def cal_scores_indices(scores_to_0,scores_to_1):
next_beam_scores_1, word_indices_1 = nn_ops.top_k(scores_to_0, k=5)
print ("ori next_beam_scores_1,word_indices_1",next_beam_scores_1)
print ("ori word_indices_1",word_indices_1)
next_beam_scores_2, word_indices_2 = nn_ops.top_k(scores_to_1, k=5)
next_beam_scores=tf.concat([next_beam_scores_1,next_beam_scores_2],1)
word_indices=tf.concat([word_indices_1,word_indices_2+9*vocab_size],1)
return next_beam_scores,word_indices
def _batch_sort_vector(x, ascending=True, name=None):
with ops.name_scope(name, "sort_each_row", [x]):
x = ops.convert_to_tensor(x, name="x")
n = array_ops.shape(x)[-1]
if ascending:
y, _ = nn_ops.top_k(-x, k=n, sorted=True)
y = -y
else:
y, _ = nn_ops.top_k(x, k=n, sorted=True)
y.set_shape(x.shape)
return y
def _batch_sort_vector(x, ascending=True, name=None):
with ops.name_scope(name, "sort_each_row", [x]):
x = ops.convert_to_tensor(x, name="x")
n = array_ops.shape(x)[-1]
if ascending:
y, _ = nn_ops.top_k(-x, k=n, sorted=True)
y = -y
else:
y, _ = nn_ops.top_k(x, k=n, sorted=True)
y.set_shape(x.shape)
return y
def sort_by_scores(scores, features_list, topn=None):
"""Sorts example features according to per-example scores.
Args:
scores: A `Tensor` of shape [batch_size, list_size] representing the
per-example scores.
features_list: A list of `Tensor`s with the same shape as scores to be
sorted.
topn: An integer as the cutoff of examples in the sorted list.
Returns:
A list of `Tensor`s as the list of sorted features by `scores`.
"""
scores = ops.convert_to_tensor(scores)
scores.get_shape().assert_has_rank(2)
batch_size, list_size = array_ops.unstack(array_ops.shape(scores))
if topn is None:
topn = list_size
topn = math_ops.minimum(topn, list_size)
_, indices = nn_ops.top_k(scores, topn, sorted=True)
list_offsets = array_ops.expand_dims(
math_ops.range(batch_size) * list_size, 1)
# The shape of `indices` is [batch_size, topn] and the shape of
# `list_offsets` is [batch_size, 1]. Broadcasting is used here.
gather_indices = array_ops.reshape(indices + list_offsets, [-1])
output_shape = array_ops.stack([batch_size, topn])
# Each feature is first flattened to a 1-D vector and then gathered by the
# indices from sorted scores and then re-shaped.
return [
array_ops.reshape(
array_ops.gather(array_ops.reshape(feature, [-1]), gather_indices),
output_shape) for feature in features_list
]
def get_best(self, n):
"""Return the indices and values of the n highest scores in the TopN."""
def refresh_shortlist():
"""Update the shortlist with the highest scores in id_to_score."""
new_scores, new_ids = nn_ops.top_k(self.id_to_score, self.shortlist_size)
smallest_new_score = math_ops.reduce_min(new_scores)
new_length = math_ops.reduce_sum(
math_ops.to_int32(math_ops.greater(new_scores, dtypes.float32.min)))
u1 = self.sl_ids.assign(
math_ops.to_int64(array_ops.concat([[new_length], new_ids], 0)))
u2 = self.sl_scores.assign(
array_ops.concat([[smallest_new_score], new_scores], 0))
self.last_ops = [u1, u2]
return control_flow_ops.group(u1, u2)
# We only need to refresh the shortlist if n is greater than the
# current shortlist size (which is stored in sl_ids[0]).
with ops.control_dependencies(self.last_ops):
cond_op = control_flow_ops.cond(n > self.sl_ids[0], refresh_shortlist,
control_flow_ops.no_op)
with ops.control_dependencies([cond_op]):
topk_values, topk_indices = nn_ops.top_k(
self.sl_scores,
math_ops.minimum(n, math_ops.to_int32(self.sl_ids[0])))
# topk_indices are the indices into the shortlist, we want to return
# the indices into id_to_score
gathered_indices = array_ops.gather(self.sl_ids, topk_indices)
return gathered_indices, topk_values
def testTopKInfinities(self):
"""Tests that positive and negative infinity sort correctly."""
supported_types = set([
dtypes.bfloat16.as_numpy_dtype, np.float16, np.float32, np.float64
])
for dtype in supported_types.intersection(self.numeric_types):
# TPU implementation is not supported for double precision
if (dtype == np.float64
or dtype == np.float16) and self.device == "TPU":
continue
with self.session() as sess:
p = array_ops.placeholder(dtype)
with self.test_scope():
topk = nn_ops.top_k(p, k=6)
results = sess.run(
topk, {
p:
np.array(
[1, 2, float("inf"), -float("inf"), -1, -2],
dtype=dtype)
})
self.assertAllEqual(
np.array(
[float("inf"), 2.0, 1.0, -1.0, -2.0, -float("inf")],
dtype=dtype), results[0])
self.assertEqual(list([2, 1, 0, 4, 5, 3]), list(results[1]))
def call(self, inputs, training=False):
input_dim = inputs.get_shape()[-1].value
_, indices = nn_ops.top_k(inputs, self.k, sorted=False)
mask = array_ops.one_hot(indices, input_dim, axis=-1)
mask = math_ops.reduce_sum(mask, axis=-2)
return utils.smart_cond(training, lambda: mask * inputs,
lambda: array_ops.identity(inputs))
def shuffle_valid_indices(is_valid, seed=None):
"""Returns a shuffle of indices with valid ones on top.
Args:
is_valid: A boolen `Tensor` for entry validity with shape [batch_size,
list_size].
seed: An int for random seed at the op level. It works together with the
seed at global graph level together to determine the random number
generation. See `tf.set_random_seed`.
Returns:
A tensor of indices with shape [batch_size, list_size, 2]. The returned
tensor can be used with `tf.gather_nd` and `tf.scatter_nd` to compose a new
[batch_size, list_size] tensor. The values in the last dimension are the
indices for an element in the input tensor.
"""
is_valid = ops.convert_to_tensor(is_valid)
is_valid.get_shape().assert_has_rank(2)
output_shape = array_ops.shape(is_valid)
rand = array_ops.where(is_valid,
random_ops.random_uniform(output_shape, seed=seed),
array_ops.ones(output_shape) * -1e-6)
# shape(indices) = [batch_size, list_size]
_, indices = nn_ops.top_k(rand, output_shape[1], sorted=True)
# shape(batch_ids) = [batch_size, list_size]
batch_ids = array_ops.ones_like(indices) * array_ops.expand_dims(
math_ops.range(output_shape[0]), 1)
return array_ops.concat([
array_ops.expand_dims(batch_ids, 2),
array_ops.expand_dims(indices, 2)
],
axis=2)
def testKNegative(self):
inputs = [[0.1, 0.2], [0.3, 0.4]]
with self.session(use_gpu=True):
k = array_ops.placeholder(dtypes.int32)
values, _ = nn_ops.top_k(inputs, k)
with self.assertRaisesOpError("Need k >= 0, got -7"):
values.eval(feed_dict={k: -7})
def benchmarkTopK(self):
for (m, n, p,
use_gpu) in itertools.product([128],
[10, 100, 1000, 10000, 100000],
[0.001, 0.01, 0.5, 0.99, 1.0],
[False, True]):
k = int(p * n)
if k == 0:
continue
name = "m_%d_n_%d_k_%g_use_gpu_%s" % (m, n, k, use_gpu)
device = "/%s:0" % ("gpu" if use_gpu else "cpu")
with ops.Graph().as_default():
with ops.device(device):
x = random_ops.random_uniform((m, n))
v = resource_variable_ops.ResourceVariable(x)
op = nn_ops.top_k(v, k)
with session.Session() as sess:
v.initializer.run()
r = self.run_op_benchmark(sess,
op,
min_iters=100,
name=name)
gb_processed_input = m * n / 1.0e9
throughput = gb_processed_input / r["wall_time"]
print("Benchmark: %s \t wall_time: %0.03g s \t "
"Throughput: %0.03g GB/s" %
(name, r["wall_time"], throughput))
sys.stdout.flush()
def get_best(self, n):
"""Return the indices and values of the n highest scores in the TopN."""
