def _ProcessSingleInput(self, source_id, src, tgt):
"""Performs strings-to-ids on the given input pair via p.tokenizer_dict."""
_, src_labels, src_paddings = self.StringsToIds(
tf.reshape(src, [1]), is_source=True, key=self._src_tokenizer_key)
tgt_ids, tgt_labels, tgt_paddings = self.StringsToIds(
tf.reshape(tgt, [1]), is_source=False, key=self._tgt_tokenizer_key)
# Mask positions to 0 where padding is 1 for consistency. We do this because
# tokenizer implementation may use EOS token to pad.
src_labels = py_utils.ApplyPadding(src_paddings, src_labels)
tgt_ids = py_utils.ApplyPadding(tgt_paddings, tgt_ids)
tgt_labels = py_utils.ApplyPadding(tgt_paddings, tgt_labels)
features = py_utils.NestedMap()
features.src = py_utils.NestedMap()
features.src.ids = src_labels
# ids_indicator is 1 if and only if the output from tokenizer has a
# non-padded id. Unlike weights, it will not mutate and can be used for
# determining actual sequence length, for example.
features.src.ids_indicator = 1 - src_paddings
features.tgt = py_utils.NestedMap()
features.tgt.ids = tgt_ids
features.tgt.labels = tgt_labels
features.tgt.ids_indicator = 1 - tgt_paddings
src_task_id, tgt_task_id = self._GetTaskIds(source_id)
# task_ids are padded with zeros.
features.src.task_ids = tf.cast(
features.src.ids_indicator, dtype=tf.int32) * src_task_id
features.tgt.task_ids = tf.cast(
features.tgt.ids_indicator, dtype=tf.int32) * tgt_task_id
if not py_utils.use_tpu():
features.src.strs = src
features.tgt.strs = tgt
return features.Transform(tf.squeeze)
def SequenceAppendToken(x, x_paddings, token, extend=False):
"""Appends <token> to sequence `x`.
Args:
x: A sequence of tokens of shape [batch_size, x_len_max].
x_paddings: The paddings of `x`.
token: The token to append (of type integer).
extend: Whether to extend `x` along the length dimension, this must be true
for any sequence length in `x` that is `x_len_max` or else an invalid
sequence will be emitted.
Returns:
A tuple.
- The new sequence, Tensor of shape [batch_size, x_len_max].
- The new paddings, Tensor of shape [batch_size, x_len_max].
"""
batch_size = py_utils.GetShape(x)[0]
x_len = tf.cast(tf.round(tf.reduce_sum(1 - x_paddings, 1)), tf.int32)
if extend:
x = tf.pad(x, [[0, 0], [0, 1]])
# Mask all invalid entries of `x` to 0.
x *= tf.sequence_mask(x_len, py_utils.GetShape(x)[1], x.dtype)
# Append the <token> based on `x_len`.
x += tf.scatter_nd(tf.stack([tf.range(batch_size), x_len], axis=1),
tf.cast(tf.fill([batch_size], token), x.dtype),
py_utils.GetShape(x))
x_paddings = 1 - tf.sequence_mask(x_len + 1,
py_utils.GetShape(x)[1],
x_paddings.dtype)
return x, x_paddings
def _InputBatch(self):
ret = py_utils.NestedMap()
ret.bucket_keys = self._bucket_keys
ret.src = py_utils.NestedMap()
ret.src.ids = tf.cast(self._src_ids, dtype=tf.int32)
ret.src.paddings = self._src_paddings
ret.tgt = py_utils.NestedMap()
ret.tgt.ids = self._tgt_ids
ret.tgt.labels = tf.cast(self._tgt_labels, dtype=tf.int32)
ret.tgt.weights = self._tgt_weights
ret.tgt.paddings = self._tgt_paddings
if (self.params.fprop_dtype is None or
self.params.dtype == self.params.fprop_dtype):
return ret
def _Cast(v):
if not v.dtype.is_floating:
return v
return tf.cast(v, self.params.fprop_dtype)
return ret.Transform(_Cast)
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, current_step):
"""Returns the current learning rate decay."""
