adding pytorch model file

modeling_pytorch.py

+# 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.
+"""Common utility functions related to TensorFlow."""
+
+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 six
+import tensorflow as tf
+import torch
+import torch.nn as nn
+
+
+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 probabilitiy 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 sttdev 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 open(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 BERTEmbeddings(nn.Module):
+
+
+class BertModel(nn.Module):
+    """BERT model ("Bidirectional Embedding Representations from a Transformer").
+
+    Example usage:
+
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[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=True,
+                             scope=None):
+        """Constructor for BertModel.
+
+        Args:
+            config: `BertConfig` instance.
+            is_training: bool. rue 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. On the TPU,
+                it is must faster if this is True, on the CPU or GPU, it is faster if
+                this is False.
+            scope: (optional) variable scope. Defaults to "bert".
+
+        Raises:
+            ValueError: The config is invalid or one of the input tensor shapes
+                is invalid.
+        """
+        super(BertModel).__init__()
+        config = copy.deepcopy(config)
+        if not is_training:
+            config.hidden_dropout_prob = 0.0
+            config.attention_probs_dropout_prob = 0.0
+
+        batch_size = input_ids.size(0)
+        seq_length = input_ids.size(1)
+
+        if input_mask is None:
+            input_mask = torch.ones(batch_size, seq_length), dtype=torch.long)
+
+        if token_type_ids is None:
+            token_type_ids = torch.zeros((batch_size, seq_length), dtype=torch.long)
+
+        self.embeddings = BERTEmbeddings(config.vocab_size, config.hidden_size)
+        self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
+            # 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(input_tensor):
+    """Gaussian Error Linear Unit.
+
+    This is a smoother version of the RELU.
+    Original paper: https://arxiv.org/abs/1606.08415
+
+    Args:
+        input_tensor: float Tensor to perform activation.
+
+    Returns:
+        `input_tensor` with the GELU activation applied.
+    """
+    cdf = 0.5 * (1.0 + tf.erf(input_tensor / tf.sqrt(2.0)))
+    return input_tensor * 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_assigment_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)
+
+    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 probabiltiy 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.nn.embedding_lookup()`. One hot is better
+            for TPUs.
+
+    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))
+
+    if use_one_hot_embeddings:
+        flat_input_ids = tf.reshape(input_ids, [-1])
+        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.nn.embedding_lookup(embedding_table, 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]
+
+    if seq_length > max_position_embeddings:
+        raise ValueError("The seq length (%d) cannot be greater than "
+                                         "`max_position_embeddings` (%d)" %
+                                         (seq_length, max_position_embeddings))
+
+    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:
+        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.
+        if seq_length < max_position_embeddings:
+            position_embeddings = tf.slice(full_position_embeddings, [0, 0],
+                                                                         [seq_length, -1])
+        else:
+            position_embeddings = full_position_embeddings
+
+        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 unchaged 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:
+        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*V]
+        context_layer = tf.reshape(
+                context_layer,
+                [batch_size * from_seq_length, num_attention_heads * size_per_head])
+    else:
+        # `context_layer` = [B, F, N*V]
+        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)))