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def import_usr_dir(usr_dir):
"""Import module at usr_dir, if provided."""
if not usr_dir:
return
if usr_dir == INTERNAL_USR_DIR_PACKAGE:
# The package has been installed with pip under this name for Cloud ML
# Engine so just import it.
importlib.import_module(INTERNAL_USR_DIR_PACKAGE)
return
... |
def basic_params1():
"""A set of basic hyperparameters."""
return hparam.HParams(
# If the problem consists of variable-length sequences
# (see problem.batch_size_means_tokens()), then this is the number
# of tokens per batch per GPU or per TPU core. Otherwise, this is
# the number of examp... |
def basic_range1(ranged_hparams):
"""A basic range of hyperparameters."""
rhp = ranged_hparams
rhp.set_discrete("batch_size", [1024, 2048, 4096])
rhp.set_discrete("num_hidden_layers", [1, 2, 3, 4, 5, 6])
rhp.set_discrete("hidden_size", [32, 64, 128, 256, 512], scale=rhp.LOG_SCALE)
rhp.set_discrete("kernel_h... |
def _check_reset_and_type_change(self, name, orig_ctr):
"""Check if name is in orig_ctr or in one of the other type containers."""
# Resetting a hyperparameter
if name in orig_ctr:
tf.logging.warning("Overwriting hparam %s", name)
ctr_names = [
(self._categorical_params, "categorical"),
... |
def to_parameter_specs(self, name_prefix=""):
"""To list of dicts suitable for Cloud ML Engine hyperparameter tuning."""
specs = []
for name, categories, _ in self._categorical_params.values():
spec = {
"parameterName": name_prefix + name,
"type": "CATEGORICAL",
"categori... |
def register_game(game_name, game_mode="NoFrameskip-v4"):
"""Create and register problems for the game.
Args:
game_name: str, one of the games in ATARI_GAMES, e.g. "bank_heist".
game_mode: the frame skip and sticky keys config.
Raises:
ValueError: if game_name or game_mode are wrong.
"""
if game... |
def _decode_png(self, encoded_observation):
"""Decodes a single observation from PNG."""
return self._session.obj.run(
self._decoded_image_t.obj,
feed_dict={self._encoded_image_p.obj: encoded_observation}
) |
def _encode_observations(self, observations):
"""Encodes observations as PNG."""
return [
Observation(
self._session.obj.run(
self._encoded_image_t.obj,
feed_dict={self._decoded_image_p.obj: observation}
),
self._decode_png
)
... |
def step(self, actions):
"""Makes a step in all environments.
Does any preprocessing and records frames.
Args:
actions: Batch of actions.
Returns:
(obs, rewards, dones) - batches of observations, rewards and done flags
respectively.
Raises:
ValueError: when the data for c... |
def reset(self, indices=None):
"""Resets environments at given indices.
Does any preprocessing and adds rollouts to history.
Args:
indices: Indices of environments to reset.
Returns:
Batch of initial observations of reset environments.
Raises:
ValueError: when there's no curren... |
def extra_reading_spec(self):
"""Additional data fields to store on disk and their decoders."""
field_names = ("frame_number", "action", "reward", "done")
data_fields = {
name: tf.FixedLenFeature([1], tf.int64) for name in field_names
}
decoders = {
name: tf.contrib.slim.tfexample_de... |
def _split_current_epoch(self):
"""Splits frames in the current epoch according to self.dataset_splits.
Rollouts can be broken on shard boundary. This is desirable when we have
few long rollouts and we want to make sure we have data in the dev set.