p = self.params
current_step = tf.cast(current_step, tf.float32)
warmup_steps = tf.cast(p.warmup_steps, tf.float32)
linear_warmup = tf.minimum(1.0, current_step / warmup_steps)
rsqrt_decay = tf.math.rsqrt(tf.maximum(current_step, warmup_steps))
return p.model_dim**-0.5 * linear_warmup * rsqrt_decay
def FProp(self, theta, current_step):
"""Returns the current learning rate decay."""
p = self.params
current_step = tf.cast(current_step, tf.float32)
warmup_steps = tf.cast(
p.warmup_examples / (p.batch_size * self._num_replicas),
tf.float32)
return tf.minimum((current_step + 1) * warmup_steps**-1.5,
(current_step + 1)**-0.5)
def _InputBatch(self):
length = tf.reduce_prod(self.shape)
counter = summary_utils.StatsCounter('CountingInputGenerator')
new_value = tf.cast(counter.IncBy(length), dtype=tf.int32) - length
new_value = tf.stop_gradient(new_value)
values = new_value + tf.range(length)
shaped_values = tf.reshape(tf.cast(values, dtype=tf.float32), self.shape)
targets = tf.reduce_sum(shaped_values, axis=0)
return py_utils.NestedMap(src_ids=shaped_values, tgt_ids=targets)
def FProp(self, theta, current_step):
"""Returns the current learning rate decay."""
p = self.params
current_step = tf.cast(current_step, tf.float32)
warmup_steps = tf.cast(p.warmup_steps * p.worker_replicas, tf.float32)
if p.decay_end is not None:
current_step = tf.where(current_step < p.decay_end, current_step,
tf.cast(p.decay_end, tf.float32))
return p.model_dim**-0.5 * tf.minimum(
(current_step + 1) * warmup_steps**-1.5, (current_step + 1)**-0.5)
def ComputePredictions(self, theta, batch):
# pyformat: disable
"""Compute the model predictions.
Args:
theta: A `.NestedMap` object containing weights' values of this layer and
its children layers.
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`.
- outputs: The contextualized output vectors of shape
[batch_size, time_dim, model_dim].
- tgt: A `.NestedMap` (optional, only during training).
- ids: The canvas ids.
- paddings: The canvas paddings.
- target_indices: The target indices.
- target_weights: The target weights.
"""
# pyformat: enable
p = self.params
# TODO(williamchan): Currently, we only support KERMIT mode (i.e., no
# encoder, unified architecture).
assert not p.encoder
# Sometimes src and tgt have different types. We reconcile here and use
# int32.
batch.src.ids = tf.cast(batch.src.ids, tf.int32)
batch.tgt.ids = tf.cast(batch.tgt.ids, tf.int32)
canvas_and_targets = self._CreateCanvasAndTargets(batch)
batch = py_utils.NestedMap(tgt=py_utils.NestedMap(
ids=canvas_and_targets.canvas,
paddings=canvas_and_targets.canvas_paddings))
predictions = super(InsertionModel,
self).ComputePredictions(theta, batch)
if not self.do_eval:
predictions.tgt = py_utils.NestedMap(
ids=canvas_and_targets.canvas,
paddings=canvas_and_targets.canvas_paddings,
target_indices=canvas_and_targets.target_indices,
target_weights=canvas_and_targets.target_weights)
return predictions
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 _GreedySearchStep(self, theta, encoder_outputs, cur_step, step_ids,
hyp_ids, hyp_lens, done_hyps, other_states,
pre_beam_search_step_callback,
post_beam_search_step_callback):
"""Extend greedy search hyps for one step.
Args:
theta: A `.NestedMap` object containing weights' values of the decoder
layer and its children layers.
encoder_outputs: A `.NestedMap` containing encoder outputs to be passed to
the callbacks.
cur_step: A scalar int tensor, the current time step, 0-based.
step_ids: An int tensor of shape [num_hyps, 1]. The input ids to the
current search step.
hyp_ids: An int tensor of shape [num_hyps, tgt_seq_len].
hyp_lens: Valid length of all the hyps. Tokens after eos ids are not
counted.
done_hyps: Whether or not a hyp has finished.
other_states: A `.NestedMap` of other beam search states. This
`.NestedMap` is managed and updated by the client. It is expected that
each of its member tensors are of rank >= 1. t[i, ...] is the state of
the i-th hyp at the beginning of this search step.
pre_beam_search_step_callback: The `PreBeamSearchStepCallback` callback.
