def FProp(self, theta, input_batch):
"""Encodes source as represented by `inputs` and `paddings`.
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].
Returns:
A NestedMap containing:
- encoded: The encoded features, a tensor of shape [time, batch, depth]
- padding: of shape [time, batch]
- segment_id: [time, batch] if packed inputs are supported by the model
(and all layers), or None otherwise.
"""
p = self.params
src_segment_id = None
with tf.name_scope(p.name):
# Now the rnn layers.
inputs = py_utils.with_dependencies([
py_utils.assert_shape_match(tf.shape(input_batch.ids),
[-1, -1]),
py_utils.assert_shape_match(tf.shape(input_batch.ids),
tf.shape(input_batch.paddings))
], tf.transpose(input_batch.ids))
paddings = tf.expand_dims(tf.transpose(input_batch.paddings), 2)
xs = self.emb.EmbLookup(theta.emb, inputs)
xs = self.ApplyClipping(theta, xs)
self._emb_out = xs
ps = paddings
# When cc_schedule is specified, make sure lstm_tpl is QuantizedLSTMCell
# with the same cc_schedule so that the RNN layer output is within
# clipping range.
xs = self.rnn[0].FProp(theta.rnn[0], xs, ps)
xs = self.dropout.FProp(theta.dropout, xs)
for i in range(1, p.num_lstm_layers):
layer = self.rnn[i]
ys, _ = layer.FProp(theta.rnn[i], xs, ps)
ys = self.dropout.FProp(theta.dropout, ys)
if hasattr(layer.params, 'cell'):
layer_params = layer.params.cell
else:
layer_params = layer.params
if layer_params.num_input_nodes == layer_params.num_output_nodes:
xs += ys # Residual skip
xs = self.ApplyClipping(theta, xs)
else:
# When cc_schedule is specified, make sure lstm_tpl is
# QuantizedLSTMCell with the same cc_schedule so that the RNN layer
# output is within clipping range.
xs = ys
return py_utils.NestedMap(encoded=xs,
padding=tf.squeeze(ps, [2]),
segment_id=src_segment_id)
def FProp(self, theta, input_batch):
p = self.params
with tf.name_scope(p.name):
inputs = py_utils.with_dependencies([
py_utils.assert_shape_match(tf.shape(input_batch.ids),
[-1, -1]),
py_utils.assert_shape_match(tf.shape(input_batch.ids),
tf.shape(input_batch.paddings))
], tf.transpose(input_batch.ids))
paddings = tf.expand_dims(tf.transpose(input_batch.paddings), 2)
if p.packed_input:
src_segment_id = tf.expand_dims(
tf.transpose(input_batch.segment_ids), 2)
else:
src_segment_id = None
xs = self.emb.EmbLookup(theta.emb, inputs)
xs = self.ApplyClipping(theta, xs)
summary_utils.histogram('input_emb', xs)
xs = self.dropout.FProp(theta.dropout, xs)
ps = paddings
# Now the rnn layers.
outputs_list = []
for i in range(0, p.num_lstm_layers):
layer = self.rnn[i]
ys = layer.FProp(theta.rnn[i],
xs,
ps,
segment_id=src_segment_id)
ys = self.dropout.FProp(theta.dropout, ys)
if i >= p.residual_start:
xs += ys # Residual skip
xs = self.ApplyClipping(theta, xs)
else:
xs = ys
outputs_list.append(xs)
summary_utils.histogram('layer_out_%s' % i, xs)
if p.is_transparent:
xs = self.transparent_merger.FProp(theta.transparent_merger,
outputs_list)
if p.lstm_cell_size * 2 != p.encoder_out_dim:
# Project to the right depth.
xs = self.final_proj.FProp(theta.final_proj, xs, ps)
summary_utils.histogram('final_proj_out', xs)
if src_segment_id is not None:
src_segment_id = tf.squeeze(src_segment_id, [2])
return py_utils.NestedMap(encoded=xs,
padding=tf.squeeze(ps, [2]),
segment_id=src_segment_id)
def FProp(self, theta, input_batch, state0=None):
p = self.params
src_segment_id = None
with tf.name_scope(p.name):
# Reshape to [t, b]
inputs = py_utils.with_dependencies([
py_utils.assert_shape_match(tf.shape(input_batch.ids),
[-1, -1]),
py_utils.assert_shape_match(tf.shape(input_batch.ids),
tf.shape(input_batch.paddings))
], tf.transpose(input_batch.ids))
paddings = tf.expand_dims(tf.transpose(input_batch.paddings), 2)
# Setup streaming states.
if not state0:
state0 = self.zero_state(theta, tf.shape(inputs)[1])
state1 = py_utils.NestedMap(rnn=[None] * p.num_lstm_layers)
xs = self.emb.EmbLookup(theta.emb, inputs)
xs = self.ApplyClipping(theta, xs)
summary_utils.histogram('input_emb', xs)
xs = self.dropout.FProp(theta.dropout, xs)
ps = paddings
# Now the rnn layers.
outputs_list = []
for i in range(0, p.num_lstm_layers):
layer = self.rnn[i]
ys, state1.rnn[i] = layer.FProp(theta.rnn[i],
xs,
ps,
state0=state0.rnn[i])
ys = self.dropout.FProp(theta.dropout, ys)
if i >= p.residual_start:
xs += ys # Residual skip
xs = self.ApplyClipping(theta, xs)
else:
xs = ys
outputs_list.append(xs)
summary_utils.histogram('layer_out_%s' % i, xs)
if p.is_transparent:
xs = self.transparent_merger.FProp(theta.transparent_merger,
outputs_list)
return py_utils.NestedMap(encoded=xs,
padding=tf.squeeze(ps, [2]),
segment_id=src_segment_id,
state=state1)
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 _ReshapeRetVal(name, t_shape):
"""Restore shape for tensors in microbatches."""
if t_shape is None:
return None
output_tensor = output_state[name]
if p.batch_dim != 0:
perm = list(range(1, p.batch_dim + 1)) + [0]
perm += list(range(p.batch_dim + 1, t_shape.rank + 1))
output_tensor = tf.transpose(output_tensor, perm=perm)
output_shape = t_shape.ToTensorShape().as_list()
output_shape[p.batch_dim] *= p.num_micro_batches
output_tensor = tf.reshape(output_tensor, output_shape)
return output_tensor
def PrepareSequenceForPlot(tensor, padding, name):
"""Prepares a sequence feature for plotting.
The sequence feature is transposed and channels are flattened.
Args:
tensor: A n-D Tensor of shape [batch, time, ...].
padding: A Tensor of shape [batch, time].
name: A string as the name of the reshaped Tensor, which will be used as the
subcaption for plotting.
Returns:
A tuple of:
reshaped_tensor: A 3-D Tensor of shape [batch, dim, time].
sequence_length: A 1-D Tensor of shape [batch].
