BoghdadyJR/codesearch_python · Datasets at Hugging Face

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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_b...
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....
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
def shakeshake2_grad(x1, x2, dy): """Overriding gradient for shake-shake of 2 tensors.""" y = shakeshake2_py(x1, x2) dx = tf.gradients(ys=[y], xs=[x1, x2], grad_ys=[dy]) return dx
def shakeshake2_indiv_grad(x1, x2, dy): """Overriding gradient for shake-shake of 2 tensors.""" y = shakeshake2_py(x1, x2, individual=True) dx = tf.gradients(ys=[y], xs=[x1, x2], grad_ys=[dy]) return dx
def shakeshake2_equal_grad(x1, x2, dy): """Overriding gradient for shake-shake of 2 tensors.""" y = shakeshake2_py(x1, x2, equal=True) dx = tf.gradients(ys=[y], xs=[x1, x2], grad_ys=[dy]) return dx
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_...
def convert_rgb_to_real(x): """Conversion of pixel values to real numbers.""" with tf.name_scope("rgb_to_real", values=[x]): x = to_float(x) x /= 255.0 return x
def convert_rgb_to_symmetric_real(x): """Conversion of pixel values to real numbers.""" with tf.name_scope("rgb_to_real", values=[x]): x = to_float(x) # Convert each pixel intensity in [0, 1, 2, ..., 255] into a real number in # the range [-1, 1]. x = (x / 127.5) - 1 return x
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
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....
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
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=d...
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 is very large. Args: x: a Tensor axis: an integer exclusive: a boolean Returns: Tensor of the same shape as x. """ if not is_xl...
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 Returns: Tensor of the same shape as x. """ if keep_prob == 1.0: return x mask = tf.less(tf.random_uniform(tf.shape(x)), keep_pr...
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 type int64 into dense vectors, reducing to max 4...
def shift_right(x, pad_value=None): """Shift the second dimension of x right by one.""" if pad_value is None: shifted_targets = tf.pad(x, [[0, 0], [1, 0], [0, 0], [0, 0]])[:, :-1, :, :] else: shifted_targets = tf.concat([pad_value, x], axis=1)[:, :-1, :, :] return shifted_targets
def shift_right_3d(x, pad_value=None): """Shift the second dimension of x right by one.""" if pad_value is None: shifted_targets = tf.pad(x, [[0, 0], [1, 0], [0, 0]])[:, :-1, :] else: shifted_targets = tf.concat([pad_value, x], axis=1)[:, :-1, :] return shifted_targets
def shift_right_2d(x, pad_value=None): """Shift the second dimension of x right by one.""" if pad_value is None: shifted_targets = tf.pad(x, [[0, 0], [1, 0]])[:, :-1] else: shifted_targets = tf.concat([pad_value, x], axis=1)[:, :-1] return shifted_targets
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 stride and filter size 2 to avoid the checkerboard problem of deconvs. As detailed in http://distill.pub/2016/deconv-checkerboard/. Args: x: a `Tens...
def deconv_stride2_multistep(x, nbr_steps, output_filters, name=None, reuse=None): """Use a deconvolution to upsample x by 2**`nbr_steps`. Args: x: a `Tensor` with shape `[batch, spatial, depth]`...
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. " ...
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" i...
def tpu_conv1d(inputs, filters, kernel_size, padding="SAME", name="tpu_conv1d"): """Version of conv1d that works on TPU (as of 11/2017). Args: inputs: a Tensor with shape [batch, length, input_depth]. filters: an integer. kernel_size: an integer. padding: a string - "SAME" or "LEFT". name: a st...
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
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],...
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(...
def group_norm(x, filters=None, num_groups=8, epsilon=1e-5): """Group normalization as in https://arxiv.org/abs/1803.08494.""" x_shape = shape_list(x) if filters is None: filters = x_shape[-1] assert len(x_shape) == 4 assert filters % num_groups == 0 # Prepare variables. scale = tf.get_variable( ...
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])))
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...
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-D Tensor, then it is reshaped such that the number of channels (last-dimension) is the same. Args: x: Tensor with the last dimension equal to t...
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": ...
def zero_add(previous_value, x, name=None, reuse=None): """Resnet connection with zero initialization. Another type of resnet connection which returns previous_value + gamma * x. gamma is a trainable scalar and initialized with zero. It is useful when a module is plugged into a trained model and we want to mak...
def layer_prepostprocess(previous_value, x, sequence, dropout_rate, norm_type, depth, epsilon, default_name, name=None, ...
def layer_preprocess(layer_input, hparams, layer_collection=None): """Apply layer preprocessing. See layer_prepostprocess() for details. A hyperparameters object is passed for convenience. The hyperparameters that may be used are: layer_preprocess_sequence layer_prepostprocess_dropout norm_type ...
def layer_postprocess(layer_input, layer_output, hparams): """Apply layer postprocessing. See layer_prepostprocess() for details. A hyperparameters object is passed for convenience. The hyperparameters that may be used are: layer_postprocess_sequence layer_prepostprocess_dropout norm_type hi...
