def __call__(self, shape, dtype=None, partition_info=None):
if dtype is None:
dtype = self.dtype
# Check the shape
if len(shape) < 3 or len(shape) > 5:
raise ValueError("The tensor to initialize must be at least "
"three-dimensional and at most five-dimensional")
if shape[-2] > shape[-1]:
raise ValueError("In_filters cannot be greater than out_filters.")
# Generate a random matrix
a = random_ops.random_normal([shape[-1], shape[-1]],
dtype=dtype, seed=self.seed)
# Compute the qr factorization
q, r = linalg_ops.qr(a, full_matrices=False)
# Make Q uniform
d = array_ops.diag_part(r)
q *= math_ops.sign(d)
q = q[:shape[-2], :]
q *= math_ops.sqrt(math_ops.cast(self.gain, dtype=dtype))
if len(shape) == 3:
weight = array_ops.scatter_nd([[(shape[0]-1)//2]],
array_ops.expand_dims(q, 0), shape)
elif len(shape) == 4:
weight = array_ops.scatter_nd([[(shape[0]-1)//2, (shape[1]-1)//2]],
array_ops.expand_dims(q, 0), shape)
else:
weight = array_ops.scatter_nd([[(shape[0]-1)//2, (shape[1]-1)//2,
(shape[2]-1)//2]],
array_ops.expand_dims(q, 0), shape)
return weight
def testInvalidShape(self):
# TODO(apassos) figure out how to unify these errors
with self.assertRaises(errors.InvalidArgumentError
if context.executing_eagerly() else ValueError):
array_ops.scatter_nd(indices=[0], # this should be indices=[[0]]
updates=[0.0],
shape=[1])
def testEmptyOutputShape1(self):
indices = array_ops.zeros([2, 2, 2], dtypes.int32)
updates = array_ops.zeros([2, 2, 2], dtypes.int32)
shape = constant_op.constant([0, 3, 2], dtypes.int32)
with self.assertRaisesWithPredicateMatch(
ValueError, "Indices and updates specified for empty output shape"):
array_ops.scatter_nd(indices, updates, shape)
def _ctc_state_trans(label_seq):
"""Compute CTC alignment model transition matrix.
Args:
label_seq: tensor of shape [batch_size, max_seq_length]
Returns:
tensor of shape [batch_size, states, states] with a state transition matrix
computed for each sequence of the batch.
"""
with ops.name_scope("ctc_state_trans"):
label_seq = ops.convert_to_tensor(label_seq, name="label_seq")
batch_size = _get_dim(label_seq, 0)
num_labels = _get_dim(label_seq, 1)
num_label_states = num_labels + 1
num_states = 2 * num_label_states
label_states = math_ops.range(num_label_states)
blank_states = label_states + num_label_states
# Start state to first label.
start_to_label = [[1, 0]]
# Blank to label transitions.
blank_to_label = array_ops.stack([label_states[1:], blank_states[:-1]], 1)
# Label to blank transitions.
label_to_blank = array_ops.stack([blank_states, label_states], 1)
# Scatter transitions that don't depend on sequence.
indices = array_ops.concat(
[start_to_label, blank_to_label, label_to_blank], 0)
values = array_ops.ones([_get_dim(indices, 0)])
trans = array_ops.scatter_nd(
indices, values, shape=[num_states, num_states])
trans += linalg_ops.eye(num_states) # Self-loops.
# Label to label transitions. Disallow transitions between repeated labels
# with no blank state in between.
batch_idx = array_ops.zeros_like(label_states[2:])
indices = array_ops.stack(
[batch_idx, label_states[2:], label_states[1:-1]], 1)
indices = array_ops.tile(
array_ops.expand_dims(indices, 0), [batch_size, 1, 1])
batch_idx = array_ops.expand_dims(math_ops.range(batch_size), 1) * [1, 0, 0]
indices += array_ops.expand_dims(batch_idx, 1)
repeats = math_ops.equal(label_seq[:, :-1], label_seq[:, 1:])
values = 1.0 - math_ops.cast(repeats, dtypes.float32)
batched_shape = [batch_size, num_states, num_states]
label_to_label = array_ops.scatter_nd(indices, values, batched_shape)
return array_ops.expand_dims(trans, 0) + label_to_label
def testEmptyOutputShape2(self):
indices = array_ops.placeholder(dtypes.int32, shape=None)
updates = array_ops.placeholder(dtypes.int32, shape=None)
shape = constant_op.constant([0, 3, 2], dtypes.int32)
with self.test_session():
array_ops.scatter_nd(indices, updates, shape).eval(feed_dict={
indices: np.zeros(
[2, 2, 2], dtype=np.int32),
updates: np.zeros(
[2, 2, 2], dtype=np.int32)
})
def testRank3InvalidShape2(self):
indices = array_ops.zeros([2, 2, 1], dtypes.int32)
updates = array_ops.zeros([2, 2], dtypes.int32)
shape = np.array([2, 2, 2])
with self.assertRaisesWithPredicateMatch(
ValueError, "The inner \\d+ dimensions of output\\.shape="):
array_ops.scatter_nd(indices, updates, shape)
ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
with self.assertRaisesWithPredicateMatch(
ValueError, "The inner \\d+ dimensions of ref\\.shape="):
state_ops.scatter_nd_update(ref, indices, updates)
def collapse_repeated(labels, seq_length, name=None):
"""Merge repeated labels into single labels.
