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tensorflow/tensor2tensor
|
tensor2tensor/utils/usr_dir.py
|
import_usr_dir
|
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
dir_path = os.path.abspath(os.path.expanduser(usr_dir).rstrip("/"))
containing_dir, module_name = os.path.split(dir_path)
tf.logging.info("Importing user module %s from path %s", module_name,
containing_dir)
sys.path.insert(0, containing_dir)
importlib.import_module(module_name)
sys.path.pop(0)
|
python
|
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
dir_path = os.path.abspath(os.path.expanduser(usr_dir).rstrip("/"))
containing_dir, module_name = os.path.split(dir_path)
tf.logging.info("Importing user module %s from path %s", module_name,
containing_dir)
sys.path.insert(0, containing_dir)
importlib.import_module(module_name)
sys.path.pop(0)
|
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Import module at usr_dir, if provided.
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/utils/usr_dir.py#L30-L46
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_hparams.py
|
basic_params1
|
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 examples per GPU or per TPU core.
batch_size=4096,
batch_shuffle_size=512,
# If True, then if the features are of variable length, the batch_size is
# used as the actual batch size (and not tokens per batch).
use_fixed_batch_size=False,
num_hidden_layers=4,
kernel_height=3,
kernel_width=1,
hidden_size=64,
compress_steps=0,
# All hyperparameters ending in "dropout" are automatically set to 0.0
# when not in training mode.
dropout=0.2,
clip_grad_norm=2.0,
grad_noise_scale=0.0,
summarize_grads=False,
# Flag for whether mlperf mode is on
mlperf_mode=False,
# Whether to log the name and size of every variable
summarize_vars=False,
initializer="orthogonal",
initializer_gain=1.5,
label_smoothing=0.1,
optimizer="adam",
optimizer_adam_epsilon=1e-6,
optimizer_adam_beta1=0.85,
optimizer_adam_beta2=0.997,
optimizer_momentum_momentum=0.9,
optimizer_momentum_nesterov=False,
optimizer_adafactor_beta1=0.0,
optimizer_adafactor_beta2=0.999,
optimizer_adafactor_factored=True,
optimizer_adafactor_decay_type="pow",
optimizer_adafactor_memory_exponent=0.8,
optimizer_adafactor_clipping_threshold=1.0,
optimizer_adafactor_multiply_by_parameter_scale=True,
# Number of accumulating steps for multi step optimizers.
optimizer_multistep_accumulate_steps=0,
# Loss scaling used.
# Generally only necessary with mixed precision training.
# Mixed precision training only supports exponential scaling currently
# To disable the scaler, see to 0/False
mixed_precision_optimizer_loss_scaler="exponential",
# Determines the initial loss scaling value for mixed precision
mixed_precision_optimizer_init_loss_scale=2**15,
# Whether to zero gradients that were not computed, so that the
# appropriate slots are created. Useful for sharing checkpoints between
# models with different sets of heads.
optimizer_zero_grads=False,
weight_decay=1e-6,
weight_noise=0.0,
# Defines the learning rate as a product of named functions.
# Available functions are listed in learning_rate._LEARNING_RATE_FUNCTIONS
# e.g. "constant*linear_warmup*rsqrt_decay*rsqrt_hidden_size"
learning_rate_schedule="legacy",
learning_rate_constant=1.0,
# If learning_rate_schedule=="legacy",
# then we specify decay scheme here. Warmup is always exponential,
# except with "noam" learning rate decay scheme.
# see optimize.legacy_learning_rate_schedule()
# TODO(noam): migrate everyone away from this.
learning_rate_decay_scheme="none",
# decay_steps and decay_staircase for learning_rate_decay_scheme=="exp"
learning_rate_decay_steps=5000,
learning_rate_decay_staircase=False,
learning_rate_minimum=None,
learning_rate_decay_rate=1.0,
learning_rate_warmup_steps=100,
learning_rate_cosine_cycle_steps=250000,
learning_rate=0.1,
sampling_method="argmax", # "argmax" or "random"
sampling_temp=1.0, # temperature for sampling
sampling_keep_top_k=-1, # If >0, ignore all but the top k logits
# expand the logits a piece at a time - saves memory.
factored_logits=False,
multiply_embedding_mode="sqrt_depth",
# Parameters related to mixtures of experts.
moe_hidden_sizes="2048", # hidden layer sizes (comma-separated)
moe_num_experts=64, # number of experts per layer
moe_k=2, # how many experts to use for each batch element
moe_loss_coef=1e-2,
# Sequences of operations to perform on layer input and layer output.
# Used by common_layers.layer_preprocess, common_layers.layer_postprocess
# Each character represents an operation:
# none: no preprocessing
# d: apply dropout
# n: apply normalization (see norm_type and norm_epsilon)
# a: add layer input (residual connection - only during postprocess)
# The special string "none" is used instead of the empty string
# to indicate no pre/postprocessing, since the empty string causes
# trouble for hyperparameter tuning.
# TODO(noam): The current settings ("", "dan") are the published version
# of the transformer. ("n", "da") seems better for harder-to-learn
# models, so it should probably be the default.
layer_preprocess_sequence="none",
layer_postprocess_sequence="dan",
# dropout rate to use during layer_preprocess and layer_postprocess
layer_prepostprocess_dropout=0.1,
# broadcast dimensions for layer_prepostprocess_dropout
# a comma-separated list of integers.
# see common_layers.dropout_with_broadcast_dims()
# Change this to "1" to save memory.
layer_prepostprocess_dropout_broadcast_dims="",
# dropout some symbols (set them to 0) before embedding.
symbol_dropout=0.0,
# What type of normalization to use
norm_type="layer", # "batch", layer", "noam", "none".
# epsilon parameter to normalization function
norm_epsilon=1e-6,
# pad vocabularies so that this value divides the vocabulary size.
vocab_divisor=1,
# During training, we drop sequences whose inputs and targets are shorter
# than min_length
min_length=0,
# During training, we drop sequences whose inputs or targets are longer
# than max_length.
# If max_length==0, we use hparams.batch_size instead.
max_length=0,
# Pack examples on the fly.
pack_dataset=False,
# Use custom ops not included in standard tensorflow.
use_custom_ops=True,
# Split targets on the first axis into chunks of this length.
split_targets_chunk_length=0,
split_targets_max_chunks=100,
split_targets_strided_training=False,
# Maximum length in the smallest length bucket. Setting this
# flag too high will result in wasteful padding of short
# sequences. Due to some (hopefully) temporary hacks in the
# data reading and batching code, setting this flag too low
# results in a very long batch-shuffling queue.
# TODO(noam): change this once the Datasets API changes.
min_length_bucket=8,
# This flag controls the number of length buckets in the data
# reader. The buckets have maximum lengths from
# min_bucket_length to (max_length or batch_size), increasing
# (approximately) by factors of length_bucket_step.
length_bucket_step=1.1,
# If set to True, drop sequences longer than max_length during eval.
# This affects the validity of the evaluation metrics.
eval_drop_long_sequences=False,
# If True, run the model autoregressively instead of teacher-forcing
# during eval
eval_run_autoregressive=False,
# (For features with symbol modality) If True, share all of the
# input embeddings, target embeddings, and softmax weights.
shared_embedding_and_softmax_weights=False,
# (For features with symbol modality) If True, share the input embeddings
# and target embeddings.
shared_embedding=False,
# (For features with symbol modality) Number to shard embeddings by.
symbol_modality_num_shards=1,
# Feature transformations are optional dictionaries comprising key-value
# pairs of a feature name (str) and its transformation (function). If not
# specified, T2TModel applies a default transformation according to the
# feature's modality. Bottom is applicable to all features; loss, top, and
# weights_fn are only applicable to target features.
# TODO(trandustin): `name` is an optional hparam for legacy reasons,
# defining variable scope names. Remove this hparam in the future.
bottom={},
loss={},
name={},
top={},
weights_fn={},
# The maximum length of "input" sequence.
# Sequences longer than this value will be truncated. 0 or negative values
# mean there is no maximum or truncation.
# You can change this behavior by overriding preprocess_example() method
# in your problem class.
max_input_seq_length=0,
# The maximum length of "target" sequence.
# Sequences longer than this value will be truncated. 0 or negative values
# mean there is no maximum or truncation.
# You can change this behavior by overriding preprocess_example() method
# in your problem class.
max_target_seq_length=0,
# if nonzero, we split the target sequences on example read.
# This is for use with language modeling problems with fixed length
# examples. e.g. The examples may be written with length 65536, but we
# want to split each example into 64 examples of length 1024.
split_to_length=0,
# Video settings: how many frames to batch on input and targets.
video_num_input_frames=1,
video_num_target_frames=1,
# This flag allows us to optionally treat a seq-to-seq problem
# as a language model. Legal values are:
#
# "none" - Do not prepend the inputs to the targets.
# "prepend_inputs_masked_attention"
# replace "targets" in preprocessing with
# tf.concat([inputs, [0], targets], axis=1)
# i.e. we prepend the inputs to the targets with a single
# padding token in between. Use masked self-attention on the
# entire resulting sequence. During training, we compute losses on
# the combined sequence. During eval, we compute the metrics
# on only the targets portion.
# "prepend_inputs_full_attention"
# similar to the previous option except that each
# position in the inputs portion can see the
# entire inputs portion. This removes the challenge of
# autoregressively predicting the inputs portion.
prepend_mode="none",
# Scheduled sampling is interesting for auto-regressive models.
# It runs an additional step using the generated output as autoregressive
# targets, which can improve the models inference results later. The
# parameter scheduled_sampling_prob determines with what probability
# will such additional step be run. It's turned off (0.0) by default.
# This probability will exponentially warm up for the number of
# steps determined by scheduled_sampling_warmup_steps.
# The tensor used for the n-th pass will consist of outputs from
# the (n-1)-th pass mixed with gold truth, with the proportion of gold
# determined by scheduled_sampling_gold_mixin_prob. Control the number
# of passes with scheduled_sampling_num_passes.
scheduled_sampling_prob=0.0,
scheduled_sampling_warmup_steps=50000,
scheduled_sampling_gold_mixin_prob=0.5,
# TODO(duckworthd): Uncomment when we can ascertain why adding an
# extra field to HParam causes test failures.
# scheduled_sampling_num_passes=1,
# This setting controls whether to copy variables around in a daisy chain
# (if true) or leave their placement to TensorFlow. It only affects multi
# device training and mostly should be turned on for performance. One
# exception are recurrent models: with dynamic loops it must be off.
daisy_chain_variables=True,
# If True in PREDICT mode, then last-position-only optimizations are not
# used.
force_full_predict=False,
# Set this for pure model parallelism. There is only one data shard.
no_data_parallelism=False,
# dtype used for activations. - "float32" or "bfloat16"
# activation_dtype="bfloat16" currently only works on TPU.
# It lowers activation-memory usage
# and does not appear to affect quality.
# You can train on TPU with activation_dtype="bfloat16" and evaluate
# on CPU/GPU with activation_dtype="float32"
activation_dtype="float32",
# dtype used for parameters: "float32" or "bfloat16"
# bfloat16 currently only works with optimizer="adafactor".
# The savings in memory allow for training larger models.
# Weights are encoded as (w*128)^8, using pseudostochastic
# roundoff. Initial experiments show that model quality is similar
# to baseline for about 3M training steps, but worse thereafter.
weight_dtype="float32",
# Directory containing a checkpoint for a pretrained model. This will only
# be used if a new run is being started. Parameters not found in the
# pretrained model will be randomly initialized. Superfluous parameters in
# the pretrained model will be ignored.
pretrained_model_dir="",
# Threshold used for two cases: the primary task probability for the
# constant mixing schedule, and the exponential schedule limit for when
# mixing should stop (eg: 0.5 means stop at 50-50 mixing, 0.8 means stop
# at 20-80 mixing for the primary-others mixing case.)
multiproblem_schedule_threshold=0.5,
# For more than 2 tasks, we may want to specify per-task thresholds here.
# In that case, this needs to be a string with as many floating point
# numbers as the number of tasks in the multi-problem. These numbers
# are later normalized to add up to 1 and taken as probabilities for
# each task. This enforces a constant mixing schedule and if this is
# empty then the threshold from above is used for the first task and
# the other tasks get the remaining probability split uniformly.
multiproblem_per_task_threshold="",
# The number of examples at which the proportion of the mixed in datasets
# is multiproblem_schedule_threshold
multiproblem_schedule_max_examples=1e7,
# When training multiproblems, we can mix the data according to different
# schedules. Example: a constant schedule mixing 20-80 between the primary
# and other tasks.
# A list of supported schedules can be found in
# `data_generators.multi_problem.py`.
multiproblem_mixing_schedule="constant",
# A boolean that decides whether input sequence losses and target label
# losses in classification problems should be reweighted.
multiproblem_reweight_label_loss=False,
# How much weight the targets in classification problems receive. Inputs
# receive 1 minus this weight.
multiproblem_label_weight=0.5,
# Hyperparameters for relative attention.
# The maximum relative positional distance to learn an embedding for.
max_relative_position=0,
# If heads share the same relative embedding.
heads_share_relative_embedding=False,
# If relative embedding terms are added to values too.
add_relative_to_values=False,
# If enable the host_call which is executed every training step.
# There could be a performance drop if host_call function is slow and
# cannot keep up with the TPU-side computation.
tpu_enable_host_call=False,
# Pad batch dim of inputs to nearest multiple of batch multiple.
pad_batch=False,
# When true, do not evaluate on the language model data when running the
# multiproblem since it can take a while. If False, set eval_steps to
# something large like 6000 or 10000.
multiproblem_target_eval_only=False,
# Max out the vocab size to a power of 2 for efficiency and to reserve
# extra space in the vocabulary for new task ids and label classes.
multiproblem_vocab_size=-1,
# When using multiproblem with generation tasks, need to truncate the
# inputs and targets manually before concatenating them.
multiproblem_max_input_length=-1,
multiproblem_max_target_length=-1,
# If positive, makes training targets fixed-length in MultiProblem.
multiproblem_fixed_train_length=-1,
# Load weights from a second model. For instance, when using
# pre-trained weights, you might want to initialize the encoder
# and decoder by loading different models.
warm_start_from_second="",
# Area attention hyper parameters
area_value_mode="none",
area_key_mode="none",
# Using area attention for the number of layers from the bottom
num_area_layers=0,
max_area_width=1,
max_area_height=1,
memory_height=1
)
|
python
|
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 examples per GPU or per TPU core.
batch_size=4096,
batch_shuffle_size=512,
# If True, then if the features are of variable length, the batch_size is
# used as the actual batch size (and not tokens per batch).
use_fixed_batch_size=False,
num_hidden_layers=4,
kernel_height=3,
kernel_width=1,
hidden_size=64,
compress_steps=0,
# All hyperparameters ending in "dropout" are automatically set to 0.0
# when not in training mode.
dropout=0.2,
clip_grad_norm=2.0,
grad_noise_scale=0.0,
summarize_grads=False,
# Flag for whether mlperf mode is on
mlperf_mode=False,
# Whether to log the name and size of every variable
summarize_vars=False,
initializer="orthogonal",
initializer_gain=1.5,
label_smoothing=0.1,
optimizer="adam",
optimizer_adam_epsilon=1e-6,
optimizer_adam_beta1=0.85,
optimizer_adam_beta2=0.997,
optimizer_momentum_momentum=0.9,
optimizer_momentum_nesterov=False,
optimizer_adafactor_beta1=0.0,
optimizer_adafactor_beta2=0.999,
optimizer_adafactor_factored=True,
optimizer_adafactor_decay_type="pow",
optimizer_adafactor_memory_exponent=0.8,
optimizer_adafactor_clipping_threshold=1.0,
optimizer_adafactor_multiply_by_parameter_scale=True,
# Number of accumulating steps for multi step optimizers.
optimizer_multistep_accumulate_steps=0,
# Loss scaling used.
# Generally only necessary with mixed precision training.
# Mixed precision training only supports exponential scaling currently
# To disable the scaler, see to 0/False
mixed_precision_optimizer_loss_scaler="exponential",
# Determines the initial loss scaling value for mixed precision
mixed_precision_optimizer_init_loss_scale=2**15,
# Whether to zero gradients that were not computed, so that the
# appropriate slots are created. Useful for sharing checkpoints between
# models with different sets of heads.
optimizer_zero_grads=False,
weight_decay=1e-6,
weight_noise=0.0,
# Defines the learning rate as a product of named functions.
# Available functions are listed in learning_rate._LEARNING_RATE_FUNCTIONS
# e.g. "constant*linear_warmup*rsqrt_decay*rsqrt_hidden_size"
learning_rate_schedule="legacy",
learning_rate_constant=1.0,
# If learning_rate_schedule=="legacy",
# then we specify decay scheme here. Warmup is always exponential,
# except with "noam" learning rate decay scheme.
# see optimize.legacy_learning_rate_schedule()
# TODO(noam): migrate everyone away from this.
learning_rate_decay_scheme="none",
# decay_steps and decay_staircase for learning_rate_decay_scheme=="exp"
learning_rate_decay_steps=5000,
learning_rate_decay_staircase=False,
learning_rate_minimum=None,
learning_rate_decay_rate=1.0,
learning_rate_warmup_steps=100,
learning_rate_cosine_cycle_steps=250000,
learning_rate=0.1,
sampling_method="argmax", # "argmax" or "random"
sampling_temp=1.0, # temperature for sampling
sampling_keep_top_k=-1, # If >0, ignore all but the top k logits
# expand the logits a piece at a time - saves memory.
factored_logits=False,
multiply_embedding_mode="sqrt_depth",
# Parameters related to mixtures of experts.
moe_hidden_sizes="2048", # hidden layer sizes (comma-separated)
moe_num_experts=64, # number of experts per layer
moe_k=2, # how many experts to use for each batch element
moe_loss_coef=1e-2,
# Sequences of operations to perform on layer input and layer output.
# Used by common_layers.layer_preprocess, common_layers.layer_postprocess
# Each character represents an operation:
# none: no preprocessing
# d: apply dropout
# n: apply normalization (see norm_type and norm_epsilon)
# a: add layer input (residual connection - only during postprocess)
# The special string "none" is used instead of the empty string
# to indicate no pre/postprocessing, since the empty string causes
# trouble for hyperparameter tuning.
# TODO(noam): The current settings ("", "dan") are the published version
# of the transformer. ("n", "da") seems better for harder-to-learn
# models, so it should probably be the default.
layer_preprocess_sequence="none",
layer_postprocess_sequence="dan",
# dropout rate to use during layer_preprocess and layer_postprocess
layer_prepostprocess_dropout=0.1,
# broadcast dimensions for layer_prepostprocess_dropout
# a comma-separated list of integers.
# see common_layers.dropout_with_broadcast_dims()
# Change this to "1" to save memory.
layer_prepostprocess_dropout_broadcast_dims="",
# dropout some symbols (set them to 0) before embedding.
symbol_dropout=0.0,
# What type of normalization to use
norm_type="layer", # "batch", layer", "noam", "none".
# epsilon parameter to normalization function
norm_epsilon=1e-6,
# pad vocabularies so that this value divides the vocabulary size.
vocab_divisor=1,
# During training, we drop sequences whose inputs and targets are shorter
# than min_length
min_length=0,
# During training, we drop sequences whose inputs or targets are longer
# than max_length.
# If max_length==0, we use hparams.batch_size instead.
max_length=0,
# Pack examples on the fly.
pack_dataset=False,
# Use custom ops not included in standard tensorflow.
use_custom_ops=True,
# Split targets on the first axis into chunks of this length.
split_targets_chunk_length=0,
split_targets_max_chunks=100,
split_targets_strided_training=False,
# Maximum length in the smallest length bucket. Setting this
# flag too high will result in wasteful padding of short
# sequences. Due to some (hopefully) temporary hacks in the
# data reading and batching code, setting this flag too low
# results in a very long batch-shuffling queue.
# TODO(noam): change this once the Datasets API changes.
min_length_bucket=8,
# This flag controls the number of length buckets in the data
# reader. The buckets have maximum lengths from
# min_bucket_length to (max_length or batch_size), increasing
# (approximately) by factors of length_bucket_step.
length_bucket_step=1.1,
# If set to True, drop sequences longer than max_length during eval.
# This affects the validity of the evaluation metrics.
eval_drop_long_sequences=False,
# If True, run the model autoregressively instead of teacher-forcing
# during eval
eval_run_autoregressive=False,
# (For features with symbol modality) If True, share all of the
# input embeddings, target embeddings, and softmax weights.
shared_embedding_and_softmax_weights=False,
# (For features with symbol modality) If True, share the input embeddings
# and target embeddings.
shared_embedding=False,
# (For features with symbol modality) Number to shard embeddings by.
symbol_modality_num_shards=1,
# Feature transformations are optional dictionaries comprising key-value
# pairs of a feature name (str) and its transformation (function). If not
# specified, T2TModel applies a default transformation according to the
# feature's modality. Bottom is applicable to all features; loss, top, and
# weights_fn are only applicable to target features.
# TODO(trandustin): `name` is an optional hparam for legacy reasons,
# defining variable scope names. Remove this hparam in the future.
bottom={},
loss={},
name={},
top={},
weights_fn={},
# The maximum length of "input" sequence.
# Sequences longer than this value will be truncated. 0 or negative values
# mean there is no maximum or truncation.
# You can change this behavior by overriding preprocess_example() method
# in your problem class.
max_input_seq_length=0,
# The maximum length of "target" sequence.
# Sequences longer than this value will be truncated. 0 or negative values
# mean there is no maximum or truncation.
# You can change this behavior by overriding preprocess_example() method
# in your problem class.
max_target_seq_length=0,
# if nonzero, we split the target sequences on example read.
# This is for use with language modeling problems with fixed length
# examples. e.g. The examples may be written with length 65536, but we
# want to split each example into 64 examples of length 1024.
split_to_length=0,
# Video settings: how many frames to batch on input and targets.
video_num_input_frames=1,
video_num_target_frames=1,
# This flag allows us to optionally treat a seq-to-seq problem
# as a language model. Legal values are:
#
# "none" - Do not prepend the inputs to the targets.
# "prepend_inputs_masked_attention"
# replace "targets" in preprocessing with
# tf.concat([inputs, [0], targets], axis=1)
# i.e. we prepend the inputs to the targets with a single
# padding token in between. Use masked self-attention on the
# entire resulting sequence. During training, we compute losses on
# the combined sequence. During eval, we compute the metrics
# on only the targets portion.
# "prepend_inputs_full_attention"
# similar to the previous option except that each
# position in the inputs portion can see the
# entire inputs portion. This removes the challenge of
# autoregressively predicting the inputs portion.
prepend_mode="none",
# Scheduled sampling is interesting for auto-regressive models.
# It runs an additional step using the generated output as autoregressive
# targets, which can improve the models inference results later. The
# parameter scheduled_sampling_prob determines with what probability
# will such additional step be run. It's turned off (0.0) by default.
# This probability will exponentially warm up for the number of
# steps determined by scheduled_sampling_warmup_steps.
# The tensor used for the n-th pass will consist of outputs from
# the (n-1)-th pass mixed with gold truth, with the proportion of gold
# determined by scheduled_sampling_gold_mixin_prob. Control the number
# of passes with scheduled_sampling_num_passes.
scheduled_sampling_prob=0.0,
scheduled_sampling_warmup_steps=50000,
scheduled_sampling_gold_mixin_prob=0.5,
# TODO(duckworthd): Uncomment when we can ascertain why adding an
# extra field to HParam causes test failures.
# scheduled_sampling_num_passes=1,
# This setting controls whether to copy variables around in a daisy chain
# (if true) or leave their placement to TensorFlow. It only affects multi
# device training and mostly should be turned on for performance. One
# exception are recurrent models: with dynamic loops it must be off.
daisy_chain_variables=True,
# If True in PREDICT mode, then last-position-only optimizations are not
# used.
force_full_predict=False,
# Set this for pure model parallelism. There is only one data shard.
no_data_parallelism=False,
# dtype used for activations. - "float32" or "bfloat16"
# activation_dtype="bfloat16" currently only works on TPU.
# It lowers activation-memory usage
# and does not appear to affect quality.
# You can train on TPU with activation_dtype="bfloat16" and evaluate
# on CPU/GPU with activation_dtype="float32"
activation_dtype="float32",
# dtype used for parameters: "float32" or "bfloat16"
# bfloat16 currently only works with optimizer="adafactor".
# The savings in memory allow for training larger models.
# Weights are encoded as (w*128)^8, using pseudostochastic
# roundoff. Initial experiments show that model quality is similar
# to baseline for about 3M training steps, but worse thereafter.
weight_dtype="float32",
# Directory containing a checkpoint for a pretrained model. This will only
# be used if a new run is being started. Parameters not found in the
# pretrained model will be randomly initialized. Superfluous parameters in
# the pretrained model will be ignored.
pretrained_model_dir="",
# Threshold used for two cases: the primary task probability for the
# constant mixing schedule, and the exponential schedule limit for when
# mixing should stop (eg: 0.5 means stop at 50-50 mixing, 0.8 means stop
# at 20-80 mixing for the primary-others mixing case.)
multiproblem_schedule_threshold=0.5,
# For more than 2 tasks, we may want to specify per-task thresholds here.
# In that case, this needs to be a string with as many floating point
# numbers as the number of tasks in the multi-problem. These numbers
# are later normalized to add up to 1 and taken as probabilities for
# each task. This enforces a constant mixing schedule and if this is
# empty then the threshold from above is used for the first task and
# the other tasks get the remaining probability split uniformly.
multiproblem_per_task_threshold="",
# The number of examples at which the proportion of the mixed in datasets
# is multiproblem_schedule_threshold
multiproblem_schedule_max_examples=1e7,
# When training multiproblems, we can mix the data according to different
# schedules. Example: a constant schedule mixing 20-80 between the primary
# and other tasks.
# A list of supported schedules can be found in
# `data_generators.multi_problem.py`.
multiproblem_mixing_schedule="constant",
# A boolean that decides whether input sequence losses and target label
# losses in classification problems should be reweighted.
multiproblem_reweight_label_loss=False,
# How much weight the targets in classification problems receive. Inputs
# receive 1 minus this weight.
multiproblem_label_weight=0.5,
# Hyperparameters for relative attention.
# The maximum relative positional distance to learn an embedding for.
max_relative_position=0,
# If heads share the same relative embedding.
heads_share_relative_embedding=False,
# If relative embedding terms are added to values too.
add_relative_to_values=False,
# If enable the host_call which is executed every training step.
# There could be a performance drop if host_call function is slow and
# cannot keep up with the TPU-side computation.
tpu_enable_host_call=False,
# Pad batch dim of inputs to nearest multiple of batch multiple.
pad_batch=False,
# When true, do not evaluate on the language model data when running the
# multiproblem since it can take a while. If False, set eval_steps to
# something large like 6000 or 10000.
multiproblem_target_eval_only=False,
# Max out the vocab size to a power of 2 for efficiency and to reserve
# extra space in the vocabulary for new task ids and label classes.
multiproblem_vocab_size=-1,
# When using multiproblem with generation tasks, need to truncate the
# inputs and targets manually before concatenating them.
multiproblem_max_input_length=-1,
multiproblem_max_target_length=-1,
# If positive, makes training targets fixed-length in MultiProblem.
multiproblem_fixed_train_length=-1,
# Load weights from a second model. For instance, when using
# pre-trained weights, you might want to initialize the encoder
# and decoder by loading different models.
warm_start_from_second="",
# Area attention hyper parameters
area_value_mode="none",
area_key_mode="none",
# Using area attention for the number of layers from the bottom
num_area_layers=0,
max_area_width=1,
max_area_height=1,
memory_height=1
)
|
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"# If the problem consists of variable-length sequences",
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"batch_size",
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"# If True, then if the features are of variable length, the batch_size is",
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"0",
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"# All hyperparameters ending in \"dropout\" are automatically set to 0.0",
"# when not in training mode.",
"dropout",
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",",
"clip_grad_norm",
"=",
"2.0",
",",
"grad_noise_scale",
"=",
"0.0",
",",
"summarize_grads",
"=",
"False",
",",
"# Flag for whether mlperf mode is on",
"mlperf_mode",
"=",
"False",
",",
"# Whether to log the name and size of every variable",
"summarize_vars",
"=",
"False",
",",
"initializer",
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"\"orthogonal\"",
",",
"initializer_gain",
"=",
"1.5",
",",
"label_smoothing",
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",",
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",",
"optimizer_adam_epsilon",
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"1e-6",
",",
"optimizer_adam_beta1",
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"0.85",
",",
"optimizer_adam_beta2",
"=",
"0.997",
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"optimizer_momentum_momentum",
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"0.9",
",",
"optimizer_momentum_nesterov",
"=",
"False",
",",
"optimizer_adafactor_beta1",
"=",
"0.0",
",",
"optimizer_adafactor_beta2",
"=",
"0.999",
",",
"optimizer_adafactor_factored",
"=",
"True",
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"optimizer_adafactor_decay_type",
"=",
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",",
"optimizer_adafactor_memory_exponent",
"=",
"0.8",
",",
"optimizer_adafactor_clipping_threshold",
"=",
"1.0",
",",
"optimizer_adafactor_multiply_by_parameter_scale",
"=",
"True",
",",
"# Number of accumulating steps for multi step optimizers.",
"optimizer_multistep_accumulate_steps",
"=",
"0",
",",
"# Loss scaling used.",
"# Generally only necessary with mixed precision training.",
"# Mixed precision training only supports exponential scaling currently",
"# To disable the scaler, see to 0/False",
"mixed_precision_optimizer_loss_scaler",
"=",
"\"exponential\"",
",",
"# Determines the initial loss scaling value for mixed precision",
"mixed_precision_optimizer_init_loss_scale",
"=",
"2",
"**",
"15",
",",
"# Whether to zero gradients that were not computed, so that the",
"# appropriate slots are created. Useful for sharing checkpoints between",
"# models with different sets of heads.",
"optimizer_zero_grads",
"=",
"False",
",",
"weight_decay",
"=",
"1e-6",
",",
"weight_noise",
"=",
"0.0",
",",
"# Defines the learning rate as a product of named functions.",
"# Available functions are listed in learning_rate._LEARNING_RATE_FUNCTIONS",
"# e.g. \"constant*linear_warmup*rsqrt_decay*rsqrt_hidden_size\"",
"learning_rate_schedule",
"=",
"\"legacy\"",
",",
"learning_rate_constant",
"=",
"1.0",
",",
"# If learning_rate_schedule==\"legacy\",",
"# then we specify decay scheme here. Warmup is always exponential,",
"# except with \"noam\" learning rate decay scheme.",
"# see optimize.legacy_learning_rate_schedule()",
"# TODO(noam): migrate everyone away from this.",
"learning_rate_decay_scheme",
"=",
"\"none\"",
",",
"# decay_steps and decay_staircase for learning_rate_decay_scheme==\"exp\"",
"learning_rate_decay_steps",
"=",
"5000",
",",
"learning_rate_decay_staircase",
"=",
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"learning_rate_minimum",
"=",
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"learning_rate_decay_rate",
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"learning_rate_warmup_steps",
"=",
"100",
",",
"learning_rate_cosine_cycle_steps",
"=",
"250000",
",",
"learning_rate",
"=",
"0.1",
",",
"sampling_method",
"=",
"\"argmax\"",
",",
"# \"argmax\" or \"random\"",
"sampling_temp",
"=",
"1.0",
",",
"# temperature for sampling",
"sampling_keep_top_k",
"=",
"-",
"1",
",",
"# If >0, ignore all but the top k logits",
"# expand the logits a piece at a time - saves memory.",
"factored_logits",
"=",
"False",
",",
"multiply_embedding_mode",
"=",
"\"sqrt_depth\"",
",",
"# Parameters related to mixtures of experts.",
"moe_hidden_sizes",
"=",
"\"2048\"",
",",
"# hidden layer sizes (comma-separated)",
"moe_num_experts",
"=",
"64",
",",
"# number of experts per layer",
"moe_k",
"=",
"2",
",",
"# how many experts to use for each batch element",
"moe_loss_coef",
"=",
"1e-2",
",",
"# Sequences of operations to perform on layer input and layer output.",
"# Used by common_layers.layer_preprocess, common_layers.layer_postprocess",
"# Each character represents an operation:",
"# none: no preprocessing",
"# d: apply dropout",
"# n: apply normalization (see norm_type and norm_epsilon)",
"# a: add layer input (residual connection - only during postprocess)",
"# The special string \"none\" is used instead of the empty string",
"# to indicate no pre/postprocessing, since the empty string causes",
"# trouble for hyperparameter tuning.",
"# TODO(noam): The current settings (\"\", \"dan\") are the published version",
"# of the transformer. (\"n\", \"da\") seems better for harder-to-learn",
"# models, so it should probably be the default.",
"layer_preprocess_sequence",
"=",
"\"none\"",
",",
"layer_postprocess_sequence",
"=",
"\"dan\"",
",",
"# dropout rate to use during layer_preprocess and layer_postprocess",
"layer_prepostprocess_dropout",
"=",
"0.1",
",",
"# broadcast dimensions for layer_prepostprocess_dropout",
"# a comma-separated list of integers.",
"# see common_layers.dropout_with_broadcast_dims()",
"# Change this to \"1\" to save memory.",
"layer_prepostprocess_dropout_broadcast_dims",
"=",
"\"\"",
",",
"# dropout some symbols (set them to 0) before embedding.",
"symbol_dropout",
"=",
"0.0",
",",
"# What type of normalization to use",
"norm_type",
"=",
"\"layer\"",
",",
"# \"batch\", layer\", \"noam\", \"none\".",
"# epsilon parameter to normalization function",
"norm_epsilon",
"=",
"1e-6",
",",
"# pad vocabularies so that this value divides the vocabulary size.",
"vocab_divisor",
"=",
"1",
",",
"# During training, we drop sequences whose inputs and targets are shorter",
"# than min_length",
"min_length",
"=",
"0",
",",
"# During training, we drop sequences whose inputs or targets are longer",
"# than max_length.",
"# If max_length==0, we use hparams.batch_size instead.",
"max_length",
"=",
"0",
",",
"# Pack examples on the fly.",
"pack_dataset",
"=",
"False",
",",
"# Use custom ops not included in standard tensorflow.",
"use_custom_ops",
"=",
"True",
",",
"# Split targets on the first axis into chunks of this length.",
"split_targets_chunk_length",
"=",
"0",
",",
"split_targets_max_chunks",
"=",
"100",
",",
"split_targets_strided_training",
"=",
"False",
",",
"# Maximum length in the smallest length bucket. Setting this",
"# flag too high will result in wasteful padding of short",
"# sequences. Due to some (hopefully) temporary hacks in the",
"# data reading and batching code, setting this flag too low",
"# results in a very long batch-shuffling queue.",
"# TODO(noam): change this once the Datasets API changes.",
"min_length_bucket",
"=",
"8",
",",
"# This flag controls the number of length buckets in the data",
"# reader. The buckets have maximum lengths from",
"# min_bucket_length to (max_length or batch_size), increasing",
"# (approximately) by factors of length_bucket_step.",
"length_bucket_step",
"=",
"1.1",
",",
"# If set to True, drop sequences longer than max_length during eval.",
"# This affects the validity of the evaluation metrics.",
"eval_drop_long_sequences",
"=",
"False",
",",
"# If True, run the model autoregressively instead of teacher-forcing",
"# during eval",
"eval_run_autoregressive",
"=",
"False",
",",
"# (For features with symbol modality) If True, share all of the",
"# input embeddings, target embeddings, and softmax weights.",
"shared_embedding_and_softmax_weights",
"=",
"False",
",",
"# (For features with symbol modality) If True, share the input embeddings",
"# and target embeddings.",
"shared_embedding",
"=",
"False",
",",
"# (For features with symbol modality) Number to shard embeddings by.",
"symbol_modality_num_shards",
"=",
"1",
",",
"# Feature transformations are optional dictionaries comprising key-value",
"# pairs of a feature name (str) and its transformation (function). If not",
"# specified, T2TModel applies a default transformation according to the",
"# feature's modality. Bottom is applicable to all features; loss, top, and",
"# weights_fn are only applicable to target features.",
"# TODO(trandustin): `name` is an optional hparam for legacy reasons,",
"# defining variable scope names. Remove this hparam in the future.",
"bottom",
"=",
"{",
"}",
",",
"loss",
"=",
"{",
"}",
",",
"name",
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",",
"# The maximum length of \"input\" sequence.",
"# Sequences longer than this value will be truncated. 0 or negative values",
"# mean there is no maximum or truncation.",
"# You can change this behavior by overriding preprocess_example() method",
"# in your problem class.",
"max_input_seq_length",
"=",
"0",
",",
"# The maximum length of \"target\" sequence.",
"# Sequences longer than this value will be truncated. 0 or negative values",
"# mean there is no maximum or truncation.",
"# You can change this behavior by overriding preprocess_example() method",
"# in your problem class.",
"max_target_seq_length",
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",",
"# if nonzero, we split the target sequences on example read.",
"# This is for use with language modeling problems with fixed length",
"# examples. e.g. The examples may be written with length 65536, but we",
"# want to split each example into 64 examples of length 1024.",
"split_to_length",
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"0",
",",
"# Video settings: how many frames to batch on input and targets.",
"video_num_input_frames",
"=",
"1",
",",
"video_num_target_frames",
"=",
"1",
",",
"# This flag allows us to optionally treat a seq-to-seq problem",
"# as a language model. Legal values are:",
"#",
"# \"none\" - Do not prepend the inputs to the targets.",
"# \"prepend_inputs_masked_attention\"",
"# replace \"targets\" in preprocessing with",
"# tf.concat([inputs, [0], targets], axis=1)",
"# i.e. we prepend the inputs to the targets with a single",
"# padding token in between. Use masked self-attention on the",
"# entire resulting sequence. During training, we compute losses on",
"# the combined sequence. During eval, we compute the metrics",
"# on only the targets portion.",
"# \"prepend_inputs_full_attention\"",
"# similar to the previous option except that each",
"# position in the inputs portion can see the",
"# entire inputs portion. This removes the challenge of",
"# autoregressively predicting the inputs portion.",
"prepend_mode",
"=",
"\"none\"",
",",
"# Scheduled sampling is interesting for auto-regressive models.",
"# It runs an additional step using the generated output as autoregressive",
"# targets, which can improve the models inference results later. The",
"# parameter scheduled_sampling_prob determines with what probability",
"# will such additional step be run. It's turned off (0.0) by default.",
"# This probability will exponentially warm up for the number of",
"# steps determined by scheduled_sampling_warmup_steps.",
"# The tensor used for the n-th pass will consist of outputs from",
"# the (n-1)-th pass mixed with gold truth, with the proportion of gold",
"# determined by scheduled_sampling_gold_mixin_prob. Control the number",
"# of passes with scheduled_sampling_num_passes.",
"scheduled_sampling_prob",
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"0.0",
",",
"scheduled_sampling_warmup_steps",
"=",
"50000",
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"scheduled_sampling_gold_mixin_prob",
"=",
"0.5",
",",
"# TODO(duckworthd): Uncomment when we can ascertain why adding an",
"# extra field to HParam causes test failures.",
"# scheduled_sampling_num_passes=1,",
"# This setting controls whether to copy variables around in a daisy chain",
"# (if true) or leave their placement to TensorFlow. It only affects multi",
"# device training and mostly should be turned on for performance. One",
"# exception are recurrent models: with dynamic loops it must be off.",
"daisy_chain_variables",
"=",
"True",
",",
"# If True in PREDICT mode, then last-position-only optimizations are not",
"# used.",
"force_full_predict",
"=",
"False",
",",
"# Set this for pure model parallelism. There is only one data shard.",
"no_data_parallelism",
"=",
"False",
",",
"# dtype used for activations. - \"float32\" or \"bfloat16\"",
"# activation_dtype=\"bfloat16\" currently only works on TPU.",
"# It lowers activation-memory usage",
"# and does not appear to affect quality.",
"# You can train on TPU with activation_dtype=\"bfloat16\" and evaluate",
"# on CPU/GPU with activation_dtype=\"float32\"",
"activation_dtype",
"=",
"\"float32\"",
",",
"# dtype used for parameters: \"float32\" or \"bfloat16\"",
"# bfloat16 currently only works with optimizer=\"adafactor\".",
"# The savings in memory allow for training larger models.",
"# Weights are encoded as (w*128)^8, using pseudostochastic",
"# roundoff. Initial experiments show that model quality is similar",
"# to baseline for about 3M training steps, but worse thereafter.",
"weight_dtype",
"=",
"\"float32\"",
",",
"# Directory containing a checkpoint for a pretrained model. This will only",
"# be used if a new run is being started. Parameters not found in the",
"# pretrained model will be randomly initialized. Superfluous parameters in",
"# the pretrained model will be ignored.",
"pretrained_model_dir",
"=",
"\"\"",
",",
"# Threshold used for two cases: the primary task probability for the",
"# constant mixing schedule, and the exponential schedule limit for when",
"# mixing should stop (eg: 0.5 means stop at 50-50 mixing, 0.8 means stop",
"# at 20-80 mixing for the primary-others mixing case.)",
"multiproblem_schedule_threshold",
"=",
"0.5",
",",
"# For more than 2 tasks, we may want to specify per-task thresholds here.",
"# In that case, this needs to be a string with as many floating point",
"# numbers as the number of tasks in the multi-problem. These numbers",
"# are later normalized to add up to 1 and taken as probabilities for",
"# each task. This enforces a constant mixing schedule and if this is",
"# empty then the threshold from above is used for the first task and",
"# the other tasks get the remaining probability split uniformly.",
"multiproblem_per_task_threshold",
"=",
"\"\"",
",",
"# The number of examples at which the proportion of the mixed in datasets",
"# is multiproblem_schedule_threshold",
"multiproblem_schedule_max_examples",
"=",
"1e7",
",",
"# When training multiproblems, we can mix the data according to different",
"# schedules. Example: a constant schedule mixing 20-80 between the primary",
"# and other tasks.",
"# A list of supported schedules can be found in",
"# `data_generators.multi_problem.py`.",
"multiproblem_mixing_schedule",
"=",
"\"constant\"",
",",
"# A boolean that decides whether input sequence losses and target label",
"# losses in classification problems should be reweighted.",
"multiproblem_reweight_label_loss",
"=",
"False",
",",
"# How much weight the targets in classification problems receive. Inputs",
"# receive 1 minus this weight.",
"multiproblem_label_weight",
"=",
"0.5",
",",
"# Hyperparameters for relative attention.",
"# The maximum relative positional distance to learn an embedding for.",
"max_relative_position",
"=",
"0",
",",
"# If heads share the same relative embedding.",
"heads_share_relative_embedding",
"=",
"False",
",",
"# If relative embedding terms are added to values too.",
"add_relative_to_values",
"=",
"False",
",",
"# If enable the host_call which is executed every training step.",
"# There could be a performance drop if host_call function is slow and",
"# cannot keep up with the TPU-side computation.",
"tpu_enable_host_call",
"=",
"False",
",",
"# Pad batch dim of inputs to nearest multiple of batch multiple.",
"pad_batch",
"=",
"False",
",",
"# When true, do not evaluate on the language model data when running the",
"# multiproblem since it can take a while. If False, set eval_steps to",
"# something large like 6000 or 10000.",
"multiproblem_target_eval_only",
"=",
"False",
",",
"# Max out the vocab size to a power of 2 for efficiency and to reserve",
"# extra space in the vocabulary for new task ids and label classes.",
"multiproblem_vocab_size",
"=",
"-",
"1",
",",
"# When using multiproblem with generation tasks, need to truncate the",
"# inputs and targets manually before concatenating them.",
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",",
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A set of basic hyperparameters.
|
[
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"set",
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"hyperparameters",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_hparams.py#L29-L351
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_hparams.py
|
basic_range1
|
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_height", [1, 3, 5, 7])
rhp.set_discrete("kernel_width", [1, 3, 5, 7])
rhp.set_discrete("compress_steps", [0, 1, 2])
rhp.set_float("dropout", 0.0, 0.5)
rhp.set_float("weight_decay", 1e-4, 10.0, scale=rhp.LOG_SCALE)
rhp.set_float("label_smoothing", 0.0, 0.2)
rhp.set_float("clip_grad_norm", 0.01, 50.0, scale=rhp.LOG_SCALE)
rhp.set_float("learning_rate", 0.005, 2.0, scale=rhp.LOG_SCALE)
rhp.set_categorical("initializer",
["uniform", "orthogonal", "uniform_unit_scaling"])
rhp.set_float("initializer_gain", 0.5, 3.5)
rhp.set_categorical("learning_rate_decay_scheme",
["none", "sqrt", "noam", "exp"])
rhp.set_float("optimizer_adam_epsilon", 1e-7, 1e-2, scale=rhp.LOG_SCALE)
rhp.set_float("optimizer_adam_beta1", 0.8, 0.9)
rhp.set_float("optimizer_adam_beta2", 0.995, 0.999)
rhp.set_categorical(
"optimizer",
["adam", "adagrad", "momentum", "rms_prop", "sgd", "yellow_fin"])
|
python
|
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_height", [1, 3, 5, 7])
rhp.set_discrete("kernel_width", [1, 3, 5, 7])
rhp.set_discrete("compress_steps", [0, 1, 2])
rhp.set_float("dropout", 0.0, 0.5)
rhp.set_float("weight_decay", 1e-4, 10.0, scale=rhp.LOG_SCALE)
rhp.set_float("label_smoothing", 0.0, 0.2)
rhp.set_float("clip_grad_norm", 0.01, 50.0, scale=rhp.LOG_SCALE)
rhp.set_float("learning_rate", 0.005, 2.0, scale=rhp.LOG_SCALE)
rhp.set_categorical("initializer",
["uniform", "orthogonal", "uniform_unit_scaling"])
rhp.set_float("initializer_gain", 0.5, 3.5)
rhp.set_categorical("learning_rate_decay_scheme",
["none", "sqrt", "noam", "exp"])
rhp.set_float("optimizer_adam_epsilon", 1e-7, 1e-2, scale=rhp.LOG_SCALE)
rhp.set_float("optimizer_adam_beta1", 0.8, 0.9)
rhp.set_float("optimizer_adam_beta2", 0.995, 0.999)
rhp.set_categorical(
"optimizer",
["adam", "adagrad", "momentum", "rms_prop", "sgd", "yellow_fin"])
|
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A basic range of hyperparameters.
|
[
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"of",
"hyperparameters",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_hparams.py#L473-L497
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_hparams.py
|
RangedHParams._check_reset_and_type_change
|
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"),
(self._discrete_params, "discrete"),
(self._float_params, "float"),
(self._int_params, "int"),
]
ctrs, names = list(zip(*ctr_names))
orig_name = names[ctrs.index(orig_ctr)]
for ctr, ctr_name in ctr_names:
if ctr is orig_ctr:
continue
# Using a different type for the same hyperparameter name
if name in ctr:
raise ValueError("Setting hyperparameter %s as type %s, but a "
"hyperparemeter of the same name was originally "
"registered as type %s" % (name, ctr_name, orig_name))
|
python
|
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"),
(self._discrete_params, "discrete"),
(self._float_params, "float"),
(self._int_params, "int"),
]
ctrs, names = list(zip(*ctr_names))
orig_name = names[ctrs.index(orig_ctr)]
for ctr, ctr_name in ctr_names:
if ctr is orig_ctr:
continue
# Using a different type for the same hyperparameter name
if name in ctr:
raise ValueError("Setting hyperparameter %s as type %s, but a "
"hyperparemeter of the same name was originally "
"registered as type %s" % (name, ctr_name, orig_name))
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_hparams.py#L374-L397
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_hparams.py
|
RangedHParams.to_parameter_specs
|
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",
"categoricalValues": categories,
}
specs.append(spec)
for name, feasible_points, scale, _ in self._discrete_params.values():
spec = {
"parameterName": name_prefix + name,
"type": "DISCRETE",
"discreteValues": feasible_points,
}
if scale:
spec["scaleType"] = self.SCALES_STR[scale]
specs.append(spec)
for name, min_val, max_val, scale, _ in self._float_params.values():
spec = {
"parameterName": name_prefix + name,
"type": "DOUBLE",
"minValue": min_val,
"maxValue": max_val,
}
if scale:
spec["scaleType"] = self.SCALES_STR[scale]
specs.append(spec)
for name, min_val, max_val, scale, _ in self._int_params.values():
spec = {
"parameterName": name_prefix + name,
"type": "INTEGER",
"minValue": min_val,
"maxValue": max_val,
}
if scale:
spec["scaleType"] = self.SCALES_STR[scale]
specs.append(spec)
return specs
|
python
|
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",
"categoricalValues": categories,
}
specs.append(spec)
for name, feasible_points, scale, _ in self._discrete_params.values():
spec = {
"parameterName": name_prefix + name,
"type": "DISCRETE",
"discreteValues": feasible_points,
}
if scale:
spec["scaleType"] = self.SCALES_STR[scale]
specs.append(spec)
for name, min_val, max_val, scale, _ in self._float_params.values():
spec = {
"parameterName": name_prefix + name,
"type": "DOUBLE",
"minValue": min_val,
"maxValue": max_val,
}
if scale:
spec["scaleType"] = self.SCALES_STR[scale]
specs.append(spec)
for name, min_val, max_val, scale, _ in self._int_params.values():
spec = {
"parameterName": name_prefix + name,
"type": "INTEGER",
"minValue": min_val,
"maxValue": max_val,
}
if scale:
spec["scaleType"] = self.SCALES_STR[scale]
specs.append(spec)
return specs
|
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To list of dicts suitable for Cloud ML Engine hyperparameter tuning.
