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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A library of helpers for use with SamplingDecoders.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import abc
import six
from tensorflow.contrib.distributions.python.ops import categorical
from tensorflow.contrib.seq2seq.python.ops import decoder
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import embedding_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import tensor_array_ops
from tensorflow.python.util import nest
__all__ = [
"Helper",
"TrainingHelper",
"GreedyEmbeddingHelper",
"CustomHelper",
"ScheduledEmbeddingTrainingHelper",
]
_transpose_batch_time = decoder._transpose_batch_time # pylint: disable=protected-access
@six.add_metaclass(abc.ABCMeta)
class Helper(object):
"""Helper interface. Helper instances are used by SamplingDecoder."""
@abc.abstractproperty
def batch_size(self):
"""Returns a scalar int32 tensor."""
raise NotImplementedError("batch_size has not been implemented")
@abc.abstractmethod
def initialize(self, name=None):
"""Returns `(initial_finished, initial_inputs)`."""
pass
@abc.abstractmethod
def sample(self, time, outputs, state, name=None):
"""Returns `sample_ids`."""
pass
@abc.abstractmethod
def next_inputs(self, time, outputs, state, sample_ids, name=None):
"""Returns `(finished, next_inputs, next_state)`."""
pass
class CustomHelper(Helper):
"""Base abstract class that allows the user to customize sampling."""
def __init__(self, initialize_fn, sample_fn, next_inputs_fn):
"""Initializer.
Args:
initialize_fn: callable that returns `(finished, next_inputs)`
for the first iteration.
sample_fn: callable that takes `(time, outputs, state)`
and emits tensor `sample_ids`.
next_inputs_fn: callable that takes `(time, outputs, state, sample_ids)`
and emits `(finished, next_inputs, next_state)`.
"""
self._initialize_fn = initialize_fn
self._sample_fn = sample_fn
self._next_inputs_fn = next_inputs_fn
self._batch_size = None
@property
def batch_size(self):
if self._batch_size is None:
raise ValueError("batch_size accessed before initialize was called")
return self._batch_size
def initialize(self, name=None):
with ops.name_scope(name, "%sInitialize" % type(self).__name__):
(finished, next_inputs) = self._initialize_fn()
if self._batch_size is None:
self._batch_size = array_ops.size(finished)
return (finished, next_inputs)
def sample(self, time, outputs, state, name=None):
with ops.name_scope(
name, "%sSample" % type(self).__name__, (time, outputs, state)):
return self._sample_fn(time=time, outputs=outputs, state=state)
def next_inputs(self, time, outputs, state, sample_ids, name=None):
with ops.name_scope(
name, "%sNextInputs" % type(self).__name__, (time, outputs, state)):
return self._next_inputs_fn(
time=time, outputs=outputs, state=state, sample_ids=sample_ids)
class TrainingHelper(Helper):
"""A helper for use during training. Only reads inputs.
Returned sample_ids are the argmax of the RNN output logits.
"""
def __init__(self, inputs, sequence_length, time_major=False, name=None):
"""Initializer.
Args:
inputs: A (structure of) input tensors.
sequence_length: An int32 vector tensor.
time_major: Python bool. Whether the tensors in `inputs` are time major.
If `False` (default), they are assumed to be batch major.
name: Name scope for any created operations.
Raises:
ValueError: if `sequence_length` is not a 1D tensor.