Args:
labels: Tensor of shape [batch, max value in seq_length]
seq_length: Tensor of shape [batch], sequence length of each batch element.
name: A name for this `Op`. Defaults to "collapse_repeated_labels".
Returns:
A tuple `(collapsed_labels, new_seq_length)` where
collapsed_labels: Tensor of shape [batch, max_seq_length] with repeated
labels collapsed and padded to max_seq_length, eg:
`[[A, A, B, B, A], [A, B, C, D, E]] => [[A, B, A, 0, 0], [A, B, C, D, E]]`
new_seq_length: int tensor of shape [batch] with new sequence lengths.
"""
with ops.name_scope(name, "collapse_repeated_labels", [labels, seq_length]):
labels = ops.convert_to_tensor(labels, name="labels")
seq_length = ops.convert_to_tensor(seq_length, name="seq_length")
# Mask labels that don't equal previous label.
label_mask = array_ops.concat([
array_ops.ones_like(labels[:, :1], dtypes.bool),
math_ops.not_equal(labels[:, 1:], labels[:, :-1])
],
axis=1)
# Filter labels that aren't in the original sequence.
maxlen = _get_dim(labels, 1)
seq_mask = array_ops.sequence_mask(seq_length, maxlen=maxlen)
label_mask = math_ops.logical_and(label_mask, seq_mask)
# Count masks for new sequence lengths.
new_seq_len = math_ops.reduce_sum(
math_ops.cast(label_mask, dtypes.int32), axis=1)
# Mask indexes based on sequence length mask.
new_maxlen = math_ops.reduce_max(new_seq_len)
idx_mask = array_ops.sequence_mask(new_seq_len, maxlen=new_maxlen)
# Flatten everything and mask out labels to keep and sparse indices.
flat_labels = array_ops.reshape(labels, [-1])
flat_label_mask = array_ops.reshape(label_mask, [-1])
flat_idx_mask = array_ops.reshape(idx_mask, [-1])
idx = math_ops.range(_get_dim(flat_idx_mask, 0))
# Scatter to flat shape.
flat = array_ops.scatter_nd(
indices=array_ops.expand_dims(
array_ops.boolean_mask(idx, flat_idx_mask), axis=1),
updates=array_ops.boolean_mask(flat_labels, flat_label_mask),
shape=array_ops.shape(flat_idx_mask))
# Reshape back to square batch.
batch_size = _get_dim(labels, 0)
new_shape = [batch_size, new_maxlen]
return (array_ops.reshape(flat, new_shape),
math_ops.cast(new_seq_len, seq_length.dtype))
def testScatterNdRepatedIndicesAdd(self):
indices = array_ops.zeros([100000, 1], dtypes.int32)
values = np.random.randn(100000)
shape = [1]
with self.test_session():
val = array_ops.scatter_nd(indices, values, shape).eval()
self.assertAllClose([np.sum(values)], val)
def _state_to_olabel_unique(labels, num_labels, states, unique):
"""Sum state log probs to ilabel log probs using unique label indices."""
num_label_states = _get_dim(labels, 1) + 1
label_states = states[:, :, 1:num_label_states]
blank_states = states[:, :, num_label_states:]
unique_y, unique_idx = unique
mul_reduce = _sum_states(unique_idx, label_states)
num_frames = states.shape[0]
batch_size = states.shape[1]
num_states = num_label_states - 1
batch_state_major = array_ops.transpose(mul_reduce, perm=[1, 2, 0])
batch_state_major = array_ops.reshape(
batch_state_major, [batch_size * num_states, num_frames])
batch_offset = math_ops.range(batch_size, dtype=unique_y.dtype) * num_labels
indices = unique_y + array_ops.expand_dims(batch_offset, axis=-1)
indices = array_ops.reshape(indices, [-1, 1])
scatter = array_ops.scatter_nd(
indices=indices,
updates=batch_state_major,
shape=[batch_size * num_labels, num_frames])
scatter = array_ops.reshape(scatter, [batch_size, num_labels, num_frames])
scatter = array_ops.where(
math_ops.equal(scatter, 0.0),
array_ops.fill(array_ops.shape(scatter), math_ops.log(0.0)),
scatter)
label_olabels = array_ops.transpose(scatter, [2, 0, 1])
label_olabels = label_olabels[:, :, 1:]
blank_olabels = math_ops.reduce_logsumexp(
blank_states, axis=2, keepdims=True)
return array_ops.concat([blank_olabels, label_olabels], axis=-1)
def testEmptyOutputShape3(self):
indices = array_ops.zeros([0], dtypes.int32)
updates = array_ops.zeros([0], dtypes.int32)
shape = constant_op.constant([0], dtypes.int32)
scatter = array_ops.scatter_nd(indices, updates, shape)
with self.test_session():
self.assertEqual(scatter.eval().size, 0)
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))
def _apply_sparse_shared(self, grad_values, grad_indices, var):
if var.get_shape()[0] <= self._max_matrix_size or self._gbar_decay != 0.0:
# The dimension is small enough, we can make the variable dense and
# do a dense update
dense_grad = array_ops.scatter_nd(
array_ops.expand_dims(grad_indices, axis=1), grad_values,
array_ops.shape(var, out_type=grad_indices.dtype))
return self._apply_gradient(dense_grad, var)
return self._apply_gradient(grad_values, var, grad_indices)
def _runScatterNd(self, indices, updates, shape):
with self.test_session():
updates_placeholder = array_ops.placeholder(updates.dtype)
indices_placeholder = array_ops.placeholder(indices.dtype)
with self.test_scope():
output = array_ops.scatter_nd(indices_placeholder, updates_placeholder,
shape)
feed_dict = {updates_placeholder: updates, indices_placeholder: indices}
return output.eval(feed_dict=feed_dict)
def _GatherNdGrad(op, grad):
ref = op.inputs[0]
indices = op.inputs[1]
ref_shape = array_ops.shape(ref, out_type=indices.dtype)
if indices.shape.ndims == 2 and indices.shape.dims[-1].value == 1:
ref_grad = ops.IndexedSlices(grad, array_ops.squeeze(indices, axis=-1),
ref_shape)
else:
ref_grad = array_ops.scatter_nd(indices, grad, ref_shape)
return [ref_grad, None]
def maybe_sample():
"""Perform scheduled sampling."""
if self._next_input_layer is None:
return array_ops.where(sample_ids, 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 = self._next_input_layer(outputs_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))
def testGradientsRank2SliceUpdate(self):
indices = constant_op.constant([[1], [0]], dtype=dtypes.int32)
updates = constant_op.constant([[3, 4], [1, 2]], dtype=dtypes.float64)
shape = constant_op.constant([2, 2], dtype=dtypes.int32)
outputs = array_ops.scatter_nd(indices, updates, shape)
grad_vals = constant_op.constant([[3, 4], [1, 2]], dtype=dtypes.float64)
grads = gradients_impl.gradients([outputs], [updates], [grad_vals])[0]
expected_grads = np.array([[1, 2], [3, 4]], dtype=np.float64)
with self.test_session():
self.assertAllEqual(expected_grads, grads.eval())
def testRank3ValidShape(self):
indices = array_ops.zeros([2, 2, 2], dtypes.int32)
updates = array_ops.zeros([2, 2, 2], dtypes.int32)
shape = np.array([2, 2, 2])
self.assertAllEqual(
array_ops.scatter_nd(indices, updates, shape).get_shape().as_list(),
shape)
ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
self.assertAllEqual(
state_ops.scatter_nd_update(ref, indices,
updates).get_shape().as_list(), shape)
def scheduled_sampling(self, batch_size, sampling_probability, true, estimate):
with variable_scope.variable_scope("ScheduledEmbedding"):
# Return -1s where we do not sample, and sample_ids elsewhere
select_sampler = bernoulli.Bernoulli(probs=sampling_probability, dtype=tf.bool)
select_sample = select_sampler.sample(sample_shape=batch_size)
sample_ids = array_ops.where(
select_sample,
tf.range(batch_size),
gen_array_ops.fill([batch_size], -1))
where_sampling = math_ops.cast(
array_ops.where(sample_ids > -1), tf.int32)
where_not_sampling = math_ops.cast(
array_ops.where(sample_ids <= -1), tf.int32)
_estimate = array_ops.gather_nd(estimate, where_sampling)
_true = array_ops.gather_nd(true, where_not_sampling)
base_shape = array_ops.shape(true)
result1 = array_ops.scatter_nd(indices=where_sampling, updates=_estimate, shape=base_shape)
result2 = array_ops.scatter_nd(indices=where_not_sampling, updates=_true, shape=base_shape)
result = result1 + result2
return result1 + result2
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_input_layer 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_input_layer(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))
def testExtraIndicesDimensions(self):
indices = array_ops.zeros([1, 1, 2], dtypes.int32)
updates = array_ops.zeros([1, 1], dtypes.int32)
shape = np.array([2, 2])
scatter = array_ops.scatter_nd(indices, updates, shape)
self.assertAllEqual(scatter.get_shape().as_list(), shape)
expected_result = np.zeros([2, 2], dtype=np.int32)
with self.test_session():
self.assertAllEqual(expected_result, scatter.eval())
ref = variables.Variable(array_ops.zeros(shape, dtypes.int32))
scatter_update = state_ops.scatter_nd_update(ref, indices, updates)
self.assertAllEqual(scatter_update.get_shape().as_list(), shape)
with self.test_session():
ref.initializer.run()
self.assertAllEqual(expected_result, scatter_update.eval())
def _TopKGrad(op, grad, _):
"""Return the gradients for TopK.
