def Bak(inputs, outputs, d_outputs):
"""Backward step."""
del inputs # unused
output_acts, step_seeds = outputs
d_outputs = d_outputs[0]
d_layer_thetas = []
for layer_idx in reversed(range(num_layers)):
f_seed, g_seed = step_seeds[layer_idx]
layer = self.sub_layers[layer_idx]
layer_theta = theta.sub_layers[layer_idx]
input_acts, d_inputs, d_theta = layer.ReverseAndGrad(
layer_theta, output_acts, d_outputs, f_seed, g_seed,
*extra_inputs)
d_layer_thetas.append(d_theta)
# Passes reconstructed inputs to the previous layer.
output_acts = input_acts
d_outputs = d_inputs
py_utils.ResetStepSeed(final_step_seed)
d_theta = py_utils.NestedMap(
global_step=tf.zeros_like(initial_step_seed))
d_theta.sub_layers = list(reversed(d_layer_thetas))
extra_grads = [tf.zeros_like(t) for t in extra_inputs]
return [
tf.zeros_like(initial_step_seed), d_theta, d_inputs,
extra_grads
]
def _Proc(record):
"""Parses a serialized tf.Example record."""
outputs = [
('inputs', tf.io.VarLenFeature(tf.int64)),
('targets', tf.io.VarLenFeature(tf.int64)),
# Default eval weight to 1.0
('eval_weight',
tf.io.FixedLenFeature([], tf.float32, default_value=1.0)),
]
features = tf.io.parse_single_example(record, dict(outputs))
for k, v in six.iteritems(features):
if k != 'eval_weight':
features[k] = v.values
else:
eval_weight = v
src_ids = features['inputs']
tgt_labels = features['targets']
# Derive trivial segmentation for unpacked input.
src_paddings, tgt_ids, tgt_paddings, tgt_weights, bucket_key = _DerivePaddingsAndIds(
src_ids, tgt_labels)
src_len = tf.shape(src_ids)[0]
tgt_len = tf.shape(tgt_ids)[0]
src_pos = tf.range(src_len, dtype=tf.int32)
src_seg = tf.zeros_like(src_paddings)
tgt_pos = tf.range(tgt_len, dtype=tf.int32)
tgt_seg = tf.zeros_like(tgt_paddings)
return [
src_ids, src_paddings, tgt_ids, tgt_paddings, tgt_labels, tgt_weights,
src_pos, src_seg, tgt_pos, tgt_seg, eval_weight
], bucket_key
def _Moments(inputs, mask, enable_cross_replica_sum_on_tpu=False):
"""Computes mean and variance over the valid data points in inputs."""
inputs = py_utils.with_dependencies([
py_utils.assert_equal(tf.rank(inputs), tf.rank(mask)),
py_utils.assert_greater_equal(mask, tf.zeros_like(mask)),
], inputs)
rank = tf.rank(mask)
reduce_over_dims = tf.range(0, rank - 1)
sum_v = tf.reduce_sum(inputs * tf.cast(mask, inputs.dtype),
reduce_over_dims)
count_v = tf.reduce_sum(mask, reduce_over_dims)
# Input shape is guaranteed to be a multiple of mask shape because the
# inputs * mask op above was successfully broadcasted.
mask_multiplier = tf.shape(inputs)[:-1] // tf.shape(mask)[:-1]
count_v *= tf.cast(tf.reduce_prod(mask_multiplier), count_v.dtype)
if py_utils.use_tpu() and enable_cross_replica_sum_on_tpu:
sum_v = tf.tpu.cross_replica_sum(sum_v)
count_v = tf.tpu.cross_replica_sum(count_v)
count_v = tf.maximum(count_v, 1.0)
mean = sum_v / count_v
sum_vv = tf.reduce_sum((inputs - mean) * (inputs - mean) * mask,
reduce_over_dims)
if py_utils.use_tpu() and enable_cross_replica_sum_on_tpu:
sum_vv = tf.tpu.cross_replica_sum(sum_vv)
variance = py_utils.with_dependencies([
py_utils.assert_greater_equal(sum_vv, tf.zeros_like(sum_vv)),
], sum_vv / count_v)
return mean, variance
def FProp(self, theta, inputs, paddings, domain_ids=None):
"""Applies data augmentation by randomly mask spectrum in inputs.
