text_prompt stringlengths 157 13.1k | code_prompt stringlengths 7 19.8k ⌀ |
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def lstm_seq2seq_internal(inputs, targets, hparams, train):
"""The basic LSTM seq2seq model, main step used for training.""" |
with tf.variable_scope("lstm_seq2seq"):
if inputs is not None:
inputs_length = common_layers.length_from_embedding(inputs)
# Flatten inputs.
inputs = common_layers.flatten4d3d(inputs)
# LSTM encoder.
inputs = tf.reverse_sequence(inputs, inputs_length, seq_axis=1)
_, final_enc... |
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def lstm_seq2seq_internal_bid_encoder(inputs, targets, hparams, train):
"""The basic LSTM seq2seq model with bidirectional encoder.""" |
with tf.variable_scope("lstm_seq2seq_bid_encoder"):
if inputs is not None:
inputs_length = common_layers.length_from_embedding(inputs)
# Flatten inputs.
inputs = common_layers.flatten4d3d(inputs)
# LSTM encoder.
_, final_encoder_state = lstm_bid_encoder(
inputs, inputs_len... |
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def lstm_seq2seq():
"""hparams for LSTM.""" |
hparams = common_hparams.basic_params1()
hparams.daisy_chain_variables = False
hparams.batch_size = 1024
hparams.hidden_size = 128
hparams.num_hidden_layers = 2
hparams.initializer = "uniform_unit_scaling"
hparams.initializer_gain = 1.0
hparams.weight_decay = 0.0
return hparams |
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def lstm_attention_base():
"""Base attention params.""" |
hparams = lstm_seq2seq()
hparams.add_hparam("attention_layer_size", hparams.hidden_size)
hparams.add_hparam("output_attention", True)
hparams.add_hparam("num_heads", 1)
return hparams |
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def lstm_asr_v1():
"""Basic LSTM Params.""" |
hparams = lstm_bahdanau_attention()
hparams.num_hidden_layers = 2
hparams.hidden_size = 256
hparams.batch_size = 36
hparams.max_input_seq_length = 600000
hparams.max_target_seq_length = 350
hparams.max_length = hparams.max_input_seq_length
hparams.min_length_bucket = hparams.max_input_seq_length // 2
... |
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def lstm_area_attention_base():
"""Hparams for LSTM with area attention.""" |
hparams = lstm_luong_attention()
hparams.batch_size = 16384
hparams.num_hidden_layers = 2
hparams.hidden_size = 1024
hparams.num_heads = 4
hparams.dropout = 0.2
hparams.learning_rate = 0.1
hparams.max_area_width = 2
hparams.area_key_mode = "mean"
hparams.area_value_mode = "sum"
return hparams |
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def prepare_data(problem, hparams, params, config):
"""Construct input pipeline.""" |
input_fn = problem.make_estimator_input_fn(
tf.estimator.ModeKeys.EVAL, hparams, force_repeat=True)
dataset = input_fn(params, config)
features, _ = dataset.make_one_shot_iterator().get_next()
inputs, labels = features["targets"], features["inputs"]
inputs = tf.to_float(inputs)
input_shape = inputs.s... |
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def encode(self, s):
"""Transform a string with a filename into a list of float32. Args: s: path to the file with a waveform. Returns: samples: list of int16s ""... |
# Make sure that the data is a single channel, 16bit, 16kHz wave.
# TODO(chorowski): the directory may not be writable, this should fallback
# to a temp path, and provide instructions for installing sox.
if s.endswith(".mp3"):
# TODO(dliebling) On Linux, check if libsox-fmt-mp3 is installed.
... |
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def decode(self, ids):
"""Transform a sequence of float32 into a waveform. Args: ids: list of integers to be converted. Returns: Path to the temporary file where... |
_, tmp_file_path = tempfile.mkstemp()
wavfile.write(tmp_file_path, self._sample_rate, np.asarray(ids))
return tmp_file_path |
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def new_vertex(self):
"""Creates and returns a new vertex. Returns: A new Vertex instance with a unique index. """ |
vertex = Vertex(len(self.vertices))
self.vertices.append(vertex)
return vertex |
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def get_vertex(self, key):
"""Returns or Creates a Vertex mapped by key. Args: key: A string reference for a vertex. May refer to a new Vertex in which case it w... |
if key in self.vertex_map:
return self.vertex_map[key]
vertex = self.new_vertex()
self.vertex_map[key] = vertex
return vertex |
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def add_edge(self, source, target):
"""Returns a new edge connecting source and target vertices. Args: source: The source Vertex. target: The target Vertex. Retu... |
edge = Edge(len(self.edges))
self.edges.append(edge)
source.out_edges.append(edge.idx)
target.in_edges.append(edge.idx)
edge.source = source.idx
edge.target = target.idx
return edge |
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def to_dict(self):
"""Returns a simplified dictionary representing the Graph. Returns: A dictionary that can easily be serialized to JSON. """ |
return {
"node": [v.to_dict() for v in self.vertices],
"edge": [e.to_dict() for e in self.edges]
} |
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def attend(x, source, hparams, name):
"""Self-attention layer with source as memory antecedent.""" |
with tf.variable_scope(name):
x = tf.squeeze(x, axis=2)
if len(source.get_shape()) > 3:
source = tf.squeeze(source, axis=2)
source = common_attention.add_timing_signal_1d(source)
y = common_attention.multihead_attention(
common_layers.layer_preprocess(x, hparams), source, None,
... |
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def ae_latent_sample(latents_dense, inputs, ed, embed, iters, hparams):
"""Sample from the latent space in the autoencoder.""" |
if hparams.num_decode_blocks < 2 and hparams.sampling_temp == 0.0:
# TODO(lukaszkaiser): beam-search only works in non-blocked mode for now.
tf.logging.info("Running beam-search for latents with beam size 1.")