def conv_block_internal(conv_fn, inputs, filters, dilation_rates_and_kernel_sizes, first_relu=True, use_elu=False, separabilities=None, **kwargs): """...
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)
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)
def separable_conv_block(inputs, filters, dilation_rates_and_kernel_sizes, kwargs): """A block of separable convolutions.""" return conv_block_internal(separable_conv, inputs, filters, dilation_rates_and_kernel_sizes, kwargs)
def subseparable_conv_block(inputs, filters, dilation_rates_and_kernel_sizes, kwargs): """A block of separable convolutions.""" return conv_block_internal(subseparable_conv, inputs, filters, dilation_rates_and_kernel_sizes, kwargs)
def pool(inputs, window_size, pooling_type, padding, strides=(1, 1)): """Pooling (supports "LEFT").""" with tf.name_scope("pool", values=[inputs]): 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.") ...
def conv_block_downsample(x, kernel, strides, padding, separability=0, name=None, reuse=None): """Implements a downwards-striding conv block, like Xception exit f...
def get_timing_signal(length, min_timescale=1, max_timescale=1e4, num_timescales=16): """Create Tensor of sinusoids of different frequencies. Args: length: Length of the Tensor to create, i.e. Number of steps. min_timescale: a float max_...
def add_timing_signal(x, min_timescale=1, max_timescale=1e4, num_timescales=16): """Adds a bunch of sinusoids of different frequencies to a Tensor. This allows attention to learn to use absolute and relative positions. The timing signal should be added to some precursor of both the source and the target of the...
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 0.0 in the padding positions and 1.0 elsewhere. Args: emb: a Tensor with shape [batch, width, height, depth]. Returns: a 0.0/1.0 Tensor wit...
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]. Returns: a Tensor with shape [batch]. """ return tf.cast(tf.reduce_sum(mask_from_embedding(emb), [1, 2, 3]), tf.int32)
def relu_density_logit(x, reduce_dims): """logit(density(x)). Useful for histograms. Args: x: a Tensor, typically the output of tf.relu reduce_dims: a list of dimensions Returns: a Tensor """ frac = tf.reduce_mean(to_float(x > 0.0), reduce_dims) scaled = tf.log(frac + math.exp(-10)) - tf.lo...
def maybe_zero_out_padding(inputs, kernel_size, nonpadding_mask): """If necessary, zero out inputs to a conv for padding positions. Args: inputs: a Tensor with shape [batch, length, ...] kernel_size: an integer or pair of integers nonpadding_mask: a Tensor with shape [batch, length] Returns: Ten...
def dense_relu_dense(inputs, filter_size, output_size, output_activation=None, dropout=0.0, dropout_broadcast_dims=None, layer_collection=None, name=None): """Hidden layer...
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...
def conv_relu_conv(inputs, filter_size, output_size, first_kernel_size=3, second_kernel_size=3, padding="SAME", nonpadding_mask=None, dropout=0.0, name=None, ...
def sepconv_relu_sepconv(inputs, filter_size, output_size, first_kernel_size=(1, 1), second_kernel_size=(1, 1), padding="LEFT", nonpadding_mask=None, ...
def conv_hidden_relu(inputs, hidden_size, output_size, kernel_size=(1, 1), second_kernel_size=(1, 1), dropout=0.0, **kwargs): """Hidden layer with RELU activation followed by linear projection...
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): ...
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 current input h_prev: Tensor of shape [batch, length, depth] of prev input filters: an integer specifying number of dimensions of the filters ...
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): ...
def diagonal_conv_gru(x, kernel_size, filters, dropout=0.0, name=None, reuse=None): """Diagonal Convolutional GRU as in https://arxiv.org/abs/1702.08727.""" # Let's make a shorthand for conv call first. ...
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)[a...
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, ...
def weights_prepend_inputs_to_targets(labels): """Assign weight 1.0 to only the "targets" portion of the labels. Weight 1.0 is assigned to all nonzero labels past the first zero. See prepend_mode in common_hparams.py Args: labels: A Tensor of int32s. Returns: A Tensor of floats. """ past_first_...
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
def weights_multi_problem(labels, taskid=-1): """Assign weight 1.0 to only the "targets" portion of the labels. Weight 1.0 is assigned to all labels past the taskid. Args: labels: A Tensor of int32s. taskid: an int32 representing the task id for a problem. Returns: A Tensor of floats. Raises: ...
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...
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
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 target French Je t'aime . ID1 source English the cat ID1 target French le chat ID1 source English ... We want to assign weight 1.0 to all words ...
def padded_cross_entropy(logits, labels, label_smoothing, weights_fn=weights_nonzero, reduce_sum=True, cutoff=0.0, gaussian=False): """Compute cross-entropy assuming 0s...
def padded_cross_entropy_mixture(logits, labels, label_smoothing, num_mixtures, weights_fn=weights_nonzero, reduce_sum=False, ...
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_mixtures*10] tensor of floats comprising one unconstrained mixture probability, three means (one per channel), three standard deviations (on...