Args:
theta: A NestedMap object containing weights' values of this layer and its
children layers.
inputs: A tensor of shape [batch, time, freq, num_channels].
paddings: A 0/1 tensor of shape [batch, time].
domain_ids: input domain_ids of shape [batch, time].
Returns:
A pair of 2 tensors:
- augmented_inputs: A tensor of shape [batch, time, freq, num_channels].
- paddings: A 0/1 tensor of shape [batch, time].
"""
p = self.params
global_seed = None # A tensor seed in case stateless random ops are needed.
if p.use_input_dependent_random_seed:
global_seed = _global_seed_from_inputs(inputs)
batch_size, series_length, _, _ = py_utils.GetShape(inputs)
if len(p.domain_ids) > 1:
augmented_inputs = tf.zeros_like(inputs)
original_inputs = inputs
for i, domain_id in enumerate(p.domain_ids):
augmented_domain = self._AugmentationNetwork(
series_length,
inputs,
paddings,
global_seed=global_seed,
domain_id_index=i)
target_domain = tf.cast(tf.expand_dims(
tf.tile([domain_id], [batch_size]), -1),
dtype=p.dtype)
# [batch, time].
domain_mask = tf.cast(tf.equal(domain_ids, target_domain),
dtype=p.dtype)
augmented_domain = self.EinsumBxycBxBxyc(
augmented_domain, domain_mask, name='einsum_domainmasking')
original_inputs = self.EinsumBxycBxBxyc(
original_inputs,
1.0 - domain_mask,
name='einsum_domainmasking2')
augmented_inputs = augmented_domain + augmented_inputs
augmented_inputs = original_inputs + augmented_inputs
else:
augmented_inputs = self._AugmentationNetwork(
series_length,
inputs,
paddings,
global_seed=global_seed,
domain_id_index=0)
return augmented_inputs, paddings
def _ApplyAndReset():
with tf.control_dependencies([
self._opt.Apply(
lr,
py_utils.ApplyGradMultiplier(var_grad,
1. / p.accum_steps))
]):
return tf.group(*[
tf.assign(a, tf.zeros_like(a))
for _, a in var_grad.Flatten()
])
def PostTrainingStepUpdate(self, global_step):
"""Updates moving_mean, moving_variance after each training step."""
p = self.params
# Get sufficient stats that accumulates over microbatches.
counts = self.accumulators.counts.GetValue()
mean_ss = self.accumulators.mean_ss.GetValue()
variance_ss = self.accumulators.variance_ss.GetValue()
# Compute batch mean and batch variance from sufficient stats
mean, variance = tf.nn.normalize_moments(counts, mean_ss, variance_ss, None)
decay = tf.convert_to_tensor(1.0 - p.decay, p.dtype)
# Update moving_mean, moving_variance from batch mean and batch variance.
with tf.name_scope(p.name) as scope:
with tf.ops.colocate_with(self.vars.moving_mean):
mean_update = tf.assign_sub(
self.vars.moving_mean,
tf.where(
tf.greater(counts, 0.5),
(self.vars.moving_mean - tf.cast(mean, p.dtype)) * decay,
tf.zeros_like(self.vars.moving_mean)),
name='moving_mean_update')
with tf.ops.colocate_with(self.vars.moving_variance):
var_update = tf.assign_sub(
self.vars.moving_variance,
tf.where(
tf.greater(counts, 0.5),
(self.vars.moving_variance - tf.cast(variance, p.dtype)) *
decay, tf.zeros_like(self.vars.moving_variance)),
name='moving_variance_update')
py_utils.CheckNumerics(
self.vars.moving_mean,
'moving mean of {} failed numeric check'.format(scope))
py_utils.CheckNumerics(
self.vars.moving_variance,
'moving variance of {} failed numeric check'.format(scope))
self.accumulators.counts.Reset()
self.accumulators.mean_ss.Reset()
self.accumulators.variance_ss.Reset()
return tf.group(mean_update, var_update)
def FProp(self, theta, inputs, paddings=None):
"""Apply batch normalization.