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] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_hparams.py#L426-L469
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/gym_env.py
|
register_game
|
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_name not in ATARI_GAMES:
raise ValueError("Game %s not in ATARI_GAMES" % game_name)
if game_mode not in ATARI_GAME_MODES:
raise ValueError("Unknown ATARI game mode: %s." % game_mode)
camel_game_name = misc_utils.snakecase_to_camelcase(game_name) + game_mode
# Create and register the Problem
cls = type("Gym%sRandom" % camel_game_name,
(T2TGymEnv,), {"base_env_name": camel_game_name})
registry.register_problem(cls)
|
python
|
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_name not in ATARI_GAMES:
raise ValueError("Game %s not in ATARI_GAMES" % game_name)
if game_mode not in ATARI_GAME_MODES:
raise ValueError("Unknown ATARI game mode: %s." % game_mode)
camel_game_name = misc_utils.snakecase_to_camelcase(game_name) + game_mode
# Create and register the Problem
cls = type("Gym%sRandom" % camel_game_name,
(T2TGymEnv,), {"base_env_name": camel_game_name})
registry.register_problem(cls)
|
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Create and register problems for the game.
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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.
|
[
"Create",
"and",
"register",
"problems",
"for",
"the",
"game",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/gym_env.py#L884-L902
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/gym_env.py
|
T2TEnv._decode_png
|
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}
)
|
python
|
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}
)
|
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Decodes a single observation from PNG.
|
[
"Decodes",
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"PNG",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/gym_env.py#L227-L232
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/gym_env.py
|
T2TEnv._encode_observations
|
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
)
for observation in observations
]
|
python
|
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
)
for observation in observations
]
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[
"Encodes",
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/gym_env.py#L234-L245
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/gym_env.py
|
T2TEnv.step
|
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 current epoch has already been loaded.
"""
if self._store_rollouts and \
self._rollouts_by_epoch_and_split[self.current_epoch]:
raise ValueError(
"Data for current epoch has already been loaded from disk."
)
(obs, unclipped_rewards, dones) = self._step(actions)
obs = self._preprocess_observations(obs)
(min_reward, max_reward) = self.reward_range
rewards = np.around(np.clip(unclipped_rewards, min_reward, max_reward))
if self._store_rollouts:
unclipped_rewards = unclipped_rewards.astype(np.float64)
encoded_obs = self._encode_observations(obs)
for (rollout, frame, action) in zip(
self._current_batch_rollouts, self._current_batch_frames, actions
):
rollout.append(frame._replace(action=action))
# orud = (observation, reward, unclipped_reward, done)
self._current_batch_frames = [
Frame(*orud, action=None)
for orud in zip(encoded_obs, rewards, unclipped_rewards, dones)
]
return (obs, rewards, dones)
|
python
|
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 current epoch has already been loaded.
"""
if self._store_rollouts and \
self._rollouts_by_epoch_and_split[self.current_epoch]:
raise ValueError(
"Data for current epoch has already been loaded from disk."
)
(obs, unclipped_rewards, dones) = self._step(actions)
obs = self._preprocess_observations(obs)
(min_reward, max_reward) = self.reward_range
rewards = np.around(np.clip(unclipped_rewards, min_reward, max_reward))
if self._store_rollouts:
unclipped_rewards = unclipped_rewards.astype(np.float64)
encoded_obs = self._encode_observations(obs)
for (rollout, frame, action) in zip(
self._current_batch_rollouts, self._current_batch_frames, actions
):
rollout.append(frame._replace(action=action))
# orud = (observation, reward, unclipped_reward, done)
self._current_batch_frames = [
Frame(*orud, action=None)
for orud in zip(encoded_obs, rewards, unclipped_rewards, dones)
]
return (obs, rewards, dones)
|
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Returns:
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respectively.
Raises:
ValueError: when the data for current epoch has already been loaded.
|
[
"Makes",
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"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/gym_env.py#L264-L301
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/gym_env.py
|
T2TEnv.reset
|
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 current epoch.
"""
if self._store_rollouts and self.current_epoch is None:
raise ValueError(
"No current epoch. start_new_epoch() should first be called."
)
if indices is None:
indices = np.arange(self.batch_size)
new_obs = self._reset(indices)
if self._should_preprocess_on_reset:
new_obs = self._preprocess_observations(new_obs)
if self._store_rollouts:
encoded_obs = self._encode_observations(new_obs)
for (index, ob) in zip(indices, encoded_obs):
frame = self._current_batch_frames[index]
if frame is not None:
rollout = self._current_batch_rollouts[index]
rollout.append(frame._replace(action=0))
self._current_epoch_rollouts.append(rollout)
self._current_batch_rollouts[index] = []
self._current_batch_frames[index] = Frame(
observation=ob, reward=0, unclipped_reward=0, done=False,
action=None
)
return new_obs
|
python
|
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 current epoch.
"""
if self._store_rollouts and self.current_epoch is None:
raise ValueError(
"No current epoch. start_new_epoch() should first be called."
)
if indices is None:
indices = np.arange(self.batch_size)
new_obs = self._reset(indices)
if self._should_preprocess_on_reset:
new_obs = self._preprocess_observations(new_obs)
if self._store_rollouts:
encoded_obs = self._encode_observations(new_obs)
for (index, ob) in zip(indices, encoded_obs):
frame = self._current_batch_frames[index]
if frame is not None:
rollout = self._current_batch_rollouts[index]
rollout.append(frame._replace(action=0))
self._current_epoch_rollouts.append(rollout)
self._current_batch_rollouts[index] = []
self._current_batch_frames[index] = Frame(
observation=ob, reward=0, unclipped_reward=0, done=False,
action=None
)
return new_obs
|
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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 current epoch.
|
[
"Resets",
"environments",
"at",
"given",
"indices",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/gym_env.py#L314-L351
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/gym_env.py
|
T2TEnv.extra_reading_spec
|
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_decoder.Tensor(tensor_key=name)
for name in field_names
}
return (data_fields, decoders)
|
python
|
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_decoder.Tensor(tensor_key=name)
for name in field_names
}
return (data_fields, decoders)
|
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[
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] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/gym_env.py#L373-L383
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/gym_env.py
|
T2TEnv._split_current_epoch
|
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._current_epoch_rollouts)
num_shards = sum(split["shards"] for split in self.dataset_splits)
shard_size = num_frames // num_shards
splits = self.dataset_splits
num_saved_frames = 0
split_index = 0
split_begin_index = 0
rollouts_by_split = collections.defaultdict(list)
def split_size(split_index):
return splits[split_index]["shards"] * shard_size
for rollout in self._current_epoch_rollouts:
num_saved_frames_current_rollout = 0
# Split the rollout into chunks corresponding to dataset splits. In most
# cases there should be only one chunk. On dataset split boundary there
# will be two. If a rollout is longer then the size of a dataset split,
# there might be more.
while num_saved_frames_current_rollout < len(rollout):
max_chunk_length = (
split_begin_index + split_size(split_index) - num_saved_frames
)
if split_index == len(splits) - 1:
# Put the remainder in the last split to preserve the ordering.
max_chunk_length = len(rollout)
rollout_chunk = rollout[
num_saved_frames_current_rollout:
(num_saved_frames_current_rollout + max_chunk_length)
]
rollouts_by_split[splits[split_index]["split"]].append(rollout_chunk)
num_saved_frames_current_rollout += len(rollout_chunk)
num_saved_frames += len(rollout_chunk)
if num_saved_frames == split_begin_index + split_size(split_index):
split_begin_index += split_size(split_index)
split_index = min(split_index + 1, len(splits) - 1)
self._rollouts_by_epoch_and_split[self.current_epoch] = rollouts_by_split
self._current_epoch_rollouts = []
|
python
|
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._current_epoch_rollouts)
num_shards = sum(split["shards"] for split in self.dataset_splits)
shard_size = num_frames // num_shards
splits = self.dataset_splits
num_saved_frames = 0
split_index = 0
split_begin_index = 0
rollouts_by_split = collections.defaultdict(list)
def split_size(split_index):
return splits[split_index]["shards"] * shard_size
for rollout in self._current_epoch_rollouts:
num_saved_frames_current_rollout = 0
# Split the rollout into chunks corresponding to dataset splits. In most
# cases there should be only one chunk. On dataset split boundary there
# will be two. If a rollout is longer then the size of a dataset split,
# there might be more.
while num_saved_frames_current_rollout < len(rollout):
max_chunk_length = (
split_begin_index + split_size(split_index) - num_saved_frames
)
if split_index == len(splits) - 1:
# Put the remainder in the last split to preserve the ordering.
max_chunk_length = len(rollout)
rollout_chunk = rollout[
num_saved_frames_current_rollout:
(num_saved_frames_current_rollout + max_chunk_length)
]
rollouts_by_split[splits[split_index]["split"]].append(rollout_chunk)
num_saved_frames_current_rollout += len(rollout_chunk)
num_saved_frames += len(rollout_chunk)
if num_saved_frames == split_begin_index + split_size(split_index):
split_begin_index += split_size(split_index)
split_index = min(split_index + 1, len(splits) - 1)
self._rollouts_by_epoch_and_split[self.current_epoch] = rollouts_by_split
self._current_epoch_rollouts = []
|
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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.
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/gym_env.py#L417-L462
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/gym_env.py
|
T2TEnv.splits_and_paths
|
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(paths):
return [
"{}.{}".format(path, self.current_epoch)
for path in paths
]
# We set shuffled=True as we don't want to shuffle on disk later.
return [
(split["split"], append_epoch(filepath_fns[split["split"]](
data_dir, split["shards"], shuffled=True
)))
for split in self.dataset_splits
]
|
python
|
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(paths):
return [
"{}.{}".format(path, self.current_epoch)
for path in paths
]
# We set shuffled=True as we don't want to shuffle on disk later.
return [
(split["split"], append_epoch(filepath_fns[split["split"]](
data_dir, split["shards"], shuffled=True
)))
for split in self.dataset_splits
]
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/gym_env.py#L464-L484
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/gym_env.py
|
T2TEnv.generate_data
|
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_epoch_and_split[self.current_epoch]
splits_and_paths = self.splits_and_paths(data_dir)
for (split, paths) in splits_and_paths:
rollouts = rollouts_by_split[split]
num_frames = self._calc_num_frames(rollouts)
shard_size = num_frames // len(paths)
frame_gen = self._generate_frames(rollouts)
for (path_index, path) in enumerate(paths):
limit = shard_size
# Put the remainder in the last shard to preserve the ordering.
if path_index == len(paths) - 1:
limit = None
generator_utils.generate_files(
itertools.islice(frame_gen, limit), [path],
cycle_every_n=float("inf")
)
|
python
|
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_epoch_and_split[self.current_epoch]
splits_and_paths = self.splits_and_paths(data_dir)
for (split, paths) in splits_and_paths:
rollouts = rollouts_by_split[split]
num_frames = self._calc_num_frames(rollouts)
shard_size = num_frames // len(paths)
frame_gen = self._generate_frames(rollouts)
for (path_index, path) in enumerate(paths):
limit = shard_size
# Put the remainder in the last shard to preserve the ordering.
if path_index == len(paths) - 1:
limit = None
generator_utils.generate_files(
itertools.islice(frame_gen, limit), [path],
cycle_every_n=float("inf")
)
|
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"dev",
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] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/gym_env.py#L494-L517
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/gym_env.py
|
T2TGymEnv.set_initial_state
|
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
|
python
|
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
|
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Sets the state that will be used on next reset.
|
[
"Sets",
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/gym_env.py#L723-L727
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/image_utils.py
|
image_to_tf_summary_value
|
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)
height, width, n_channels = curr_image.shape
# If monochrome image, then reshape to [height, width]
if n_channels == 1:
curr_image = np.reshape(curr_image, [height, width])
s = io.BytesIO()
matplotlib_pyplot().imsave(s, curr_image, format="png")
img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
height=height, width=width,
colorspace=n_channels)
return tf.Summary.Value(tag=tag, image=img_sum)
|
python
|
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)
height, width, n_channels = curr_image.shape
# If monochrome image, then reshape to [height, width]
if n_channels == 1:
curr_image = np.reshape(curr_image, [height, width])
s = io.BytesIO()
matplotlib_pyplot().imsave(s, curr_image, format="png")
img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
height=height, width=width,
colorspace=n_channels)
return tf.Summary.Value(tag=tag, image=img_sum)
|
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Converts a NumPy image to a tf.Summary.Value object.
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/image_utils.py#L43-L62
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/image_utils.py
|
convert_predictions_to_image_summaries
|
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_hparams.display_decoded_images:
return []
predictions = hook_args.predictions[0]
# Display ten random inputs and outputs so that tensorboard does not hang.
all_summaries = []
rand_predictions = np.random.choice(predictions, size=10)
for ind, prediction in enumerate(rand_predictions):
output_summary = image_to_tf_summary_value(
prediction["outputs"], tag="%d_output" % ind)
input_summary = image_to_tf_summary_value(
prediction["inputs"], tag="%d_input" % ind)
all_summaries.append(input_summary)
all_summaries.append(output_summary)
return all_summaries
|
python
|
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_hparams.display_decoded_images:
return []
predictions = hook_args.predictions[0]
# Display ten random inputs and outputs so that tensorboard does not hang.
all_summaries = []
rand_predictions = np.random.choice(predictions, size=10)
for ind, prediction in enumerate(rand_predictions):
output_summary = image_to_tf_summary_value(
prediction["outputs"], tag="%d_output" % ind)
input_summary = image_to_tf_summary_value(
prediction["inputs"], tag="%d_input" % ind)
all_summaries.append(input_summary)
all_summaries.append(output_summary)
return all_summaries
|
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Optionally converts images from hooks_args to image summaries.
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hook_args: DecodeHookArgs namedtuple
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|
[
"Optionally",
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"from",
"hooks_args",
"to",
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] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/image_utils.py#L65-L88
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/image_utils.py
|
resize_by_area
|
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))
|
python
|
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))
|
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image resize function used by quite a few image problems.
|
[
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"image",
"problems",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/image_utils.py#L91-L94
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/image_utils.py
|
make_multiscale
|
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 height is resized to.
resize_method: tf.image.ResizeMethod.
num_channels: Number of channels in image.
Returns:
List of Tensors, one for each resolution with shape given by
[resolutions[i], resolutions[i], num_channels].
"""
scaled_images = []
for height in resolutions:
scaled_image = tf.image.resize_images(
image,
size=[height, height], # assuming that height = width
method=resize_method)
scaled_image = tf.to_int64(scaled_image)
scaled_image.set_shape([height, height, num_channels])
scaled_images.append(scaled_image)
return scaled_images
|
python
|
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 height is resized to.
resize_method: tf.image.ResizeMethod.
num_channels: Number of channels in image.
Returns:
List of Tensors, one for each resolution with shape given by
[resolutions[i], resolutions[i], num_channels].
"""
scaled_images = []
for height in resolutions:
scaled_image = tf.image.resize_images(
image,
size=[height, height], # assuming that height = width
method=resize_method)
scaled_image = tf.to_int64(scaled_image)
scaled_image.set_shape([height, height, num_channels])
scaled_images.append(scaled_image)
return scaled_images
|
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resize_method: tf.image.ResizeMethod.
num_channels: Number of channels in image.
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/image_utils.py#L97-L122
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/image_utils.py
|
make_multiscale_dilated
|
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
assumes VALID padding, so the original image's height must be divisible
by each resolution's height to return the exact resolution size.
num_channels: Number of channels in image.
Returns:
List of Tensors, one for each resolution with shape given by
[resolutions[i], resolutions[i], num_channels] if resolutions properly
divide the original image's height; otherwise shape height and width is up
to valid skips.
"""
image_height = common_layers.shape_list(image)[0]
scaled_images = []
for height in resolutions:
dilation_rate = image_height // height # assuming height = width
scaled_image = image[::dilation_rate, ::dilation_rate]
scaled_image = tf.to_int64(scaled_image)
scaled_image.set_shape([None, None, num_channels])
scaled_images.append(scaled_image)
return scaled_images
|
python
|
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
assumes VALID padding, so the original image's height must be divisible
by each resolution's height to return the exact resolution size.
num_channels: Number of channels in image.
Returns:
List of Tensors, one for each resolution with shape given by
[resolutions[i], resolutions[i], num_channels] if resolutions properly
divide the original image's height; otherwise shape height and width is up
to valid skips.
"""
image_height = common_layers.shape_list(image)[0]
scaled_images = []
for height in resolutions:
dilation_rate = image_height // height # assuming height = width
scaled_image = image[::dilation_rate, ::dilation_rate]
scaled_image = tf.to_int64(scaled_image)
scaled_image.set_shape([None, None, num_channels])
scaled_images.append(scaled_image)
return scaled_images
|
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resolutions: List of heights that image's height is resized to. The function
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num_channels: Number of channels in image.
Returns:
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[
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/image_utils.py#L125-L151
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/image_utils.py
|
encode_images_as_png
|
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=(height, width, channels))
encoded_image_t = tf.image.encode_png(image_t)
with tf.Session() as sess:
for image in images:
enc_string = sess.run(encoded_image_t, feed_dict={image_t: image})
yield enc_string
|
python
|
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=(height, width, channels))
encoded_image_t = tf.image.encode_png(image_t)
with tf.Session() as sess:
for image in images:
enc_string = sess.run(encoded_image_t, feed_dict={image_t: image})
yield enc_string
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/image_utils.py#L266-L279
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/image_utils.py
|
image_generator
|
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 following fields:
* image/encoded: the string encoding the image as PNG,
* image/format: the string "png" representing image format,
* image/class/label: an integer representing the label,
* image/height: an integer representing the height,
* image/width: an integer representing the width.
Every field is actually a singleton list of the corresponding type.
Raises:
ValueError: if images is an empty list.
"""
if not images:
raise ValueError("Must provide some images for the generator.")
width, height, _ = images[0].shape
for (enc_image, label) in zip(encode_images_as_png(images), labels):
yield {
"image/encoded": [enc_image],
"image/format": ["png"],
"image/class/label": [int(label)],
"image/height": [height],
"image/width": [width]
}
|
python
|
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 following fields:
* image/encoded: the string encoding the image as PNG,
* image/format: the string "png" representing image format,
* image/class/label: an integer representing the label,
* image/height: an integer representing the height,
* image/width: an integer representing the width.
Every field is actually a singleton list of the corresponding type.
Raises:
ValueError: if images is an empty list.
"""
if not images:
raise ValueError("Must provide some images for the generator.")
width, height, _ = images[0].shape
for (enc_image, label) in zip(encode_images_as_png(images), labels):
yield {
"image/encoded": [enc_image],
"image/format": ["png"],
"image/class/label": [int(label)],
"image/height": [height],
"image/width": [width]
}
|
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A dictionary representing the images with the following fields:
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* image/format: the string "png" representing image format,
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* image/height: an integer representing the height,
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/image_utils.py#L282-L311
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/image_utils.py
|
image_augmentation
|
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 augmentation, but might be slow.
images = tf.image.random_brightness(images, max_delta=32. / 255.)
images = tf.image.random_saturation(images, lower=0.5, upper=1.5)
images = tf.image.random_hue(images, max_delta=0.2)
images = tf.image.random_contrast(images, lower=0.5, upper=1.5)
return images
|
python
|
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 augmentation, but might be slow.
images = tf.image.random_brightness(images, max_delta=32. / 255.)
images = tf.image.random_saturation(images, lower=0.5, upper=1.5)
images = tf.image.random_hue(images, max_delta=0.2)
images = tf.image.random_contrast(images, lower=0.5, upper=1.5)
return images
|
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|
[
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/image_utils.py#L378-L389
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/image_utils.py
|
cifar_image_augmentation
|
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 = tf.random_crop(images, [32, 32, 3])
images = tf.image.random_flip_left_right(images)
return images
|
python
|
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 = tf.random_crop(images, [32, 32, 3])
images = tf.image.random_flip_left_right(images)
return images
|
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[
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/image_utils.py#L392-L405
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/data_generators/image_utils.py
|
random_shift
|
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 of the width.
Returns:
images: images translated by the provided wsr and hsr.
"""
height, width, _ = common_layers.shape_list(image)
width_range, height_range = wsr*width, hsr*height
height_translations = tf.random_uniform((1,), -height_range, height_range)
width_translations = tf.random_uniform((1,), -width_range, width_range)
translations = tf.concat((height_translations, width_translations), axis=0)
return tf.contrib.image.translate(image, translations=translations)
|
python
|
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 of the width.
Returns:
images: images translated by the provided wsr and hsr.
"""
height, width, _ = common_layers.shape_list(image)
width_range, height_range = wsr*width, hsr*height
height_translations = tf.random_uniform((1,), -height_range, height_range)
width_translations = tf.random_uniform((1,), -width_range, width_range)
translations = tf.concat((height_translations, width_translations), axis=0)
return tf.contrib.image.translate(image, translations=translations)
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/data_generators/image_utils.py#L408-L425
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
get_standardized_layers
|
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 within the devices.
If moe wants to be used, both dp and model need to be set.
Args:
hparams (tf.HParams): the model hparameters
dp (expert_utils.Parallelism): A data parallelism object. If not given,
the dp calls are simply ignored.
Returns:
dict[str:fct]: A dictionary containing the standardized functions
"""
def partial(fct, *args, **kwargs):
"""Same as functools.partial but with functools.wraps."""
return functools.wraps(fct)(functools.partial(fct, *args, **kwargs))
def register_layer(
fct_in,
default_args=None,
default_kwargs=None,
use_dp=True,
recompute_grad=False,
):
"""Turn a function into its standardized version.
Args:
fct_in (fct): The function to register
default_args (list): The default parameters to add to the function.
default_kwargs (dict): The default parameters to add to the function.
Those arguments can be overwritten when calling the function.
use_dp (bool): Wrap the function call within a dataparallelism object if
dp is available. Some layers (like MOE) must be called without dp.
recompute_grad (bool): If True, recompute the function during the
backward pass to save memory
Returns:
fct: the standardized layer function.
"""
# The kwargs given when calling the function overwrite the default ones
fct_in = partial(fct_in, *(default_args or []), **(default_kwargs or {}))
@functools.wraps(fct_in)
def decorator(x, *args, **kwargs):
"""Call the layer function."""
fct = fct_in # For closure. Could use nonlocal with Python 3
# Eventually create the memory optimized version of the function
if recompute_grad:
fct = partial(fct, **kwargs) # recompute_grad only accept args
fct = common_layers.recompute_grad(fct)
kwargs = {}
# Eventually use dp (if given and not MoE)
if use_dp and dp is not None:
y = dp(fct, x, *args, **kwargs)
else:
y = fct(x, *args, **kwargs)
# Eventually capture the extra loss
extra_loss = 0.0
if isinstance(y, tuple):
y, extra_loss = y
return y, extra_loss
return decorator
total_key_depth = hparams.attention_key_channels or hparams.hidden_size
total_value_depth = hparams.attention_value_channels or hparams.hidden_size
# Attention layers:
# === Multi-head full attention layer ===
multihead_attention_fn = register_layer(
multihead_attention,
default_kwargs=dict(
memory_antecedent=None, # Self-attention by default
bias=None,
total_key_depth=total_key_depth,
total_value_depth=total_value_depth,
output_depth=hparams.hidden_size,
num_heads=hparams.num_heads,
dropout_rate=hparams.attention_dropout,
))
# === Memory efficient full-attention layer ===
# Save memory by not storing the activations and
# recomputing them during the backward pass
memeff_attention_base_fn = register_layer(
multihead_attention,
default_kwargs=dict(
total_key_depth=total_key_depth,
total_value_depth=total_value_depth,
output_depth=hparams.hidden_size,
num_heads=hparams.num_heads,
dropout_rate=hparams.attention_dropout,
),
recompute_grad=True,
)
def memeff_attention_fn(*args, **kwargs):
"""Modify args/kwargs for compatibility with recompute_grad."""
kwargs = kwargs.copy()
assert len(args) == 1
x = args[0]
memory_antecedent = kwargs.pop("memory_antecedent", x) # Same as x if None
if kwargs.get("bias", None) is not None: # Case where bias has been set
args = (x, memory_antecedent, kwargs.pop("bias"))
else:
# Otherwise, only 2 args. This is necessary as recompute_grad does not
# support None values.
args = (x, memory_antecedent)
return memeff_attention_base_fn(*args, **kwargs)
# === Local attention (unmasked) layer ===
# Reuse same parameters as multihead_attention
# Don't mask the future
local_attention_fn = partial(
multihead_attention_fn,
block_length=hparams.attention_loc_block_length,
block_width=hparams.attention_loc_block_width,
attention_type="local_unmasked",
)
# === Local attention (masked) layer ===
# Reuse same parameters as multihead_attention
# Only works for self attention. Always mask the future.
local_attention_masked_fn = partial(
multihead_attention_fn,
block_length=hparams.attention_loc_block_length,
attention_type="local_mask_right",
)
# === Masked memory-compressed multihead self attention layer ===
# Only works for self attention. Always mask the future.
compressed_attention_masked_fn = register_layer(
multihead_self_attention_reduced,
default_kwargs=dict(
factor=hparams.attention_red_factor,
nonlinearity=hparams.attention_red_nonlinearity,
reduction_type=hparams.attention_red_type,
multihead_params=dict(
total_key_depth=total_key_depth,
total_value_depth=total_value_depth,
num_heads=hparams.num_heads,
dropout_rate=hparams.attention_dropout,
),
),
)
# === Unmasked memory-compressed multihead self attention layer ===
# Only works for self attention. Never mask the future. Bias never added
compressed_attention_fn = partial(
compressed_attention_masked_fn,
add_mask=False,
)
# Feed-forwards layers:
# === FC layer ===
conv_hidden_relu = register_layer(
common_layers.conv_hidden_relu,
default_kwargs=dict(
hidden_size=hparams.filter_size,
output_size=hparams.hidden_size,
dropout=hparams.relu_dropout,
),
)
# === Separable convolution layer ===
# No mask applied
sep_conv_relu = partial(
conv_hidden_relu,
padding="SAME",
# Parameters copied from the transformer model, could add hparams
kernel_size=(3, 1),
second_kernel_size=(31, 1),
)
# === Separable convolution layer (masked version) ===
# Mask the future
sep_conv_relu_masked = partial(
sep_conv_relu,
padding="LEFT", # Mask future for decoder
)
# Define all available layers
cur_layers = dict(
# Attention layers:
a=multihead_attention_fn, # Multihead full attention
loc=local_attention_fn, # Local attention
locm=local_attention_masked_fn, # Local attention (masked)
red=compressed_attention_fn, # Memory-compressed attention
redm=compressed_attention_masked_fn, # Memory-compressed att (masked)
mem=memeff_attention_fn, # Memory efficient
# Feed-forward layers:
fc=conv_hidden_relu, # Fully connected
sep=sep_conv_relu, # Separable convolution (unmasked)
sepm=sep_conv_relu_masked, # Separable convolution (masked)
)
return cur_layers
|
python
|
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 within the devices.
If moe wants to be used, both dp and model need to be set.
Args:
hparams (tf.HParams): the model hparameters
dp (expert_utils.Parallelism): A data parallelism object. If not given,
the dp calls are simply ignored.
Returns:
dict[str:fct]: A dictionary containing the standardized functions
"""
def partial(fct, *args, **kwargs):
"""Same as functools.partial but with functools.wraps."""
return functools.wraps(fct)(functools.partial(fct, *args, **kwargs))
def register_layer(
fct_in,
default_args=None,
default_kwargs=None,
use_dp=True,
recompute_grad=False,
):
"""Turn a function into its standardized version.
Args:
fct_in (fct): The function to register
default_args (list): The default parameters to add to the function.
default_kwargs (dict): The default parameters to add to the function.
Those arguments can be overwritten when calling the function.
use_dp (bool): Wrap the function call within a dataparallelism object if
dp is available. Some layers (like MOE) must be called without dp.
recompute_grad (bool): If True, recompute the function during the
backward pass to save memory
Returns:
fct: the standardized layer function.
"""
# The kwargs given when calling the function overwrite the default ones
fct_in = partial(fct_in, *(default_args or []), **(default_kwargs or {}))
@functools.wraps(fct_in)
def decorator(x, *args, **kwargs):
"""Call the layer function."""
fct = fct_in # For closure. Could use nonlocal with Python 3
# Eventually create the memory optimized version of the function
if recompute_grad:
fct = partial(fct, **kwargs) # recompute_grad only accept args
fct = common_layers.recompute_grad(fct)
kwargs = {}
# Eventually use dp (if given and not MoE)
if use_dp and dp is not None:
y = dp(fct, x, *args, **kwargs)
else:
y = fct(x, *args, **kwargs)
# Eventually capture the extra loss
extra_loss = 0.0
if isinstance(y, tuple):
y, extra_loss = y
return y, extra_loss
return decorator
total_key_depth = hparams.attention_key_channels or hparams.hidden_size
total_value_depth = hparams.attention_value_channels or hparams.hidden_size
# Attention layers:
# === Multi-head full attention layer ===
multihead_attention_fn = register_layer(
multihead_attention,
default_kwargs=dict(
memory_antecedent=None, # Self-attention by default
bias=None,
total_key_depth=total_key_depth,
total_value_depth=total_value_depth,
output_depth=hparams.hidden_size,
num_heads=hparams.num_heads,
dropout_rate=hparams.attention_dropout,
))
# === Memory efficient full-attention layer ===
# Save memory by not storing the activations and
# recomputing them during the backward pass
memeff_attention_base_fn = register_layer(
multihead_attention,
default_kwargs=dict(
total_key_depth=total_key_depth,
total_value_depth=total_value_depth,
output_depth=hparams.hidden_size,
num_heads=hparams.num_heads,
dropout_rate=hparams.attention_dropout,
),
recompute_grad=True,
)
def memeff_attention_fn(*args, **kwargs):
"""Modify args/kwargs for compatibility with recompute_grad."""
kwargs = kwargs.copy()
assert len(args) == 1
x = args[0]
memory_antecedent = kwargs.pop("memory_antecedent", x) # Same as x if None
if kwargs.get("bias", None) is not None: # Case where bias has been set
args = (x, memory_antecedent, kwargs.pop("bias"))
else:
# Otherwise, only 2 args. This is necessary as recompute_grad does not
# support None values.
args = (x, memory_antecedent)
return memeff_attention_base_fn(*args, **kwargs)
# === Local attention (unmasked) layer ===
# Reuse same parameters as multihead_attention
# Don't mask the future
local_attention_fn = partial(
multihead_attention_fn,
block_length=hparams.attention_loc_block_length,
block_width=hparams.attention_loc_block_width,
attention_type="local_unmasked",
)
# === Local attention (masked) layer ===
# Reuse same parameters as multihead_attention
# Only works for self attention. Always mask the future.
local_attention_masked_fn = partial(
multihead_attention_fn,
block_length=hparams.attention_loc_block_length,
attention_type="local_mask_right",
)
# === Masked memory-compressed multihead self attention layer ===
# Only works for self attention. Always mask the future.
compressed_attention_masked_fn = register_layer(
multihead_self_attention_reduced,
default_kwargs=dict(
factor=hparams.attention_red_factor,
nonlinearity=hparams.attention_red_nonlinearity,
reduction_type=hparams.attention_red_type,
multihead_params=dict(
total_key_depth=total_key_depth,
total_value_depth=total_value_depth,
num_heads=hparams.num_heads,
dropout_rate=hparams.attention_dropout,
),
),
)
# === Unmasked memory-compressed multihead self attention layer ===
# Only works for self attention. Never mask the future. Bias never added
compressed_attention_fn = partial(
compressed_attention_masked_fn,
add_mask=False,
)
# Feed-forwards layers:
# === FC layer ===
conv_hidden_relu = register_layer(
common_layers.conv_hidden_relu,
default_kwargs=dict(
hidden_size=hparams.filter_size,
output_size=hparams.hidden_size,
dropout=hparams.relu_dropout,
),
)
# === Separable convolution layer ===
# No mask applied
sep_conv_relu = partial(
conv_hidden_relu,
padding="SAME",
# Parameters copied from the transformer model, could add hparams
kernel_size=(3, 1),
second_kernel_size=(31, 1),
)
# === Separable convolution layer (masked version) ===
# Mask the future
sep_conv_relu_masked = partial(
sep_conv_relu,
padding="LEFT", # Mask future for decoder
)
# Define all available layers
cur_layers = dict(
# Attention layers:
a=multihead_attention_fn, # Multihead full attention
loc=local_attention_fn, # Local attention
locm=local_attention_masked_fn, # Local attention (masked)
red=compressed_attention_fn, # Memory-compressed attention
redm=compressed_attention_masked_fn, # Memory-compressed att (masked)
mem=memeff_attention_fn, # Memory efficient
# Feed-forward layers:
fc=conv_hidden_relu, # Fully connected
sep=sep_conv_relu, # Separable convolution (unmasked)
sepm=sep_conv_relu_masked, # Separable convolution (masked)
)
return cur_layers
|
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")",
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] |
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 within the devices.
If moe wants to be used, both dp and model need to be set.
Args:
hparams (tf.HParams): the model hparameters
dp (expert_utils.Parallelism): A data parallelism object. If not given,
the dp calls are simply ignored.
Returns:
dict[str:fct]: A dictionary containing the standardized functions
|
[
"Get",
"the",
"common",
"attention",
"and",
"feed",
"-",
"forward",
"layers",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L91-L299
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
add_standard_attention_hparams
|
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
# hparams.mode
# hparams.hidden_size
# Pre-post processing flags
# hparams.layer_preprocess_sequence
# hparams.layer_postprocess_sequence
# hparams.layer_prepostprocess_dropout
# hparams.norm_type
# hparams.norm_epsilon
# Mixture-of-Expert flags
# hparams.moe_hidden_sizes
# hparams.moe_num_experts
# hparams.moe_k
# hparams.moe_loss_coef
# Attention layers flags
hparams.add_hparam("num_heads", 8)
hparams.add_hparam("attention_key_channels", 0)
hparams.add_hparam("attention_value_channels", 0)
hparams.add_hparam("attention_dropout", 0.0)
# Attention: Local
hparams.add_hparam("attention_loc_block_length", 256)
# Attention: Local (unmasked only): How much to look left.
hparams.add_hparam("attention_loc_block_width", 128)
# Attention: Memory-compressed
hparams.add_hparam("attention_red_factor", 3)
hparams.add_hparam("attention_red_type", "conv")
hparams.add_hparam("attention_red_nonlinearity", "none")
# Fully connected layers flags
# To be more consistent, should use filter_size to also control the MOE
# size if moe_hidden_sizes not set.
hparams.add_hparam("filter_size", 2048)
hparams.add_hparam("relu_dropout", 0.0)
return hparams
|
python
|
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
# hparams.mode
# hparams.hidden_size
# Pre-post processing flags
# hparams.layer_preprocess_sequence
# hparams.layer_postprocess_sequence
# hparams.layer_prepostprocess_dropout
# hparams.norm_type
# hparams.norm_epsilon
# Mixture-of-Expert flags
# hparams.moe_hidden_sizes
# hparams.moe_num_experts
# hparams.moe_k
# hparams.moe_loss_coef
# Attention layers flags
hparams.add_hparam("num_heads", 8)
hparams.add_hparam("attention_key_channels", 0)
hparams.add_hparam("attention_value_channels", 0)
hparams.add_hparam("attention_dropout", 0.0)
# Attention: Local
hparams.add_hparam("attention_loc_block_length", 256)
# Attention: Local (unmasked only): How much to look left.
hparams.add_hparam("attention_loc_block_width", 128)
# Attention: Memory-compressed
hparams.add_hparam("attention_red_factor", 3)
hparams.add_hparam("attention_red_type", "conv")
hparams.add_hparam("attention_red_nonlinearity", "none")
# Fully connected layers flags
# To be more consistent, should use filter_size to also control the MOE
# size if moe_hidden_sizes not set.
hparams.add_hparam("filter_size", 2048)
hparams.add_hparam("relu_dropout", 0.0)
return hparams
|
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Adds the hparams used by get_standardized_layers.
|
[
"Adds",
"the",
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"used",
"by",
"get_standardized_layers",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L302-L344
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
encoder_decoder_attention_loss
|
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:
expected_attention_logits: Tensor storing the expected encoder-decoder
attention logits with shape [batch_size, target_length, input_length].
actual_attentions: Dictionary with actual attention logits for different
attention types and hidden layers.
loss_type: type of the loss function.
loss_multiplier: multiplier for the attention loss.
Returns:
KL_divergence loss between the actual and expected attention logits.
"""
def combine_attentions(attention_list):
"""Combine different layer attentions and then average over layers/heads."""
# Stack all hidden layer attention tensors to get a tensor with shape
# [num_hidden_layers, batch_size, num_heads, target_length, input_length].
attentions = tf.stack(attention_list)
# Reduce mean across all layers (axis=0) and all heads (axis=2) to get a
# tensor with shape [batch_size, target_length, input_length].
return tf.reduce_mean(attentions, [0, 2])
def kl_divergence_loss(expected_logits, actual_logits):
p = tfp.distributions.Categorical(logits=expected_logits)
q = tfp.distributions.Categorical(logits=actual_logits)
return tfp.distributions.kl_divergence(p, q)
def mse_loss(expected_logits, actual_weights):
expected_weights = tf.nn.softmax(expected_logits)
return tf.losses.mean_squared_error(expected_weights, actual_weights)
# For each hidden layer, we have attention-logit and attention-weight tensors
# with shape [batch_size, num_heads, target_length, input_length].
loss = 0.0
if loss_type == "mse":
actual_encdec_attention_weights = [
t for layer_key, t in actual_attentions.items()
if "encdec_attention" in layer_key and not layer_key.endswith("/logits")
]
actual_attention_weights = combine_attentions(
actual_encdec_attention_weights)
loss = mse_loss(expected_attention_logits, actual_attention_weights)
else:
actual_encdec_attention_logits = [
t for layer_key, t in actual_attentions.items()
if "encdec_attention" in layer_key and layer_key.endswith("/logits")
]
actual_attention_logits = combine_attentions(actual_encdec_attention_logits)
loss = kl_divergence_loss(expected_attention_logits,
actual_attention_logits)
return loss * loss_multiplier
|
python
|
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:
expected_attention_logits: Tensor storing the expected encoder-decoder
attention logits with shape [batch_size, target_length, input_length].
actual_attentions: Dictionary with actual attention logits for different
attention types and hidden layers.
loss_type: type of the loss function.
loss_multiplier: multiplier for the attention loss.
Returns:
KL_divergence loss between the actual and expected attention logits.
"""
def combine_attentions(attention_list):
"""Combine different layer attentions and then average over layers/heads."""
# Stack all hidden layer attention tensors to get a tensor with shape
# [num_hidden_layers, batch_size, num_heads, target_length, input_length].
attentions = tf.stack(attention_list)
# Reduce mean across all layers (axis=0) and all heads (axis=2) to get a
# tensor with shape [batch_size, target_length, input_length].
return tf.reduce_mean(attentions, [0, 2])
def kl_divergence_loss(expected_logits, actual_logits):
p = tfp.distributions.Categorical(logits=expected_logits)
q = tfp.distributions.Categorical(logits=actual_logits)
return tfp.distributions.kl_divergence(p, q)
def mse_loss(expected_logits, actual_weights):
expected_weights = tf.nn.softmax(expected_logits)
return tf.losses.mean_squared_error(expected_weights, actual_weights)
# For each hidden layer, we have attention-logit and attention-weight tensors
# with shape [batch_size, num_heads, target_length, input_length].
loss = 0.0
if loss_type == "mse":
actual_encdec_attention_weights = [
t for layer_key, t in actual_attentions.items()
if "encdec_attention" in layer_key and not layer_key.endswith("/logits")
]
actual_attention_weights = combine_attentions(
actual_encdec_attention_weights)
loss = mse_loss(expected_attention_logits, actual_attention_weights)
else:
actual_encdec_attention_logits = [
t for layer_key, t in actual_attentions.items()
if "encdec_attention" in layer_key and layer_key.endswith("/logits")
]
actual_attention_logits = combine_attentions(actual_encdec_attention_logits)
loss = kl_divergence_loss(expected_attention_logits,
actual_attention_logits)
return loss * loss_multiplier
|
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",",
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")",
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Computes encdec attention loss between expected and actual attentions.