def split_to_discretized_mix_logistic_params(inputs): """Splits input tensor into parameters of discretized mixture logistic. Args: inputs: A [batch, height, width, num_mixtures*10] tensor of floats comprising one unconstrained mixture probability, three means (one per channel), three standard devi...
def discretized_mix_logistic_loss(pred, labels): """Computes negative log probability for the discretized mixture of logistics. The distribution of a whole pixel is a mixture of 3-dimensional discretized logistic distributions. The 3-D discretized logistic factorizes as 3 1-D discretized logistic distributions...
def sample_from_discretized_mix_logistic(pred, seed=None): """Sampling from a discretized mixture of logistics. Args: pred: A [batch, height, width, num_mixtures*10] tensor of floats comprising one unconstrained mixture probability, three means (one per channel), three standard deviations (one per ...
def smoothing_cross_entropy(logits, labels, vocab_size, confidence, gaussian=False): """Cross entropy with label smoothing to limit over-confidence. Args: logits: Tensor of shape [batch_size, ?, ?, ?...
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 so as to get a representation of a set. Args: inputs: A tensor of shape [batch_size, sequence_length, input_dims] containing the sequences o...
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 state of future elements is not known. Takes no mask as all elements up to the current element are assumed to exist. Currently only supports maximum. Eq...
def gated_linear_unit_layer(x, name=None): """Gated linear unit layer. Paper: Language Modeling with Gated Convolutional Networks. Link: https://arxiv.org/abs/1612.08083 x = Wx * sigmoid(W'x). Args: x: A tensor name: A string Returns: A tensor of the same shape as x. """ with tf.variable_...
def sru_with_scan(x, num_layers=2, activation=None, initial_state=None, name=None, reuse=None): """SRU cell as in https://arxiv.org/abs/1709.02755. This implementation uses tf.scan and can incur overhead, see the full SRU f...
def sru(x, num_layers=2, activation=None, initial_state=None, name=None, reuse=None): """SRU cell as in https://arxiv.org/abs/1709.02755. As defined in the paper: (1) x'_t = W x_t (2) f_t = sigmoid(Wf x_t + bf) (3) r_t = sigmoid(Wr x_t + br) (4) c_t = f_t * c_{t-1} +...
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. Applies a linear transformation to each element in...
def ravanbakhsh_set_layer(layer_size, inputs, mask=None, sequential=False, activation_fn=tf.nn.tanh, dropout=0.0, name=None): """Layer from Deep Sets paper: https...
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
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)): a...
def underlying_variable_ref(t): """Find the underlying variable ref. Traverses through Identity, ReadVariableOp, and Enter ops. Stops when op type has Variable or VarHandle in name. Args: t: a Tensor Returns: a Tensor that is a variable ref, or None on error. """ while t.op.type in ["Identity",...
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"): ...
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. Returns: a list of num_splits Tensors. """ size = shape_list(x)[axis] size_splits = [tf.div(size + i, num_splits) for i in range(nu...
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 = approximat...
def smoothing_cross_entropy_factored(a, b, labels, confidence): """Memory-efficient computation of smoothing cross-entropy. Avoids realizing the entire logits matrix at once. Args: a: a Tensor with shape [batch, inner_dim] b: a Tensor with shape [vocab_size, inner_dim] labels: an integer Tensor with...
def padded_cross_entropy_factored(factored_logits, labels, label_smoothing, weights_fn=weights_nonzero, reduce_sum=True): """Memory-efficient computation of smoothing cross-entropy. ...
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 function is NOT put in a Defun and so does not suffer from the limitations of the Defun (all subgraph ops on the same device, no summaries). Args:...
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 arguments and produces 1 or more Tensors. inputs: list<Tensor>, will be passed as fn(*inputs). grad_fn: function with signature (inputs, vars,...
def conv_hidden_relu_memory_efficient(x, filter_size, epsilon=1e-6, forget=True, test_vars=None, name=None): """LayerNorm, Conv,...
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 ...
def sample_with_temperature(logits, temperature, sampling_keep_top_k=-1): """Either argmax or random sampling. Args: logits: a Tensor. temperature: a float 0.0=argmax 1.0=random sampling_keep_top_k: If not -1, only sample from the top k logits. Returns: a Tensor with one fewer dimension than log...
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 cols: int num_lower: int, maximum distance backward. Negative values indicate unlimited. num_upper: int, maximum distance forward. Neg...
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
def recompute_grad(fn): """Decorator that recomputes the function on the backwards pass. Args: fn: a function that takes Tensors (all as positional arguments) and returns a tuple of Tensors. Returns: A wrapped fn that is identical to fn when called, but its activations will be discarded and re...
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 ...
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. Otherwi...
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 matrices. Each input matrix is multiplied by the corresponding parameter mat...
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 towards x1.""" with tf.name_scope("mix"): if not is_training: if max_prob >= 1.0: ...