return ae_latent_sample_beam(latents_dense, inputs, ed, embed, hparams)
latents_pred = decode... |
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def imagetransformer_ae_cifar():
"""Hyperparameters for CIFAR-10 experiments.""" |
hparams = transformer_ae_small()
hparams.filter_size = 512
hparams.num_compress_steps = 3
hparams.startup_steps = 10000
hparams.is_2d = 0
hparams.learning_rate_warmup_steps = 8000
hparams.learning_rate = 0.2
hparams.hidden_size = 512
hparams.batch_size = 1
hparams.max_length = 256
hparams.dropout... |
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def imagetransformer_ae_imagenet():
"""For 64x64 ImageNet. ~56M trainable variables.""" |
hparams = imagetransformer_ae_cifar()
hparams.max_length = int(64 * 64 * 3)
hparams.img_len = 64
hparams.num_heads = 4 # Heads are expensive on TPUs.
# Reduce architecture from 32x32 CIFAR-10 in order to fit in memory.
hparams.num_decoder_layers = 8
hparams.num_compress_steps = 2
return hparams |
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def transformer_sketch():
"""Basic transformer_sketch hparams.""" |
hparams = transformer.transformer_small()
hparams.num_compress_steps = 4
hparams.batch_size = 32
hparams.clip_grad_norm = 2.
hparams.sampling_method = "random"
return hparams |
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def layers():
"""Get the layers module good for TF 1 and TF 2 work for now.""" |
global _cached_layers
if _cached_layers is not None:
return _cached_layers
layers_module = tf.layers
try:
from tensorflow.python import tf2 # pylint: disable=g-direct-tensorflow-import,g-import-not-at-top
if tf2.enabled():
tf.logging.info("Running in V2 mode, using Keras layers.")
laye... |
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def dropout_with_broadcast_dims(x, keep_prob, broadcast_dims=None, **kwargs):
"""Like tf.nn.dropout but takes broadcast_dims instead of noise_shape. Instead of s... |
assert "noise_shape" not in kwargs
if broadcast_dims:
shape = tf.shape(x)
ndims = len(x.get_shape())
# Allow dimensions like "-1" as well.
broadcast_dims = [dim + ndims if dim < 0 else dim for dim in broadcast_dims]
kwargs["noise_shape"] = [
1 if i in broadcast_dims else shape[i] for i ... |
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def inverse_exp_decay(max_step, min_value=0.01, step=None):
"""Inverse-decay exponentially from 0.01 to 1.0 reached at max_step.""" |
inv_base = tf.exp(tf.log(min_value) / float(max_step))
if step is None:
step = tf.train.get_global_step()
if step is None:
return 1.0
step = to_float(step)
return inv_base**tf.maximum(float(max_step) - step, 0.0) |
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def inverse_lin_decay(max_step, min_value=0.01, step=None):
"""Inverse-decay linearly from 0.01 to 1.0 reached at max_step.""" |
if step is None:
step = tf.train.get_global_step()
if step is None:
return 1.0
step = to_float(step)
progress = tf.minimum(step / float(max_step), 1.0)
return progress * (1.0 - min_value) + min_value |
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def shakeshake2_py(x, y, equal=False, individual=False):
"""The shake-shake sum of 2 tensors, python version.""" |
if equal:
alpha = 0.5
elif individual:
alpha = tf.random_uniform(tf.get_shape(x)[:1])
else:
alpha = tf.random_uniform([])
return alpha * x + (1.0 - alpha) * y |
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def shakeshake(xs, equal_grad=False):
"""Multi-argument shake-shake, currently approximated by sums of 2.""" |
if len(xs) == 1:
return xs[0]
div = (len(xs) + 1) // 2
arg1 = shakeshake(xs[:div], equal_grad=equal_grad)
arg2 = shakeshake(xs[div:], equal_grad=equal_grad)
if equal_grad:
return shakeshake2_eqgrad(arg1, arg2)
return shakeshake2(arg1, arg2) |
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def expand_squeeze_to_nd(x, n, squeeze_dim=2, expand_dim=-1):
"""Make x n-d with squeeze and expand_dims.""" |
if len(x.shape) > n:
while len(x.shape) != n:
x = tf.squeeze(x, [squeeze_dim])
else:
while len(x.shape) != n:
x = tf.expand_dims(x, expand_dim)
return x |
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def standardize_images(x):
"""Image standardization on batches and videos.""" |
with tf.name_scope("standardize_images", values=[x]):
x_shape = shape_list(x)
x = to_float(tf.reshape(x, [-1] + x_shape[-3:]))
x_mean = tf.reduce_mean(x, axis=[1, 2], keepdims=True)
x_variance = tf.reduce_mean(
tf.squared_difference(x, x_mean), axis=[1, 2], keepdims=True)
num_pixels = to_... |
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def flatten4d3d(x):
"""Flatten a 4d-tensor into a 3d-tensor by joining width and height.""" |
xshape = shape_list(x)
result = tf.reshape(x, [xshape[0], xshape[1] * xshape[2], xshape[3]])
return result |
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def gather(params, indices, dtype=tf.float32):
"""Version of tf.gather that works faster on tpu.""" |
if not is_xla_compiled():
return tf.gather(params, indices)
vocab_size = params.get_shape().as_list()[0]
indices_flat = tf.reshape(indices, [-1])
out = tf.matmul(tf.one_hot(indices_flat, vocab_size, dtype=dtype), params)
out = reshape_like(out, tf.expand_dims(indices, -1))
return out |