Args:
theta: A `.NestedMap` object containing weights' values of this layer and
its children layers.
inputs: The inputs tensor. Shaped [..., dim].
paddings: The paddings tensor. Shaped [..., 1], with the same rank as the
input tensor.
Returns:
Output after applying batch normalization, with the same shape as
'inputs'.
"""
p = self.params
if paddings is None:
paddings = self._GetDefaultPaddings(inputs)
with tf.name_scope(p.name):
norm_mean, norm_variance, beta, gamma = self.ComputeAndUpdateMoments(
theta, inputs, paddings)
with tf.control_dependencies([
py_utils.assert_greater_equal(norm_variance,
tf.zeros_like(norm_variance)),
py_utils.assert_shape_match([tf.shape(inputs)[-1]],
tf.shape(norm_mean)),
py_utils.assert_shape_match([tf.shape(inputs)[-1]],
tf.shape(norm_variance)),
]):
if p.use_fused_batch_norm_for_eval and self.do_eval:
bn_output, _, _ = nn.fused_batch_norm(
inputs,
gamma,
beta,
norm_mean,
norm_variance,
self._epsilon,
is_training=False)
else:
bn_output = tf.nn.batch_normalization(inputs, norm_mean,
norm_variance, beta, gamma,
self._epsilon)
if p.set_padded_output_to_zero:
bn_output *= 1.0 - paddings
return bn_output
def _TokenizeOneSentence(i, strs, token_ids_ta, target_ids_ta,
paddings_ta):
"""Tokenizes a single sentence."""
ids, _ = self._wpm_encoder.Encode(strs[i])
if append_eos:
ids = tf.concat([ids, [self.eos_id]], axis=0)
# This truncates after the eos is added, so some sentences might
# not have </s> at the end.
token_ids_ta = token_ids_ta.write(
i,
py_utils.PadOrTrimTo(tf.concat([[self.sos_id], ids], axis=0),
[max_length], self.eos_id))
target_ids_ta = target_ids_ta.write(
i, py_utils.PadOrTrimTo(ids, [max_length], self.eos_id))
paddings_ta = paddings_ta.write(
i,
py_utils.PadOrTrimTo(tf.zeros_like(ids, dtype=tf.float32),
[max_length], 1.))
return i + 1, strs, token_ids_ta, target_ids_ta, paddings_ta
def FProp(self, theta, input_batch):
"""Embeds source ids and transforms with TransformerStack.
Args:
theta: A `.NestedMap` object containing weights' values of this
layer and its children layers.
input_batch: A `.NestedMap` with fields:
- ids: The inputs tensor. It is expected to be of shape [batch, time].
- paddings: The paddings tensor. Expected shape [batch, time].
- task_ids: If p.task_emb is provided, must contain per-token task
ids of shape [batch, time].
Returns:
A NestedMap containing
- encoded: The encoded features, either a tensor of shape
[time, batch, depth], or a list of tensors if is_transparent is set in
transformer_stack.
- padding: of shape [time, batch]
- segment_id: [time, batch] if packed inputs are supported by the model
(and all layers), or None otherwise.
- embedded_inputs: [time, batch, depth] embedded inputs tokens without
positional encodings.