def refresh_shortlist():
"""Update the shortlist with the highest scores in id_to_score."""
new_scores, new_ids = nn_ops.top_k(self.id_to_score,
self.shortlist_size)
smallest_new_score = math_ops.reduce_min(new_scores)
new_length = math_ops.reduce_sum(
math_ops.to_int32(
math_ops.greater(new_scores, dtypes.float32.min)))
u1 = self.sl_ids.assign(
math_ops.to_int64(array_ops.concat([[new_length], new_ids],
0)))
u2 = self.sl_scores.assign(
array_ops.concat([[smallest_new_score], new_scores], 0))
self.last_ops = [u1, u2]
return control_flow_ops.group(u1, u2)
# We only need to refresh the shortlist if n is greater than the
# current shortlist size (which is stored in sl_ids[0]).
with ops.control_dependencies(self.last_ops):
cond_op = control_flow_ops.cond(n > self.sl_ids[0],
refresh_shortlist,
control_flow_ops.no_op)
with ops.control_dependencies([cond_op]):
topk_values, topk_indices = nn_ops.top_k(
self.sl_scores,
math_ops.minimum(n, math_ops.to_int32(self.sl_ids[0])))
# topk_indices are the indices into the shortlist, we want to return
# the indices into id_to_score
gathered_indices = array_ops.gather(self.sl_ids, topk_indices)
return gathered_indices, topk_values
def GetParams(self):
"""Testing that output type of engine using Top-K is set correctly."""
dtype = dtypes.float32
input_name = "input"
input_dims = [100, 100]
k = 5
g = ops.Graph()
with g.as_default():
x = array_ops.placeholder(dtype=dtype,
shape=input_dims,
name=input_name)
k_tensor = constant_op.constant(k,
dtype=dtypes.int32,
name="Const")
values, indices = nn_ops.top_k(x, k_tensor, name="TopK")
# Reshape will act as a layer between the TopK output and the engine
# output, requiring the output tensor of reshape to be set explicitly to
# int32.
indices = array_ops.reshape(indices, [100, 1, 5], name="Reshape")
values = array_ops.identity(values, name="output_values")
indices = array_ops.identity(indices, name="output_indices")
return trt_test.TfTrtIntegrationTestParams(
gdef=g.as_graph_def(),
input_names=[input_name],
input_dims=[[input_dims]],
output_names=["output_values", "output_indices"],
expected_output_dims=[[[100, k], [100, 1, k]]])
def _cond_restrict_fn(self):
"""
This will only be execute when size of variable is larger than trigger.
"""
restrict_var_ops, restrict_status_ops, restrict_slot_ops = [], [], []
for i, dev in enumerate(self.freq_var.devices):
with ops.device(dev):
partial_keys, partial_counts = self.freq_var.tables[i].export()
partial_reserved = int(self._num_reserved / self.freq_var.shard_num)
partial_counts = array_ops.reshape(partial_counts, (-1,))
first_dim = array_ops.shape(partial_counts)[0]
k_on_top = math_ops.cast(first_dim - partial_reserved,
dtype=dtypes.int32)
k_on_top = math_ops.maximum(k_on_top, 0)
_, removed_key_indices = nn_ops.top_k(-partial_counts,
k_on_top,
sorted=False)
removed_keys = array_ops.gather(partial_keys, removed_key_indices)
restrict_var_ops.append(self.var.tables[i].remove(removed_keys))
restrict_status_ops.append(self.freq_var.tables[i].remove(removed_keys))
for slot_param in self.params_in_slots:
restrict_slot_ops.append(slot_param.tables[i].remove(removed_keys))
return control_flow_ops.group(restrict_var_ops, restrict_status_ops,
restrict_slot_ops)
def allocmem(u_tm1, ww_tm1, wr_tm1_ls, fg_ls):
"""Allocate Memory.
Parameters
----------
u_tm1 : `[batch_size, mem_size]`.
ww_tm1 : `[batch_size, mem_size]`.
wr_tm1_ls : a list of R read weights. each element in the list has size of:
`[batch_size, mem_size]`.
fg_ls : a list of R free gates. each element in the list has size of:
`[batch_size, 1]`.
Returns
-------
u : `[batch_size, mem_size]`.
alloc_vec : `[batch_size, mem_size]`
"""
mem_size = shape(u_tm1)[1]
retention = functools.reduce(
multiply, [1 - fg * wr_tm1 for fg, wr_tm1 in zip(fg_ls, wr_tm1_ls)])
u = (u_tm1 + ww_tm1 - u_tm1 * ww_tm1) * retention
asd_u, asd_u_idx = top_k(u, k=mem_size)
idx = reverse(asd_u_idx, axis=[1])
prod_phi = cumprod(reverse(asd_u, axis=[1]), axis=1, exclusive=True)
alloc_vec = (1 - u) * prod_phi
return alloc_vec, u
def dnw_fn(mask, sparsity, dtype):
"""Creates a mask with smallest magnitudes with deterministic sparsity.
Args:
mask: tf.Tensor, used to obtain correct corresponding gradient.
sparsity: float, between 0 and 1.
dtype: tf.dtype, type of the return value.
Returns:
tf.Tensor
"""
del dtype
var_name = sparse_utils.mask_extract_name_fn(mask.name)
v = vars_dict[var_name]
score_drop = math_ops.abs(v)
n_total = np.prod(score_drop.shape.as_list())
n_prune = sparse_utils.get_n_zeros(n_total, sparsity)
n_keep = n_total - 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.dtype),
array_ops.zeros_like(sorted_indices, dtype=mask.dtype))
new_mask = array_ops.scatter_nd(sorted_indices_ex, new_values,
new_values.shape)
return array_ops.reshape(new_mask, mask.shape)
def GraphFn(self, x):
k = 5
k_tensor = constant_op.constant(k, dtype=dtypes.int32, name="Const")
values, indices = nn_ops.top_k(x, k_tensor, name="TopK")
values = array_ops.identity(values, name="output_0")
indices = array_ops.identity(indices, name="output_1")
return values, indices
def testKNegative(self):
inputs = [[0.1, 0.2], [0.3, 0.4]]
with self.test_session(use_gpu=True):
k = array_ops.placeholder(dtypes.int32)
values, _ = nn_ops.top_k(inputs, k)
with self.assertRaisesOpError("Need k >= 0, got -7"):
values.eval(feed_dict={k: -7})
def testTopKInfinities(self):
"""Tests that positive and negative infinity sort correctly."""
# TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU.
if self.device in ["XLA_CPU", "XLA_GPU"]:
return
# Only bfloat16 is implemented.
bfloat16 = dtypes.bfloat16.as_numpy_dtype
if bfloat16 not in self.numeric_types:
return
with self.test_session() as sess:
p = array_ops.placeholder(dtypes.bfloat16)
with self.test_scope():
topk = nn_ops.top_k(p, k=6)
results = sess.run(
topk, {
p:
np.array([1, 2, float("inf"), -float("inf"), -1, -2],
dtype=bfloat16)
})
self.assertAllEqual(
np.array([float("inf"), 2.0, 1.0, -1.0, -2.0, -float("inf")],
dtype=bfloat16), results[0])
self.assertEqual(list([2, 1, 0, 4, 5, 3]), list(results[1]))
def _descending_sort(values, axis, return_argsort=False):
"""Sorts values in reverse using `top_k`.
Args:
values: Tensor of numeric values.
axis: Index of the axis which values should be sorted along.
return_argsort: If False, return the sorted values. If True, return the
indices that would sort the values.
Returns:
The sorted values.