"""
# Flatten any dimensions beyond the third into the third.
batch_size, max_len = py_utils.GetShape(tensor, 2)
plot_tensor = tf.reshape(tensor, [batch_size, max_len, -1])
plot_tensor = tf.transpose(plot_tensor, [0, 2, 1], name=name)
return (plot_tensor, SequenceLength(padding))
def AddAttentionSummary(name,
attention_tensors,
src_paddings,
tgt_paddings,
transcripts=None,
max_outputs=3):
"""Adds an image summary showing the attention probability matrix and state.
Tensors are in sequence tensor format with the batch dimension in axis 1.
Args:
name: Summary name.
attention_tensors: A list of 3D tensors shaped [target_len, batch_size,
source_len] where attention[i, j, k] is the probability for the i-th
output attending to the k-th input for element j in the batch.
src_paddings: A tensor of binary paddings shaped [source_len, batch] for the
source sequence. Or a list of tensors of the same length as
attention_tensors with a separate paddings for each entry in
attention_tensors.
tgt_paddings: A tensor of binary paddings shaped [target_len, batch] for the
target sequence. Or a list of tensors of the same length as
attention_tensors with a separate paddings for each entry in
attention_tensors.
transcripts: Optional, transcripts shaped [batch, source_len] for the source
sequence.
max_outputs: Integer maximum number of elements of the batch to plot.
"""
def Transpose(paddings):
paddings = paddings if isinstance(paddings, list) else [paddings]
return [tf.transpose(p) for p in paddings]
AddAttentionSummaryBatchMajor(
name, [tf.transpose(a, [1, 0, 2]) for a in attention_tensors],
Transpose(src_paddings), Transpose(tgt_paddings), transcripts,
max_outputs)
def GreedySearchDecode(self,
theta,
encoder_outputs,
init_beam_search_state=None,
pre_beam_search_step_callback=None,
post_beam_search_step_callback=None,
max_steps=None):
"""Performs greedy-search based decoding.
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.
init_beam_search_state: The `InitBeamSearchState` callback. Please refer
to the class header comments for more details.
pre_beam_search_step_callback: The `PreBeamSearchStepCallback` callback.
Please refer to the class header comments for more details.
post_beam_search_step_callback: The `PostBeamSearchStepCallback` callback.
Please refer to the class header comments for more details.
max_steps: maximum beam search steps. If None, use
self.params.target_seq_len.
Returns:
A tuple (hyp_ids, hyp_lens, done_hyps). Note that num_hyps is same as
src_batch_size.
- hyp_ids: [num_hyps, max_step]. Hyps end with <eos> token if the <eos>
token is encountered during search.
- hyp_lens: [num_hyps].
- done_hyps: [num_hyps], whether or not an eos is encountered.
"""
p = self.params
if max_steps is None:
max_steps = p.target_seq_len
initial_results, other_states = init_beam_search_state(
theta,
encoder_outputs,
1 # num_hyps_per_beam
)
num_hyps = tf.shape(initial_results.log_probs)[0]
if 'step_ids' in initial_results:
# [num_hyps, 1]
step_ids = tf.ensure_shape(initial_results.step_ids, [None, 1])
else:
step_ids = tf.fill([num_hyps, 1],
tf.constant(p.target_sos_id, dtype=tf.int32))
cur_step = tf.constant(0, dtype=tf.int32)
done_hyps = inplace_ops.empty(shape=[num_hyps],
dtype=tf.bool,
init=True,
name='done_hyps')
hyp_lens = inplace_ops.empty(shape=[num_hyps],
dtype=tf.int32,
init=True,
name='hyp_lens')
hyp_ids = inplace_ops.empty(shape=[max_steps, num_hyps],
dtype=tf.int32,
init=True,
name='hyp_ids')
def LoopContinue(cur_step, unused_step_ids, unused_hyp_ids,
unused_hyp_lens, done_hyps, unused_other_states_list):
return tf.math.logical_and(
cur_step < max_steps,
tf.math.logical_not(tf.reduce_all(done_hyps)))
def LoopBody(cur_step, step_ids, hyp_ids, hyp_lens, done_hyps,
other_states_list):
(cur_step, new_step_ids, hyp_ids, hyp_lens, done_hyps,
new_other_states) = self._GreedySearchStep(
theta, encoder_outputs, cur_step, step_ids, hyp_ids, hyp_lens,
done_hyps, other_states.Pack(other_states_list),
pre_beam_search_step_callback, post_beam_search_step_callback)
return (cur_step, new_step_ids, hyp_ids, hyp_lens, done_hyps,
new_other_states.Flatten())
flat_other_states = other_states.Flatten()
_, _, final_hyp_ids, final_hyp_lens, final_done_hyps, _ = tf.while_loop(
LoopContinue,
LoopBody,
loop_vars=(cur_step, step_ids, hyp_ids, hyp_lens, done_hyps,
flat_other_states),
parallel_iterations=10,
back_prop=False,
swap_memory=False,
shape_invariants=(tf.TensorShape(cur_step.get_shape()),
tf.TensorShape(step_ids.get_shape()),
tf.TensorShape(hyp_ids.get_shape()),
tf.TensorShape(hyp_lens.get_shape()),
tf.TensorShape(done_hyps.get_shape()),
_GetShapes(flat_other_states, none_shapes=True)))
# transpose hyp_ids so it matches BeamSearchDecode's output
final_hyp_ids = tf.transpose(final_hyp_ids)
return final_hyp_ids, final_hyp_lens, final_done_hyps
def MergeBeamSearchOutputs(max_hyps_per_beam, beam_search_outputs):
"""Merges beam search hyps from multiple decoders.
Args:
max_hyps_per_beam: the number of top hyps in the merged results. Must be
less than or equal to total number of input hyps.
beam_search_outputs: a list of BeamSearchDecodeOutput objects. Must share
the same source_batch and max sequence length.
Returns:
A BeamSearchDecodeOutput object containing max_hyps_per_beam hypotheses per
beam.