"""
p = self.params
with tf.name_scope(p.name):
src_segment_id = None
src_segment_pos = None
input_ids = py_utils.with_dependencies([
py_utils.assert_shape_match(tf.shape(input_batch.ids),
tf.shape(input_batch.paddings)),
py_utils.assert_equal(tf.rank(input_batch.ids), 2)
], input_batch.ids)
if (not py_utils.use_tpu()
and tf.flags.FLAGS.transformer_encoder_truncates_inputs):
max_seq_length = tf.cast(
tf.reduce_max(tf.reduce_sum(1.0 - input_batch.paddings,
1)), tf.int32)
paddings = py_utils.with_dependencies([
py_utils.assert_equal(
tf.constant(True, tf.bool),
tf.reduce_all(
input_batch.paddings[:, max_seq_length:] > 0.5))
], input_batch.paddings)
input_ids = input_ids[:, :max_seq_length]
paddings = paddings[:, :max_seq_length]
if p.packed_input:
src_segment_id = input_batch.segment_ids[:, :
max_seq_length]
src_segment_pos = input_batch.segment_pos[:, :
max_seq_length]
else:
paddings = input_batch.paddings
if p.packed_input:
src_segment_id = input_batch.segment_ids
src_segment_pos = input_batch.segment_pos
max_time = tf.shape(input_ids)[1]
# Input token embeddings + positional embeddings
if not p.shared_emb:
input_embs = self.token_emb.EmbLookup(
theta.token_emb, tf.reshape(input_ids, [-1]))
else:
input_embs = self.softmax.EmbLookup(
theta.softmax, tf.reshape(input_ids, [-1]))
input_embs = tf.reshape(input_embs,
[-1, max_time, p.token_emb.embedding_dim])
# [time, batch, dim]
orig_input_embs = tf.transpose(input_embs, [1, 0, 2])
if p.packed_input:
position_embs = self.position_emb.FPropWithPosition(
theta.position_emb, src_segment_pos)
else:
position_embs = self.position_emb.FProp(
theta.position_emb, max_time)
position_embs = tf.reshape(
position_embs, [1, max_time, p.token_emb.embedding_dim])
input_embs += position_embs
if p.task_emb:
input_embs += self.task_emb.EmbLookup(theta.task_emb,
input_batch.task_ids)
if p.model_dim != p.token_emb.embedding_dim:
input_embs = self.emb_proj.FProp(theta.emb_proj, input_embs)
paddings = tf.cast(tf.transpose(paddings), py_utils.FPropDtype(p))
if p.packed_input:
src_segment_id = tf.transpose(src_segment_id)
input_embs = self.input_dropout.FProp(theta.input_dropout,
input_embs)
# [time, batch, dim]
transformer_input = tf.transpose(input_embs, [1, 0, 2])
if not self.do_eval and p.apply_source_mask:
# Augment padding for masked source word positions.
dtype = paddings.dtype
source_mask = tf.where(tf.equal(input_ids, p.source_mask_id),
tf.ones_like(input_ids, dtype=dtype),
tf.zeros_like(input_ids, dtype=dtype))
# Make sure padding is between 0 and 1.
paddings = tf.clip_by_value(paddings + tf.transpose(source_mask),
0.0, 1.0)
encoded, padding, segment_id = self.transformer_stack.FProp(
theta.transformer_stack, transformer_input, paddings,
src_segment_id)
return py_utils.NestedMap(encoded=encoded,
padding=padding,
segment_id=segment_id,
embedded_inputs=orig_input_embs)
def FProp(self,
theta,
x,
x_paddings=None,
eos_id=1,
force_sample_last_token=True):
"""Applies SymbolInsertionLayer.
We take in a `x`, which represents the groundtruth sequence (i.e., English
sequence). We return a sampled rollin (observed) canvas (i.e., random subset
of the English sequence), as well as the target (indices) for an
insertion-based model (i.e., the targets given the random observed subset).
Args:
theta: Ignored, this can be None.
x: The symbol ids of shape `[batch_size, time_dim]`.
x_paddings: The paddings (1 or 0) of shape `[batch_size, time_dim]` where
0 is valid and 1 is invalid.
eos_id: The <eos> token id to represent end-of-slot.
force_sample_last_token: Set True to force sample the last token of `x`.
