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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.seq2seq.python.ops import decoder
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
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 gen_array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import tensor_array_ops
from tensorflow.python.ops.distributions import bernoulli
from tensorflow.python.ops.distributions import categorical
from tensorflow.python.util import nest
__all__ = [
"Helper",
"TrainingHelper",
"GreedyEmbeddingHelper",
"SampleEmbeddingHelper",
"CustomHelper",
"ScheduledEmbeddingTrainingHelper",
"ScheduledOutputTrainingHelper",
"InferenceHelper",
]
_transpose_batch_time = decoder._transpose_batch_time # pylint: disable=protected-access
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)
@six.add_metaclass(abc.ABCMeta)
class Helper(object):
"""Interface for implementing sampling in seq2seq decoders.
Helper instances are used by `BasicDecoder`.
"""
@abc.abstractproperty
def batch_size(self):
"""Batch size of tensor returned by `sample`.
Returns a scalar int32 tensor.
"""
raise NotImplementedError("batch_size has not been implemented")
@abc.abstractproperty
def sample_ids_shape(self):
"""Shape of tensor returned by `sample`, excluding the batch dimension.
Returns a `TensorShape`.
"""
raise NotImplementedError("sample_ids_shape has not been implemented")
@abc.abstractproperty
def sample_ids_dtype(self):
"""DType of tensor returned by `sample`.
Returns a DType.
"""
raise NotImplementedError("sample_ids_dtype 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,
sample_ids_shape=None, sample_ids_dtype=None):
"""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)`.
sample_ids_shape: Either a list of integers, or a 1-D Tensor of type
`int32`, the shape of each value in the `sample_ids` batch. Defaults to
a scalar.
sample_ids_dtype: The dtype of the `sample_ids` tensor. Defaults to int32.
"""
self._initialize_fn = initialize_fn
self._sample_fn = sample_fn
self._next_inputs_fn = next_inputs_fn
self._batch_size = None
self._sample_ids_shape = tensor_shape.TensorShape(sample_ids_shape or [])
self._sample_ids_dtype = sample_ids_dtype or dtypes.int32
@property
def batch_size(self):
if self._batch_size is None:
raise ValueError("batch_size accessed before initialize was called")
return self._batch_size
@property
def sample_ids_shape(self):
return self._sample_ids_shape
@property
def sample_ids_dtype(self):
return self._sample_ids_dtype
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")
self._inputs = inputs
if not time_major:
inputs = nest.map_structure(_transpose_batch_time, inputs)
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 inputs(self):
return self._inputs
@property
def sequence_length(self):
return self._sequence_length
@property
def batch_size(self):
return self._batch_size
@property
def sample_ids_shape(self):
return tensor_shape.TensorShape([])
@property
def sample_ids_dtype(self):
return dtypes.int32
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_sampler = bernoulli.Bernoulli(
probs=self._sampling_probability, dtype=dtypes.bool)
select_sample = select_sampler.sample(
sample_shape=self.batch_size, seed=self._scheduling_seed)
sample_id_sampler = categorical.Categorical(logits=outputs)
return array_ops.where(
select_sample,
sample_id_sampler.sample(seed=self._seed),
gen_array_ops.fill([self.batch_size], -1))
def next_inputs(self, time, outputs, state, sample_ids, name=None):
with ops.name_scope(name, "ScheduledEmbeddingTrainingHelperNextInputs",
[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)
sample_ids_sampling = array_ops.gather_nd(sample_ids, where_sampling)
inputs_not_sampling = array_ops.gather_nd(
base_next_inputs, where_not_sampling)
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 ScheduledOutputTrainingHelper(TrainingHelper):
"""A training helper that adds scheduled sampling directly to outputs.
Returns False for sample_ids where no sampling took place; True elsewhere.
"""
def __init__(self, inputs, sequence_length, sampling_probability,
time_major=False, seed=None, next_inputs_fn=None,
auxiliary_inputs=None, name=None):
"""Initializer.