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_b...

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....

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

def shakeshake2_grad(x1, x2, dy): """Overriding gradient for shake-shake of 2 tensors.""" y = shakeshake2_py(x1, x2) dx = tf.gradients(ys=[y], xs=[x1, x2], grad_ys=[dy]) return dx

def shakeshake2_indiv_grad(x1, x2, dy): """Overriding gradient for shake-shake of 2 tensors.""" y = shakeshake2_py(x1, x2, individual=True) dx = tf.gradients(ys=[y], xs=[x1, x2], grad_ys=[dy]) return dx

def shakeshake2_equal_grad(x1, x2, dy): """Overriding gradient for shake-shake of 2 tensors.""" y = shakeshake2_py(x1, x2, equal=True) dx = tf.gradients(ys=[y], xs=[x1, x2], grad_ys=[dy]) return dx

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_...

def convert_rgb_to_real(x): """Conversion of pixel values to real numbers.""" with tf.name_scope("rgb_to_real", values=[x]): x = to_float(x) x /= 255.0 return x

def convert_rgb_to_symmetric_real(x): """Conversion of pixel values to real numbers.""" with tf.name_scope("rgb_to_real", values=[x]): x = to_float(x) # Convert each pixel intensity in [0, 1, 2, ..., 255] into a real number in # the range [-1, 1]. x = (x / 127.5) - 1 return x

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

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....

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

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=d...

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 is very large. Args: x: a Tensor axis: an integer exclusive: a boolean Returns: Tensor of the same shape as x. """ if not is_xl...

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 Returns: Tensor of the same shape as x. """ if keep_prob == 1.0: return x mask = tf.less(tf.random_uniform(tf.shape(x)), keep_pr...

