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- # coding=utf-8
- # Copyright 2018 The Google AI Language Team Authors.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- """The main BERT model and related functions."""
-
- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
-
- import collections
- import copy
- import json
- import math
- import re
- import numpy as np
- import six
- import tensorflow as tf
-
-
- class BertConfig(object):
- """Configuration for `BertModel`."""
-
- def __init__(self,
- vocab_size,
- hidden_size=768,
- num_hidden_layers=12,
- num_attention_heads=12,
- intermediate_size=3072,
- hidden_act="gelu",
- hidden_dropout_prob=0.1,
- attention_probs_dropout_prob=0.1,
- max_position_embeddings=512,
- type_vocab_size=16,
- initializer_range=0.02):
- """Constructs BertConfig.
-
- Args:
- vocab_size: Vocabulary size of `inputs_ids` in `BertModel`.
- hidden_size: Size of the encoder layers and the pooler layer.
- num_hidden_layers: Number of hidden layers in the Transformer encoder.
- num_attention_heads: Number of attention heads for each attention layer in
- the Transformer encoder.
- intermediate_size: The size of the "intermediate" (i.e., feed-forward)
- layer in the Transformer encoder.
- hidden_act: The non-linear activation function (function or string) in the
- encoder and pooler.
- hidden_dropout_prob: The dropout probability for all fully connected
- layers in the embeddings, encoder, and pooler.
- attention_probs_dropout_prob: The dropout ratio for the attention
- probabilities.
- max_position_embeddings: The maximum sequence length that this model might
- ever be used with. Typically set this to something large just in case
- (e.g., 512 or 1024 or 2048).
- type_vocab_size: The vocabulary size of the `token_type_ids` passed into
- `BertModel`.
- initializer_range: The stdev of the truncated_normal_initializer for
- initializing all weight matrices.
- """
- self.vocab_size = vocab_size
- self.hidden_size = hidden_size
- self.num_hidden_layers = num_hidden_layers
- self.num_attention_heads = num_attention_heads
- self.hidden_act = hidden_act
- self.intermediate_size = intermediate_size
- self.hidden_dropout_prob = hidden_dropout_prob
- self.attention_probs_dropout_prob = attention_probs_dropout_prob
- self.max_position_embeddings = max_position_embeddings
- self.type_vocab_size = type_vocab_size
- self.initializer_range = initializer_range
-
- @classmethod
- def from_dict(cls, json_object):
- """Constructs a `BertConfig` from a Python dictionary of parameters."""
- config = BertConfig(vocab_size=None)
- for (key, value) in six.iteritems(json_object):
- config.__dict__[key] = value
- return config
-
- @classmethod
- def from_json_file(cls, json_file):
- """Constructs a `BertConfig` from a json file of parameters."""
- with tf.gfile.GFile(json_file, "r") as reader:
- text = reader.read()
- return cls.from_dict(json.loads(text))
-
- def to_dict(self):
- """Serializes this instance to a Python dictionary."""
- output = copy.deepcopy(self.__dict__)
- return output
-
- def to_json_string(self):
- """Serializes this instance to a JSON string."""
- return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
-
-
- class BertModel(object):
- """BERT model ("Bidirectional Encoder Representations from Transformers").
-
- Example usage:
-
- ```python
- # Already been converted into WordPiece token ids
- input_ids = tf.constant([[31, 51, 99], [15, 5, 0]])
- input_mask = tf.constant([[1, 1, 1], [1, 1, 0]])
- token_type_ids = tf.constant([[0, 0, 1], [0, 2, 0]])
-
- config = modeling.BertConfig(vocab_size=32000, hidden_size=512,
- num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
-
- model = modeling.BertModel(config=config, is_training=True,
- input_ids=input_ids, input_mask=input_mask, token_type_ids=token_type_ids)
-
- label_embeddings = tf.get_variable(...)
- pooled_output = model.get_pooled_output()
- logits = tf.matmul(pooled_output, label_embeddings)
- ...
- ```
- """
-
- def __init__(self,
- config,
- is_training,
- input_ids,
- input_mask=None,
- token_type_ids=None,
- use_one_hot_embeddings=False,
- scope=None):
- """Constructor for BertModel.
-
- Args:
- config: `BertConfig` instance.
- is_training: bool. true for training model, false for eval model. Controls
- whether dropout will be applied.
- input_ids: int32 Tensor of shape [batch_size, seq_length].