Args:
inputs: A (structure) of input tensors.
sequence_length: An int32 vector tensor.
sampling_probability: A 0D `float32` tensor: the probability of sampling
from the outputs 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.
next_inputs_fn: (Optional) callable to apply to the RNN outputs to create
the next input when sampling. If `None` (default), the RNN outputs will
be used as the next inputs.
auxiliary_inputs: An optional (structure of) auxiliary input tensors with
a shape that matches `inputs` in all but (potentially) the final
dimension. These tensors will be concatenated to the sampled output or
the `inputs` when not sampling for use as the next input.
name: Name scope for any created operations.
Raises:
ValueError: if `sampling_probability` is not a scalar or vector.
"""
with ops.name_scope(name, "ScheduledOutputTrainingHelper",
[inputs, auxiliary_inputs, sampling_probability]):
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()))
if auxiliary_inputs is None:
maybe_concatenated_inputs = inputs
else:
inputs = ops.convert_to_tensor(inputs, name="inputs")
auxiliary_inputs = ops.convert_to_tensor(
auxiliary_inputs, name="auxiliary_inputs")
maybe_concatenated_inputs = nest.map_structure(
lambda x, y: array_ops.concat((x, y), -1),
inputs, auxiliary_inputs)
if not time_major:
auxiliary_inputs = nest.map_structure(
_transpose_batch_time, auxiliary_inputs)
self._auxiliary_input_tas = (
nest.map_structure(_unstack_ta, auxiliary_inputs)
if auxiliary_inputs is not None else None)
self._seed = seed
self._next_inputs_fn = next_inputs_fn
super(ScheduledOutputTrainingHelper, self).__init__(
inputs=maybe_concatenated_inputs,
sequence_length=sequence_length,
time_major=time_major,
name=name)
def initialize(self, name=None):
return super(ScheduledOutputTrainingHelper, self).initialize(name=name)
def sample(self, time, outputs, state, name=None):
with ops.name_scope(name, "ScheduledOutputTrainingHelperSample",
[time, outputs, state]):
sampler = bernoulli.Bernoulli(probs=self._sampling_probability)
return sampler.sample(sample_shape=self.batch_size, seed=self._seed)
def next_inputs(self, time, outputs, state, sample_ids, name=None):
with ops.name_scope(name, "ScheduledOutputTrainingHelperNextInputs",
[time, outputs, state, sample_ids]):
(finished, base_next_inputs, state) = (
super(ScheduledOutputTrainingHelper, self).next_inputs(
time=time,
outputs=outputs,
state=state,
sample_ids=sample_ids,
name=name))
sample_ids = math_ops.cast(sample_ids, dtypes.bool)
def maybe_sample():
"""Perform scheduled sampling."""
def maybe_concatenate_auxiliary_inputs(outputs_, indices=None):
"""Concatenate outputs with auxiliary inputs, if they exist."""
if self._auxiliary_input_tas is None:
return outputs_
next_time = time + 1
auxiliary_inputs = nest.map_structure(
lambda ta: ta.read(next_time), self._auxiliary_input_tas)
if indices is not None:
auxiliary_inputs = array_ops.gather_nd(auxiliary_inputs, indices)
return nest.map_structure(
lambda x, y: array_ops.concat((x, y), -1),
outputs_, auxiliary_inputs)
if self._next_inputs_fn is None:
return array_ops.where(
sample_ids, maybe_concatenate_auxiliary_inputs(outputs),
base_next_inputs)
where_sampling = math_ops.cast(
array_ops.where(sample_ids), dtypes.int32)
where_not_sampling = math_ops.cast(
array_ops.where(math_ops.logical_not(sample_ids)), dtypes.int32)
outputs_sampling = array_ops.gather_nd(outputs, where_sampling)
inputs_not_sampling = array_ops.gather_nd(base_next_inputs,
where_not_sampling)
sampled_next_inputs = maybe_concatenate_auxiliary_inputs(
self._next_inputs_fn(outputs_sampling), where_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)
no_samples = math_ops.logical_not(math_ops.reduce_any(sample_ids))
next_inputs = control_flow_ops.cond(
math_ops.logical_or(all_finished, no_samples),
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`. The returned tensor
will be passed to the decoder input.
start_tokens: `int32` vector shaped `[batch_size]`, the start tokens.
end_token: `int32` scalar, the token that marks end of decoding.