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 type int64 into dense vectors, reducing to max 4...

def shift_right(x, pad_value=None): """Shift the second dimension of x right by one.""" if pad_value is None: shifted_targets = tf.pad(x, [[0, 0], [1, 0], [0, 0], [0, 0]])[:, :-1, :, :] else: shifted_targets = tf.concat([pad_value, x], axis=1)[:, :-1, :, :] return shifted_targets

def shift_right_3d(x, pad_value=None): """Shift the second dimension of x right by one.""" if pad_value is None: shifted_targets = tf.pad(x, [[0, 0], [1, 0], [0, 0]])[:, :-1, :] else: shifted_targets = tf.concat([pad_value, x], axis=1)[:, :-1, :] return shifted_targets

def shift_right_2d(x, pad_value=None): """Shift the second dimension of x right by one.""" if pad_value is None: shifted_targets = tf.pad(x, [[0, 0], [1, 0]])[:, :-1] else: shifted_targets = tf.concat([pad_value, x], axis=1)[:, :-1] return shifted_targets

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 stride and filter size 2 to avoid the checkerboard problem of deconvs. As detailed in http://distill.pub/2016/deconv-checkerboard/. Args: x: a `Tens...

def deconv_stride2_multistep(x, nbr_steps, output_filters, name=None, reuse=None): """Use a deconvolution to upsample x by 2**`nbr_steps`. Args: x: a `Tensor` with shape `[batch, spatial, depth]`...

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. " ...

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" i...

def tpu_conv1d(inputs, filters, kernel_size, padding="SAME", name="tpu_conv1d"): """Version of conv1d that works on TPU (as of 11/2017). Args: inputs: a Tensor with shape [batch, length, input_depth]. filters: an integer. kernel_size: an integer. padding: a string - "SAME" or "LEFT". name: a st...

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

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],...

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(...

def group_norm(x, filters=None, num_groups=8, epsilon=1e-5): """Group normalization as in https://arxiv.org/abs/1803.08494.""" x_shape = shape_list(x) if filters is None: filters = x_shape[-1] assert len(x_shape) == 4 assert filters % num_groups == 0 # Prepare variables. scale = tf.get_variable( ...

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])))

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...

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-D Tensor, then it is reshaped such that the number of channels (last-dimension) is the same. Args: x: Tensor with the last dimension equal to t...

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": ...

def zero_add(previous_value, x, name=None, reuse=None): """Resnet connection with zero initialization. Another type of resnet connection which returns previous_value + gamma * x. gamma is a trainable scalar and initialized with zero. It is useful when a module is plugged into a trained model and we want to mak...

def layer_prepostprocess(previous_value, x, sequence, dropout_rate, norm_type, depth, epsilon, default_name, name=None, ...

def layer_preprocess(layer_input, hparams, layer_collection=None): """Apply layer preprocessing. See layer_prepostprocess() for details. A hyperparameters object is passed for convenience. The hyperparameters that may be used are: layer_preprocess_sequence layer_prepostprocess_dropout norm_type ...

def layer_postprocess(layer_input, layer_output, hparams): """Apply layer postprocessing. See layer_prepostprocess() for details. A hyperparameters object is passed for convenience. The hyperparameters that may be used are: layer_postprocess_sequence layer_prepostprocess_dropout norm_type hi...

def conv_block_internal(conv_fn, inputs, filters, dilation_rates_and_kernel_sizes, first_relu=True, use_elu=False, separabilities=None, **kwargs): """...

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)

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)

def separable_conv_block(inputs, filters, dilation_rates_and_kernel_sizes, **kwargs): """A block of separable convolutions.""" return conv_block_internal(separable_conv, inputs, filters, dilation_rates_and_kernel_sizes, **kwargs)

def subseparable_conv_block(inputs, filters, dilation_rates_and_kernel_sizes, **kwargs): """A block of separable convolutions.""" return conv_block_internal(subseparable_conv, inputs, filters, dilation_rates_and_kernel_sizes, **kwargs)

def pool(inputs, window_size, pooling_type, padding, strides=(1, 1)): """Pooling (supports "LEFT").""" with tf.name_scope("pool", values=[inputs]): 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.") ...

def conv_block_downsample(x, kernel, strides, padding, separability=0, name=None, reuse=None): """Implements a downwards-striding conv block, like Xception exit f...

def get_timing_signal(length, min_timescale=1, max_timescale=1e4, num_timescales=16): """Create Tensor of sinusoids of different frequencies. Args: length: Length of the Tensor to create, i.e. Number of steps. min_timescale: a float max_...

def add_timing_signal(x, min_timescale=1, max_timescale=1e4, num_timescales=16): """Adds a bunch of sinusoids of different frequencies to a Tensor. This allows attention to learn to use absolute and relative positions. The timing signal should be added to some precursor of both the source and the target of the...