See class header comments for more details.
post_beam_search_step_callback: The `PostBeamSearchStepCallback` callback.
See class header comments for more details.
Returns:
A tuple of following elements for the next greedy search step,
(next step, new_step_ids, hyp_ids, hyp_lens, done_hyps, other_states)
"""
p = self.params
# Increment hyp_lens by 1 if the hyp is not finished yet.
hyp_lens = hyp_lens + (1 - tf.cast(done_hyps, tf.int32))
bs_results, new_other_states = pre_beam_search_step_callback(
theta, encoder_outputs, step_ids, other_states,
1) # num_hyps_per_beam
new_step_ids = tf.math.argmax(bs_results.log_probs, 1)
new_step_ids = tf.cast(new_step_ids, tf.int32)
new_step_ids = tf.reshape(new_step_ids, tf.shape(step_ids))
final_other_states = post_beam_search_step_callback(
theta, encoder_outputs, new_step_ids, new_other_states)
# Stash new_step_ids into the right slot.
new_step_ids_1d = tf.reshape(new_step_ids, [-1])
hyp_ids = inplace_ops.alias_inplace_update(hyp_ids, cur_step,
new_step_ids_1d)
# Update done_hyps if the current step_ids is the end of sequence token.
done_hyps = tf.math.logical_or(
done_hyps, tf.equal(new_step_ids_1d, p.target_eos_id))
return (cur_step + 1, new_step_ids, hyp_ids, hyp_lens, done_hyps,
final_other_states)
def UnstackFeatures(self, src_inputs, src_paddings):
"""Unstacks src_input and src_paddings based off stack height."""
sh = self.params.stack_height
bs, old_series_length, _, channels = py_utils.GetShape(src_inputs)
unstacked_series_length = old_series_length * sh
src_inputs = tf.reshape(src_inputs,
[bs, unstacked_series_length, -1, channels])
content = 1 - src_paddings
lengths = tf.cast(sh * tf.reduce_sum(content, axis=1), tf.int32)
mask = tf.sequence_mask(lengths, maxlen=unstacked_series_length)
src_paddings = 1 - tf.cast(mask, tf.int32)
return src_inputs, src_paddings
def FProp(self, theta, current_step):
"""Returns the current learning rate decay."""
params = self.params
warmup_steps = tf.cast(params.decay_start * params.worker_replicas,
tf.float32)
current_step = tf.cast(current_step, tf.float32)
if params.decay_end is not None:
current_step = tf.where(current_step < params.decay_end,
current_step,
tf.cast(params.decay_end, tf.float32))
peak_learning_rate = (warmup_steps**-0.5)
return (params.model_dim**-0.5) * tf.minimum(
tf.minimum((current_step + 1),
(current_step + 1)**-0.5), peak_learning_rate)
def FProp(self, theta, current_step):
p = self.params
current_step = tf.cast(current_step, tf.int64)
interval_starts = [0] + p.boundaries
values = []
for interval_start, schedule, schedule_theta in zip(
interval_starts, self.schedules, theta.schedules):
relative_step = tf.maximum(
tf.cast(0, current_step.dtype),
current_step - tf.cast(interval_start, current_step.dtype))
values.append(schedule.FProp(schedule_theta, relative_step))
return py_utils.PiecewiseConstant(current_step, p.boundaries, values,
values[0].dtype)
def _Value(self, current_step):
"""Returns the current clipping cap."""
p = self.params
start_step = tf.cast(p.start_step, tf.float32)
end_step = tf.cast(p.end_step, tf.float32)
current_step = tf.cast(current_step, tf.float32)
steps_ratio = (
tf.minimum(end_step - start_step, current_step - start_step) /
(end_step - start_step))
rmax_tensor = (steps_ratio * p.end_cap +
(1.0 - steps_ratio) * p.start_cap)
return tf.cond(tf.less(current_step, p.start_step),
lambda: tf.cast(p.start_cap, tf.float32),
lambda: tf.cast(rmax_tensor, tf.float32))
def SequenceConcat(x, x_paddings, y, y_paddings, pad=0):
"""Concats sequence `x` with sequence `y`.