Args:
op: The TopKOp for which we need to generate gradients.
grad: Tensor. The gradients passed to the TopKOp.
Returns:
A list of two tensors, the first being the gradient w.r.t to the input and
TopK, and the second being the gradient w.r.t. to the indices (all zero).
"""
in_shape = array_ops.shape(op.inputs[0])
ind_shape = array_ops.shape(op.outputs[1])
# int32 is not supported on GPU hence up-casting
ind_lastdim = array_ops.gather(
math_ops.cast(ind_shape, dtypes.int64),
array_ops.size(ind_shape) - 1)
# Flatten indices to 2D.
ind_2d = array_ops.reshape(op.outputs[1], array_ops.stack([-1, ind_lastdim]))
in_lastdim = array_ops.gather(
math_ops.cast(in_shape, dtypes.int64),
array_ops.size(in_shape) - 1)
outerdim = array_ops.shape(ind_2d)[0]
# Compute linear indices (flattened to 1D).
ind = array_ops.reshape(
ind_2d + math_ops.cast(
array_ops.expand_dims(
math_ops.range(0,
math_ops.cast(outerdim, dtypes.int64) * in_lastdim,
in_lastdim), -1), dtypes.int32), [-1])
# Substitute grad to appropriate locations and fill the rest with zeros,
# finally reshaping it to the original input shape.
return [
array_ops.reshape(
array_ops.scatter_nd(
array_ops.expand_dims(ind, -1), array_ops.reshape(grad, [-1]),
[math_ops.reduce_prod(in_shape)]), in_shape),
array_ops.zeros([], dtype=dtypes.int32)
]
def ScatterUpdateGrads(op, grad):
var, indices, updates = op.inputs
updates_grad = array_ops.gather(grad, indices)
# dynamic stitch approach (this seems to be a bit slower)
# grad_range = math_ops.range(grad.get_shape()[0].value)
# var_grad = data_flow_ops.dynamic_stitch(
# [grad_range, indices],
# [grad, array_ops.zeros(updates.get_shape())])
if isinstance(grad, ops.IndexedSlices):
# note: we could use this approach for everything, but the
# temporary variable approach seems to be slightly faster (but we
# can't use that on indexedslices)
var_grad = grad - array_ops.scatter_nd(
array_ops.expand_dims(indices, 1), updates_grad,
var.get_shape())
else:
shape = tuple(grad.get_shape().as_list())
dtype = grad.dtype.base_dtype
with variable_scope.variable_scope(
"gradient_vars", reuse=variable_scope.AUTO_REUSE):
var_grad = variable_scope.get_variable(
"tmp" + "_%s" * (len(grad.get_shape()) + 1) % (
shape + (dtype.name,)),
shape=shape, dtype=dtype, trainable=False,
collections=["gradient_vars"])
var_grad = state_ops.assign(var_grad, grad)
var_grad = state_ops.scatter_update(
var_grad, indices, array_ops.zeros_like(updates))
# we need to force a copy so that any future assignments to the
# variable will not affect the value we return here
# TODO: check if this is still necessary in TensorFlow 2.0
var_grad = var_grad + 0
return var_grad, None, updates_grad
def testUndefinedOutputShape(self): indices = array_ops.placeholder(dtypes.int32, shape=[2, 2, 2]) updates = array_ops.placeholder(dtypes.int32, shape=[2, 2, 2]) shape = array_ops.placeholder(dtypes.int32, shape=[None]) array_ops.scatter_nd(indices, updates, shape)
def _GatherNdGrad(op, grad): ref = op.inputs[0] indices = op.inputs[1] ref_shape = array_ops.shape(ref, out_type=indices.dtype) ref_grad = array_ops.scatter_nd(indices, grad, ref_shape) return [ref_grad, None]
def scheduled_sampling_vocab_dist(hps,
sampling_probability,
output,
embedding,
inp,
alpha=0):
# borrowed ideas from https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/ScheduledEmbeddingTrainingHelper
def soft_argmax(alpha, output):
# alpha_exp = tf.exp(alpha * output) # (batch_size, vocab_size)
# one_hot_scores = alpha_exp / tf.reshape(tf.reduce_sum(alpha_exp, axis=1),[-1,1]) #(batch_size, vocab_size)
one_hot_scores = tf.nn.softmax(alpha * output)
return one_hot_scores
def soft_top_k(alpha, output, K):
copy = tf.identity(output)
p = []
arg_top_k = []
for k in range(K):
sargmax = soft_argmax(alpha, copy)
copy = (1 - sargmax) * copy