"""
num_frames = self._calc_num_frames(self._curre... |
def splits_and_paths(self, data_dir):
"""List of pairs (split, paths) for the current epoch."""
filepath_fns = {
problem.DatasetSplit.TRAIN: self.training_filepaths,
problem.DatasetSplit.EVAL: self.dev_filepaths,
problem.DatasetSplit.TEST: self.test_filepaths,
}
def append_epoch... |
def generate_data(self, data_dir, tmp_dir=None, task_id=-1):
"""Saves the current epoch rollouts to disk, split into train/dev sets."""
if not self._rollouts_by_epoch_and_split[self.current_epoch]:
# Data not loaded from disk.
self._split_current_epoch()
rollouts_by_split = self._rollouts_by_ep... |
def set_initial_state(self, initial_state, initial_frames):
"""Sets the state that will be used on next reset."""
self._initial_state = initial_state
self._initial_frames = initial_frames[:, -1, ...]
self._should_preprocess_on_reset = False |
def image_to_tf_summary_value(image, tag):
"""Converts a NumPy image to a tf.Summary.Value object.
Args:
image: 3-D NumPy array.
tag: name for tf.Summary.Value for display in tensorboard.
Returns:
image_summary: A tf.Summary.Value object.
"""
curr_image = np.asarray(image, dtype=np.uint8)
heigh... |
def convert_predictions_to_image_summaries(hook_args):
"""Optionally converts images from hooks_args to image summaries.
Args:
hook_args: DecodeHookArgs namedtuple
Returns:
summaries: list of tf.Summary values if hook_args.decode_hpara
"""
decode_hparams = hook_args.decode_hparams
if not decode_hpa... |
def resize_by_area(img, size):
"""image resize function used by quite a few image problems."""
return tf.to_int64(
tf.image.resize_images(img, [size, size], tf.image.ResizeMethod.AREA)) |
def make_multiscale(image, resolutions,
resize_method=tf.image.ResizeMethod.BICUBIC,
num_channels=3):
"""Returns list of scaled images, one for each resolution.
Args:
image: Tensor of shape [height, height, num_channels].
resolutions: List of heights that image's hei... |
def make_multiscale_dilated(image, resolutions, num_channels=3):
"""Returns list of scaled images, one for each resolution.
Resizes by skipping every nth pixel.
Args:
image: Tensor of shape [height, height, num_channels].
resolutions: List of heights that image's height is resized to. The function
... |
def encode_images_as_png(images):
"""Yield images encoded as pngs."""
if tf.executing_eagerly():
for image in images:
yield tf.image.encode_png(image).numpy()
else:
(height, width, channels) = images[0].shape
with tf.Graph().as_default():
image_t = tf.placeholder(dtype=tf.uint8, shape=(hei... |
def image_generator(images, labels):
"""Generator for images that takes image and labels lists and creates pngs.
Args:
images: list of images given as [width x height x channels] numpy arrays.
labels: list of ints, same length as images.
Yields:
A dictionary representing the images with the followin... |
def image_augmentation(images, do_colors=False, crop_size=None):
"""Image augmentation: cropping, flipping, and color transforms."""
if crop_size is None:
crop_size = [299, 299]
images = tf.random_crop(images, crop_size + [3])
images = tf.image.random_flip_left_right(images)
if do_colors: # More augmenta... |
def cifar_image_augmentation(images):
"""Image augmentation suitable for CIFAR-10/100.
As described in https://arxiv.org/pdf/1608.06993v3.pdf (page 5).
Args:
images: a Tensor.
Returns:
Tensor of the same shape as images.
"""
images = tf.image.resize_image_with_crop_or_pad(images, 40, 40)
images ... |
def random_shift(image, wsr=0.1, hsr=0.1):
"""Apply random horizontal and vertical shift to images.
This is the default data-augmentation strategy used on CIFAR in Glow.
Args:
image: a 3-D Tensor
wsr: Width shift range, as a float fraction of the width.
hsr: Height shift range, as a float fraction o... |
def get_standardized_layers(hparams, dp=None):
"""Get the common attention and feed-forward layers.
The returned layer functions will have the following signature:
y, extra_loss = fct(x)
extra_loss is set to 0.0 if the layer doesn't have extra loss.
If dp is provided, the layers will be distributed withi... |
def add_standard_attention_hparams(hparams):
"""Adds the hparams used by get_standardized_layers."""