- input_mask: (optional) int32 Tensor of shape [batch_size, seq_length].
- token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
- use_one_hot_embeddings: (optional) bool. Whether to use one-hot word
- embeddings or tf.embedding_lookup() for the word embeddings.
- scope: (optional) variable scope. Defaults to "bert".
-
- Raises:
- ValueError: The config is invalid or one of the input tensor shapes
- is invalid.
- """
- config = copy.deepcopy(config)
- if not is_training:
- config.hidden_dropout_prob = 0.0
- config.attention_probs_dropout_prob = 0.0
-
- input_shape = get_shape_list(input_ids, expected_rank=2)
- batch_size = input_shape[0]
- seq_length = input_shape[1]
-
- if input_mask is None:
- input_mask = tf.ones(shape=[batch_size, seq_length], dtype=tf.int32)
-
- if token_type_ids is None:
- token_type_ids = tf.zeros(shape=[batch_size, seq_length], dtype=tf.int32)
-
- with tf.variable_scope(scope, default_name="bert"):
- with tf.variable_scope("embeddings"):
- # Perform embedding lookup on the word ids.
- (self.embedding_output, self.embedding_table) = embedding_lookup(
- input_ids=input_ids,
- vocab_size=config.vocab_size,
- embedding_size=config.hidden_size,
- initializer_range=config.initializer_range,
- word_embedding_name="word_embeddings",
- use_one_hot_embeddings=use_one_hot_embeddings)
-
- # Add positional embeddings and token type embeddings, then layer
- # normalize and perform dropout.
- self.embedding_output = embedding_postprocessor(
- input_tensor=self.embedding_output,
- use_token_type=True,
- token_type_ids=token_type_ids,
- token_type_vocab_size=config.type_vocab_size,
- token_type_embedding_name="token_type_embeddings",
- use_position_embeddings=True,
- position_embedding_name="position_embeddings",
- initializer_range=config.initializer_range,
- max_position_embeddings=config.max_position_embeddings,
- dropout_prob=config.hidden_dropout_prob)
-
- with tf.variable_scope("encoder"):
- # This converts a 2D mask of shape [batch_size, seq_length] to a 3D
- # mask of shape [batch_size, seq_length, seq_length] which is used
- # for the attention scores.
- attention_mask = create_attention_mask_from_input_mask(
- input_ids, input_mask)
-
- # Run the stacked transformer.
- # `sequence_output` shape = [batch_size, seq_length, hidden_size].
- self.all_encoder_layers = transformer_model(
- input_tensor=self.embedding_output,
- attention_mask=attention_mask,
- hidden_size=config.hidden_size,
- num_hidden_layers=config.num_hidden_layers,
- num_attention_heads=config.num_attention_heads,
- intermediate_size=config.intermediate_size,
- intermediate_act_fn=get_activation(config.hidden_act),
- hidden_dropout_prob=config.hidden_dropout_prob,
- attention_probs_dropout_prob=config.attention_probs_dropout_prob,
- initializer_range=config.initializer_range,
- do_return_all_layers=True)
-
- self.sequence_output = self.all_encoder_layers[-1]
- # The "pooler" converts the encoded sequence tensor of shape
- # [batch_size, seq_length, hidden_size] to a tensor of shape
- # [batch_size, hidden_size]. This is necessary for segment-level
- # (or segment-pair-level) classification tasks where we need a fixed
- # dimensional representation of the segment.
- with tf.variable_scope("pooler"):
- # We "pool" the model by simply taking the hidden state corresponding
- # to the first token. We assume that this has been pre-trained
- first_token_tensor = tf.squeeze(self.sequence_output[:, 0:1, :], axis=1)
- self.pooled_output = tf.layers.dense(
- first_token_tensor,
- config.hidden_size,
- activation=tf.tanh,
- kernel_initializer=create_initializer(config.initializer_range))
-
- def get_pooled_output(self):
- return self.pooled_output
-
- def get_sequence_output(self):
- """Gets final hidden layer of encoder.
-
- Returns:
- float Tensor of shape [batch_size, seq_length, hidden_size] corresponding
- to the final hidden of the transformer encoder.
- """
- return self.sequence_output
-
- def get_all_encoder_layers(self):
- return self.all_encoder_layers
-
- def get_embedding_output(self):
- """Gets output of the embedding lookup (i.e., input to the transformer).