Args:
expected_attention_logits: Tensor storing the expected encoder-decoder
attention logits with shape [batch_size, target_length, input_length].
actual_attentions: Dictionary with actual attention logits for different
attention types and hidden layers.
loss_type: type of the loss function.
loss_multiplier: multiplier for the attention loss.
Returns:
KL_divergence loss between the actual and expected attention logits.
|
[
"Computes",
"encdec",
"attention",
"loss",
"between",
"expected",
"and",
"actual",
"attentions",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L347-L402
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
get_timing_signal_1d
|
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 of a different
frequency and phase.
This allows attention to learn to use absolute and relative positions.
Timing signals should be added to some precursors of both the query and the
memory inputs to attention.
The use of relative position is possible because sin(x+y) and cos(x+y) can be
expressed in terms of y, sin(x) and cos(x).
In particular, we use a geometric sequence of timescales starting with
min_timescale and ending with max_timescale. The number of different
timescales is equal to channels / 2. For each timescale, we
generate the two sinusoidal signals sin(timestep/timescale) and
cos(timestep/timescale). All of these sinusoids are concatenated in
the channels dimension.
Args:
length: scalar, length of timing signal sequence.
channels: scalar, size of timing embeddings to create. The number of
different timescales is equal to channels / 2.
min_timescale: a float
max_timescale: a float
start_index: index of first position
Returns:
a Tensor of timing signals [1, length, channels]
"""
position = tf.to_float(tf.range(length) + start_index)
num_timescales = channels // 2
log_timescale_increment = (
math.log(float(max_timescale) / float(min_timescale)) /
tf.maximum(tf.to_float(num_timescales) - 1, 1))
inv_timescales = min_timescale * tf.exp(
tf.to_float(tf.range(num_timescales)) * -log_timescale_increment)
scaled_time = tf.expand_dims(position, 1) * tf.expand_dims(inv_timescales, 0)
signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1)
signal = tf.pad(signal, [[0, 0], [0, tf.mod(channels, 2)]])
signal = tf.reshape(signal, [1, length, channels])
return signal
|
python
|
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 of a different
frequency and phase.
This allows attention to learn to use absolute and relative positions.
Timing signals should be added to some precursors of both the query and the
memory inputs to attention.
The use of relative position is possible because sin(x+y) and cos(x+y) can be
expressed in terms of y, sin(x) and cos(x).
In particular, we use a geometric sequence of timescales starting with
min_timescale and ending with max_timescale. The number of different
timescales is equal to channels / 2. For each timescale, we
generate the two sinusoidal signals sin(timestep/timescale) and
cos(timestep/timescale). All of these sinusoids are concatenated in
the channels dimension.
Args:
length: scalar, length of timing signal sequence.
channels: scalar, size of timing embeddings to create. The number of
different timescales is equal to channels / 2.
min_timescale: a float
max_timescale: a float
start_index: index of first position
Returns:
a Tensor of timing signals [1, length, channels]
"""
position = tf.to_float(tf.range(length) + start_index)
num_timescales = channels // 2
log_timescale_increment = (
math.log(float(max_timescale) / float(min_timescale)) /
tf.maximum(tf.to_float(num_timescales) - 1, 1))
inv_timescales = min_timescale * tf.exp(
tf.to_float(tf.range(num_timescales)) * -log_timescale_increment)
scaled_time = tf.expand_dims(position, 1) * tf.expand_dims(inv_timescales, 0)
signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1)
signal = tf.pad(signal, [[0, 0], [0, tf.mod(channels, 2)]])
signal = tf.reshape(signal, [1, length, channels])
return signal
|
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frequency and phase.
This allows attention to learn to use absolute and relative positions.
Timing signals should be added to some precursors of both the query and the
memory inputs to attention.
The use of relative position is possible because sin(x+y) and cos(x+y) can be
expressed in terms of y, sin(x) and cos(x).
In particular, we use a geometric sequence of timescales starting with
min_timescale and ending with max_timescale. The number of different
timescales is equal to channels / 2. For each timescale, we
generate the two sinusoidal signals sin(timestep/timescale) and
cos(timestep/timescale). All of these sinusoids are concatenated in
the channels dimension.
Args:
length: scalar, length of timing signal sequence.
channels: scalar, size of timing embeddings to create. The number of
different timescales is equal to channels / 2.
min_timescale: a float
max_timescale: a float
start_index: index of first position
Returns:
a Tensor of timing signals [1, length, channels]
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L406-L452
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
add_timing_signal_1d
|
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 and phase.
This allows attention to learn to use absolute and relative positions.
Timing signals should be added to some precursors of both the query and the
memory inputs to attention.
The use of relative position is possible because sin(x+y) and cos(x+y) can be
expressed in terms of y, sin(x) and cos(x).
In particular, we use a geometric sequence of timescales starting with
min_timescale and ending with max_timescale. The number of different
timescales is equal to channels / 2. For each timescale, we
generate the two sinusoidal signals sin(timestep/timescale) and
cos(timestep/timescale). All of these sinusoids are concatenated in
the channels dimension.
Args:
x: a Tensor with shape [batch, length, channels]
min_timescale: a float
max_timescale: a float
start_index: index of first position
Returns:
a Tensor the same shape as x.
"""
length = common_layers.shape_list(x)[1]
channels = common_layers.shape_list(x)[2]
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale,
start_index)
return x + common_layers.cast_like(signal, x)
|
python
|
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 and phase.
This allows attention to learn to use absolute and relative positions.
Timing signals should be added to some precursors of both the query and the
memory inputs to attention.
The use of relative position is possible because sin(x+y) and cos(x+y) can be
expressed in terms of y, sin(x) and cos(x).
In particular, we use a geometric sequence of timescales starting with
min_timescale and ending with max_timescale. The number of different
timescales is equal to channels / 2. For each timescale, we
generate the two sinusoidal signals sin(timestep/timescale) and
cos(timestep/timescale). All of these sinusoids are concatenated in
the channels dimension.
Args:
x: a Tensor with shape [batch, length, channels]
min_timescale: a float
max_timescale: a float
start_index: index of first position
Returns:
a Tensor the same shape as x.
"""
length = common_layers.shape_list(x)[1]
channels = common_layers.shape_list(x)[2]
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale,
start_index)
return x + common_layers.cast_like(signal, x)
|
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This allows attention to learn to use absolute and relative positions.
Timing signals should be added to some precursors of both the query and the
memory inputs to attention.
The use of relative position is possible because sin(x+y) and cos(x+y) can be
expressed in terms of y, sin(x) and cos(x).
In particular, we use a geometric sequence of timescales starting with
min_timescale and ending with max_timescale. The number of different
timescales is equal to channels / 2. For each timescale, we
generate the two sinusoidal signals sin(timestep/timescale) and
cos(timestep/timescale). All of these sinusoids are concatenated in
the channels dimension.
Args:
x: a Tensor with shape [batch, length, channels]
min_timescale: a float
max_timescale: a float
start_index: index of first position
Returns:
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L456-L492
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
get_layer_timing_signal_learned_1d
|
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 layers
Returns:
a Tensor of timing signals [1, 1, channels].
"""
shape = [num_layers, 1, 1, channels]
layer_embedding = (
tf.get_variable(
"layer_embedding",
shape,
initializer=tf.random_normal_initializer(0, channels**-0.5)) *
(channels**0.5))
return layer_embedding[layer, :, :, :]
|
python
|
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 layers
Returns:
a Tensor of timing signals [1, 1, channels].
"""
shape = [num_layers, 1, 1, channels]
layer_embedding = (
tf.get_variable(
"layer_embedding",
shape,
initializer=tf.random_normal_initializer(0, channels**-0.5)) *
(channels**0.5))
return layer_embedding[layer, :, :, :]
|
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get n-dimensional embedding as the layer (vertical) timing signal.
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layer: layer num
num_layers: total number of layers
Returns:
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L496-L516
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
add_layer_timing_signal_learned_1d
|
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 layers
Returns:
a Tensor the same shape as x.
"""
channels = common_layers.shape_list(x)[-1]
signal = get_layer_timing_signal_learned_1d(channels, layer, num_layers)
x += signal
return x
|
python
|
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 layers
Returns:
a Tensor the same shape as x.
"""
channels = common_layers.shape_list(x)[-1]
signal = get_layer_timing_signal_learned_1d(channels, layer, num_layers)
x += signal
return x
|
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layer: layer num
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L520-L536
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
get_layer_timing_signal_sinusoid_1d
|
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, channels].
"""
signal = get_timing_signal_1d(num_layers, channels)
layer_signal = tf.expand_dims(signal[:, layer, :], axis=1)
return layer_signal
|
python
|
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, channels].
"""
signal = get_timing_signal_1d(num_layers, channels)
layer_signal = tf.expand_dims(signal[:, layer, :], axis=1)
return layer_signal
|
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Add sinusoids of different frequencies as layer (vertical) timing signal.
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layer: layer num
num_layers: total number of layers
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|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L540-L555
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
add_layer_timing_signal_sinusoid_1d
|
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.
"""
channels = common_layers.shape_list(x)[-1]
signal = get_layer_timing_signal_sinusoid_1d(channels, layer, num_layers)
return x + signal
|
python
|
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.
"""
channels = common_layers.shape_list(x)[-1]
signal = get_layer_timing_signal_sinusoid_1d(channels, layer, num_layers)
return x + signal
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L559-L574
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
add_timing_signal_1d_given_position
|
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 with shape [batch, length, channels]
position: a Tensor with shape [batch, length]
min_timescale: a float
max_timescale: a float
Returns:
a Tensor the same shape as x.
"""
channels = common_layers.shape_list(x)[2]
num_timescales = channels // 2
log_timescale_increment = (
math.log(float(max_timescale) / float(min_timescale)) /
(tf.to_float(num_timescales) - 1))
inv_timescales = min_timescale * tf.exp(
tf.to_float(tf.range(num_timescales)) * -log_timescale_increment)
scaled_time = (
tf.expand_dims(tf.to_float(position), 2) * tf.expand_dims(
tf.expand_dims(inv_timescales, 0), 0))
signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=2)
signal = tf.pad(signal, [[0, 0], [0, 0], [0, tf.mod(channels, 2)]])
signal = common_layers.cast_like(signal, x)
return x + signal
|
python
|
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 with shape [batch, length, channels]
position: a Tensor with shape [batch, length]
min_timescale: a float
max_timescale: a float
Returns:
a Tensor the same shape as x.
"""
channels = common_layers.shape_list(x)[2]
num_timescales = channels // 2
log_timescale_increment = (
math.log(float(max_timescale) / float(min_timescale)) /
(tf.to_float(num_timescales) - 1))
inv_timescales = min_timescale * tf.exp(
tf.to_float(tf.range(num_timescales)) * -log_timescale_increment)
scaled_time = (
tf.expand_dims(tf.to_float(position), 2) * tf.expand_dims(
tf.expand_dims(inv_timescales, 0), 0))
signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=2)
signal = tf.pad(signal, [[0, 0], [0, 0], [0, tf.mod(channels, 2)]])
signal = common_layers.cast_like(signal, x)
return x + signal
|
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min_timescale: a float
max_timescale: a float
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L578-L606
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
add_timing_signal_nd
|
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 absolute and relative positions.
Timing signals should be added to some precursors of both the query and the
memory inputs to attention.
The use of relative position is possible because sin(a+b) and cos(a+b) can be
experessed in terms of b, sin(a) and cos(a).
x is a Tensor with n "positional" dimensions, e.g. one dimension for a
sequence or two dimensions for an image
We use a geometric sequence of timescales starting with
min_timescale and ending with max_timescale. The number of different
timescales is equal to channels // (n * 2). For each timescale, we
generate the two sinusoidal signals sin(timestep/timescale) and
cos(timestep/timescale). All of these sinusoids are concatenated in
the channels dimension.
Args:
x: a Tensor with shape [batch, d1 ... dn, channels]
min_timescale: a float
max_timescale: a float
Returns:
a Tensor the same shape as x.
"""
num_dims = len(x.get_shape().as_list()) - 2
channels = common_layers.shape_list(x)[-1]
num_timescales = channels // (num_dims * 2)
log_timescale_increment = (
math.log(float(max_timescale) / float(min_timescale)) /
(tf.to_float(num_timescales) - 1))
inv_timescales = min_timescale * tf.exp(
tf.to_float(tf.range(num_timescales)) * -log_timescale_increment)
for dim in range(num_dims):
length = common_layers.shape_list(x)[dim + 1]
position = tf.to_float(tf.range(length))
scaled_time = tf.expand_dims(position, 1) * tf.expand_dims(
inv_timescales, 0)
signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1)
prepad = dim * 2 * num_timescales
postpad = channels - (dim + 1) * 2 * num_timescales
signal = tf.pad(signal, [[0, 0], [prepad, postpad]])
for _ in range(1 + dim):
signal = tf.expand_dims(signal, 0)
for _ in range(num_dims - 1 - dim):
signal = tf.expand_dims(signal, -2)
x += signal
return x
|
python
|
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 absolute and relative positions.
Timing signals should be added to some precursors of both the query and the
memory inputs to attention.
The use of relative position is possible because sin(a+b) and cos(a+b) can be
experessed in terms of b, sin(a) and cos(a).
x is a Tensor with n "positional" dimensions, e.g. one dimension for a
sequence or two dimensions for an image
We use a geometric sequence of timescales starting with
min_timescale and ending with max_timescale. The number of different
timescales is equal to channels // (n * 2). For each timescale, we
generate the two sinusoidal signals sin(timestep/timescale) and
cos(timestep/timescale). All of these sinusoids are concatenated in
the channels dimension.
Args:
x: a Tensor with shape [batch, d1 ... dn, channels]
min_timescale: a float
max_timescale: a float
Returns:
a Tensor the same shape as x.
"""
num_dims = len(x.get_shape().as_list()) - 2
channels = common_layers.shape_list(x)[-1]
num_timescales = channels // (num_dims * 2)
log_timescale_increment = (
math.log(float(max_timescale) / float(min_timescale)) /
(tf.to_float(num_timescales) - 1))
inv_timescales = min_timescale * tf.exp(
tf.to_float(tf.range(num_timescales)) * -log_timescale_increment)
for dim in range(num_dims):
length = common_layers.shape_list(x)[dim + 1]
position = tf.to_float(tf.range(length))
scaled_time = tf.expand_dims(position, 1) * tf.expand_dims(
inv_timescales, 0)
signal = tf.concat([tf.sin(scaled_time), tf.cos(scaled_time)], axis=1)
prepad = dim * 2 * num_timescales
postpad = channels - (dim + 1) * 2 * num_timescales
signal = tf.pad(signal, [[0, 0], [prepad, postpad]])
for _ in range(1 + dim):
signal = tf.expand_dims(signal, 0)
for _ in range(num_dims - 1 - dim):
signal = tf.expand_dims(signal, -2)
x += signal
return x
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This allows attention to learn to use absolute and relative positions.
Timing signals should be added to some precursors of both the query and the
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The use of relative position is possible because sin(a+b) and cos(a+b) can be
experessed in terms of b, sin(a) and cos(a).
x is a Tensor with n "positional" dimensions, e.g. one dimension for a
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We use a geometric sequence of timescales starting with
min_timescale and ending with max_timescale. The number of different
timescales is equal to channels // (n * 2). For each timescale, we
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Args:
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|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L610-L663
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
add_positional_embedding
|
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 with shape [batch, length].
Returns:
Tensor of same shape as x.
"""
with tf.name_scope("add_positional_embedding"):
_, length, depth = common_layers.shape_list(x)
var = tf.cast(tf.get_variable(name, [max_length, depth]), x.dtype)
if positions is None:
pad_length = tf.maximum(0, length - max_length)
sliced = tf.cond(
tf.less(length, max_length),
lambda: tf.slice(var, [0, 0], [length, -1]),
lambda: tf.pad(var, [[0, pad_length], [0, 0]]))
return x + tf.expand_dims(sliced, 0)
else:
return x + tf.gather(var, tf.to_int32(positions))
|
python
|
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 with shape [batch, length].
Returns:
Tensor of same shape as x.
"""
with tf.name_scope("add_positional_embedding"):
_, length, depth = common_layers.shape_list(x)
var = tf.cast(tf.get_variable(name, [max_length, depth]), x.dtype)
if positions is None:
pad_length = tf.maximum(0, length - max_length)
sliced = tf.cond(
tf.less(length, max_length),
lambda: tf.slice(var, [0, 0], [length, -1]),
lambda: tf.pad(var, [[0, pad_length], [0, 0]]))
return x + tf.expand_dims(sliced, 0)
else:
return x + tf.gather(var, tf.to_int32(positions))
|
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positions: Tensor with shape [batch, length].
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|
[
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L666-L689
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
add_positional_embedding_nd
|
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.
max_length: int representing static maximum size of any dimension.
name: str representing name of the embedding tf.Variable.
Returns:
Tensor of same shape as x.
"""
with tf.name_scope("add_positional_embedding_nd"):
x_shape = common_layers.shape_list(x)
num_dims = len(x_shape) - 2
depth = x_shape[-1]
base_shape = [1] * (num_dims + 1) + [depth]
base_start = [0] * (num_dims + 2)
base_size = [-1] + [1] * num_dims + [depth]
for i in range(num_dims):
shape = base_shape[:]
start = base_start[:]
size = base_size[:]
shape[i + 1] = max_length
size[i + 1] = x_shape[i + 1]
var = tf.get_variable(
name + "_%d" % i,
shape,
initializer=tf.random_normal_initializer(0, depth**-0.5))
var = var * depth**0.5
x += tf.slice(var, start, size)
return x
|
python
|
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.
max_length: int representing static maximum size of any dimension.
name: str representing name of the embedding tf.Variable.
Returns:
Tensor of same shape as x.
"""
with tf.name_scope("add_positional_embedding_nd"):
x_shape = common_layers.shape_list(x)
num_dims = len(x_shape) - 2
depth = x_shape[-1]
base_shape = [1] * (num_dims + 1) + [depth]
base_start = [0] * (num_dims + 2)
base_size = [-1] + [1] * num_dims + [depth]
for i in range(num_dims):
shape = base_shape[:]
start = base_start[:]
size = base_size[:]
shape[i + 1] = max_length
size[i + 1] = x_shape[i + 1]
var = tf.get_variable(
name + "_%d" % i,
shape,
initializer=tf.random_normal_initializer(0, depth**-0.5))
var = var * depth**0.5
x += tf.slice(var, start, size)
return x
|
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max_length: int representing static maximum size of any dimension.
name: str representing name of the embedding tf.Variable.
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[
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L692-L725
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
make_edge_vectors
|
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
Returns:
A [batch, num_nodes, num_nodes, depth] vector of tensors
"""
with tf.variable_scope(name, default_name="edge_vectors"):
att_adj_vectors_shape = [num_edge_types, depth]
adjacency_matrix_shape = common_layers.shape_list(adjacency_matrix)
adj_vectors = (
tf.get_variable(
"adj_vectors",
att_adj_vectors_shape,
initializer=tf.random_normal_initializer(0, depth**-0.5)) *
(depth**0.5))
# Avoiding gathers so that it works on TPUs
# adjacency_matrix_one_hot has shape
# [batch, num_nodes, num_nodes, num_edge_types]
adjacency_matrix_one_hot = tf.one_hot(adjacency_matrix, num_edge_types)
att_adj_vectors = tf.matmul(
tf.reshape(tf.to_float(adjacency_matrix_one_hot), [-1, num_edge_types]),
adj_vectors)
return tf.reshape(att_adj_vectors,
[adjacency_matrix_shape[0], adjacency_matrix_shape[1],
adjacency_matrix_shape[2], depth])
|
python
|
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
Returns:
A [batch, num_nodes, num_nodes, depth] vector of tensors
"""
with tf.variable_scope(name, default_name="edge_vectors"):
att_adj_vectors_shape = [num_edge_types, depth]
adjacency_matrix_shape = common_layers.shape_list(adjacency_matrix)
adj_vectors = (
tf.get_variable(
"adj_vectors",
att_adj_vectors_shape,
initializer=tf.random_normal_initializer(0, depth**-0.5)) *
(depth**0.5))
# Avoiding gathers so that it works on TPUs
# adjacency_matrix_one_hot has shape
# [batch, num_nodes, num_nodes, num_edge_types]
adjacency_matrix_one_hot = tf.one_hot(adjacency_matrix, num_edge_types)
att_adj_vectors = tf.matmul(
tf.reshape(tf.to_float(adjacency_matrix_one_hot), [-1, num_edge_types]),
adj_vectors)
return tf.reshape(att_adj_vectors,
[adjacency_matrix_shape[0], adjacency_matrix_shape[1],
adjacency_matrix_shape[2], depth])
|
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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
Returns:
A [batch, num_nodes, num_nodes, depth] vector of tensors
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L728-L759
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
padding_to_length
|
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))
|
python
|
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))
|
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Calculate the length of mask based on padding.
Args:
padding: a Tensor with shape [..., length].
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a Tensor with shape [...].
|
[
"Calculate",
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L860-L869
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
attention_bias_local
|
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 distance backward to attend. Negative values
indicate unlimited.
max_forward: int, maximum distance forward to attend. Negative values
indicate unlimited.
Returns:
a `Tensor` with shape [1, 1, length, length].
"""
band = common_layers.ones_matrix_band_part(
length,
length,
max_backward,
max_forward,
out_shape=[1, 1, length, length])
return -1e9 * (1.0 - band)
|
python
|
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 distance backward to attend. Negative values
indicate unlimited.
max_forward: int, maximum distance forward to attend. Negative values
indicate unlimited.
Returns:
a `Tensor` with shape [1, 1, length, length].
"""
band = common_layers.ones_matrix_band_part(
length,
length,
max_backward,
max_forward,
out_shape=[1, 1, length, length])
return -1e9 * (1.0 - band)
|
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Create an bias tensor to be added to attention logits.
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This does not actually save any computation.
Args:
length: int
max_backward: int, maximum distance backward to attend. Negative values
indicate unlimited.
max_forward: int, maximum distance forward to attend. Negative values
indicate unlimited.
Returns:
a `Tensor` with shape [1, 1, length, length].
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L873-L897
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
attention_bias_same_segment
|
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 shape [batch, memory_length].
Returns:
a `Tensor` with shape [batch, 1, query_length, memory_length].
"""
ret = (tf.to_float(
tf.not_equal(
tf.expand_dims(query_segment_id, 2),
tf.expand_dims(memory_segment_id, 1))) *
large_compatible_negative(memory_segment_id.dtype))
return tf.expand_dims(ret, axis=1)
|
python
|
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 shape [batch, memory_length].
Returns:
a `Tensor` with shape [batch, 1, query_length, memory_length].
"""
ret = (tf.to_float(
tf.not_equal(
tf.expand_dims(query_segment_id, 2),
tf.expand_dims(memory_segment_id, 1))) *
large_compatible_negative(memory_segment_id.dtype))
return tf.expand_dims(ret, axis=1)
|
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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 shape [batch, memory_length].
Returns:
a `Tensor` with shape [batch, 1, query_length, memory_length].
|
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"to",
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"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L916-L933
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
attention_bias_ignore_padding
|
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(memory_padding.dtype)
return tf.expand_dims(tf.expand_dims(ret, axis=1), axis=1)
|
python
|
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(memory_padding.dtype)
return tf.expand_dims(tf.expand_dims(ret, axis=1), axis=1)
|
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Create an bias tensor to be added to attention logits.
Args:
memory_padding: a float `Tensor` with shape [batch, memory_length].
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a `Tensor` with shape [batch, 1, 1, memory_length].
|
[
"Create",
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"to",
"be",
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"to",
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"logits",
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] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L937-L947
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
attention_bias_to_padding
|
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 Tensor with shape [batch, memory_length] with 1.0 in padding positions
and 0.0 in non-padding positions. Type is determined by cast_fn.
"""
# `attention_bias` is a large negative number in padding positions and 0.0
# elsewhere.
return tf.squeeze(cast_fn(tf.less(attention_bias, -1)), axis=[1, 2])
|
python
|
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 Tensor with shape [batch, memory_length] with 1.0 in padding positions
and 0.0 in non-padding positions. Type is determined by cast_fn.
"""
# `attention_bias` is a large negative number in padding positions and 0.0
# elsewhere.
return tf.squeeze(cast_fn(tf.less(attention_bias, -1)), axis=[1, 2])
|
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Inverse of attention_bias_ignore_padding().
Args:
attention_bias: a `Tensor` with shape [batch, 1, 1, memory_length], as
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cast_fn: function used to cast to output type.
Returns:
a Tensor with shape [batch, memory_length] with 1.0 in padding positions
and 0.0 in non-padding positions. Type is determined by cast_fn.
|
[
"Inverse",
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L951-L965
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
attention_bias_prepend_inputs_full_attention
|
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 sequence and masked connectivity in the targets part.
Args:
padding: a float `Tensor` with shape [batch, length] with
ones in positions corresponding to padding. In each row, a single
padding position separates the input part from the target part.
Returns:
a `Tensor` with shape [batch, 1, length, length].
"""
# Everything past the first padding position is part of the target.
# This Tensor has zeros for the source portion and separator,
# and ones for the target portion.
in_target = tf.cumsum(padding, axis=1, exclusive=True)
# The position within the target, or 0 if part of the source.
target_pos = tf.cumsum(in_target, axis=1)
# A position with a lesser target_pos cannot see a position with greater
# target_pos.
illegal_connections = tf.greater(
tf.expand_dims(target_pos, 1), tf.expand_dims(target_pos, 2))
bias = tf.to_float(illegal_connections) * -1e9
bias = tf.expand_dims(bias, 1)
return bias
|
python
|
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 sequence and masked connectivity in the targets part.
Args:
padding: a float `Tensor` with shape [batch, length] with
ones in positions corresponding to padding. In each row, a single
padding position separates the input part from the target part.
Returns:
a `Tensor` with shape [batch, 1, length, length].
"""
# Everything past the first padding position is part of the target.
# This Tensor has zeros for the source portion and separator,
# and ones for the target portion.
in_target = tf.cumsum(padding, axis=1, exclusive=True)
# The position within the target, or 0 if part of the source.
target_pos = tf.cumsum(in_target, axis=1)
# A position with a lesser target_pos cannot see a position with greater
# target_pos.
illegal_connections = tf.greater(
tf.expand_dims(target_pos, 1), tf.expand_dims(target_pos, 2))
bias = tf.to_float(illegal_connections) * -1e9
bias = tf.expand_dims(bias, 1)
return bias
|
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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 sequence and masked connectivity in the targets part.
Args:
padding: a float `Tensor` with shape [batch, length] with
ones in positions corresponding to padding. In each row, a single
padding position separates the input part from the target part.
Returns:
a `Tensor` with shape [batch, 1, length, length].
|
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] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L969-L999
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
attention_bias_proximal
|
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.expand_dims(tf.expand_dims(-tf.log1p(tf.abs(diff)), 0), 0)
|
python
|
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.expand_dims(tf.expand_dims(-tf.log1p(tf.abs(diff)), 0), 0)
|
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Bias for self-attention to encourage attention to close positions.
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[
"Bias",
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] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1003-L1014
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
attention_bias_batch
|
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 the batches
batch_coordinates_k: Int-like Tensor of shape [length_k, 1] containing the
coordinates of the batches. If None, do self-attention.
condition_fn: Callable defining the attention mask.
Returns:
Float-like Tensor of shape [length_q, length_k] containing either 0 or
-infinity (-1e9).
"""
if batch_coordinates_k is None:
batch_coordinates_k = batch_coordinates_q
# Convert to float first because of b/25387198.
def to_float(bc):
bc = tf.squeeze(bc, 1)
bc = tf.to_float(bc)
return bc
# Broadcast to create [length_q, length_k] mask.
bc_v = tf.expand_dims(to_float(batch_coordinates_q), 1)
bc_h = tf.expand_dims(to_float(batch_coordinates_k), 0)
bias_batch = bc_h - bc_v
bias_batch = condition_fn(bias_batch)
bias_batch *= -1e9
return bias_batch
|
python
|
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 the batches
batch_coordinates_k: Int-like Tensor of shape [length_k, 1] containing the
coordinates of the batches. If None, do self-attention.
condition_fn: Callable defining the attention mask.
Returns:
Float-like Tensor of shape [length_q, length_k] containing either 0 or
-infinity (-1e9).
"""
if batch_coordinates_k is None:
batch_coordinates_k = batch_coordinates_q
# Convert to float first because of b/25387198.
def to_float(bc):
bc = tf.squeeze(bc, 1)
bc = tf.to_float(bc)
return bc
# Broadcast to create [length_q, length_k] mask.
bc_v = tf.expand_dims(to_float(batch_coordinates_q), 1)
bc_h = tf.expand_dims(to_float(batch_coordinates_k), 0)
bias_batch = bc_h - bc_v
bias_batch = condition_fn(bias_batch)
bias_batch *= -1e9
return bias_batch
|
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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 the batches
batch_coordinates_k: Int-like Tensor of shape [length_k, 1] containing the
coordinates of the batches. If None, do self-attention.
condition_fn: Callable defining the attention mask.
Returns:
Float-like Tensor of shape [length_q, length_k] containing either 0 or
-infinity (-1e9).
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1018-L1049
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
split_last_dimension
|
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]
if isinstance(m, int) and isinstance(n, int):
assert m % n == 0
return tf.reshape(x, x_shape[:-1] + [n, m // n])
|
python
|
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]
if isinstance(m, int) and isinstance(n, int):
assert m % n == 0
return tf.reshape(x, x_shape[:-1] + [n, m // n])
|
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Reshape x so that the last dimension becomes two dimensions.
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1069-L1085
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
combine_last_two_dimensions
|
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])
|
python
|
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])
|
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Reshape x so that the last two dimension become one.
Args:
x: a Tensor with shape [..., a, b]
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a Tensor with shape [..., ab]
|
[
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1089-L1100
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
combine_first_two_dimensions
|
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_shape[:2]
new_shape = [a * b if a and b else None] + old_shape[2:]
ret.set_shape(new_shape)
return ret
|
python
|
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_shape[:2]
new_shape = [a * b if a and b else None] + old_shape[2:]
ret.set_shape(new_shape)
return ret
|
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Reshape x so that the first two dimension become one.
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[
"Reshape",
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1104-L1118
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
attention_image_summary
|
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 images, then pass in their dimensions:
(query_rows, query_cols, memory_rows, memory_cols).
If the query positions and memory positions represent the
pixels x channels of flattened images, then pass in their dimensions:
(query_rows, query_cols, query_channels,
memory_rows, memory_cols, memory_channels).
"""
attn = tf.cast(attn, tf.float32)
num_heads = common_layers.shape_list(attn)[1]
# [batch, query_length, memory_length, num_heads]
image = tf.transpose(attn, [0, 2, 3, 1])
image = tf.pow(image, 0.2) # for high-dynamic-range
# Each head will correspond to one of RGB.
# pad the heads to be a multiple of 3
image = tf.pad(image, [[0, 0], [0, 0], [0, 0], [0, tf.mod(-num_heads, 3)]])
image = split_last_dimension(image, 3)
image = tf.reduce_max(image, 4)
if image_shapes is not None:
if len(image_shapes) == 4:
q_rows, q_cols, m_rows, m_cols = list(image_shapes)
image = tf.reshape(image, [-1, q_rows, q_cols, m_rows, m_cols, 3])
image = tf.transpose(image, [0, 1, 3, 2, 4, 5])
image = tf.reshape(image, [-1, q_rows * m_rows, q_cols * m_cols, 3])
else:
assert len(image_shapes) == 6
q_rows, q_cols, q_channnels, m_rows, m_cols, m_channels = list(
image_shapes)
image = tf.reshape(
image,
[-1, q_rows, q_cols, q_channnels, m_rows, m_cols, m_channels, 3])
image = tf.transpose(image, [0, 1, 4, 3, 2, 5, 6, 7])
image = tf.reshape(
image,
[-1, q_rows * m_rows * q_channnels, q_cols * m_cols * m_channels, 3])
tf.summary.image("attention", image, max_outputs=1)
|
python
|
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 images, then pass in their dimensions:
(query_rows, query_cols, memory_rows, memory_cols).
If the query positions and memory positions represent the
pixels x channels of flattened images, then pass in their dimensions:
(query_rows, query_cols, query_channels,
memory_rows, memory_cols, memory_channels).
"""
attn = tf.cast(attn, tf.float32)
num_heads = common_layers.shape_list(attn)[1]
# [batch, query_length, memory_length, num_heads]
image = tf.transpose(attn, [0, 2, 3, 1])
image = tf.pow(image, 0.2) # for high-dynamic-range
# Each head will correspond to one of RGB.
# pad the heads to be a multiple of 3
image = tf.pad(image, [[0, 0], [0, 0], [0, 0], [0, tf.mod(-num_heads, 3)]])
image = split_last_dimension(image, 3)
image = tf.reduce_max(image, 4)
if image_shapes is not None:
if len(image_shapes) == 4:
q_rows, q_cols, m_rows, m_cols = list(image_shapes)
image = tf.reshape(image, [-1, q_rows, q_cols, m_rows, m_cols, 3])
image = tf.transpose(image, [0, 1, 3, 2, 4, 5])
image = tf.reshape(image, [-1, q_rows * m_rows, q_cols * m_cols, 3])
else:
assert len(image_shapes) == 6
q_rows, q_cols, q_channnels, m_rows, m_cols, m_channels = list(
image_shapes)
image = tf.reshape(
image,
[-1, q_rows, q_cols, q_channnels, m_rows, m_cols, m_channels, 3])
image = tf.transpose(image, [0, 1, 4, 3, 2, 5, 6, 7])
image = tf.reshape(
image,
[-1, q_rows * m_rows * q_channnels, q_cols * m_cols * m_channels, 3])
tf.summary.image("attention", image, max_outputs=1)
|
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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 images, then pass in their dimensions:
(query_rows, query_cols, memory_rows, memory_cols).
If the query positions and memory positions represent the
pixels x channels of flattened images, then pass in their dimensions:
(query_rows, query_cols, query_channels,
memory_rows, memory_cols, memory_channels).
|
[
"Compute",
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] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1176-L1217
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
grouped_attention_multihead
|
def grouped_attention_multihead(query_antecedent,
memory_antecedent,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
num_groups,
memory_target_density=2.0,
multiplicative_overhead=1.25,
additive_overhead=8.0,
mask_right=False,
make_image_summary=True,
name=None):
"""Multi-head dot-product attention with sparsity.
For each attention head, the queries are partitioned into groups.
For each group, only a subset of the key-value pairs are considered.
The choices of groups are selected based on trained predictors of
the total attention given the group inclusion.
memory_target_density indicates the average how many groups in which
a key-value pair should participate.
We use auxiliary losses to ensure that each group contains roughly
the same number of queries and the same number of key-value pairs.
If for a given sequence, the actual number of queries/pairs sent to
an expert exceeds this target by a factor of more than
multiplicative_overhead, then the last ones are dropped. We use
this drop-last policy to avoid bleeding information backwards, which
is necessary when using this function with autoregressive
prediction.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels]
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
num_groups: an integer
memory_target_density: a floating point scalar
multiplicative_overhead: a floating point scalar
additive_overhead: a floating point scalar
mask_right: a boolean
make_image_summary: a boolean
name: an optional string
Returns:
A Tensor with shape [batch, length_q, output_depth]
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads.
"""
batch = common_layers.shape_list(query_antecedent)[0]
length_q = common_layers.shape_list(query_antecedent)[1]
length_kv = common_layers.shape_list(memory_antecedent)[1]
if total_key_depth % num_heads != 0:
raise ValueError("Key depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_key_depth, num_heads))
depth_qk = total_key_depth // num_heads
if total_value_depth % num_heads != 0:
raise ValueError("Value depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_value_depth, num_heads))
depth_v = total_value_depth // num_heads
with tf.variable_scope(
name, default_name="multihead_attention_sparse",
values=[query_antecedent, memory_antecedent]):
q = common_layers.dense(
query_antecedent, total_key_depth, use_bias=False, name="q_transform")
kv = common_layers.dense(
memory_antecedent,
total_key_depth + total_value_depth,
use_bias=False,
name="kv_transform")
q = split_heads(q, num_heads)
kv = split_heads(kv, num_heads)
# Make predictions about q_total and m_total.
# These are used to determine group inclusion.
# We will train these by auxiliary losses. We use stop_gradient here
# to keep these losses from back-propagating to the rest of the model.
# We add biases that help balance the usage of the experts.
q_pred = common_layers.dense(
tf.stop_gradient(query_antecedent),
num_heads * num_groups,
use_bias=False,
name="q_pred")
q_pred = split_heads(q_pred, num_heads)
q_bias = tf.get_variable("q_bias", [1, num_heads, 1, num_groups])
q_pred_biased = q_pred + q_bias
m_pred = common_layers.dense(
tf.stop_gradient(memory_antecedent),
num_heads * num_groups,
use_bias=False,
name="m_pred")
m_pred = split_heads(m_pred, num_heads)
m_bias = tf.get_variable("m_bias", [1, num_heads, 1, num_groups])
m_pred_biased = m_pred + m_bias
q *= depth_qk**-0.5
# q, kv, q_pred, m_pred are all [batch, heads, length_[q/m], ?]
# now reshape them all to [batch * heads, length, ?]
q = combine_first_two_dimensions(q)
kv = combine_first_two_dimensions(kv)
q_pred = combine_first_two_dimensions(q_pred)
m_pred = combine_first_two_dimensions(m_pred)
q_pred_biased = combine_first_two_dimensions(q_pred_biased)
m_pred_biased = combine_first_two_dimensions(m_pred_biased)
q_group = tf.argmax(q_pred_biased, axis=2)
q_requests = tf.one_hot(q_group, num_groups, axis=-1)
m_requests = tf.to_float(tf.greater(m_pred_biased, 0.0))
# include first memory position in all groups, to avoid division by zero.
m_requests = tf.maximum(
m_requests, tf.reshape(tf.one_hot([0], length_kv), [1, length_kv, 1]))
q_group_size = tf.reduce_sum(q_requests, 1)
m_group_size = tf.reduce_sum(m_requests, 1)
q_group_target_size = tf.to_float(length_q) / tf.to_float(num_groups)
m_group_target_size = (
tf.to_float(length_kv) * memory_target_density /
tf.to_float(num_groups))
capacity_q = tf.minimum(
length_q,
tf.to_int32(q_group_target_size * multiplicative_overhead +
additive_overhead))
capacity_m = tf.minimum(
length_kv,
tf.to_int32(m_group_target_size * multiplicative_overhead +
additive_overhead))
q_dispatcher = expert_utils.TruncatingDispatcher(q_requests, capacity_q)
m_dispatcher = expert_utils.TruncatingDispatcher(m_requests, capacity_m)
q_gates = q_dispatcher.gates()
m_gates = m_dispatcher.gates()
dispatched_q = q_dispatcher.dispatch(q)
dispatched_kv = m_dispatcher.dispatch(kv)
# dispatched_q: [batch * num_heads, num_groups, capacity_q, depth_qk]
# dispatched_kv:
# [batch * num_heads, num_groups, capacity_m, depth_qk + depth_v]
k, v = tf.split(dispatched_kv, [depth_qk, depth_v], axis=3)
logits = tf.matmul(dispatched_q, k, transpose_b=True)
bias = tf.expand_dims((m_dispatcher.nonpadding() - 1.0) * 1e9, 2)
if mask_right:
q_coordinate = tf.to_float(
tf.expand_dims(q_dispatcher.length_coordinate(), 3))
m_coordinate = tf.to_float(
tf.expand_dims(m_dispatcher.length_coordinate(), 2))
bias += tf.to_float(tf.greater(m_coordinate, q_coordinate)) * -1e9
logits += bias
log_weights = tf.nn.log_softmax(logits)
weights = tf.exp(log_weights)
# For each query, this is the log of the sum of the unnormalized weights.
q_total = tf.stop_gradient(logits[:, :, :, :1] - log_weights[:, :, :, :1])
# For each key, this is the sum of the normalized weights.
m_total = tf.expand_dims(
tf.reduce_sum(tf.stop_gradient(weights), axis=2), -1)
o = tf.matmul(weights, v)
o = q_dispatcher.combine(o)
o = tf.reshape(o, [batch, num_heads, length_q, depth_v])
o = combine_heads(o)
o = common_layers.dense(
o, output_depth, use_bias=False, name="output_transform")
m_total = m_dispatcher.combine(m_total)
q_total = q_dispatcher.combine(q_total)
q_total = tf.squeeze(q_total, -1)
m_total = tf.squeeze(m_total, -1)
# Compute summed m predictions for all groups
m_pred_used = tf.reduce_sum(tf.exp(m_pred) * m_dispatcher.gates(), axis=2)
q_pred_used = tf.reduce_sum(q_pred * q_dispatcher.gates(), axis=2)
epsilon = 1e-3
m_pred_used = tf.log(m_pred_used + epsilon)
m_total = tf.log(m_total + epsilon)
m_loss = tf.nn.l2_loss(m_total - m_pred_used)
q_loss = tf.nn.l2_loss(
(q_total - q_pred_used) * tf.reduce_sum(q_gates, axis=2))
q_loss /= tf.to_float(batch * length_q)
m_loss /= tf.to_float(batch * length_kv)
# We would like the query groups to be equal sized. The group
# size is discrete, so we need some trick here. We add a loss
# proportional to the product of the group size and the
# predictions for that group. This encourages the predictions to
# decrease for groups that are too big.
q_group_deviation = (q_group_size / q_group_target_size) - 1.0
q_balance_loss = tf.reduce_sum(
tf.reduce_mean(q_pred_biased, axis=1) *
q_group_deviation) / tf.to_float(batch)
m_group_deviation = (m_group_size / m_group_target_size) - 1.0
m_balance_loss = tf.reduce_sum(
tf.reduce_mean(m_pred_biased, axis=1) *
m_group_deviation) / tf.to_float(batch)
# The losses in this function only propagate back to variables
# defined in this function, and the losses outside of this
# function only propagate back to variables outside of this
# function. Assuming some kind of adaptive learning algorithm,
# it should not matter how much we scale the losses in this function.
# Still we scale them down a lot so that they should not show up
# much in the overall loss for the model.
extra_loss_multiplier = 1e-3
extra_loss = q_loss + m_loss + q_balance_loss + m_balance_loss
extra_loss *= extra_loss_multiplier
# Show a bunch of summaries.
if common_layers.should_generate_summaries() and make_image_summary:
tf.summary.histogram("q_group_size", q_group_size)
tf.summary.histogram("m_group_size", m_group_size)
tf.summary.scalar("q_loss", q_loss)
tf.summary.scalar("m_loss", m_loss)
tf.summary.scalar("q_balance_loss", q_balance_loss)
tf.summary.scalar("m_balance_loss", m_balance_loss)
tf.summary.histogram("m_pred_used", m_pred_used)
tf.summary.histogram("m_total", m_total)
tf.summary.histogram("q_pred_used", q_pred_used)
tf.summary.histogram("q_total", q_total)
if make_image_summary:
# image summaries are expensive.
# So we restrict them to head_num<4, query_position<512, batch_index=0.
trunc_heads = min(4, num_heads)
trunc_length_q = tf.minimum(length_q, 512)
# We recompute the attention for the first example, in an inefficient
# way - masking. This lets us show pretty pictures.
# [trunc_heads, length_q, group]
q_gates_trunc = q_gates[:trunc_heads, :trunc_length_q, :]
# [trunc_heads, length_kv, group]
m_gates_trunc = m_gates[:trunc_heads, :, :]
grouping_mask = tf.matmul(
q_gates_trunc, m_gates_trunc, transpose_b=True)
q_trunc = q[:trunc_heads, :trunc_length_q, :]
k_trunc = kv[:trunc_heads, :, :depth_qk]
logits_trunc = tf.matmul(q_trunc, k_trunc, transpose_b=True)
if mask_right:
band = common_layers.ones_matrix_band_part(trunc_length_q, length_kv,
-1, 0)
trunc_bias = tf.expand_dims((1.0 - band) * -1e9, 0)
logits_trunc += trunc_bias
att_trunc = tf.nn.softmax(logits_trunc)
mask_coverage = tf.reduce_sum(grouping_mask * att_trunc) / (
tf.to_float(trunc_length_q) * trunc_heads)
tf.summary.scalar("coverage", mask_coverage)
att_trunc_hdr = tf.pow(att_trunc, 0.2) # for high-dynamic-range
mask_channel = grouping_mask * tf.maximum(att_trunc_hdr, 0.3)
image = tf.stack([att_trunc_hdr, mask_channel, mask_channel], axis=3)
tf.summary.image("att", image, max_outputs=trunc_heads)
# show one group for each head.
att_per_group = tf.expand_dims(weights[:trunc_heads, 0, :, :], -1)
tf.summary.image(
"att_per_group_%d",
tf.pow(att_per_group, 0.2),
max_outputs=trunc_heads)
return o, extra_loss
|
python
|
def grouped_attention_multihead(query_antecedent,
memory_antecedent,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
num_groups,
memory_target_density=2.0,
multiplicative_overhead=1.25,
additive_overhead=8.0,
mask_right=False,
make_image_summary=True,
name=None):
"""Multi-head dot-product attention with sparsity.