# All hyperparameters ending in "dropout" are automatically set to 0.0
# when not in training mode.
# hparams used and which should have been defined outside (in
# common_hparams):
# Global flags
# hparam... |
def encoder_decoder_attention_loss(expected_attention_logits,
actual_attentions,
loss_type="kl_divergence",
loss_multiplier=1.0):
"""Computes encdec attention loss between expected and actual attentions.
Args:
... |
def get_timing_signal_1d(length,
channels,
min_timescale=1.0,
max_timescale=1.0e4,
start_index=0):
"""Gets a bunch of sinusoids of different frequencies.
Each channel of the input Tensor is incremented by a sinusoid... |
def add_timing_signal_1d(x,
min_timescale=1.0,
max_timescale=1.0e4,
start_index=0):
"""Adds a bunch of sinusoids of different frequencies to a Tensor.
Each channel of the input Tensor is incremented by a sinusoid of a different
frequency ... |
def get_layer_timing_signal_learned_1d(channels, layer, num_layers):
"""get n-dimensional embedding as the layer (vertical) timing signal.
Adds embeddings to represent the position of the layer in the tower.
Args:
channels: dimension of the timing signal
layer: layer num
num_layers: total number of ... |
def add_layer_timing_signal_learned_1d(x, layer, num_layers):
"""Add n-dimensional embedding as the layer (vertical) timing signal.
Adds embeddings to represent the position of the layer in the tower.
Args:
x: a tensor with shape [batch, length, depth]
layer: layer num
num_layers: total number of la... |
def get_layer_timing_signal_sinusoid_1d(channels, layer, num_layers):
"""Add sinusoids of different frequencies as layer (vertical) timing signal.
Args:
channels: dimension of the timing signal
layer: layer num
num_layers: total number of layers
Returns:
a Tensor of timing signals [1, 1, channel... |
def add_layer_timing_signal_sinusoid_1d(x, layer, num_layers):
"""Add sinusoids of different frequencies as layer (vertical) timing signal.
Args:
x: a Tensor with shape [batch, length, channels]
layer: layer num
num_layers: total number of layers
Returns:
a Tensor the same shape as x.
"""
c... |
def add_timing_signal_1d_given_position(x,
position,
min_timescale=1.0,
max_timescale=1.0e4):
"""Adds sinusoids of diff frequencies to a Tensor, with timing position given.
Args:
x: a Tensor ... |
def add_timing_signal_nd(x, min_timescale=1.0, max_timescale=1.0e4):
"""Adds a bunch of sinusoids of different frequencies to a Tensor.
Each channel of the input Tensor is incremented by a sinusoid of a different
frequency and phase in one of the positional dimensions.
This allows attention to learn to use ab... |
def add_positional_embedding(x, max_length, name=None, positions=None):
"""Adds positional embedding.
Args:
x: Tensor with shape [batch, length, depth].
max_length: int representing static maximum size of any dimension.
name: str representing name of the embedding tf.Variable.
positions: Tensor wit... |
def add_positional_embedding_nd(x, max_length, name=None):
"""Adds n-dimensional positional embedding.
The embeddings add to all positional dimensions of the tensor.
Args:
x: Tensor with shape [batch, p1 ... pn, depth]. It has n positional
dimensions, i.e., 1 for text, 2 for images, 3 for video, etc.
... |
def make_edge_vectors(adjacency_matrix, num_edge_types, depth, name=None):
"""Gets edge vectors for the edge types in the adjacency matrix.
Args:
adjacency_matrix: A [batch, num_nodes, num_nodes] tensor of ints.
num_edge_types: Number of different edge types
depth: Number of channels
name: a string... |
def padding_to_length(padding):
"""Calculate the length of mask based on padding.
Args:
padding: a Tensor with shape [..., length].
Returns:
a Tensor with shape [...].
"""
non_padding = 1.0 - padding
return tf.to_int32(tf.reduce_sum(non_padding, axis=-1)) |
def attention_bias_local(length, max_backward, max_forward):
"""Create an bias tensor to be added to attention logits.
A position may attend to positions at most max_distance from it,
forward and backwards.