"""
source_batch = tf.shape(beam_search_outputs[0].topk_hyps)[0]
value_dict = {}
for output in beam_search_outputs:
hyps_per_beam = py_utils.with_dependencies([
py_utils.assert_equal(source_batch,
tf.shape(output.topk_hyps)[0]),
],
tf.shape(
output.topk_hyps)[1])
for k, v in six.iteritems(output._asdict()):
if v is None:
continue
if k == 'done_hyps':
v = tf.transpose(v)
if k not in value_dict:
value_dict[k] = []
value_dict[k].append(
tf.reshape(v, [source_batch, hyps_per_beam, -1]))
# Concatenate the tensors along the 'num_hyps_per_beam' dimension.
concatenated = {}
for k, values in six.iteritems(value_dict):
if len(values) != len(beam_search_outputs):
raise ValueError('Incomplete values for %s: %s' %
(k, beam_search_outputs))
concatenated[k] = tf.concat(values, axis=1)
scores = concatenated['topk_scores']
scores = tf.where(tf.equal(concatenated['topk_lens'], 0),
tf.fill(tf.shape(scores), -1e6), scores)
scores = tf.squeeze(scores, -1)
# Select top max_hyps_per_beam indices per beam.
_, top_indices = tf.nn.top_k(scores, max_hyps_per_beam)
batch_ids = tf.tile(tf.expand_dims(tf.range(source_batch), -1),
[1, max_hyps_per_beam])
# [source_batch, max_hyps_per_beam, 2]
gather_indices = tf.stack([batch_ids, top_indices], axis=-1)
# Gather the merged top hyps according to 'gather_indices'.
top = beam_search_outputs[0]._asdict()
total_hyps = source_batch * max_hyps_per_beam
for k, v in six.iteritems(concatenated):
v = tf.gather_nd(v, gather_indices)
if k == 'done_hyps':
v = tf.transpose(tf.reshape(v, [total_hyps, -1]))
elif k == 'topk_hyps':
v = tf.reshape(v, [source_batch, max_hyps_per_beam])
elif k == 'topk_ids':
v = tf.reshape(v, [total_hyps, -1])
elif k in ('topk_lens', 'topk_scores', 'topk_decoded'):
v = tf.reshape(v, [total_hyps])
else:
raise ValueError('Unexpected field: %s' % k)
top[k] = v
return BeamSearchDecodeOutput(**top)
def BeamSearchDecode(self,
theta,
encoder_outputs,
num_hyps_per_beam_override=0,
init_beam_search_state=None,
pre_beam_search_step_callback=None,
post_beam_search_step_callback=None,
max_steps=None):
"""Performs beam-search based decoding.
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. Mostly opaque to BeamSearchHelper, except that it should
contain either a 'seq_lengths' field of shape [source_batch_size] or
a 'paddings' field of shape [source_max_lengths, source_batch_size].
num_hyps_per_beam_override: If set to a value <= 0, this parameter is
ignored. If set to a value > 0, then this value will be used to override
`p.num_hyps_per_beam`.
init_beam_search_state: The `InitBeamSearchState` callback. Please refer
to the class header comments for more details.
pre_beam_search_step_callback: The `PreBeamSearchStepCallback` callback.
Please refer to the class header comments for more details.
post_beam_search_step_callback: The `PostBeamSearchStepCallback` callback.
Please refer to the class header comments for more details.
max_steps: maximum beam search steps. If None, use
self.params.target_seq_len.
Returns:
A `BeamSearchDecodeOutput`.
"""
p = self.params
num_hyps_per_beam = p.num_hyps_per_beam
if num_hyps_per_beam_override > 0:
num_hyps_per_beam = num_hyps_per_beam_override
if max_steps is None:
max_steps = p.target_seq_len
initial_results, other_states = init_beam_search_state(
theta, encoder_outputs, num_hyps_per_beam)
num_hyps = tf.shape(initial_results.log_probs)[0]
num_beams = num_hyps // num_hyps_per_beam
if 'step_ids' in initial_results:
# [num_hyps, 1]
step_ids = tf.ensure_shape(initial_results.step_ids, [None, 1])
else:
step_ids = tf.fill([num_hyps, 1],
tf.constant(p.target_sos_id, dtype=tf.int32))
min_score = -1e36
best_scores = (tf.zeros(shape=[num_beams], dtype=p.dtype) + min_score)
cumulative_scores = tf.zeros(shape=[num_hyps], dtype=p.dtype)
in_scores = tf.zeros([max_steps, num_hyps], dtype=p.dtype)
in_hyps = tf.zeros([max_steps, num_hyps], dtype=tf.int32)
in_prev_hyps = tf.zeros([max_steps, num_hyps], dtype=tf.int32)
in_done_hyps = tf.zeros([max_steps, num_hyps], dtype=tf.string)
bs_atten_probs = tf.zeros(
[max_steps, num_hyps,
tf.shape(initial_results.atten_probs)[1]],
dtype=p.dtype)
cur_step = tf.constant(0, dtype=tf.int32)
all_done = tf.constant(False, dtype=tf.bool)
core_bs_states = (best_scores, cumulative_scores, in_scores, in_hyps,
in_prev_hyps, in_done_hyps, bs_atten_probs)
def LoopContinue(cur_step, all_done, unused_step_ids,
unused_core_bs_states, unused_other_states_list):
return tf.math.logical_and(cur_step < max_steps,
tf.math.logical_not(all_done))
def LoopBody(cur_step, unused_all_done, step_ids, core_bs_states,
other_states_list):
(cur_step, all_done, new_step_ids, new_bs_states,
new_other_states) = self._BeamSearchStep(
theta, encoder_outputs, cur_step, step_ids, core_bs_states,
other_states.Pack(other_states_list), num_hyps_per_beam,
pre_beam_search_step_callback, post_beam_search_step_callback)
return (cur_step, all_done, new_step_ids, new_bs_states,
new_other_states.Flatten())
flat_other_states = other_states.Flatten()
_, _, _, final_bs_states, flat_final_other_states = tf.while_loop(
LoopContinue,
LoopBody,
loop_vars=(cur_step, all_done, step_ids, core_bs_states,
flat_other_states),
parallel_iterations=10,
back_prop=False,
swap_memory=False,
shape_invariants=(tf.TensorShape(cur_step.get_shape()),
tf.TensorShape(all_done.get_shape()),
tf.TensorShape(step_ids.get_shape()),
_GetShapes(core_bs_states),
_GetShapes(flat_other_states, none_shapes=True)))
# [target_seq_len, num_beams * num_hyps_per_beam].
final_done_hyps = final_bs_states[5]
final_other_states = other_states.Pack(flat_final_other_states)
# Assume that `paddings` has shape [source_max_lengths, source_batch_size]
# by default, and compute `encoded_seq_lengths` accordingly. This can be
# overridden by directly passing `seq_lengths` in the `encoder_outputs`
# NestedMap.
encoded_seq_lengths = getattr(encoder_outputs, 'seq_lengths', None)
if encoded_seq_lengths is None:
source_paddings = encoder_outputs.padding
if isinstance(source_paddings, py_utils.NestedMap):
encoded_seq_lengths = tf.cast(
tf.round(
tf.reduce_sum(
1.0 - tf.transpose(source_paddings.Flatten()[0]),
1)), tf.int32)
else:
encoded_seq_lengths = tf.cast(
tf.round(
tf.reduce_sum(1.0 - tf.transpose(source_paddings), 1)),
tf.int32)
# [num_beams, num_hyps_per_beam].
topk_hyps = ops.top_k_terminated_hyps(
final_done_hyps,
encoded_seq_lengths,
k=num_hyps_per_beam,
num_hyps_per_beam=num_hyps_per_beam,
length_normalization=p.length_normalization,
coverage_penalty=p.coverage_penalty,
target_seq_length_ratio=p.target_seq_length_ratio,
eoc_id=p.target_eoc_id,
merge_paths=p.merge_paths)
# [num_beams * num_hyps_per_beam, ...].
max_seq_length = 0 if isinstance(max_steps, tf.Tensor) else max_steps
topk_ids, topk_lens, topk_scores = ops.unpack_hyp(
tf.reshape(topk_hyps, [-1]), max_seq_length=max_seq_length)
# [num_beams, num_hyps_per_beam].
topk_scores = tf.reshape(topk_scores, tf.shape(topk_hyps))
return BeamSearchDecodeOutput(final_done_hyps, topk_hyps, topk_ids,
topk_lens, topk_scores, None,
final_other_states)
def _BeamSearchDecodeIds(self,
theta,
encoder_outputs,
num_hyps_per_beam,
init_beam_search_state=None,
pre_beam_search_step_callback=None,
post_beam_search_step_callback=None,
max_steps=None):
"""Performs beam-search based decoding.