For each attention head, the queries are partitioned into groups.
For each group, only a subset of the key-value pairs are considered.
The choices of groups are selected based on trained predictors of
the total attention given the group inclusion.
memory_target_density indicates the average how many groups in which
a key-value pair should participate.
We use auxiliary losses to ensure that each group contains roughly
the same number of queries and the same number of key-value pairs.
If for a given sequence, the actual number of queries/pairs sent to
an expert exceeds this target by a factor of more than
multiplicative_overhead, then the last ones are dropped. We use
this drop-last policy to avoid bleeding information backwards, which
is necessary when using this function with autoregressive
prediction.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels]
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
num_groups: an integer
memory_target_density: a floating point scalar
multiplicative_overhead: a floating point scalar
additive_overhead: a floating point scalar
mask_right: a boolean
make_image_summary: a boolean
name: an optional string
Returns:
A Tensor with shape [batch, length_q, output_depth]
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads.
"""
batch = common_layers.shape_list(query_antecedent)[0]
length_q = common_layers.shape_list(query_antecedent)[1]
length_kv = common_layers.shape_list(memory_antecedent)[1]
if total_key_depth % num_heads != 0:
raise ValueError("Key depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_key_depth, num_heads))
depth_qk = total_key_depth // num_heads
if total_value_depth % num_heads != 0:
raise ValueError("Value depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_value_depth, num_heads))
depth_v = total_value_depth // num_heads
with tf.variable_scope(
name, default_name="multihead_attention_sparse",
values=[query_antecedent, memory_antecedent]):
q = common_layers.dense(
query_antecedent, total_key_depth, use_bias=False, name="q_transform")
kv = common_layers.dense(
memory_antecedent,
total_key_depth + total_value_depth,
use_bias=False,
name="kv_transform")
q = split_heads(q, num_heads)
kv = split_heads(kv, num_heads)
# Make predictions about q_total and m_total.
# These are used to determine group inclusion.
# We will train these by auxiliary losses. We use stop_gradient here
# to keep these losses from back-propagating to the rest of the model.
# We add biases that help balance the usage of the experts.
q_pred = common_layers.dense(
tf.stop_gradient(query_antecedent),
num_heads * num_groups,
use_bias=False,
name="q_pred")
q_pred = split_heads(q_pred, num_heads)
q_bias = tf.get_variable("q_bias", [1, num_heads, 1, num_groups])
q_pred_biased = q_pred + q_bias
m_pred = common_layers.dense(
tf.stop_gradient(memory_antecedent),
num_heads * num_groups,
use_bias=False,
name="m_pred")
m_pred = split_heads(m_pred, num_heads)
m_bias = tf.get_variable("m_bias", [1, num_heads, 1, num_groups])
m_pred_biased = m_pred + m_bias
q *= depth_qk**-0.5
# q, kv, q_pred, m_pred are all [batch, heads, length_[q/m], ?]
# now reshape them all to [batch * heads, length, ?]
q = combine_first_two_dimensions(q)
kv = combine_first_two_dimensions(kv)
q_pred = combine_first_two_dimensions(q_pred)
m_pred = combine_first_two_dimensions(m_pred)
q_pred_biased = combine_first_two_dimensions(q_pred_biased)
m_pred_biased = combine_first_two_dimensions(m_pred_biased)
q_group = tf.argmax(q_pred_biased, axis=2)
q_requests = tf.one_hot(q_group, num_groups, axis=-1)
m_requests = tf.to_float(tf.greater(m_pred_biased, 0.0))
# include first memory position in all groups, to avoid division by zero.
m_requests = tf.maximum(
m_requests, tf.reshape(tf.one_hot([0], length_kv), [1, length_kv, 1]))
q_group_size = tf.reduce_sum(q_requests, 1)
m_group_size = tf.reduce_sum(m_requests, 1)
q_group_target_size = tf.to_float(length_q) / tf.to_float(num_groups)
m_group_target_size = (
tf.to_float(length_kv) * memory_target_density /
tf.to_float(num_groups))
capacity_q = tf.minimum(
length_q,
tf.to_int32(q_group_target_size * multiplicative_overhead +
additive_overhead))
capacity_m = tf.minimum(
length_kv,
tf.to_int32(m_group_target_size * multiplicative_overhead +
additive_overhead))
q_dispatcher = expert_utils.TruncatingDispatcher(q_requests, capacity_q)
m_dispatcher = expert_utils.TruncatingDispatcher(m_requests, capacity_m)
q_gates = q_dispatcher.gates()
m_gates = m_dispatcher.gates()
dispatched_q = q_dispatcher.dispatch(q)
dispatched_kv = m_dispatcher.dispatch(kv)
# dispatched_q: [batch * num_heads, num_groups, capacity_q, depth_qk]
# dispatched_kv:
# [batch * num_heads, num_groups, capacity_m, depth_qk + depth_v]
k, v = tf.split(dispatched_kv, [depth_qk, depth_v], axis=3)
logits = tf.matmul(dispatched_q, k, transpose_b=True)
bias = tf.expand_dims((m_dispatcher.nonpadding() - 1.0) * 1e9, 2)
if mask_right:
q_coordinate = tf.to_float(
tf.expand_dims(q_dispatcher.length_coordinate(), 3))
m_coordinate = tf.to_float(
tf.expand_dims(m_dispatcher.length_coordinate(), 2))
bias += tf.to_float(tf.greater(m_coordinate, q_coordinate)) * -1e9
logits += bias
log_weights = tf.nn.log_softmax(logits)
weights = tf.exp(log_weights)
# For each query, this is the log of the sum of the unnormalized weights.
q_total = tf.stop_gradient(logits[:, :, :, :1] - log_weights[:, :, :, :1])
# For each key, this is the sum of the normalized weights.
m_total = tf.expand_dims(
tf.reduce_sum(tf.stop_gradient(weights), axis=2), -1)
o = tf.matmul(weights, v)
o = q_dispatcher.combine(o)
o = tf.reshape(o, [batch, num_heads, length_q, depth_v])
o = combine_heads(o)
o = common_layers.dense(
o, output_depth, use_bias=False, name="output_transform")
m_total = m_dispatcher.combine(m_total)
q_total = q_dispatcher.combine(q_total)
q_total = tf.squeeze(q_total, -1)
m_total = tf.squeeze(m_total, -1)
# Compute summed m predictions for all groups
m_pred_used = tf.reduce_sum(tf.exp(m_pred) * m_dispatcher.gates(), axis=2)
q_pred_used = tf.reduce_sum(q_pred * q_dispatcher.gates(), axis=2)
epsilon = 1e-3
m_pred_used = tf.log(m_pred_used + epsilon)
m_total = tf.log(m_total + epsilon)
m_loss = tf.nn.l2_loss(m_total - m_pred_used)
q_loss = tf.nn.l2_loss(
(q_total - q_pred_used) * tf.reduce_sum(q_gates, axis=2))
q_loss /= tf.to_float(batch * length_q)
m_loss /= tf.to_float(batch * length_kv)
# We would like the query groups to be equal sized. The group
# size is discrete, so we need some trick here. We add a loss
# proportional to the product of the group size and the
# predictions for that group. This encourages the predictions to
# decrease for groups that are too big.
q_group_deviation = (q_group_size / q_group_target_size) - 1.0
q_balance_loss = tf.reduce_sum(
tf.reduce_mean(q_pred_biased, axis=1) *
q_group_deviation) / tf.to_float(batch)
m_group_deviation = (m_group_size / m_group_target_size) - 1.0
m_balance_loss = tf.reduce_sum(
tf.reduce_mean(m_pred_biased, axis=1) *
m_group_deviation) / tf.to_float(batch)
# The losses in this function only propagate back to variables
# defined in this function, and the losses outside of this
# function only propagate back to variables outside of this
# function. Assuming some kind of adaptive learning algorithm,
# it should not matter how much we scale the losses in this function.
# Still we scale them down a lot so that they should not show up
# much in the overall loss for the model.
extra_loss_multiplier = 1e-3
extra_loss = q_loss + m_loss + q_balance_loss + m_balance_loss
extra_loss *= extra_loss_multiplier
# Show a bunch of summaries.
if common_layers.should_generate_summaries() and make_image_summary:
tf.summary.histogram("q_group_size", q_group_size)
tf.summary.histogram("m_group_size", m_group_size)
tf.summary.scalar("q_loss", q_loss)
tf.summary.scalar("m_loss", m_loss)
tf.summary.scalar("q_balance_loss", q_balance_loss)
tf.summary.scalar("m_balance_loss", m_balance_loss)
tf.summary.histogram("m_pred_used", m_pred_used)
tf.summary.histogram("m_total", m_total)
tf.summary.histogram("q_pred_used", q_pred_used)
tf.summary.histogram("q_total", q_total)
if make_image_summary:
# image summaries are expensive.
# So we restrict them to head_num<4, query_position<512, batch_index=0.
trunc_heads = min(4, num_heads)
trunc_length_q = tf.minimum(length_q, 512)
# We recompute the attention for the first example, in an inefficient
# way - masking. This lets us show pretty pictures.
# [trunc_heads, length_q, group]
q_gates_trunc = q_gates[:trunc_heads, :trunc_length_q, :]
# [trunc_heads, length_kv, group]
m_gates_trunc = m_gates[:trunc_heads, :, :]
grouping_mask = tf.matmul(
q_gates_trunc, m_gates_trunc, transpose_b=True)
q_trunc = q[:trunc_heads, :trunc_length_q, :]
k_trunc = kv[:trunc_heads, :, :depth_qk]
logits_trunc = tf.matmul(q_trunc, k_trunc, transpose_b=True)
if mask_right:
band = common_layers.ones_matrix_band_part(trunc_length_q, length_kv,
-1, 0)
trunc_bias = tf.expand_dims((1.0 - band) * -1e9, 0)
logits_trunc += trunc_bias
att_trunc = tf.nn.softmax(logits_trunc)
mask_coverage = tf.reduce_sum(grouping_mask * att_trunc) / (
tf.to_float(trunc_length_q) * trunc_heads)
tf.summary.scalar("coverage", mask_coverage)
att_trunc_hdr = tf.pow(att_trunc, 0.2) # for high-dynamic-range
mask_channel = grouping_mask * tf.maximum(att_trunc_hdr, 0.3)
image = tf.stack([att_trunc_hdr, mask_channel, mask_channel], axis=3)
tf.summary.image("att", image, max_outputs=trunc_heads)
# show one group for each head.
att_per_group = tf.expand_dims(weights[:trunc_heads, 0, :, :], -1)
tf.summary.image(
"att_per_group_%d",
tf.pow(att_per_group, 0.2),
max_outputs=trunc_heads)
return o, extra_loss
|
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] |
Multi-head dot-product attention with sparsity.
For each attention head, the queries are partitioned into groups.
For each group, only a subset of the key-value pairs are considered.
The choices of groups are selected based on trained predictors of
the total attention given the group inclusion.
memory_target_density indicates the average how many groups in which
a key-value pair should participate.
We use auxiliary losses to ensure that each group contains roughly
the same number of queries and the same number of key-value pairs.
If for a given sequence, the actual number of queries/pairs sent to
an expert exceeds this target by a factor of more than
multiplicative_overhead, then the last ones are dropped. We use
this drop-last policy to avoid bleeding information backwards, which
is necessary when using this function with autoregressive
prediction.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels]
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
num_groups: an integer
memory_target_density: a floating point scalar
multiplicative_overhead: a floating point scalar
additive_overhead: a floating point scalar
mask_right: a boolean
make_image_summary: a boolean
name: an optional string
Returns:
A Tensor with shape [batch, length_q, output_depth]
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads.
|
[
"Multi",
"-",
"head",
"dot",
"-",
"product",
"attention",
"with",
"sparsity",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1220-L1472
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
harden_attention_weights
|
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 this.
weights -= common_layers.top_kth_iterative(weights, hard_attention_k)
weights = tf.nn.relu(weights)
# Re-normalize the weights.
weights_sum = tf.reduce_sum(weights, axis=-1, keep_dims=True)
weights_sum = tf.maximum(weights_sum, 1e-6) # Avoid division by 0.
weights /= weights_sum
return weights
|
python
|
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 this.
weights -= common_layers.top_kth_iterative(weights, hard_attention_k)
weights = tf.nn.relu(weights)
# Re-normalize the weights.
weights_sum = tf.reduce_sum(weights, axis=-1, keep_dims=True)
weights_sum = tf.maximum(weights_sum, 1e-6) # Avoid division by 0.
weights /= weights_sum
return weights
|
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"weights",
"/=",
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] |
Make attention weights non-0 only on the top-hard_attention_k ones.
|
[
"Make",
"attention",
"weights",
"non",
"-",
"0",
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"on",
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"top",
"-",
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"ones",
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] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1475-L1486
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
dot_product_attention
|
def dot_product_attention(q,
k,
v,
bias,
dropout_rate=0.0,
image_shapes=None,
name=None,
make_image_summary=True,
save_weights_to=None,
dropout_broadcast_dims=None,
activation_dtype=None,
weight_dtype=None,
hard_attention_k=0):
"""Dot-product attention.
Args:
q: Tensor with shape [..., length_q, depth_k].
k: Tensor with shape [..., length_kv, depth_k]. Leading dimensions must
match with q.
v: Tensor with shape [..., length_kv, depth_v] Leading dimensions must
match with q.
bias: bias Tensor (see attention_bias())
dropout_rate: a float.
image_shapes: optional tuple of integer scalars.
see comments for attention_image_summary()
name: an optional string
make_image_summary: True if you want an image summary.
save_weights_to: an optional dictionary to capture attention weights
for visualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
dropout_broadcast_dims: an optional list of integers less than rank of q.
Specifies in which dimensions to broadcast the dropout decisions.
activation_dtype: Used to define function activation dtype when using
mixed precision.
weight_dtype: The dtype weights are stored in when using mixed precision
hard_attention_k: integer, if > 0 triggers hard attention (picking top-k)
Returns:
Tensor with shape [..., length_q, depth_v].
"""
with tf.variable_scope(
name, default_name="dot_product_attention", values=[q, k, v]) as scope:
logits = tf.matmul(q, k, transpose_b=True) # [..., length_q, length_kv]
if bias is not None:
bias = common_layers.cast_like(bias, logits)
logits += bias
# If logits are fp16, upcast before softmax
logits = maybe_upcast(logits, activation_dtype, weight_dtype)
weights = tf.nn.softmax(logits, name="attention_weights")
if hard_attention_k > 0:
weights = harden_attention_weights(weights, hard_attention_k)
weights = common_layers.cast_like(weights, q)
if save_weights_to is not None:
save_weights_to[scope.name] = weights
save_weights_to[scope.name + "/logits"] = logits
# Drop out attention links for each head.
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
return tf.matmul(weights, v)
|
python
|
def dot_product_attention(q,
k,
v,
bias,
dropout_rate=0.0,
image_shapes=None,
name=None,
make_image_summary=True,
save_weights_to=None,
dropout_broadcast_dims=None,
activation_dtype=None,
weight_dtype=None,
hard_attention_k=0):
"""Dot-product attention.
Args:
q: Tensor with shape [..., length_q, depth_k].
k: Tensor with shape [..., length_kv, depth_k]. Leading dimensions must
match with q.
v: Tensor with shape [..., length_kv, depth_v] Leading dimensions must
match with q.
bias: bias Tensor (see attention_bias())
dropout_rate: a float.
image_shapes: optional tuple of integer scalars.
see comments for attention_image_summary()
name: an optional string
make_image_summary: True if you want an image summary.
save_weights_to: an optional dictionary to capture attention weights
for visualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
dropout_broadcast_dims: an optional list of integers less than rank of q.
Specifies in which dimensions to broadcast the dropout decisions.
activation_dtype: Used to define function activation dtype when using
mixed precision.
weight_dtype: The dtype weights are stored in when using mixed precision
hard_attention_k: integer, if > 0 triggers hard attention (picking top-k)
Returns:
Tensor with shape [..., length_q, depth_v].
"""
with tf.variable_scope(
name, default_name="dot_product_attention", values=[q, k, v]) as scope:
logits = tf.matmul(q, k, transpose_b=True) # [..., length_q, length_kv]
if bias is not None:
bias = common_layers.cast_like(bias, logits)
logits += bias
# If logits are fp16, upcast before softmax
logits = maybe_upcast(logits, activation_dtype, weight_dtype)
weights = tf.nn.softmax(logits, name="attention_weights")
if hard_attention_k > 0:
weights = harden_attention_weights(weights, hard_attention_k)
weights = common_layers.cast_like(weights, q)
if save_weights_to is not None:
save_weights_to[scope.name] = weights
save_weights_to[scope.name + "/logits"] = logits
# Drop out attention links for each head.
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
return tf.matmul(weights, v)
|
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k: Tensor with shape [..., length_kv, depth_k]. Leading dimensions must
match with q.
v: Tensor with shape [..., length_kv, depth_v] Leading dimensions must
match with q.
bias: bias Tensor (see attention_bias())
dropout_rate: a float.
image_shapes: optional tuple of integer scalars.
see comments for attention_image_summary()
name: an optional string
make_image_summary: True if you want an image summary.
save_weights_to: an optional dictionary to capture attention weights
for visualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
dropout_broadcast_dims: an optional list of integers less than rank of q.
Specifies in which dimensions to broadcast the dropout decisions.
activation_dtype: Used to define function activation dtype when using
mixed precision.
weight_dtype: The dtype weights are stored in when using mixed precision
hard_attention_k: integer, if > 0 triggers hard attention (picking top-k)
Returns:
Tensor with shape [..., length_q, depth_v].
|
[
"Dot",
"-",
"product",
"attention",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1489-L1549
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
_generate_relative_positions_matrix
|
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 = tf.range(length_q)
else:
range_vec_k = tf.range(length_k)
range_vec_q = range_vec_k[-length_q:]
distance_mat = range_vec_k[None, :] - range_vec_q[:, None]
else:
distance_mat = tf.expand_dims(tf.range(-length_k+1, 1, 1), 0)
distance_mat_clipped = tf.clip_by_value(distance_mat, -max_relative_position,
max_relative_position)
# Shift values to be >= 0. Each integer still uniquely identifies a relative
# position difference.
final_mat = distance_mat_clipped + max_relative_position
return final_mat
|
python
|
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 = tf.range(length_q)
else:
range_vec_k = tf.range(length_k)
range_vec_q = range_vec_k[-length_q:]
distance_mat = range_vec_k[None, :] - range_vec_q[:, None]
else:
distance_mat = tf.expand_dims(tf.range(-length_k+1, 1, 1), 0)
distance_mat_clipped = tf.clip_by_value(distance_mat, -max_relative_position,
max_relative_position)
# Shift values to be >= 0. Each integer still uniquely identifies a relative
# position difference.
final_mat = distance_mat_clipped + max_relative_position
return final_mat
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1552-L1570
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
_generate_relative_positions_embeddings
|
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):
relative_positions_matrix = _generate_relative_positions_matrix(
length_q, length_k, max_relative_position, cache=cache)
vocab_size = max_relative_position * 2 + 1
# Generates embedding for each relative position of dimension depth.
embeddings_table = tf.get_variable("embeddings", [vocab_size, depth])
embeddings = tf.gather(embeddings_table, relative_positions_matrix)
return embeddings
|
python
|
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):
relative_positions_matrix = _generate_relative_positions_matrix(
length_q, length_k, max_relative_position, cache=cache)
vocab_size = max_relative_position * 2 + 1
# Generates embedding for each relative position of dimension depth.
embeddings_table = tf.get_variable("embeddings", [vocab_size, depth])
embeddings = tf.gather(embeddings_table, relative_positions_matrix)
return embeddings
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1573-L1584
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
_relative_attention_inner
|
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_size, heads, length or 1, depth].
z: Tensor with shape [length or 1, length, depth].
transpose: Whether to transpose inner matrices of y and z. Should be true if
last dimension of x is depth, not length.
Returns:
A Tensor with shape [batch_size, heads, length, length or depth].
"""
batch_size = tf.shape(x)[0]
heads = x.get_shape().as_list()[1]
length = tf.shape(x)[2]
# xy_matmul is [batch_size, heads, length or 1, length or depth]
xy_matmul = tf.matmul(x, y, transpose_b=transpose)
# x_t is [length or 1, batch_size, heads, length or depth]
x_t = tf.transpose(x, [2, 0, 1, 3])
# x_t_r is [length or 1, batch_size * heads, length or depth]
x_t_r = tf.reshape(x_t, [length, heads * batch_size, -1])
# x_tz_matmul is [length or 1, batch_size * heads, length or depth]
x_tz_matmul = tf.matmul(x_t_r, z, transpose_b=transpose)
# x_tz_matmul_r is [length or 1, batch_size, heads, length or depth]
x_tz_matmul_r = tf.reshape(x_tz_matmul, [length, batch_size, heads, -1])
# x_tz_matmul_r_t is [batch_size, heads, length or 1, length or depth]
x_tz_matmul_r_t = tf.transpose(x_tz_matmul_r, [1, 2, 0, 3])
return xy_matmul + x_tz_matmul_r_t
|
python
|
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_size, heads, length or 1, depth].
z: Tensor with shape [length or 1, length, depth].
transpose: Whether to transpose inner matrices of y and z. Should be true if
last dimension of x is depth, not length.
Returns:
A Tensor with shape [batch_size, heads, length, length or depth].
"""
batch_size = tf.shape(x)[0]
heads = x.get_shape().as_list()[1]
length = tf.shape(x)[2]
# xy_matmul is [batch_size, heads, length or 1, length or depth]
xy_matmul = tf.matmul(x, y, transpose_b=transpose)
# x_t is [length or 1, batch_size, heads, length or depth]
x_t = tf.transpose(x, [2, 0, 1, 3])
# x_t_r is [length or 1, batch_size * heads, length or depth]
x_t_r = tf.reshape(x_t, [length, heads * batch_size, -1])
# x_tz_matmul is [length or 1, batch_size * heads, length or depth]
x_tz_matmul = tf.matmul(x_t_r, z, transpose_b=transpose)
# x_tz_matmul_r is [length or 1, batch_size, heads, length or depth]
x_tz_matmul_r = tf.reshape(x_tz_matmul, [length, batch_size, heads, -1])
# x_tz_matmul_r_t is [batch_size, heads, length or 1, length or depth]
x_tz_matmul_r_t = tf.transpose(x_tz_matmul_r, [1, 2, 0, 3])
return xy_matmul + x_tz_matmul_r_t
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z: Tensor with shape [length or 1, length, depth].
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|
[
"Relative",
"position",
"-",
"aware",
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"-",
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"attention",
"inner",
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"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1587-L1618
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
dot_product_attention_relative
|
def dot_product_attention_relative(q,
k,
v,
bias,
max_relative_position,
dropout_rate=0.0,
image_shapes=None,
save_weights_to=None,
name=None,
make_image_summary=True,
cache=False,
allow_memory=False,
hard_attention_k=0):
"""Calculate relative position-aware dot-product self-attention.
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v.
Args:
q: a Tensor with shape [batch, heads, length, depth].
k: a Tensor with shape [batch, heads, length, depth].
v: a Tensor with shape [batch, heads, length, depth].
bias: bias Tensor.
max_relative_position: an integer specifying the maximum distance between
inputs that unique position embeddings should be learned for.
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
save_weights_to: an optional dictionary to capture attention weights
for visualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
name: an optional string.
make_image_summary: Whether to make an attention image summary.
cache: whether use cache mode
allow_memory: whether to assume that recurrent memory is in use. If True,
the length dimension of k/v/bias may be longer than the queries, and it is
assumed that the extra memory entries precede the non-memory entries.
hard_attention_k: integer, if > 0 triggers hard attention (picking top-k)
Returns:
A Tensor.
Raises:
ValueError: if max_relative_position is not > 0.
"""
if not max_relative_position:
raise ValueError("Max relative position (%s) should be > 0 when using "
"relative self attention." % (max_relative_position))
with tf.variable_scope(
name, default_name="dot_product_attention_relative",
values=[q, k, v]) as scope:
# This calculation only works for self attention.
# q, k and v must therefore have the same shape, unless memory is enabled.
if not cache and not allow_memory:
q.get_shape().assert_is_compatible_with(k.get_shape())
q.get_shape().assert_is_compatible_with(v.get_shape())
# Use separate embeddings suitable for keys and values.
depth = k.get_shape().as_list()[3]
length_k = common_layers.shape_list(k)[2]
length_q = common_layers.shape_list(q)[2] if allow_memory else length_k
relations_keys = _generate_relative_positions_embeddings(
length_q, length_k, depth, max_relative_position,
"relative_positions_keys", cache=cache)
relations_values = _generate_relative_positions_embeddings(
length_q, length_k, depth, max_relative_position,
"relative_positions_values", cache=cache)
# Compute self attention considering the relative position embeddings.
logits = _relative_attention_inner(q, k, relations_keys, True)
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
if hard_attention_k > 0:
weights = harden_attention_weights(weights, hard_attention_k)
if save_weights_to is not None:
save_weights_to[scope.name] = weights
save_weights_to[scope.name + "/logits"] = logits
weights = tf.nn.dropout(weights, 1.0 - dropout_rate)
if not tf.get_variable_scope().reuse and make_image_summary:
attention_image_summary(weights, image_shapes)
return _relative_attention_inner(weights, v, relations_values, False)
|
python
|
def dot_product_attention_relative(q,
k,
v,
bias,
max_relative_position,
dropout_rate=0.0,
image_shapes=None,
save_weights_to=None,
name=None,
make_image_summary=True,
cache=False,
allow_memory=False,
hard_attention_k=0):
"""Calculate relative position-aware dot-product self-attention.
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v.
Args:
q: a Tensor with shape [batch, heads, length, depth].
k: a Tensor with shape [batch, heads, length, depth].
v: a Tensor with shape [batch, heads, length, depth].
bias: bias Tensor.
max_relative_position: an integer specifying the maximum distance between
inputs that unique position embeddings should be learned for.
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
save_weights_to: an optional dictionary to capture attention weights
for visualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
name: an optional string.
make_image_summary: Whether to make an attention image summary.
cache: whether use cache mode
allow_memory: whether to assume that recurrent memory is in use. If True,
the length dimension of k/v/bias may be longer than the queries, and it is
assumed that the extra memory entries precede the non-memory entries.
hard_attention_k: integer, if > 0 triggers hard attention (picking top-k)
Returns:
A Tensor.
Raises:
ValueError: if max_relative_position is not > 0.
"""
if not max_relative_position:
raise ValueError("Max relative position (%s) should be > 0 when using "
"relative self attention." % (max_relative_position))
with tf.variable_scope(
name, default_name="dot_product_attention_relative",
values=[q, k, v]) as scope:
# This calculation only works for self attention.
# q, k and v must therefore have the same shape, unless memory is enabled.
if not cache and not allow_memory:
q.get_shape().assert_is_compatible_with(k.get_shape())
q.get_shape().assert_is_compatible_with(v.get_shape())
# Use separate embeddings suitable for keys and values.
depth = k.get_shape().as_list()[3]
length_k = common_layers.shape_list(k)[2]
length_q = common_layers.shape_list(q)[2] if allow_memory else length_k
relations_keys = _generate_relative_positions_embeddings(
length_q, length_k, depth, max_relative_position,
"relative_positions_keys", cache=cache)
relations_values = _generate_relative_positions_embeddings(
length_q, length_k, depth, max_relative_position,
"relative_positions_values", cache=cache)
# Compute self attention considering the relative position embeddings.
logits = _relative_attention_inner(q, k, relations_keys, True)
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
if hard_attention_k > 0:
weights = harden_attention_weights(weights, hard_attention_k)
if save_weights_to is not None:
save_weights_to[scope.name] = weights
save_weights_to[scope.name + "/logits"] = logits
weights = tf.nn.dropout(weights, 1.0 - dropout_rate)
if not tf.get_variable_scope().reuse and make_image_summary:
attention_image_summary(weights, image_shapes)
return _relative_attention_inner(weights, v, relations_values, False)
|
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Calculate relative position-aware dot-product self-attention.
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Args:
q: a Tensor with shape [batch, heads, length, depth].
k: a Tensor with shape [batch, heads, length, depth].
v: a Tensor with shape [batch, heads, length, depth].
bias: bias Tensor.
max_relative_position: an integer specifying the maximum distance between
inputs that unique position embeddings should be learned for.
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
save_weights_to: an optional dictionary to capture attention weights
for visualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
name: an optional string.
make_image_summary: Whether to make an attention image summary.
cache: whether use cache mode
allow_memory: whether to assume that recurrent memory is in use. If True,
the length dimension of k/v/bias may be longer than the queries, and it is
assumed that the extra memory entries precede the non-memory entries.
hard_attention_k: integer, if > 0 triggers hard attention (picking top-k)
Returns:
A Tensor.
Raises:
ValueError: if max_relative_position is not > 0.
|
[
"Calculate",
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"position",
"-",
"aware",
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"-",
"product",
"self",
"-",
"attention",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1621-L1702
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
_relative_position_to_absolute_position_masked
|
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 represent:
[batch, heads, query_position, memory_position]
Only works with masked_attention. Undefined behavior for regions of the
input where memory_position > query_position.
Args:
x: a Tensor with shape [batch, heads, length, length]
Returns:
a Tensor with shape [batch, heads, length, length]
"""
batch, heads, length, _ = common_layers.shape_list(x)
x = tf.pad(x, [[0, 0], [0, 0], [0, 0], [1, 0]])
x = tf.reshape(x, [batch, heads, 1 + length, length])
x = tf.slice(x, [0, 0, 1, 0], [-1, -1, -1, -1])
return x
|
python
|
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 represent:
[batch, heads, query_position, memory_position]
Only works with masked_attention. Undefined behavior for regions of the
input where memory_position > query_position.
Args:
x: a Tensor with shape [batch, heads, length, length]
Returns:
a Tensor with shape [batch, heads, length, length]
"""
batch, heads, length, _ = common_layers.shape_list(x)
x = tf.pad(x, [[0, 0], [0, 0], [0, 0], [1, 0]])
x = tf.reshape(x, [batch, heads, 1 + length, length])
x = tf.slice(x, [0, 0, 1, 0], [-1, -1, -1, -1])
return x
|
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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 represent:
[batch, heads, query_position, memory_position]
Only works with masked_attention. Undefined behavior for regions of the
input where memory_position > query_position.
Args:
x: a Tensor with shape [batch, heads, length, length]
Returns:
a Tensor with shape [batch, heads, length, length]
|
[
"Helper",
"to",
"dot_product_self_attention_relative_v2",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1705-L1729
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
dot_product_self_attention_relative_v2
|
def dot_product_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-aware dot-product self-attention.
Only works for masked self-attention (no looking forward).
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v.
Args:
q: a Tensor with shape [batch, heads, length, depth].
k: a Tensor with shape [batch, heads, length, depth].
v: a Tensor with shape [batch, heads, length, depth].
bias: bias Tensor.
max_relative_position: an integer indicating the maximum relative distance
to look back - changing this invalidates checkpoints
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
heads_share_relative_embedding: a boolean indicating wheather to share
relative embeddings between attention heads.
add_relative_to_values: a boolean for whether to add relative component to
values.
Returns:
A Tensor.
Raises:
ValueError: if max_relative_position is not > 0.
"""
if not max_relative_position:
raise ValueError("Max relative position (%s) should be > 0 when using "
"relative self attention." % (max_relative_position))
with tf.variable_scope(
name,
default_name="dot_product_self_attention_relative_v2",
values=[q, k, v]):
# This calculation only works for self attention.
# q, k and v must therefore have the same shape.
q.get_shape().assert_is_compatible_with(k.get_shape())
q.get_shape().assert_is_compatible_with(v.get_shape())
# Use separate embeddings suitable for keys and values.
_, num_heads, length, depth_k = common_layers.shape_list(k)
# [batch, num_heads, query_length, memory_length]
logits = tf.matmul(q, k, transpose_b=True)
key_relative_embeddings = get_relative_embeddings_left(
max_relative_position, length, depth_k, num_heads,
heads_share_relative_embedding, "key_relative_embeddings")
rel_logits = matmul_with_relative_keys(q, key_relative_embeddings,
heads_share_relative_embedding)
rel_logits = _relative_position_to_absolute_position_masked(rel_logits)
logits += rel_logits
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
# Dropping out the attention links for each of the heads.
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
output = tf.matmul(weights, v)
if add_relative_to_values:
# [batch, num_heads, query_length, memory_length]
relative_weights = _absolute_position_to_relative_position_masked(weights)
depth_v = common_layers.shape_list(v)[3]
value_relative_embeddings = get_relative_embeddings_left(
max_relative_position, length, depth_v, num_heads,
heads_share_relative_embedding, "value_relative_embeddings")
output += matmul_with_relative_values(
relative_weights, value_relative_embeddings,
heads_share_relative_embedding)
return output
|
python
|
def dot_product_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-aware dot-product self-attention.
Only works for masked self-attention (no looking forward).
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v.
Args:
q: a Tensor with shape [batch, heads, length, depth].
k: a Tensor with shape [batch, heads, length, depth].
v: a Tensor with shape [batch, heads, length, depth].
bias: bias Tensor.
max_relative_position: an integer indicating the maximum relative distance
to look back - changing this invalidates checkpoints
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
heads_share_relative_embedding: a boolean indicating wheather to share
relative embeddings between attention heads.
add_relative_to_values: a boolean for whether to add relative component to
values.
Returns:
A Tensor.
Raises:
ValueError: if max_relative_position is not > 0.
"""
if not max_relative_position:
raise ValueError("Max relative position (%s) should be > 0 when using "
"relative self attention." % (max_relative_position))
with tf.variable_scope(
name,
default_name="dot_product_self_attention_relative_v2",
values=[q, k, v]):
# This calculation only works for self attention.
# q, k and v must therefore have the same shape.
q.get_shape().assert_is_compatible_with(k.get_shape())
q.get_shape().assert_is_compatible_with(v.get_shape())
# Use separate embeddings suitable for keys and values.
_, num_heads, length, depth_k = common_layers.shape_list(k)
# [batch, num_heads, query_length, memory_length]
logits = tf.matmul(q, k, transpose_b=True)
key_relative_embeddings = get_relative_embeddings_left(
max_relative_position, length, depth_k, num_heads,
heads_share_relative_embedding, "key_relative_embeddings")
rel_logits = matmul_with_relative_keys(q, key_relative_embeddings,
heads_share_relative_embedding)
rel_logits = _relative_position_to_absolute_position_masked(rel_logits)
logits += rel_logits
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
# Dropping out the attention links for each of the heads.
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
output = tf.matmul(weights, v)
if add_relative_to_values:
# [batch, num_heads, query_length, memory_length]
relative_weights = _absolute_position_to_relative_position_masked(weights)
depth_v = common_layers.shape_list(v)[3]
value_relative_embeddings = get_relative_embeddings_left(
max_relative_position, length, depth_v, num_heads,
heads_share_relative_embedding, "value_relative_embeddings")
output += matmul_with_relative_values(
relative_weights, value_relative_embeddings,
heads_share_relative_embedding)
return output
|
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Calculate relative position-aware dot-product self-attention.
Only works for masked self-attention (no looking forward).
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v.
Args:
q: a Tensor with shape [batch, heads, length, depth].
k: a Tensor with shape [batch, heads, length, depth].
v: a Tensor with shape [batch, heads, length, depth].
bias: bias Tensor.
max_relative_position: an integer indicating the maximum relative distance
to look back - changing this invalidates checkpoints
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
heads_share_relative_embedding: a boolean indicating wheather to share
relative embeddings between attention heads.
add_relative_to_values: a boolean for whether to add relative component to
values.
Returns:
A Tensor.
Raises:
ValueError: if max_relative_position is not > 0.
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1809-L1899
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
_absolute_position_to_relative_position_unmasked
|
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:
[batch, heads, query_position, memory_position - query_position + length - 1]
Only works with unmasked_attention.
Args:
x: a Tensor with shape [batch, heads, length, length]
Returns:
a Tensor with shape [batch, heads, length, 2*length-1]
"""
batch, heads, length, _ = common_layers.shape_list(x)
# padd along column
x = tf.pad(x, [[0, 0], [0, 0], [0, 0], [0, length-1]])
x_flat = tf.reshape(x, [batch, heads, length**2 + length*(length -1)])
# add 0's in the beginning that will skew the elements after reshape
x_flat = tf.pad(x_flat, [[0, 0], [0, 0], [length, 0]])
x = tf.reshape(x_flat, [batch, heads, length, 2*length])
x = tf.slice(x, [0, 0, 0, 1], [batch, heads, length,
2*length -1])
return x
|
python
|
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:
[batch, heads, query_position, memory_position - query_position + length - 1]
Only works with unmasked_attention.
Args:
x: a Tensor with shape [batch, heads, length, length]
Returns:
a Tensor with shape [batch, heads, length, 2*length-1]
"""
batch, heads, length, _ = common_layers.shape_list(x)
# padd along column
x = tf.pad(x, [[0, 0], [0, 0], [0, 0], [0, length-1]])
x_flat = tf.reshape(x, [batch, heads, length**2 + length*(length -1)])
# add 0's in the beginning that will skew the elements after reshape
x_flat = tf.pad(x_flat, [[0, 0], [0, 0], [length, 0]])
x = tf.reshape(x_flat, [batch, heads, length, 2*length])
x = tf.slice(x, [0, 0, 0, 1], [batch, heads, length,
2*length -1])
return x
|
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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:
[batch, heads, query_position, memory_position - query_position + length - 1]
Only works with unmasked_attention.
Args:
x: a Tensor with shape [batch, heads, length, length]
Returns:
a Tensor with shape [batch, heads, length, 2*length-1]
|
[
"Helper",
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1902-L1930
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
get_relative_embeddings_left_right
|
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.
Use for unmasked case where the relative attention looks both left and right.
Args:
max_relative_position: an Integer for the number of entries in the relative
embedding, which corresponds to the max relative distance that is
considered.
length: an Integer, specifies the length of the input sequence for which
this relative embedding is retrieved for.
depth: an Integer, specifies the depth for relative embeddings.
num_heads: an Integer, specifies the number of heads.
heads_share_relative_embedding: a Boolean specifying if the relative
embedding is shared across heads.
name: a string giving the name of the embedding variables.
Returns:
a Tensor with shape [length, depth]
"""
initializer_stddev = depth**-0.5
max_relative_position_unmasked = 2 * max_relative_position - 1
if heads_share_relative_embedding:
embedding_shape = (max_relative_position_unmasked, depth)
else:
embedding_shape = (num_heads, max_relative_position_unmasked, depth)
relative_embeddings = tf.get_variable(
name=name, shape=embedding_shape,
initializer=tf.random_normal_initializer(stddev=initializer_stddev))
# Pad first before slice to avoid using tf.cond.
pad_length = tf.maximum(length - max_relative_position, 0)
slice_start_position = tf.maximum(max_relative_position-length, 0)
if heads_share_relative_embedding:
padded_relative_embeddings = tf.pad(
relative_embeddings,
[[pad_length, pad_length], [0, 0]])
used_relative_embeddings = tf.slice(
padded_relative_embeddings,
[slice_start_position, 0], [2 * length - 1, -1])
else:
padded_relative_embeddings = tf.pad(
relative_embeddings,
[[0, 0], [pad_length, pad_length], [0, 0]])
used_relative_embeddings = tf.slice(
padded_relative_embeddings,
[0, slice_start_position, 0], [-1, 2 * length - 1, -1])
return used_relative_embeddings
|
python
|
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.
Use for unmasked case where the relative attention looks both left and right.
Args:
max_relative_position: an Integer for the number of entries in the relative
embedding, which corresponds to the max relative distance that is
considered.
length: an Integer, specifies the length of the input sequence for which
this relative embedding is retrieved for.
depth: an Integer, specifies the depth for relative embeddings.
num_heads: an Integer, specifies the number of heads.
heads_share_relative_embedding: a Boolean specifying if the relative
embedding is shared across heads.
name: a string giving the name of the embedding variables.
Returns:
a Tensor with shape [length, depth]
"""
initializer_stddev = depth**-0.5
max_relative_position_unmasked = 2 * max_relative_position - 1
if heads_share_relative_embedding:
embedding_shape = (max_relative_position_unmasked, depth)
else:
embedding_shape = (num_heads, max_relative_position_unmasked, depth)
relative_embeddings = tf.get_variable(
name=name, shape=embedding_shape,
initializer=tf.random_normal_initializer(stddev=initializer_stddev))
# Pad first before slice to avoid using tf.cond.
pad_length = tf.maximum(length - max_relative_position, 0)
slice_start_position = tf.maximum(max_relative_position-length, 0)
if heads_share_relative_embedding:
padded_relative_embeddings = tf.pad(
relative_embeddings,
[[pad_length, pad_length], [0, 0]])
used_relative_embeddings = tf.slice(
padded_relative_embeddings,
[slice_start_position, 0], [2 * length - 1, -1])
else:
padded_relative_embeddings = tf.pad(
relative_embeddings,
[[0, 0], [pad_length, pad_length], [0, 0]])
used_relative_embeddings = tf.slice(
padded_relative_embeddings,
[0, slice_start_position, 0], [-1, 2 * length - 1, -1])
return used_relative_embeddings
|
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Args:
max_relative_position: an Integer for the number of entries in the relative
embedding, which corresponds to the max relative distance that is
considered.
length: an Integer, specifies the length of the input sequence for which
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depth: an Integer, specifies the depth for relative embeddings.
num_heads: an Integer, specifies the number of heads.
heads_share_relative_embedding: a Boolean specifying if the relative
embedding is shared across heads.
name: a string giving the name of the embedding variables.
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|
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] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1933-L1982
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
dot_product_unmasked_self_attention_relative_v2
|
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-aware dot-product self-attention.
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v.
Args:
q: a Tensor with shape [batch, heads, length, depth].
k: a Tensor with shape [batch, heads, length, depth].
v: a Tensor with shape [batch, heads, length, depth].
bias: bias Tensor.
max_relative_position: an integer the max relative embedding considered.
Changing this invalidates checkpoints.
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
heads_share_relative_embedding: a boolean indicating wheather to share
relative embeddings between attention heads.
add_relative_to_values: a boolean for whether to add relative component to
values.
Returns:
A Tensor.
Raises:
ValueError: if max_relative_position is not > 0.
"""
if not max_relative_position:
raise ValueError("Max relative position (%s) should be > 0 when using "
"relative self attention." % (max_relative_position))
with tf.variable_scope(
name,
default_name="dot_product_unmasked_self_attention_relative_v2",
values=[q, k, v]):
# This calculation only works for self attention.
# q, k and v must therefore have the same shape.
q.get_shape().assert_is_compatible_with(k.get_shape())
q.get_shape().assert_is_compatible_with(v.get_shape())
# [batch, num_heads, query_length, memory_length]
logits = tf.matmul(q, k, transpose_b=True)
length = common_layers.shape_list(q)[2]
k_shape = common_layers.shape_list(k)
num_heads = k_shape[1]
depth_k = k_shape[-1]
key_relative_embeddings = get_relative_embeddings_left_right(
max_relative_position, length, depth_k, num_heads,
heads_share_relative_embedding,
"key_relative_embeddings")
unmasked_rel_logits = matmul_with_relative_keys(
q, key_relative_embeddings, heads_share_relative_embedding)
unmasked_rel_logits = _relative_position_to_absolute_position_unmasked(
unmasked_rel_logits)
logits += unmasked_rel_logits
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
# dropping out the attention links for each of the heads
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
# relative_weights.set_shape([None, None, None, max_length])
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
ret = tf.matmul(weights, v)
if add_relative_to_values:
# Adds the contribution of the weighted relative embeddings to the values.
# [batch, num_heads, query_length, 2*memory_length-1]
relative_weights = _absolute_position_to_relative_position_unmasked(
weights)
depth_v = common_layers.shape_list(v)[3]
value_relative_embeddings = get_relative_embeddings_left_right(
max_relative_position, length, depth_v, num_heads,
heads_share_relative_embedding, "value_relative_embeddings")
ret += matmul_with_relative_values(
relative_weights, value_relative_embeddings,
heads_share_relative_embedding)
return ret
|
python
|
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-aware dot-product self-attention.