"""
k = array_ops.shape(values)[axis]
rank = array_ops.rank(values)
static_rank = values.shape.ndims
# Fast path: sorting the last axis.
if axis == -1 or axis + 1 == values.get_shape().ndims:
top_k_input = values
transposition = None
else:
# Otherwise, transpose the array. Swap axes `axis` and `rank - 1`.
if axis < 0:
# Calculate the actual axis index if counting from the end. Use the static
# rank if available, or else make the axis back into a tensor.
axis += static_rank or rank
if static_rank is not None:
# Prefer to calculate the transposition array in NumPy and make it a
# constant.
transposition = constant_op.constant(
np.r_[
# Axes up to axis are unchanged.
np.arange(axis),
# Swap axis and rank - 1.
[static_rank - 1],
# Axes in [axis + 1, rank - 1) are unchanged.
np.arange(axis + 1, static_rank - 1),
# Swap axis and rank - 1.
[axis]],
name='transposition')
else:
# Generate the transposition array from the tensors.
transposition = array_ops.concat(
[
# Axes up to axis are unchanged.
math_ops.range(axis),
# Swap axis and rank - 1.
[rank - 1],
# Axes in [axis + 1, rank - 1) are unchanged.
math_ops.range(axis + 1, rank - 1),
# Swap axis and rank - 1.
[axis]
],
axis=0)
top_k_input = array_ops.transpose(values, transposition)
values, indices = nn_ops.top_k(top_k_input, k)
return_value = indices if return_argsort else values
if transposition is not None:
# transposition contains a single cycle of length 2 (swapping 2 elements),
# so it is an involution (it is its own inverse).
return_value = array_ops.transpose(return_value, transposition)
return return_value
def cal_scores_indices_t1(scores_final,next_beam_size):
next_beam_scores_1, word_indices_1=nn_ops.top_k(scores_final, k=5)
#next_beam_scores_1, word_indices_1=sample(next_beam_scores_1,word_indices_1)
print ("next_beam_scores_1", next_beam_scores_1)
print ("word_indices_1",word_indices_1)
next_beam_scores=tf.concat([next_beam_scores_1,next_beam_scores_1],1)
word_indices=tf.concat([word_indices_1,word_indices_1+5*vocab_size],1)
return next_beam_scores, word_indices
def _validateTopK(self,
inputs,
k,
expected_values,
expected_indices,
sorted=True): # pylint: disable=redefined-builtin
np_expected_values = np.array(expected_values)
np_expected_indices = np.array(expected_indices)
with self.cached_session(use_gpu=True) as sess:
values_op, indices_op = nn_ops.top_k(inputs, k, sorted=sorted)
values, indices = self.evaluate([values_op, indices_op])
self.assertShapeEqual(np_expected_values, values_op)
self.assertShapeEqual(np_expected_indices, indices_op)
if sorted:
self.assertAllClose(np_expected_values, values)
# Do some special casing of equality of indices: if indices
# are not the same, but values are floating type, ensure that
# the values are within epsilon of each other.
if not np.issubdtype(np_expected_values.dtype, np.floating):
# Values are not floating point type; check indices exactly
self.assertAllEqual(np_expected_indices, indices)
else:
# Values are floating point; indices may be swapped for
# values near each other.
indices_not_equal = np_expected_indices != indices
if np.any(indices_not_equal):
values_unsure = values[indices_not_equal]
expected_values_unsure = expected_values[
indices_not_equal]
self.assertAllClose(expected_values_unsure,
values_unsure)
else:
np_inputs = np.array(inputs)
# Check that the indices are valid.
for result_index, src_index in np.ndenumerate(indices):
value = values[result_index]
expected_value = np_inputs[result_index[0], src_index]
np.testing.assert_almost_equal(value, expected_value)
# Check that if two elements are equal, the lower-index element appears
# first.
shape = values.shape
for batch_index in range(shape[0]):
for index in range(shape[1] - 1):
if np.isclose(values[batch_index, index],
values[batch_index, index + 1]):
self.assertLess(indices[batch_index, index],
indices[batch_index, index + 1])
# Now check the results, ignoring order.
self.assertAllEqual(np.sort(np_expected_indices),
np.sort(indices))
self.assertAllClose(np.sort(np_expected_values),
np.sort(values))
def testTopKGradients(self):
with self.test_session(use_gpu=True) as sess:
inputs = array_ops.placeholder(dtypes.int32, shape=[2, 5])
values, _ = nn_ops.top_k(inputs, 3)
grad = sess.run(
gradients_impl.gradients(
values, inputs, grad_ys=[[[1, 2, 3], [4, 5, 6]]]),
feed_dict={inputs: [[2, -1, 1000, 3, 4], [1, 5, 2, 4, 3]]})[0]
self.assertEqual(grad.tolist(), [[0, 0, 1, 3, 2], [0, 4, 0, 5, 6]])
def testTopKGradients(self):
with self.test_session(use_gpu=True) as sess:
inputs = array_ops.placeholder(dtypes.int32, shape=[2, 5])
values, _ = nn_ops.top_k(inputs, 3)
grad = sess.run(
gradients_impl.gradients(
values, inputs, grad_ys=[[[1, 2, 3], [4, 5, 6]]]),
feed_dict={inputs: [[2, -1, 1000, 3, 4], [1, 5, 2, 4, 3]]})[0]
self.assertEqual(grad.tolist(), [[0, 0, 1, 3, 2], [0, 4, 0, 5, 6]])
def prune_by_bbb(variable_metadata, percentage):
"""Prune a percentage of variables based on their signal to noise ratios.
Arguments:
variable_metadata: `list` of `bbb._VariableMetadata`, suggest using
`bbb.get_variable_metadata()`.
percentage: a `tf.Tensor` that is scalar representing what percentage
of variables to prune.
"""
if not variable_metadata:
return []
signal_to_noise_ratios = []
variable_estimates = []
variable_info = []
# get signal to noise and mean posterior
for meta in variable_metadata:
posterior_dist = meta.posterior
signal_to_noise_ratios.append(
array_utils.flatten(
distribution_utils.signal_to_noise_ratio(posterior_dist)))
variable_estimates.append(array_utils.flatten(meta.posterior_estimate))
variable_info.append((meta.raw_variable_name, meta.raw_variable_shape))
# flatten variables
flat_variable_estimates = array_ops.concat(variable_estimates, 0)
flat_signal_to_noise_ratios = array_ops.concat(signal_to_noise_ratios, 0)
flat_variable_size = flat_variable_estimates.get_shape().as_list()[-1]
flat_drop_size = math_ops.cast(flat_variable_size * percentage,
dtypes.int32)
# sort by signal to noise ratio
_, indices = nn_ops.top_k(flat_signal_to_noise_ratios,
k=flat_variable_size,
sorted=True)
zero_indices = array_ops.expand_dims(indices[:flat_drop_size], -1)
mask = math_ops.cast(
sparse_ops.sparse_to_dense(zero_indices, [flat_variable_size],
sparse_values=0,
default_value=1,
validate_indices=False),
flat_variable_estimates.dtype)
flat_variable_estimates *= mask
# unflatten variables
start = 0
dsts = []
for name, shape in variable_info:
end = array_utils.product(shape)
dst = gen_array_ops.reshape(flat_variable_estimates[start:start + end],
shape,
name=name)
dsts.append(dst)
start += end
return dsts
def call(self, inputs, training=False):
input_dim = inputs.get_shape()[-1].value
k = random_ops.random_uniform([1],
maxval=input_dim,
dtype=dtypes.int32)[0]
_, indices = nn_ops.top_k(inputs, k, sorted=False)
mask = array_ops.one_hot(indices, input_dim, axis=-1)
mask = math_ops.reduce_sum(mask, axis=-2)
return utils.smart_cond(training, lambda: mask * inputs,
lambda: array_ops.identity(inputs))
def GraphFn(self, x):
k = 5
k_tensor = constant_op.constant(k, dtype=dtypes.int32, name="Const")
values, indices = nn_ops.top_k(x, k_tensor, name="TopK")
# Reshape will act as a layer between the TopK output and the engine
# output, requiring the output tensor of reshape to be set explicitly to
# int32.
indices = array_ops.reshape(indices, [100, 1, 5], name="Reshape")
values = array_ops.identity(values, name="output_0")
indices = array_ops.identity(indices, name="output_1")
return values, indices
def _sort_rows(matrix, num_rows):
"""Sort matrix rows by the last column.