"""
with ops.name_scope(name, "TrainingHelper", [inputs, sequence_length]):
inputs = ops.convert_to_tensor(inputs, name="inputs")
if not time_major:
inputs = nest.map_structure(_transpose_batch_time, inputs)
def _unstack_ta(inp):
return tensor_array_ops.TensorArray(
dtype=inp.dtype, size=array_ops.shape(inp)[0],
element_shape=inp.get_shape()[1:]).unstack(inp)
self._input_tas = nest.map_structure(_unstack_ta, inputs)
self._sequence_length = ops.convert_to_tensor(
sequence_length, name="sequence_length")
if self._sequence_length.get_shape().ndims != 1:
raise ValueError(
"Expected sequence_length to be a vector, but received shape: %s" %
self._sequence_length.get_shape())
self._zero_inputs = nest.map_structure(
lambda inp: array_ops.zeros_like(inp[0, :]), inputs)
self._batch_size = array_ops.size(sequence_length)
@property
def batch_size(self):
return self._batch_size
def initialize(self, name=None):
with ops.name_scope(name, "TrainingHelperInitialize"):
finished = math_ops.equal(0, self._sequence_length)
all_finished = math_ops.reduce_all(finished)
next_inputs = control_flow_ops.cond(
all_finished, lambda: self._zero_inputs,
lambda: nest.map_structure(lambda inp: inp.read(0), self._input_tas))
return (finished, next_inputs)
def sample(self, time, outputs, name=None, **unused_kwargs):
with ops.name_scope(name, "TrainingHelperSample", [time, outputs]):
sample_ids = math_ops.cast(
math_ops.argmax(outputs, axis=-1), dtypes.int32)
return sample_ids
def next_inputs(self, time, outputs, state, name=None, **unused_kwargs):
"""next_inputs_fn for TrainingHelper."""
with ops.name_scope(name, "TrainingHelperNextInputs",
[time, outputs, state]):
next_time = time + 1
finished = (next_time >= self._sequence_length)
all_finished = math_ops.reduce_all(finished)
def read_from_ta(inp):
return inp.read(next_time)
next_inputs = control_flow_ops.cond(
all_finished, lambda: self._zero_inputs,
lambda: nest.map_structure(read_from_ta, self._input_tas))
return (finished, next_inputs, state)
class ScheduledEmbeddingTrainingHelper(TrainingHelper):
"""A training helper that adds scheduled sampling.
Returns -1s for sample_ids where no sampling took place; valid sample id
values elsewhere.
"""
def __init__(self, inputs, sequence_length, embedding, sampling_probability,
time_major=False, seed=None, scheduling_seed=None, name=None):
"""Initializer.
Args:
inputs: A (structure of) input tensors.
sequence_length: An int32 vector tensor.
embedding: A callable that takes a vector tensor of `ids` (argmax ids),
or the `params` argument for `embedding_lookup`.
sampling_probability: A 0D `float32` tensor: the probability of sampling
categorically from the output ids instead of reading directly from the
inputs.
time_major: Python bool. Whether the tensors in `inputs` are time major.
If `False` (default), they are assumed to be batch major.
seed: The sampling seed.
scheduling_seed: The schedule decision rule sampling seed.
name: Name scope for any created operations.
Raises:
ValueError: if `sampling_probability` is not a scalar or vector.
"""
with ops.name_scope(name, "ScheduledEmbeddingSamplingWrapper",
[embedding, sampling_probability]):
if callable(embedding):
self._embedding_fn = embedding
else:
self._embedding_fn = (
lambda ids: embedding_ops.embedding_lookup(embedding, ids))
self._sampling_probability = ops.convert_to_tensor(
sampling_probability, name="sampling_probability")
if self._sampling_probability.get_shape().ndims not in (0, 1):
raise ValueError(
"sampling_probability must be either a scalar or a vector. "
"saw shape: %s" % (self._sampling_probability.get_shape()))
self._seed = seed
self._scheduling_seed = scheduling_seed
super(ScheduledEmbeddingTrainingHelper, self).__init__(
inputs=inputs,
sequence_length=sequence_length,
time_major=time_major,
name=name)
def initialize(self, name=None):
return super(ScheduledEmbeddingTrainingHelper, self).initialize(name=name)
def sample(self, time, outputs, state, name=None):
with ops.name_scope(name, "ScheduledEmbeddingTrainingHelperSample",
[time, outputs, state]):
# Return -1s where we did not sample, and sample_ids elsewhere
select_sample_noise = random_ops.random_uniform(
[self.batch_size], seed=self._scheduling_seed)
select_sample = (self._sampling_probability > select_sample_noise)
sample_id_sampler = categorical.Categorical(logits=outputs)
return array_ops.where(
select_sample,
sample_id_sampler.sample(seed=self._seed),
array_ops.tile([-1], [self.batch_size]))
def next_inputs(self, time, outputs, state, sample_ids, name=None):
with ops.name_scope(name, "ScheduledEmbeddingTrainingHelperSample",
[time, outputs, state, sample_ids]):
(finished, base_next_inputs, state) = (
super(ScheduledEmbeddingTrainingHelper, self).next_inputs(
time=time,
outputs=outputs,
state=state,
sample_ids=sample_ids,
name=name))
def maybe_sample():
"""Perform scheduled sampling."""