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def cumsum(x, axis=0, exclusive=False):
"""TPU hack for tf.cumsum. This is equivalent to tf.cumsum and is faster on TPU as of 04/2018 unless the axis dimension i... |
if not is_xla_compiled():
return tf.cumsum(x, axis=axis, exclusive=exclusive)
x_shape = shape_list(x)
rank = len(x_shape)
length = x_shape[axis]
my_range = tf.range(length)
comparator = tf.less if exclusive else tf.less_equal
mask = tf.cast(
comparator(tf.expand_dims(my_range, 1), tf.expand_dim... |
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def dropout_no_scaling(x, keep_prob):
"""Like tf.nn.dropout, but does not scale up. Works on integers also. Args: x: a Tensor keep_prob: a floating point number ... |
if keep_prob == 1.0:
return x
mask = tf.less(tf.random_uniform(tf.shape(x)), keep_prob)
return x * cast_like(mask, x) |
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def embedding(x, vocab_size, dense_size, name=None, reuse=None, multiplier=1.0, symbol_dropout_rate=0.0, embedding_var=None, dtype=tf.float32):
"""Embed x of typ... |
with tf.variable_scope(
name, default_name="embedding", values=[x], reuse=reuse, dtype=dtype):
if embedding_var is None:
embedding_var = tf.get_variable("kernel", [vocab_size, dense_size])
# On the backwards pass, we want to convert the gradient from
# an indexed-slices to a regular tensor be... |
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def conv_stride2_multistep(x, nbr_steps, output_filters, name=None, reuse=None):
"""Use a strided convolution to downsample x by 2, `nbr_steps` times. We use str... |
with tf.variable_scope(
name, default_name="conv_stride2_multistep", values=[x], reuse=reuse):
if nbr_steps == 0:
out = conv(x, output_filters, (1, 1))
return out, [out]
hidden_layers = [x]
for i in range(nbr_steps):
hidden_layers.append(
conv(
hidden_layer... |
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def conv_internal(conv_fn, inputs, filters, kernel_size, **kwargs):
"""Conditional conv_fn making kernel 1d or 2d depending on inputs shape.""" |
static_shape = inputs.get_shape()
if not static_shape or len(static_shape) != 4:
raise ValueError("Inputs to conv must have statically known rank 4. "
"Shape: " + str(static_shape))
# Add support for left padding.
if kwargs.get("padding") == "LEFT":
dilation_rate = (1, 1)
if "d... |
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def subseparable_conv(inputs, filters, kernel_size, **kwargs):
"""Sub-separable convolution. If separability == 0 it's a separable_conv.""" |
def conv_fn(inputs, filters, kernel_size, **kwargs):
"""Sub-separable convolution, splits into separability-many blocks."""
separability = None
if "separability" in kwargs:
separability = kwargs.pop("separability")
if separability:
parts = []
abs_sep = separability if separability ... |
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def layer_norm_vars(filters):
"""Create Variables for layer norm.""" |
scale = tf.get_variable(
"layer_norm_scale", [filters], initializer=tf.ones_initializer())
bias = tf.get_variable(
"layer_norm_bias", [filters], initializer=tf.zeros_initializer())
return scale, bias |
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def layer_norm_compute(x, epsilon, scale, bias, layer_collection=None):
"""Layer norm raw computation.""" |
# Save these before they get converted to tensors by the casting below
params = (scale, bias)
epsilon, scale, bias = [cast_like(t, x) for t in [epsilon, scale, bias]]
mean = tf.reduce_mean(x, axis=[-1], keepdims=True)
variance = tf.reduce_mean(
tf.squared_difference(x, mean), axis=[-1], keepdims=True... |
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def layer_norm(x, filters=None, epsilon=1e-6, name=None, reuse=None, layer_collection=None):
"""Layer normalize the tensor x, averaging over the last dimension."... |
if filters is None:
filters = shape_list(x)[-1]
with tf.variable_scope(
name, default_name="layer_norm", values=[x], reuse=reuse):
scale, bias = layer_norm_vars(filters)
return layer_norm_compute(x, epsilon, scale, bias,
layer_collection=layer_collection) |
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def noam_norm(x, epsilon=1.0, name=None):
"""One version of layer normalization.""" |
with tf.name_scope(name, default_name="noam_norm", values=[x]):
shape = x.get_shape()
ndims = len(shape)
return (tf.nn.l2_normalize(x, ndims - 1, epsilon=epsilon) * tf.sqrt(
to_float(shape[-1]))) |
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def l2_norm(x, filters=None, epsilon=1e-6, name=None, reuse=None):
"""Layer normalization with l2 norm.""" |
if filters is None:
filters = shape_list(x)[-1]
with tf.variable_scope(name, default_name="l2_norm", values=[x], reuse=reuse):
scale = tf.get_variable(
"l2_norm_scale", [filters], initializer=tf.ones_initializer())
bias = tf.get_variable(
"l2_norm_bias", [filters], initializer=tf.zeros_... |
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def apply_spectral_norm(x):
"""Normalizes x using the spectral norm. The implementation follows Algorithm 1 of https://arxiv.org/abs/1802.05957. If x is not a 2-... |
weights_shape = shape_list(x)
other, num_filters = tf.reduce_prod(weights_shape[:-1]), weights_shape[-1]