Args:
matrix: a matrix of values (row,col).
num_rows: (int) number of sorted rows to return from the matrix.
Returns:
Tensor (num_rows, col) of the sorted matrix top K rows.
"""
tmatrix = array_ops.transpose(matrix, [1, 0])
sorted_tmatrix = nn_ops.top_k(tmatrix, num_rows)[0]
return array_ops.transpose(sorted_tmatrix, [1, 0])
def testTopKGradients(self):
with self.session(use_gpu=True) as sess:
inputs = array_ops.placeholder(dtypes.float32, shape=[2, 5])
values, _ = nn_ops.top_k(inputs, 3)
grad = sess.run(
gradients_impl.gradients(
values, inputs, grad_ys=[[[1., 2., 3.], [4., 5., 6.]]]),
feed_dict={inputs: [[2., -1., 1000., 3., 4.],
[1., 5., 2., 4., 3.]]})[0]
self.assertEqual(
grad.tolist(), [[0., 0., 1., 3., 2.], [0., 4., 0., 5., 6.]])
def _validateTopK(self,
inputs,
k,
expected_values,
expected_indices,
sorted=True): # pylint: disable=redefined-builtin
np_expected_values = np.array(expected_values)
np_expected_indices = np.array(expected_indices)
with self.test_session(use_gpu=True) as sess:
values_op, indices_op = nn_ops.top_k(inputs, k, sorted=sorted)
values, indices = sess.run([values_op, indices_op])
self.assertShapeEqual(np_expected_values, values_op)
self.assertShapeEqual(np_expected_indices, indices_op)
if sorted:
self.assertAllClose(np_expected_values, values)
# Do some special casing of equality of indices: if indices
# are not the same, but values are floating type, ensure that
# the values are within epsilon of each other.
if not np.issubdtype(np_expected_values.dtype, np.float):
# Values are not floating point type; check indices exactly
self.assertAllEqual(np_expected_indices, indices)
else:
# Values are floating point; indices may be swapped for
# values near each other.
indices_not_equal = np_expected_indices != indices
if np.any(indices_not_equal):
values_unsure = values[indices_not_equal]
expected_values_unsure = expected_values[indices_not_equal]
self.assertAllClose(expected_values_unsure, values_unsure)
else:
np_inputs = np.array(inputs)
# Check that the indices are valid.
for result_index, src_index in np.ndenumerate(indices):
value = values[result_index]
expected_value = np_inputs[result_index[0], src_index]
np.testing.utils.assert_almost_equal(value, expected_value)
# Check that if two elements are equal, the lower-index element appears
# first.
shape = values.shape
for batch_index in range(shape[0]):
for index in range(shape[1] - 1):
if np.isclose(values[batch_index, index],
values[batch_index, index + 1]):
self.assertLess(indices[batch_index, index],
indices[batch_index, index + 1])
# Now check the results, ignoring order.
self.assertAllEqual(np.sort(np_expected_indices), np.sort(indices))
self.assertAllClose(np.sort(np_expected_values), np.sort(values))
def refresh_shortlist():
"""Update the shortlist with the highest scores in id_to_score."""
new_scores, new_ids = nn_ops.top_k(self.id_to_score, self.shortlist_size)
smallest_new_score = math_ops.reduce_min(new_scores)
new_length = math_ops.reduce_sum(
math_ops.to_int32(math_ops.greater(new_scores, dtypes.float32.min)))
u1 = self.sl_ids.assign(
math_ops.to_int64(array_ops.concat([[new_length], new_ids], 0)))
u2 = self.sl_scores.assign(
array_ops.concat([[smallest_new_score], new_scores], 0))
self.last_ops = [u1, u2]
return control_flow_ops.group(u1, u2)
def _sort_rows(matrix, num_rows):
"""Sort matrix rows by the last column.
Args:
matrix: a matrix of values (row,col).
num_rows: (int) number of sorted rows to return from the matrix.
Returns:
Tensor (num_rows, col) of the sorted matrix top K rows.
"""
tmatrix = array_ops.transpose(matrix, [1, 0])
sorted_tmatrix = nn_ops.top_k(tmatrix, num_rows)[0]
return array_ops.transpose(sorted_tmatrix, [1, 0])
def testTopKZeros(self):
"""Tests that positive and negative zeros sort correctly."""
supported_types = set([dtypes.bfloat16.as_numpy_dtype, np.float32])
for dtype in supported_types.intersection(self.numeric_types):
with self.session() as sess:
p = array_ops.placeholder(dtype)
with self.test_scope():
topk = nn_ops.top_k(p, k=4)
results = sess.run(
topk,
{p: np.array([0., -0., 0., 3., -0., -4., 0., -0.], dtype=dtype)})
self.assertAllEqual(np.array([3., 0., 0., 0.], dtype=dtype), results[0])
self.assertEqual(list([3, 0, 2, 6]), list(results[1]))
def _descending_sort(values, axis):
"""Sorts values in reverse using `top_k`.
Args:
values: Tensor of numeric values.
axis: Index of the axis which values should be sorted along.
Returns:
The sorted values.
"""
k = array_ops.shape(values)[axis]
rank = array_ops.rank(values)
# Fast path: sorting the last axis.
if axis == -1 or axis + 1 == values.get_shape().ndims:
return nn_ops.top_k(values, k)[0]
# Otherwise, transpose the array. Swap axes `axis` and `rank - 1`.
if axis < 0:
# Make axis a Tensor with the real axis index if needed.
axis += rank
transposition = array_ops.concat(
[
# Axes up to axis are unchanged.
math_ops.range(axis),
# Swap axis and rank - 1.
[rank - 1],
# Axes in [axis + 1, rank - 1) are unchanged.
math_ops.range(axis + 1, rank - 1),
# Swap axis and rank - 1.
[axis]
],
axis=0)
top_k_input = array_ops.transpose(values, transposition)
values, unused_indices = nn_ops.top_k(top_k_input, k)
# transposition contains a single cycle of length 2 (swapping 2 elements),
# so it is an involution (it is its own inverse).
return array_ops.transpose(values, transposition)
def _update_mask(self, weights, threshold):
"""Updates the mask for a given weight tensor.
This functions first computes the cdf of the weight tensor, and estimates
the threshold value such that 'desired_sparsity' fraction of weights
have magnitude less than the threshold.
Args:
weights: The weight tensor that needs to be masked.
threshold: The current threshold value. The function will compute a new
threshold and return the exponential moving average using the current
value of threshold
Returns:
new_threshold: The new value of the threshold based on weights, and
sparsity at the current global_step
new_mask: A numpy array of the same size and shape as weights containing
0 or 1 to indicate which of the values in weights falls below
the threshold
Raises:
ValueError: if sparsity is not defined
"""
if self._sparsity is None:
raise ValueError('Sparsity variable undefined')
sparsity = self._get_sparsity(weights.op.name)
with ops.name_scope(weights.op.name + '_pruning_ops'):
abs_weights = math_ops.abs(weights)
k = math_ops.cast(
math_ops.round(
math_ops.cast(array_ops.size(abs_weights), dtypes.float32)
* (1 - sparsity)), dtypes.int32)
# Sort the entire array
values, _ = nn_ops.top_k(array_ops.reshape(abs_weights, [-1]),
k=array_ops.size(abs_weights))
# Grab the (k-1) th value
current_threshold = array_ops.gather(values, k - 1)
smoothed_threshold = math_ops.add_n([
math_ops.multiply(current_threshold,
1 - self._spec.threshold_decay),
math_ops.multiply(threshold, self._spec.threshold_decay)
])
new_mask = math_ops.cast(
math_ops.greater_equal(abs_weights, smoothed_threshold),
dtypes.float32)
return smoothed_threshold, new_mask
def refresh_shortlist():
"""Update the shortlist with the highest scores in id_to_score."""
new_scores, new_ids = nn_ops.top_k(self.id_to_score,
self.shortlist_size)
smallest_new_score = math_ops.reduce_min(new_scores)
new_length = math_ops.reduce_sum(
math_ops.to_int32(
math_ops.greater(new_scores, dtypes.float32.min)))
u1 = self.sl_ids.assign(
math_ops.to_int64(array_ops.concat([[new_length], new_ids],
0)))
u2 = self.sl_scores.assign(
array_ops.concat([[smallest_new_score], new_scores], 0))
self.last_ops = [u1, u2]
return control_flow_ops.group(u1, u2)
def _update_mask(self, weights, threshold):
"""Updates the mask for a given weight tensor.
