenly divide batch size (1).
obs_batch = obs.shape[0]
if n_devices > obs_batch:
obs = jnp.repeat(obs, int(n_devices / obs_batch), axis=0)
values, _ = self._value_eval_jit(obs, weights, state, rng)
values = values[:obs_batch]
values *= self._value_network_scale
return values
def __init__(self, loss_layer, optimizer, n_devices=None):
self._loss_layer = loss_layer
self._optimizer = optimizer
self._n_devices = n_devices or fastmath.device_count()
# optimizer slots and opt_params may need to be replicated
self._slots, self._opt_params = tl.for_n_devices(
(self._optimizer.slots, self._optimizer.opt_params), self._n_devices)
# accelerated version of loss layer to replicate weights and state
self._accelerated_loss_layer = tl.Accelerate(
loss_layer, n_devices=n_devices)
# Signature:
# (batch, weights, state, rng) -> ((loss, state), gradients)
self._forward_and_backward_fn = (
fastmath.value_and_grad(
loss_layer.pure_fn,
argnums=1, # arg1 of pure_fn: weights
has_aux=True)) # return (loss, state), gradients
# Signature:
# (weights, slots), step, opt_params, batch, state, rng ->
# (weights, slots), state, stats
self._accelerated_update_fn = (
_accelerate_update_fn(
self._forward_and_backward_fn,
self._optimizer,
n_devices=self._n_devices,
accelerate=True,
)
)
def _run_value_model(self, observations, dist_inputs):
if dist_inputs is None:
dist_inputs = jnp.zeros(observations.shape[:2] +
(self._policy_dist.n_inputs, ))
actions = None
if self._q_value:
if self._sample_all_discrete_actions:
# Since we want to sample all actions, start by creating their list.
act = np.arange(self._vocab_size)
# Now act is a vector [0, ..., vocab_size-1], but we'll need to tile it.
# Add extra dimenstions so it's the same dimensionality as dist_inputs.
act = jnp.reshape(act,
[-1] + [1] * (len(dist_inputs.shape) - 1))
# Now act is [vocab_size, 1, ..., 1], dimensionality of dist_inputs.
dist_inputs = jnp.broadcast_to(
dist_inputs, (self._q_value_n_samples, ) + dist_inputs.shape)
if self._sample_all_discrete_actions:
actions = act + jnp.zeros(dist_inputs.shape[:-1],
dtype=jnp.int32)
actions = jnp.swapaxes(actions, 0, 1)
# Swapping the n_samples and batch_size axes, so the input is split
# between accelerators along the batch_size axis.
dist_inputs = jnp.swapaxes(dist_inputs, 0, 1)
if not self._sample_all_discrete_actions:
actions = self._policy_dist.sample(dist_inputs)
log_probs = self._policy_dist.log_prob(dist_inputs, actions)
obs = observations
obs = jnp.reshape(obs, [obs.shape[0], 1] + list(obs.shape[1:]))
inputs = (obs, actions)
else:
log_probs = None
inputs = (observations, )
n_devices = fastmath.device_count()
weights = tl.for_n_devices(self._value_eval_model.weights, n_devices)
state = tl.for_n_devices(self._value_eval_model.state, n_devices)
rng = self._value_eval_model.rng
values, _ = self._value_eval_jit(inputs, weights, state, rng)
values *= self._value_network_scale
values = jnp.squeeze(values,
axis=-1) # Remove the singleton depth dim.
return (values, actions, log_probs)
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 _run_value_model(self, obs, use_eval_model=True):
"""Runs value model."""
n_devices = fastmath.device_count()
if use_eval_model:
weights = tl.for_n_devices(self._value_eval_model.weights, n_devices)
state = tl.for_n_devices(self._value_eval_model.state, n_devices)
rng = self._value_eval_model.rng
else:
# TODO(henrykm): this strangely looking solution address the problem that
# value_batches_stream calls _run_value_model _once_ before
# the trainer is initialized.
try:
weights = tl.for_n_devices(self._value_trainer.model_weights, n_devices)
state = tl.for_n_devices(self._value_trainer.model_state, n_devices)
rng = self._value_trainer._rng # pylint: disable=protected-access
except AttributeError:
weights = tl.for_n_devices(self._value_eval_model.weights, n_devices)
state = tl.for_n_devices(self._value_eval_model.state, n_devices)
rng = self._value_eval_model.rng
# TODO(henrykm): the line below fails on TPU with the error
# ValueError: Number of devices (8) does not evenly divide batch size (1).
obs_batch = obs.shape[0]
if n_devices > obs_batch:
obs = jnp.repeat(obs, int(n_devices / obs_batch), axis=0)
values, _ = self._value_eval_jit(obs, weights, state, rng)
values = values[:obs_batch]
values *= self._value_network_scale
return values
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 __init__(self,
model_with_loss,
optimizer,
n_devices=None,
adasum=False):
self._model_with_loss = model_with_loss
self._optimizer = optimizer
self._n_devices = n_devices or fastmath.local_device_count()
self._adasum = adasum
# optimizer slots and opt_params may need to be replicated
self._slots, self._opt_params = tl.on_cpu(
tl.for_n_devices(
(self._optimizer.slots, self._optimizer.opt_params),
self._n_devices))
# accelerated version of model+loss to replicate weights and state
self._accelerated_model_with_loss = tl.Accelerate(model_with_loss,
n_devices=n_devices)
# Signature:
# (batch, weights, state, rng) -> ((loss, state), gradients)
self._forward_and_backward_fn = (
fastmath.value_and_grad(
model_with_loss.pure_fn,
argnums=1, # arg1 of pure_fn: weights
has_aux=True)) # return (loss, state), gradients
# Signature:
# (weights, slots), step, opt_params, batch, state, rng ->
# (weights, slots), state, stats
self._accelerated_update_fn = (_accelerate_update_fn(
self._forward_and_backward_fn,
self._optimizer,
n_devices=self._n_devices,
accelerate=True,
adasum=self._adasum))
def _for_n_devices(self, x):
"""Replicates/broadcasts `x` for n devices if `self.n_devicess > 1`."""
return tl.for_n_devices(x, self.n_devices) # pylint: disable=protected-access
def _for_n_devices(self, x): """Replicates/broadcasts `x` for n devices if `self.n_devicess > 1`.""" return tl.for_n_devices(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 _replicate(self, x):
if self._n_devices > 1:
return tl.for_n_devices(x, self._n_devices)
return tl.on_accelerator(x)
def slots(self, slots):
"""Sets the slots of the optimizers and this class (replicated)."""
self._optimizer.slots = slots
self._slots = tl.on_cpu(tl.for_n_devices(slots, 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:
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