# Reshape into a 2-D matrix with outer size num_filters.
weights_2d = tf.reshape(x, (other, num_filters))
# v = Wu / ||W u||
with tf.variable_scope("u", reuse=tf.AUTO_REUSE):
u = tf.get_variable(
... |
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def apply_norm(x, norm_type, depth, epsilon, layer_collection=None):
"""Apply Normalization.""" |
if layer_collection is not None:
assert norm_type == "layer"
if norm_type == "layer":
return layer_norm(
x, filters=depth, epsilon=epsilon, layer_collection=layer_collection)
if norm_type == "group":
return group_norm(x, filters=depth, epsilon=epsilon)
if norm_type == "batch":
return la... |
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def zero_add(previous_value, x, name=None, reuse=None):
"""Resnet connection with zero initialization. Another type of resnet connection which returns previous_v... |
with tf.variable_scope(name, default_name="zero_add", reuse=reuse):
gamma = tf.get_variable("gamma", (), initializer=tf.zeros_initializer())
return previous_value + gamma * x |
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def layer_prepostprocess(previous_value, x, sequence, dropout_rate, norm_type, depth, epsilon, default_name, name=None, dropout_broadcast_dims=None, layer_collect... |
with tf.variable_scope(name, default_name=default_name):
if sequence == "none":
return x
for c in sequence:
if c == "a":
x += previous_value
elif c == "z":
x = zero_add(previous_value, x)
elif c == "n":
x = apply_norm(
x, norm_type, depth, epsilon, ... |
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def layer_preprocess(layer_input, hparams, layer_collection=None):
"""Apply layer preprocessing. See layer_prepostprocess() for details. A hyperparameters object... |
assert "a" not in hparams.layer_preprocess_sequence, (
"No residual connections allowed in hparams.layer_preprocess_sequence")
assert "z" not in hparams.layer_preprocess_sequence, (
"No residual connections allowed in hparams.layer_preprocess_sequence")
return layer_prepostprocess(
None,
... |
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def layer_postprocess(layer_input, layer_output, hparams):
"""Apply layer postprocessing. See layer_prepostprocess() for details. A hyperparameters object is pas... |
return layer_prepostprocess(
layer_input,
layer_output,
sequence=hparams.layer_postprocess_sequence,
dropout_rate=hparams.layer_prepostprocess_dropout,
norm_type=hparams.norm_type,
depth=None,
epsilon=hparams.norm_epsilon,
dropout_broadcast_dims=comma_separated_string_... |
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def conv_block_internal(conv_fn, inputs, filters, dilation_rates_and_kernel_sizes, first_relu=True, use_elu=False, separabilities=None, **kwargs):
"""A block of ... |
name = kwargs.pop("name") if "name" in kwargs else None
mask = kwargs.pop("mask") if "mask" in kwargs else None
# Usage for normalize_fn kwarg:
# if not specified, use layer norm
# if given normalize_fn=None, don't use any normalization
# if given normalize_fn=norm, use the specified norm function
use... |
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def conv_block(inputs, filters, dilation_rates_and_kernel_sizes, **kwargs):
"""A block of standard 2d convolutions.""" |
return conv_block_internal(conv, inputs, filters,
dilation_rates_and_kernel_sizes, **kwargs) |
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def conv1d_block(inputs, filters, dilation_rates_and_kernel_sizes, **kwargs):
"""A block of standard 1d convolutions.""" |
return conv_block_internal(conv1d, inputs, filters,
dilation_rates_and_kernel_sizes, **kwargs) |
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def conv_block_downsample(x, kernel, strides, padding, separability=0, name=None, reuse=None):
"""Implements a downwards-striding conv block, like Xception exit ... |
with tf.variable_scope(
name, default_name="conv_block_downsample", values=[x], reuse=reuse):
hidden_size = int(x.get_shape()[-1])
res = conv_block(
x,
int(1.25 * hidden_size), [((1, 1), kernel)],
padding=padding,
strides=strides,
name="res_conv")
x = subsep... |
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def get_timing_signal(length, min_timescale=1, max_timescale=1e4, num_timescales=16):
"""Create Tensor of sinusoids of different frequencies. Args: length: Lengt... |
positions = to_float(tf.range(length))
log_timescale_increment = (
math.log(max_timescale / min_timescale) / (num_timescales - 1))
inv_timescales = min_timescale * tf.exp(
to_float(tf.range(num_timescales)) * -log_timescale_increment)
scaled_time = tf.expand_dims(positions, 1) * tf.expand_dims(inv_... |
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def mask_from_embedding(emb):
"""Input embeddings -> padding mask. We have hacked symbol_modality to return all-zero embeddings for padding. Returns a mask with ... |
return weights_nonzero(tf.reduce_sum(tf.abs(emb), axis=3, keepdims=True)) |
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def length_from_embedding(emb):
"""Compute the length of each sequence in the batch. Args: emb: a sequence embedding Tensor with shape [batch, max_time, 1, depth... |
return tf.cast(tf.reduce_sum(mask_from_embedding(emb), [1, 2, 3]), tf.int32) |
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def maybe_zero_out_padding(inputs, kernel_size, nonpadding_mask):
"""If necessary, zero out inputs to a conv for padding positions. Args: kernel_size: an integer... |
if (kernel_size != 1 and kernel_size != (1, 1) and
nonpadding_mask is not None):
while nonpadding_mask.get_shape().ndims < inputs.get_shape().ndims:
nonpadding_mask = tf.expand_dims(nonpadding_mask, -1)
return inputs * nonpadding_mask
return inputs |
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def dense_dropconnect(inputs, output_size, dropconnect_dropout=0.0, name="dense_dropconnect", **kwargs):
"""Dense layer with dropconnect.""" |
if dropconnect_dropout != 0.0:
tf.logging.info("Applying dropconnect as the kernel regularization.")