This function is length aware (based off the paddings).
Args:
x: A sequence of tokens of shape [batch_size, x_len_max].
x_paddings: The paddings of `x`.
y: A sequence of tokens of shape [batch_size, y_len_max].
y_paddings: The paddings of `y`.
pad: The <pad> token to fill the concatenated sequence (of type integer).
Returns:
A tuple.
- Concatenation of `x` and `y` of shape
[batch_size, x_len_max + y_len_max].
- Paddings of the concatenation of shape
[batch_size, x_len_max + y_len_max].
"""
# Get the length (w/ eos).
x_len = tf.cast(tf.round(tf.reduce_sum(1 - x_paddings, 1)), tf.int32)
y_len = tf.cast(tf.round(tf.reduce_sum(1 - y_paddings, 1)), tf.int32)
batch_size = py_utils.GetShape(x)[0]
y_len_max = py_utils.GetShape(y)[1]
# Pad `x` with necessary <pad>.
x = tf.concat([x, tf.fill(py_utils.GetShape(y), pad)], 1)
# Replace all <pad> with 0.
x = tf.where(tf.not_equal(x, pad), x, tf.fill(py_utils.GetShape(x), 0))
# Compute the write indices of `y` in `xy`.
indices = tf.stack([
tf.tile(tf.expand_dims(tf.range(batch_size), 1), [1, y_len_max]),
(tf.tile(tf.expand_dims(tf.range(y_len_max), 0), [batch_size, 1]) +
tf.expand_dims(x_len, 1)),
], 2)
xy = x + tf.scatter_nd(indices, y, py_utils.GetShape(x))
# We need to remap all <pad> to `pad`.
xy = tf.where(
tf.less(tf.expand_dims(tf.range(py_utils.GetShape(xy)[1]), 0),
tf.expand_dims(x_len + y_len, 1)), xy,
tf.fill(py_utils.GetShape(xy), pad))
xy_paddings = 1 - tf.sequence_mask(x_len + y_len,
py_utils.GetShape(xy)[1],
x_paddings.dtype)
return xy, xy_paddings
def _global_seed_from_inputs(input_floats):
"""Generates a random seed tensor based on input floats and mode key.
Args:
input_floats: a set of float input tensors that are derived from the input
data (for example, input tokens). The important thing is that these are
usually different for each batch.
Returns:
A tensor of shape=[2] with integer seed tensors derived from the inputs.
"""
timestamp = tf.math.floormod(tf.cast(tf.timestamp(), dtype=tf.int64),
10000000)
input_sum = tf.cast(tf.reduce_sum(tf.math.abs(input_floats)),
dtype=tf.int64)
return tf.stack([timestamp + input_sum, timestamp - input_sum], axis=-1)
def _ProcessBeamSearchDecodeOut(self, input_batch, encoder_outputs,
decoder_outs):
self.r1_shape = decoder_outs[0]
self.r2_shape = decoder_outs[1]
self.r3_shape = decoder_outs[2]
tf.logging.info('r1_shape: %s', self.r1_shape)
tf.logging.info('r2_shape: %s', self.r2_shape)
tf.logging.info('r3_shape: %s', self.r3_shape)
hyps = decoder_outs[3]
prev_hyps = decoder_outs[4]
done_hyps = decoder_outs[5]
scores = decoder_outs[6]
atten_probs = decoder_outs[7]
eos_scores = decoder_outs[8]
eos_atten_probs = decoder_outs[9]
source_seq_lengths = decoder_outs[10]
tlen = tf.cast(
tf.round(tf.reduce_sum(1.0 - input_batch.tgt.paddings, 1) - 1.0),
tf.int32)
ret_dict = {
'target_ids': input_batch.tgt.ids[:, 1:],
'eval_weight': input_batch.eval_weight,
'tlen': tlen,
'hyps': hyps,
'prev_hyps': prev_hyps,
'done_hyps': done_hyps,
'scores': scores,
'atten_probs': atten_probs,
'eos_scores': eos_scores,
'eos_atten_probs': eos_atten_probs,
'source_seq_lengths': source_seq_lengths,
}
return ret_dict
def Polynomial(x):
"""Polynomial function of x."""