Args:
theta: A NestedMap object containing weights' values of the decoder layer
and its children layers.
encoder_outputs: A NestedMap computed by encoder.
num_hyps_per_beam: Number of hyps per beam.
init_beam_search_state: The InitBeamSearchState callback. Please refer to
the class header comments for more details.
pre_beam_search_step_callback: The PreBeamSearchStepCallback callback.
Please refer to the class header comments for more details.
post_beam_search_step_callback: The PostBeamSearchStepCallback callback.
Please refer to the class header comments for more details.
max_steps: maximum beam search steps. If None, use
self.params.target_seq_len.
Returns:
hyps: A tensor of shape [time, b * k] with ids of the token selected.
prev_hyps: A tensor of shape [time, b * k] with index to the previous hyps
which was selected.
done_hyps: A boolean tensor of shape [time, b * k] where value
indicates if hyps was terminated.
scores: A tensor of shape [time, b * k] with scores of the token
selected.
atten_probs: A tensor of shape [time, b * k, seq_len] which contain the
attention probabilities over the source words against word in the
previous hyps.
eos_scores: A tensor of shape [time, b * k] with scores of the eos token
selected.
eos_atten_probs: A tensor of shape [time, b * k, seq_len] which contain
the attention probabilities over the source words against word in the
previous hyps.
source_seq_lengths: A tensor of shape [time] containing the source
seq_lengths.
flat_final_other_states: A array of tensors that are part of other states.
"""
p = self.params
source_paddings = encoder_outputs.padding
initial_results, other_states = init_beam_search_state(
theta, encoder_outputs, num_hyps_per_beam)
num_hyps = tf.shape(initial_results.log_probs)[0]
num_beams = num_hyps // num_hyps_per_beam
# We cache the NestedMap as member variable so that we can use it to
# pack the final outputs. Tpu rewrite methods forces us to strictly pass
# in Tensors, and output Tensors
self._other_states = other_states
step_ids = tf.fill([num_hyps, 1],
tf.constant(p.target_sos_id, dtype=tf.int32))
min_score = -1e36
fprop_dtype = py_utils.FPropDtype(p)
best_scores = (tf.zeros(shape=[num_beams], dtype=fprop_dtype) +
min_score)
cumulative_scores = tf.zeros(shape=[num_hyps], dtype=fprop_dtype)
histories = tf.zeros(shape=[num_hyps], dtype=tf.int32)
in_scores = tf.TensorArray(dtype=fprop_dtype, size=max_steps)
in_hyps = tf.TensorArray(dtype=tf.int32, size=max_steps)
in_prev_hyps = tf.TensorArray(dtype=tf.int32, size=max_steps)
in_done_hyps = tf.TensorArray(dtype=tf.int32, size=max_steps)
in_atten_probs = tf.TensorArray(dtype=fprop_dtype, size=max_steps)
in_eos_scores = tf.TensorArray(dtype=fprop_dtype, size=max_steps)
in_eos_atten_probs = tf.TensorArray(dtype=fprop_dtype, size=max_steps)
cur_step = tf.constant(0, dtype=tf.int32)
all_done = tf.constant(False, dtype=tf.bool)
# States for beam search that are inputs into Beam search step.
accum_bs_states = [best_scores, cumulative_scores, histories]
# States that are not accumulators.
non_accum_bs_states = [
in_scores,
in_hyps,
in_prev_hyps,
in_done_hyps,
in_atten_probs,
in_eos_scores,
in_eos_atten_probs,
]
core_bs_states = tuple(accum_bs_states + non_accum_bs_states)
flat_other_states = other_states.Flatten()
# If there is an optimized implementation for short sequence, LoopBodyShort
# will run first for short_seq_limit steps (after which the
# LoopBodyShort does not have performance benefit). Then LoopBodyLong (the
# default implementation) is used to continue the rest of the steps. For
# decoders which do not have the short sequence specific implementation,
# only the LoopBodyLong (the default implementation) will run.
if p.short_seq_limit > 0:
def LoopContinueShort(cur_step, all_done, unused_step_ids,
unused_core_bs_states,
unused_other_states_list):
"""Use short_seq optimization when cur_step is smaller than limit."""
return tf.math.logical_and(cur_step < p.short_seq_limit,
tf.math.logical_not(all_done))
def LoopBodyShort(cur_step, unused_all_done, step_ids,
core_bs_states, other_states_list):
"""Loop body of short_seq optimization.
Instead of doing computation for the entire padded sequence, while loop
with early exit is used within each _BeamSearchStep to do computation
for only the actual sequence (seq_length <= cur_step).
use_short_seq_opt is used as the flag to pass this information down to
the decoder implementation.
Args:
cur_step: A scalar int tensor, the current time step, 0-based.
unused_all_done: A tf.bool, indicating whether the decoding finishes.
step_ids: An int32 tensor of shape [num_hyps, 1]. The input ids to the
current search step.
core_bs_states: A tuple of core beam search states.
other_states_list: A flattened NestedMap of other beam search states.
Returns:
The updated input tuple, with the same shape.
"""
(cur_step, all_done, new_step_ids, new_bs_states,
new_other_states) = self._BeamSearchStep(
theta,
encoder_outputs,
cur_step,
step_ids,
core_bs_states,
other_states.Pack(other_states_list),
num_hyps_per_beam,
pre_beam_search_step_callback,
post_beam_search_step_callback,
use_short_seq_opt=True)
return (cur_step, all_done, new_step_ids, new_bs_states,
new_other_states.Flatten())
(cur_step, all_done, step_ids, core_bs_states,
flat_other_states) = tf.while_loop(
LoopContinueShort,
LoopBodyShort,
loop_vars=(cur_step, all_done, step_ids, core_bs_states,
flat_other_states),
parallel_iterations=10,
back_prop=False,
swap_memory=False,
shape_invariants=(
tf.TensorShape(cur_step.get_shape()),
tf.TensorShape(all_done.get_shape()),
tf.TensorShape(step_ids.get_shape()),
tuple(
list(_GetShapes(accum_bs_states)) +
list(_GetShapes(non_accum_bs_states,
none_shapes=True))),
_GetShapes(flat_other_states, none_shapes=True)),
maximum_iterations=max_steps)
def LoopContinueLong(cur_step, all_done, unused_step_ids,
unused_core_bs_states, unused_other_states_list):
"""Continue default implementation until decoding finishes."""
return tf.math.logical_and(cur_step < max_steps,
tf.math.logical_not(all_done))
def LoopBodyLong(cur_step, unused_all_done, step_ids, core_bs_states,
other_states_list):
"""Loop body of default long_seq implementation."""