This does not actually save any computation.
Args:
length: int
max_backward: int, maximum di... |
def attention_bias_same_segment(query_segment_id, memory_segment_id):
"""Create an bias tensor to be added to attention logits.
Positions with the same segment_ids can see each other.
Args:
query_segment_id: a float `Tensor` with shape [batch, query_length].
memory_segment_id: a float `Tensor` with shap... |
def attention_bias_ignore_padding(memory_padding):
"""Create an bias tensor to be added to attention logits.
Args:
memory_padding: a float `Tensor` with shape [batch, memory_length].
Returns:
a `Tensor` with shape [batch, 1, 1, memory_length].
"""
ret = memory_padding * large_compatible_negative(mem... |
def attention_bias_to_padding(attention_bias, cast_fn=tf.to_float):
"""Inverse of attention_bias_ignore_padding().
Args:
attention_bias: a `Tensor` with shape [batch, 1, 1, memory_length], as
returned by attention_bias_ignore_padding().
cast_fn: function used to cast to output type.
Returns:
a... |
def attention_bias_prepend_inputs_full_attention(padding):
"""Create a bias tensor for prepend_mode="prepend_inputs_full_attention".
See prepend_inputs in common_hparams.py.
Produces a bias tensor to be used in self-attention.
This bias tensor allows for full connectivity in the "inputs" part of
the sequen... |
def attention_bias_proximal(length):
"""Bias for self-attention to encourage attention to close positions.
Args:
length: an integer scalar.
Returns:
a Tensor with shape [1, 1, length, length]
"""
r = tf.to_float(tf.range(length))
diff = tf.expand_dims(r, 0) - tf.expand_dims(r, 1)
return tf.expan... |
def attention_bias_batch(batch_coordinates_q,
batch_coordinates_k=None,
condition_fn=None):
"""Generate a mask to prevent the batch to attend to each others.
Args:
batch_coordinates_q: Int-like Tensor of shape [length_q, 1] containing the
coordinates of t... |
def split_last_dimension(x, n):
"""Reshape x so that the last dimension becomes two dimensions.
The first of these two dimensions is n.
Args:
x: a Tensor with shape [..., m]
n: an integer.
Returns:
a Tensor with shape [..., n, m/n]
"""
x_shape = common_layers.shape_list(x)
m = x_shape[-1]
... |
def combine_last_two_dimensions(x):
"""Reshape x so that the last two dimension become one.
Args:
x: a Tensor with shape [..., a, b]
Returns:
a Tensor with shape [..., ab]
"""
x_shape = common_layers.shape_list(x)
a, b = x_shape[-2:]
return tf.reshape(x, x_shape[:-2] + [a * b]) |
def combine_first_two_dimensions(x):
"""Reshape x so that the first two dimension become one.
Args:
x: a Tensor with shape [a, b, ...]
Returns:
a Tensor with shape [ab, ...]
"""
ret = tf.reshape(x, tf.concat([[-1], common_layers.shape_list(x)[2:]], 0))
old_shape = x.get_shape().dims
a, b = old_s... |
def attention_image_summary(attn, image_shapes=None):
"""Compute color image summary.
Args:
attn: a Tensor with shape [batch, num_heads, query_length, memory_length]
image_shapes: optional tuple of integer scalars.
If the query positions and memory positions represent the
pixels of flattened im... |
def grouped_attention_multihead(query_antecedent,
memory_antecedent,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
... |
def harden_attention_weights(weights, hard_attention_k):
"""Make attention weights non-0 only on the top-hard_attention_k ones."""
# Subtract the top-kth weight and zero-out all lower ones.
# Note that currently in case of numerical ties it will retain more
# than k elements. In the future, we may want to avoid... |
def dot_product_attention(q,
k,
v,
bias,
dropout_rate=0.0,
image_shapes=None,
name=None,
make_image_summary=True,
... |
def _generate_relative_positions_matrix(length_q, length_k,
max_relative_position,
cache=False):
"""Generates matrix of relative positions between inputs."""
if not cache:
if length_q == length_k:
range_vec_q = range_vec_k = t... |
def _generate_relative_positions_embeddings(length_q, length_k, depth,
max_relative_position, name,
cache=False):
"""Generates tensor of size [1 if cache else length_q, length_k, depth]."""
with tf.variable_scope(name):
rela... |
def _relative_attention_inner(x, y, z, transpose):
"""Relative position-aware dot-product attention inner calculation.