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v.
Args:
q: a Tensor with shape [batch, heads, length, depth].
k: a Tensor with shape [batch, heads, length, depth].
v: a Tensor with shape [batch, heads, length, depth].
bias: bias Tensor.
max_relative_position: an integer the max relative embedding considered.
Changing this invalidates checkpoints.
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
heads_share_relative_embedding: a boolean indicating wheather to share
relative embeddings between attention heads.
add_relative_to_values: a boolean for whether to add relative component to
values.
Returns:
A Tensor.
Raises:
ValueError: if max_relative_position is not > 0.
"""
if not max_relative_position:
raise ValueError("Max relative position (%s) should be > 0 when using "
"relative self attention." % (max_relative_position))
with tf.variable_scope(
name,
default_name="dot_product_unmasked_self_attention_relative_v2",
values=[q, k, v]):
# This calculation only works for self attention.
# q, k and v must therefore have the same shape.
q.get_shape().assert_is_compatible_with(k.get_shape())
q.get_shape().assert_is_compatible_with(v.get_shape())
# [batch, num_heads, query_length, memory_length]
logits = tf.matmul(q, k, transpose_b=True)
length = common_layers.shape_list(q)[2]
k_shape = common_layers.shape_list(k)
num_heads = k_shape[1]
depth_k = k_shape[-1]
key_relative_embeddings = get_relative_embeddings_left_right(
max_relative_position, length, depth_k, num_heads,
heads_share_relative_embedding,
"key_relative_embeddings")
unmasked_rel_logits = matmul_with_relative_keys(
q, key_relative_embeddings, heads_share_relative_embedding)
unmasked_rel_logits = _relative_position_to_absolute_position_unmasked(
unmasked_rel_logits)
logits += unmasked_rel_logits
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
# dropping out the attention links for each of the heads
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
# relative_weights.set_shape([None, None, None, max_length])
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
ret = tf.matmul(weights, v)
if add_relative_to_values:
# Adds the contribution of the weighted relative embeddings to the values.
# [batch, num_heads, query_length, 2*memory_length-1]
relative_weights = _absolute_position_to_relative_position_unmasked(
weights)
depth_v = common_layers.shape_list(v)[3]
value_relative_embeddings = get_relative_embeddings_left_right(
max_relative_position, length, depth_v, num_heads,
heads_share_relative_embedding, "value_relative_embeddings")
ret += matmul_with_relative_values(
relative_weights, value_relative_embeddings,
heads_share_relative_embedding)
return ret
|
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Calculate relative position-aware dot-product self-attention.
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v.
Args:
q: a Tensor with shape [batch, heads, length, depth].
k: a Tensor with shape [batch, heads, length, depth].
v: a Tensor with shape [batch, heads, length, depth].
bias: bias Tensor.
max_relative_position: an integer the max relative embedding considered.
Changing this invalidates checkpoints.
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
heads_share_relative_embedding: a boolean indicating wheather to share
relative embeddings between attention heads.
add_relative_to_values: a boolean for whether to add relative component to
values.
Returns:
A Tensor.
Raises:
ValueError: if max_relative_position is not > 0.
|
[
"Calculate",
"relative",
"position",
"-",
"aware",
"dot",
"-",
"product",
"self",
"-",
"attention",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L1985-L2074
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
_matmul_with_relative_keys_2d
|
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
|
python
|
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
|
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Helper function for dot_product_unmasked_self_attention_relative_2d.
|
[
"Helper",
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"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L2077-L2083
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
dot_product_unmasked_self_attention_relative_2d
|
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 unmasked dot-product self-attention 2d.
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v in
height and width dimensions. for query index (i,j) and key index (l, m),
the logit is q_i k_j^T + q_i rh_{l-i}^T + q_i rw_{m-j}^T, where rh and ry are
the set of relative embeddings in height and width spatial dimensions,
respectively.
Args:
q: a Tensor with shape [batch, heads, height, width, depth].
k: a Tensor with shape [batch, heads, height, width, depth].
v: a Tensor with shape [batch, heads, height, width, depth].
bias: bias Tensor.
max_relative_position: an integer the max relative embedding considered.
Changing this invalidates checkpoints.
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
heads_share_relative_embedding: a boolean indicating wheather to share
relative embeddings between attention heads.
add_relative_to_values: a boolean for adding relative embeddings to values.
Returns:
[batch, heads, height, width, depth] tensor, the output of attention.
height_key_relative_embeddings: a 3d or 2d tensor, depending on head sharing
settings, which are the relative embeddings for height.
width_key_relative_embeddings: a 3d or 2d tensor, depending on head sharing
settings, which are the relative embeddings for width.
Raises:
ValueError: if max_relative_position is not > 0.
"""
if not max_relative_position:
raise ValueError("Max relative position (%s) should be > 0 when using "
"relative self attention." % (max_relative_position))
if add_relative_to_values:
raise ValueError("Adding relative embeddings to values is not implemented")
with tf.variable_scope(
name,
default_name="dot_product_self_attention_relative_v2",
values=[q, k, v]):
# This calculation only works for self attention.
# q, k and v must therefore have the same shape.
q.get_shape().assert_is_compatible_with(k.get_shape())
q.get_shape()[:-1].assert_is_compatible_with(v.get_shape()[:-1])
(height, width) = (common_layers.shape_list(q)[2],
common_layers.shape_list(q)[3])
k_shape = common_layers.shape_list(k)
num_heads = k_shape[1]
depth_k = k_shape[-1]
depth_v = common_layers.shape_list(v)[-1]
# flatten height width
flatten_hw = lambda x, d: tf.reshape(x, [-1, num_heads, height*width, d])
# [batch, num_heads, query_length, memory_length]
logits = tf.matmul(flatten_hw(q, depth_k), flatten_hw(k, depth_k),
transpose_b=True)
def _compute_2d_relative_logits(
query, key_relative_embeddings, height, width,
heads_share_relative_embedding, transpose_mask):
"""compute relative logits."""
unmasked_rel_logits = _matmul_with_relative_keys_2d(
query, key_relative_embeddings, heads_share_relative_embedding)
# collapse height and heads
unmasked_rel_logits = tf.reshape(unmasked_rel_logits,
[-1, num_heads*height, width,
2*width-1])
unmasked_rel_logits = (
_relative_position_to_absolute_position_unmasked(
unmasked_rel_logits))
# shape it back for tiling
unmasked_rel_logits = tf.reshape(
unmasked_rel_logits, [-1, num_heads, height, width, width])
# tiling it height times
unmasked_rel_logits = tf.expand_dims(
unmasked_rel_logits, axis=3)
unmasked_rel_logits = tf.tile(unmasked_rel_logits,
[1, 1, 1, height, 1, 1])
# bringing it to the right shape for adding to the logits.
unmasked_rel_logits = tf.transpose(unmasked_rel_logits, transpose_mask)
unmasked_rel_logits = tf.reshape(unmasked_rel_logits,
[-1, num_heads, height*width,
height*width])
return unmasked_rel_logits
# Relative logits in width dimension first.
width_key_relative_embeddings = get_relative_embeddings_left_right(
max_relative_position, width, depth_k, num_heads,
heads_share_relative_embedding,
"width_key_relative_embeddings")
# [batch, heads, height, 2*width-1, 2*width-1]
width_unmasked_rel_logits = _compute_2d_relative_logits(
q, width_key_relative_embeddings, height, width,
heads_share_relative_embedding, [0, 1, 2, 4, 3, 5])
logits += width_unmasked_rel_logits
# Relative logits in height dimension next. For ease, we transpose
# height and width and repeat the above steps, and transpose to eventually
# put the logits in their right positions.
# [batch, heads, height, 2*height-1, 2*width-1]
height_key_relative_embeddings = get_relative_embeddings_left_right(
max_relative_position, height, depth_k, num_heads,
heads_share_relative_embedding,
"height_key_relative_embeddings")
height_unmasked_rel_logits = _compute_2d_relative_logits(
tf.transpose(q, [0, 1, 3, 2, 4]),
height_key_relative_embeddings,
width,
height,
heads_share_relative_embedding, [0, 1, 4, 2, 5, 3])
logits += height_unmasked_rel_logits
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
# dropping out the attention links for each of the heads
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
ret = tf.matmul(weights, flatten_hw(v, depth_v))
# reshape back the same spatial dimensions as q
return (
tf.reshape(ret, [-1, num_heads, height, width, depth_v]),
height_key_relative_embeddings,
width_key_relative_embeddings)
|
python
|
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 unmasked dot-product self-attention 2d.
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v in
height and width dimensions. for query index (i,j) and key index (l, m),
the logit is q_i k_j^T + q_i rh_{l-i}^T + q_i rw_{m-j}^T, where rh and ry are
the set of relative embeddings in height and width spatial dimensions,
respectively.
Args:
q: a Tensor with shape [batch, heads, height, width, depth].
k: a Tensor with shape [batch, heads, height, width, depth].
v: a Tensor with shape [batch, heads, height, width, depth].
bias: bias Tensor.
max_relative_position: an integer the max relative embedding considered.
Changing this invalidates checkpoints.
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
heads_share_relative_embedding: a boolean indicating wheather to share
relative embeddings between attention heads.
add_relative_to_values: a boolean for adding relative embeddings to values.
Returns:
[batch, heads, height, width, depth] tensor, the output of attention.
height_key_relative_embeddings: a 3d or 2d tensor, depending on head sharing
settings, which are the relative embeddings for height.
width_key_relative_embeddings: a 3d or 2d tensor, depending on head sharing
settings, which are the relative embeddings for width.
Raises:
ValueError: if max_relative_position is not > 0.
"""
if not max_relative_position:
raise ValueError("Max relative position (%s) should be > 0 when using "
"relative self attention." % (max_relative_position))
if add_relative_to_values:
raise ValueError("Adding relative embeddings to values is not implemented")
with tf.variable_scope(
name,
default_name="dot_product_self_attention_relative_v2",
values=[q, k, v]):
# This calculation only works for self attention.
# q, k and v must therefore have the same shape.
q.get_shape().assert_is_compatible_with(k.get_shape())
q.get_shape()[:-1].assert_is_compatible_with(v.get_shape()[:-1])
(height, width) = (common_layers.shape_list(q)[2],
common_layers.shape_list(q)[3])
k_shape = common_layers.shape_list(k)
num_heads = k_shape[1]
depth_k = k_shape[-1]
depth_v = common_layers.shape_list(v)[-1]
# flatten height width
flatten_hw = lambda x, d: tf.reshape(x, [-1, num_heads, height*width, d])
# [batch, num_heads, query_length, memory_length]
logits = tf.matmul(flatten_hw(q, depth_k), flatten_hw(k, depth_k),
transpose_b=True)
def _compute_2d_relative_logits(
query, key_relative_embeddings, height, width,
heads_share_relative_embedding, transpose_mask):
"""compute relative logits."""
unmasked_rel_logits = _matmul_with_relative_keys_2d(
query, key_relative_embeddings, heads_share_relative_embedding)
# collapse height and heads
unmasked_rel_logits = tf.reshape(unmasked_rel_logits,
[-1, num_heads*height, width,
2*width-1])
unmasked_rel_logits = (
_relative_position_to_absolute_position_unmasked(
unmasked_rel_logits))
# shape it back for tiling
unmasked_rel_logits = tf.reshape(
unmasked_rel_logits, [-1, num_heads, height, width, width])
# tiling it height times
unmasked_rel_logits = tf.expand_dims(
unmasked_rel_logits, axis=3)
unmasked_rel_logits = tf.tile(unmasked_rel_logits,
[1, 1, 1, height, 1, 1])
# bringing it to the right shape for adding to the logits.
unmasked_rel_logits = tf.transpose(unmasked_rel_logits, transpose_mask)
unmasked_rel_logits = tf.reshape(unmasked_rel_logits,
[-1, num_heads, height*width,
height*width])
return unmasked_rel_logits
# Relative logits in width dimension first.
width_key_relative_embeddings = get_relative_embeddings_left_right(
max_relative_position, width, depth_k, num_heads,
heads_share_relative_embedding,
"width_key_relative_embeddings")
# [batch, heads, height, 2*width-1, 2*width-1]
width_unmasked_rel_logits = _compute_2d_relative_logits(
q, width_key_relative_embeddings, height, width,
heads_share_relative_embedding, [0, 1, 2, 4, 3, 5])
logits += width_unmasked_rel_logits
# Relative logits in height dimension next. For ease, we transpose
# height and width and repeat the above steps, and transpose to eventually
# put the logits in their right positions.
# [batch, heads, height, 2*height-1, 2*width-1]
height_key_relative_embeddings = get_relative_embeddings_left_right(
max_relative_position, height, depth_k, num_heads,
heads_share_relative_embedding,
"height_key_relative_embeddings")
height_unmasked_rel_logits = _compute_2d_relative_logits(
tf.transpose(q, [0, 1, 3, 2, 4]),
height_key_relative_embeddings,
width,
height,
heads_share_relative_embedding, [0, 1, 4, 2, 5, 3])
logits += height_unmasked_rel_logits
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
# dropping out the attention links for each of the heads
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
ret = tf.matmul(weights, flatten_hw(v, depth_v))
# reshape back the same spatial dimensions as q
return (
tf.reshape(ret, [-1, num_heads, height, width, depth_v]),
height_key_relative_embeddings,
width_key_relative_embeddings)
|
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Calculate relative position unmasked dot-product self-attention 2d.
The attention calculation is augmented with learned representations for the
relative position between each element in q and each element in k and v in
height and width dimensions. for query index (i,j) and key index (l, m),
the logit is q_i k_j^T + q_i rh_{l-i}^T + q_i rw_{m-j}^T, where rh and ry are
the set of relative embeddings in height and width spatial dimensions,
respectively.
Args:
q: a Tensor with shape [batch, heads, height, width, depth].
k: a Tensor with shape [batch, heads, height, width, depth].
v: a Tensor with shape [batch, heads, height, width, depth].
bias: bias Tensor.
max_relative_position: an integer the max relative embedding considered.
Changing this invalidates checkpoints.
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
heads_share_relative_embedding: a boolean indicating wheather to share
relative embeddings between attention heads.
add_relative_to_values: a boolean for adding relative embeddings to values.
Returns:
[batch, heads, height, width, depth] tensor, the output of attention.
height_key_relative_embeddings: a 3d or 2d tensor, depending on head sharing
settings, which are the relative embeddings for height.
width_key_relative_embeddings: a 3d or 2d tensor, depending on head sharing
settings, which are the relative embeddings for width.
Raises:
ValueError: if max_relative_position is not > 0.
|
[
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"dot",
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"self",
"-",
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"2d",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L2086-L2225
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
_split_along_width
|
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, num_w_blocks,
height, width, depth] tensor
Returns:
x_left_blocks, x_right_blocks: two [batch, num_h_blocks,
(num_w_blocks-2)/2, height, width,
depth] tensors
"""
(_, x_num_h_blocks, x_num_outer_w_blocks, x_memory_flange_h,
x_memory_flange_w, depth) = common_layers.shape_list(x_left_right_blocks)
x_num_w_blocks = (x_num_outer_w_blocks-1)//2
# get it ready for splitting the left and right memory blocks
x_left_right_blocks = tf.reshape(x_left_right_blocks,
[-1,
x_num_h_blocks,
x_num_outer_w_blocks//2, 2,
x_memory_flange_h,
x_memory_flange_w, depth])
x_left_blocks, x_right_blocks = tf.split(x_left_right_blocks,
num_or_size_splits=2, axis=3)
x_left_blocks = tf.squeeze(x_left_blocks, axis=3)
x_right_blocks = tf.squeeze(x_right_blocks, axis=3)
x_left_blocks = tf.slice(x_left_blocks, [0, 0, 0, 0, 0, 0],
[-1, -1, x_num_w_blocks, -1, -1, -1])
x_right_blocks = tf.slice(x_right_blocks, [0, 0, 1, 0, 0, 0],
[-1, -1, x_num_w_blocks, -1, -1, -1])
return x_left_blocks, x_right_blocks
|
python
|
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, num_w_blocks,
height, width, depth] tensor
Returns:
x_left_blocks, x_right_blocks: two [batch, num_h_blocks,
(num_w_blocks-2)/2, height, width,
depth] tensors
"""
(_, x_num_h_blocks, x_num_outer_w_blocks, x_memory_flange_h,
x_memory_flange_w, depth) = common_layers.shape_list(x_left_right_blocks)
x_num_w_blocks = (x_num_outer_w_blocks-1)//2
# get it ready for splitting the left and right memory blocks
x_left_right_blocks = tf.reshape(x_left_right_blocks,
[-1,
x_num_h_blocks,
x_num_outer_w_blocks//2, 2,
x_memory_flange_h,
x_memory_flange_w, depth])
x_left_blocks, x_right_blocks = tf.split(x_left_right_blocks,
num_or_size_splits=2, axis=3)
x_left_blocks = tf.squeeze(x_left_blocks, axis=3)
x_right_blocks = tf.squeeze(x_right_blocks, axis=3)
x_left_blocks = tf.slice(x_left_blocks, [0, 0, 0, 0, 0, 0],
[-1, -1, x_num_w_blocks, -1, -1, -1])
x_right_blocks = tf.slice(x_right_blocks, [0, 0, 1, 0, 0, 0],
[-1, -1, x_num_w_blocks, -1, -1, -1])
return x_left_blocks, x_right_blocks
|
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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, num_w_blocks,
height, width, depth] tensor
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(num_w_blocks-2)/2, height, width,
depth] tensors
|
[
"Helper",
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] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L2228-L2265
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
_get_left_right_blocks
|
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 dimension.
Args:
x: a 6-d tensor.
Returns:
x_left_blocks, x_right_blocks: Two 6-d tensors
"""
(_, x_num_outer_h_blocks, x_num_outer_w_blocks, x_memory_flange_h,
x_memory_flange_w, depth) = common_layers.shape_list(x)
x_left_right_blocks = tf.slice(x,
[0, 1, 0, 0, 0, 0],
[-1, x_num_outer_h_blocks-2, -1, -1,
-1, -1])
num_blocks_h = (x_num_outer_h_blocks-2)//2
x_left_right_blocks = tf.reshape(x_left_right_blocks,
[-1,
num_blocks_h,
2, x_num_outer_w_blocks,
x_memory_flange_h,
x_memory_flange_w, depth])
x_left_right_blocks = tf.transpose(x_left_right_blocks,
[0, 1, 3, 2, 4, 5, 6])
x_left_right_blocks = tf.reshape(x_left_right_blocks,
[-1, num_blocks_h,
x_num_outer_w_blocks, 2*x_memory_flange_h,
x_memory_flange_w, depth])
# get it ready for splitting the left and right memory blocks
x_left_blocks, x_right_blocks = _split_along_width(x_left_right_blocks)
return x_left_blocks, x_right_blocks
|
python
|
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 dimension.
Args:
x: a 6-d tensor.
Returns:
x_left_blocks, x_right_blocks: Two 6-d tensors
"""
(_, x_num_outer_h_blocks, x_num_outer_w_blocks, x_memory_flange_h,
x_memory_flange_w, depth) = common_layers.shape_list(x)
x_left_right_blocks = tf.slice(x,
[0, 1, 0, 0, 0, 0],
[-1, x_num_outer_h_blocks-2, -1, -1,
-1, -1])
num_blocks_h = (x_num_outer_h_blocks-2)//2
x_left_right_blocks = tf.reshape(x_left_right_blocks,
[-1,
num_blocks_h,
2, x_num_outer_w_blocks,
x_memory_flange_h,
x_memory_flange_w, depth])
x_left_right_blocks = tf.transpose(x_left_right_blocks,
[0, 1, 3, 2, 4, 5, 6])
x_left_right_blocks = tf.reshape(x_left_right_blocks,
[-1, num_blocks_h,
x_num_outer_w_blocks, 2*x_memory_flange_h,
x_memory_flange_w, depth])
# get it ready for splitting the left and right memory blocks
x_left_blocks, x_right_blocks = _split_along_width(x_left_right_blocks)
return x_left_blocks, x_right_blocks
|
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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 dimension.
Args:
x: a 6-d tensor.
Returns:
x_left_blocks, x_right_blocks: Two 6-d tensors
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L2268-L2303
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
_extract_blocks
|
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, width, depth) = common_layers.shape_list(x)
assert height % block_h == 0
assert width % block_w == 0
x = tf.reshape(x, [-1, height//block_h, block_h,
width//block_w, block_w, depth])
return tf.transpose(x, [0, 1, 3, 2, 4, 5])
|
python
|
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, width, depth) = common_layers.shape_list(x)
assert height % block_h == 0
assert width % block_w == 0
x = tf.reshape(x, [-1, height//block_h, block_h,
width//block_w, block_w, depth])
return tf.transpose(x, [0, 1, 3, 2, 4, 5])
|
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Helper function for local 2d attention.
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[
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L2307-L2323
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
get_2d_local_memory
|
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,
query_shape[0]+2*memory_flange[0],query_shape[1]+2*memory_flange[1]]
tensor.
"""
(_, height, width, depth_x) = common_layers.shape_list(x)
x_center_blocks = _extract_blocks(x, query_shape[0], query_shape[1])
# add extra padding to x so that we can extract the memory region
# around the center
paddings = [[0, 0], [memory_flange[0], memory_flange[0]],
[memory_flange[1], memory_flange[1]], [0, 0]]
padded_x = tf.pad(x, paddings)
padded_x.set_shape([None, height+2*memory_flange[0],
width+2*memory_flange[1], depth_x])
x_outer_memory_blocks = _extract_blocks(padded_x,
memory_flange[0], memory_flange[1])
# We'll extract left and right memory blocks, top and bottom memory blocks,
# and then the corner memory blocks
# Each of these after will have shape
# [batch, num_h_blocks, num_w_blocks, query_shape[0],
# memory_flange[1], depth]
x_left_blocks, x_right_blocks = _get_left_right_blocks(
x_outer_memory_blocks)
t_hw_block = lambda x: tf.transpose(x, [0, 2, 1, 4, 3, 5])
# now to get top and bottom blocks, we should just transpose the outer
# blocks, call the same function and transpose back to get shape
# [batch, num_h_blocks, num_w_blocks, memory_flange[0],
# query_shape[1], depth]
x_top_center_blocks, x_bottom_center_blocks = (
map(t_hw_block, _get_left_right_blocks(
t_hw_block(x_outer_memory_blocks))))
# now to get the corner blocks
x_left_corner_blocks, x_right_corner_blocks = _split_along_width(
x_outer_memory_blocks)
# now to extract top and bottom for both k and v
# we need to transpose because _split_along_width separates along
# the width
# each of these should have shape [batch, num_h_blocks,
# num_w_blocks, memory_flange[0], memory_flange[1], depth]
t_hw = lambda x: tf.transpose(x, [0, 2, 1, 3, 4, 5])
x_top_left_corner_blocks, x_bottom_left_corner_blocks = (
map(t_hw, _split_along_width(t_hw(x_left_corner_blocks))))
x_top_right_corner_blocks, x_bottom_right_corner_blocks = (
map(t_hw, _split_along_width(t_hw(x_right_corner_blocks))))
# The memory is top_left top_center top_right
# left_center middle right_center
# bottom_left bottom_center bottom_right
# Assembling the above row by row
# first [x_top_left, x_top, x_top_right]
# to get [batch, num_h_blocks, num_w_blocks, memory_flange[0],
# query_shape[1]+2*memory_flange[1], depth]
# then [x_left, x_center, x_right]
# then [x_bottom_left, x_bottom, x_bottom_right]
x_top_memory = tf.concat(
[x_top_left_corner_blocks,
x_top_center_blocks,
x_top_right_corner_blocks], axis=4)
x_middle_memory = tf.concat(
[x_left_blocks, x_center_blocks, x_right_blocks], axis=4)
x_bottom_memory = tf.concat(
[x_bottom_left_corner_blocks,
x_bottom_center_blocks,
x_bottom_right_corner_blocks], axis=4)
# concat along height
x = tf.concat([x_top_memory, x_middle_memory, x_bottom_memory], axis=3)
return x
|
python
|
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,
query_shape[0]+2*memory_flange[0],query_shape[1]+2*memory_flange[1]]
tensor.
"""
(_, height, width, depth_x) = common_layers.shape_list(x)
x_center_blocks = _extract_blocks(x, query_shape[0], query_shape[1])
# add extra padding to x so that we can extract the memory region
# around the center
paddings = [[0, 0], [memory_flange[0], memory_flange[0]],
[memory_flange[1], memory_flange[1]], [0, 0]]
padded_x = tf.pad(x, paddings)
padded_x.set_shape([None, height+2*memory_flange[0],
width+2*memory_flange[1], depth_x])
x_outer_memory_blocks = _extract_blocks(padded_x,
memory_flange[0], memory_flange[1])
# We'll extract left and right memory blocks, top and bottom memory blocks,
# and then the corner memory blocks
# Each of these after will have shape
# [batch, num_h_blocks, num_w_blocks, query_shape[0],
# memory_flange[1], depth]
x_left_blocks, x_right_blocks = _get_left_right_blocks(
x_outer_memory_blocks)
t_hw_block = lambda x: tf.transpose(x, [0, 2, 1, 4, 3, 5])
# now to get top and bottom blocks, we should just transpose the outer
# blocks, call the same function and transpose back to get shape
# [batch, num_h_blocks, num_w_blocks, memory_flange[0],
# query_shape[1], depth]
x_top_center_blocks, x_bottom_center_blocks = (
map(t_hw_block, _get_left_right_blocks(
t_hw_block(x_outer_memory_blocks))))
# now to get the corner blocks
x_left_corner_blocks, x_right_corner_blocks = _split_along_width(
x_outer_memory_blocks)
# now to extract top and bottom for both k and v
# we need to transpose because _split_along_width separates along
# the width
# each of these should have shape [batch, num_h_blocks,
# num_w_blocks, memory_flange[0], memory_flange[1], depth]
t_hw = lambda x: tf.transpose(x, [0, 2, 1, 3, 4, 5])
x_top_left_corner_blocks, x_bottom_left_corner_blocks = (
map(t_hw, _split_along_width(t_hw(x_left_corner_blocks))))
x_top_right_corner_blocks, x_bottom_right_corner_blocks = (
map(t_hw, _split_along_width(t_hw(x_right_corner_blocks))))
# The memory is top_left top_center top_right
# left_center middle right_center
# bottom_left bottom_center bottom_right
# Assembling the above row by row
# first [x_top_left, x_top, x_top_right]
# to get [batch, num_h_blocks, num_w_blocks, memory_flange[0],
# query_shape[1]+2*memory_flange[1], depth]
# then [x_left, x_center, x_right]
# then [x_bottom_left, x_bottom, x_bottom_right]
x_top_memory = tf.concat(
[x_top_left_corner_blocks,
x_top_center_blocks,
x_top_right_corner_blocks], axis=4)
x_middle_memory = tf.concat(
[x_left_blocks, x_center_blocks, x_right_blocks], axis=4)
x_bottom_memory = tf.concat(
[x_bottom_left_corner_blocks,
x_bottom_center_blocks,
x_bottom_right_corner_blocks], axis=4)
# concat along height
x = tf.concat([x_top_memory, x_middle_memory, x_bottom_memory], axis=3)
return x
|
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Stitches together the local 2d memory blocks.
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memory_flange: 2-d integer list of memory flanges
Returns:
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tensor.
|
[
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L2326-L2404
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
get_2d_local_memory_v2
|
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 integer list of memory flanges
Returns:
x: A [batch, num_h_blocks, num_w_blocks,
query_shape[0]+2*memory_flange[0],query_shape[1]+2*memory_flange[1]]
tensor.
"""
(_, height, width, depth_x) = common_layers.shape_list(x)
# add extra padding to x so that we can extract the memory region
# around the center
paddings = [[0, 0], [memory_flange[0], memory_flange[0]],
[memory_flange[1], memory_flange[1]], [0, 0]]
padded_x = tf.pad(x, paddings)
padded_x.set_shape([None, height+2*memory_flange[0],
width+2*memory_flange[1], depth_x])
num_h_memory_blocks = height//query_shape[0] + 1
num_w_memory_blocks = width//query_shape[1] + 1
x_memory_blocks = _extract_blocks(padded_x,
query_shape[0], query_shape[1])
x_width_blocks = tf.split(x_memory_blocks, num_w_memory_blocks,
2)
x_left_width = tf.concat(x_width_blocks[:num_w_memory_blocks - 1], axis=2)
x_right_width = tf.concat(x_width_blocks[1:], axis=2)
x_memory_blocks = tf.concat([x_left_width, x_right_width], axis=4)
x_height_blocks = tf.split(x_memory_blocks, num_h_memory_blocks, 1)
x_top_height = tf.concat(x_height_blocks[:num_h_memory_blocks - 1], axis=1)
x_bottom_height = tf.concat(x_height_blocks[1:], axis=1)
x = tf.concat([x_top_height, x_bottom_height], axis=3)
return x
|
python
|
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 integer list of memory flanges
Returns:
x: A [batch, num_h_blocks, num_w_blocks,
query_shape[0]+2*memory_flange[0],query_shape[1]+2*memory_flange[1]]
tensor.
"""
(_, height, width, depth_x) = common_layers.shape_list(x)
# add extra padding to x so that we can extract the memory region
# around the center
paddings = [[0, 0], [memory_flange[0], memory_flange[0]],
[memory_flange[1], memory_flange[1]], [0, 0]]
padded_x = tf.pad(x, paddings)
padded_x.set_shape([None, height+2*memory_flange[0],
width+2*memory_flange[1], depth_x])
num_h_memory_blocks = height//query_shape[0] + 1
num_w_memory_blocks = width//query_shape[1] + 1
x_memory_blocks = _extract_blocks(padded_x,
query_shape[0], query_shape[1])
x_width_blocks = tf.split(x_memory_blocks, num_w_memory_blocks,
2)
x_left_width = tf.concat(x_width_blocks[:num_w_memory_blocks - 1], axis=2)
x_right_width = tf.concat(x_width_blocks[1:], axis=2)
x_memory_blocks = tf.concat([x_left_width, x_right_width], axis=4)
x_height_blocks = tf.split(x_memory_blocks, num_h_memory_blocks, 1)
x_top_height = tf.concat(x_height_blocks[:num_h_memory_blocks - 1], axis=1)
x_bottom_height = tf.concat(x_height_blocks[1:], axis=1)
x = tf.concat([x_top_height, x_bottom_height], axis=3)
return x
|
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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 integer list of memory flanges
Returns:
x: A [batch, num_h_blocks, num_w_blocks,
query_shape[0]+2*memory_flange[0],query_shape[1]+2*memory_flange[1]]
tensor.
|
[
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"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L2407-L2445
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
dot_product_unmasked_attention_local_2d_tpu
|
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 with shape [batch, heads, height, width, depth].
k: a Tensor with shape [batch, heads, height, width, depth].
v: a Tensor with shape [batch, heads, height, width, depth].
bias: bias Tensor.
max_relative_position: an integer the max relative embedding considered.
Changing this invalidates checkpoints.
query_shape: a two tuple indicating query shape
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
Returns:
[batch, heads, height, width, depth] tensor, the output of attention.
"""
if max_relative_position:
raise ValueError("Relative local 2d attention not implemented")
with tf.variable_scope(
name,
default_name="dot_product_unmasked_attention_local_2d_tpu",
values=[q, k, v]):
# This calculation only works for self attention.
# q, k and v must therefore have the same shape.
q.get_shape().assert_is_compatible_with(k.get_shape())
q.get_shape().assert_is_compatible_with(v.get_shape())
orig_q_shape = common_layers.shape_list(q)
# Pad query, key, value to ensure multiple of corresponding lengths.
memory_flange = [int(query_shape[0]//2), int(query_shape[1]//2)]
q = pad_to_multiple_2d(q, query_shape)
k = pad_to_multiple_2d(k, query_shape)
v = pad_to_multiple_2d(v, query_shape)
q_shape = common_layers.shape_list(q)
(height, width) = (q_shape[2],
q_shape[3])
_, num_heads, height, width, depth_k = common_layers.shape_list(k)
depth_v = common_layers.shape_list(v)[-1]
num_h_blocks = height//query_shape[0]
num_w_blocks = width//query_shape[1]
# Extract center queries, keys, and values
q = tf.reshape(q, [-1, height, width, depth_k])
queries = _extract_blocks(
q, query_shape[0], query_shape[1])
k = tf.reshape(k, [-1, height, width, depth_k])
keys = get_2d_local_memory_v2(
k, query_shape, memory_flange)
v = tf.reshape(v, [-1, height, width, depth_v])
values = get_2d_local_memory_v2(
v, query_shape, memory_flange)
memory_h = query_shape[0] + 2*memory_flange[0]
memory_w = query_shape[1] + 2*memory_flange[1]
queries = tf.reshape(queries, [-1, num_heads, num_h_blocks, num_w_blocks,
query_shape[0]*query_shape[1], depth_k])
keys = tf.reshape(keys, [-1, num_heads, num_h_blocks, num_w_blocks,
memory_h*memory_w, depth_k])
values = tf.reshape(values, [-1, num_heads, num_h_blocks, num_w_blocks,
memory_h*memory_w, depth_v])
logits = tf.matmul(queries, keys, transpose_b=True)
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
# Dropping out the attention links for each of the heads
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
ret = tf.matmul(weights, values)
# we need to get it back to shape [batch, heads, height, width]
ret = tf.reshape(ret, [-1, num_heads, num_h_blocks, num_w_blocks,
query_shape[0], query_shape[1], depth_v])
ret = tf.transpose(ret, [0, 1, 2, 4, 3, 5, 6])
ret = tf.reshape(ret, [-1, num_heads, num_h_blocks*query_shape[0],
num_w_blocks*query_shape[1], depth_v])
# slice if padding was introduced
ret = tf.slice(ret, [0, 0, 0, 0, 0], [-1, -1, orig_q_shape[2],
orig_q_shape[3], -1])
return ret
|
python
|
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 with shape [batch, heads, height, width, depth].
k: a Tensor with shape [batch, heads, height, width, depth].
v: a Tensor with shape [batch, heads, height, width, depth].
bias: bias Tensor.
max_relative_position: an integer the max relative embedding considered.
Changing this invalidates checkpoints.
query_shape: a two tuple indicating query shape
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
Returns:
[batch, heads, height, width, depth] tensor, the output of attention.
"""
if max_relative_position:
raise ValueError("Relative local 2d attention not implemented")
with tf.variable_scope(
name,
default_name="dot_product_unmasked_attention_local_2d_tpu",
values=[q, k, v]):
# This calculation only works for self attention.
# q, k and v must therefore have the same shape.
q.get_shape().assert_is_compatible_with(k.get_shape())
q.get_shape().assert_is_compatible_with(v.get_shape())
orig_q_shape = common_layers.shape_list(q)
# Pad query, key, value to ensure multiple of corresponding lengths.
memory_flange = [int(query_shape[0]//2), int(query_shape[1]//2)]
q = pad_to_multiple_2d(q, query_shape)
k = pad_to_multiple_2d(k, query_shape)
v = pad_to_multiple_2d(v, query_shape)
q_shape = common_layers.shape_list(q)
(height, width) = (q_shape[2],
q_shape[3])
_, num_heads, height, width, depth_k = common_layers.shape_list(k)
depth_v = common_layers.shape_list(v)[-1]
num_h_blocks = height//query_shape[0]
num_w_blocks = width//query_shape[1]
# Extract center queries, keys, and values
q = tf.reshape(q, [-1, height, width, depth_k])
queries = _extract_blocks(
q, query_shape[0], query_shape[1])
k = tf.reshape(k, [-1, height, width, depth_k])
keys = get_2d_local_memory_v2(
k, query_shape, memory_flange)
v = tf.reshape(v, [-1, height, width, depth_v])
values = get_2d_local_memory_v2(
v, query_shape, memory_flange)
memory_h = query_shape[0] + 2*memory_flange[0]
memory_w = query_shape[1] + 2*memory_flange[1]
queries = tf.reshape(queries, [-1, num_heads, num_h_blocks, num_w_blocks,
query_shape[0]*query_shape[1], depth_k])
keys = tf.reshape(keys, [-1, num_heads, num_h_blocks, num_w_blocks,
memory_h*memory_w, depth_k])
values = tf.reshape(values, [-1, num_heads, num_h_blocks, num_w_blocks,
memory_h*memory_w, depth_v])
logits = tf.matmul(queries, keys, transpose_b=True)
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
# Dropping out the attention links for each of the heads
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
ret = tf.matmul(weights, values)
# we need to get it back to shape [batch, heads, height, width]
ret = tf.reshape(ret, [-1, num_heads, num_h_blocks, num_w_blocks,
query_shape[0], query_shape[1], depth_v])
ret = tf.transpose(ret, [0, 1, 2, 4, 3, 5, 6])
ret = tf.reshape(ret, [-1, num_heads, num_h_blocks*query_shape[0],
num_w_blocks*query_shape[1], depth_v])
# slice if padding was introduced
ret = tf.slice(ret, [0, 0, 0, 0, 0], [-1, -1, orig_q_shape[2],
orig_q_shape[3], -1])
return ret
|
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Calculate unmasked dot-product local self-attention 2d on tpu.
Args:
q: a Tensor with shape [batch, heads, height, width, depth].
k: a Tensor with shape [batch, heads, height, width, depth].
v: a Tensor with shape [batch, heads, height, width, depth].
bias: bias Tensor.
max_relative_position: an integer the max relative embedding considered.
Changing this invalidates checkpoints.
query_shape: a two tuple indicating query shape
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
name: an optional string.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
Returns:
[batch, heads, height, width, depth] tensor, the output of attention.
|
[
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] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L2448-L2537
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
dot_product_unmasked_attention_local_2d_tpu_simple
|
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.
The query, key, and value blocks are the same. We do not do a second linear
transformation after computing the values
Args:
x: a Tensor with shape [batch, height, width, depth].
bias: bias Tensor.
total_key_depth: the dimensions of the keys
total_value_depth: the dimensions of the values
num_heads: number of heads
query_shape: a two tuple indicating query shape
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
Returns:
ret: [batch, height, width, total_value_depth] tensor,
the output of attention.
q: [batch, height, width, total_key_depth] query tensor
k: [batch, height, width, total_key_depth] key tensor
v: [batch, height, width, total_value_depth] value tensor
"""
# This calculation only works for self attention.
# q, k and v must therefore have the same shape.
orig_x_shape = common_layers.shape_list(x)
# Pad query, key, value to ensure multiple of corresponding lengths if
# necessary
is_padded = False
if (orig_x_shape[1]%query_shape[0]) != 0 or (
orig_x_shape[2]%query_shape[1]) != 0:
x = pad_to_multiple_2d(x, query_shape)
is_padded = True
_, height, width, depth = common_layers.shape_list(x)
assert depth%num_heads == 0
num_h_blocks = height//query_shape[0]
num_w_blocks = width//query_shape[1]
# Extract center queries, keys, and values
x_blocks = _extract_blocks(x, query_shape[0], query_shape[1])
x_blocks = tf.reshape(x_blocks, [-1, query_shape[0]*query_shape[1], depth])
q, k, v = compute_qkv(x_blocks, None, total_key_depth, total_value_depth)
hsplit = lambda x: split_heads(x, num_heads)
q, k, v = map(hsplit, [q, k, v])
logits = tf.matmul(q, k, transpose_b=True)
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
# Dropping out the attention links for each of the heads
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
output = tf.matmul(weights, v)
output = combine_heads(output)
# we need to get it back to shape [batch, height, width]
ret = tf.reshape(output, [-1, num_h_blocks, num_w_blocks,
query_shape[0], query_shape[1], total_value_depth])
ret = tf.transpose(ret, [0, 1, 3, 2, 4, 5])
ret = tf.reshape(ret, [-1, num_h_blocks*query_shape[0],
num_w_blocks*query_shape[1], total_value_depth])
# slice if padding was introduced
if is_padded:
ret = tf.slice(ret, [0, 0, 0, 0], [-1, orig_x_shape[1],
orig_x_shape[2], -1])
return ret, q, k, v
|
python
|
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.
The query, key, and value blocks are the same. We do not do a second linear
transformation after computing the values
Args:
x: a Tensor with shape [batch, height, width, depth].
bias: bias Tensor.
total_key_depth: the dimensions of the keys
total_value_depth: the dimensions of the values
num_heads: number of heads
query_shape: a two tuple indicating query shape
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
Returns:
ret: [batch, height, width, total_value_depth] tensor,
the output of attention.
q: [batch, height, width, total_key_depth] query tensor
k: [batch, height, width, total_key_depth] key tensor
v: [batch, height, width, total_value_depth] value tensor
"""
# This calculation only works for self attention.
# q, k and v must therefore have the same shape.
orig_x_shape = common_layers.shape_list(x)
# Pad query, key, value to ensure multiple of corresponding lengths if
# necessary
is_padded = False
if (orig_x_shape[1]%query_shape[0]) != 0 or (
orig_x_shape[2]%query_shape[1]) != 0:
x = pad_to_multiple_2d(x, query_shape)
is_padded = True
_, height, width, depth = common_layers.shape_list(x)
assert depth%num_heads == 0
num_h_blocks = height//query_shape[0]
num_w_blocks = width//query_shape[1]
# Extract center queries, keys, and values
x_blocks = _extract_blocks(x, query_shape[0], query_shape[1])
x_blocks = tf.reshape(x_blocks, [-1, query_shape[0]*query_shape[1], depth])
q, k, v = compute_qkv(x_blocks, None, total_key_depth, total_value_depth)
hsplit = lambda x: split_heads(x, num_heads)
q, k, v = map(hsplit, [q, k, v])
logits = tf.matmul(q, k, transpose_b=True)
if bias is not None:
logits += bias
weights = tf.nn.softmax(logits, name="attention_weights")
# Dropping out the attention links for each of the heads
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate, broadcast_dims=dropout_broadcast_dims)
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(weights, image_shapes)
output = tf.matmul(weights, v)
output = combine_heads(output)
# we need to get it back to shape [batch, height, width]
ret = tf.reshape(output, [-1, num_h_blocks, num_w_blocks,
query_shape[0], query_shape[1], total_value_depth])
ret = tf.transpose(ret, [0, 1, 3, 2, 4, 5])
ret = tf.reshape(ret, [-1, num_h_blocks*query_shape[0],
num_w_blocks*query_shape[1], total_value_depth])
# slice if padding was introduced
if is_padded:
ret = tf.slice(ret, [0, 0, 0, 0], [-1, orig_x_shape[1],
orig_x_shape[2], -1])
return ret, q, k, v
|
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Calculate simple unmasked dot-product local self-attention 2d on tpu.
The query, key, and value blocks are the same. We do not do a second linear
transformation after computing the values
Args:
x: a Tensor with shape [batch, height, width, depth].
bias: bias Tensor.
total_key_depth: the dimensions of the keys
total_value_depth: the dimensions of the values
num_heads: number of heads
query_shape: a two tuple indicating query shape
dropout_rate: a floating point number.
image_shapes: optional tuple of integer scalars.