where_sampling = math_ops.cast(
array_ops.where(sample_ids > -1), dtypes.int32)
where_not_sampling = math_ops.cast(
array_ops.where(sample_ids <= -1), dtypes.int32)
where_sampling_flat = array_ops.reshape(where_sampling, [-1])
where_not_sampling_flat = array_ops.reshape(where_not_sampling, [-1])
sample_ids_sampling = array_ops.gather(sample_ids, where_sampling_flat)
inputs_not_sampling = array_ops.gather(
base_next_inputs, where_not_sampling_flat)
sampled_next_inputs = self._embedding_fn(sample_ids_sampling)
base_shape = array_ops.shape(base_next_inputs)
return (array_ops.scatter_nd(indices=where_sampling,
updates=sampled_next_inputs,
shape=base_shape)
+ array_ops.scatter_nd(indices=where_not_sampling,
updates=inputs_not_sampling,
shape=base_shape))
all_finished = math_ops.reduce_all(finished)
next_inputs = control_flow_ops.cond(
all_finished, lambda: base_next_inputs, maybe_sample)
return (finished, next_inputs, state)
class GreedyEmbeddingHelper(Helper):
"""A helper for use during inference.
Uses the argmax of the output (treated as logits) and passes the
result through an embedding layer to get the next input.
"""
def __init__(self, embedding, start_tokens, end_token):
"""Initializer.
Args:
embedding: A callable that takes a vector tensor of `ids` (argmax ids),
or the `params` argument for `embedding_lookup`.
start_tokens: `int32` vector shaped `[batch_size]`, the start tokens.
end_token: `int32` scalar, the token that marks end of decoding.
Raises:
ValueError: if `sequence_length` is not a 1D tensor.
"""
if callable(embedding):
self._embedding_fn = embedding
else:
self._embedding_fn = (
lambda ids: embedding_ops.embedding_lookup(embedding, ids))
self._start_tokens = ops.convert_to_tensor(
start_tokens, dtype=dtypes.int32, name="start_tokens")
self._end_token = ops.convert_to_tensor(
end_token, dtype=dtypes.int32, name="end_token")
if self._start_tokens.get_shape().ndims != 1:
raise ValueError("start_tokens must be a vector")
self._batch_size = array_ops.size(start_tokens)
if self._end_token.get_shape().ndims != 0:
raise ValueError("end_token must be a scalar")
self._start_inputs = self._embedding_fn(self._start_tokens)
@property
def batch_size(self):
return self._batch_size
def initialize(self, name=None):
finished = array_ops.tile([False], [self._batch_size])
return (finished, self._start_inputs)
def sample(self, time, outputs, state, name=None):
"""sample for GreedyEmbeddingHelper."""
del time, state # unused by sample_fn
# Outputs are logits, use argmax to get the most probable id
if not isinstance(outputs, ops.Tensor):
raise TypeError("Expected outputs to be a single Tensor, got: %s" %
outputs)
sample_ids = math_ops.cast(
math_ops.argmax(outputs, axis=-1), dtypes.int32)
return sample_ids
def next_inputs(self, time, outputs, state, sample_ids, name=None):
"""next_inputs_fn for GreedyEmbeddingHelper."""
del time, outputs # unused by next_inputs_fn
finished = math_ops.equal(sample_ids, self._end_token)
all_finished = math_ops.reduce_all(finished)
next_inputs = control_flow_ops.cond(
all_finished,
# If we're finished, the next_inputs value doesn't matter
lambda: self._start_inputs,
lambda: self._embedding_fn(sample_ids))
return (finished, next_inputs, state)