This batches matrix multiply calculations to avoid unnecessary broadcasting.
Args:
x: Tensor with shape [batch_size, heads, length or 1, length or depth].
y: Tensor with shape [batch_si... |
def dot_product_attention_relative(q,
k,
v,
bias,
max_relative_position,
dropout_rate=0.0,
image_shapes=None,
... |
def _relative_position_to_absolute_position_masked(x):
"""Helper to dot_product_self_attention_relative_v2.
Rearrange an attention logits or weights Tensor.
The dimensions of the input represent:
[batch, heads, query_position, memory_position - query_position + length - 1]
The dimensions of the output repr... |
def dot_product_self_attention_relative_v2(q,
k,
v,
bias,
max_relative_position=None,
dropout_rate=0.0,
... |
def _absolute_position_to_relative_position_unmasked(x):
"""Helper function for dot_product_unmasked_self_attention_relative_v2.
Rearrange an attention logits or weights Tensor.
The dimensions of the input represent:
[batch, heads, query_position, memory_position]
The dimensions of the output represent:
... |
def get_relative_embeddings_left_right(max_relative_position, length, depth,
num_heads,
heads_share_relative_embedding,
name):
"""Instantiate or retrieve relative embeddings, sliced according to length... |
def dot_product_unmasked_self_attention_relative_v2(
q, k, v, bias, max_relative_position=None, dropout_rate=0.0,
image_shapes=None, name=None, make_image_summary=True,
dropout_broadcast_dims=None, heads_share_relative_embedding=False,
add_relative_to_values=False):
"""Calculate relative position-awar... |
def _matmul_with_relative_keys_2d(x, y, heads_share_relative_embedding):
"""Helper function for dot_product_unmasked_self_attention_relative_2d."""
if heads_share_relative_embedding:
ret = tf.einsum("bhxyd,md->bhxym", x, y)
else:
ret = tf.einsum("bhxyd,hmd->bhxym", x, y)
return ret |
def dot_product_unmasked_self_attention_relative_2d(
q, k, v, bias, max_relative_position=None, dropout_rate=0.0,
image_shapes=None, name=None, make_image_summary=True,
dropout_broadcast_dims=None, heads_share_relative_embedding=False,
add_relative_to_values=False):
"""Calculate relative position unma... |
def _split_along_width(x_left_right_blocks):
"""Helper function for local 2d attention.
Takes a tensor of [batch, heads, num_h_blocks, num_w_blocks,
height, width, depth] and returns two tensors which contain every alternate
position along the width
Args:
x_left_right_blocks: A [batch, num_h_blocks, nu... |
def _get_left_right_blocks(x):
"""Helper function. Assumes that memory_flange is half of query sizes.
This function splits the tensor of width 'n' into two halves, where the
first half gets the width indices 0, 2, 4.. and the second half gets the
width indices 3, 5, ... We also fuse two blocks along the h dime... |
def _extract_blocks(x, block_h, block_w):
"""Helper function for local 2d attention.
Args:
x: a [batch, height, width, depth] tensor
block_h: An integer. block height
block_w: An inteter. block width
returns:
a [batch, num_heads, height/block_h, width/block_w, depth] tensor
"""
(_, height, w... |
def get_2d_local_memory(x, query_shape, memory_flange):
"""Stitches together the local 2d memory blocks.
Args:
x: a [batch, height, width, depth tensor]
query_shape: 2-d integer list of query shape
memory_flange: 2-d integer list of memory flanges
Returns:
x: A [batch, num_h_blocks, num_w_blocks... |
def get_2d_local_memory_v2(x, query_shape, memory_flange):
"""Gathering memory blocks around query blocks. flange is half of query .