Raises:
ValueError: if `start_tokens` is not a 1D tensor or `end_token` is not a
scalar.
"""
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
@property
def sample_ids_shape(self):
return tensor_shape.TensorShape([])
@property
def sample_ids_dtype(self):
return dtypes.int32
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" %
type(outputs))
sample_ids = math_ops.argmax(outputs, axis=-1, output_type=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)
class SampleEmbeddingHelper(GreedyEmbeddingHelper):
"""A helper for use during inference.
Uses sampling (from a distribution) instead of argmax and passes the
result through an embedding layer to get the next input.
"""
def __init__(self, embedding, start_tokens, end_token,
softmax_temperature=None, seed=None):
"""Initializer.
Args:
embedding: A callable that takes a vector tensor of `ids` (argmax ids),
or the `params` argument for `embedding_lookup`. The returned tensor
will be passed to the decoder input.
start_tokens: `int32` vector shaped `[batch_size]`, the start tokens.
end_token: `int32` scalar, the token that marks end of decoding.
softmax_temperature: (Optional) `float32` scalar, value to divide the
logits by before computing the softmax. Larger values (above 1.0) result
in more random samples, while smaller values push the sampling
distribution towards the argmax. Must be strictly greater than 0.
Defaults to 1.0.
seed: (Optional) The sampling seed.
Raises:
ValueError: if `start_tokens` is not a 1D tensor or `end_token` is not a
scalar.
"""
super(SampleEmbeddingHelper, self).__init__(
embedding, start_tokens, end_token)
self._softmax_temperature = softmax_temperature
self._seed = seed
def sample(self, time, outputs, state, name=None):
"""sample for SampleEmbeddingHelper."""
del time, state # unused by sample_fn
# Outputs are logits, we sample instead of argmax (greedy).
if not isinstance(outputs, ops.Tensor):
raise TypeError("Expected outputs to be a single Tensor, got: %s" %
type(outputs))
if self._softmax_temperature is None:
logits = outputs
else:
logits = outputs / self._softmax_temperature
sample_id_sampler = categorical.Categorical(logits=logits)
sample_ids = sample_id_sampler.sample(seed=self._seed)
return sample_ids
class InferenceHelper(Helper):
"""A helper to use during inference with a custom sampling function."""
def __init__(self, sample_fn, sample_shape, sample_dtype,
start_inputs, end_fn, next_inputs_fn=None):
"""Initializer.
Args:
sample_fn: A callable that takes `outputs` and emits tensor `sample_ids`.
sample_shape: Either a list of integers, or a 1-D Tensor of type `int32`,
the shape of the each sample in the batch returned by `sample_fn`.
sample_dtype: the dtype of the sample returned by `sample_fn`.
start_inputs: The initial batch of inputs.
end_fn: A callable that takes `sample_ids` and emits a `bool` vector
shaped `[batch_size]` indicating whether each sample is an end token.
next_inputs_fn: (Optional) A callable that takes `sample_ids` and returns
the next batch of inputs. If not provided, `sample_ids` is used as the
next batch of inputs.
"""
self._sample_fn = sample_fn
self._end_fn = end_fn
self._sample_shape = tensor_shape.TensorShape(sample_shape)
self._sample_dtype = sample_dtype
self._next_inputs_fn = next_inputs_fn
self._batch_size = array_ops.shape(start_inputs)[0]
self._start_inputs = ops.convert_to_tensor(
start_inputs, name="start_inputs")
@property
def batch_size(self):
return self._batch_size
@property
def sample_ids_shape(self):
return self._sample_shape
@property
def sample_ids_dtype(self):
return self._sample_dtype
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):
del time, state # unused by sample
return self._sample_fn(outputs)
def next_inputs(self, time, outputs, state, sample_ids, name=None):
del time, outputs # unused by next_inputs
if self._next_inputs_fn is None:
next_inputs = sample_ids
else:
next_inputs = self._next_inputs_fn(sample_ids)
finished = self._end_fn(sample_ids)
return (finished, next_inputs, state)