This functions first computes the cdf of the weight tensor, and estimates
the threshold value such that 'desired_sparsity' fraction of weights
have magnitude less than the threshold.
Args:
weights: The weight tensor that needs to be masked.
threshold: The current threshold value. The function will compute a new
threshold and return the exponential moving average using the current
value of threshold
Returns:
new_threshold: The new value of the threshold based on weights, and
sparsity at the current global_step
new_mask: A numpy array of the same size and shape as weights containing
0 or 1 to indicate which of the values in weights falls below
the threshold
Raises:
ValueError: if sparsity is not defined
"""
if self._sparsity is None:
raise ValueError('Sparsity variable undefined')
sparsity = self._get_sparsity(weights.op.name)
with ops.name_scope(weights.op.name + '_pruning_ops'):
abs_weights = math_ops.abs(weights)
k = math_ops.cast(
math_ops.round(
math_ops.cast(array_ops.size(abs_weights), dtypes.float32) *
(1 - sparsity)), dtypes.int32)
# Sort the entire array
values, _ = nn_ops.top_k(
array_ops.reshape(abs_weights, [-1]), k=array_ops.size(abs_weights))
# Grab the (k-1) th value
current_threshold = array_ops.gather(values, k - 1)
smoothed_threshold = math_ops.add_n([
math_ops.multiply(current_threshold, 1 - self._spec.threshold_decay),
math_ops.multiply(threshold, self._spec.threshold_decay)
])
new_mask = math_ops.cast(
math_ops.greater_equal(abs_weights, smoothed_threshold),
dtypes.float32)
return smoothed_threshold, new_mask
def _validateTopK(self,
inputs,
k,
expected_values,
expected_indices,
sorted=True):
np_values = np.array(expected_values)
np_indices = np.array(expected_indices)
with self.test_session():
values_op, indices_op = nn_ops.top_k(inputs, k, sorted=sorted)
values = values_op.eval()
indices = indices_op.eval()
self.assertAllClose(np_values, values)
self.assertAllEqual(np_indices, indices)
self.assertShapeEqual(np_values, values_op)
self.assertShapeEqual(np_indices, indices_op)
def _validateTopK(self,
inputs,
k,
expected_values,
expected_indices,
sorted=True):
np_values = np.array(expected_values)
np_indices = np.array(expected_indices)
with self.test_session():
values_op, indices_op = nn_ops.top_k(inputs, k, sorted=sorted)
values = values_op.eval()
indices = indices_op.eval()
self.assertShapeEqual(np_values, values_op)
self.assertShapeEqual(np_indices, indices_op)
self.assertAllEqual(np_indices, indices)
self.assertAllClose(np_values, values)
def _filter_top_k(x, k):
"""Filters top-k values in the last dim of x and set the rest to NEG_INF.
Used for computing top-k prediction values in dense labels (which has the same
shape as predictions) for recall and precision top-k metrics.
Args:
x: tensor with any dimensions.
k: the number of values to keep.
Returns:
tensor with same shape and dtype as x.
"""
_, top_k_idx = nn_ops.top_k(x, k, sorted=False)
top_k_mask = math_ops.reduce_sum(
array_ops.one_hot(top_k_idx, x.shape[-1], axis=-1), axis=-2)
return x * top_k_mask + NEG_INF * (1 - top_k_mask)
def _filter_top_k(x, k):
"""Filters top-k values in the last dim of x and set the rest to NEG_INF.
Used for computing top-k prediction values in dense labels (which has the same
shape as predictions) for recall and precision top-k metrics.
Args:
x: tensor with any dimensions.
k: the number of values to keep.
Returns:
tensor with same shape and dtype as x.
"""
_, top_k_idx = nn_ops.top_k(x, k, sorted=False)
top_k_mask = math_ops.reduce_sum(
array_ops.one_hot(top_k_idx, array_ops.shape(x)[-1], axis=-1), axis=-2)
return x * top_k_mask + NEG_INF * (1 - top_k_mask)
def testTopKZeros(self):
"""Tests that positive and negative zeros sort correctly."""
# Only bfloat16 is implemented.
bfloat16 = dtypes.bfloat16.as_numpy_dtype
if bfloat16 not in self.numeric_types:
return
with self.cached_session() as sess:
p = array_ops.placeholder(dtypes.bfloat16)
with self.test_scope():
topk = nn_ops.top_k(p, k=4)
results = sess.run(
topk,
{p: np.array([0., -0., 0., 3., -0., -4., 0., -0.], dtype=bfloat16)})
self.assertAllEqual(
np.array([3., 0., 0., 0.], dtype=bfloat16), results[0])
self.assertEqual(list([3, 0, 2, 6]), list(results[1]))
def __init__(self, permutation, validate_args=False, name=None):
"""Creates the `Permute` bijector.
Args:
permutation: An `int`-like vector-shaped `Tensor` representing the
permutation to apply to the rightmost dimension of the transformed
`Tensor`.
validate_args: Python `bool` indicating whether arguments should be
checked for correctness.
name: Python `str`, name given to ops managed by this object.
Raises:
TypeError: if `not permutation.dtype.is_integer`.
ValueError: if `permutation` does not contain exactly one of each of
`{0, 1, ..., d}`.
"""
with ops.name_scope(name, "permute", values=[permutation]):
permutation = ops.convert_to_tensor(
permutation,
name="permutation")
if not permutation.dtype.is_integer:
raise TypeError("permutation.dtype ({}) should be `int`-like.".format(
permutation.dtype.name))
p = tensor_util.constant_value(permutation)
if p is not None:
if set(p) != set(np.arange(p.size)):
raise ValueError("Permutation over `d` must contain exactly one of "
"each of `{0, 1, ..., d}`.")
elif validate_args:
p, _ = nn_ops.top_k(-permutation,
k=array_ops.shape(permutation)[-1],
sorted=True)
permutation = control_flow_ops.with_dependencies([
check_ops.assert_equal(
-p, math_ops.range(array_ops.size(p)),
message=("Permutation over `d` must contain exactly one of "
"each of `{0, 1, ..., d}`.")),
], permutation)
self._permutation = permutation
super(Permute, self).__init__(
forward_min_event_ndims=1,
is_constant_jacobian=True,
validate_args=validate_args,
name=name or "permute")
def GetParams(self):
"""Testing Top-K in TF-TRT conversion."""
dtype = dtypes.float32
input_name = "input"
input_dims = [100, 100]
k = 5
g = ops.Graph()
with g.as_default():
x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name)
k_tensor = constant_op.constant(k, dtype=dtypes.int32, name="Const")
values, indices = nn_ops.top_k(x, k_tensor, name="TopK")
values = array_ops.identity(values, name="output_values")
indices = array_ops.identity(indices, name="output_indices")
return trt_test.TfTrtIntegrationTestParams(
gdef=g.as_graph_def(),
input_names=[input_name],
input_dims=[[input_dims]],
output_names=["output_values", "output_indices"],
expected_output_dims=[[[100, k], [100, k]]])
def testTopKInfinities(self):
"""Tests that positive and negative infinity sort correctly."""
# Only bfloat16 is implemented.
bfloat16 = dtypes.bfloat16.as_numpy_dtype
if bfloat16 not in self.numeric_types:
return
with self.cached_session() as sess:
p = array_ops.placeholder(dtypes.bfloat16)
with self.test_scope():
topk = nn_ops.top_k(p, k=6)
results = sess.run(topk, {
p: np.array(
[1, 2, float("inf"), -float("inf"), -1, -2], dtype=bfloat16)
})
self.assertAllEqual(
np.array(
[float("inf"), 2.0, 1.0, -1.0, -2.0, -float("inf")],
dtype=bfloat16), results[0])
self.assertEqual(list([2, 1, 0, 4, 5, 3]), list(results[1]))
def testTopKZeros(self):
"""Tests that positive and negative zeros sort correctly."""