-
- Returns:
- float Tensor of shape [batch_size, seq_length, hidden_size] corresponding
- to the output of the embedding layer, after summing the word
- embeddings with the positional embeddings and the token type embeddings,
- then performing layer normalization. This is the input to the transformer.
- """
- return self.embedding_output
-
- def get_embedding_table(self):
- return self.embedding_table
-
-
- 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 perform activation.
-
- Returns:
- `x` with the GELU activation applied.
- """
- cdf = 0.5 * (1.0 + tf.tanh(
- (np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
- return x * cdf
-
-
- def get_activation(activation_string):
- """Maps a string to a Python function, e.g., "relu" => `tf.nn.relu`.
-
- Args:
- activation_string: String name of the activation function.
-
- Returns:
- A Python function corresponding to the activation function. If
- `activation_string` is None, empty, or "linear", this will return None.
- If `activation_string` is not a string, it will return `activation_string`.
-
- Raises:
- ValueError: The `activation_string` does not correspond to a known
- activation.
- """
-
- # We assume that anything that"s not a string is already an activation
- # function, so we just return it.
- if not isinstance(activation_string, six.string_types):
- return activation_string
-
- if not activation_string:
- return None
-
- act = activation_string.lower()
- if act == "linear":
- return None
- elif act == "relu":
- return tf.nn.relu
- elif act == "gelu":
- return gelu
- elif act == "tanh":
- return tf.tanh
- else:
- raise ValueError("Unsupported activation: %s" % act)
-
-
- def get_assignment_map_from_checkpoint(tvars, init_checkpoint):
- """Compute the union of the current variables and checkpoint variables."""
- assignment_map = {}
- initialized_variable_names = {}
-
- name_to_variable = collections.OrderedDict()
- for var in tvars:
- name = var.name
- m = re.match("^(.*):\\d+$", name)
- if m is not None:
- name = m.group(1)
- name_to_variable[name] = var
-
- init_vars = tf.train.list_variables(init_checkpoint)
-
- # for var in init_vars:
- # init_string = ""
- # tf.logging.info(" name = %s, shape = %s%s", var[0], var[1],
- # init_string)
-
- assignment_map = collections.OrderedDict()
- for x in init_vars:
- (name, var) = (x[0], x[1])
- if name not in name_to_variable:
- continue
- assignment_map[name] = name
- initialized_variable_names[name] = 1
- initialized_variable_names[name + ":0"] = 1
-
- return (assignment_map, initialized_variable_names)
-
-
- def dropout(input_tensor, dropout_prob):
- """Perform dropout.
-
- Args:
- input_tensor: float Tensor.
- dropout_prob: Python float. The probability of dropping out a value (NOT of
- *keeping* a dimension as in `tf.nn.dropout`).
-
- Returns:
- A version of `input_tensor` with dropout applied.
- """
- if dropout_prob is None or dropout_prob == 0.0:
- return input_tensor
-
- output = tf.nn.dropout(input_tensor, 1.0 - dropout_prob)
- return output
-
-
- def layer_norm(input_tensor, name=None):
- """Run layer normalization on the last dimension of the tensor."""
- return tf.contrib.layers.layer_norm(
- inputs=input_tensor, begin_norm_axis=-1, begin_params_axis=-1, scope=name)
-
-
- def layer_norm_and_dropout(input_tensor, dropout_prob, name=None):
- """Runs layer normalization followed by dropout."""
- output_tensor = layer_norm(input_tensor, name)
- output_tensor = dropout(output_tensor, dropout_prob)
- return output_tensor
-
-
- def create_initializer(initializer_range=0.02):
- """Creates a `truncated_normal_initializer` with the given range."""
- return tf.truncated_normal_initializer(stddev=initializer_range)
-
-
- def embedding_lookup(input_ids,
- vocab_size,
- embedding_size=128,
- initializer_range=0.02,
- word_embedding_name="word_embeddings",
- use_one_hot_embeddings=False):
- """Looks up words embeddings for id tensor.
-
- Args:
- input_ids: int32 Tensor of shape [batch_size, seq_length] containing word
- ids.
- vocab_size: int. Size of the embedding vocabulary.
- embedding_size: int. Width of the word embeddings.
- initializer_range: float. Embedding initialization range.
- word_embedding_name: string. Name of the embedding table.
- use_one_hot_embeddings: bool. If True, use one-hot method for word
- embeddings. If False, use `tf.gather()`.
-
- Returns:
- float Tensor of shape [batch_size, seq_length, embedding_size].