p.append(tf.reduce_sum(sargmax * output, axis=1))
arg_top_k.append(sargmax)
return tf.stack(p, axis=1), tf.stack(arg_top_k)
with variable_scope.variable_scope("ScheduledEmbedding"):
# Return -1s where we did not sample, and sample_ids elsewhere
select_sampler = bernoulli.Bernoulli(probs=sampling_probability,
dtype=tf.bool)
select_sample = select_sampler.sample(sample_shape=hps.batch_size)
sample_id_sampler = categorical.Categorical(
probs=output
) # equals to argmax{ Multinomial(output, total_count=1) }, our greedy search selection
sample_ids = array_ops.where(select_sample,
sample_id_sampler.sample(seed=123),
gen_array_ops.fill([hps.batch_size], -1))
where_sampling = math_ops.cast(array_ops.where(sample_ids > -1),
tf.int32)
where_not_sampling = math_ops.cast(array_ops.where(sample_ids <= -1),
tf.int32)
if hps.greedy_scheduled_sampling:
sample_ids = tf.argmax(output, axis=1, output_type=tf.int32)
sample_ids_sampling = array_ops.gather_nd(sample_ids, where_sampling)
inputs_not_sampling = array_ops.gather_nd(inp, where_not_sampling)
if hps.E2EBackProp:
if hps.hard_argmax:
greedy_search_prob, greedy_search_sample = tf.nn.top_k(
output, k=hps.k) # (batch_size, k)
greedy_search_prob_normalized = greedy_search_prob / tf.reshape(
tf.reduce_sum(greedy_search_prob, axis=1), [-1, 1])
greedy_embedding = tf.nn.embedding_lookup(
embedding, greedy_search_sample)
normalized_embedding = tf.multiply(
tf.reshape(greedy_search_prob_normalized,
[hps.batch_size, hps.k, 1]), greedy_embedding)
e2e_embedding = tf.reduce_mean(normalized_embedding, axis=1)
else:
e = []
greedy_search_prob, greedy_search_sample = soft_top_k(
alpha, output,
K=hps.k) # (batch_size, k), (k, batch_size, vocab_size)
greedy_search_prob_normalized = greedy_search_prob / tf.reshape(
tf.reduce_sum(greedy_search_prob, axis=1), [-1, 1])
for _ in range(hps.k):
a_k = greedy_search_sample[_]
e_k = tf.matmul(
tf.reshape(greedy_search_prob_normalized[:, _],
[-1, 1]) * a_k, embedding)
e.append(e_k)
e2e_embedding = tf.reduce_sum(e,
axis=0) # (batch_size, emb_dim)
sampled_next_inputs = array_ops.gather_nd(e2e_embedding,
where_sampling)
else:
if hps.hard_argmax:
sampled_next_inputs = tf.nn.embedding_lookup(
embedding, sample_ids_sampling)
else: # using soft armax (greedy) proposed in: https://arxiv.org/abs/1704.06970
# alpha_exp = tf.exp(alpha * (output_not_extended + G)) # (batch_size, vocab_size)
# one_hot_scores = alpha_exp / tf.reduce_sum(alpha_exp, axis=1) #(batch_size, vocab_size)
one_hot_scores = soft_argmax(
alpha, output) # (batch_size, vocab_size)
soft_argmax_embedding = tf.matmul(
one_hot_scores, embedding) # (batch_size, emb_size)
sampled_next_inputs = array_ops.gather_nd(
soft_argmax_embedding, where_sampling)
base_shape = array_ops.shape(inp)
result1 = array_ops.scatter_nd(indices=where_sampling,
updates=sampled_next_inputs,
shape=base_shape)
result2 = array_ops.scatter_nd(indices=where_not_sampling,
updates=inputs_not_sampling,
shape=base_shape)
return result1 + result2
def scatter_nd(self, indices, updates, shape, input_=None): del input_ # input_ is not used in scatter_nd return array_ops.scatter_nd(indices, updates, shape)
def _GatherNdGrad(op, grad):
ref = op.inputs[0]
indices = op.inputs[1]
ref_shape = array_ops.shape(ref, out_type=indices.dtype)
ref_grad = array_ops.scatter_nd(indices, grad, ref_shape)
return [ref_grad, None]
def _get_update_op(self,
score_drop,
score_grow,
mask,
weights,
reinit_when_same=False):
"""Prunes+grows connections, all tensors same shape."""
old_dtype = mask.dtype
mask_casted = math_ops.cast(mask, dtypes.float32)
n_total = array_ops.size(score_drop)
n_ones = math_ops.cast(math_ops.reduce_sum(mask_casted),
dtype=dtypes.int32)
n_prune = math_ops.cast(
math_ops.cast(n_ones, dtype=dtypes.float32) * self.drop_fraction,
dtypes.int32)