kwargs["kernel_regularizer"] = functools.partial(
tf.nn.dropout, keep_prob=1.0 - dropconnect_dropout)
return dense(inputs, output_size, use_bias=True, name=name, **kwargs) |
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def conv_gru(x, kernel_size, filters, padding="SAME", dilation_rate=(1, 1), name=None, reuse=None):
"""Convolutional GRU in 1 dimension.""" |
# Let's make a shorthand for conv call first.
def do_conv(args, name, bias_start, padding):
return conv(
args,
filters,
kernel_size,
padding=padding,
dilation_rate=dilation_rate,
bias_initializer=tf.constant_initializer(bias_start),
name=name)
# Here ... |
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def gru_feedfwd(a_t, h_prev, filters, name=None):
"""position-wise Feed-fwd GRU gates following the MPNN. Args: a_t: Tensor of shape [batch, length, depth] of cu... |
with tf.variable_scope(name, default_name="GRU", values=[a_t, h_prev]):
# we use right matrix multiplication to handle batches
# W_z and W_r have shape 2d, d. U_z U_r have shape d,d
z_t = (
tf.sigmoid(
tpu_conv1d(a_t, filters, 1, padding="SAME", name="W_z") +
tpu_conv1d(h... |
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def conv_lstm(x, kernel_size, filters, padding="SAME", dilation_rate=(1, 1), name=None, reuse=None):
"""Convolutional LSTM in 1 dimension.""" |
with tf.variable_scope(
name, default_name="conv_lstm", values=[x], reuse=reuse):
gates = conv(
x,
4 * filters,
kernel_size,
padding=padding,
dilation_rate=dilation_rate)
g = tf.split(layer_norm(gates, 4 * filters), 4, axis=3)
new_cell = tf.sigmoid(g[0]) * x ... |
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def pad_to_same_length(x, y, final_length_divisible_by=1, axis=1):
"""Pad tensors x and y on axis 1 so that they have the same length.""" |
if axis not in [1, 2]:
raise ValueError("Only axis=1 and axis=2 supported for now.")
with tf.name_scope("pad_to_same_length", values=[x, y]):
x_length = shape_list(x)[axis]
y_length = shape_list(y)[axis]
if (isinstance(x_length, int) and isinstance(y_length, int) and
x_length == y_length an... |
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def pad_with_zeros(logits, labels):
"""Pad labels on the length dimension to match logits length.""" |
with tf.name_scope("pad_with_zeros", values=[logits, labels]):
logits, labels = pad_to_same_length(logits, labels)
if len(labels.shape) == 3: # 2-d labels.
logits, labels = pad_to_same_length(logits, labels, axis=2)
return logits, labels |
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def check_nonnegative(value):
"""Check that the value is nonnegative.""" |
if isinstance(value, tf.Tensor):
with tf.control_dependencies([tf.assert_greater_equal(value, 0)]):
value = tf.identity(value)
elif value < 0:
raise ValueError("Value must be non-negative.")
return value |
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def weights_multi_problem_all(labels, taskid=-1):
"""Assign weight 1.0 to only examples from the given task.""" |
taskid = check_nonnegative(taskid)
weights = to_float(tf.not_equal(labels, 0))
past_taskid = tf.cumsum(to_float(tf.equal(labels, taskid)), axis=1)
# Additionally zero out the task id location
past_taskid *= to_float(tf.not_equal(labels, taskid))
non_taskid = to_float(labels)
example_mask = to_float(tf.no... |
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def weights_multi_problem_input(labels, taskid=-1):
"""Assign weight 1.0 to only the inputs for the given task.""" |
taskid = check_nonnegative(taskid)
weights_all_tokens = weights_multi_problem_all(labels, taskid)
weights_target = weights_multi_problem(labels, taskid)
return weights_all_tokens - weights_target |
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def weights_concatenated(labels):
"""Assign weight 1.0 to the "target" part of the concatenated labels. The labels look like: source English I love you . ID1 tar... |
eos_mask = tf.to_int32(tf.equal(labels, 1))
sentence_num = tf.cumsum(eos_mask, axis=1, exclusive=True)
in_target = tf.equal(tf.mod(sentence_num, 2), 1)