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
Returns:
ret: [batch, height, width, total_value_depth] tensor,
the output of attention.
q: [batch, height, width, total_key_depth] query tensor
k: [batch, height, width, total_key_depth] key tensor
v: [batch, height, width, total_value_depth] value tensor
|
[
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"unmasked",
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"-",
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"self",
"-",
"attention",
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"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L2540-L2615
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
masked_within_block_local_attention_1d
|
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
query position in the corresponding block.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v]
"""
with tf.variable_scope(
name, default_name="within_local_attention_1d", values=[q, k, v]):
batch, heads, length, depth_k = common_layers.shape_list(q)
depth_v = common_layers.shape_list(v)[-1]
if isinstance(block_length, tf.Tensor):
const = tf.contrib.util.constant_value(block_length)
if const is not None:
block_length = int(const)
# Pad query, key, value to ensure multiple of block length.
original_length = length
padding_size = tf.mod(-length, block_length)
length += padding_size
padding = [[0, 0], [0, 0], [0, padding_size], [0, 0]]
q = tf.pad(q, padding)
k = tf.pad(k, padding)
v = tf.pad(v, padding)
# Compute attention for all subsequent query blocks.
num_blocks = tf.div(length, block_length)
q = tf.reshape(q, [batch, heads, num_blocks, block_length, depth_k])
k = tf.reshape(k, [batch, heads, num_blocks, block_length, depth_k])
v = tf.reshape(v, [batch, heads, num_blocks, block_length, depth_v])
# [batch, heads, num_blocks, block_length, block_length]
attention = tf.matmul(q, k, transpose_b=True)
attention += tf.reshape(attention_bias_lower_triangle(block_length),
[1, 1, 1, block_length, block_length])
attention = tf.nn.softmax(attention)
# [batch, heads, num_blocks, block_length, depth_v]
output = tf.matmul(attention, v)
output = tf.reshape(output, [batch, heads, -1, depth_v])
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output.set_shape([None if isinstance(dim, tf.Tensor) else dim for dim in
(batch, heads, length, depth_v)])
return output
|
python
|
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
query position in the corresponding block.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v]
"""
with tf.variable_scope(
name, default_name="within_local_attention_1d", values=[q, k, v]):
batch, heads, length, depth_k = common_layers.shape_list(q)
depth_v = common_layers.shape_list(v)[-1]
if isinstance(block_length, tf.Tensor):
const = tf.contrib.util.constant_value(block_length)
if const is not None:
block_length = int(const)
# Pad query, key, value to ensure multiple of block length.
original_length = length
padding_size = tf.mod(-length, block_length)
length += padding_size
padding = [[0, 0], [0, 0], [0, padding_size], [0, 0]]
q = tf.pad(q, padding)
k = tf.pad(k, padding)
v = tf.pad(v, padding)
# Compute attention for all subsequent query blocks.
num_blocks = tf.div(length, block_length)
q = tf.reshape(q, [batch, heads, num_blocks, block_length, depth_k])
k = tf.reshape(k, [batch, heads, num_blocks, block_length, depth_k])
v = tf.reshape(v, [batch, heads, num_blocks, block_length, depth_v])
# [batch, heads, num_blocks, block_length, block_length]
attention = tf.matmul(q, k, transpose_b=True)
attention += tf.reshape(attention_bias_lower_triangle(block_length),
[1, 1, 1, block_length, block_length])
attention = tf.nn.softmax(attention)
# [batch, heads, num_blocks, block_length, depth_v]
output = tf.matmul(attention, v)
output = tf.reshape(output, [batch, heads, -1, depth_v])
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output.set_shape([None if isinstance(dim, tf.Tensor) else dim for dim in
(batch, heads, length, depth_v)])
return output
|
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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
query position in the corresponding block.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
name: an optional string
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a Tensor of shape [batch, heads, length, depth_v]
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L2618-L2671
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
_relative_position_to_absolute_position_unmasked
|
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), heads, length, length-1]
"""
x_shape = common_layers.shape_list(x)
batch = x_shape[0]
heads = x_shape[1]
length = x_shape[2]
# Concat columns of pad to shift from relative to absolute indexing.
col_pad = tf.zeros((batch, heads, length, 1))
x = tf.concat([x, col_pad], axis=3)
# Concat extra elements so to add up to shape (len+1, 2*len-1).
flat_x = tf.reshape(x, [batch, heads, length * 2 * length])
flat_pad = tf.zeros((batch, heads, length-1))
flat_x_padded = tf.concat([flat_x, flat_pad], axis=2)
# Reshape and slice out the padded elements.
final_x = tf.reshape(flat_x_padded, [batch, heads, length+1, 2*length-1])
final_x = final_x[:, :, :, length-1:]
final_x = final_x[:, :, :length, :]
return final_x
|
python
|
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), heads, length, length-1]
"""
x_shape = common_layers.shape_list(x)
batch = x_shape[0]
heads = x_shape[1]
length = x_shape[2]
# Concat columns of pad to shift from relative to absolute indexing.
col_pad = tf.zeros((batch, heads, length, 1))
x = tf.concat([x, col_pad], axis=3)
# Concat extra elements so to add up to shape (len+1, 2*len-1).
flat_x = tf.reshape(x, [batch, heads, length * 2 * length])
flat_pad = tf.zeros((batch, heads, length-1))
flat_x_padded = tf.concat([flat_x, flat_pad], axis=2)
# Reshape and slice out the padded elements.
final_x = tf.reshape(flat_x_padded, [batch, heads, length+1, 2*length-1])
final_x = final_x[:, :, :, length-1:]
final_x = final_x[:, :, :length, :]
return final_x
|
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Converts tensor from relative to aboslute indexing for local attention.
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Returns:
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|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L2674-L2701
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
masked_local_attention_1d
|
def masked_local_attention_1d(q,
k,
v,
block_length=128,
make_image_summary=False,
dropout_rate=0.,
name=None):
"""Attention to the source position and a neighborhood to the left of it.
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
query position, in the corresponding block and the previous block.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
make_image_summary: a boolean, whether to make an attention image summary.
dropout_rate: Dropout rate for attention dropout
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v]
"""
with tf.variable_scope(
name, default_name="local_attention_1d", values=[q, k, v]):
batch, heads, length, depth_k = common_layers.shape_list(q)
depth_v = common_layers.shape_list(v)[-1]
if isinstance(block_length, tf.Tensor):
const = tf.contrib.util.constant_value(block_length)
if const is not None:
block_length = int(const)
# If (length < 2 * block_length), then we use only one block.
if isinstance(length, int) and isinstance(block_length, int):
block_length = length if length < block_length * 2 else block_length
else:
block_length = tf.where(
tf.less(length, block_length * 2), length, block_length)
# Pad query, key, value to ensure multiple of block length.
original_length = length
padding_size = tf.mod(-length, block_length)
length += padding_size
padding = [[0, 0], [0, 0], [0, padding_size], [0, 0]]
q = tf.pad(q, padding)
k = tf.pad(k, padding)
v = tf.pad(v, padding)
if isinstance(length, int) and isinstance(block_length, int):
num_blocks = length // block_length
else:
num_blocks = tf.div(length, block_length)
# Compute attention for the first query block.
first_q = tf.slice(q, [0, 0, 0, 0], [-1, -1, block_length, -1])
first_k = tf.slice(k, [0, 0, 0, 0], [-1, -1, block_length, -1])
first_v = tf.slice(v, [0, 0, 0, 0], [-1, -1, block_length, -1])
first_output = dot_product_attention(
first_q,
first_k,
first_v,
attention_bias_lower_triangle(block_length),
dropout_rate=dropout_rate,
make_image_summary=make_image_summary,
name="first_block")
# Compute attention for all subsequent query blocks.
q = tf.reshape(q, [batch, heads, num_blocks, block_length, depth_k])
k = tf.reshape(k, [batch, heads, num_blocks, block_length, depth_k])
v = tf.reshape(v, [batch, heads, num_blocks, block_length, depth_v])
local_k = _make_local_block(k, depth_k, batch, heads, num_blocks,
block_length)
local_v = _make_local_block(v, depth_v, batch, heads, num_blocks,
block_length)
tail_q = tf.slice(q, [0, 0, 1, 0, 0], [-1, -1, -1, -1, -1])
tail_q = tf.reshape(tail_q,
[batch, heads, num_blocks - 1, block_length, depth_k])
local_length = common_layers.shape_list(local_k)[3]
# make sure source_pos <= target_pos
good_part = common_layers.ones_matrix_band_part(
block_length,
local_length,
-1,
block_length,
out_shape=[1, 1, 1, block_length, local_length])
bias = (1.0 - good_part) * -1e9
# TODO(noam): figure out how to show a summary for the remaining blocks.
# The naive way currently causes errors due to empty tensors.
# output: [batch, heads, num_blocks-1, block_length, depth_v]
tail_output = dot_product_attention(
tail_q,
local_k,
local_v,
bias,
dropout_rate=dropout_rate,
make_image_summary=False,
name="tail_block")
tail_output = tf.reshape(
tail_output, [batch, heads, (num_blocks - 1) * block_length, depth_v])
output = tf.concat([first_output, tail_output], axis=2)
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output = tf.reshape(output, [batch, heads, original_length, depth_v])
return output
|
python
|
def masked_local_attention_1d(q,
k,
v,
block_length=128,
make_image_summary=False,
dropout_rate=0.,
name=None):
"""Attention to the source position and a neighborhood to the left of it.
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
query position, in the corresponding block and the previous block.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
make_image_summary: a boolean, whether to make an attention image summary.
dropout_rate: Dropout rate for attention dropout
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v]
"""
with tf.variable_scope(
name, default_name="local_attention_1d", values=[q, k, v]):
batch, heads, length, depth_k = common_layers.shape_list(q)
depth_v = common_layers.shape_list(v)[-1]
if isinstance(block_length, tf.Tensor):
const = tf.contrib.util.constant_value(block_length)
if const is not None:
block_length = int(const)
# If (length < 2 * block_length), then we use only one block.
if isinstance(length, int) and isinstance(block_length, int):
block_length = length if length < block_length * 2 else block_length
else:
block_length = tf.where(
tf.less(length, block_length * 2), length, block_length)
# Pad query, key, value to ensure multiple of block length.
original_length = length
padding_size = tf.mod(-length, block_length)
length += padding_size
padding = [[0, 0], [0, 0], [0, padding_size], [0, 0]]
q = tf.pad(q, padding)
k = tf.pad(k, padding)
v = tf.pad(v, padding)
if isinstance(length, int) and isinstance(block_length, int):
num_blocks = length // block_length
else:
num_blocks = tf.div(length, block_length)
# Compute attention for the first query block.
first_q = tf.slice(q, [0, 0, 0, 0], [-1, -1, block_length, -1])
first_k = tf.slice(k, [0, 0, 0, 0], [-1, -1, block_length, -1])
first_v = tf.slice(v, [0, 0, 0, 0], [-1, -1, block_length, -1])
first_output = dot_product_attention(
first_q,
first_k,
first_v,
attention_bias_lower_triangle(block_length),
dropout_rate=dropout_rate,
make_image_summary=make_image_summary,
name="first_block")
# Compute attention for all subsequent query blocks.
q = tf.reshape(q, [batch, heads, num_blocks, block_length, depth_k])
k = tf.reshape(k, [batch, heads, num_blocks, block_length, depth_k])
v = tf.reshape(v, [batch, heads, num_blocks, block_length, depth_v])
local_k = _make_local_block(k, depth_k, batch, heads, num_blocks,
block_length)
local_v = _make_local_block(v, depth_v, batch, heads, num_blocks,
block_length)
tail_q = tf.slice(q, [0, 0, 1, 0, 0], [-1, -1, -1, -1, -1])
tail_q = tf.reshape(tail_q,
[batch, heads, num_blocks - 1, block_length, depth_k])
local_length = common_layers.shape_list(local_k)[3]
# make sure source_pos <= target_pos
good_part = common_layers.ones_matrix_band_part(
block_length,
local_length,
-1,
block_length,
out_shape=[1, 1, 1, block_length, local_length])
bias = (1.0 - good_part) * -1e9
# TODO(noam): figure out how to show a summary for the remaining blocks.
# The naive way currently causes errors due to empty tensors.
# output: [batch, heads, num_blocks-1, block_length, depth_v]
tail_output = dot_product_attention(
tail_q,
local_k,
local_v,
bias,
dropout_rate=dropout_rate,
make_image_summary=False,
name="tail_block")
tail_output = tf.reshape(
tail_output, [batch, heads, (num_blocks - 1) * block_length, depth_v])
output = tf.concat([first_output, tail_output], axis=2)
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output = tf.reshape(output, [batch, heads, original_length, depth_v])
return output
|
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Attention to the source position and a neighborhood to the left of it.
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
query position, in the corresponding block and the previous block.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
make_image_summary: a boolean, whether to make an attention image summary.
dropout_rate: Dropout rate for attention dropout
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v]
|
[
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"it",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L2704-L2812
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
_make_local_block
|
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])
local_block = tf.concat([prev_block, cur_block], 3)
return tf.reshape(local_block,
[batch, heads, num_blocks - 1, block_length * 2, depth])
|
python
|
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])
local_block = tf.concat([prev_block, cur_block], 3)
return tf.reshape(local_block,
[batch, heads, num_blocks - 1, block_length * 2, depth])
|
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Helper function to create a local version of the keys or values for 1d.
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L2815-L2822
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
masked_relative_local_attention_1d
|
def masked_relative_local_attention_1d(q,
k,
v,
block_length=128,
make_image_summary=False,
dropout_rate=0.,
heads_share_relative_embedding=False,
add_relative_to_values=False,
name=None):
"""Masked local 1d attention with relative positions.
The sequence is divided into blocks of length block_size.
Attention for a given query position can only see memory positions
less than or equal to the query position, in the corresponding block
and the previous block.
If mask_right is True, then a target position cannot see greater source
positions.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
make_image_summary: a boolean, whether to make an attention image summary.
dropout_rate: Dropout rate for attention dropout
heads_share_relative_embedding: a boolean for sharing relative embeddings.
add_relative_to_values: a boolean for whether to add relative component to
values.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v]
Raises:
ValueError: wwhen the name for the variable scope is not passed.
"""
if not name:
raise ValueError("Name must be assigned since reuse for variable scope is "
"set to tf.AUTO_REUSE, in order to reuse relative "
"embeddings of keys and values.")
# Reuse flag is set to auto_reuse to reuse relative embeddings of keys and
# values across blocks (first and tail blocks).
with tf.variable_scope(
name, default_name="masked_relative_local_attention_1d",
values=[q, k, v], reuse=tf.AUTO_REUSE):
default_block_length = block_length
batch = common_layers.shape_list(q)[0]
heads = common_layers.shape_list(q)[1]
length = common_layers.shape_list(q)[2]
# If (length < 2 * block_length), then we use only one block.
if isinstance(length, int) and isinstance(block_length, int):
block_length = length if length < block_length * 2 else block_length
else:
block_length = tf.where(
tf.less(length, block_length * 2), length, block_length)
depth_k = common_layers.shape_list(k)[3]
depth_v = common_layers.shape_list(v)[3]
original_length = length
padding_size = tf.mod(-length, block_length)
length += padding_size
padding = [[0, 0], [0, 0], [0, padding_size], [0, 0]]
q = tf.pad(q, padding)
k = tf.pad(k, padding)
v = tf.pad(v, padding)
num_blocks = length // block_length
# compute attention for the first query block.
first_q = tf.slice(q, [0, 0, 0, 0], [-1, -1, block_length, -1])
first_k = tf.slice(k, [0, 0, 0, 0], [-1, -1, block_length, -1])
first_v = tf.slice(v, [0, 0, 0, 0], [-1, -1, block_length, -1])
# Relative embeddings will be used later as well.
# TODO(avaswani,annahuang): check why 2*bl was breaking for music
# Needs to be known at static shape inference time, hence cannot be
# 2 * block_length.
rel_embed_length = 4 * default_block_length
# We only multiply with the needed embeddings as we slice them out.
first_rel_embeddings = get_relative_embeddings_left(
rel_embed_length, block_length, depth_k, heads,
heads_share_relative_embedding, "relative_embeddings")
first_rel_logits = matmul_with_relative_keys(
first_q, first_rel_embeddings, heads_share_relative_embedding)
first_logits = tf.matmul(first_q, first_k, transpose_b=True)
first_logits += (
_relative_position_to_absolute_position_masked(first_rel_logits))
# adding a mask
first_logits += (
common_layers.cast_like(attention_bias_lower_triangle(block_length),
first_logits))
first_att = tf.nn.softmax(first_logits,
name="first_attention_weights")
# dropping out the attention links for each of the heads
first_att = common_layers.dropout_with_broadcast_dims(
first_att, 1.0 - dropout_rate,
broadcast_dims=None)
# only call image summary for the first block
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(first_att, None)
first_output = tf.matmul(first_att, first_v)
# compute attention for all subsequent query blocks.
q = tf.reshape(q, [batch, heads, num_blocks, block_length, depth_k])
k = tf.reshape(k, [batch, heads, num_blocks, block_length, depth_k])
v = tf.reshape(v, [batch, heads, num_blocks, block_length, depth_v])
local_k = _make_local_block(k, depth_k, batch, heads, num_blocks,
block_length)
local_v = _make_local_block(v, depth_v, batch, heads, num_blocks,
block_length)
tail_q = tf.slice(q, [0, 0, 1, 0, 0], [-1, -1, -1, -1, -1])
tail_q = tf.reshape(tail_q,
[batch, heads, num_blocks - 1, block_length, depth_k])
local_length = common_layers.shape_list(local_k)[3]
# collapsing num blocks and batch size so that we can reuse
# functions
def _reshape_for_relative(x):
x_shape = common_layers.shape_list(x)
# [batch, num_blocks, heads, length, depth]
x = tf.transpose(x, [0, 2, 1, 3, 4])
x = tf.reshape(x, [batch*x_shape[2], heads, x_shape[3],
x_shape[4]])
return x
rel_tail_q = _reshape_for_relative(tail_q)
rel_k = _reshape_for_relative(local_k)
rel_v = _reshape_for_relative(local_v)
rel_embeddings = get_relative_embeddings_left(
rel_embed_length, 2 * block_length, depth_k, heads,
heads_share_relative_embedding, "relative_embeddings")
rel_logits = matmul_with_relative_keys(
rel_tail_q, rel_embeddings, heads_share_relative_embedding)
# Computing relative logits separately for the masked and unmasked parts
# because the reshaping logic is different for both
masked_rel_logits = tf.slice(rel_logits, [0, 0, 0, block_length],
[-1, -1, -1, -1])
masked_rel_logits = _relative_position_to_absolute_position_masked(
masked_rel_logits)
unmasked_rel_logits = tf.slice(rel_logits, [0, 0, 0, 0],
[-1, -1, -1, 2*block_length-1])
unmasked_rel_logits = _relative_position_to_absolute_position_unmasked(
unmasked_rel_logits)
all_rel_logits = tf.concat([unmasked_rel_logits, masked_rel_logits],
axis=3)
all_logits = (
tf.matmul(rel_tail_q, rel_k, transpose_b=True) + all_rel_logits)
# make sure source_pos <= target_pos
good_part = common_layers.ones_matrix_band_part(block_length,
local_length,
-1, block_length)
mask = (1.0 - good_part) * -1e9
mask = common_layers.cast_like(mask, all_logits)
all_logits += tf.reshape(mask, [1, 1, block_length, local_length])
weights = tf.nn.softmax(all_logits, name="attention_weights")
# [batch (* num_blocks), heads, query_length (=block_length),
# key_length (=2*block_length)]
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate,
broadcast_dims=None)
output = tf.matmul(weights, rel_v)
if add_relative_to_values:
# Adds the contribution of the weighted relative embeddings to the values.
weights_for_unmasked, weights_for_masked = (
tf.split(weights, 2, axis=3))
rel_weights_unmasked = _absolute_position_to_relative_position_unmasked(
weights_for_unmasked)
rel_weights_masked = _absolute_position_to_relative_position_masked(
weights_for_masked)
value_rel_embeddings_unmasked = get_relative_embeddings_left(
rel_embed_length, 2 * block_length, depth_v,
heads, heads_share_relative_embedding,
"value_relative_embeddings")
# The unmasked part starts with index -1 as opposed 0 has take uptil last.
if heads_share_relative_embedding:
value_rel_embeddings_unmasked = value_rel_embeddings_unmasked[:-1, :]
else:
value_rel_embeddings_unmasked = value_rel_embeddings_unmasked[:, :-1, :]
value_rel_embeddings_masked = get_relative_embeddings_left(
rel_embed_length, block_length, depth_v,
heads, heads_share_relative_embedding,
"value_relative_embeddings")
# [batch (*num_blocks), heads, query length, key length]
rel_weights = tf.concat(
[rel_weights_unmasked, rel_weights_masked], axis=3)
if heads_share_relative_embedding:
value_rel_embeddings_concat_axis = 0
else:
value_rel_embeddings_concat_axis = 1
value_rel_embeddings = tf.concat(
[value_rel_embeddings_unmasked, value_rel_embeddings_masked],
axis=value_rel_embeddings_concat_axis)
output_rel = matmul_with_relative_values(
rel_weights, value_rel_embeddings, heads_share_relative_embedding)
output += output_rel
# bring to [batch, heads, num_blocks-1, block_length, depth]
output = tf.reshape(output,
[batch, num_blocks-1, heads, block_length, depth_v])
output = tf.transpose(output, [0, 2, 1, 3, 4])
output = tf.reshape(
output, [batch, heads, (num_blocks - 1) * block_length, depth_v])
output = tf.concat([first_output, output], axis=2)
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output = tf.reshape(output, [batch, heads, original_length, depth_v])
return output
|
python
|
def masked_relative_local_attention_1d(q,
k,
v,
block_length=128,
make_image_summary=False,
dropout_rate=0.,
heads_share_relative_embedding=False,
add_relative_to_values=False,
name=None):
"""Masked local 1d attention with relative positions.
The sequence is divided into blocks of length block_size.
Attention for a given query position can only see memory positions
less than or equal to the query position, in the corresponding block
and the previous block.
If mask_right is True, then a target position cannot see greater source
positions.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
make_image_summary: a boolean, whether to make an attention image summary.
dropout_rate: Dropout rate for attention dropout
heads_share_relative_embedding: a boolean for sharing relative embeddings.
add_relative_to_values: a boolean for whether to add relative component to
values.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v]
Raises:
ValueError: wwhen the name for the variable scope is not passed.
"""
if not name:
raise ValueError("Name must be assigned since reuse for variable scope is "
"set to tf.AUTO_REUSE, in order to reuse relative "
"embeddings of keys and values.")
# Reuse flag is set to auto_reuse to reuse relative embeddings of keys and
# values across blocks (first and tail blocks).
with tf.variable_scope(
name, default_name="masked_relative_local_attention_1d",
values=[q, k, v], reuse=tf.AUTO_REUSE):
default_block_length = block_length
batch = common_layers.shape_list(q)[0]
heads = common_layers.shape_list(q)[1]
length = common_layers.shape_list(q)[2]
# If (length < 2 * block_length), then we use only one block.
if isinstance(length, int) and isinstance(block_length, int):
block_length = length if length < block_length * 2 else block_length
else:
block_length = tf.where(
tf.less(length, block_length * 2), length, block_length)
depth_k = common_layers.shape_list(k)[3]
depth_v = common_layers.shape_list(v)[3]
original_length = length
padding_size = tf.mod(-length, block_length)
length += padding_size
padding = [[0, 0], [0, 0], [0, padding_size], [0, 0]]
q = tf.pad(q, padding)
k = tf.pad(k, padding)
v = tf.pad(v, padding)
num_blocks = length // block_length
# compute attention for the first query block.
first_q = tf.slice(q, [0, 0, 0, 0], [-1, -1, block_length, -1])
first_k = tf.slice(k, [0, 0, 0, 0], [-1, -1, block_length, -1])
first_v = tf.slice(v, [0, 0, 0, 0], [-1, -1, block_length, -1])
# Relative embeddings will be used later as well.
# TODO(avaswani,annahuang): check why 2*bl was breaking for music
# Needs to be known at static shape inference time, hence cannot be
# 2 * block_length.
rel_embed_length = 4 * default_block_length
# We only multiply with the needed embeddings as we slice them out.
first_rel_embeddings = get_relative_embeddings_left(
rel_embed_length, block_length, depth_k, heads,
heads_share_relative_embedding, "relative_embeddings")
first_rel_logits = matmul_with_relative_keys(
first_q, first_rel_embeddings, heads_share_relative_embedding)
first_logits = tf.matmul(first_q, first_k, transpose_b=True)
first_logits += (
_relative_position_to_absolute_position_masked(first_rel_logits))
# adding a mask
first_logits += (
common_layers.cast_like(attention_bias_lower_triangle(block_length),
first_logits))
first_att = tf.nn.softmax(first_logits,
name="first_attention_weights")
# dropping out the attention links for each of the heads
first_att = common_layers.dropout_with_broadcast_dims(
first_att, 1.0 - dropout_rate,
broadcast_dims=None)
# only call image summary for the first block
if common_layers.should_generate_summaries() and make_image_summary:
attention_image_summary(first_att, None)
first_output = tf.matmul(first_att, first_v)
# compute attention for all subsequent query blocks.
q = tf.reshape(q, [batch, heads, num_blocks, block_length, depth_k])
k = tf.reshape(k, [batch, heads, num_blocks, block_length, depth_k])
v = tf.reshape(v, [batch, heads, num_blocks, block_length, depth_v])
local_k = _make_local_block(k, depth_k, batch, heads, num_blocks,
block_length)
local_v = _make_local_block(v, depth_v, batch, heads, num_blocks,
block_length)
tail_q = tf.slice(q, [0, 0, 1, 0, 0], [-1, -1, -1, -1, -1])
tail_q = tf.reshape(tail_q,
[batch, heads, num_blocks - 1, block_length, depth_k])
local_length = common_layers.shape_list(local_k)[3]
# collapsing num blocks and batch size so that we can reuse
# functions
def _reshape_for_relative(x):
x_shape = common_layers.shape_list(x)
# [batch, num_blocks, heads, length, depth]
x = tf.transpose(x, [0, 2, 1, 3, 4])
x = tf.reshape(x, [batch*x_shape[2], heads, x_shape[3],
x_shape[4]])
return x
rel_tail_q = _reshape_for_relative(tail_q)
rel_k = _reshape_for_relative(local_k)
rel_v = _reshape_for_relative(local_v)
rel_embeddings = get_relative_embeddings_left(
rel_embed_length, 2 * block_length, depth_k, heads,
heads_share_relative_embedding, "relative_embeddings")
rel_logits = matmul_with_relative_keys(
rel_tail_q, rel_embeddings, heads_share_relative_embedding)
# Computing relative logits separately for the masked and unmasked parts
# because the reshaping logic is different for both
masked_rel_logits = tf.slice(rel_logits, [0, 0, 0, block_length],
[-1, -1, -1, -1])
masked_rel_logits = _relative_position_to_absolute_position_masked(
masked_rel_logits)
unmasked_rel_logits = tf.slice(rel_logits, [0, 0, 0, 0],
[-1, -1, -1, 2*block_length-1])
unmasked_rel_logits = _relative_position_to_absolute_position_unmasked(
unmasked_rel_logits)
all_rel_logits = tf.concat([unmasked_rel_logits, masked_rel_logits],
axis=3)
all_logits = (
tf.matmul(rel_tail_q, rel_k, transpose_b=True) + all_rel_logits)
# make sure source_pos <= target_pos
good_part = common_layers.ones_matrix_band_part(block_length,
local_length,
-1, block_length)
mask = (1.0 - good_part) * -1e9
mask = common_layers.cast_like(mask, all_logits)
all_logits += tf.reshape(mask, [1, 1, block_length, local_length])
weights = tf.nn.softmax(all_logits, name="attention_weights")
# [batch (* num_blocks), heads, query_length (=block_length),
# key_length (=2*block_length)]
weights = common_layers.dropout_with_broadcast_dims(
weights, 1.0 - dropout_rate,
broadcast_dims=None)
output = tf.matmul(weights, rel_v)
if add_relative_to_values:
# Adds the contribution of the weighted relative embeddings to the values.
weights_for_unmasked, weights_for_masked = (
tf.split(weights, 2, axis=3))
rel_weights_unmasked = _absolute_position_to_relative_position_unmasked(
weights_for_unmasked)
rel_weights_masked = _absolute_position_to_relative_position_masked(
weights_for_masked)
value_rel_embeddings_unmasked = get_relative_embeddings_left(
rel_embed_length, 2 * block_length, depth_v,
heads, heads_share_relative_embedding,
"value_relative_embeddings")
# The unmasked part starts with index -1 as opposed 0 has take uptil last.
if heads_share_relative_embedding:
value_rel_embeddings_unmasked = value_rel_embeddings_unmasked[:-1, :]
else:
value_rel_embeddings_unmasked = value_rel_embeddings_unmasked[:, :-1, :]
value_rel_embeddings_masked = get_relative_embeddings_left(
rel_embed_length, block_length, depth_v,
heads, heads_share_relative_embedding,
"value_relative_embeddings")
# [batch (*num_blocks), heads, query length, key length]
rel_weights = tf.concat(
[rel_weights_unmasked, rel_weights_masked], axis=3)
if heads_share_relative_embedding:
value_rel_embeddings_concat_axis = 0
else:
value_rel_embeddings_concat_axis = 1
value_rel_embeddings = tf.concat(
[value_rel_embeddings_unmasked, value_rel_embeddings_masked],
axis=value_rel_embeddings_concat_axis)
output_rel = matmul_with_relative_values(
rel_weights, value_rel_embeddings, heads_share_relative_embedding)
output += output_rel
# bring to [batch, heads, num_blocks-1, block_length, depth]
output = tf.reshape(output,
[batch, num_blocks-1, heads, block_length, depth_v])
output = tf.transpose(output, [0, 2, 1, 3, 4])
output = tf.reshape(
output, [batch, heads, (num_blocks - 1) * block_length, depth_v])
output = tf.concat([first_output, output], axis=2)
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output = tf.reshape(output, [batch, heads, original_length, depth_v])
return output
|
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] |
Masked local 1d attention with relative positions.
The sequence is divided into blocks of length block_size.
Attention for a given query position can only see memory positions
less than or equal to the query position, in the corresponding block
and the previous block.
If mask_right is True, then a target position cannot see greater source
positions.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
make_image_summary: a boolean, whether to make an attention image summary.
dropout_rate: Dropout rate for attention dropout
heads_share_relative_embedding: a boolean for sharing relative embeddings.
add_relative_to_values: a boolean for whether to add relative component to
values.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v]
Raises:
ValueError: wwhen the name for the variable scope is not passed.
|
[
"Masked",
"local",
"1d",
"attention",
"with",
"relative",
"positions",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L2825-L3033
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
local_attention_1d
|
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 left and right of the block.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
filter_width: an integer indicating how much to look left and right of the
block.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v]
"""
with tf.variable_scope(
name, default_name="local_self_attention_1d", values=[q, k, v]):
# Check that q, k, v have the same shape except in their depth dimension.
q.get_shape()[:-1].assert_is_compatible_with(k.get_shape()[:-1])
q.get_shape()[:-1].assert_is_compatible_with(v.get_shape()[:-1])
batch_size, num_heads, original_length, _ = common_layers.shape_list(q)
# Pad query, key, value to ensure multiple of corresponding lengths.
def pad_to_multiple(x, pad_length):
x_length = common_layers.shape_list(x)[2]
return tf.pad(x, [[0, 0], [0, 0], [0, -x_length % pad_length], [0, 0]])
def pad_l_and_r(x, pad_length):
return tf.pad(x, [[0, 0], [0, 0], [pad_length, pad_length], [0, 0]])
# Set up query blocks.
# [batch, heads, blocks_q, block_length, depth_k]
q = pad_to_multiple(q, block_length)
q = reshape_by_blocks(q, common_layers.shape_list(q), block_length)
total_query_blocks = common_layers.shape_list(q)[2]
# Set up key and value blocks.
# [batch, heads, blocks_k, block_length, depth_k]
blocks_per_filter_width = filter_width // block_length
remaining_items = filter_width % block_length
k = pad_to_multiple(k, block_length)
v = pad_to_multiple(v, block_length)
k = pad_l_and_r(k, filter_width + block_length - remaining_items)
v = pad_l_and_r(v, filter_width + block_length - remaining_items)
k = reshape_by_blocks(k, common_layers.shape_list(k), block_length)
v = reshape_by_blocks(v, common_layers.shape_list(v), block_length)
total_kv_blocks = common_layers.shape_list(k)[2]
slices = []
# prepare the left-most and right-most partial blocks if needed
if remaining_items:
first_partial_block_k = tf.slice(
k, [0, 0, 0, block_length - remaining_items, 0],
[-1, -1, total_query_blocks, -1, -1])
first_partial_block_v = tf.slice(
v, [0, 0, 0, block_length - remaining_items, 0],
[-1, -1, total_query_blocks, -1, -1])
last_partial_block_k = tf.slice(
k, [0, 0, total_kv_blocks - total_query_blocks, 0, 0],
[-1, -1, -1, remaining_items, -1])
last_partial_block_v = tf.slice(
v, [0, 0, total_kv_blocks - total_query_blocks, 0, 0],
[-1, -1, -1, remaining_items, -1])
slices.append((first_partial_block_k, first_partial_block_v))
slices.append((last_partial_block_k, last_partial_block_v))
# Prepare the rest of the blocks
first_block_index = 1 if remaining_items else 0
attention_blocks = 2 * blocks_per_filter_width + 1
for i in range(first_block_index, attention_blocks + first_block_index):
block_k = tf.slice(k, [0, 0, i, 0, 0],
[-1, -1, total_query_blocks, -1, -1])
block_v = tf.slice(v, [0, 0, i, 0, 0],
[-1, -1, total_query_blocks, -1, -1])
slices.append((block_k, block_v))
# [batch, heads, blocks_q, block_length + 2 * filter_width, depth_k]
k = tf.concat([s[0] for s in slices], axis=3)
v = tf.concat([s[1] for s in slices], axis=3)
attention_bias = tf.expand_dims(embedding_to_padding(k) * -1e9, axis=-2)
depth_v = common_layers.shape_list(v)[-1]
output = dot_product_attention(
q,
k,
v,
attention_bias,
dropout_rate=0.,
name="local_1d",
make_image_summary=False)
output = tf.reshape(output, [batch_size, num_heads, -1, depth_v])
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output.set_shape([None if isinstance(dim, tf.Tensor) else dim for dim in
(batch_size, num_heads, original_length, depth_v)])
return output
|
python
|
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 left and right of the block.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
filter_width: an integer indicating how much to look left and right of the
block.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v]
"""
with tf.variable_scope(
name, default_name="local_self_attention_1d", values=[q, k, v]):
# Check that q, k, v have the same shape except in their depth dimension.
q.get_shape()[:-1].assert_is_compatible_with(k.get_shape()[:-1])
q.get_shape()[:-1].assert_is_compatible_with(v.get_shape()[:-1])
batch_size, num_heads, original_length, _ = common_layers.shape_list(q)
# Pad query, key, value to ensure multiple of corresponding lengths.
def pad_to_multiple(x, pad_length):
x_length = common_layers.shape_list(x)[2]
return tf.pad(x, [[0, 0], [0, 0], [0, -x_length % pad_length], [0, 0]])
def pad_l_and_r(x, pad_length):
return tf.pad(x, [[0, 0], [0, 0], [pad_length, pad_length], [0, 0]])
# Set up query blocks.
# [batch, heads, blocks_q, block_length, depth_k]
q = pad_to_multiple(q, block_length)
q = reshape_by_blocks(q, common_layers.shape_list(q), block_length)
total_query_blocks = common_layers.shape_list(q)[2]
# Set up key and value blocks.
# [batch, heads, blocks_k, block_length, depth_k]
blocks_per_filter_width = filter_width // block_length
remaining_items = filter_width % block_length
k = pad_to_multiple(k, block_length)
v = pad_to_multiple(v, block_length)
k = pad_l_and_r(k, filter_width + block_length - remaining_items)
v = pad_l_and_r(v, filter_width + block_length - remaining_items)
k = reshape_by_blocks(k, common_layers.shape_list(k), block_length)
v = reshape_by_blocks(v, common_layers.shape_list(v), block_length)
total_kv_blocks = common_layers.shape_list(k)[2]
slices = []
# prepare the left-most and right-most partial blocks if needed
if remaining_items:
first_partial_block_k = tf.slice(
k, [0, 0, 0, block_length - remaining_items, 0],
[-1, -1, total_query_blocks, -1, -1])
first_partial_block_v = tf.slice(
v, [0, 0, 0, block_length - remaining_items, 0],
[-1, -1, total_query_blocks, -1, -1])
last_partial_block_k = tf.slice(
k, [0, 0, total_kv_blocks - total_query_blocks, 0, 0],
[-1, -1, -1, remaining_items, -1])
last_partial_block_v = tf.slice(
v, [0, 0, total_kv_blocks - total_query_blocks, 0, 0],
[-1, -1, -1, remaining_items, -1])
slices.append((first_partial_block_k, first_partial_block_v))
slices.append((last_partial_block_k, last_partial_block_v))
# Prepare the rest of the blocks
first_block_index = 1 if remaining_items else 0
attention_blocks = 2 * blocks_per_filter_width + 1
for i in range(first_block_index, attention_blocks + first_block_index):
block_k = tf.slice(k, [0, 0, i, 0, 0],
[-1, -1, total_query_blocks, -1, -1])
block_v = tf.slice(v, [0, 0, i, 0, 0],
[-1, -1, total_query_blocks, -1, -1])
slices.append((block_k, block_v))
# [batch, heads, blocks_q, block_length + 2 * filter_width, depth_k]
k = tf.concat([s[0] for s in slices], axis=3)
v = tf.concat([s[1] for s in slices], axis=3)
attention_bias = tf.expand_dims(embedding_to_padding(k) * -1e9, axis=-2)
depth_v = common_layers.shape_list(v)[-1]
output = dot_product_attention(
q,
k,
v,
attention_bias,
dropout_rate=0.,
name="local_1d",
make_image_summary=False)
output = tf.reshape(output, [batch_size, num_heads, -1, depth_v])
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output.set_shape([None if isinstance(dim, tf.Tensor) else dim for dim in
(batch_size, num_heads, original_length, depth_v)])
return output
|
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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 left and right of the block.
Args:
q: a Tensor with shape [batch, heads, length, depth_k]
k: a Tensor with shape [batch, heads, length, depth_k]
v: a Tensor with shape [batch, heads, length, depth_v]
block_length: an integer
filter_width: an integer indicating how much to look left and right of the
block.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth_v]
|
[
"Strided",
"block",
"local",
"self",
"-",
"attention",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3052-L3154
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
reshape_by_blocks
|
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
[batch, heads, length // memory_block_size, memory_block_size, depth].
"""
x = tf.reshape(x, [
x_shape[0], x_shape[1], x_shape[2] // memory_block_size,
memory_block_size, x_shape[3]
])
return x
|
python
|
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
[batch, heads, length // memory_block_size, memory_block_size, depth].
"""
x = tf.reshape(x, [
x_shape[0], x_shape[1], x_shape[2] // memory_block_size,
memory_block_size, x_shape[3]
])
return x
|
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x_shape: tf.TensorShape of x.
memory_block_size: Integer which divides length.
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Tensor with shape
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|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3157-L3173
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
dilated_self_attention_1d
|
def dilated_self_attention_1d(q,
k,
v,
query_block_size=128,
memory_block_size=128,
gap_size=2,
num_memory_blocks=2,
name=None):
"""Dilated self-attention.
Args:
q: a Tensor with shape [batch, heads, length, depth]
k: a Tensor with shape [batch, heads, length, depth]
v: a Tensor with shape [batch, heads, length, depth]
query_block_size: an integer indicating size of query block
memory_block_size: an integer indicating the size of a memory block.
gap_size: an integer indicating the gap size
num_memory_blocks: how many memory blocks to look at to the left and right.
Each will be separated by gap_size.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth]
"""
with tf.variable_scope(
name, default_name="dilated_self_attention_1d", values=[q, k, v]):
v_list_shape = v.get_shape().as_list()
assert v_list_shape == k.shape.as_list(), "K and V depths must be equal"
v_shape = common_layers.shape_list(v)
depth_v = v_shape[3]
batch_size = v_shape[0]
num_heads = v_shape[1]
original_length = common_layers.shape_list(q)[2]
# Pad query, key, value to ensure multiple of corresponding lengths.
def pad_to_multiple(x, pad_length):
x_length = common_layers.shape_list(x)[2]
return tf.pad(x, [[0, 0], [0, 0], [0, -x_length % pad_length], [0, 0]])
def pad_l_and_r(x, pad_length):
return tf.pad(x, [[0, 0], [0, 0], [pad_length, pad_length], [0, 0]])
q = pad_to_multiple(q, query_block_size)
v = pad_to_multiple(v, query_block_size)
k = pad_to_multiple(k, query_block_size)
# Set up query blocks.
new_q_shape = common_layers.shape_list(q)
q = reshape_by_blocks(q, new_q_shape, query_block_size)
self_k_part = reshape_by_blocks(k, new_q_shape, query_block_size)
self_v_part = reshape_by_blocks(v, new_q_shape, query_block_size)
# Set up key and value windows.
k_v_padding = (gap_size + memory_block_size) * num_memory_blocks
k = pad_l_and_r(k, k_v_padding)
v = pad_l_and_r(v, k_v_padding)
# Get gather indices.
index_length = (new_q_shape[2] - query_block_size + memory_block_size)
indices = tf.range(0, index_length, delta=1, name="index_range")
indices = tf.reshape(indices, [1, -1, 1]) # [1, length, 1] for convs
kernel = tf.expand_dims(tf.eye(memory_block_size), axis=1)
gather_indices = tf.nn.conv1d(
tf.cast(indices, tf.float32),
kernel,
query_block_size,
padding="VALID",
name="gather_conv")
gather_indices = tf.squeeze(tf.cast(gather_indices, tf.int32), axis=0)
# Get left and right memory blocks for each query.
# [length, batch, heads, dim]
k_t = tf.transpose(k, [2, 0, 1, 3])
v_t = tf.transpose(v, [2, 0, 1, 3])
left_k = gather_dilated_memory_blocks(
k_t[:-k_v_padding, :, :, :], num_memory_blocks, gap_size,
query_block_size, memory_block_size, gather_indices)
left_v = gather_dilated_memory_blocks(
v_t[:-k_v_padding, :, :, :], num_memory_blocks, gap_size,
query_block_size, memory_block_size, gather_indices)
right_k = gather_dilated_memory_blocks(
k_t[k_v_padding:, :, :, :],
num_memory_blocks,
gap_size,
query_block_size,
memory_block_size,
gather_indices,
direction="right")
right_v = gather_dilated_memory_blocks(
v_t[k_v_padding:, :, :, :],
num_memory_blocks,
gap_size,
query_block_size,
memory_block_size,
gather_indices,
direction="right")
k_windows = tf.concat([left_k, self_k_part, right_k], axis=3)
v_windows = tf.concat([left_v, self_v_part, right_v], axis=3)
attention_bias = tf.expand_dims(
embedding_to_padding(k_windows) * -1e9, axis=-2)
output = dot_product_attention(
q,
k_windows,
v_windows,
attention_bias,
dropout_rate=0.,
name="dilated_1d",
make_image_summary=False)
output = tf.reshape(output, [batch_size, num_heads, -1, depth_v])
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output.set_shape(v_list_shape)
return output
|
python
|
def dilated_self_attention_1d(q,
k,
v,
query_block_size=128,
memory_block_size=128,
gap_size=2,
num_memory_blocks=2,
name=None):
"""Dilated self-attention.
Args:
q: a Tensor with shape [batch, heads, length, depth]
k: a Tensor with shape [batch, heads, length, depth]
v: a Tensor with shape [batch, heads, length, depth]
query_block_size: an integer indicating size of query block
memory_block_size: an integer indicating the size of a memory block.
gap_size: an integer indicating the gap size
num_memory_blocks: how many memory blocks to look at to the left and right.