# TODO(b/26783907): The Sort HLO is not implemented on CPU or GPU.
if self.device in ["XLA_CPU", "XLA_GPU"]:
return
# Only bfloat16 is implemented.
bfloat16 = dtypes.bfloat16.as_numpy_dtype
if bfloat16 not in self.numeric_types:
return
with self.test_session() as sess:
p = array_ops.placeholder(dtypes.bfloat16)
with self.test_scope():
topk = nn_ops.top_k(p, k=4)
results = sess.run(
topk,
{p: np.array([0., -0., 0., 3., -0., -4., 0., -0.], dtype=bfloat16)})
self.assertAllEqual(
np.array([3., 0., 0., 0.], dtype=bfloat16), results[0])
self.assertEqual(list([3, 0, 1, 2]), list(results[1]))
def GetParams(self):
"""Testing that output type of engine using Top-K is set correctly."""
dtype = dtypes.float32
input_name = "input"
input_dims = [100, 100]
k = 5
g = ops.Graph()
with g.as_default():
x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name)
k_tensor = constant_op.constant(k, dtype=dtypes.int32, name="Const")
values, indices = nn_ops.top_k(x, k_tensor, name="TopK")
# Reshape will act as a layer between the TopK output and the engine
# output, requiring the output tensor of reshape to be set explicitly to
# int32.
indices = array_ops.reshape(indices, [100, 1, 5], name="Reshape")
values = array_ops.identity(values, name="output_values")
indices = array_ops.identity(indices, name="output_indices")
return trt_test.TfTrtIntegrationTestParams(
gdef=g.as_graph_def(),
input_names=[input_name],
input_dims=[[input_dims]],
output_names=["output_values", "output_indices"],
expected_output_dims=[[[100, k], [100, 1, k]]])
def benchmarkTopK(self):
for (m, n, p, use_gpu) in itertools.product(
[128],
[10, 100, 1000, 10000, 100000],
[0.001, 0.01, 0.5, 0.99, 1.0],
[False, True]):
k = int(p * n)
if k == 0:
continue
name = "m_%d_n_%d_k_%g_use_gpu_%s" % (m, n, k, use_gpu)
device = "/%s:0" % ("gpu" if use_gpu else "cpu")
with ops.Graph().as_default():
with ops.device(device):
x = random_ops.random_uniform((m, n))
v = resource_variable_ops.ResourceVariable(x)
op = nn_ops.top_k(v, k)
with session.Session() as sess:
v.initializer.run()
r = self.run_op_benchmark(sess, op, min_iters=100, name=name)
gb_processed_input = m * n / 1.0e9
throughput = gb_processed_input / r["wall_time"]
print("Benchmark: %s \t wall_time: %0.03g s \t "
"Throughput: %0.03g GB/s" % (name, r["wall_time"], throughput))
sys.stdout.flush()
def sample_symbols_new(logits, log_probs, finished, lengths, time):
"""
:param logits: [batch_size * beam_size, target_vocab_size]
:param log_probs: [batch_size * beam_size, ]
:param finished: [batch_size * beam_size, ]
:param lengths: decoding length [batch_size * beam_size, ]
:param time:
:return:
"""
# [batch_size * beam_size,]
prev_finished_float = math_ops.to_float(finished)
# [batch_size * beam_size, ]
prev_log_probs = log_probs
# [batch_size * beam_size, target_vocab_size]
probs = advanced_log_softmax(logits) # negative
# mask the finished beam except only one entrance (target_eos_id)
# [target_vocab_size, ]: [float_min, float_min, float_min, ..., 0]
# this forces the beam with EOS continue to generate EOS
finished_beam_bias = finished_beam_one_entry_bias(
on_entry=eos_id, num_entries=vocab_size)
# [batch_size * beam_size, target_vocab_size]: outer product
finished_beam_bias = expand_to_beam_size(
finished_beam_bias, beam_size * batch_size, axis=0)
finished_beam_bias *= array_ops.expand_dims(prev_finished_float, 1)
# compute new probs, with finished flags & mask
probs = probs * array_ops.expand_dims(1. - prev_finished_float, 1) + finished_beam_bias
# [batch_size * beam_size, target_vocab_size]
# compute new log_probs
log_probs = probs + array_ops.expand_dims(prev_log_probs, 1)
# new decoding length: [batch_size * beam_size]
lengths = lengths + 1 - math_ops.to_int32(finished)
# compute beam score
# length_penalty: [batch_size * beam_size,]
length_penalty = math_ops.pow(
((5.0 + math_ops.to_float(lengths)) / 6.0), -alpha)
scores = log_probs * array_ops.expand_dims(length_penalty, axis=1)
# flatten
# [batch_size, beam_size * target_vocab_size]
scores = array_ops.reshape(array_ops.reshape(scores, [-1]),
[batch_size, -1])
ret_log_probs = array_ops.reshape(array_ops.reshape(log_probs, [-1]),
[batch_size, -1])
scores_flat = control_flow_ops.cond(
ops.convert_to_tensor(time) > 0, lambda: scores, # time > 0: all
lambda: array_ops.slice(scores, [0, 0],
[-1, vocab_size])) # time = 0: first logits in each batch
# [batch_size, beam_size] will restore top live_k
sample_scores, sample_ids = nn_ops.top_k(scores_flat, k=beam_size)
ret_sample_ids = array_ops.reshape(sample_ids, [-1])
# flatten: [batch_size * beam_size,]
sample_ids = array_ops.reshape(sample_ids, [-1])
# because we do topk to scores with dim:[batch, beam * vocab]
# we need to cover the true word ids
word_ids = math_ops.mod(sample_ids, vocab_size)
# beam ids should be adjusted according to batch_size
# batch_pos, [batch_size, beam_size]: [[0, 0, ...], [1, 1,...], [batch_size,...] ]
batch_pos = compute_batch_indices(batch_size, beam_size)
# compute new beam_ids, [batch_size * beam_size, ]
beam_ids = math_ops.div(sample_ids, vocab_size) \
+ array_ops.reshape(batch_pos * beam_size, [-1])
# we need to recover log_probs from score
# flatten sample_scores: [batch_size * beam_size,]
sample_scores_flatten = array_ops.reshape(sample_scores, [-1])
# gather each length penalty
length_penalty = gather_states(length_penalty, beam_ids)
# recover log probabilities
next_log_probs = sample_scores_flatten / length_penalty
# gather states according to beam_ids
next_lengths = gather_states(lengths, beam_ids)
# [batch_size * beam_size * vocab_size, ]
log_probs_flat = array_ops.reshape(log_probs, [-1])
log_probs_index = array_ops.reshape(batch_pos, [-1]) * beam_size * vocab_size + sample_ids
next_log_probs = array_ops.gather(log_probs_flat, log_probs_index)
return word_ids, beam_ids, next_log_probs, next_lengths, ret_log_probs, ret_sample_ids, length_penalty
def _descending_sort(values, axis, return_argsort=False):
"""Sorts values in reverse using `top_k`.
Args:
values: Tensor of numeric values.
axis: Index of the axis which values should be sorted along.
return_argsort: If False, return the sorted values. If True, return the
indices that would sort the values.
Returns:
The sorted values.