Only works if memory flanges are half of query sizes.
Args:
x: a [batch, height, width, depth tensor]
query_shape: 2-d integer list of query shape
memory_flange: 2-d... |
def dot_product_unmasked_attention_local_2d_tpu(
q, k, v, bias, max_relative_position=None, query_shape=(8, 8),
dropout_rate=0.0, image_shapes=None, name=None, make_image_summary=False,
dropout_broadcast_dims=None):
"""Calculate unmasked dot-product local self-attention 2d on tpu.
Args:
q: a Tensor... |
def dot_product_unmasked_attention_local_2d_tpu_simple(
x, bias, total_key_depth, total_value_depth, num_heads,
query_shape=(8, 8),
dropout_rate=0.0, image_shapes=None, make_image_summary=False,
dropout_broadcast_dims=None):
"""Calculate simple unmasked dot-product local self-attention 2d on tpu.
... |
def masked_within_block_local_attention_1d(q, k, v, block_length=64, name=None):
"""Attention to the source and a neighborhood to the left within a block.
The sequence is divided into blocks of length block_length. Attention for a
given query position can only see memory positions less than or equal to the
que... |
def _relative_position_to_absolute_position_unmasked(x):
"""Converts tensor from relative to aboslute indexing for local attention.
Args:
x: a Tensor of shape [batch (or batch*num_blocks), heads,
length, 2 * length - 1]
Returns:
A Tensor of shape [batch (or batch*num_blocks), h... |
def masked_local_attention_1d(q,
k,
v,
block_length=128,
make_image_summary=False,
dropout_rate=0.,
name=None):
"""Attention to the source... |
def _make_local_block(x, depth, batch, heads, num_blocks, block_length):
"""Helper function to create a local version of the keys or values for 1d."""
prev_block = tf.slice(x, [0, 0, 0, 0, 0],
[-1, -1, num_blocks - 1, -1, -1])
cur_block = tf.slice(x, [0, 0, 1, 0, 0], [-1, -1, -1, -1, -1])
... |
def masked_relative_local_attention_1d(q,
k,
v,
block_length=128,
make_image_summary=False,
dropout_rate=0.,
... |
def local_attention_1d(q, k, v, block_length=128, filter_width=100, name=None):
"""Strided block local self-attention.
The sequence is divided into blocks of length block_length. Attention for a
given query position can see all memory positions in the corresponding block
and filter_width many positions to the ... |
def reshape_by_blocks(x, x_shape, memory_block_size):
"""Reshapes input by splitting its length over blocks of memory_block_size.
Args:
x: a Tensor with shape [batch, heads, length, depth]
x_shape: tf.TensorShape of x.
memory_block_size: Integer which divides length.
Returns:
Tensor with shape
... |
def dilated_self_attention_1d(q,
k,
v,
query_block_size=128,
memory_block_size=128,
gap_size=2,
num_memory_blocks=2,
... |
def gather_dilated_memory_blocks(x,
num_memory_blocks,
gap_size,
query_block_size,
memory_block_size,
gather_indices,
dire... |
def masked_dilated_self_attention_1d(q,
k,
v,
query_block_size=64,
memory_block_size=64,
gap_size=2,
... |
def local_attention_2d(q,
k,
v,
query_shape=(8, 16),
memory_flange=(8, 16),
name=None):
"""Strided block local self-attention.
The 2-D sequence is divided into 2-D blocks of shape query_shape. Attenti... |
def pad_to_multiple_2d(x, block_shape):
"""Making sure x is a multiple of shape.