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 0.0 in the padding positions and 1.0 elsewhere. Args: emb: a Tensor with shape [batch, width, height, depth]. Returns: a 0.0/1.0 Tensor wit...

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]. Returns: a Tensor with shape [batch]. """ return tf.cast(tf.reduce_sum(mask_from_embedding(emb), [1, 2, 3]), tf.int32)

def relu_density_logit(x, reduce_dims): """logit(density(x)). Useful for histograms. Args: x: a Tensor, typically the output of tf.relu reduce_dims: a list of dimensions Returns: a Tensor """ frac = tf.reduce_mean(to_float(x > 0.0), reduce_dims) scaled = tf.log(frac + math.exp(-10)) - tf.lo...

def maybe_zero_out_padding(inputs, kernel_size, nonpadding_mask): """If necessary, zero out inputs to a conv for padding positions. Args: inputs: a Tensor with shape [batch, length, ...] kernel_size: an integer or pair of integers nonpadding_mask: a Tensor with shape [batch, length] Returns: Ten...

def dense_relu_dense(inputs, filter_size, output_size, output_activation=None, dropout=0.0, dropout_broadcast_dims=None, layer_collection=None, name=None): """Hidden layer...

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...

def conv_relu_conv(inputs, filter_size, output_size, first_kernel_size=3, second_kernel_size=3, padding="SAME", nonpadding_mask=None, dropout=0.0, name=None, ...

def sepconv_relu_sepconv(inputs, filter_size, output_size, first_kernel_size=(1, 1), second_kernel_size=(1, 1), padding="LEFT", nonpadding_mask=None, ...

def conv_hidden_relu(inputs, hidden_size, output_size, kernel_size=(1, 1), second_kernel_size=(1, 1), dropout=0.0, **kwargs): """Hidden layer with RELU activation followed by linear projection...

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): ...

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 current input h_prev: Tensor of shape [batch, length, depth] of prev input filters: an integer specifying number of dimensions of the filters ...

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): ...

def diagonal_conv_gru(x, kernel_size, filters, dropout=0.0, name=None, reuse=None): """Diagonal Convolutional GRU as in https://arxiv.org/abs/1702.08727.""" # Let's make a shorthand for conv call first. ...

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)[a...

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, ...

def weights_prepend_inputs_to_targets(labels): """Assign weight 1.0 to only the "targets" portion of the labels. Weight 1.0 is assigned to all nonzero labels past the first zero. See prepend_mode in common_hparams.py Args: labels: A Tensor of int32s. Returns: A Tensor of floats. """ past_first_...

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

def weights_multi_problem(labels, taskid=-1): """Assign weight 1.0 to only the "targets" portion of the labels. Weight 1.0 is assigned to all labels past the taskid. Args: labels: A Tensor of int32s. taskid: an int32 representing the task id for a problem. Returns: A Tensor of floats. Raises: ...

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...

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

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 target French Je t'aime . ID1 source English the cat ID1 target French le chat ID1 source English ... We want to assign weight 1.0 to all words ...

def padded_cross_entropy(logits, labels, label_smoothing, weights_fn=weights_nonzero, reduce_sum=True, cutoff=0.0, gaussian=False): """Compute cross-entropy assuming 0s...

def padded_cross_entropy_mixture(logits, labels, label_smoothing, num_mixtures, weights_fn=weights_nonzero, reduce_sum=False, ...

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_mixtures*10] tensor of floats comprising one unconstrained mixture probability, three means (one per channel), three standard deviations (on...

def split_to_discretized_mix_logistic_params(inputs): """Splits input tensor into parameters of discretized mixture logistic. Args: inputs: A [batch, height, width, num_mixtures*10] tensor of floats comprising one unconstrained mixture probability, three means (one per channel), three standard devi...

def discretized_mix_logistic_loss(pred, labels): """Computes negative log probability for the discretized mixture of logistics. The distribution of a whole pixel is a mixture of 3-dimensional discretized logistic distributions. The 3-D discretized logistic factorizes as 3 1-D discretized logistic distributions...

def sample_from_discretized_mix_logistic(pred, seed=None): """Sampling from a discretized mixture of logistics. Args: pred: A [batch, height, width, num_mixtures*10] tensor of floats comprising one unconstrained mixture probability, three means (one per channel), three standard deviations (one per ...

def smoothing_cross_entropy(logits, labels, vocab_size, confidence, gaussian=False): """Cross entropy with label smoothing to limit over-confidence. Args: logits: Tensor of shape [batch_size, ?, ?, ?...