Returns:
A `NestedMap`.
- canvas: The canvas (based off of the `rollin_policy`) of shape
[batch_size, c_dim]. Note that, `c_dim` <= `time_dim` but need not be
equal.
- canvas_indices: The canvas indices (into `x`).
- canvas_paddings: The paddings of `canvas_indices`.
- target_indices: The target indices of shape [num_targets, 3].
`num_targets` is the number of total targets in the entire batch.
[:, 0] captures the batch, [:, 1] captures the slot, and [:, 2]
captures the token. Each row [batch, slot, vocab] represents the
indices of the target -- i.e., the batch, slot and vocab combination
of the target. Typical usage of these indices is to tf.gather_nd
the log-probs (from the softmax layer).
- target_weights: The target weights.
Raises:
ValueError: If invalid params.
"""
p = self.params
batch_size = py_utils.GetShape(x)[0]
time_dim = py_utils.GetShape(x)[1]
if x_paddings is None:
x_paddings = tf.zeros([batch_size, time_dim], tf.float32)
oracle_policy = p.oracle_policy
rollin_policy = (oracle_policy
if p.rollin_policy == 'oracle' else p.rollin_policy)
if rollin_policy != 'uniform':
raise ValueError('Unknown or unsupported rollin policy: %s' %
rollin_policy)
if oracle_policy != 'uniform':
raise ValueError('Unknown or unsupported oracle policy: %s' %
oracle_policy)
x_len = tf.cast(tf.round(tf.reduce_sum(1 - x_paddings, 1)), tf.int32)
# Compute the desired length per example in the batch.
ratio = tf.random.uniform([batch_size], 0.0, 1.0, seed=p.random_seed)
if force_sample_last_token:
c_len = tf.minimum(
tf.cast(ratio * tf.cast(x_len, tf.float32), tf.int32),
x_len - 1) + 1
else:
c_len = tf.minimum(
tf.cast(ratio * tf.cast(x_len + 1, tf.float32), tf.int32),
x_len)
# Compute the maximum length across the batch.
c_len_max = tf.reduce_max(c_len)
# Grab subset of random valid indices per example.
z_logits = tf.cast(
tf.expand_dims(tf.range(time_dim), 0) >= tf.expand_dims(x_len, 1),
tf.float32) * -1e9
if force_sample_last_token:
# Force sample the last token -- i.e., as indexed by `x_len - 1`. We can
# accomplish this by add +LARGE_NUMBER to the logits.
z_logits += tf.cast(
tf.equal(tf.expand_dims(tf.range(time_dim), 0),
tf.expand_dims(x_len - 1, 1)), tf.float32) * 1e9
# Gumbel-max trick to sample (we only sample valid positions per sample in
# the batch).
z = -tf.math.log(-tf.math.log(
tf.random.uniform([batch_size, time_dim], seed=p.random_seed)))
unused_c_values, c_indices = tf.nn.top_k(z_logits + z, time_dim)
# Trim everything > c_len_max.
c_indices = c_indices[:, :c_len_max]
# Invalidate any indices >= c_len, we use the last index as the default
# invalid index.
c_indices = tf.where(
tf.expand_dims(tf.range(c_len_max), 0) < tf.expand_dims(c_len, 1),
c_indices, tf.fill(py_utils.GetShape(c_indices), time_dim - 1))
# Materialize the canvas.
c_indices = tf.sort(c_indices)
c = tf.gather_nd(
x,
tf.stack([
tf.reshape(
tf.tile(tf.expand_dims(tf.range(batch_size), 1),
[1, c_len_max]), [-1]),
tf.reshape(c_indices, [-1])
], 1))
c = tf.reshape(c, [batch_size, c_len_max])