(cur_step, all_done, new_step_ids, new_bs_states,
new_other_states) = self._BeamSearchStep(
theta,
encoder_outputs,
cur_step,
step_ids,
core_bs_states,
other_states.Pack(other_states_list),
num_hyps_per_beam,
pre_beam_search_step_callback,
post_beam_search_step_callback,
use_short_seq_opt=False)
return (cur_step, all_done, new_step_ids, new_bs_states,
new_other_states.Flatten())
_, _, _, final_bs_states, flat_final_other_states = tf.while_loop(
LoopContinueLong,
LoopBodyLong,
loop_vars=(cur_step, all_done, step_ids, core_bs_states,
flat_other_states),
parallel_iterations=10,
back_prop=False,
swap_memory=False,
shape_invariants=(
tf.TensorShape(cur_step.get_shape()),
tf.TensorShape(all_done.get_shape()),
tf.TensorShape(step_ids.get_shape()),
tuple(
list(_GetShapes(accum_bs_states)) +
list(_GetShapes(non_accum_bs_states, none_shapes=True))),
_GetShapes(flat_other_states, none_shapes=False)),
maximum_iterations=max_steps)
if isinstance(source_paddings, py_utils.NestedMap):
source_seq_lengths = tf.cast(tf.round(
tf.reduce_sum(1.0 - tf.transpose(source_paddings.Flatten()[0]),
1)),
dtype=tf.int32)
else:
source_seq_lengths = tf.cast(tf.round(
tf.reduce_sum(1.0 - tf.transpose(source_paddings), 1)),
dtype=tf.int32)
# Concatenate all outputs on axis=0.
scores = final_bs_states[3].stack()
hyps = final_bs_states[4].stack()
prev_hyps = final_bs_states[5].stack()
done_hyps = tf.cast(final_bs_states[6].stack(), tf.bool)
atten_probs = final_bs_states[7].stack()
eos_scores = final_bs_states[8].stack()
eos_atten_probs = final_bs_states[9].stack()
rets = (hyps, prev_hyps, done_hyps, scores, atten_probs, eos_scores,
eos_atten_probs, source_seq_lengths)
# TODO(rohananil): Only send a single R1 tensor to host instead of 3 after
# b/111131551 is resolved.
# Canonical shapes for tensors of various. ranks
r_shapes = [
py_utils.GetShape(source_seq_lengths),
py_utils.GetShape(hyps),
py_utils.GetShape(atten_probs)
]
# Reshape all tensors to [-1] to avoid cost of copy due to padding.
rets_r1 = [tf.reshape(r, [-1]) for r in rets]
return tuple(r_shapes) + tuple(rets_r1) + tuple(
flat_final_other_states)
def _BeamSearchStep(self,
theta,
encoder_outputs,
cur_step,
step_ids,
core_bs_states,
other_states,
num_hyps_per_beam,
pre_beam_search_step_callback,
post_beam_search_step_callback,
use_short_seq_opt=False):
"""Extend beam search hyps for one step.
num_beams = Number of source sequences to be decoded.
num_hyps_per_beam = Number of hyps to keep per source sequence.
num_hyps = num_beams * num_hyps_per_beam
src_seq_len = Number of time steps in the source sequence.
tgt_seq_len = Maximum allowed time steps in the target sequence.
Args:
theta: A NestedMap object containing weights' values of the decoder
layer and its children layers.
encoder_outputs: A NestedMap computed by encoder.
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.
core_bs_states: A tuple of core beam search states. This list is
maintained by this helper class.
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.
num_hyps_per_beam: Num of hyps to keep per beam.
pre_beam_search_step_callback: The PreBeamSearchStepCallback callback.
Please refer to the class header comments for more details.
post_beam_search_step_callback: The PostBeamSearchStepCallback callback.
Please refer to the class header comments for more details.
use_short_seq_opt: A bool, whether using short sequence optimization.
Returns:
A tuple of following elements for the next beam search step:
(next step, all_done, step_ids, core_bs_states, other_states)
"""
p = self.params
if use_short_seq_opt:
bs_results, other_states = pre_beam_search_step_callback(
theta, encoder_outputs, step_ids, other_states,
num_hyps_per_beam, use_short_seq_opt)
else:
bs_results, other_states = pre_beam_search_step_callback(
theta, encoder_outputs, step_ids, other_states,
num_hyps_per_beam)
(best_scores, cumulative_scores, histories, in_scores, in_hyps,
in_prev_hyps, in_done_hyps, in_atten_probs, in_eos_scores,
in_eos_atten_probs) = core_bs_states
(out_best_scores, out_cumulative_scores, out_scores, out_eos_scores,
out_hyps, out_prev_hyps, out_done_hyps, out_atten_probs,
out_eos_atten_probs, all_done,
out_histories) = beam_search_tpu_ops.beam_search_step(
bs_results.log_probs,
bs_results.atten_probs,
best_scores,
cumulative_scores,
histories,
cur_step,
eos_id=p.target_eos_id,
beam_size=p.beam_size,
num_beams=tf.shape(best_scores)[0],
num_hyps_per_beam=num_hyps_per_beam,
valid_eos_max_logit_delta=p.valid_eos_max_logit_delta,
merge_paths=p.merge_paths,
eoc_id=p.target_eoc_id if p.merge_paths else -1,
is_last_chunk=bs_results.get('is_last_chunk'))
# Write out values into TensorArray's corresponding to each output.
arr_scores = in_scores.write(cur_step, out_scores)
arr_eos_scores = in_eos_scores.write(cur_step, out_eos_scores)
arr_hyps = in_hyps.write(cur_step, out_hyps)
arr_prev_hyps = in_prev_hyps.write(cur_step, out_prev_hyps)
# TODO(rohananil): Change the implementation of TensorArray write for
# tf.bool from false += current_value to logical_and(true, current_value) as
# addition operator for bool is not defined.
arr_done_hyps = in_done_hyps.write(cur_step,
tf.cast(out_done_hyps, tf.int32))
arr_atten_probs = in_atten_probs.write(cur_step, out_atten_probs)
arr_eos_atten_probs = in_eos_atten_probs.write(cur_step,
out_eos_atten_probs)
# New beam search states.
new_bs_states = (out_best_scores, out_cumulative_scores, out_histories,
arr_scores, arr_hyps, arr_prev_hyps, arr_done_hyps,
arr_atten_probs, arr_eos_scores, arr_eos_atten_probs)
old_hyp_ids = tf.reshape(out_prev_hyps, [-1])
if p.batch_major_compute:
# Transformed the indices into the key/value cache for fast decoding
# (prefix_states in other_states) due to the num_hyps dimension of
# cache is computed as num_beams by num_hyps_per_beam, which is different
# from the old_hyp_ids assumption (num_hyps_per_beam by num_beams).
# Both transpose and recomputation are required to correct the indices.
num_beams = tf.shape(best_scores)[0]
old_hyp_ids_in_cache_order = tf.reshape(
tf.transpose(tf.reshape(old_hyp_ids, [num_hyps_per_beam, -1])),
[-1])
old_hyp_ids_in_cache_order = (
(old_hyp_ids_in_cache_order % num_beams) * num_hyps_per_beam +
old_hyp_ids_in_cache_order // num_beams)
def ReOrderHyps(x_in):
"""Reorders x_in based on prev hyp ids."""
if isinstance(x_in, tf.Tensor) and x_in.shape.ndims > 0:
# For rank > 1 tensors we make use of an efficient matmul based gather
# on tpu that takes in account the range of the values. For R1, we
# rely on the tf.gather and xla to optimize it efficiently for R1
# layout.
if x_in.shape.ndims > 1:
if p.batch_major_state:
num_hyps = tf.shape(old_hyp_ids)[0]
x_out = beam_search_tpu_ops.fast_gather(
x_in,
old_hyp_ids,
num_hyps,
max_value=None,
batch_major_state=p.batch_major_state)
else:
# Use corrected indices only here for batch major compute as
# key/value caches are the states being affected.
correct_old_hyp_ids = (old_hyp_ids_in_cache_order
if p.batch_major_compute else
old_hyp_ids)
def _GatherStep(x_in, t):
"""Gather for one time step.
Args:
x_in: in the shape of [T, B, ...] we first get slice(t) from the
tensors, then gather old_hyp_ids from the slice and write the
interpolated slice inplace to update the original x_in.
t: current time step
Returns:
Updated x_in and time step
"""
x = tf.gather(tf.gather(x_in, t),
correct_old_hyp_ids)
return inplace_ops.alias_inplace_update(
x_in, t, x), t + 1
x_out, _ = tf.while_loop(
lambda _, t: t <= cur_step, _GatherStep,
(x_in, tf.zeros([], tf.int32)))
else:
x_out = tf.gather(x_in, old_hyp_ids)
x_out.set_shape(x_in.get_shape())
return x_out
else:
return x_in
new_other_states = other_states.Transform(ReOrderHyps)
new_step_ids = tf.reshape(out_hyps, [-1, 1])
final_other_states = post_beam_search_step_callback(
theta, encoder_outputs, new_step_ids, new_other_states)
return (cur_step + 1, all_done, new_step_ids, new_bs_states,
final_other_states)
def Sample(self, decoder_theta, encoder_outputs, random_seed,
init_state_callback, pre_step_callback, post_step_callback):
"""Samples target sequences, one target sequence per source sequence.
(Please see beam_search_helper.py for description of decoder callbacks.)
Args:
decoder_theta: A NestedMap object containing weights' values of the
decoder layer and its children layers, to be passed to decoder
callbacks.
encoder_outputs: the outputs of the encoder, to be passed to callbacks.
random_seed: a scalar int32 tensor representing the random seed.
init_state_callback: decoder._InitBeamSearchStateCallback.
pre_step_callback: decoder._PreBeamSearchStepCallback.
post_step_callback: decoder._PostBeamSearchStepCallback.
Returns:
A NestedMap containing the following tensors
- 'logits': [batch, max_target_length, vocab_size], representing the
distribution from which target sequences are sampled.
- 'ids': [batch, max_target_length] of int32, representing the target
sequence ids, not including target_sos_id, but maybe ending with
target_eos_id if end-of-sequence is reached before target_seq_len.
- 'paddings': [batch, max_target_length] of 0/1, where 1 represents
a padded timestep.
"""
p = self.params
assert p.temperature > 0
if getattr(encoder_outputs, 'segment_id', 1) is None:
# Remove None values, which are not supported by recurrent.
del encoder_outputs['segment_id']
# init_state_callback may modify 'encoder_outputs', e.g., by inserting
# 'packed_src'.
bs_result, bs_state = init_state_callback(decoder_theta,
encoder_outputs,
num_hyps_per_beam=1)
# 'recurrent_theta' represents all cross-timestep information used by the
# recurrent loop below, including layer theta and encoder outputs.
recurrent_theta = py_utils.NestedMap(theta=decoder_theta,
random_seed=random_seed,
encoder_outputs=encoder_outputs)
batch = tf.shape(bs_result.log_probs)[0]
recurrent_state0 = py_utils.NestedMap(
timestep=tf.zeros(shape=[], dtype=tf.int32),
logits=bs_result.log_probs,
# Start with target_sos_id.
ids=tf.fill([batch], tf.cast(p.target_sos_id, tf.int32)),
bs_state=bs_state)
inputs = py_utils.NestedMap(dummy=tf.zeros([p.target_seq_len, batch]))
def Step(recurrent_theta, state0, inputs):
"""Computes one decoder step."""
del inputs
with tf.name_scope('single_sampler_step'):
# Compute logits and states.
bs_result, bs_state1 = pre_step_callback(
recurrent_theta.theta,
recurrent_theta.encoder_outputs,
tf.expand_dims(state0.ids, 1), # [batch, 1].
state0.bs_state,
num_hyps_per_beam=1)
batch = tf.shape(bs_result.log_probs)[0]
state1 = py_utils.NestedMap(timestep=state0.timestep + 1)
state1.logits = bs_result.log_probs
# Sample ids from logits. [batch].
state1.ids = tf.reshape(
tf.random.stateless_categorical(
state1.logits / p.temperature,
num_samples=1,
seed=tf.stack(
[recurrent_theta.random_seed, state0.timestep]),
dtype=state0.ids.dtype,
name='sample_next_id'), [batch])
if 'is_last_chunk' in bs_result and p.target_eoc_id >= 0:
state1.ids = tf.where(
tf.math.logical_and(
bs_result.is_last_chunk,
tf.equal(state1.ids, p.target_eoc_id)),
tf.fill(tf.shape(state1.ids), p.target_eos_id),
state1.ids)
state1.bs_state = post_step_callback(
recurrent_theta.theta, recurrent_theta.encoder_outputs,
state1.ids, bs_state1)
return state1, py_utils.NestedMap()
accumulated_states, _ = recurrent.Recurrent(
recurrent_theta,
recurrent_state0,
inputs,
Step,
allow_implicit_capture=True)
result = py_utils.NestedMap(logits=tf.transpose(
accumulated_states.logits, [1, 0, 2]),
ids=tf.transpose(accumulated_states.ids))
result.paddings = tf.cast(
_ComputePaddings(result.ids, p.target_eos_id), result.logits.dtype)
# Force ids to be eos_id if the timestep is padded.
result.ids = tf.where(tf.equal(result.paddings, 0), result.ids,
tf.fill(tf.shape(result.ids), p.target_eos_id))
static_batch_size = bs_result.log_probs.shape[0]
result.ids.set_shape([static_batch_size, p.target_seq_len])
result.paddings.set_shape([static_batch_size, p.target_seq_len])
return result
def Transpose(paddings): paddings = paddings if isinstance(paddings, list) else [paddings] return [tf.transpose(p) for p in paddings]
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 _BeamSearchStep(self, theta, encoder_outputs, cur_step, step_ids,
core_bs_states, other_states, num_hyps_per_beam,
pre_beam_search_step_callback,
post_beam_search_step_callback):
"""Extend beam search hyps for one step.