Args:
x: a [batch, heads, h, w, depth] or [batch, h, w, depth] tensor
block_shape: a 2-d list of integer shapes
Returns:
padded_x: a [batch, heads, h, w, depth] or [batch, h, w, depth] tensor
"""
old_shape = x.get_s... |
def reshape_range(tensor, i, j, shape):
"""Reshapes a tensor between dimensions i and j."""
t_shape = common_layers.shape_list(tensor)
target_shape = t_shape[:i] + shape + t_shape[j:]
return tf.reshape(tensor, target_shape) |
def gather_blocks_2d(x, indices):
"""Gathers flattened blocks from x."""
x_shape = common_layers.shape_list(x)
x = reshape_range(x, 2, 4, [tf.reduce_prod(x_shape[2:4])])
# [length, batch, heads, dim]
x_t = tf.transpose(x, [2, 0, 1, 3])
x_new = tf.gather(x_t, indices)
# returns [batch, heads, num_blocks, b... |
def scatter_blocks_2d(x, indices, shape):
"""scatters blocks from x into shape with indices."""
x_shape = common_layers.shape_list(x)
# [length, batch, heads, dim]
x_t = tf.transpose(
tf.reshape(x, [x_shape[0], x_shape[1], -1, x_shape[-1]]), [2, 0, 1, 3])
x_t_shape = common_layers.shape_list(x_t)
indi... |
def gather_indices_2d(x, block_shape, block_stride):
"""Getting gather indices."""
# making an identity matrix kernel
kernel = tf.eye(block_shape[0] * block_shape[1])
kernel = reshape_range(kernel, 0, 1, [block_shape[0], block_shape[1], 1])
# making indices [1, h, w, 1] to appy convs
x_shape = common_layers... |
def make_2d_block_raster_mask(query_shape, memory_flange):
"""Creates a mask for 2d block raster scan.
The query mask can look to the left, top left, top, and top right, but
not to the right. Inside the query, we have the standard raster scan
masking.
Args:
query_shape: A tuple of ints (query_height, que... |
def get_memory_region(x, query_block_shape, memory_flange, q_indices):
"""Get the memory regions that surround a 2d query.
The memory regions will be the left and top right.
Args:
x: A tensor with shape [batch, heads, height, width, depth]
query_block_shape: a 2-d tuple of integers
memory_flange: ... |
def get_shifted_center_blocks(x, indices):
"""Get right shifted blocks for masked local attention 2d.
Args:
x: A tensor with shape [batch, heads, height, width, depth]
indices: The indices to gather blocks
Returns:
x_shifted: a tensor of extracted blocks, each block right shifted along
length.... |
def right_shift_blockwise(x, query_shape, name=None):
"""Right shifts once in every block.
Args:
x: a tensor of shape [batch, height, width, depth]
query_shape: A 2d tuple of ints
name: a string
Returns:
output: a tensor of the same shape as x
"""
with tf.variable_scope(
name, default_... |
def masked_local_attention_2d(q,
k,
v,
query_shape=(8, 16),
memory_flange=(8, 16),
name=None):
"""Strided block local self-attention.
Each position in a query block ... |
def compute_attention_component(antecedent,
total_depth,
filter_width=1,
padding="VALID",
name="c",
vars_3d_num_heads=0,
layer_c... |
def compute_qkv(query_antecedent,
memory_antecedent,
total_key_depth,
total_value_depth,
q_filter_width=1,
kv_filter_width=1,
q_padding="VALID",
kv_padding="VALID",
vars_3d_num_heads=0,
... |
def multihead_attention(query_antecedent,
memory_antecedent,
bias,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
dropout_rate,
... |
def multihead_attention_2d(query_antecedent,
memory_antecedent,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
attention_type="local_attention_2... |
def ffn_self_attention_layer(x,
filter_depth,
output_depth,
num_parts,
dropout_rate,
share_kv=False,
name=None):
"""Self-attention feedforward l... |
def parameter_attention(x,
total_key_depth,
total_value_depth,
output_depth,
memory_rows,
num_heads,
dropout_rate,
name=None):
"""Attention over param... |
def coordinate_tensor(shape, axis):
"""Return a tensor with given shape containing coordinate along given axis.
Args:
shape: a Tensor representing the shape of the output Tensor
axis: an integer
Returns:
A tensor with shape shape and type tf.int32, where each elements its
coordinate along the gi... |
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