p = self.params
x0, y0 = p.start
x1, y1 = p.limit
assert x0 < x1, '%s must be < %s' % (x0, x1)
x0 = tf.cast(x0, dtype=x.dtype)
x1 = tf.cast(x1, dtype=x.dtype)
y0 = tf.cast(y0, dtype=x.dtype)
y1 = tf.cast(y1, dtype=x.dtype)
f_x = ((x - x0) / (x1 - x0))**p.power
y = y0 + f_x * (y1 - y0)
return tf.where(x < x0, y0, tf.where(x >= x1, y1, y))
def ApplyBias():
"""Bias and update log_probs and consistent."""
def TileForBeamAndFlatten(tensor):
tensor = tf.reshape(tensor, [1, -1]) # [1, src_batch]
tensor = tf.tile(
tensor, [num_hyps_per_beam, 1]) # [num_hyps_per_beam, src_batch]
tgt_batch = tf.shape(step_ids)[0] # num_hyps_per_beam*src_batch
return tf.reshape(tensor, [tgt_batch])
# Consistent if step_ids == labels from previous step
# TODO(navari): Consider updating consistent only if weights > 0. Then
# re-evaluate the need for bias_only_if_consistent=True.
# Note that prev_label is incorrrect for step 0 but is overridden later
prev_label = TileForBeamAndFlatten(
tf.gather(labels, tf.maximum(time_step - 1, 0), axis=1))
is_step0 = tf.equal(time_step, 0)
local_consistence = tf.math.logical_or(
is_step0, tf.equal(prev_label, tf.squeeze(step_ids, 1)))
consistent = tf.math.logical_and(states.consistent, local_consistence)
# get label, weight slices corresponding to current time_step
label = TileForBeamAndFlatten(tf.gather(labels, time_step, axis=1))
weight = TileForBeamAndFlatten(tf.gather(weights, time_step, axis=1))
if p.bias_only_if_consistent:
weight = weight * tf.cast(consistent, p.dtype)
# convert from dense label to sparse label probs
vocab_size = tf.shape(bs_results.log_probs)[1]
uncertainty = tf.constant(
1e-10, p.dtype) # avoid 0 probs which may cause issues with log
label_probs = tf.one_hot(
label,
vocab_size,
on_value=1 - uncertainty,
off_value=uncertainty / tf.cast(vocab_size - 1, p.dtype),
dtype=p.dtype) # [tgt_batch, vocab_size]
pred_probs = tf.exp(bs_results.log_probs)
# interpolate predicted probs and label probs
weight = tf.expand_dims(weight, 1)
probs = py_utils.with_dependencies([
py_utils.assert_less_equal(weight, 1.),
py_utils.assert_greater_equal(weight, 0.)
], (1.0 - weight) * pred_probs + weight * label_probs)
return tf.math.log(probs), consistent
def IncBy(self, delta):
"""Increment the counter by delta and return the new value."""
# NOTE: We must ensure _value is computed (_var + 0) before
# updating _var with delta.
delta = tf.cast(delta, tf.int64)
with tf.control_dependencies([self._value]):
scalar(self._name, self._value)
return tf.identity(tf.assign_add(self._var, delta))
def restore(self, restored_tensors, restored_shapes):
restored_tensor = restored_tensors[0]
if restored_shapes is not None:
restored_tensor = tf.reshape(restored_tensor, restored_shapes[0])
return tf.assign(
self.op,
tf.cast(restored_tensor, tf.bfloat16),
validate_shape=restored_shapes is None and
self.op.get_shape().is_fully_defined())
def _matmul_gather(self, values, axis=0, batch_major_state=True):
"""Returns values gathered.