n_keep = n_ones - n_prune
# Sort the entire array since the k needs to be constant for TPU.
_, sorted_indices = nn_ops.top_k(array_ops.reshape(score_drop, [-1]),
k=n_total)
sorted_indices_ex = array_ops.expand_dims(sorted_indices, 1)
# We will have zeros after having `n_keep` many ones.
new_values = array_ops.where(
math_ops.range(n_total) < n_keep,
array_ops.ones_like(sorted_indices, dtype=mask_casted.dtype),
array_ops.zeros_like(sorted_indices, dtype=mask_casted.dtype))
mask1 = array_ops.scatter_nd(sorted_indices_ex, new_values,
new_values.shape)
# Flatten the scores
score_grow = array_ops.reshape(score_grow, [-1])
# Set scores of the enabled connections(ones) to min(s) - 1, so that they
# have the lowest scores.
score_grow_lifted = array_ops.where(
math_ops.equal(mask1, 1),
array_ops.ones_like(mask1) * (math_ops.reduce_min(score_grow) - 1),
score_grow)
_, sorted_indices = nn_ops.top_k(score_grow_lifted, k=n_total)
sorted_indices_ex = array_ops.expand_dims(sorted_indices, 1)
new_values = array_ops.where(
math_ops.range(n_total) < n_prune,
array_ops.ones_like(sorted_indices, dtype=mask_casted.dtype),
array_ops.zeros_like(sorted_indices, dtype=mask_casted.dtype))
mask2 = array_ops.scatter_nd(sorted_indices_ex, new_values,
new_values.shape)
# Ensure masks are disjoint.
assert_op = control_flow_ops.Assert(
math_ops.equal(math_ops.reduce_sum(mask1 * mask2), 0.),
[mask1, mask2])
with ops.control_dependencies([assert_op]):
# Let's set the weights of the growed connections.
mask2_reshaped = array_ops.reshape(mask2, mask.shape)
# Set the values of the new connections.
grow_tensor = self.get_grow_tensor(weights, self._grow_init)
if reinit_when_same:
# If dropped and grown, we re-initialize.
new_connections = math_ops.equal(mask2_reshaped, 1)
else:
new_connections = math_ops.logical_and(
math_ops.equal(mask2_reshaped, 1),
math_ops.equal(mask_casted, 0))
new_weights = array_ops.where(new_connections, grow_tensor, weights)
weights_update = state_ops.assign(weights, new_weights)
# Ensure there is no momentum value for new connections
reset_op = self.reset_momentum(weights, new_connections)
with ops.control_dependencies([weights_update, reset_op]):
mask_combined = array_ops.reshape(mask1 + mask2, mask.shape)
mask_combined = math_ops.cast(mask_combined, dtype=old_dtype)
new_mask = state_ops.assign(mask, mask_combined)
return new_mask
def scheduled_sampling(hps,
sampling_probability,
output,
embedding,
inp,
alpha=0):
# borrowed ideas from https://www.tensorflow.org/api_docs/python/tf/contrib/seq2seq/ScheduledEmbeddingTrainingHelper
vocab_size = embedding.get_shape()[0].value
def soft_argmax(alpha, output):
alpha_exp = tf.exp(alpha * output) # (batch_size, vocab_size)
one_hot_scores = alpha_exp / tf.reshape(
tf.reduce_sum(alpha_exp, axis=1),
[-1, 1]) #(batch_size, vocab_size)
return one_hot_scores
def soft_top_k(alpha, output, K):
copy = tf.identity(output)
p = []
arg_top_k = []
for k in range(K):
sargmax = soft_argmax(alpha, copy)
copy = (1 - sargmax) * copy
p.append(tf.reduce_sum(sargmax * output, axis=1))
# replace oov with unk if necessary
mask = tf.equal(tf.reduce_max(sargmax, axis=1),
tf.reduce_max(sargmax[:, 0:vocab_size], axis=1))
sargmax_truncated = tf.where(
mask, sargmax[:, 0:vocab_size],
tf.stack([
tf.one_hot(0, vocab_size) for _ in range(hps.batch_size)
]))
arg_top_k.append(sargmax_truncated)
return p, tf.stack(arg_top_k)
with variable_scope.variable_scope("ScheduledEmbedding"):
# Return -1s where we did not sample, and sample_ids elsewhere
select_sampler = bernoulli.Bernoulli(probs=sampling_probability,
dtype=tf.bool)
select_sample = select_sampler.sample(sample_shape=hps.batch_size)
sample_id_sampler = categorical.Categorical(
probs=output
) # equals to argmax{ Multinomial(output, total_count=1) }, our greedy search selection
sample_ids = array_ops.where(select_sample,
sample_id_sampler.sample(seed=123),
gen_array_ops.fill([hps.batch_size], -1))
where_sampling = math_ops.cast(array_ops.where(sample_ids > -1),
tf.int32)
where_not_sampling = math_ops.cast(array_ops.where(sample_ids <= -1),
tf.int32)
if hps.greedy_scheduled_sampling:
sample_ids = tf.argmax(output, axis=1, output_type=tf.int32)
sample_ids_sampling = array_ops.gather_nd(sample_ids, where_sampling)
cond = tf.less(sample_ids_sampling, vocab_size) # replace oov with unk
sample_ids_sampling = tf.cast(cond, tf.int32) * sample_ids_sampling
inputs_not_sampling = array_ops.gather_nd(inp, where_not_sampling)
if hps.E2EBackProp:
if hps.hard_argmax:
greedy_search_prob, greedy_search_sample = tf.nn.top_k(
output, k=hps.k) # (batch_size, k)
greedy_search_prob_normalized = greedy_search_prob / tf.reshape(
tf.reduce_sum(greedy_search_prob, axis=1), [-1, 1])
cond = tf.less(greedy_search_sample,
vocab_size) # replace oov with unk