- """
- # This function assumes that the input is of shape [batch_size, seq_length,
- # num_inputs].
- #
- # If the input is a 2D tensor of shape [batch_size, seq_length], we
- # reshape to [batch_size, seq_length, 1].
- if input_ids.shape.ndims == 2:
- input_ids = tf.expand_dims(input_ids, axis=[-1])
-
- embedding_table = tf.get_variable(
- name=word_embedding_name,
- shape=[vocab_size, embedding_size],
- initializer=create_initializer(initializer_range))
-
- flat_input_ids = tf.reshape(input_ids, [-1])
- if use_one_hot_embeddings:
- one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size)
- output = tf.matmul(one_hot_input_ids, embedding_table)
- else:
- output = tf.gather(embedding_table, flat_input_ids)
-
- input_shape = get_shape_list(input_ids)
-
- output = tf.reshape(output,
- input_shape[0:-1] + [input_shape[-1] * embedding_size])
- return (output, embedding_table)
-
-
- def embedding_postprocessor(input_tensor,
- use_token_type=False,
- token_type_ids=None,
- token_type_vocab_size=16,
- token_type_embedding_name="token_type_embeddings",
- use_position_embeddings=True,
- position_embedding_name="position_embeddings",
- initializer_range=0.02,
- max_position_embeddings=512,
- dropout_prob=0.1):
- """Performs various post-processing on a word embedding tensor.
-
- Args:
- input_tensor: float Tensor of shape [batch_size, seq_length,
- embedding_size].
- use_token_type: bool. Whether to add embeddings for `token_type_ids`.
- token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
- Must be specified if `use_token_type` is True.
- token_type_vocab_size: int. The vocabulary size of `token_type_ids`.
- token_type_embedding_name: string. The name of the embedding table variable
- for token type ids.
- use_position_embeddings: bool. Whether to add position embeddings for the
- position of each token in the sequence.
- position_embedding_name: string. The name of the embedding table variable
- for positional embeddings.
- initializer_range: float. Range of the weight initialization.
- max_position_embeddings: int. Maximum sequence length that might ever be
- used with this model. This can be longer than the sequence length of
- input_tensor, but cannot be shorter.
- dropout_prob: float. Dropout probability applied to the final output tensor.
-
- Returns:
- float tensor with same shape as `input_tensor`.
-
- Raises:
- ValueError: One of the tensor shapes or input values is invalid.
- """
- input_shape = get_shape_list(input_tensor, expected_rank=3)
- batch_size = input_shape[0]
- seq_length = input_shape[1]
- width = input_shape[2]
-
- output = input_tensor
-
- if use_token_type:
- if token_type_ids is None:
- raise ValueError("`token_type_ids` must be specified if"
- "`use_token_type` is True.")
- token_type_table = tf.get_variable(
- name=token_type_embedding_name,
- shape=[token_type_vocab_size, width],
- initializer=create_initializer(initializer_range))
- # This vocab will be small so we always do one-hot here, since it is always
- # faster for a small vocabulary.
- flat_token_type_ids = tf.reshape(token_type_ids, [-1])
- one_hot_ids = tf.one_hot(flat_token_type_ids, depth=token_type_vocab_size)
- token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
- token_type_embeddings = tf.reshape(token_type_embeddings,
- [batch_size, seq_length, width])
- output += token_type_embeddings
-
- if use_position_embeddings:
- assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
- with tf.control_dependencies([assert_op]):
- full_position_embeddings = tf.get_variable(
- name=position_embedding_name,
- shape=[max_position_embeddings, width],
- initializer=create_initializer(initializer_range))
- # Since the position embedding table is a learned variable, we create it
- # using a (long) sequence length `max_position_embeddings`. The actual
- # sequence length might be shorter than this, for faster training of
- # tasks that do not have long sequences.
- #
- # So `full_position_embeddings` is effectively an embedding table
- # for position [0, 1, 2, ..., max_position_embeddings-1], and the current
- # sequence has positions [0, 1, 2, ... seq_length-1], so we can just
- # perform a slice.
- position_embeddings = tf.slice(full_position_embeddings, [0, 0],
- [seq_length, -1])
- num_dims = len(output.shape.as_list())
-
- # Only the last two dimensions are relevant (`seq_length` and `width`), so
- # we broadcast among the first dimensions, which is typically just
- # the batch size.