| num_beams = Number of source sequences to be decoded.
| num_hyps_per_beam = Number of hyps to keep per source sequence.
| num_hyps = num_beams * num_hyps_per_beam
| src_seq_len = Number of time steps in the source sequence.
| src_batch = Number of examples in the source sequence.
| tgt_seq_len = Maximum allowed time steps in the target sequence.
| tgt_batch = num_hyps_per_beam * src_batch
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.
core_bs_states: A tuple of core beam search states. This list is
maintained by this helper class.
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.
num_hyps_per_beam: Num of hyps to keep per beam.
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 beam search step,
(next step, all_done, step_ids, core_bs_states, other_states)
"""
p = self.params
bs_results, other_states = pre_beam_search_step_callback(
theta, encoder_outputs, step_ids, other_states, num_hyps_per_beam)
(best_scores, cumulative_scores, in_scores, in_hyps, in_prev_hyps,
in_done_hyps, in_atten_probs) = core_bs_states
(out_best_scores, out_cumulative_scores, out_scores, out_hyps,
out_prev_hyps, out_done_hyps, out_atten_probs,
all_done) = ops.beam_search_step(
tf.cast(bs_results.log_probs, dtype=p.dtype),
tf.cast(bs_results.atten_probs, dtype=p.dtype),
best_scores,
cumulative_scores,
in_scores,
in_hyps,
in_prev_hyps,
in_done_hyps,
in_atten_probs,
bs_results.is_last_chunk if self._model_uses_eoc_id else [],
cur_step,
eoc_id=p.target_eoc_id,
eos_id=p.target_eos_id,
beam_size=p.beam_size,
num_hyps_per_beam=num_hyps_per_beam,
valid_eos_max_logit_delta=p.valid_eos_max_logit_delta,
merge_paths=p.merge_paths,
allow_empty_terminated_hyp=p.allow_empty_terminated_hyp,
ensure_full_beam=p.ensure_full_beam,
force_eos_in_last_step=p.force_eos_in_last_step,
local_eos_threshold=p.local_eos_threshold)
new_step_ids = tf.reshape(out_hyps[cur_step, :], tf.shape(step_ids))
new_step_ids.set_shape(step_ids.get_shape())
old_hyp_ids = tf.reshape(
tf.slice(out_prev_hyps, begin=[cur_step, 0], size=[1, -1]), [-1])
if p.batch_major_compute:
# Transformed the indices into the key/value cache for fast decoding
# (prefix_states in other_states) due to the num_hyps dimension of
# cache is computed as num_beams by num_hyps_per_beam, which is different
# from the old_hyp_ids assumption (num_hyps_per_beam by num_beams).
# Both transpose and recomputation are required to correct the indices.
num_beams = tf.shape(best_scores)[0]
old_hyp_ids_in_cache_order = tf.reshape(
tf.transpose(tf.reshape(old_hyp_ids, [num_hyps_per_beam, -1])),
[-1])
old_hyp_ids_in_cache_order = (
(old_hyp_ids_in_cache_order % num_beams) * num_hyps_per_beam +
old_hyp_ids_in_cache_order // num_beams)
new_bs_states = (out_best_scores, out_cumulative_scores, out_scores,
out_hyps, out_prev_hyps, out_done_hyps,
out_atten_probs)
def ReOrderHyps(x_in):
"""Reorders x_in based on prev hyp ids."""
if (isinstance(x_in, tf.Tensor) and x_in.shape.ndims
and x_in.shape.ndims > 0):
if x_in.shape.ndims > 2 and not p.batch_major_state:
# Use corrected indices only here for batch major compute as key/value
# caches are the states being affected.
correct_old_hyp_ids = (old_hyp_ids_in_cache_order
if p.batch_major_compute else
old_hyp_ids)
x_out = tf.gather(x_in, correct_old_hyp_ids, axis=1)
else:
x_out = tf.gather(x_in, old_hyp_ids)
x_out.set_shape(x_in.get_shape())
return x_out
else:
return x_in
new_other_states = other_states.Transform(ReOrderHyps)
final_other_states = post_beam_search_step_callback(
theta, encoder_outputs, new_step_ids, new_other_states)
return (cur_step + 1, all_done, new_step_ids, new_bs_states,
final_other_states)
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` object containing: ids - The inputs tensor of
shape [batch, time]. paddings - The ids' paddings of shape [batch,
time].
Returns:
A '.NestedMap' object containing:
encoded - The encoded features of shape [time, batch, dim] or [batch,
time, dim], depending p.output_data_format.
padding - The encoded features' padding of shape [time, batch] or
[batch, time].
segment_id - The segmentation of packed inputs of shape [time, batch] or
[batch, time] if it is supported by the model, or None otherwise.
embedded_inputs - The embedded inputs tokens without positional
encodings of shape [time, batch, dim] or [batch, time, dim].
"""
p = self.params
with tf.name_scope(p.name):
# [batch, time]
input_ids = input_batch.ids
# [batch, time]
paddings = input_batch.paddings
# [batch, time]
segment_ids = input_batch.segment_ids if p.packed_input else None
batch = py_utils.GetShape(input_ids)[0]
time = py_utils.GetShape(input_ids)[1]