"""
k = array_ops.shape(values)[axis]
rank = array_ops.rank(values)
static_rank = values.shape.ndims
# Fast path: sorting the last axis.
if axis == -1 or axis + 1 == values.get_shape().ndims:
top_k_input = values
transposition = None
else:
# Otherwise, transpose the array. Swap axes `axis` and `rank - 1`.
if axis < 0:
# Calculate the actual axis index if counting from the end. Use the static
# rank if available, or else make the axis back into a tensor.
axis += static_rank or rank
if static_rank is not None:
# Prefer to calculate the transposition array in NumPy and make it a
# constant.
transposition = constant_op.constant(
np.r_[
# Axes up to axis are unchanged.
np.arange(axis),
# Swap axis and rank - 1.
[static_rank - 1],
# Axes in [axis + 1, rank - 1) are unchanged.
np.arange(axis + 1, static_rank - 1),
# Swap axis and rank - 1.
[axis]],
name='transposition')
else:
# Generate the transposition array from the tensors.
transposition = array_ops.concat(
[
# Axes up to axis are unchanged.
math_ops.range(axis),
# Swap axis and rank - 1.
[rank - 1],
# Axes in [axis + 1, rank - 1) are unchanged.
math_ops.range(axis + 1, rank - 1),
# Swap axis and rank - 1.
[axis]
],
axis=0)
top_k_input = array_ops.transpose(values, transposition)
values, indices = nn_ops.top_k(top_k_input, k)
return_value = indices if return_argsort else values
if transposition is not None:
# transposition contains a single cycle of length 2 (swapping 2 elements),
# so it is an involution (it is its own inverse).
return_value = array_ops.transpose(return_value, transposition)
return return_value
def topk(v, k=k): return nn_ops.top_k(v, k=k, sorted=True)
def _beam_search_step(time, logits, beam_state, batch_size, beam_width,
end_token, length_penalty_weight):
"""Performs a single step of Beam Search Decoding.
Args:
time: Beam search time step, should start at 0. At time 0 we assume
that all beams are equal and consider only the first beam for
continuations.
logits: Logits at the current time step. A tensor of shape `[B, vocab_size]`
beam_state: Current state of the beam search. An instance of `BeamState`
batch_size: The batch size for this input.
beam_width: The size of the beams.
end_token: The int32 end token.
length_penalty_weight: Float weight to penalize length. Disabled with 0.0.
Returns:
A new beam state.
"""
static_batch_size = tensor_util.constant_value(batch_size)
# Calculate the current lengths of the predictions
prediction_lengths = beam_state.lengths
previously_finished = beam_state.finished
# Calculate the total log probs for the new hypotheses
# Final Shape: [batch_size, beam_width, vocab_size]
probs = nn_ops.log_softmax(logits)
probs = _mask_probs(probs, end_token, previously_finished)
total_probs = array_ops.expand_dims(beam_state.log_probs, 2) + probs
# Calculate the continuation lengths by adding to all continuing beams.
vocab_size = logits.get_shape().as_list()[-1]
lengths_to_add = array_ops.one_hot(
array_ops.tile(
array_ops.reshape(end_token, [1, 1]), [batch_size, beam_width]),
vocab_size, 0, 1)
add_mask = (1 - math_ops.to_int32(previously_finished))
lengths_to_add = array_ops.expand_dims(add_mask, 2) * lengths_to_add
new_prediction_lengths = array_ops.expand_dims(prediction_lengths,
2) + lengths_to_add
# Calculate the scores for each beam
scores = _get_scores(
log_probs=total_probs,
sequence_lengths=new_prediction_lengths,
length_penalty_weight=length_penalty_weight)
scores_flat = array_ops.reshape(scores, [batch_size, -1])
# During the first time step we only consider the initial beam
scores_flat = control_flow_ops.cond(
ops.convert_to_tensor(time) > 0, lambda: scores_flat,
lambda: scores[:, 0])
# Pick the next beams according to the specified successors function
next_beam_scores, word_indices = nn_ops.top_k(scores_flat, k=beam_width)
next_beam_scores.set_shape([static_batch_size, beam_width])
word_indices.set_shape([static_batch_size, beam_width])
# Pick out the probs, beam_ids, and states according to the chosen predictions
next_beam_probs = _tensor_gather_helper(
gather_indices=word_indices,
gather_from=total_probs,
range_input=batch_size,
range_size=beam_width * vocab_size,
final_shape=[static_batch_size, beam_width])
next_word_ids = math_ops.to_int32(word_indices % vocab_size)
next_beam_ids = math_ops.to_int32(word_indices / vocab_size)
# Append new ids to current predictions
previously_finished = _tensor_gather_helper(
gather_indices=next_beam_ids,
gather_from=previously_finished,
range_input=batch_size,
range_size=beam_width,
final_shape=[static_batch_size, beam_width])
next_finished = math_ops.logical_or(previously_finished,
math_ops.equal(next_word_ids, end_token))
# Calculate the length of the next predictions.
# 1. Finished beams remain unchanged
# 2. Beams that are now finished (EOS predicted) remain unchanged
# 3. Beams that are not yet finished have their length increased by 1
lengths_to_add = math_ops.to_int32(
math_ops.not_equal(next_word_ids, end_token))
lengths_to_add = (1 - math_ops.to_int32(next_finished)) * lengths_to_add
next_prediction_len = _tensor_gather_helper(
gather_indices=next_beam_ids,
gather_from=beam_state.lengths,
range_input=batch_size,
range_size=beam_width,
final_shape=[static_batch_size, beam_width])
next_prediction_len += lengths_to_add
next_state = BeamSearchDecoderState(
cell_state=beam_state.cell_state,
log_probs=next_beam_probs,
lengths=next_prediction_len,
finished=next_finished)
output = BeamSearchDecoderOutput(
scores=next_beam_scores,
predicted_ids=next_word_ids,
parent_ids=next_beam_ids)
return output, next_state
def _beam_search_step(time, logits, next_cell_state, beam_state, batch_size,
beam_width, end_token, length_penalty_weight):
"""Performs a single step of Beam Search Decoding.
Args:
time: Beam search time step, should start at 0. At time 0 we assume
that all beams are equal and consider only the first beam for
continuations.
logits: Logits at the current time step. A tensor of shape
`[batch_size, beam_width, vocab_size]`
next_cell_state: The next state from the cell, e.g. an instance of
AttentionWrapperState if the cell is attentional.
beam_state: Current state of the beam search.
An instance of `BeamSearchDecoderState`.
batch_size: The batch size for this input.
beam_width: Python int. The size of the beams.
end_token: The int32 end token.
length_penalty_weight: Float weight to penalize length. Disabled with 0.0.
Returns:
A new beam state.
"""
static_batch_size = tensor_util.constant_value(batch_size)
# Calculate the current lengths of the predictions
prediction_lengths = beam_state.lengths
previously_finished = beam_state.finished
# Calculate the total log probs for the new hypotheses
# Final Shape: [batch_size, beam_width, vocab_size]
step_log_probs = nn_ops.log_softmax(logits)
step_log_probs = _mask_probs(
step_log_probs, end_token, previously_finished)
total_probs = tf.expand_dims(
beam_state.log_probs, axis=2) + step_log_probs
# Calculate the continuation lengths by adding to all continuing beams.
vocab_size = logits.shape[-1].value
lengths_to_add = tf.one_hot(
indices=tf.tile(
tf.reshape(end_token, [1, 1]), [batch_size, beam_width]),
depth=vocab_size,
on_value=0,
off_value=1)
add_mask = (1 - tf.to_int32(previously_finished))
lengths_to_add = tf.expand_dims(add_mask, 2) * lengths_to_add
new_prediction_lengths = (
lengths_to_add + tf.expand_dims(prediction_lengths, 2))
# Calculate the scores for each beam
scores = _get_scores(
log_probs=total_probs,
sequence_lengths=new_prediction_lengths,
length_penalty_weight=length_penalty_weight)
time = ops.convert_to_tensor(time, name="time")
# During the first time step we only consider the initial beam
scores_shape = tf.shape(scores)
scores_flat = tf.cond(
time > 0,
lambda: tf.reshape(scores, [batch_size, -1]),
lambda: scores[:, 0])
num_available_beam = tf.cond(
time > 0, lambda: tf.reduce_prod(scores_shape[1:]),
lambda: tf.reduce_prod(scores_shape[2:]))
# Pick the next beams according to the specified successors function
next_beam_size = tf.minimum(
ops.convert_to_tensor(
beam_width, dtype=dtypes.int32, name="beam_width"),
num_available_beam)
next_beam_scores, word_indices = nn_ops.top_k(
scores_flat, k=next_beam_size)
next_beam_scores.set_shape([static_batch_size, beam_width])
word_indices.set_shape([static_batch_size, beam_width])
# Pick out the probs, beam_ids, and states according to the chosen
# predictions
next_beam_probs = _tensor_gather_helper(
gather_indices=word_indices,
gather_from=total_probs,
batch_size=batch_size,
range_size=beam_width * vocab_size,
gather_shape=[-1])
next_word_ids = tf.to_int32(word_indices % vocab_size)
next_beam_ids = tf.to_int32(word_indices / vocab_size)
# Append new ids to current predictions
previously_finished = _tensor_gather_helper(
gather_indices=next_beam_ids,
gather_from=previously_finished,
batch_size=batch_size,
range_size=beam_width,
gather_shape=[-1])
next_finished = tf.logical_or(previously_finished,
tf.equal(next_word_ids, end_token))