Each will be separated by gap_size.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth]
"""
with tf.variable_scope(
name, default_name="dilated_self_attention_1d", values=[q, k, v]):
v_list_shape = v.get_shape().as_list()
assert v_list_shape == k.shape.as_list(), "K and V depths must be equal"
v_shape = common_layers.shape_list(v)
depth_v = v_shape[3]
batch_size = v_shape[0]
num_heads = v_shape[1]
original_length = common_layers.shape_list(q)[2]
# Pad query, key, value to ensure multiple of corresponding lengths.
def pad_to_multiple(x, pad_length):
x_length = common_layers.shape_list(x)[2]
return tf.pad(x, [[0, 0], [0, 0], [0, -x_length % pad_length], [0, 0]])
def pad_l_and_r(x, pad_length):
return tf.pad(x, [[0, 0], [0, 0], [pad_length, pad_length], [0, 0]])
q = pad_to_multiple(q, query_block_size)
v = pad_to_multiple(v, query_block_size)
k = pad_to_multiple(k, query_block_size)
# Set up query blocks.
new_q_shape = common_layers.shape_list(q)
q = reshape_by_blocks(q, new_q_shape, query_block_size)
self_k_part = reshape_by_blocks(k, new_q_shape, query_block_size)
self_v_part = reshape_by_blocks(v, new_q_shape, query_block_size)
# Set up key and value windows.
k_v_padding = (gap_size + memory_block_size) * num_memory_blocks
k = pad_l_and_r(k, k_v_padding)
v = pad_l_and_r(v, k_v_padding)
# Get gather indices.
index_length = (new_q_shape[2] - query_block_size + memory_block_size)
indices = tf.range(0, index_length, delta=1, name="index_range")
indices = tf.reshape(indices, [1, -1, 1]) # [1, length, 1] for convs
kernel = tf.expand_dims(tf.eye(memory_block_size), axis=1)
gather_indices = tf.nn.conv1d(
tf.cast(indices, tf.float32),
kernel,
query_block_size,
padding="VALID",
name="gather_conv")
gather_indices = tf.squeeze(tf.cast(gather_indices, tf.int32), axis=0)
# Get left and right memory blocks for each query.
# [length, batch, heads, dim]
k_t = tf.transpose(k, [2, 0, 1, 3])
v_t = tf.transpose(v, [2, 0, 1, 3])
left_k = gather_dilated_memory_blocks(
k_t[:-k_v_padding, :, :, :], num_memory_blocks, gap_size,
query_block_size, memory_block_size, gather_indices)
left_v = gather_dilated_memory_blocks(
v_t[:-k_v_padding, :, :, :], num_memory_blocks, gap_size,
query_block_size, memory_block_size, gather_indices)
right_k = gather_dilated_memory_blocks(
k_t[k_v_padding:, :, :, :],
num_memory_blocks,
gap_size,
query_block_size,
memory_block_size,
gather_indices,
direction="right")
right_v = gather_dilated_memory_blocks(
v_t[k_v_padding:, :, :, :],
num_memory_blocks,
gap_size,
query_block_size,
memory_block_size,
gather_indices,
direction="right")
k_windows = tf.concat([left_k, self_k_part, right_k], axis=3)
v_windows = tf.concat([left_v, self_v_part, right_v], axis=3)
attention_bias = tf.expand_dims(
embedding_to_padding(k_windows) * -1e9, axis=-2)
output = dot_product_attention(
q,
k_windows,
v_windows,
attention_bias,
dropout_rate=0.,
name="dilated_1d",
make_image_summary=False)
output = tf.reshape(output, [batch_size, num_heads, -1, depth_v])
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output.set_shape(v_list_shape)
return output
|
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] |
Dilated self-attention.
Args:
q: a Tensor with shape [batch, heads, length, depth]
k: a Tensor with shape [batch, heads, length, depth]
v: a Tensor with shape [batch, heads, length, depth]
query_block_size: an integer indicating size of query block
memory_block_size: an integer indicating the size of a memory block.
gap_size: an integer indicating the gap size
num_memory_blocks: how many memory blocks to look at to the left and right.
Each will be separated by gap_size.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth]
|
[
"Dilated",
"self",
"-",
"attention",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3176-L3293
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
gather_dilated_memory_blocks
|
def gather_dilated_memory_blocks(x,
num_memory_blocks,
gap_size,
query_block_size,
memory_block_size,
gather_indices,
direction="left"):
"""Gathers blocks with gaps in between.
Args:
x: Tensor of shape [length, batch, heads, depth]
num_memory_blocks: how many memory blocks to look in "direction". Each will
be separated by gap_size.
gap_size: an integer indicating the gap size
query_block_size: an integer indicating size of query block
memory_block_size: an integer indicating the size of a memory block.
gather_indices: The indices to gather from.
direction: left or right
Returns:
Tensor of shape [batch, heads, blocks, block_length, depth]
"""
gathered_blocks = []
# gathering memory blocks
for block_id in range(num_memory_blocks):
block_end_index = -(query_block_size + gap_size *
(block_id + 1) + memory_block_size * block_id)
block_start_index = (
(memory_block_size + gap_size) * (num_memory_blocks - (block_id + 1)))
if direction != "left":
[block_end_index,
block_start_index] = [-block_start_index, -block_end_index]
if block_end_index == 0:
x_block = x[block_start_index:]
else:
x_block = x[block_start_index:block_end_index]
def gather_dilated_1d_blocks(x, gather_indices):
x_new = tf.gather(x, gather_indices)
# [batch, heads, blocks, block_length, dim]
return tf.transpose(x_new, [2, 3, 0, 1, 4])
gathered_blocks.append(gather_dilated_1d_blocks(x_block, gather_indices))
return tf.concat(gathered_blocks, 3)
|
python
|
def gather_dilated_memory_blocks(x,
num_memory_blocks,
gap_size,
query_block_size,
memory_block_size,
gather_indices,
direction="left"):
"""Gathers blocks with gaps in between.
Args:
x: Tensor of shape [length, batch, heads, depth]
num_memory_blocks: how many memory blocks to look in "direction". Each will
be separated by gap_size.
gap_size: an integer indicating the gap size
query_block_size: an integer indicating size of query block
memory_block_size: an integer indicating the size of a memory block.
gather_indices: The indices to gather from.
direction: left or right
Returns:
Tensor of shape [batch, heads, blocks, block_length, depth]
"""
gathered_blocks = []
# gathering memory blocks
for block_id in range(num_memory_blocks):
block_end_index = -(query_block_size + gap_size *
(block_id + 1) + memory_block_size * block_id)
block_start_index = (
(memory_block_size + gap_size) * (num_memory_blocks - (block_id + 1)))
if direction != "left":
[block_end_index,
block_start_index] = [-block_start_index, -block_end_index]
if block_end_index == 0:
x_block = x[block_start_index:]
else:
x_block = x[block_start_index:block_end_index]
def gather_dilated_1d_blocks(x, gather_indices):
x_new = tf.gather(x, gather_indices)
# [batch, heads, blocks, block_length, dim]
return tf.transpose(x_new, [2, 3, 0, 1, 4])
gathered_blocks.append(gather_dilated_1d_blocks(x_block, gather_indices))
return tf.concat(gathered_blocks, 3)
|
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Gathers blocks with gaps in between.
Args:
x: Tensor of shape [length, batch, heads, depth]
num_memory_blocks: how many memory blocks to look in "direction". Each will
be separated by gap_size.
gap_size: an integer indicating the gap size
query_block_size: an integer indicating size of query block
memory_block_size: an integer indicating the size of a memory block.
gather_indices: The indices to gather from.
direction: left or right
Returns:
Tensor of shape [batch, heads, blocks, block_length, depth]
|
[
"Gathers",
"blocks",
"with",
"gaps",
"in",
"between",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3296-L3339
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
masked_dilated_self_attention_1d
|
def masked_dilated_self_attention_1d(q,
k,
v,
query_block_size=64,
memory_block_size=64,
gap_size=2,
num_memory_blocks=2,
name=None):
"""Dilated self-attention. TODO(avaswani): Try it and write a paper on it.
Args:
q: a Tensor with shape [batch, heads, length, depth]
k: a Tensor with shape [batch, heads, length, depth]
v: a Tensor with shape [batch, heads, length, depth]
query_block_size: an integer
memory_block_size: an integer indicating how much to look left.
gap_size: an integer indicating the gap size
num_memory_blocks: how many memory blocks to look at to the left. Each will
be separated by gap_size.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth]
"""
with tf.variable_scope(
name, default_name="masked_dilated_self_attention_1d", values=[q, k, v]):
v_list_shape = v.get_shape().as_list()
assert v_list_shape == k.shape.as_list(), "K and V depths must be equal"
v_shape = common_layers.shape_list(v)
depth_v = v_shape[3]
batch_size = v_shape[0]
num_heads = v_shape[1]
original_length = common_layers.shape_list(q)[2]
# Pad query, key, value to ensure multiple of corresponding lengths.
def pad_to_multiple(x, pad_length):
x_length = common_layers.shape_list(x)[2]
return tf.pad(x, [[0, 0], [0, 0], [0, -x_length % pad_length], [0, 0]])
def pad_l(x, left_pad_length):
return tf.pad(x, [[0, 0], [0, 0], [left_pad_length, 0], [0, 0]])
q = pad_to_multiple(q, query_block_size)
v = pad_to_multiple(v, query_block_size)
k = pad_to_multiple(k, query_block_size)
# Set up query blocks.
new_q_shape = common_layers.shape_list(q)
q = reshape_by_blocks(q, new_q_shape, query_block_size)
# Set up key and value windows.
self_k_part = reshape_by_blocks(k, new_q_shape, query_block_size)
self_v_part = reshape_by_blocks(v, new_q_shape, query_block_size)
k_v_padding = (gap_size + memory_block_size) * num_memory_blocks
k = pad_l(k, k_v_padding)
v = pad_l(v, k_v_padding)
# Get gather indices.
index_length = (new_q_shape[2] - query_block_size + memory_block_size)
indices = tf.range(0, index_length, delta=1, name="index_range")
indices = tf.reshape(indices, [1, -1, 1]) # [1, length, 1] for convs
kernel = tf.expand_dims(tf.eye(memory_block_size), axis=1)
gather_indices = tf.nn.conv1d(
tf.cast(indices, tf.float32),
kernel,
query_block_size,
padding="VALID",
name="gather_conv")
gather_indices = tf.squeeze(tf.cast(gather_indices, tf.int32), axis=0)
# Get left and right memory blocks for each query.
# [length, batch, heads, dim]
k_t = tf.transpose(k, [2, 0, 1, 3])
v_t = tf.transpose(v, [2, 0, 1, 3])
k_unmasked_windows = gather_dilated_memory_blocks(
k_t, num_memory_blocks, gap_size, query_block_size, memory_block_size,
gather_indices)
v_unmasked_windows = gather_dilated_memory_blocks(
v_t, num_memory_blocks, gap_size, query_block_size, memory_block_size,
gather_indices)
# Combine memory windows.
block_q_shape = common_layers.shape_list(q)
masked_attention_bias = tf.tile(
tf.expand_dims(attention_bias_lower_triangle(query_block_size), axis=0),
[block_q_shape[0], block_q_shape[1], block_q_shape[2], 1, 1])
padding_attention_bias = tf.expand_dims(
embedding_to_padding(k_unmasked_windows) * -1e9, axis=-2)
padding_attention_bias = tf.tile(padding_attention_bias,
[1, 1, 1, query_block_size, 1])
attention_bias = tf.concat(
[masked_attention_bias, padding_attention_bias], axis=-1)
# combine memory windows
k_windows = tf.concat([self_k_part, k_unmasked_windows], 3)
v_windows = tf.concat([self_v_part, v_unmasked_windows], 3)
output = dot_product_attention(
q,
k_windows,
v_windows,
attention_bias,
dropout_rate=0.,
name="dilated_1d",
make_image_summary=False)
output = tf.reshape(output, [batch_size, num_heads, -1, depth_v])
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output.set_shape(v_list_shape)
return output
|
python
|
def masked_dilated_self_attention_1d(q,
k,
v,
query_block_size=64,
memory_block_size=64,
gap_size=2,
num_memory_blocks=2,
name=None):
"""Dilated self-attention. TODO(avaswani): Try it and write a paper on it.
Args:
q: a Tensor with shape [batch, heads, length, depth]
k: a Tensor with shape [batch, heads, length, depth]
v: a Tensor with shape [batch, heads, length, depth]
query_block_size: an integer
memory_block_size: an integer indicating how much to look left.
gap_size: an integer indicating the gap size
num_memory_blocks: how many memory blocks to look at to the left. Each will
be separated by gap_size.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth]
"""
with tf.variable_scope(
name, default_name="masked_dilated_self_attention_1d", values=[q, k, v]):
v_list_shape = v.get_shape().as_list()
assert v_list_shape == k.shape.as_list(), "K and V depths must be equal"
v_shape = common_layers.shape_list(v)
depth_v = v_shape[3]
batch_size = v_shape[0]
num_heads = v_shape[1]
original_length = common_layers.shape_list(q)[2]
# Pad query, key, value to ensure multiple of corresponding lengths.
def pad_to_multiple(x, pad_length):
x_length = common_layers.shape_list(x)[2]
return tf.pad(x, [[0, 0], [0, 0], [0, -x_length % pad_length], [0, 0]])
def pad_l(x, left_pad_length):
return tf.pad(x, [[0, 0], [0, 0], [left_pad_length, 0], [0, 0]])
q = pad_to_multiple(q, query_block_size)
v = pad_to_multiple(v, query_block_size)
k = pad_to_multiple(k, query_block_size)
# Set up query blocks.
new_q_shape = common_layers.shape_list(q)
q = reshape_by_blocks(q, new_q_shape, query_block_size)
# Set up key and value windows.
self_k_part = reshape_by_blocks(k, new_q_shape, query_block_size)
self_v_part = reshape_by_blocks(v, new_q_shape, query_block_size)
k_v_padding = (gap_size + memory_block_size) * num_memory_blocks
k = pad_l(k, k_v_padding)
v = pad_l(v, k_v_padding)
# Get gather indices.
index_length = (new_q_shape[2] - query_block_size + memory_block_size)
indices = tf.range(0, index_length, delta=1, name="index_range")
indices = tf.reshape(indices, [1, -1, 1]) # [1, length, 1] for convs
kernel = tf.expand_dims(tf.eye(memory_block_size), axis=1)
gather_indices = tf.nn.conv1d(
tf.cast(indices, tf.float32),
kernel,
query_block_size,
padding="VALID",
name="gather_conv")
gather_indices = tf.squeeze(tf.cast(gather_indices, tf.int32), axis=0)
# Get left and right memory blocks for each query.
# [length, batch, heads, dim]
k_t = tf.transpose(k, [2, 0, 1, 3])
v_t = tf.transpose(v, [2, 0, 1, 3])
k_unmasked_windows = gather_dilated_memory_blocks(
k_t, num_memory_blocks, gap_size, query_block_size, memory_block_size,
gather_indices)
v_unmasked_windows = gather_dilated_memory_blocks(
v_t, num_memory_blocks, gap_size, query_block_size, memory_block_size,
gather_indices)
# Combine memory windows.
block_q_shape = common_layers.shape_list(q)
masked_attention_bias = tf.tile(
tf.expand_dims(attention_bias_lower_triangle(query_block_size), axis=0),
[block_q_shape[0], block_q_shape[1], block_q_shape[2], 1, 1])
padding_attention_bias = tf.expand_dims(
embedding_to_padding(k_unmasked_windows) * -1e9, axis=-2)
padding_attention_bias = tf.tile(padding_attention_bias,
[1, 1, 1, query_block_size, 1])
attention_bias = tf.concat(
[masked_attention_bias, padding_attention_bias], axis=-1)
# combine memory windows
k_windows = tf.concat([self_k_part, k_unmasked_windows], 3)
v_windows = tf.concat([self_v_part, v_unmasked_windows], 3)
output = dot_product_attention(
q,
k_windows,
v_windows,
attention_bias,
dropout_rate=0.,
name="dilated_1d",
make_image_summary=False)
output = tf.reshape(output, [batch_size, num_heads, -1, depth_v])
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, -1, original_length, -1])
output.set_shape(v_list_shape)
return output
|
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Dilated self-attention. TODO(avaswani): Try it and write a paper on it.
Args:
q: a Tensor with shape [batch, heads, length, depth]
k: a Tensor with shape [batch, heads, length, depth]
v: a Tensor with shape [batch, heads, length, depth]
query_block_size: an integer
memory_block_size: an integer indicating how much to look left.
gap_size: an integer indicating the gap size
num_memory_blocks: how many memory blocks to look at to the left. Each will
be separated by gap_size.
name: an optional string
Returns:
a Tensor of shape [batch, heads, length, depth]
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3342-L3452
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
local_attention_2d
|
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. Attention
for a given query position can only see memory positions less than or equal to
the query position. The memory positions are the corresponding block with
memory_flange many positions to add to the height and width of the block
(namely, left, top, and right).
Args:
q: a Tensor with shape [batch, heads, h, w, depth_k]
k: a Tensor with shape [batch, heads, h, w, depth_k]
v: a Tensor with shape [batch, heads, h, w, depth_v]. In the current
implementation, depth_v must be equal to depth_k.
query_shape: an tuple indicating the height and width of each query block.
memory_flange: an integer indicating how much to look in height and width
from each query block.
name: an optional string
Returns:
a Tensor of shape [batch, heads, h, w, depth_v]
"""
with tf.variable_scope(
name, default_name="local_self_attention_2d", values=[q, k, v]):
v_shape = common_layers.shape_list(v)
# Pad query, key, value to ensure multiple of corresponding lengths.
q = pad_to_multiple_2d(q, query_shape)
k = pad_to_multiple_2d(k, query_shape)
v = pad_to_multiple_2d(v, query_shape)
paddings = [[0, 0], [0, 0], [memory_flange[0], memory_flange[1]],
[memory_flange[0], memory_flange[1]], [0, 0]]
k = tf.pad(k, paddings)
v = tf.pad(v, paddings)
# Set up query blocks.
q_indices = gather_indices_2d(q, query_shape, query_shape)
q_new = gather_blocks_2d(q, q_indices)
# Set up key and value blocks.
memory_shape = (query_shape[0] + 2 * memory_flange[0],
query_shape[1] + 2 * memory_flange[1])
k_and_v_indices = gather_indices_2d(k, memory_shape, query_shape)
k_new = gather_blocks_2d(k, k_and_v_indices)
v_new = gather_blocks_2d(v, k_and_v_indices)
attention_bias = tf.expand_dims(
tf.to_float(embedding_to_padding(k_new)) * -1e9, axis=-2)
output = dot_product_attention(
q_new,
k_new,
v_new,
attention_bias,
dropout_rate=0.,
name="local_2d",
make_image_summary=False)
# Put representations back into original shapes.
padded_q_shape = common_layers.shape_list(q)
output = scatter_blocks_2d(output, q_indices, padded_q_shape)
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0, 0],
[-1, -1, v_shape[2], v_shape[3], -1])
return output
|
python
|
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. Attention
for a given query position can only see memory positions less than or equal to
the query position. The memory positions are the corresponding block with
memory_flange many positions to add to the height and width of the block
(namely, left, top, and right).
Args:
q: a Tensor with shape [batch, heads, h, w, depth_k]
k: a Tensor with shape [batch, heads, h, w, depth_k]
v: a Tensor with shape [batch, heads, h, w, depth_v]. In the current
implementation, depth_v must be equal to depth_k.
query_shape: an tuple indicating the height and width of each query block.
memory_flange: an integer indicating how much to look in height and width
from each query block.
name: an optional string
Returns:
a Tensor of shape [batch, heads, h, w, depth_v]
"""
with tf.variable_scope(
name, default_name="local_self_attention_2d", values=[q, k, v]):
v_shape = common_layers.shape_list(v)
# Pad query, key, value to ensure multiple of corresponding lengths.
q = pad_to_multiple_2d(q, query_shape)
k = pad_to_multiple_2d(k, query_shape)
v = pad_to_multiple_2d(v, query_shape)
paddings = [[0, 0], [0, 0], [memory_flange[0], memory_flange[1]],
[memory_flange[0], memory_flange[1]], [0, 0]]
k = tf.pad(k, paddings)
v = tf.pad(v, paddings)
# Set up query blocks.
q_indices = gather_indices_2d(q, query_shape, query_shape)
q_new = gather_blocks_2d(q, q_indices)
# Set up key and value blocks.
memory_shape = (query_shape[0] + 2 * memory_flange[0],
query_shape[1] + 2 * memory_flange[1])
k_and_v_indices = gather_indices_2d(k, memory_shape, query_shape)
k_new = gather_blocks_2d(k, k_and_v_indices)
v_new = gather_blocks_2d(v, k_and_v_indices)
attention_bias = tf.expand_dims(
tf.to_float(embedding_to_padding(k_new)) * -1e9, axis=-2)
output = dot_product_attention(
q_new,
k_new,
v_new,
attention_bias,
dropout_rate=0.,
name="local_2d",
make_image_summary=False)
# Put representations back into original shapes.
padded_q_shape = common_layers.shape_list(q)
output = scatter_blocks_2d(output, q_indices, padded_q_shape)
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0, 0],
[-1, -1, v_shape[2], v_shape[3], -1])
return output
|
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query_shape: an tuple indicating the height and width of each query block.
memory_flange: an integer indicating how much to look in height and width
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name: an optional string
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3455-L3523
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
pad_to_multiple_2d
|
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_shape().dims
last = old_shape[-1]
if len(old_shape) == 4:
height_padding = -common_layers.shape_list(x)[1] % block_shape[0]
width_padding = -common_layers.shape_list(x)[2] % block_shape[1]
paddings = [[0, 0], [0, height_padding], [0, width_padding], [0, 0]]
elif len(old_shape) == 5:
height_padding = -common_layers.shape_list(x)[2] % block_shape[0]
width_padding = -common_layers.shape_list(x)[3] % block_shape[1]
paddings = [[0, 0], [0, 0], [0, height_padding], [0, width_padding], [0, 0]]
padded_x = tf.pad(x, paddings)
padded_shape = padded_x.get_shape().as_list()
padded_shape = padded_shape[:-1] + [last]
padded_x.set_shape(padded_shape)
return padded_x
|
python
|
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_shape().dims
last = old_shape[-1]
if len(old_shape) == 4:
height_padding = -common_layers.shape_list(x)[1] % block_shape[0]
width_padding = -common_layers.shape_list(x)[2] % block_shape[1]
paddings = [[0, 0], [0, height_padding], [0, width_padding], [0, 0]]
elif len(old_shape) == 5:
height_padding = -common_layers.shape_list(x)[2] % block_shape[0]
width_padding = -common_layers.shape_list(x)[3] % block_shape[1]
paddings = [[0, 0], [0, 0], [0, height_padding], [0, width_padding], [0, 0]]
padded_x = tf.pad(x, paddings)
padded_shape = padded_x.get_shape().as_list()
padded_shape = padded_shape[:-1] + [last]
padded_x.set_shape(padded_shape)
return padded_x
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[
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3526-L3551
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
reshape_range
|
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)
|
python
|
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)
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3554-L3558
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
gather_blocks_2d
|
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, block_length ** 2, dim]
return tf.transpose(x_new, [2, 3, 0, 1, 4])
|
python
|
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, block_length ** 2, dim]
return tf.transpose(x_new, [2, 3, 0, 1, 4])
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3561-L3569
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
scatter_blocks_2d
|
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)
indices = tf.reshape(indices, [-1, 1])
scattered_x = tf.scatter_nd(indices, x_t, x_t_shape)
scattered_x = tf.transpose(scattered_x, [1, 2, 0, 3])
return tf.reshape(scattered_x, shape)
|
python
|
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)
indices = tf.reshape(indices, [-1, 1])
scattered_x = tf.scatter_nd(indices, x_t, x_t_shape)
scattered_x = tf.transpose(scattered_x, [1, 2, 0, 3])
return tf.reshape(scattered_x, shape)
|
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scatters blocks from x into shape with indices.
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3572-L3582
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
gather_indices_2d
|
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.shape_list(x)
indices = tf.range(x_shape[2] * x_shape[3])
indices = tf.reshape(indices, [1, x_shape[2], x_shape[3], 1])
indices = tf.nn.conv2d(
tf.cast(indices, tf.float32),
kernel,
strides=[1, block_stride[0], block_stride[1], 1],
padding="VALID")
# making indices [num_blocks, dim] to gather
dims = common_layers.shape_list(indices)[:3]
if all([isinstance(dim, int) for dim in dims]):
num_blocks = functools.reduce(operator.mul, dims, 1)
else:
num_blocks = tf.reduce_prod(dims)
indices = tf.reshape(indices, [num_blocks, -1])
return tf.cast(indices, tf.int32)
|
python
|
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.shape_list(x)
indices = tf.range(x_shape[2] * x_shape[3])
indices = tf.reshape(indices, [1, x_shape[2], x_shape[3], 1])
indices = tf.nn.conv2d(
tf.cast(indices, tf.float32),
kernel,
strides=[1, block_stride[0], block_stride[1], 1],
padding="VALID")
# making indices [num_blocks, dim] to gather
dims = common_layers.shape_list(indices)[:3]
if all([isinstance(dim, int) for dim in dims]):
num_blocks = functools.reduce(operator.mul, dims, 1)
else:
num_blocks = tf.reduce_prod(dims)
indices = tf.reshape(indices, [num_blocks, -1])
return tf.cast(indices, tf.int32)
|
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Getting gather indices.
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3585-L3606
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
make_2d_block_raster_mask
|
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, query_width)
memory_flange: A tuple of ints
(memory_flange_height, memory_flange_width)
Returns:
A tensor of shape query_size, memory_size
"""
# mask inside the query block
query_triangle = common_layers.ones_matrix_band_part(
np.prod(query_shape), np.prod(query_shape), -1, 0)
split_query_masks = tf.split(query_triangle, query_shape[0], axis=1)
# adding mask for left and right
mask_pieces = [
tf.concat( # pylint: disable=g-complex-comprehension
[tf.ones([np.prod(query_shape), memory_flange[1]]),
split_query_masks[i],
tf.zeros([np.prod(query_shape), memory_flange[1]])],
axis=1) for i in range(query_shape[0])
]
# adding mask for top
final_mask = tf.concat(
[
tf.ones([
np.prod(query_shape),
(query_shape[1] + 2 * memory_flange[1]) * memory_flange[0]
]),
tf.concat(mask_pieces, axis=1)
],
axis=1)
# 0.0 is visible location, 1.0 is masked.
return 1. - final_mask
|
python
|
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, query_width)
memory_flange: A tuple of ints
(memory_flange_height, memory_flange_width)
Returns:
A tensor of shape query_size, memory_size
"""
# mask inside the query block
query_triangle = common_layers.ones_matrix_band_part(
np.prod(query_shape), np.prod(query_shape), -1, 0)
split_query_masks = tf.split(query_triangle, query_shape[0], axis=1)
# adding mask for left and right
mask_pieces = [
tf.concat( # pylint: disable=g-complex-comprehension
[tf.ones([np.prod(query_shape), memory_flange[1]]),
split_query_masks[i],
tf.zeros([np.prod(query_shape), memory_flange[1]])],
axis=1) for i in range(query_shape[0])
]
# adding mask for top
final_mask = tf.concat(
[
tf.ones([
np.prod(query_shape),
(query_shape[1] + 2 * memory_flange[1]) * memory_flange[0]
]),
tf.concat(mask_pieces, axis=1)
],
axis=1)
# 0.0 is visible location, 1.0 is masked.
return 1. - final_mask
|
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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, query_width)
memory_flange: A tuple of ints
(memory_flange_height, memory_flange_width)
Returns:
A tensor of shape query_size, memory_size
|
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] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3609-L3646
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
get_memory_region
|
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: a 2-d tuple of integers
q_indices: a tensor of indices for each of the center blocks.
[num_blocks, block_length]
Returns:
x_flange: A tensor of shape [batch, heads, #blocks, block_length, depth]
"""
# Padding x to be multiple of query_shape and then
# extracting the memory blocks from the same regions as the query blocks
x_query_padded = pad_to_multiple_2d(x, query_block_shape)
x_center = gather_blocks_2d(x_query_padded, q_indices)
# Then padding the flange region
paddings = [[0, 0], [0, 0], [memory_flange[0], 0],
[memory_flange[1], memory_flange[1]], [0, 0]]
x_memory_padded = tf.pad(x_query_padded, paddings)
left_x = None
top_x = None
# Extracting the memory regions around the query block. left_x_region extends
# to the left and the top_x_region is the combination of top left, top, and
# top right of the query block
# if no left region
if memory_flange[1] > 0:
left_x_region = x_memory_padded[:, :, memory_flange[
0]:, :-(query_block_shape[1] + memory_flange[1]), :]
left_memory_shape = (query_block_shape[0], memory_flange[1])
left_indices = gather_indices_2d(left_x_region, left_memory_shape,
query_block_shape)
left_x = gather_blocks_2d(left_x_region, left_indices)
# if no top region
if memory_flange[0] > 0:
top_x_region = x_memory_padded[:, :, :-query_block_shape[0], :, :]
top_memory_shape = (memory_flange[0],
query_block_shape[1] + 2 * memory_flange[1])
top_indices = gather_indices_2d(top_x_region, top_memory_shape,
query_block_shape)
top_x = gather_blocks_2d(top_x_region, top_indices)
x_flange = None
if top_x is not None and left_x is not None:
x_flange = tf.concat([top_x, left_x], axis=3)
else:
x_flange = top_x if top_x is not None else left_x
return x_flange, x_center
|
python
|
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: a 2-d tuple of integers
q_indices: a tensor of indices for each of the center blocks.
[num_blocks, block_length]
Returns:
x_flange: A tensor of shape [batch, heads, #blocks, block_length, depth]
"""
# Padding x to be multiple of query_shape and then
# extracting the memory blocks from the same regions as the query blocks
x_query_padded = pad_to_multiple_2d(x, query_block_shape)
x_center = gather_blocks_2d(x_query_padded, q_indices)
# Then padding the flange region
paddings = [[0, 0], [0, 0], [memory_flange[0], 0],
[memory_flange[1], memory_flange[1]], [0, 0]]
x_memory_padded = tf.pad(x_query_padded, paddings)
left_x = None
top_x = None
# Extracting the memory regions around the query block. left_x_region extends
# to the left and the top_x_region is the combination of top left, top, and
# top right of the query block
# if no left region
if memory_flange[1] > 0:
left_x_region = x_memory_padded[:, :, memory_flange[
0]:, :-(query_block_shape[1] + memory_flange[1]), :]
left_memory_shape = (query_block_shape[0], memory_flange[1])
left_indices = gather_indices_2d(left_x_region, left_memory_shape,
query_block_shape)
left_x = gather_blocks_2d(left_x_region, left_indices)
# if no top region
if memory_flange[0] > 0:
top_x_region = x_memory_padded[:, :, :-query_block_shape[0], :, :]
top_memory_shape = (memory_flange[0],
query_block_shape[1] + 2 * memory_flange[1])
top_indices = gather_indices_2d(top_x_region, top_memory_shape,
query_block_shape)
top_x = gather_blocks_2d(top_x_region, top_indices)
x_flange = None
if top_x is not None and left_x is not None:
x_flange = tf.concat([top_x, left_x], axis=3)
else:
x_flange = top_x if top_x is not None else left_x
return x_flange, x_center
|
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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: a 2-d tuple of integers
q_indices: a tensor of indices for each of the center blocks.
[num_blocks, block_length]
Returns:
x_flange: A tensor of shape [batch, heads, #blocks, block_length, depth]
|
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3649-L3700
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
get_shifted_center_blocks
|
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.
"""
center_x = gather_blocks_2d(x, indices)
# Shift right along the length dimension
def shift_right_2d_blocks(x):
"""Shift the second to last dimension of x right by one."""
shifted_targets = (
tf.pad(x, [[0, 0], [0, 0], [0, 0], [1, 0], [0, 0]])[:, :, :, :-1, :])
return shifted_targets
x_shifted = shift_right_2d_blocks(center_x)
return x_shifted
|
python
|
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.
"""
center_x = gather_blocks_2d(x, indices)
# Shift right along the length dimension
def shift_right_2d_blocks(x):
"""Shift the second to last dimension of x right by one."""
shifted_targets = (
tf.pad(x, [[0, 0], [0, 0], [0, 0], [1, 0], [0, 0]])[:, :, :, :-1, :])
return shifted_targets
x_shifted = shift_right_2d_blocks(center_x)
return x_shifted
|
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Get right shifted blocks for masked local attention 2d.
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indices: The indices to gather blocks
Returns:
x_shifted: a tensor of extracted blocks, each block right shifted along
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272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3703-L3724
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
right_shift_blockwise
|
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_name="right_shift_blockwise", values=[x]):
x_list_shape = x.get_shape().as_list()
x_shape = common_layers.shape_list(x)
# Add a dummy dimension for heads.
x = tf.expand_dims(x, axis=1)
x = pad_to_multiple_2d(x, query_shape)
padded_x_shape = common_layers.shape_list(x)
# Set up q blocks.
x_indices = gather_indices_2d(x, query_shape, query_shape)
x_new = get_shifted_center_blocks(x, x_indices)
# Put representations back into original shapes.
output = scatter_blocks_2d(x_new, x_indices, padded_x_shape)
# Remove the dummy head dimension.
output = tf.squeeze(output, axis=1)
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, x_shape[1], x_shape[2], -1])
output.set_shape(x_list_shape)
return output
|
python
|
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_name="right_shift_blockwise", values=[x]):
x_list_shape = x.get_shape().as_list()
x_shape = common_layers.shape_list(x)
# Add a dummy dimension for heads.
x = tf.expand_dims(x, axis=1)
x = pad_to_multiple_2d(x, query_shape)
padded_x_shape = common_layers.shape_list(x)
# Set up q blocks.
x_indices = gather_indices_2d(x, query_shape, query_shape)
x_new = get_shifted_center_blocks(x, x_indices)
# Put representations back into original shapes.
output = scatter_blocks_2d(x_new, x_indices, padded_x_shape)
# Remove the dummy head dimension.
output = tf.squeeze(output, axis=1)
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0], [-1, x_shape[1], x_shape[2], -1])
output.set_shape(x_list_shape)
return output
|
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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
|
[
"Right",
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] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3727-L3757
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
masked_local_attention_2d
|
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 can attend to all the generated queries in
the query block, which are generated in raster scan, and positions that are
generated to the left and top. The shapes are specified by query shape and
memory flange. Note that if you're using this function, you do not need to
right shift. Right shifting happens inside this function separately for each
block.
Args:
q: a Tensor with shape [batch, heads, h, w, depth_k]
k: a Tensor with shape [batch, heads, h, w, depth_k]
v: a Tensor with shape [batch, heads, h, w, depth_v]. In the current
implementation, depth_v must be equal to depth_k.
query_shape: an tuple indicating the height and width of each query block.
query_shape = block_shape
memory_flange: an integer indicating how much to look in height and width
from each query block.
memory shape = query_shape + (block_flange[0], 2*block_flange[1])
name: an optional string
Returns:
a Tensor of shape [batch, heads, h, w, depth_v]
"""
with tf.variable_scope(
name, default_name="local_masked_self_attention_2d", values=[q, k, v]):
v_shape = common_layers.shape_list(v)
# Pad query to ensure multiple of corresponding lengths.
q = pad_to_multiple_2d(q, query_shape)
# Set up query blocks.
q_indices = gather_indices_2d(q, query_shape, query_shape)
q_new = gather_blocks_2d(q, q_indices)
# Set up key and value blocks.
k_flange, k_center = get_memory_region(k, query_shape, memory_flange,
q_indices)
v_flange, v_center = get_memory_region(v, query_shape, memory_flange,
q_indices)
if k_flange is not None:
k_new = tf.concat([k_flange, k_center], axis=3)
v_new = tf.concat([v_flange, v_center], axis=3)
else:
k_new = k_center
v_new = v_center
# Set up the masks.
query_elements = np.prod(query_shape)
padding_mask = None
if k_flange is not None:
padding_mask = tf.expand_dims(
embedding_to_padding(k_flange) * -1e9, axis=-2)
padding_mask = tf.tile(padding_mask, [1, 1, 1, query_elements, 1])
center_attention_bias = attention_bias_lower_triangle(
np.prod(query_elements))
center_attention_bias = tf.reshape(
center_attention_bias, [1, 1, 1, query_elements, query_elements])
v_center_shape = common_layers.shape_list(v_center)
center_attention_bias = tf.tile(
center_attention_bias,
[v_center_shape[0], v_center_shape[1], v_center_shape[2], 1, 1])
if padding_mask is not None:
# Combine the mask for padding and visible region.
attention_bias = tf.concat([padding_mask, center_attention_bias], axis=4)
else:
attention_bias = center_attention_bias
output = dot_product_attention(
q_new,
k_new,
v_new,
attention_bias,
dropout_rate=0.,
name="masked_local_2d",
make_image_summary=False)
# Put representations back into original shapes.
padded_q_shape = common_layers.shape_list(q)
output = scatter_blocks_2d(output, q_indices, padded_q_shape)
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0, 0],
[-1, -1, v_shape[2], v_shape[3], -1])
return output
|
python
|
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 can attend to all the generated queries in
the query block, which are generated in raster scan, and positions that are
generated to the left and top. The shapes are specified by query shape and
memory flange. Note that if you're using this function, you do not need to
right shift. Right shifting happens inside this function separately for each
block.
Args:
q: a Tensor with shape [batch, heads, h, w, depth_k]
k: a Tensor with shape [batch, heads, h, w, depth_k]
v: a Tensor with shape [batch, heads, h, w, depth_v]. In the current
implementation, depth_v must be equal to depth_k.
query_shape: an tuple indicating the height and width of each query block.
query_shape = block_shape
memory_flange: an integer indicating how much to look in height and width
from each query block.
memory shape = query_shape + (block_flange[0], 2*block_flange[1])
name: an optional string
Returns:
a Tensor of shape [batch, heads, h, w, depth_v]
"""
with tf.variable_scope(
name, default_name="local_masked_self_attention_2d", values=[q, k, v]):
v_shape = common_layers.shape_list(v)
# Pad query to ensure multiple of corresponding lengths.
q = pad_to_multiple_2d(q, query_shape)
# Set up query blocks.
q_indices = gather_indices_2d(q, query_shape, query_shape)
q_new = gather_blocks_2d(q, q_indices)
# Set up key and value blocks.
k_flange, k_center = get_memory_region(k, query_shape, memory_flange,
q_indices)
v_flange, v_center = get_memory_region(v, query_shape, memory_flange,
q_indices)
if k_flange is not None:
k_new = tf.concat([k_flange, k_center], axis=3)
v_new = tf.concat([v_flange, v_center], axis=3)
else:
k_new = k_center
v_new = v_center
# Set up the masks.
query_elements = np.prod(query_shape)
padding_mask = None
if k_flange is not None:
padding_mask = tf.expand_dims(
embedding_to_padding(k_flange) * -1e9, axis=-2)
padding_mask = tf.tile(padding_mask, [1, 1, 1, query_elements, 1])
center_attention_bias = attention_bias_lower_triangle(
np.prod(query_elements))
center_attention_bias = tf.reshape(
center_attention_bias, [1, 1, 1, query_elements, query_elements])
v_center_shape = common_layers.shape_list(v_center)
center_attention_bias = tf.tile(
center_attention_bias,
[v_center_shape[0], v_center_shape[1], v_center_shape[2], 1, 1])
if padding_mask is not None:
# Combine the mask for padding and visible region.
attention_bias = tf.concat([padding_mask, center_attention_bias], axis=4)
else:
attention_bias = center_attention_bias
output = dot_product_attention(
q_new,
k_new,
v_new,
attention_bias,
dropout_rate=0.,
name="masked_local_2d",
make_image_summary=False)
# Put representations back into original shapes.
padded_q_shape = common_layers.shape_list(q)
output = scatter_blocks_2d(output, q_indices, padded_q_shape)
# Remove the padding if introduced.
output = tf.slice(output, [0, 0, 0, 0, 0],
[-1, -1, v_shape[2], v_shape[3], -1])
return output
|
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"return",
"output"
] |
Strided block local self-attention.
Each position in a query block can attend to all the generated queries in
the query block, which are generated in raster scan, and positions that are
generated to the left and top. The shapes are specified by query shape and
memory flange. Note that if you're using this function, you do not need to
right shift. Right shifting happens inside this function separately for each
block.
Args:
q: a Tensor with shape [batch, heads, h, w, depth_k]
k: a Tensor with shape [batch, heads, h, w, depth_k]
v: a Tensor with shape [batch, heads, h, w, depth_v]. In the current
implementation, depth_v must be equal to depth_k.
query_shape: an tuple indicating the height and width of each query block.
query_shape = block_shape
memory_flange: an integer indicating how much to look in height and width
from each query block.
memory shape = query_shape + (block_flange[0], 2*block_flange[1])
name: an optional string
Returns:
a Tensor of shape [batch, heads, h, w, depth_v]
|
[
"Strided",
"block",
"local",
"self",
"-",
"attention",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3760-L3850
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
compute_attention_component
|
def compute_attention_component(antecedent,
total_depth,
filter_width=1,
padding="VALID",
name="c",
vars_3d_num_heads=0,
layer_collection=None):
"""Computes attention compoenent (query, key or value).
Args:
antecedent: a Tensor with shape [batch, length, channels]
total_depth: an integer
filter_width: An integer specifying how wide you want the attention
component to be.
padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
name: a string specifying scope name.
vars_3d_num_heads: an optional integer (if we want to use 3d variables)
layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
KFAC optimizer. Default is None.
Returns:
c : [batch, length, depth] tensor
"""
if layer_collection is not None:
if filter_width != 1 or vars_3d_num_heads != 0:
raise ValueError(
"KFAC implementation only supports filter_width=1 (actual: {}) and "
"vars_3d_num_heads=0 (actual: {}).".format(
filter_width, vars_3d_num_heads))
if vars_3d_num_heads > 0:
assert filter_width == 1
input_depth = antecedent.get_shape().as_list()[-1]
depth_per_head = total_depth // vars_3d_num_heads
initializer_stddev = input_depth ** -0.5
if "q" in name:
initializer_stddev *= depth_per_head ** -0.5
var = tf.get_variable(
name, [input_depth,
vars_3d_num_heads,
total_depth // vars_3d_num_heads],
initializer=tf.random_normal_initializer(stddev=initializer_stddev))
var = tf.cast(var, antecedent.dtype)
var = tf.reshape(var, [input_depth, total_depth])
return tf.tensordot(antecedent, var, axes=1)
if filter_width == 1:
return common_layers.dense(
antecedent, total_depth, use_bias=False, name=name,
layer_collection=layer_collection)
else:
return common_layers.conv1d(
antecedent, total_depth, filter_width, padding=padding, name=name)
|
python
|
def compute_attention_component(antecedent,
total_depth,
filter_width=1,
padding="VALID",
name="c",
vars_3d_num_heads=0,
layer_collection=None):
"""Computes attention compoenent (query, key or value).
Args:
antecedent: a Tensor with shape [batch, length, channels]
total_depth: an integer
filter_width: An integer specifying how wide you want the attention
component to be.
padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
name: a string specifying scope name.
vars_3d_num_heads: an optional integer (if we want to use 3d variables)
layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
KFAC optimizer. Default is None.
Returns:
c : [batch, length, depth] tensor
"""
if layer_collection is not None:
if filter_width != 1 or vars_3d_num_heads != 0:
raise ValueError(
"KFAC implementation only supports filter_width=1 (actual: {}) and "
"vars_3d_num_heads=0 (actual: {}).".format(
filter_width, vars_3d_num_heads))
if vars_3d_num_heads > 0:
assert filter_width == 1
input_depth = antecedent.get_shape().as_list()[-1]
depth_per_head = total_depth // vars_3d_num_heads
initializer_stddev = input_depth ** -0.5
if "q" in name:
initializer_stddev *= depth_per_head ** -0.5
var = tf.get_variable(
name, [input_depth,
vars_3d_num_heads,
total_depth // vars_3d_num_heads],
initializer=tf.random_normal_initializer(stddev=initializer_stddev))
var = tf.cast(var, antecedent.dtype)
var = tf.reshape(var, [input_depth, total_depth])
return tf.tensordot(antecedent, var, axes=1)
if filter_width == 1:
return common_layers.dense(
antecedent, total_depth, use_bias=False, name=name,
layer_collection=layer_collection)
else:
return common_layers.conv1d(
antecedent, total_depth, filter_width, padding=padding, name=name)
|
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Computes attention compoenent (query, key or value).
Args:
antecedent: a Tensor with shape [batch, length, channels]
total_depth: an integer
filter_width: An integer specifying how wide you want the attention
component to be.
padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
name: a string specifying scope name.
vars_3d_num_heads: an optional integer (if we want to use 3d variables)
layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
KFAC optimizer. Default is None.
Returns:
c : [batch, length, depth] tensor
|
[
"Computes",
"attention",
"compoenent",
"(",
"query",
"key",
"or",
"value",
")",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3853-L3903
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
compute_qkv
|
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,
layer_collection=None):
"""Computes query, key and value.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels]
total_key_depth: an integer
total_value_depth: an integer
q_filter_width: An integer specifying how wide you want the query to be.
kv_filter_width: An integer specifying how wide you want the keys and values
to be.
q_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
kv_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
vars_3d_num_heads: an optional (if we want to use 3d variables)
layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
KFAC optimizer. Default is None.