# Compute the paddings.
c_paddings = 1 - tf.sequence_mask(
c_len, c_len_max, dtype=x_paddings.dtype)
c *= tf.cast(1 - c_paddings, tf.int32)
indices = tf.concat([
tf.reshape(
tf.tile(tf.expand_dims(tf.range(batch_size), 1),
[1, c_len_max]), [batch_size * c_len_max, 1]),
tf.reshape(c_indices, [batch_size * c_len_max, 1])
], 1)
x_token_is_observed = tf.scatter_nd(
indices, tf.ones([batch_size * c_len_max], tf.int32),
py_utils.GetShape(x))
# `x_segments` captures which slot each `x` belongs to (both observed and
# tokens that need to be observed).
x_segments = tf.cumsum(x_token_is_observed, 1, exclusive=True)
x_token_is_observed = tf.cast(x_token_is_observed, tf.bool)
prev_x_token_is_observed = tf.pad(x_token_is_observed[:, :-1],
[[0, 0], [1, 0]],
constant_values=True)
x_token_is_observed = tf.reshape(x_token_is_observed, [-1])
prev_x_token_is_observed = tf.reshape(prev_x_token_is_observed, [-1])
x_is_valid = tf.cast(1 - x_paddings, tf.bool)
x_is_valid = tf.reshape(x_is_valid, [-1])
# Remap all the observed to <eos>, note some of these need a zero weight
# (or else there would be <eos> and valid token in the same slot).
target_indices = tf.cast(tf.reshape(x, [-1, 1]), tf.int32)
target_indices = tf.where(
x_token_is_observed,
tf.fill(py_utils.GetShape(target_indices), eos_id), target_indices)
# TODO(williamchan): We give uniform 1.0 weight, however, math suggests
# we may want to weigh this term by the original sequence length.
target_weights = tf.ones_like(target_indices, tf.float32)
# We need to set all the weights for <eos> which actually have valid tokens
# in the slot to zero.
target_weights = tf.where(
x_token_is_observed & ~prev_x_token_is_observed,
tf.zeros_like(target_weights), target_weights)
# TODO(williamchan): Consider dropping the entries w/ weight zero.
# Add the batch and slot indices.
target_indices = tf.concat([
tf.reshape(
tf.tile(tf.expand_dims(tf.range(batch_size), 1),
[1, time_dim]), [batch_size * time_dim, 1]),
tf.reshape(x_segments, [-1, 1]), target_indices
], 1)
# Select only the valid indices. The selected valid ones include slots w/
# <eos>.
target_indices = target_indices[x_is_valid]
target_weights = target_weights[x_is_valid]
return py_utils.NestedMap(canvas=c,
canvas_indices=c_indices,
canvas_paddings=c_paddings,
target_indices=target_indices,
target_weights=target_weights)
def _CreateCanvasAndTargets(self, batch):
# pyformat: disable
"""Create the canvas and targets.
Args:
batch: A `.NestedMap`.
- src: A `.NestedMap`.
- ids: The source ids, ends in <eos>.
- paddings: The source paddings.
- tgt: A `.NestedMap`.
- ids: The target ids, ends in <eos>.
- paddings: The target paddings.
Returns:
A `NestedMap`.
- canvas: The canvas (based off of the `rollin_policy`) of shape
[batch_size, c_dim].
- canvas_paddings: The paddings of `canvas_indices`.
- target_indices: The target indices (i.e., use these indices to
tf.gather_nd the log-probs). Optional, only during training.
- target_weights: The target weights. Optional, only during training.