# Embedding layer.
# [batch, time, dim]
if not p.shared_emb:
input_embs = self.token_emb.EmbLookup(theta.token_emb,
input_ids)
else:
input_embs = self.softmax.EmbLookup(theta.softmax, input_ids)
orig_input_embs = input_embs
# [1, time, dim]
if p.packed_input:
positions = input_batch.segment_pos
position_embs = tf.expand_dims(
self.position_emb.FPropWithPosition(
theta.position_emb, positions), 0)
else:
position_embs = tf.expand_dims(
self.position_emb.FProp(theta.position_emb, time), 0)
# [batch, time, dim]
input_embs += tf.cast(position_embs, tf.bfloat16)
if p.input_dropout_tpl.fprop_dtype:
input_embs = tf.cast(input_embs,
p.input_dropout_tpl.fprop_dtype)
paddings = tf.cast(paddings, p.input_dropout_tpl.fprop_dtype)
input_embs = self.input_dropout.FProp(theta.input_dropout,
input_embs)
# [batch, time, dim]
transformer_input = input_embs
# Explicitly set the input shape of Transformer layers, to avoid
# unknown shape error occurred to tf.einsum on nonTPU devices.
transformer_input = tf.reshape(transformer_input,
[batch, time, p.model_dim])
# Compute self-attention segment mask once.
if p.packed_input:
segment_mask = batch_major_attention.SegmentMask(
segment_ids, segment_ids, dtype=transformer_input.dtype)
else:
segment_mask = tf.zeros([batch, 1, time, time])
encoded, padding = self.transformer_stack.FProp(
theta.transformer_stack, transformer_input, paddings,
segment_mask)
if p.final_layer_norm:
encoded = self.final_ln.FProp(theta.final_ln, encoded)
seq_lengths = tf.cast(tf.reduce_sum(1. - padding, axis=1),
tf.int32)
if p.output_data_format == 'TBC':
encoded = tf.transpose(encoded,
[1, 0, 2]) # [time, batch, dim]
padding = tf.transpose(padding) # [time, batch]
segment_ids = tf.transpose(
segment_ids) if p.packed_input else None
orig_input_embs = tf.transpose(orig_input_embs, [1, 0, 2])
return py_utils.NestedMap(
encoded=encoded,
padding=padding,
seq_lengths=seq_lengths, # used by beam_search_helper.
segment_id=segment_ids,
embedded_inputs=orig_input_embs)
def _ConstructWarpMatrix(self, batch_size, matrix_size, origin,
destination, choose_range, dtype):
"""Returns warp matrices according to origin, destination and choose_range.
This function constructs a batch of warp matrices which maps the batch
of origin points to the batch of destination points with fixed boundary
coordinates at 0 and choose_range.
The warping function, defined by the origin anchor point `origin`,
the destination of the origin anchor point `destination` and the
length of the domain in the warping axis `choose_range` is a piecewise
linear map that fixes the points 0 and `choose_range` and maps
`origin` to `destination`.
For the warping matrix to be non-singular, destination must lie in the
range 1<= destination <= choose_range - 1, so a destination
out of this range is adjusted to be in this range before the warping
matrix is constructed.
The warping map can be explicitly written by first defining the slopes:
1) slope_0 = origin / destination.
2) slope_1 = (choose_range - origin) / (choose_range - destination).
3) slope_2 = 1.0.
Then the origin point orig_i of the mapped coordinate i is given by:
1) i < destination: orig_i = slope_0 * i.
2) destination <= i < choose_range:
orig_i = slope_1 * i - (slope_1 - slope_0) * destination.
3) i >= choose_range: orig_i = i.
Denoting n_i = ceil(orig_i), the warp matrix element warp[i][j] is given by:
1) j = n_i: 1 - n_i + orig_i.
2) j = n_i - 1: n_i - orig_i.
3) Otherwise: 0.
Applying the warp matrix to an array of pixels, i.e.,
warped_pixel[i] = sum_j warp[i][j] * pixel[j], one would get
warped_pixel[i] = (n_i-orig_i) pixel[n_i-1] + (1-n_i+orig_i) pixel[n_i].
Args:
batch_size: Batch size. Integer number.
matrix_size: Dimension of the vector space the warp matrix is applied to.
Integer number.
origin: Origin anchor point for warping. Tensor of shape (batch_size,) and
data type dtype.
destination: Destination of the origin anchor point upon warping. Tensor
of shape (batch_size,) and data type dtype.
choose_range: Range within which the warp reference points must lie.
Tensor of shape (batch_size,) data type dtype.
dtype: Data type of origin, destination, choose_range and the output warp
matrix.
Returns:
warp_matrix: An array of fixed size warp matrices with shape
(batch_size, matrix_size, matrix_size).
"""
p = self.params
# Entries of destination must be in the range
# 1 <= destination <= choose_range - 1
# for warp matrix to have non-singular values.
destination = tf.minimum(tf.maximum(destination, 1.0),
choose_range - 1.0)
# Construct piece-wise linear function fixing boundary points
# specified by zero, choose_range and matrix size and maps
# the origin anchor point to the destination.
destination_bc = tf.broadcast_to(destination,
(matrix_size, batch_size))
destination_bc = tf.transpose(destination_bc)
choose_range_bc = tf.broadcast_to(choose_range,
(matrix_size, batch_size))
choose_range_bc = tf.transpose(choose_range_bc)
# Slopes of piece-wise linear function.
slope_0 = origin / destination
slope_1 = (choose_range - origin) / (choose_range - destination)
slope_2 = 1.0
# x is a batch of origin matrices.
# The origin matrix is the matrix such that
# origin[i][j] = Origin coordinate of coordinate i for the warp map.
# Denoting the destination of the origin anchor point in the
# warp map as "dest," the origin coordinate of point i is given by:
# 1) i < dest: slope_0 * i.
# 2) dest <= i < choose_range: slope_1 * i - (slope_1 - slope_0) * dest.
# 3) i >= choose_range: i.
x = tf.broadcast_to(tf.cast(tf.range(matrix_size), dtype=dtype),
(batch_size, matrix_size))
x = (self.EinsumBBmBm(slope_0, x) + self.EinsumBBmBm(
slope_1 - slope_0, tf.nn.relu(x - destination_bc)) +
self.EinsumBBmBm(slope_2 - slope_1,
tf.nn.relu(x - choose_range_bc)))
x = tf.broadcast_to(x, (matrix_size, batch_size, matrix_size))
x = tf.transpose(x, perm=[1, 2, 0])
# y is a batch of coordinate matrices.
# A coordinate matrix is a matrix such that
# coordinate[i][j] = j.
y = tf.broadcast_to(tf.cast(tf.range(matrix_size), dtype=dtype),
(batch_size, matrix_size, matrix_size))
# Warp matrix is obtained by applying hat function element-wise to (x-y).
# Denoting the origin point of i under the warp map as orig_i,
# and n_i = ceil(orig_i), the warp matrix element warp[i][j] is given by:
# 1) j = n_i: 1 - n_i + orig_i.
# 2) j = n_i - 1: n_i - orig_i.
# 3) Otherwise: 0.
# Applying the warp matrix to pixels, i.e.,
# warped_pixel[i] = sum_j warp[i][j] * original_pixel[j], one would get
# warped_pixel[i] = (n_i - orig_i) * original_pixel[n_i-1]
# + (1 - n_i + orig_i) * original_pixel[n_i].
warp_matrix = x - y
warp_matrix = _hat(warp_matrix)
if p.fprop_dtype is not None and p.fprop_dtype != dtype:
warp_matrix = tf.cast(warp_matrix, p.fprop_dtype)
return warp_matrix