greedy_search_sample = tf.cast(cond,
tf.int32) * greedy_search_sample
greedy_embedding = tf.nn.embedding_lookup(
embedding, greedy_search_sample)
normalized_embedding = tf.multiply(
tf.reshape(greedy_search_prob_normalized,
[hps.batch_size, hps.k, 1]), greedy_embedding)
e2e_embedding = tf.reduce_mean(normalized_embedding, axis=1)
else:
e = []
greedy_search_prob, greedy_search_sample = soft_top_k(
alpha, output, K=hps.k) # (batch_size, k), (k, vocab_size)
greedy_search_prob_normalized = greedy_search_prob / tf.reshape(
tf.reduce_sum(greedy_search_prob, axis=1), [-1, 1])
for _ in range(hps.k):
a_k = greedy_search_sample[_]
e_k = tf.matmul(
tf.reshape(greedy_search_prob_normalized[:, _],
[-1, 1]) * a_k, embedding)
e.append(e_k)
e2e_embedding = tf.reduce_sum(e,
axis=0) # (batch_size, emb_dim)
sampled_next_inputs = array_ops.gather_nd(e2e_embedding,
where_sampling)
else:
if hps.hard_argmax:
sampled_next_inputs = tf.nn.embedding_lookup(
embedding, sample_ids_sampling)
else: # using soft armax (greedy) proposed in: https://arxiv.org/abs/1704.06970
if not hps.greedy_scheduled_sampling:
# Gumbel reparametrization trick: https://arxiv.org/abs/1704.06970
U = tf.random_uniform(
(hps.batch_size, vocab_size), 10e-12,
(1 - 10e-12)) # add a small number to avoid log(0)
G = -tf.log(-tf.log(U))
else:
G = tf.zeros((hps.batch_size, vocab_size))
#alpha_exp = tf.exp(alpha * (output_not_extended + G)) # (batch_size, vocab_size)
#one_hot_scores = alpha_exp / tf.reduce_sum(alpha_exp, axis=1) #(batch_size, vocab_size)
one_hot_scores = soft_argmax(
alpha, (output + G)) #(batch_size, vocab_size)
sampled_next_inputs = tf.matmul(
one_hot_scores, embedding) #(batch_size, emb_size)
base_shape = array_ops.shape(inp)
result1 = array_ops.scatter_nd(indices=where_sampling,
updates=sampled_next_inputs,
shape=base_shape)
result2 = array_ops.scatter_nd(indices=where_not_sampling,
updates=inputs_not_sampling,
shape=base_shape)
return result1 + result2
def _groupwise_dnn_v2(features, labels, mode, params, config):
"""Defines the dnn for groupwise scoring functions."""
with ops.name_scope('transform'):
context_features, per_example_features = _call_transform_fn(
features, mode)
def _score_fn(context_features, group_features, reuse):
with variable_scope.variable_scope('group_score', reuse=reuse):
return group_score_fn(context_features, group_features, mode,
params, config)
# Scatter/Gather per-example scores through groupwise comparison. Each
# instance in a mini-batch will form a number of groups. Each groups of
# examples are scored by 'score_fn' and socres for individual examples
# accumulated over groups.
with ops.name_scope('groupwise_dnn_v2'):
with ops.name_scope('infer_sizes'):
if labels is not None:
batch_size, list_size = array_ops.unstack(
array_ops.shape(labels))
is_valid = utils.is_label_valid(labels)
else:
# Infer batch_size and list_size from a feature.
example_tensor_shape = array_ops.shape(
next(six.itervalues(per_example_features)))
batch_size = example_tensor_shape[0]
list_size = example_tensor_shape[1]
is_valid = utils.is_label_valid(
array_ops.ones([batch_size, list_size]))
if batch_size is None or list_size is None:
raise ValueError('Invalid batch_size=%s or list_size=%s' %
(batch_size, list_size))
# For each example feature, assume the shape is [batch_size, list_size,
# feature_size], the groups are formed along the 2nd dim. Each group has a
# 'group_size' number of indices in [0, list_size). Based on these
# indices, we can gather the example feature into a sub-tensor for each
# group. The total number of groups we have for a mini-batch is batch_size
# * num_groups. Inside each group, we have a 'group_size' number of
# examples.
indices, mask = _form_group_indices_nd(
is_valid,
group_size,
shuffle=(mode != model_fn.ModeKeys.PREDICT))
num_groups = array_ops.shape(mask)[1]
with ops.name_scope('group_features'):
# For context features, We have shape [batch_size * num_groups, ...].
large_batch_context_features = {}
for name, value in six.iteritems(context_features):
# [batch_size, 1, ...].
value = array_ops.expand_dims(value, axis=1)
# [batch_size, num_groups, ...].
value = array_ops.gather(value,
array_ops.zeros([num_groups],
dtypes.int32),
axis=1)
# [batch_size * num_groups, ...]
large_batch_context_features[
name] = utils.reshape_first_ndims(
value, 2, [batch_size * num_groups])
# For example feature, we have shape [batch_size * num_groups,
# group_size, ...].
large_batch_group_features = {}
for name, value in six.iteritems(per_example_features):
# [batch_size, num_groups, group_size, ...].
value = array_ops.gather_nd(value, indices)
# [batch_size * num_groups, group_size, ...].
large_batch_group_features[
name] = utils.reshape_first_ndims(
value, 3, [batch_size * num_groups, group_size])