# first two tokens of each sentence are boilerplate.
sentence_num_plus_one = sentence_num + 1
shifted = tf.pad(sentence_num_plus_one,
[[... |
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def dml_loss(pred, labels, weights_fn=_weights_one_third, reduce_sum=True):
"""Discretized mixture of logistics loss. Args: pred: A [batch, height, width, num_mi... |
real_labels = convert_rgb_to_symmetric_real(labels)
dml_loss_value = discretized_mix_logistic_loss(pred=pred, labels=real_labels)
weights = weights_fn(labels)
loss_num = weights * dml_loss_value
loss_den = weights_nonzero(weights)
if reduce_sum:
loss_num = tf.reduce_sum(loss_num)
loss_den = tf.redu... |
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def split_to_discretized_mix_logistic_params(inputs):
"""Splits input tensor into parameters of discretized mixture logistic. Args: inputs: A [batch, height, wid... |
batch, height, width, output_dim = shape_list(inputs) # pylint: disable=unbalanced-tuple-unpacking
num_mixtures = output_dim // 10
logits, locs, log_scales, coeffs = tf.split(
inputs,
num_or_size_splits=[
num_mixtures, num_mixtures * 3, num_mixtures * 3, num_mixtures * 3
],
axi... |
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def discretized_mix_logistic_loss(pred, labels):
"""Computes negative log probability for the discretized mixture of logistics. The distribution of a whole pixel... |
logits, locs, log_scales, coeffs = split_to_discretized_mix_logistic_params(
pred)
# Tile labels to broadcast compute across the mixture dimension.
batch, height, width, num_mixtures = shape_list(logits) # pylint: disable=unbalanced-tuple-unpacking
labels = tf.tile(
tf.reshape(labels, [batch, he... |
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def sample_from_discretized_mix_logistic(pred, seed=None):
"""Sampling from a discretized mixture of logistics. Args: pred: A [batch, height, width, num_mixtures... |
logits, locs, log_scales, coeffs = split_to_discretized_mix_logistic_params(
pred)
# Sample mixture indicator given logits using the gumbel max trick.
num_mixtures = shape_list(logits)[-1]
gumbel_noise = -tf.log(-tf.log(
tf.random_uniform(
tf.shape(logits), minval=1e-5, maxval=1. - 1e-5... |
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def smoothing_cross_entropy(logits, labels, vocab_size, confidence, gaussian=False):
"""Cross entropy with label smoothing to limit over-confidence. Args: logits... |
with tf.name_scope("smoothing_cross_entropy", values=[logits, labels]):
# Low confidence is given to all non-true labels, uniformly.
low_confidence = (1.0 - confidence) / to_float(vocab_size - 1)
# Normalizing constant is the best cross-entropy value with soft targets.
# We subtract it just for reada... |
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def global_pool_1d(inputs, pooling_type="MAX", mask=None):
"""Pool elements across the last dimension. Useful to convert a list of vectors into a single vector s... |
with tf.name_scope("global_pool", values=[inputs]):
if mask is not None:
mask = tf.expand_dims(mask, axis=2)
inputs = tf.multiply(inputs, mask)
if pooling_type == "MAX":
# A tf.pool can be used here, but reduce is cleaner
output = tf.reduce_max(inputs, axis=1)
elif pooling_type =... |
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def running_global_pool_1d(inputs, pooling_type="MAX"):
"""Same global pool, but only for the elements up to the current element. Useful for outputs where the st... |
del pooling_type
with tf.name_scope("running_global_pool", values=[inputs]):
scan_fct = tf.maximum
# Permute inputs so seq_length is first.
elems = tf.transpose(inputs, [1, 0, 2])
# Perform scan.
cumulatives = tf.scan(scan_fct, elems, swap_memory=True)
# Permute output to get back to origin... |
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def gated_linear_unit_layer(x, name=None):
"""Gated linear unit layer. Paper: Language Modeling with Gated Convolutional Networks. Link: https://arxiv.org/abs/16... |
with tf.variable_scope(name, default_name="glu_layer", values=[x]):
depth = shape_list(x)[-1]
x = layers().Dense(depth * 2, activation=None)(x)
x, gating_x = tf.split(x, 2, axis=-1)
return x * tf.nn.sigmoid(gating_x) |
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def linear_set_layer(layer_size, inputs, context=None, activation_fn=tf.nn.relu, dropout=0.0, name=None):
"""Basic layer type for doing funky things with sets. A... |
with tf.variable_scope(
name, default_name="linear_set_layer", values=[inputs]):
# Apply 1D convolution to apply linear filter to each element
# along the 2nd dimension.
outputs = conv1d(inputs, layer_size, 1, activation=None, name="set_conv")