# Calculate the length of the next predictions.
# 1. Finished beams remain unchanged
# 2. Beams that are now finished (EOS predicted) remain unchanged
# 3. Beams that are not yet finished have their length increased by 1
lengths_to_add = tf.to_int32(
tf.not_equal(next_word_ids, end_token))
lengths_to_add = (1 - tf.to_int32(next_finished)) * lengths_to_add
next_prediction_len = _tensor_gather_helper(
gather_indices=next_beam_ids,
gather_from=beam_state.lengths,
batch_size=batch_size,
range_size=beam_width,
gather_shape=[-1])
next_prediction_len += lengths_to_add
# Pick out the cell_states according to the next_beam_ids. We use a
# different gather_shape here because the cell_state tensors, i.e.
# the tensors that would be gathered from, all have dimension
# greater than two and we need to preserve those dimensions.
next_cell_state = nest.map_structure(
lambda gather_from: _maybe_tensor_gather_helper(
gather_indices=next_beam_ids,
gather_from=gather_from,
batch_size=batch_size,
range_size=beam_width,
gather_shape=[batch_size * beam_width, -1]),
next_cell_state)
next_state = BeamSearchDecoderState(
cell_state=next_cell_state,
log_probs=next_beam_probs,
lengths=next_prediction_len,
finished=next_finished)
output = BeamSearchDecoderOutput(
scores=next_beam_scores,
predicted_ids=next_word_ids,
parent_ids=next_beam_ids)
return output, next_state
def testKTooLarge(self):
inputs = [[0.1, 0.2], [0.3, 0.4]]
with self.assertRaisesRegexp(ValueError,
r"must have last dimension >= k = 4"):
nn_ops.top_k(inputs, 4)
def tftop_k(_): x = array_ops.placeholder(dtypes.int32, shape=[5], name='x') output = nn_ops.top_k(x, 2, name='values') array_ops.identity(output[1], name='indices')
def _sort_tensor(tensor): """Use `top_k` to sort a `Tensor` along the last dimension.""" sorted_, _ = nn_ops.top_k(tensor, k=array_ops.shape(tensor)[-1]) return sorted_
def _beam_search_step(time, logits, next_cell_state, beam_state, batch_size,
beam_width, end_token, length_penalty_weight):
"""Performs a single step of Beam Search Decoding.
Args:
time: Beam search time step, should start at 0. At time 0 we assume
that all beams are equal and consider only the first beam for
continuations.
logits: Logits at the current time step. A tensor of shape
`[batch_size, beam_width, vocab_size]`
next_cell_state: The next state from the cell, e.g. an instance of
AttentionWrapperState if the cell is attentional.
beam_state: Current state of the beam search.
An instance of `BeamSearchDecoderState`.
batch_size: The batch size for this input.
beam_width: Python int. The size of the beams.
end_token: The int32 end token.
length_penalty_weight: Float weight to penalize length. Disabled with 0.0.
Returns:
A new beam state.
"""
static_batch_size = tensor_util.constant_value(batch_size)
# Calculate the current lengths of the predictions
prediction_lengths = beam_state.lengths
previously_finished = beam_state.finished
# Calculate the total log probs for the new hypotheses
# Final Shape: [batch_size, beam_width, vocab_size]
step_log_probs = nn_ops.log_softmax(logits)
step_log_probs = _mask_probs(step_log_probs, end_token, previously_finished)
total_probs = array_ops.expand_dims(beam_state.log_probs, 2) + step_log_probs
# Calculate the continuation lengths by adding to all continuing beams.
vocab_size = logits.shape[-1].value or array_ops.shape(logits)[-1]
lengths_to_add = array_ops.one_hot(
indices=array_ops.fill([batch_size, beam_width], end_token),
depth=vocab_size,
on_value=np.int64(0),
off_value=np.int64(1),
dtype=dtypes.int64)
add_mask = math_ops.to_int64(math_ops.logical_not(previously_finished))
lengths_to_add *= array_ops.expand_dims(add_mask, 2)
new_prediction_lengths = (
lengths_to_add + array_ops.expand_dims(prediction_lengths, 2))
# Calculate the scores for each beam
scores = _get_scores(
log_probs=total_probs,
sequence_lengths=new_prediction_lengths,
length_penalty_weight=length_penalty_weight)
time = ops.convert_to_tensor(time, name="time")
# During the first time step we only consider the initial beam
scores_flat = array_ops.reshape(scores, [batch_size, -1])
# Pick the next beams according to the specified successors function
next_beam_size = ops.convert_to_tensor(
beam_width, dtype=dtypes.int32, name="beam_width")
next_beam_scores, word_indices = nn_ops.top_k(scores_flat, k=next_beam_size)
next_beam_scores.set_shape([static_batch_size, beam_width])
word_indices.set_shape([static_batch_size, beam_width])
# Pick out the probs, beam_ids, and states according to the chosen predictions
next_beam_probs = _tensor_gather_helper(
gather_indices=word_indices,
gather_from=total_probs,
batch_size=batch_size,
range_size=beam_width * vocab_size,
gather_shape=[-1],
name="next_beam_probs")
# Note: just doing the following
# math_ops.to_int32(word_indices % vocab_size,
# name="next_beam_word_ids")
# would be a lot cleaner but for reasons unclear, that hides the results of
# the op which prevents capturing it with tfdbg debug ops.
raw_next_word_ids = math_ops.mod(
word_indices, vocab_size, name="next_beam_word_ids")
next_word_ids = math_ops.to_int32(raw_next_word_ids)
next_beam_ids = math_ops.to_int32(
word_indices / vocab_size, name="next_beam_parent_ids")
# Append new ids to current predictions
previously_finished = _tensor_gather_helper(
gather_indices=next_beam_ids,
gather_from=previously_finished,
batch_size=batch_size,
range_size=beam_width,
gather_shape=[-1])
next_finished = math_ops.logical_or(
previously_finished,
math_ops.equal(next_word_ids, end_token),
name="next_beam_finished")
# Calculate the length of the next predictions.
# 1. Finished beams remain unchanged.
# 2. Beams that are now finished (EOS predicted) have their length
# increased by 1.
# 3. Beams that are not yet finished have their length increased by 1.
lengths_to_add = math_ops.to_int64(math_ops.logical_not(previously_finished))
next_prediction_len = _tensor_gather_helper(
gather_indices=next_beam_ids,
gather_from=beam_state.lengths,
batch_size=batch_size,
range_size=beam_width,
gather_shape=[-1])
next_prediction_len += lengths_to_add
# Pick out the cell_states according to the next_beam_ids. We use a
# different gather_shape here because the cell_state tensors, i.e.
# the tensors that would be gathered from, all have dimension
# greater than two and we need to preserve those dimensions.
# pylint: disable=g-long-lambda
next_cell_state = nest.map_structure(
lambda gather_from: _maybe_tensor_gather_helper(
gather_indices=next_beam_ids,
gather_from=gather_from,
batch_size=batch_size,
range_size=beam_width,
gather_shape=[batch_size * beam_width, -1]),
next_cell_state)
# pylint: enable=g-long-lambda
next_state = BeamSearchDecoderState(
cell_state=next_cell_state,
log_probs=next_beam_probs,
lengths=next_prediction_len,
finished=next_finished)
output = BeamSearchDecoderOutput(
scores=next_beam_scores,
predicted_ids=next_word_ids,
parent_ids=next_beam_ids)
return output, next_state