# Do the inference and get scores for the large batch.
# [batch_size * num_groups, group_size].
scores = _score_fn(large_batch_context_features,
large_batch_group_features,
reuse=False)
with ops.name_scope('accumulate_scores'):
scores = array_ops.reshape(
scores, [batch_size, num_groups, group_size])
# Reset invalid scores to 0 based on mask.
scores = array_ops.where(
array_ops.gather(array_ops.expand_dims(mask, 2),
array_ops.zeros([group_size],
dtypes.int32),
axis=2), scores,
array_ops.zeros_like(scores))
# [batch_size, num_groups, group_size].
list_scores = array_ops.scatter_nd(indices, scores,
[batch_size, list_size])
# Use average.
list_scores /= math_ops.to_float(group_size)
if mode == model_fn.ModeKeys.PREDICT:
return list_scores
else:
features.update(context_features)
features.update(per_example_features)
return list_scores
def scatter_nd(self, indices, updates, shape, input_=None): del input_ # input_ is not used in scatter_nd return array_ops.scatter_nd(indices, updates, shape)
def _replicate_rows(tensor, multiple):
tensor_shape = tensor.shape.as_list()
expanded_shape = [tensor_shape[0] * multiple, tensor_shape[1]]
indices = constant_op.constant(_generate_indices(tensor_shape[0], multiple))
return array_ops.scatter_nd(indices, _tile_rows(tensor, multiple),
expanded_shape)
def testUndefinedUpdatesShape(self): indices = array_ops.placeholder(dtypes.int32, shape=[2, 2, 2]) updates = array_ops.placeholder(dtypes.int32, shape=None) shape = constant_op.constant([2, 2, 2], dtypes.int32) array_ops.scatter_nd(indices, updates, shape)
def collapse_repeated(labels, seq_length, name=None):
"""Merge repeated labels into single labels.
Args:
labels: Tensor of shape (batch, max value in seq_length)
seq_length: Tensor of shape (batch), sequence length of each batch element.
name: A name for this `Op`. Defaults to "collapse_repeated_labels".
Returns:
tuple of Tensor of shape (batch, max_seq_length) with repeated labels
collapsed and padded to max_seq_length, eg:
[[A, A, B, B, A],
[A, B, C, D, E]] => [[A, B, A, 0, 0],
[A, B, C, D, E]]
and int tensor of shape [batch] with new sequence lengths.
"""
with ops.name_scope(name, "collapse_repeated_labels",
[labels, seq_length]):
labels = ops.convert_to_tensor(labels, name="labels")
seq_length = ops.convert_to_tensor(seq_length, name="seq_length")
# Mask labels that don't equal previous label.
label_mask = array_ops.concat([
array_ops.ones_like(labels[:, :1], dtypes.bool),
math_ops.not_equal(labels[:, 1:], labels[:, :-1])
],
axis=1)
# Filter labels that aren't in the original sequence.
maxlen = _get_dim(labels, 1)
seq_mask = array_ops.sequence_mask(seq_length, maxlen=maxlen)
label_mask = math_ops.logical_and(label_mask, seq_mask)
# Count masks for new sequence lengths.
new_seq_len = math_ops.reduce_sum(math_ops.cast(
label_mask, dtypes.int32),
axis=1)
# Mask indexes based on sequence length mask.
new_maxlen = math_ops.reduce_max(new_seq_len)
idx_mask = array_ops.sequence_mask(new_seq_len, maxlen=new_maxlen)
# Flatten everything and mask out labels to keep and sparse indices.
flat_labels = array_ops.reshape(labels, [-1])
flat_label_mask = array_ops.reshape(label_mask, [-1])
flat_idx_mask = array_ops.reshape(idx_mask, [-1])
idx = math_ops.range(_get_dim(flat_idx_mask, 0))
# Scatter to flat shape.
flat = array_ops.scatter_nd(indices=array_ops.expand_dims(
array_ops.boolean_mask(idx, flat_idx_mask), axis=1),
updates=array_ops.boolean_mask(
flat_labels, flat_label_mask),
shape=array_ops.shape(flat_idx_mask))
# Reshape back to square batch.
batch_size = _get_dim(labels, 0)
new_shape = [batch_size, new_maxlen]
return (array_ops.reshape(flat, new_shape),
math_ops.cast(new_seq_len, seq_length.dtype))