# Apply the context if it exists.
if context i... |
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def fn_device_dependency_dict():
"""State container for fn_device_dependency.""" |
default_graph = tf.get_default_graph()
if not hasattr(default_graph, "dependency_dict"):
default_graph.dependency_dict = collections.defaultdict(list)
return default_graph.dependency_dict |
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def fn_device_dependency(name, device=""):
"""Add control deps for name and device.""" |
key = name + "_" + device
outs = []
def body():
with tf.control_dependencies(fn_device_dependency_dict()[key]):
yield outs
assert outs
deps = outs
if isinstance(outs[0], (list, tuple)):
assert len(outs) == 1
deps = outs[0]
fn_device_dependency_dict()[key] = dep... |
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def underlying_variable_ref(t):
"""Find the underlying variable ref. Traverses through Identity, ReadVariableOp, and Enter ops. Stops when op type has Variable o... |
while t.op.type in ["Identity", "ReadVariableOp", "Enter"]:
t = t.op.inputs[0]
op_type = t.op.type
if "Variable" in op_type or "VarHandle" in op_type:
return t
else:
return None |
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def underlying_variable(t):
"""Find the underlying tf.Variable object. Args: t: a Tensor Returns: tf.Variable. """ |
t = underlying_variable_ref(t)
assert t is not None
# make sure that the graph has a variable index and that it is up-to-date
if not hasattr(tf.get_default_graph(), "var_index"):
tf.get_default_graph().var_index = {}
var_index = tf.get_default_graph().var_index
for v in tf.global_variables()[len(var_in... |
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def approximate_split(x, num_splits, axis=0):
"""Split approximately equally into num_splits parts. Args: x: a Tensor num_splits: an integer axis: an integer. Re... |
size = shape_list(x)[axis]
size_splits = [tf.div(size + i, num_splits) for i in range(num_splits)]
return tf.split(x, size_splits, axis=axis) |
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def smoothing_cross_entropy_factored_grad(op, dy):
"""Gradient function for smoothing_cross_entropy_factored.""" |
a = op.inputs[0]
b = op.inputs[1]
labels = op.inputs[2]
confidence = op.inputs[3]
num_splits = 16
vocab_size = shape_list(b)[0]
labels = approximate_split(labels, num_splits)
a = approximate_split(a, num_splits)
dy = approximate_split(dy, num_splits)
b_grad = None
a_grad_parts = []
deps = []
... |
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def fn_with_custom_grad(grad_fn, use_global_vars=False):
"""Decorator to create a subgraph with a custom gradient function. The subgraph created by the decorated... |
def dec(fn):
@functools.wraps(fn)
def wrapped(*args):
return _fn_with_custom_grad(
fn, args, grad_fn, use_global_vars=use_global_vars)
return wrapped
return dec |
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def _fn_with_custom_grad(fn, inputs, grad_fn, use_global_vars=False):
"""Create a subgraph with a custom gradient. Args: fn: function that takes inputs as argume... |
vs = tf.get_variable_scope()
get_vars_fn = (
vs.global_variables if use_global_vars else vs.trainable_variables)
len_before_vars = len(get_vars_fn())
inputs = list(inputs)
outputs = fn(*inputs)
train_vars = get_vars_fn()[len_before_vars:]
if grad_fn is None:
return outputs
if not isinstance... |
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def shape_list(x):
"""Return list of dims, statically where possible.""" |
x = tf.convert_to_tensor(x)
# If unknown rank, return dynamic shape
if x.get_shape().dims is None:
return tf.shape(x)
static = x.get_shape().as_list()
shape = tf.shape(x)
ret = []
for i, dim in enumerate(static):
if dim is None:
dim = shape[i]
ret.append(dim)
return ret |
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def ones_matrix_band_part(rows, cols, num_lower, num_upper, out_shape=None):
"""Matrix band part of ones. Args: rows: int determining number of rows in output co... |
if all([isinstance(el, int) for el in [rows, cols, num_lower, num_upper]]):
# Needed info is constant, so we construct in numpy
if num_lower < 0:
num_lower = rows - 1
if num_upper < 0:
num_upper = cols - 1
lower_mask = np.tri(cols, rows, num_lower).T
upper_mask = np.tri(rows, cols, nu... |
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def reshape_like_all_dims(a, b):
"""Reshapes a to match the shape of b.""" |
ret = tf.reshape(a, tf.shape(b))
if not tf.executing_eagerly():
ret.set_shape(b.get_shape())
return ret |
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def recompute_grad(fn):
"""Decorator that recomputes the function on the backwards pass. Args: fn: a function that takes Tensors (all as positional arguments) an... |
@functools.wraps(fn)
def wrapped(*args):
return _recompute_grad(fn, args)
return wrapped |
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def _recompute_grad(fn, args):
"""See recompute_grad.""" |
cached_vs = []
cached_arg_scope = []
def grad_fn(inputs, variables, outputs, output_grads):
"""Recompute outputs for gradient computation."""
del outputs
variables = [underlying_variable_ref(v) for v in variables]
# Recompute outputs
with tf.control_dependencies(output_grads):
with tf... |
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def dense(x, units, **kwargs):
"""Identical to layers.dense.""" |
layer_collection = kwargs.pop("layer_collection", None)
activations = layers().Dense(units, **kwargs)(x)
if layer_collection:
# We need to find the layer parameters using scope name for the layer, so
# check that the layer is named. Otherwise parameters for different layers
# may get mixed up.
la... |
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def batch_dense(inputs, units, activation=None, kernel_initializer=None, reuse=None, name=None):
"""Multiply a batch of input matrices by a batch of parameter ma... |
inputs_shape = shape_list(inputs)
if len(inputs_shape) != 3:
raise ValueError("inputs must have 3 dimensions")
batch = inputs_shape[0]
input_units = inputs_shape[2]
if not isinstance(batch, int) or not isinstance(input_units, int):
raise ValueError("inputs must have static dimensions 0 and 2")
with... |
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def mix(x1, x2, steps, is_training, min_prob=0.0, max_prob=1.0, mode="lin", simple=False, broadcast_last=False):
"""Mix starting with x2, mixing mixing, going to... |
with tf.name_scope("mix"):
if not is_training:
if max_prob >= 1.0:
return x1
alpha_shape = shape_list(x1)
if broadcast_last:
alpha_shape = alpha_shape[:-1] + [1]
alpha = tf.random_uniform(alpha_shape)
alpha = to_float(tf.less(alpha, max_prob))
return alpha * x1... |
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Description:
def gelu(x):
"""Gaussian Error Linear Unit. This is a smoother version of the RELU. Original paper: https://arxiv.org/abs/1606.08415 Args: x: float Tensor to per... |
cdf = 0.5 * (1.0 + tf.tanh(
(np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
return x * cdf |
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def argmax_with_score(logits, axis=None):
"""Argmax along with the value.""" |
axis = axis or len(logits.get_shape()) - 1
predictions = tf.argmax(logits, axis=axis)
logits_shape = shape_list(logits)
prefix_shape, vocab_size = logits_shape[:-1], logits_shape[-1]
prefix_size = 1
for d in prefix_shape:
prefix_size *= d
# Flatten to extract scores
flat_logits = tf.reshape(logit... |
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def top_kth_iterative(x, k):
"""Compute the k-th top element of x on the last axis iteratively. This assumes values in x are non-negative, rescale if needed. It ... |
# The iterative computation is as follows:
#
# cur_x = x
# for _ in range(k):
# top_x = maximum of elements of cur_x on the last axis
# cur_x = cur_x where cur_x < top_x and 0 everywhere else (top elements)
#
# We encode this computation in a TF graph using tf.foldl, so the inner
# part of the ab... |
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def top_1_tpu(inputs):
"""find max and argmax over the last dimension. Works well on TPU Args: Returns: """ |
inputs_max = tf.reduce_max(inputs, axis=-1, keepdims=True)
mask = tf.to_int32(tf.equal(inputs_max, inputs))
index = tf.range(tf.shape(inputs)[-1]) * mask
return tf.squeeze(inputs_max, -1), tf.reduce_max(index, axis=-1) |
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def index_last_dim_with_indices(x, indices):
"""Use indices to index into the last axis of x. This can be useful for recovering the actual probabilities of a sam... |
assert len(x.shape) == len(indices.shape) + 1
x_shape = shape_list(x)
vocab_size = x_shape[-1]
flat_x = tf.reshape(x, [list_product(x_shape[:-1]), vocab_size])
flat_indices = tf.reshape(indices, [list_product(x_shape[:-1])])
idx = tf.stack(
[
tf.range(tf.to_int64(shape_list(flat_indices)... |
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def should_generate_summaries():
"""Is this an appropriate context to generate summaries. Returns: a boolean """ |
name_scope = tf.contrib.framework.get_name_scope()
if name_scope and "while/" in name_scope:
# Summaries don't work well within tf.while_loop()
return False
if tf.get_variable_scope().reuse:
# Avoid generating separate summaries for different data shards
return False
return True |
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def reshape_like(a, b):
"""Reshapes a to match the shape of b in all but the last dimension.""" |
ret = tf.reshape(a, tf.concat([tf.shape(b)[:-1], tf.shape(a)[-1:]], 0))
if not tf.executing_eagerly():
ret.set_shape(b.get_shape().as_list()[:-1] + a.get_shape().as_list()[-1:])
return ret |
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def summarize_video(video, prefix, max_outputs=1):
"""Summarize the video using image summaries starting with prefix.""" |
video_shape = shape_list(video)
if len(video_shape) != 5:
raise ValueError("Assuming videos given as tensors in the format "
"[batch, time, height, width, channels] but got one "
"of shape: %s" % str(video_shape))
if tf.executing_eagerly():
return
if video.get_... |
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def cast_like(x, y):
"""Cast x to y's dtype, if necessary.""" |
x = tf.convert_to_tensor(x)
y = tf.convert_to_tensor(y)
if x.dtype.base_dtype == y.dtype.base_dtype:
return x
cast_x = tf.cast(x, y.dtype)
if cast_x.device != x.device:
x_name = "(eager Tensor)"
try:
x_name = x.name
except AttributeError:
pass
tf.logging.warning("Cast for %s... |
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def make_even_size(x):
"""Pad x to be even-sized on axis 1 and 2, but only if necessary.""" |
x_shape = x.get_shape().as_list()
assert len(x_shape) > 2, "Only 3+-dimensional tensors supported."
shape = [dim if dim is not None else -1 for dim in x_shape]
new_shape = x_shape # To make sure constant shapes remain constant.
if x_shape[1] is not None:
new_shape[1] = 2 * int(math.ceil(x_shape[1] * 0.5... |
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def instance_norm(x):
"""Instance normalization layer.""" |
with tf.variable_scope("instance_norm"):
epsilon = 1e-5
mean, var = tf.nn.moments(x, [1, 2], keep_dims=True)
scale = tf.get_variable(
"scale", [x.get_shape()[-1]],
initializer=tf.truncated_normal_initializer(mean=1.0, stddev=0.02))
offset = tf.get_variable(
"offset", [x.get_sh... |
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def general_conv(x, num_filters=64, filter_size=7, stride=1, stddev=0.02, padding="VALID", name="conv", do_norm="instance", do_relu=True, relufactor=0):
"""Gener... |
with tf.variable_scope(name):
x = layers().Conv2D(
num_filters,
filter_size,
stride,
padding,
activation=None,
kernel_initializer=tf.truncated_normal_initializer(stddev=stddev),
bias_initializer=tf.constant_initializer(0.0))(x)
if do_norm == "layer":
... |
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