Args:
values: Values to gather from.
axis: Axis to gather on. Defaults to 0 (rows).
batch_major_state: Whether the values to gather from use batch major or
not. Defaults to True. For Transformer model, batch_major_state is set
to False (time is the major dim).
Returns:
Gathered values.
Raises:
NotImplemented error if axis is not 0 nor 1.
"""
dtype = values.dtype
if dtype != tf.float32 and dtype != tf.bfloat16:
values = tf.cast(values, tf.float32)
if axis == 0:
if values.shape.rank is not None and values.shape.rank > 2:
if not batch_major_state:
values = tf.transpose(values, [1, 0, 2])
results = tf.cast(
tf.gather(values, tf.cast(self._ids, tf.int32)), dtype)
# pylint:disable=g-long-ternary
return (tf.transpose(results, [1, 0, 2])
if not batch_major_state else results)
# pylint:enable=g-long-ternary
else:
one_hot_ids = tf.one_hot(self._ids,
self._ids_size,
dtype=values.dtype)
return tf.cast(tf.matmul(one_hot_ids, values), dtype)
elif axis == 1:
one_hot_ids = tf.one_hot(self._ids,
self._ids_size,
dtype=values.dtype,
axis=0)
return tf.cast(tf.matmul(values, one_hot_ids), dtype)
else:
raise NotImplementedError("Only row/col-wise gather implemented.")
def _Apply():
if self.params.use_bf16_gradients_ar:
return optimizer.apply_gradients(
[(tf.cast(g, tf.float32), v)
for (v, g) in var_grad.Flatten()],
name='meta_backprop')
else:
return optimizer.apply_gradients(
[(g, v) for (v, g) in var_grad.Flatten()],
name='meta_backprop')
def FProp(self, theta, current_step):
p = self.params
assert p.total_steps > 0
assert p.initial_value > p.final_value
with tf.name_scope(p.name):
decay_gap = p.initial_value - p.final_value
return p.final_value + 0.5 * decay_gap * (1 + tf.cos(
math.pi *
tf.minimum(1.0,
tf.cast(current_step, tf.float32) / p.total_steps)))
def MakeCausalPadding(seq_len, block_size, left_context, right_context):
"""Makes the causal padding tensor for a full sequence.
Args:
seq_len: int or scalar int tensor. Sequence length.
block_size: int. Number of time frames in a block.
left_context: int. Left context size.
right_context: int. Right context size.
Returns:
A tensor of [num_blocks, block_size, context_size] taking values in {0, 1},
where context_size = block_size + (left_context - 1) + right_context.
Element b, i, j is zero if in the b-th block, the i-th frame can access
the j-th frame in the context.
"""
seq_len = py_utils.with_dependencies([
py_utils.assert_greater_equal(
seq_len, 1, message='seq_len must be at least 1')
], seq_len)
num_blocks = (seq_len + block_size - 1) // block_size
context_size = block_size + (left_context - 1) + right_context
# [num_blocks, block_size]: source positions in the original sequence.
src_positions = tf.reshape(tf.range(num_blocks * block_size),
[num_blocks, block_size])
# [num_blocks,]: source positions at the start of each block.
block_start_positions = tf.range(0, num_blocks * block_size, block_size)
# [context_size]: positions relative to the block start.
relative_context_positions = tf.range(context_size) - (left_context - 1)
# [num_blocks, context_size]: target positions in the original sequence.
tgt_positions = (block_start_positions[:, tf.newaxis] +
relative_context_positions[tf.newaxis, :])
# [num_blocks, block_size, context_size]: position differences between source-
# target pairs.
position_diff = src_positions[:, :,
tf.newaxis] - tgt_positions[:, tf.newaxis, :]
# [num_blocks, block_size, context_size]: if attention is allowed between
# source-target pairs.
valid_atten = tf.math.logical_and(-right_context <= position_diff,
position_diff < left_context)
# [num_blocks, block_size]: if the source position is valid, not padded.
valid_src = src_positions < seq_len
# [num_blocks, context_size]: if the target position is valid, not padded.
valid_tgt = tf.math.logical_and(0 <= tgt_positions,
tgt_positions < seq_len)
valid_atten &= tf.math.logical_and(valid_src[:, :, tf.newaxis],
valid_tgt[:, tf.newaxis, :])
padding = 1.0 - tf.cast(valid_atten, dtype=tf.float32)
return padding
def FProp(self, theta, inputs):
"""Apply projection to inputs.