- position_broadcast_shape = []
- for _ in range(num_dims - 2):
- position_broadcast_shape.append(1)
- position_broadcast_shape.extend([seq_length, width])
- position_embeddings = tf.reshape(position_embeddings,
- position_broadcast_shape)
- output += position_embeddings
-
- output = layer_norm_and_dropout(output, dropout_prob)
- return output
-
-
- def create_attention_mask_from_input_mask(from_tensor, to_mask):
- """Create 3D attention mask from a 2D tensor mask.
-
- Args:
- from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...].
- to_mask: int32 Tensor of shape [batch_size, to_seq_length].
-
- Returns:
- float Tensor of shape [batch_size, from_seq_length, to_seq_length].
- """
- from_shape = get_shape_list(from_tensor, expected_rank=[2, 3])
- batch_size = from_shape[0]
- from_seq_length = from_shape[1]
-
- to_shape = get_shape_list(to_mask, expected_rank=2)
- to_seq_length = to_shape[1]
-
- to_mask = tf.cast(
- tf.reshape(to_mask, [batch_size, 1, to_seq_length]), tf.float32)
-
- # We don't assume that `from_tensor` is a mask (although it could be). We
- # don't actually care if we attend *from* padding tokens (only *to* padding)
- # tokens so we create a tensor of all ones.
- #
- # `broadcast_ones` = [batch_size, from_seq_length, 1]
- broadcast_ones = tf.ones(
- shape=[batch_size, from_seq_length, 1], dtype=tf.float32)
-
- # Here we broadcast along two dimensions to create the mask.
- mask = broadcast_ones * to_mask
-
- return mask
-
-
- def attention_layer(from_tensor,
- to_tensor,
- attention_mask=None,
- num_attention_heads=1,
- size_per_head=512,
- query_act=None,
- key_act=None,
- value_act=None,
- attention_probs_dropout_prob=0.0,
- initializer_range=0.02,
- do_return_2d_tensor=False,
- batch_size=None,
- from_seq_length=None,
- to_seq_length=None):
- """Performs multi-headed attention from `from_tensor` to `to_tensor`.
-
- This is an implementation of multi-headed attention based on "Attention
- is all you Need". If `from_tensor` and `to_tensor` are the same, then
- this is self-attention. Each timestep in `from_tensor` attends to the
- corresponding sequence in `to_tensor`, and returns a fixed-with vector.
-
- This function first projects `from_tensor` into a "query" tensor and
- `to_tensor` into "key" and "value" tensors. These are (effectively) a list
- of tensors of length `num_attention_heads`, where each tensor is of shape
- [batch_size, seq_length, size_per_head].
-
- Then, the query and key tensors are dot-producted and scaled. These are
- softmaxed to obtain attention probabilities. The value tensors are then
- interpolated by these probabilities, then concatenated back to a single
- tensor and returned.
-
- In practice, the multi-headed attention are done with transposes and
- reshapes rather than actual separate tensors.
-
- Args:
- from_tensor: float Tensor of shape [batch_size, from_seq_length,
- from_width].
- to_tensor: float Tensor of shape [batch_size, to_seq_length, to_width].
- attention_mask: (optional) int32 Tensor of shape [batch_size,
- from_seq_length, to_seq_length]. The values should be 1 or 0. The
- attention scores will effectively be set to -infinity for any positions in
- the mask that are 0, and will be unchanged for positions that are 1.
- num_attention_heads: int. Number of attention heads.
- size_per_head: int. Size of each attention head.
- query_act: (optional) Activation function for the query transform.
- key_act: (optional) Activation function for the key transform.
- value_act: (optional) Activation function for the value transform.
- attention_probs_dropout_prob: (optional) float. Dropout probability of the
- attention probabilities.
- initializer_range: float. Range of the weight initializer.
- do_return_2d_tensor: bool. If True, the output will be of shape [batch_size
- * from_seq_length, num_attention_heads * size_per_head]. If False, the
- output will be of shape [batch_size, from_seq_length, num_attention_heads
- * size_per_head].
- batch_size: (Optional) int. If the input is 2D, this might be the batch size
- of the 3D version of the `from_tensor` and `to_tensor`.
- from_seq_length: (Optional) If the input is 2D, this might be the seq length
- of the 3D version of the `from_tensor`.
- to_seq_length: (Optional) If the input is 2D, this might be the seq length
- of the 3D version of the `to_tensor`.
-
- Returns:
- float Tensor of shape [batch_size, from_seq_length,
- num_attention_heads * size_per_head]. (If `do_return_2d_tensor` is
- true, this will be of shape [batch_size * from_seq_length,
- num_attention_heads * size_per_head]).