"""
# pyformat: enable
p = self.params
if not self.do_eval:
# Sample our src and tgt canvas.
src_descriptor = self._SampleCanvasAndTargets(
batch.src.ids, batch.src.paddings)
tgt_descriptor = self._SampleCanvasAndTargets(
batch.tgt.ids, batch.tgt.paddings)
# Offset the src ids (to unshare embeddings between src/tgt). Note, we
# only offset the canvas ids, but we do not offset the vocab ids. This
# will result in unshared embeddings, but shared softmax. This is due to
# GPU/TPU memory limitations, empirically it is known that unsharing
# everything results in better performance.
vocab_size = p.decoder.softmax.num_classes
src_descriptor.canvas = tf.where(
tf.equal(src_descriptor.canvas_paddings, 0),
src_descriptor.canvas + vocab_size, src_descriptor.canvas)
# Offset the tgt indices (need shift according to src length).
batch_size = py_utils.GetShape(batch.src.ids)[0]
# `target_batch` is a [num_targets, batch_size] tensor where each row
# identifies which batch the target belongs to. Note the observation that,
# tf.reduce_sum(target_batch, 1) == 1 \forall rows.
target_batch = tf.cast(
tf.equal(
tf.expand_dims(tf.range(batch_size), 0),
tf.expand_dims(tgt_descriptor.target_indices[:, 0], 1)),
tf.int32)
src_lens = tf.cast(
tf.reduce_sum(1 - src_descriptor.canvas_paddings, 1), tf.int32)
# `tgt_offset` is shape [num_targets] where each entry corresponds to the
# offset needed for that target (due to the source length).
tgt_offset = tf.matmul(target_batch, tf.expand_dims(src_lens, 1))
# We shift the tgt slot without touching the batch or vocab.
tgt_descriptor.target_indices += tf.concat([
tf.zeros_like(tgt_offset), tgt_offset,
tf.zeros_like(tgt_offset)
], 1)
# The canvas is simply the sequence-level concat of the src and tgt.
canvas, canvas_paddings = insertion.SequenceConcat(
src_descriptor.canvas, src_descriptor.canvas_paddings,
tgt_descriptor.canvas, tgt_descriptor.canvas_paddings)
target_indices = tf.concat(
[src_descriptor.target_indices, tgt_descriptor.target_indices],
0)
target_weights = tf.concat(
[src_descriptor.target_weights, tgt_descriptor.target_weights],
0)
return py_utils.NestedMap(canvas=canvas,
canvas_paddings=canvas_paddings,
target_indices=target_indices,
target_weights=target_weights)
def FProp(self, theta, inputs, paddings, state0=None, segment_id=None):
"""Computes LSTM forward pass.
Args:
theta: A `.NestedMap` object containing weights' values of this layer and
its children layers.
inputs: A single tensor or a tuple of tensors with cardinality equal to
rnn_cell.inputs_arity. For every input tensor, the first dimension is
assumed to be time, second dimension batch, and third dimension depth.
paddings: A tensor. First dim is time, second dim is batch, and third dim
is expected to be 1.
state0: If not None, the initial rnn state in a `.NestedMap`. Defaults to
the cell's zero-state.
segment_id: A tensor to support packed inputs. First dim is time, second
dim is batch, and third dim is expected to be 1.
Returns:
A tensor of [time, batch, dims].
The final recurrent state.
"""
p = self.params
rcell = self.cell
assert isinstance(rcell, (rnn_cell.RNNCell))
if not isinstance(inputs, (list, tuple)):
inputs = [inputs]