Returns:
q, k, v : [batch, length, depth] tensors
"""
if memory_antecedent is None:
memory_antecedent = query_antecedent
q = compute_attention_component(
query_antecedent,
total_key_depth,
q_filter_width,
q_padding,
"q",
vars_3d_num_heads=vars_3d_num_heads,
layer_collection=layer_collection)
k = compute_attention_component(
memory_antecedent,
total_key_depth,
kv_filter_width,
kv_padding,
"k",
vars_3d_num_heads=vars_3d_num_heads,
layer_collection=layer_collection)
v = compute_attention_component(
memory_antecedent,
total_value_depth,
kv_filter_width,
kv_padding,
"v",
vars_3d_num_heads=vars_3d_num_heads,
layer_collection=layer_collection)
return q, k, v
|
python
|
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,
layer_collection=None):
"""Computes query, key and value.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels]
total_key_depth: an integer
total_value_depth: an integer
q_filter_width: An integer specifying how wide you want the query to be.
kv_filter_width: An integer specifying how wide you want the keys and values
to be.
q_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
kv_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
vars_3d_num_heads: an optional (if we want to use 3d variables)
layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
KFAC optimizer. Default is None.
Returns:
q, k, v : [batch, length, depth] tensors
"""
if memory_antecedent is None:
memory_antecedent = query_antecedent
q = compute_attention_component(
query_antecedent,
total_key_depth,
q_filter_width,
q_padding,
"q",
vars_3d_num_heads=vars_3d_num_heads,
layer_collection=layer_collection)
k = compute_attention_component(
memory_antecedent,
total_key_depth,
kv_filter_width,
kv_padding,
"k",
vars_3d_num_heads=vars_3d_num_heads,
layer_collection=layer_collection)
v = compute_attention_component(
memory_antecedent,
total_value_depth,
kv_filter_width,
kv_padding,
"v",
vars_3d_num_heads=vars_3d_num_heads,
layer_collection=layer_collection)
return q, k, v
|
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"=",
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"q",
",",
"k",
",",
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Computes query, key and value.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels]
total_key_depth: an integer
total_value_depth: an integer
q_filter_width: An integer specifying how wide you want the query to be.
kv_filter_width: An integer specifying how wide you want the keys and values
to be.
q_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
kv_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
vars_3d_num_heads: an optional (if we want to use 3d variables)
layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
KFAC optimizer. Default is None.
Returns:
q, k, v : [batch, length, depth] tensors
|
[
"Computes",
"query",
"key",
"and",
"value",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3906-L3961
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
multihead_attention
|
def multihead_attention(query_antecedent,
memory_antecedent,
bias,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
dropout_rate,
attention_type="dot_product",
max_relative_position=None,
heads_share_relative_embedding=False,
add_relative_to_values=False,
image_shapes=None,
block_length=128,
block_width=128,
q_filter_width=1,
kv_filter_width=1,
q_padding="VALID",
kv_padding="VALID",
cache=None,
gap_size=0,
num_memory_blocks=2,
name="multihead_attention",
save_weights_to=None,
make_image_summary=True,
dropout_broadcast_dims=None,
vars_3d=False,
layer_collection=None,
recurrent_memory=None,
chunk_number=None,
hard_attention_k=0,
max_area_width=1,
max_area_height=1,
memory_height=1,
area_key_mode="mean",
area_value_mode="sum",
training=True,
**kwargs):
"""Multihead scaled-dot-product attention with input/output transformations.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels] or None
bias: bias Tensor (see attention_bias())
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
dropout_rate: a floating point number
attention_type: a string, either "dot_product", "dot_product_relative",
"local_mask_right", "local_unmasked", "masked_dilated_1d",
"unmasked_dilated_1d", graph, or any attention function
with the signature (query, key, value, **kwargs)
max_relative_position: Maximum distance between inputs to generate
unique relation embeddings for. Only relevant
when using "dot_product_relative" attention.
heads_share_relative_embedding: boolean to share relative embeddings
add_relative_to_values: a boolean for whether to add relative component to
values.
image_shapes: optional tuple of integer scalars.
see comments for attention_image_summary()
block_length: an integer - relevant for "local_mask_right"
block_width: an integer - relevant for "local_unmasked"
q_filter_width: An integer specifying how wide you want the query to be.
kv_filter_width: An integer specifying how wide you want the keys and values
to be.
q_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
kv_padding: One of "VALID", "SAME" or "LEFT". Default is "VALID":
no padding.
cache: dict containing Tensors which are the results of previous
attentions, used for fast decoding. Expects the dict to contrain two
keys ('k' and 'v'), for the initial call the values for these keys
should be empty Tensors of the appropriate shape.
'k' [batch_size, 0, key_channels]
'v' [batch_size, 0, value_channels]
gap_size: Integer option for dilated attention to indicate spacing between
memory blocks.
num_memory_blocks: Integer option to indicate how many memory blocks to look
at.
name: an optional string.
save_weights_to: an optional dictionary to capture attention weights
for vizualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
vars_3d: use 3-dimensional variables for input/output transformations
layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
KFAC optimizer. Default is None.
recurrent_memory: An optional transformer_memory.RecurrentMemory, which
retains state across chunks. Default is None.
chunk_number: an optional integer Tensor with shape [batch] used to operate
the recurrent_memory.
hard_attention_k: integer, if > 0 triggers hard attention (picking top-k).
max_area_width: the max width allowed for an area.
max_area_height: the max height allowed for an area.
memory_height: the height of the memory.
area_key_mode: the mode for computing area keys, which can be "mean",
"concat", "sum", "sample_concat", and "sample_sum".
area_value_mode: the mode for computing area values, which can be either
"mean", or "sum".
training: indicating if it is in the training mode.
**kwargs (dict): Parameters for the attention function.
Caching:
WARNING: For decoder self-attention, i.e. when memory_antecedent == None,
the caching assumes that the bias contains future masking.
The caching works by saving all the previous key and value values so that
you are able to send just the last query location to this attention
function. I.e. if the cache dict is provided it assumes the query is of the
shape [batch_size, 1, hidden_dim] rather than the full memory.
Returns:
The result of the attention transformation. The output shape is
[batch_size, length_q, hidden_dim]
unless the cache dict is provided in which case only the last memory
position is calculated and the output shape is [batch_size, 1, hidden_dim]
Optionally returns an additional loss parameters (ex: load balance loss for
the experts) returned by the attention_type function.
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads.
"""
if total_key_depth % num_heads != 0:
raise ValueError("Key depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_key_depth, num_heads))
if total_value_depth % num_heads != 0:
raise ValueError("Value depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_value_depth, num_heads))
vars_3d_num_heads = num_heads if vars_3d else 0
if layer_collection is not None:
if cache is not None:
raise ValueError("KFAC implementation only supports cache is None.")
if vars_3d:
raise ValueError("KFAC implementation does not support 3d vars.")
if recurrent_memory is not None:
if memory_antecedent is not None:
raise ValueError("Recurrent memory requires memory_antecedent is None.")
if cache is not None:
raise ValueError("Cache is not supported when using recurrent memory.")
if vars_3d:
raise ValueError("3d vars are not supported when using recurrent memory.")
if layer_collection is not None:
raise ValueError("KFAC is not supported when using recurrent memory.")
if chunk_number is None:
raise ValueError("chunk_number is required when using recurrent memory.")
with tf.variable_scope(name, default_name="multihead_attention",
values=[query_antecedent, memory_antecedent]):
if recurrent_memory is not None:
(
recurrent_memory_transaction,
query_antecedent, memory_antecedent, bias,
) = recurrent_memory.pre_attention(
chunk_number,
query_antecedent, memory_antecedent, bias,
)
if cache is None or memory_antecedent is None:
q, k, v = compute_qkv(query_antecedent, memory_antecedent,
total_key_depth, total_value_depth, q_filter_width,
kv_filter_width, q_padding, kv_padding,
vars_3d_num_heads=vars_3d_num_heads,
layer_collection=layer_collection)
if cache is not None:
if attention_type not in ["dot_product", "dot_product_relative"]:
# TODO(petershaw): Support caching when using relative position
# representations, i.e. "dot_product_relative" attention.
raise NotImplementedError(
"Caching is not guaranteed to work with attention types other than"
" dot_product.")
if bias is None:
raise ValueError("Bias required for caching. See function docstring "
"for details.")
if memory_antecedent is not None:
# Encoder-Decoder Attention Cache
q = compute_attention_component(query_antecedent, total_key_depth,
q_filter_width, q_padding, "q",
vars_3d_num_heads=vars_3d_num_heads)
k = cache["k_encdec"]
v = cache["v_encdec"]
else:
k = split_heads(k, num_heads)
v = split_heads(v, num_heads)
decode_loop_step = kwargs.get("decode_loop_step")
if decode_loop_step is None:
k = cache["k"] = tf.concat([cache["k"], k], axis=2)
v = cache["v"] = tf.concat([cache["v"], v], axis=2)
else:
# Inplace update is required for inference on TPU.
# Inplace_ops only supports inplace_update on the first dimension.
# The performance of current implementation is better than updating
# the tensor by adding the result of matmul(one_hot,
# update_in_current_step)
tmp_k = tf.transpose(cache["k"], perm=[2, 0, 1, 3])
tmp_k = inplace_ops.alias_inplace_update(
tmp_k, decode_loop_step, tf.squeeze(k, axis=2))
k = cache["k"] = tf.transpose(tmp_k, perm=[1, 2, 0, 3])
tmp_v = tf.transpose(cache["v"], perm=[2, 0, 1, 3])
tmp_v = inplace_ops.alias_inplace_update(
tmp_v, decode_loop_step, tf.squeeze(v, axis=2))
v = cache["v"] = tf.transpose(tmp_v, perm=[1, 2, 0, 3])
q = split_heads(q, num_heads)
if cache is None:
k = split_heads(k, num_heads)
v = split_heads(v, num_heads)
key_depth_per_head = total_key_depth // num_heads
if not vars_3d:
q *= key_depth_per_head**-0.5
additional_returned_value = None
if callable(attention_type): # Generic way to extend multihead_attention
x = attention_type(q, k, v, **kwargs)
if isinstance(x, tuple):
x, additional_returned_value = x # Unpack
elif attention_type == "dot_product":
if max_area_width > 1 or max_area_height > 1:
x = area_attention.dot_product_area_attention(
q, k, v, bias, dropout_rate, image_shapes,
save_weights_to=save_weights_to,
dropout_broadcast_dims=dropout_broadcast_dims,
max_area_width=max_area_width,
max_area_height=max_area_height,
memory_height=memory_height,
area_key_mode=area_key_mode,
area_value_mode=area_value_mode,
training=training)
else:
x = dot_product_attention(q, k, v, bias, dropout_rate, image_shapes,
save_weights_to=save_weights_to,
make_image_summary=make_image_summary,
dropout_broadcast_dims=dropout_broadcast_dims,
activation_dtype=kwargs.get(
"activation_dtype"),
hard_attention_k=hard_attention_k)
elif attention_type == "dot_product_relative":
x = dot_product_attention_relative(
q,
k,
v,
bias,
max_relative_position,
dropout_rate,
image_shapes,
save_weights_to=save_weights_to,
make_image_summary=make_image_summary,
cache=cache is not None,
allow_memory=recurrent_memory is not None,
hard_attention_k=hard_attention_k)
elif attention_type == "dot_product_unmasked_relative_v2":
x = dot_product_unmasked_self_attention_relative_v2(
q,
k,
v,
bias,
max_relative_position,
dropout_rate,
image_shapes,
make_image_summary=make_image_summary,
dropout_broadcast_dims=dropout_broadcast_dims,
heads_share_relative_embedding=heads_share_relative_embedding,
add_relative_to_values=add_relative_to_values)
elif attention_type == "dot_product_relative_v2":
x = dot_product_self_attention_relative_v2(
q,
k,
v,
bias,
max_relative_position,
dropout_rate,
image_shapes,
make_image_summary=make_image_summary,
dropout_broadcast_dims=dropout_broadcast_dims,
heads_share_relative_embedding=heads_share_relative_embedding,
add_relative_to_values=add_relative_to_values)
elif attention_type == "local_within_block_mask_right":
x = masked_within_block_local_attention_1d(
q, k, v, block_length=block_length)
elif attention_type == "local_relative_mask_right":
x = masked_relative_local_attention_1d(
q,
k,
v,
block_length=block_length,
make_image_summary=make_image_summary,
dropout_rate=dropout_rate,
heads_share_relative_embedding=heads_share_relative_embedding,
add_relative_to_values=add_relative_to_values,
name="masked_relative_local_attention_1d")
elif attention_type == "local_mask_right":
x = masked_local_attention_1d(
q,
k,
v,
block_length=block_length,
make_image_summary=make_image_summary)
elif attention_type == "local_unmasked":
x = local_attention_1d(
q, k, v, block_length=block_length, filter_width=block_width)
elif attention_type == "masked_dilated_1d":
x = masked_dilated_self_attention_1d(q, k, v, block_length, block_width,
gap_size, num_memory_blocks)
else:
assert attention_type == "unmasked_dilated_1d"
x = dilated_self_attention_1d(q, k, v, block_length, block_width,
gap_size, num_memory_blocks)
x = combine_heads(x)
# Set last dim specifically.
x.set_shape(x.shape.as_list()[:-1] + [total_value_depth])
if vars_3d:
o_var = tf.get_variable(
"o", [num_heads, total_value_depth // num_heads, output_depth])
o_var = tf.cast(o_var, x.dtype)
o_var = tf.reshape(o_var, [total_value_depth, output_depth])
x = tf.tensordot(x, o_var, axes=1)
else:
x = common_layers.dense(
x, output_depth, use_bias=False, name="output_transform",
layer_collection=layer_collection)
if recurrent_memory is not None:
x = recurrent_memory.post_attention(recurrent_memory_transaction, x)
if additional_returned_value is not None:
return x, additional_returned_value
return x
|
python
|
def multihead_attention(query_antecedent,
memory_antecedent,
bias,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
dropout_rate,
attention_type="dot_product",
max_relative_position=None,
heads_share_relative_embedding=False,
add_relative_to_values=False,
image_shapes=None,
block_length=128,
block_width=128,
q_filter_width=1,
kv_filter_width=1,
q_padding="VALID",
kv_padding="VALID",
cache=None,
gap_size=0,
num_memory_blocks=2,
name="multihead_attention",
save_weights_to=None,
make_image_summary=True,
dropout_broadcast_dims=None,
vars_3d=False,
layer_collection=None,
recurrent_memory=None,
chunk_number=None,
hard_attention_k=0,
max_area_width=1,
max_area_height=1,
memory_height=1,
area_key_mode="mean",
area_value_mode="sum",
training=True,
**kwargs):
"""Multihead scaled-dot-product attention with input/output transformations.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels] or None
bias: bias Tensor (see attention_bias())
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
dropout_rate: a floating point number
attention_type: a string, either "dot_product", "dot_product_relative",
"local_mask_right", "local_unmasked", "masked_dilated_1d",
"unmasked_dilated_1d", graph, or any attention function
with the signature (query, key, value, **kwargs)
max_relative_position: Maximum distance between inputs to generate
unique relation embeddings for. Only relevant
when using "dot_product_relative" attention.
heads_share_relative_embedding: boolean to share relative embeddings
add_relative_to_values: a boolean for whether to add relative component to
values.
image_shapes: optional tuple of integer scalars.
see comments for attention_image_summary()
block_length: an integer - relevant for "local_mask_right"
block_width: an integer - relevant for "local_unmasked"
q_filter_width: An integer specifying how wide you want the query to be.
kv_filter_width: An integer specifying how wide you want the keys and values
to be.
q_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
kv_padding: One of "VALID", "SAME" or "LEFT". Default is "VALID":
no padding.
cache: dict containing Tensors which are the results of previous
attentions, used for fast decoding. Expects the dict to contrain two
keys ('k' and 'v'), for the initial call the values for these keys
should be empty Tensors of the appropriate shape.
'k' [batch_size, 0, key_channels]
'v' [batch_size, 0, value_channels]
gap_size: Integer option for dilated attention to indicate spacing between
memory blocks.
num_memory_blocks: Integer option to indicate how many memory blocks to look
at.
name: an optional string.
save_weights_to: an optional dictionary to capture attention weights
for vizualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
vars_3d: use 3-dimensional variables for input/output transformations
layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
KFAC optimizer. Default is None.
recurrent_memory: An optional transformer_memory.RecurrentMemory, which
retains state across chunks. Default is None.
chunk_number: an optional integer Tensor with shape [batch] used to operate
the recurrent_memory.
hard_attention_k: integer, if > 0 triggers hard attention (picking top-k).
max_area_width: the max width allowed for an area.
max_area_height: the max height allowed for an area.
memory_height: the height of the memory.
area_key_mode: the mode for computing area keys, which can be "mean",
"concat", "sum", "sample_concat", and "sample_sum".
area_value_mode: the mode for computing area values, which can be either
"mean", or "sum".
training: indicating if it is in the training mode.
**kwargs (dict): Parameters for the attention function.
Caching:
WARNING: For decoder self-attention, i.e. when memory_antecedent == None,
the caching assumes that the bias contains future masking.
The caching works by saving all the previous key and value values so that
you are able to send just the last query location to this attention
function. I.e. if the cache dict is provided it assumes the query is of the
shape [batch_size, 1, hidden_dim] rather than the full memory.
Returns:
The result of the attention transformation. The output shape is
[batch_size, length_q, hidden_dim]
unless the cache dict is provided in which case only the last memory
position is calculated and the output shape is [batch_size, 1, hidden_dim]
Optionally returns an additional loss parameters (ex: load balance loss for
the experts) returned by the attention_type function.
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads.
"""
if total_key_depth % num_heads != 0:
raise ValueError("Key depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_key_depth, num_heads))
if total_value_depth % num_heads != 0:
raise ValueError("Value depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_value_depth, num_heads))
vars_3d_num_heads = num_heads if vars_3d else 0
if layer_collection is not None:
if cache is not None:
raise ValueError("KFAC implementation only supports cache is None.")
if vars_3d:
raise ValueError("KFAC implementation does not support 3d vars.")
if recurrent_memory is not None:
if memory_antecedent is not None:
raise ValueError("Recurrent memory requires memory_antecedent is None.")
if cache is not None:
raise ValueError("Cache is not supported when using recurrent memory.")
if vars_3d:
raise ValueError("3d vars are not supported when using recurrent memory.")
if layer_collection is not None:
raise ValueError("KFAC is not supported when using recurrent memory.")
if chunk_number is None:
raise ValueError("chunk_number is required when using recurrent memory.")
with tf.variable_scope(name, default_name="multihead_attention",
values=[query_antecedent, memory_antecedent]):
if recurrent_memory is not None:
(
recurrent_memory_transaction,
query_antecedent, memory_antecedent, bias,
) = recurrent_memory.pre_attention(
chunk_number,
query_antecedent, memory_antecedent, bias,
)
if cache is None or memory_antecedent is None:
q, k, v = compute_qkv(query_antecedent, memory_antecedent,
total_key_depth, total_value_depth, q_filter_width,
kv_filter_width, q_padding, kv_padding,
vars_3d_num_heads=vars_3d_num_heads,
layer_collection=layer_collection)
if cache is not None:
if attention_type not in ["dot_product", "dot_product_relative"]:
# TODO(petershaw): Support caching when using relative position
# representations, i.e. "dot_product_relative" attention.
raise NotImplementedError(
"Caching is not guaranteed to work with attention types other than"
" dot_product.")
if bias is None:
raise ValueError("Bias required for caching. See function docstring "
"for details.")
if memory_antecedent is not None:
# Encoder-Decoder Attention Cache
q = compute_attention_component(query_antecedent, total_key_depth,
q_filter_width, q_padding, "q",
vars_3d_num_heads=vars_3d_num_heads)
k = cache["k_encdec"]
v = cache["v_encdec"]
else:
k = split_heads(k, num_heads)
v = split_heads(v, num_heads)
decode_loop_step = kwargs.get("decode_loop_step")
if decode_loop_step is None:
k = cache["k"] = tf.concat([cache["k"], k], axis=2)
v = cache["v"] = tf.concat([cache["v"], v], axis=2)
else:
# Inplace update is required for inference on TPU.
# Inplace_ops only supports inplace_update on the first dimension.
# The performance of current implementation is better than updating
# the tensor by adding the result of matmul(one_hot,
# update_in_current_step)
tmp_k = tf.transpose(cache["k"], perm=[2, 0, 1, 3])
tmp_k = inplace_ops.alias_inplace_update(
tmp_k, decode_loop_step, tf.squeeze(k, axis=2))
k = cache["k"] = tf.transpose(tmp_k, perm=[1, 2, 0, 3])
tmp_v = tf.transpose(cache["v"], perm=[2, 0, 1, 3])
tmp_v = inplace_ops.alias_inplace_update(
tmp_v, decode_loop_step, tf.squeeze(v, axis=2))
v = cache["v"] = tf.transpose(tmp_v, perm=[1, 2, 0, 3])
q = split_heads(q, num_heads)
if cache is None:
k = split_heads(k, num_heads)
v = split_heads(v, num_heads)
key_depth_per_head = total_key_depth // num_heads
if not vars_3d:
q *= key_depth_per_head**-0.5
additional_returned_value = None
if callable(attention_type): # Generic way to extend multihead_attention
x = attention_type(q, k, v, **kwargs)
if isinstance(x, tuple):
x, additional_returned_value = x # Unpack
elif attention_type == "dot_product":
if max_area_width > 1 or max_area_height > 1:
x = area_attention.dot_product_area_attention(
q, k, v, bias, dropout_rate, image_shapes,
save_weights_to=save_weights_to,
dropout_broadcast_dims=dropout_broadcast_dims,
max_area_width=max_area_width,
max_area_height=max_area_height,
memory_height=memory_height,
area_key_mode=area_key_mode,
area_value_mode=area_value_mode,
training=training)
else:
x = dot_product_attention(q, k, v, bias, dropout_rate, image_shapes,
save_weights_to=save_weights_to,
make_image_summary=make_image_summary,
dropout_broadcast_dims=dropout_broadcast_dims,
activation_dtype=kwargs.get(
"activation_dtype"),
hard_attention_k=hard_attention_k)
elif attention_type == "dot_product_relative":
x = dot_product_attention_relative(
q,
k,
v,
bias,
max_relative_position,
dropout_rate,
image_shapes,
save_weights_to=save_weights_to,
make_image_summary=make_image_summary,
cache=cache is not None,
allow_memory=recurrent_memory is not None,
hard_attention_k=hard_attention_k)
elif attention_type == "dot_product_unmasked_relative_v2":
x = dot_product_unmasked_self_attention_relative_v2(
q,
k,
v,
bias,
max_relative_position,
dropout_rate,
image_shapes,
make_image_summary=make_image_summary,
dropout_broadcast_dims=dropout_broadcast_dims,
heads_share_relative_embedding=heads_share_relative_embedding,
add_relative_to_values=add_relative_to_values)
elif attention_type == "dot_product_relative_v2":
x = dot_product_self_attention_relative_v2(
q,
k,
v,
bias,
max_relative_position,
dropout_rate,
image_shapes,
make_image_summary=make_image_summary,
dropout_broadcast_dims=dropout_broadcast_dims,
heads_share_relative_embedding=heads_share_relative_embedding,
add_relative_to_values=add_relative_to_values)
elif attention_type == "local_within_block_mask_right":
x = masked_within_block_local_attention_1d(
q, k, v, block_length=block_length)
elif attention_type == "local_relative_mask_right":
x = masked_relative_local_attention_1d(
q,
k,
v,
block_length=block_length,
make_image_summary=make_image_summary,
dropout_rate=dropout_rate,
heads_share_relative_embedding=heads_share_relative_embedding,
add_relative_to_values=add_relative_to_values,
name="masked_relative_local_attention_1d")
elif attention_type == "local_mask_right":
x = masked_local_attention_1d(
q,
k,
v,
block_length=block_length,
make_image_summary=make_image_summary)
elif attention_type == "local_unmasked":
x = local_attention_1d(
q, k, v, block_length=block_length, filter_width=block_width)
elif attention_type == "masked_dilated_1d":
x = masked_dilated_self_attention_1d(q, k, v, block_length, block_width,
gap_size, num_memory_blocks)
else:
assert attention_type == "unmasked_dilated_1d"
x = dilated_self_attention_1d(q, k, v, block_length, block_width,
gap_size, num_memory_blocks)
x = combine_heads(x)
# Set last dim specifically.
x.set_shape(x.shape.as_list()[:-1] + [total_value_depth])
if vars_3d:
o_var = tf.get_variable(
"o", [num_heads, total_value_depth // num_heads, output_depth])
o_var = tf.cast(o_var, x.dtype)
o_var = tf.reshape(o_var, [total_value_depth, output_depth])
x = tf.tensordot(x, o_var, axes=1)
else:
x = common_layers.dense(
x, output_depth, use_bias=False, name="output_transform",
layer_collection=layer_collection)
if recurrent_memory is not None:
x = recurrent_memory.post_attention(recurrent_memory_transaction, x)
if additional_returned_value is not None:
return x, additional_returned_value
return x
|
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] |
Multihead scaled-dot-product attention with input/output transformations.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels] or None
bias: bias Tensor (see attention_bias())
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
dropout_rate: a floating point number
attention_type: a string, either "dot_product", "dot_product_relative",
"local_mask_right", "local_unmasked", "masked_dilated_1d",
"unmasked_dilated_1d", graph, or any attention function
with the signature (query, key, value, **kwargs)
max_relative_position: Maximum distance between inputs to generate
unique relation embeddings for. Only relevant
when using "dot_product_relative" attention.
heads_share_relative_embedding: boolean to share relative embeddings
add_relative_to_values: a boolean for whether to add relative component to
values.
image_shapes: optional tuple of integer scalars.
see comments for attention_image_summary()
block_length: an integer - relevant for "local_mask_right"
block_width: an integer - relevant for "local_unmasked"
q_filter_width: An integer specifying how wide you want the query to be.
kv_filter_width: An integer specifying how wide you want the keys and values
to be.
q_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
kv_padding: One of "VALID", "SAME" or "LEFT". Default is "VALID":
no padding.
cache: dict containing Tensors which are the results of previous
attentions, used for fast decoding. Expects the dict to contrain two
keys ('k' and 'v'), for the initial call the values for these keys
should be empty Tensors of the appropriate shape.
'k' [batch_size, 0, key_channels]
'v' [batch_size, 0, value_channels]
gap_size: Integer option for dilated attention to indicate spacing between
memory blocks.
num_memory_blocks: Integer option to indicate how many memory blocks to look
at.
name: an optional string.
save_weights_to: an optional dictionary to capture attention weights
for vizualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
vars_3d: use 3-dimensional variables for input/output transformations
layer_collection: A tensorflow_kfac.LayerCollection. Only used by the
KFAC optimizer. Default is None.
recurrent_memory: An optional transformer_memory.RecurrentMemory, which
retains state across chunks. Default is None.
chunk_number: an optional integer Tensor with shape [batch] used to operate
the recurrent_memory.
hard_attention_k: integer, if > 0 triggers hard attention (picking top-k).
max_area_width: the max width allowed for an area.
max_area_height: the max height allowed for an area.
memory_height: the height of the memory.
area_key_mode: the mode for computing area keys, which can be "mean",
"concat", "sum", "sample_concat", and "sample_sum".
area_value_mode: the mode for computing area values, which can be either
"mean", or "sum".
training: indicating if it is in the training mode.
**kwargs (dict): Parameters for the attention function.
Caching:
WARNING: For decoder self-attention, i.e. when memory_antecedent == None,
the caching assumes that the bias contains future masking.
The caching works by saving all the previous key and value values so that
you are able to send just the last query location to this attention
function. I.e. if the cache dict is provided it assumes the query is of the
shape [batch_size, 1, hidden_dim] rather than the full memory.
Returns:
The result of the attention transformation. The output shape is
[batch_size, length_q, hidden_dim]
unless the cache dict is provided in which case only the last memory
position is calculated and the output shape is [batch_size, 1, hidden_dim]
Optionally returns an additional loss parameters (ex: load balance loss for
the experts) returned by the attention_type function.
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads.
|
[
"Multihead",
"scaled",
"-",
"dot",
"-",
"product",
"attention",
"with",
"input",
"/",
"output",
"transformations",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L3964-L4299
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
multihead_attention_2d
|
def multihead_attention_2d(query_antecedent,
memory_antecedent,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
attention_type="local_attention_2d",
query_shape=(8, 16),
memory_flange=(8, 16),
name=None):
"""2d Multihead scaled-dot-product attention with inp/output transformations.
Args:
query_antecedent: a Tensor with shape [batch, h, w, depth_k]
memory_antecedent: a Tensor with shape [batch, h, w, depth_k]
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
attention_type: String, type of attention function to use.
query_shape: an tuple indicating the height and width of each query block.
memory_flange: an integer indicating how much to look in height and width
name: an optional string
Returns:
A Tensor of shape [batch, h, w, output_depth]
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads.
"""
if total_key_depth % num_heads != 0:
raise ValueError("Key depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_key_depth, num_heads))
if total_value_depth % num_heads != 0:
raise ValueError("Value depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_value_depth, num_heads))
with tf.variable_scope(
name,
default_name="multihead_attention_2d",
values=[query_antecedent, memory_antecedent]):
q, k, v = compute_qkv(query_antecedent, memory_antecedent, total_key_depth,
total_value_depth)
# after splitting, shape is [batch, heads, h, w, depth]
q = split_heads_2d(q, num_heads)
k = split_heads_2d(k, num_heads)
v = split_heads_2d(v, num_heads)
key_depth_per_head = total_key_depth // num_heads
q *= key_depth_per_head**-0.5
if attention_type == "local_attention_2d":
x = local_attention_2d(
q, k, v, query_shape=query_shape, memory_flange=memory_flange)
elif attention_type == "masked_local_attention_2d":
assert attention_type == "masked_local_attention_2d"
x = masked_local_attention_2d(
q, k, v, query_shape=query_shape, memory_flange=memory_flange)
else:
assert attention_type == "unmasked_local_attention_2d_tpu"
x = dot_product_unmasked_attention_local_2d_tpu(
q, k, v, None, max_relative_position=None, query_shape=query_shape)
x = combine_heads_2d(x)
x = common_layers.dense(
x, output_depth, use_bias=False, name="output_transform")
return x
|
python
|
def multihead_attention_2d(query_antecedent,
memory_antecedent,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
attention_type="local_attention_2d",
query_shape=(8, 16),
memory_flange=(8, 16),
name=None):
"""2d Multihead scaled-dot-product attention with inp/output transformations.
Args:
query_antecedent: a Tensor with shape [batch, h, w, depth_k]
memory_antecedent: a Tensor with shape [batch, h, w, depth_k]
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
attention_type: String, type of attention function to use.
query_shape: an tuple indicating the height and width of each query block.
memory_flange: an integer indicating how much to look in height and width
name: an optional string
Returns:
A Tensor of shape [batch, h, w, output_depth]
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads.
"""
if total_key_depth % num_heads != 0:
raise ValueError("Key depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_key_depth, num_heads))
if total_value_depth % num_heads != 0:
raise ValueError("Value depth (%d) must be divisible by the number of "
"attention heads (%d)." % (total_value_depth, num_heads))
with tf.variable_scope(
name,
default_name="multihead_attention_2d",
values=[query_antecedent, memory_antecedent]):
q, k, v = compute_qkv(query_antecedent, memory_antecedent, total_key_depth,
total_value_depth)
# after splitting, shape is [batch, heads, h, w, depth]
q = split_heads_2d(q, num_heads)
k = split_heads_2d(k, num_heads)
v = split_heads_2d(v, num_heads)
key_depth_per_head = total_key_depth // num_heads
q *= key_depth_per_head**-0.5
if attention_type == "local_attention_2d":
x = local_attention_2d(
q, k, v, query_shape=query_shape, memory_flange=memory_flange)
elif attention_type == "masked_local_attention_2d":
assert attention_type == "masked_local_attention_2d"
x = masked_local_attention_2d(
q, k, v, query_shape=query_shape, memory_flange=memory_flange)
else:
assert attention_type == "unmasked_local_attention_2d_tpu"
x = dot_product_unmasked_attention_local_2d_tpu(
q, k, v, None, max_relative_position=None, query_shape=query_shape)
x = combine_heads_2d(x)
x = common_layers.dense(
x, output_depth, use_bias=False, name="output_transform")
return x
|
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] |
2d Multihead scaled-dot-product attention with inp/output transformations.
Args:
query_antecedent: a Tensor with shape [batch, h, w, depth_k]
memory_antecedent: a Tensor with shape [batch, h, w, depth_k]
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
attention_type: String, type of attention function to use.
query_shape: an tuple indicating the height and width of each query block.
memory_flange: an integer indicating how much to look in height and width
name: an optional string
Returns:
A Tensor of shape [batch, h, w, output_depth]
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads.
|
[
"2d",
"Multihead",
"scaled",
"-",
"dot",
"-",
"product",
"attention",
"with",
"inp",
"/",
"output",
"transformations",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L4302-L4365
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
ffn_self_attention_layer
|
def ffn_self_attention_layer(x,
filter_depth,
output_depth,
num_parts,
dropout_rate,
share_kv=False,
name=None):
"""Self-attention feedforward layer.
We use self-attention to do feedforward computations. We apply this function
positionwise where for each position, we linearly transform the output to have
depth filter_depth, and break up the result depth-wise into num_parts
contiguous parts. The parts self-attend, we concatenate the results
depth-wise, and we linearly transform to a depth of output_depth. The goal is
to get multiplicative interactions between components of a representation.
Args:
x: a Tensor with shape [batch, length, channels]
filter_depth: an integer
output_depth: an integer
num_parts: an integer dividing filter depth
dropout_rate: a floating point number
share_kv: Share the key value transform
name: an optional string
Returns:
A Tensor with shape [batch, length, output_depth].
"""
with tf.variable_scope(
name, default_name="feedforward_self_attention", values=[x]):
x_shape = common_layers.shape_list(x)
part_depth = filter_depth // num_parts
if not share_kv:
combined = common_layers.dense(
x, filter_depth * 3, use_bias=False, name="qkv_transform")
combined = tf.expand_dims(combined, axis=2)
q, k, v = tf.split(combined, 3, axis=3)
else:
q = tf.expand_dims(
common_layers.dense(
x, filter_depth, use_bias=False, name="q_transform"),
axis=2)
kv_combined = tf.expand_dims(
common_layers.dense(
tf.concat([x, x], axis=1),
filter_depth,
use_bias=False,
name="kv_transform"),
axis=2)
k, v = tf.split(kv_combined, [x_shape[1], x_shape[1]], axis=1)
batch_q = tf.reshape(q, [-1, 1, num_parts, part_depth])
batch_k = tf.reshape(k, [-1, 1, num_parts, part_depth])
batch_v = tf.reshape(v, [-1, 1, num_parts, part_depth])
batch_q *= part_depth**-0.5
# non-masked bias
bias = None
x = dot_product_attention(batch_q, batch_k, batch_v, bias, dropout_rate)
x = tf.reshape(x, [x_shape[0], x_shape[1], filter_depth])
x = common_layers.dense(
x, output_depth, use_bias=False, name="output_transform")
return x
|
python
|
def ffn_self_attention_layer(x,
filter_depth,
output_depth,
num_parts,
dropout_rate,
share_kv=False,
name=None):
"""Self-attention feedforward layer.
We use self-attention to do feedforward computations. We apply this function
positionwise where for each position, we linearly transform the output to have
depth filter_depth, and break up the result depth-wise into num_parts
contiguous parts. The parts self-attend, we concatenate the results
depth-wise, and we linearly transform to a depth of output_depth. The goal is
to get multiplicative interactions between components of a representation.
Args:
x: a Tensor with shape [batch, length, channels]
filter_depth: an integer
output_depth: an integer
num_parts: an integer dividing filter depth
dropout_rate: a floating point number
share_kv: Share the key value transform
name: an optional string
Returns:
A Tensor with shape [batch, length, output_depth].
"""
with tf.variable_scope(
name, default_name="feedforward_self_attention", values=[x]):
x_shape = common_layers.shape_list(x)
part_depth = filter_depth // num_parts
if not share_kv:
combined = common_layers.dense(
x, filter_depth * 3, use_bias=False, name="qkv_transform")
combined = tf.expand_dims(combined, axis=2)
q, k, v = tf.split(combined, 3, axis=3)
else:
q = tf.expand_dims(
common_layers.dense(
x, filter_depth, use_bias=False, name="q_transform"),
axis=2)
kv_combined = tf.expand_dims(
common_layers.dense(
tf.concat([x, x], axis=1),
filter_depth,
use_bias=False,
name="kv_transform"),
axis=2)
k, v = tf.split(kv_combined, [x_shape[1], x_shape[1]], axis=1)
batch_q = tf.reshape(q, [-1, 1, num_parts, part_depth])
batch_k = tf.reshape(k, [-1, 1, num_parts, part_depth])
batch_v = tf.reshape(v, [-1, 1, num_parts, part_depth])
batch_q *= part_depth**-0.5
# non-masked bias
bias = None
x = dot_product_attention(batch_q, batch_k, batch_v, bias, dropout_rate)
x = tf.reshape(x, [x_shape[0], x_shape[1], filter_depth])
x = common_layers.dense(
x, output_depth, use_bias=False, name="output_transform")
return x
|
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Self-attention feedforward layer.
We use self-attention to do feedforward computations. We apply this function
positionwise where for each position, we linearly transform the output to have
depth filter_depth, and break up the result depth-wise into num_parts
contiguous parts. The parts self-attend, we concatenate the results
depth-wise, and we linearly transform to a depth of output_depth. The goal is
to get multiplicative interactions between components of a representation.
Args:
x: a Tensor with shape [batch, length, channels]
filter_depth: an integer
output_depth: an integer
num_parts: an integer dividing filter depth
dropout_rate: a floating point number
share_kv: Share the key value transform
name: an optional string
Returns:
A Tensor with shape [batch, length, output_depth].
|
[
"Self",
"-",
"attention",
"feedforward",
"layer",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L4368-L4430
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
parameter_attention
|
def parameter_attention(x,
total_key_depth,
total_value_depth,
output_depth,
memory_rows,
num_heads,
dropout_rate,
name=None):
"""Attention over parameters.
We use the same multi-headed attention as in the other layers, but the memory
keys and values are model parameters. There are no linear transformation on
the keys or values.
We are also a bit more careful about memory usage, since the number of
memory positions may be very large.
Args:
x: a Tensor with shape [batch, length_q, channels]
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
memory_rows: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
dropout_rate: a floating point number
name: an optional string
Returns:
A Tensor with shape [batch, length_q, output_depth].
"""
with tf.variable_scope(name, default_name="parameter_attention", values=[x]):
head_size_k = total_key_depth // num_heads
head_size_v = total_value_depth // num_heads
var_shape_k = [num_heads, memory_rows, head_size_k]
var_shape_v = [num_heads, memory_rows, head_size_v]
k = tf.get_variable(
"k",
var_shape_k,
initializer=tf.random_normal_initializer(
0, output_depth**-0.5 * (num_heads**0.5)))
v = tf.get_variable(
"v",
var_shape_v,
initializer=tf.random_normal_initializer(
0, output_depth**-0.5 * (output_depth**0.5)))
batch_size = common_layers.shape_list(x)[0]
length = common_layers.shape_list(x)[1]
q = common_layers.dense(
x, total_key_depth, use_bias=False, name="q_transform")
if dropout_rate:
# This is a cheaper form of attention dropout where we use to use
# the same dropout decisions across batch elements and query positions,
# but different decisions across heads and memory positions.
v = tf.nn.dropout(
v, 1.0 - dropout_rate, noise_shape=[num_heads, memory_rows, 1])
# query is [batch, length, hidden_size]
# reshape and transpose it to [heads, batch * length, head_size]
q = tf.reshape(q, [batch_size, length, num_heads, head_size_k])
q = tf.transpose(q, [2, 0, 1, 3])
q = tf.reshape(q, [num_heads, batch_size * length, head_size_k])
weights = tf.matmul(q, k, transpose_b=True)
weights = tf.nn.softmax(weights)
y = tf.matmul(weights, v)
y = tf.reshape(y, [num_heads, batch_size, length, head_size_v])
y = tf.transpose(y, [1, 2, 0, 3])
y = tf.reshape(y, [batch_size, length, total_value_depth])
y.set_shape([None, None, total_value_depth])
y = common_layers.dense(
y, output_depth, use_bias=False, name="output_transform")
return y
|
python
|
def parameter_attention(x,
total_key_depth,
total_value_depth,
output_depth,
memory_rows,
num_heads,
dropout_rate,
name=None):
"""Attention over parameters.
We use the same multi-headed attention as in the other layers, but the memory
keys and values are model parameters. There are no linear transformation on
the keys or values.
We are also a bit more careful about memory usage, since the number of
memory positions may be very large.
Args:
x: a Tensor with shape [batch, length_q, channels]
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
memory_rows: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
dropout_rate: a floating point number
name: an optional string
Returns:
A Tensor with shape [batch, length_q, output_depth].
"""
with tf.variable_scope(name, default_name="parameter_attention", values=[x]):
head_size_k = total_key_depth // num_heads
head_size_v = total_value_depth // num_heads
var_shape_k = [num_heads, memory_rows, head_size_k]
var_shape_v = [num_heads, memory_rows, head_size_v]
k = tf.get_variable(
"k",
var_shape_k,
initializer=tf.random_normal_initializer(
0, output_depth**-0.5 * (num_heads**0.5)))
v = tf.get_variable(
"v",
var_shape_v,
initializer=tf.random_normal_initializer(
0, output_depth**-0.5 * (output_depth**0.5)))
batch_size = common_layers.shape_list(x)[0]
length = common_layers.shape_list(x)[1]
q = common_layers.dense(
x, total_key_depth, use_bias=False, name="q_transform")
if dropout_rate:
# This is a cheaper form of attention dropout where we use to use
# the same dropout decisions across batch elements and query positions,
# but different decisions across heads and memory positions.
v = tf.nn.dropout(
v, 1.0 - dropout_rate, noise_shape=[num_heads, memory_rows, 1])
# query is [batch, length, hidden_size]
# reshape and transpose it to [heads, batch * length, head_size]
q = tf.reshape(q, [batch_size, length, num_heads, head_size_k])
q = tf.transpose(q, [2, 0, 1, 3])
q = tf.reshape(q, [num_heads, batch_size * length, head_size_k])
weights = tf.matmul(q, k, transpose_b=True)
weights = tf.nn.softmax(weights)
y = tf.matmul(weights, v)
y = tf.reshape(y, [num_heads, batch_size, length, head_size_v])
y = tf.transpose(y, [1, 2, 0, 3])
y = tf.reshape(y, [batch_size, length, total_value_depth])
y.set_shape([None, None, total_value_depth])
y = common_layers.dense(
y, output_depth, use_bias=False, name="output_transform")
return y
|
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] |
Attention over parameters.
We use the same multi-headed attention as in the other layers, but the memory
keys and values are model parameters. There are no linear transformation on
the keys or values.
We are also a bit more careful about memory usage, since the number of
memory positions may be very large.
Args:
x: a Tensor with shape [batch, length_q, channels]
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
memory_rows: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
dropout_rate: a floating point number
name: an optional string
Returns:
A Tensor with shape [batch, length_q, output_depth].
|
[
"Attention",
"over",
"parameters",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L4433-L4502
|
train
|
tensorflow/tensor2tensor
|
tensor2tensor/layers/common_attention.py
|
coordinate_tensor
|
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 given axis.
"""
if axis < 0:
axis = tf.size(shape) + axis # Convert to positive for the one_hot indice
r = tf.range(shape[axis])
r_shape = tf.one_hot(
axis, tf.size(shape), on_value=-1, off_value=1, dtype=tf.int32)
return tf.zeros(shape, dtype=tf.int32) + tf.reshape(r, r_shape)
|
python
|
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 given axis.
"""
if axis < 0:
axis = tf.size(shape) + axis # Convert to positive for the one_hot indice
r = tf.range(shape[axis])
r_shape = tf.one_hot(
axis, tf.size(shape), on_value=-1, off_value=1, dtype=tf.int32)
return tf.zeros(shape, dtype=tf.int32) + tf.reshape(r, r_shape)
|
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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 given axis.
|
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"Return",
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"tensor",
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"shape",
"containing",
"coordinate",
"along",
"given",
"axis",
"."
] |
272500b6efe353aeb638d2745ed56e519462ca31
|
https://github.com/tensorflow/tensor2tensor/blob/272500b6efe353aeb638d2745ed56e519462ca31/tensor2tensor/layers/common_attention.py#L4506-L4523
|
train
|
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