-
- Raises:
- ValueError: Any of the arguments or tensor shapes are invalid.
- """
-
- def transpose_for_scores(input_tensor, batch_size, num_attention_heads,
- seq_length, width):
- output_tensor = tf.reshape(
- input_tensor, [batch_size, seq_length, num_attention_heads, width])
-
- output_tensor = tf.transpose(output_tensor, [0, 2, 1, 3])
- return output_tensor
-
- from_shape = get_shape_list(from_tensor, expected_rank=[2, 3])
- to_shape = get_shape_list(to_tensor, expected_rank=[2, 3])
-
- if len(from_shape) != len(to_shape):
- raise ValueError(
- "The rank of `from_tensor` must match the rank of `to_tensor`.")
-
- if len(from_shape) == 3:
- batch_size = from_shape[0]
- from_seq_length = from_shape[1]
- to_seq_length = to_shape[1]
- elif len(from_shape) == 2:
- if (batch_size is None or from_seq_length is None or to_seq_length is None):
- raise ValueError(
- "When passing in rank 2 tensors to attention_layer, the values "
- "for `batch_size`, `from_seq_length`, and `to_seq_length` "
- "must all be specified.")
-
- # Scalar dimensions referenced here:
- # B = batch size (number of sequences)
- # F = `from_tensor` sequence length
- # T = `to_tensor` sequence length
- # N = `num_attention_heads`
- # H = `size_per_head`
-
- from_tensor_2d = reshape_to_matrix(from_tensor)
- to_tensor_2d = reshape_to_matrix(to_tensor)
-
- # `query_layer` = [B*F, N*H]
- query_layer = tf.layers.dense(
- from_tensor_2d,
- num_attention_heads * size_per_head,
- activation=query_act,
- name="query",
- kernel_initializer=create_initializer(initializer_range))
-
- # `key_layer` = [B*T, N*H]
- key_layer = tf.layers.dense(
- to_tensor_2d,
- num_attention_heads * size_per_head,
- activation=key_act,
- name="key",
- kernel_initializer=create_initializer(initializer_range))
-
- # `value_layer` = [B*T, N*H]
- value_layer = tf.layers.dense(
- to_tensor_2d,
- num_attention_heads * size_per_head,
- activation=value_act,
- name="value",
- kernel_initializer=create_initializer(initializer_range))
-
- # `query_layer` = [B, N, F, H]
- query_layer = transpose_for_scores(query_layer, batch_size,
- num_attention_heads, from_seq_length,
- size_per_head)
-
- # `key_layer` = [B, N, T, H]
- key_layer = transpose_for_scores(key_layer, batch_size, num_attention_heads,
- to_seq_length, size_per_head)
-
- # Take the dot product between "query" and "key" to get the raw
- # attention scores.
- # `attention_scores` = [B, N, F, T]
- attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
- attention_scores = tf.multiply(attention_scores,
- 1.0 / math.sqrt(float(size_per_head)))
-
- if attention_mask is not None:
- # `attention_mask` = [B, 1, F, T]
- attention_mask = tf.expand_dims(attention_mask, axis=[1])
-
- # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
- # masked positions, this operation will create a tensor which is 0.0 for
- # positions we want to attend and -10000.0 for masked positions.
- adder = (1.0 - tf.cast(attention_mask, tf.float32)) * -10000.0
-
- # Since we are adding it to the raw scores before the softmax, this is
- # effectively the same as removing these entirely.
- attention_scores += adder
-
- # Normalize the attention scores to probabilities.
- # `attention_probs` = [B, N, F, T]
- attention_probs = tf.nn.softmax(attention_scores)
-
- # This is actually dropping out entire tokens to attend to, which might
- # seem a bit unusual, but is taken from the original Transformer paper.