# Slicing wm to wm_{i,h} outside the loop to get 20% speedup over regular
# LSTM baseline.
# Keeping slicing within the loop gives only < 3% speedup.
cell_theta = theta.cell.copy()
num_input_nodes = p.cell.num_input_nodes
cell_theta['wm_i'] = cell_theta.wm[:num_input_nodes, :]
cell_theta['wm_h'] = cell_theta.wm[num_input_nodes:, :]
tf.logging.vlog(1, 'cell_theta: %r', cell_theta)
if p.packed_input:
assert segment_id is not None
reset_mask = rnn_layers.GeneratePackedInputResetMask(
segment_id, is_reverse=False)
reset_mask = py_utils.HasShape(reset_mask, tf.shape(paddings))
else:
reset_mask = tf.zeros_like(paddings)
if p.reverse:
inputs = [tf.reverse(x, [0]) for x in inputs]
paddings = tf.reverse(paddings, [0])
reset_mask = tf.reverse(reset_mask, [0])
if not state0:
batch_size = py_utils.GetShape(paddings)[1]
state0 = rcell.zero_state(cell_theta, batch_size)
# [T, B, H]
proj_inputs = rcell.ProjectInputSequence(
cell_theta, py_utils.NestedMap(act=inputs))
proj_inputs = py_utils.NestedMap(proj_inputs=proj_inputs,
padding=paddings,
reset_mask=reset_mask)
acc_state, final_state = recurrent.Recurrent(
theta=cell_theta,
state0=state0,
inputs=proj_inputs,
cell_fn=rcell.FPropWithProjectedInput,
cell_type=rcell.layer_type,
accumulator_layer=self,
allow_implicit_capture=p.allow_implicit_capture)
act = rcell.GetOutput(acc_state)
if p.reverse:
act = tf.reverse(act, [0])
return act, final_state
def _ComputePaddings(ids, eos_id):
is_eos = tf.cast(tf.equal(ids, eos_id), tf.int32)
# eos_in_prefix[i, j] = any(ids[i, k] == eos_id for k in range(j))
eos_in_prefix = tf.cumsum(is_eos, axis=-1, exclusive=True)
return tf.where(tf.equal(eos_in_prefix, 0), tf.zeros_like(ids),
tf.ones_like(ids))
def ReverseAndGrad(self, theta, outputs, d_outputs, f_seed, g_seed,
*extra_inputs):
"""Implements Algorithm 1 in the revnet paper.
Args:
theta: A NestedMap object containing weights' values of this layer and its
children layers.
outputs: A NestedMap: .split1 and .split2 corresponding to y1 and y2.
d_outputs: A NestedMap: .split1 and .split2 corresponding to dy1 and dy2,
the total derivatives.
f_seed: Scalar tensor. The step seed used in forward for the f block.
g_seed: Scalar tensor. The step seed used in forward for the g block. The
step seeds are needed for deterministic randomness, e.g. to ensure
dropout generate the same random mask in forward and reverse_grad.
*extra_inputs: additional inputs that will be passed to both f and g. No
gradient will be computed for these inputs.
Returns:
A tuple of NestedMaps
- inputs: .split1 and .split2 corresponding to x1 and x2.
- d_inputs: .split1 and .split2 corresponding to dx1 and dx2, the total
derivatives with respect to inputs.
- d_theta: has the same structure as theta. The total derivatives with
respect to weights.
"""
# Stop gradient on the outputs to avoid circular symbolic dependency.
y1 = tf.stop_gradient(outputs.split1)
y2 = tf.stop_gradient(outputs.split2)
dy1 = d_outputs.split1
dy2 = d_outputs.split2
# Computes the reverse.
z1 = y1
py_utils.ResetStepSeed(g_seed)
gz1 = self.g_block.FProp(theta.g_block, z1, *extra_inputs)
x2 = y2 - gz1
py_utils.ResetStepSeed(f_seed)
fx2 = self.f_block.FProp(theta.f_block, x2, *extra_inputs)
x1 = z1 - fx2
# Computes the gradients.
dz1 = dy1 + tf.gradients(gz1, z1, dy2)[0]
dx2 = dy2 + tf.gradients(fx2, x2, dz1)[0]
dgw = tf.gradients(gz1,
theta.g_block.Flatten(),
dy2,
unconnected_gradients=tf.UnconnectedGradients.ZERO)
dgw = theta.g_block.Pack(dgw)
dfw = tf.gradients(fx2,
theta.f_block.Flatten(),
dz1,
unconnected_gradients=tf.UnconnectedGradients.ZERO)
dfw = theta.f_block.Pack(dfw)
return (py_utils.NestedMap(split1=x1, split2=x2),
py_utils.NestedMap(split1=dz1, split2=dx2),
py_utils.NestedMap(f_block=dfw,
g_block=dgw,
global_step=tf.zeros_like(
theta.global_step)))