Args:
theta: A NestedMap object containing weights' values of this layer and its
children layers.
inputs: The inputs tensor. Shaped [..., input_dims].
Returns:
Projected inputs.
"""
p = self.params
with tf.name_scope(p.name):
computation_cost.Add(
self, 'flops',
tf.reduce_prod(tf.cast(tf.shape(inputs)[:-1], tf.int64)) *
tf.cast(symbolic.ToTensor(p.input_dims * p.output_dims),
tf.int64) * 2)
return py_utils.ProjectLastDim(inputs, theta.w, p.input_dims,
p.output_dims)
def ApplyClippingWithState(self, state, x):
"""Applies clipping to x.
Args:
state: Clipping state.
x: Input tensor to clip.
Returns:
Clipped (or identity) x.
"""
cap = tf.cast(state, x.dtype)
return tf.clip_by_value(x, -cap, cap)
def GenerateStepSeedPair(p, unused_global_step=None, op_seed=None):
"""Override py_utils.GenerateStepSeedPair to use GetOverWriteGlobalStep."""
seed_dtype = tf.int32 if py_utils.use_tpu() else tf.int64
if p.is_inference and p.random_seed is None:
# Unlike tf.random*, stateless random ops are completely determined by the
# passed-in seeds. This means at inference time the same inputs will produce
# the same outputs, even if the model is supposed to have randomness such as
# dropout during inference. We inject additional randomness only during
# inference if the graph is exported with random_seed=None as a workaround.
return tf.random.uniform([2], maxval=seed_dtype.max, dtype=seed_dtype)
with tf.name_scope('op_seed') as scope:
global_step = tf.cast(GetOverWriteGlobalStep(), seed_dtype)
step_seed = tf.cast(py_utils.GenerateSeedFromName(scope), seed_dtype)
seeds = tf.stack([global_step, step_seed])
if p.random_seed is not None:
seeds += p.random_seed
if op_seed is not None:
seeds += op_seed
return seeds
def _TimeWarp(self,
inputs,
seq_lengths,
global_seed,
dtype=tf.float32,
domain_id_index=0):
"""Applies time warping with given degree to inputs.
Args:
inputs: Batch of input features of shape (batch_size, time_length,
num_freq, channels).
seq_lengths: The actual sequence lengths which mask been sampled of shape
(batch_size,).
global_seed: an integer seed tensor for stateless random ops.
dtype: Data type.
domain_id_index: Domain ID index.
Returns:
Inputs with random time warping applied.
"""
p = self.params
batch_size, time_length, _, _ = py_utils.GetShape(inputs)
# Get parameters for warping.
time_warp_max_frames = p.time_warp_max_frames[domain_id_index]
max_ratio = p.time_warp_max_ratio[domain_id_index]
time_warp_bound = p.time_warp_bound[domain_id_index]
assert time_warp_bound in ('static', 'dynamic')
# If maximum warp length is zero, do nothing.
if ((time_warp_max_frames == 0 and time_warp_bound == 'static')
or max_ratio <= 0.0):
return inputs
seq_lengths = tf.cast(seq_lengths, tf.int32)
# Discard upper-bound on time-warp frames when
# dynamic time warping is used.
if time_warp_bound == 'dynamic':
time_warp_max_frames = None
# Create warping matrix in time direction and apply
warp_matrix = self._GetWarpMatrix(batch_size,
choose_range=seq_lengths,
matrix_size=time_length,
global_seed=global_seed,
max_warp_frames=time_warp_max_frames,
dtype=dtype,
max_ratio=max_ratio)
return self.EinsumBxycBzxBzyc(inputs,
warp_matrix,
name='einsum_forwarping')