- attention_probs = dropout(attention_probs, attention_probs_dropout_prob)
-
- # `value_layer` = [B, T, N, H]
- value_layer = tf.reshape(
- value_layer,
- [batch_size, to_seq_length, num_attention_heads, size_per_head])
-
- # `value_layer` = [B, N, T, H]
- value_layer = tf.transpose(value_layer, [0, 2, 1, 3])
-
- # `context_layer` = [B, N, F, H]
- context_layer = tf.matmul(attention_probs, value_layer)
-
- # `context_layer` = [B, F, N, H]
- context_layer = tf.transpose(context_layer, [0, 2, 1, 3])
-
- if do_return_2d_tensor:
- # `context_layer` = [B*F, N*H]
- context_layer = tf.reshape(
- context_layer,
- [batch_size * from_seq_length, num_attention_heads * size_per_head])
- else:
- # `context_layer` = [B, F, N*H]
- context_layer = tf.reshape(
- context_layer,
- [batch_size, from_seq_length, num_attention_heads * size_per_head])
-
- return context_layer
-
-
- def transformer_model(input_tensor,
- attention_mask=None,
- hidden_size=768,
- num_hidden_layers=12,
- num_attention_heads=12,
- intermediate_size=3072,
- intermediate_act_fn=gelu,
- hidden_dropout_prob=0.1,
- attention_probs_dropout_prob=0.1,
- initializer_range=0.02,
- do_return_all_layers=False):
- """Multi-headed, multi-layer Transformer from "Attention is All You Need".
-
- This is almost an exact implementation of the original Transformer encoder.
-
- See the original paper:
- https://arxiv.org/abs/1706.03762
-
- Also see:
- https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py
-
- Args:
- input_tensor: float Tensor of shape [batch_size, seq_length, hidden_size].
- attention_mask: (optional) int32 Tensor of shape [batch_size, seq_length,
- seq_length], with 1 for positions that can be attended to and 0 in
- positions that should not be.
- hidden_size: int. Hidden size of the Transformer.
- num_hidden_layers: int. Number of layers (blocks) in the Transformer.
- num_attention_heads: int. Number of attention heads in the Transformer.
- intermediate_size: int. The size of the "intermediate" (a.k.a., feed
- forward) layer.
- intermediate_act_fn: function. The non-linear activation function to apply
- to the output of the intermediate/feed-forward layer.
- hidden_dropout_prob: float. Dropout probability for the hidden layers.
- attention_probs_dropout_prob: float. Dropout probability of the attention
- probabilities.
- initializer_range: float. Range of the initializer (stddev of truncated
- normal).
- do_return_all_layers: Whether to also return all layers or just the final
- layer.
-
- Returns:
- float Tensor of shape [batch_size, seq_length, hidden_size], the final
- hidden layer of the Transformer.
-
- Raises:
- ValueError: A Tensor shape or parameter is invalid.
- """
- if hidden_size % num_attention_heads != 0:
- raise ValueError(
- "The hidden size (%d) is not a multiple of the number of attention "
- "heads (%d)" % (hidden_size, num_attention_heads))
-
- attention_head_size = int(hidden_size / num_attention_heads)
- input_shape = get_shape_list(input_tensor, expected_rank=3)
- batch_size = input_shape[0]
- seq_length = input_shape[1]
- input_width = input_shape[2]
-
- # The Transformer performs sum residuals on all layers so the input needs
- # to be the same as the hidden size.
- if input_width != hidden_size:
- raise ValueError("The width of the input tensor (%d) != hidden size (%d)" %
- (input_width, hidden_size))
-
- # We keep the representation as a 2D tensor to avoid re-shaping it back and
- # forth from a 3D tensor to a 2D tensor. Re-shapes are normally free on
- # the GPU/CPU but may not be free on the TPU, so we want to minimize them to
- # help the optimizer.
- prev_output = reshape_to_matrix(input_tensor)
-
- all_layer_outputs = []
- for layer_idx in range(num_hidden_layers):
- with tf.variable_scope("layer_%d" % layer_idx):
- layer_input = prev_output
-
- with tf.variable_scope("attention"):
- attention_heads = []
- with tf.variable_scope("self"):
- attention_head = attention_layer(
- from_tensor=layer_input,
- to_tensor=layer_input,
- attention_mask=attention_mask,
- num_attention_heads=num_attention_heads,
- size_per_head=attention_head_size,
- attention_probs_dropout_prob=attention_probs_dropout_prob,
- initializer_range=initializer_range,
- do_return_2d_tensor=True,
- batch_size=batch_size,
- from_seq_length=seq_length,
- to_seq_length=seq_length)
- attention_heads.append(attention_head)
-
- attention_output = None
- if len(attention_heads) == 1:
- attention_output = attention_heads[0]
- else:
- # In the case where we have other sequences, we just concatenate
- # them to the self-attention head before the projection.
- attention_output = tf.concat(attention_heads, axis=-1)
-
- # Run a linear projection of `hidden_size` then add a residual
- # with `layer_input`.