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 so as to get a representation of a set. Args: inputs: A tensor of shape [batch_size, sequence_length, input_dims] containing the sequences o...

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 state of future elements is not known. Takes no mask as all elements up to the current element are assumed to exist. Currently only supports maximum. Eq...

def gated_linear_unit_layer(x, name=None): """Gated linear unit layer. Paper: Language Modeling with Gated Convolutional Networks. Link: https://arxiv.org/abs/1612.08083 x = Wx * sigmoid(W'x). Args: x: A tensor name: A string Returns: A tensor of the same shape as x. """ with tf.variable_...

def sru_with_scan(x, num_layers=2, activation=None, initial_state=None, name=None, reuse=None): """SRU cell as in https://arxiv.org/abs/1709.02755. This implementation uses tf.scan and can incur overhead, see the full SRU f...

def sru(x, num_layers=2, activation=None, initial_state=None, name=None, reuse=None): """SRU cell as in https://arxiv.org/abs/1709.02755. As defined in the paper: (1) x'_t = W x_t (2) f_t = sigmoid(Wf x_t + bf) (3) r_t = sigmoid(Wr x_t + br) (4) c_t = f_t * c_{t-1} +...

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. Applies a linear transformation to each element in...

def ravanbakhsh_set_layer(layer_size, inputs, mask=None, sequential=False, activation_fn=tf.nn.tanh, dropout=0.0, name=None): """Layer from Deep Sets paper: https...

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

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)): a...

def underlying_variable_ref(t): """Find the underlying variable ref. Traverses through Identity, ReadVariableOp, and Enter ops. Stops when op type has Variable or VarHandle in name. Args: t: a Tensor Returns: a Tensor that is a variable ref, or None on error. """ while t.op.type in ["Identity",...

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"): ...

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. Returns: a list of num_splits Tensors. """ size = shape_list(x)[axis] size_splits = [tf.div(size + i, num_splits) for i in range(nu...

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 = approximat...

def smoothing_cross_entropy_factored(a, b, labels, confidence): """Memory-efficient computation of smoothing cross-entropy. Avoids realizing the entire logits matrix at once. Args: a: a Tensor with shape [batch, inner_dim] b: a Tensor with shape [vocab_size, inner_dim] labels: an integer Tensor with...

def padded_cross_entropy_factored(factored_logits, labels, label_smoothing, weights_fn=weights_nonzero, reduce_sum=True): """Memory-efficient computation of smoothing cross-entropy. ...

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 function is NOT put in a Defun and so does not suffer from the limitations of the Defun (all subgraph ops on the same device, no summaries). Args:...

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 arguments and produces 1 or more Tensors. inputs: list<Tensor>, will be passed as fn(*inputs). grad_fn: function with signature (inputs, vars,...

def conv_hidden_relu_memory_efficient(x, filter_size, epsilon=1e-6, forget=True, test_vars=None, name=None): """LayerNorm, Conv,...

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 ...

def sample_with_temperature(logits, temperature, sampling_keep_top_k=-1): """Either argmax or random sampling. Args: logits: a Tensor. temperature: a float 0.0=argmax 1.0=random sampling_keep_top_k: If not -1, only sample from the top k logits. Returns: a Tensor with one fewer dimension than log...

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 cols: int num_lower: int, maximum distance backward. Negative values indicate unlimited. num_upper: int, maximum distance forward. Neg...

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

def recompute_grad(fn): """Decorator that recomputes the function on the backwards pass. Args: fn: a function that takes Tensors (all as positional arguments) and returns a tuple of Tensors. Returns: A wrapped fn that is identical to fn when called, but its activations will be discarded and re...

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 ...

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. Otherwi...

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 matrices. Each input matrix is multiplied by the corresponding parameter mat...

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 towards x1.""" with tf.name_scope("mix"): if not is_training: if max_prob >= 1.0: ...