- with tf.variable_scope("output"):
- attention_output = tf.layers.dense(
- attention_output,
- hidden_size,
- kernel_initializer=create_initializer(initializer_range))
- attention_output = dropout(attention_output, hidden_dropout_prob)
- attention_output = layer_norm(attention_output + layer_input)
-
- # The activation is only applied to the "intermediate" hidden layer.
- with tf.variable_scope("intermediate"):
- intermediate_output = tf.layers.dense(
- attention_output,
- intermediate_size,
- activation=intermediate_act_fn,
- kernel_initializer=create_initializer(initializer_range))
-
- # Down-project back to `hidden_size` then add the residual.
- with tf.variable_scope("output"):
- layer_output = tf.layers.dense(
- intermediate_output,
- hidden_size,
- kernel_initializer=create_initializer(initializer_range))
- layer_output = dropout(layer_output, hidden_dropout_prob)
- layer_output = layer_norm(layer_output + attention_output)
- prev_output = layer_output
- all_layer_outputs.append(layer_output)
-
- if do_return_all_layers:
- final_outputs = []
- for layer_output in all_layer_outputs:
- final_output = reshape_from_matrix(layer_output, input_shape)
- final_outputs.append(final_output)
- return final_outputs
- else:
- final_output = reshape_from_matrix(prev_output, input_shape)
- return final_output
-
-
- def get_shape_list(tensor, expected_rank=None, name=None):
- """Returns a list of the shape of tensor, preferring static dimensions.
-
- Args:
- tensor: A tf.Tensor object to find the shape of.
- expected_rank: (optional) int. The expected rank of `tensor`. If this is
- specified and the `tensor` has a different rank, and exception will be
- thrown.
- name: Optional name of the tensor for the error message.
-
- Returns:
- A list of dimensions of the shape of tensor. All static dimensions will
- be returned as python integers, and dynamic dimensions will be returned
- as tf.Tensor scalars.
- """
- if name is None:
- name = tensor.name
-
- if expected_rank is not None:
- assert_rank(tensor, expected_rank, name)
-
- shape = tensor.shape.as_list()
-
- non_static_indexes = []
- for (index, dim) in enumerate(shape):
- if dim is None:
- non_static_indexes.append(index)
-
- if not non_static_indexes:
- return shape
-
- dyn_shape = tf.shape(tensor)
- for index in non_static_indexes:
- shape[index] = dyn_shape[index]
- return shape
-
-
- def reshape_to_matrix(input_tensor):
- """Reshapes a >= rank 2 tensor to a rank 2 tensor (i.e., a matrix)."""
- ndims = input_tensor.shape.ndims
- if ndims < 2:
- raise ValueError("Input tensor must have at least rank 2. Shape = %s" %
- (input_tensor.shape))
- if ndims == 2:
- return input_tensor
-
- width = input_tensor.shape[-1]
- output_tensor = tf.reshape(input_tensor, [-1, width])
- return output_tensor
-
-
- def reshape_from_matrix(output_tensor, orig_shape_list):
- """Reshapes a rank 2 tensor back to its original rank >= 2 tensor."""
- if len(orig_shape_list) == 2:
- return output_tensor
-
- output_shape = get_shape_list(output_tensor)
-
- orig_dims = orig_shape_list[0:-1]
- width = output_shape[-1]
-
- return tf.reshape(output_tensor, orig_dims + [width])
-
-
- def assert_rank(tensor, expected_rank, name=None):
- """Raises an exception if the tensor rank is not of the expected rank.
-
- Args:
- tensor: A tf.Tensor to check the rank of.
- expected_rank: Python integer or list of integers, expected rank.
- name: Optional name of the tensor for the error message.
-
- Raises:
- ValueError: If the expected shape doesn't match the actual shape.
- """
- if name is None:
- name = tensor.name
-
- expected_rank_dict = {}
- if isinstance(expected_rank, six.integer_types):
- expected_rank_dict[expected_rank] = True
- else:
- for x in expected_rank:
- expected_rank_dict[x] = True
-
- actual_rank = tensor.shape.ndims
- if actual_rank not in expected_rank_dict:
- scope_name = tf.get_variable_scope().name
- raise ValueError(
- "For the tensor `%s` in scope `%s`, the actual rank "
- "`%d` (shape = %s) is not equal to the expected rank `%s`" %
- (name, scope_name, actual_rank, str(tensor.shape), str(expected_rank)))
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