modeling_replace_sa_ff.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.
"""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.io.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,
                 use_estimator=True,
                 use_approximator=[],
                 approximator_setting=None,
                 add_GeLU_att=False,
                 layer_order=[],
                 add_weight=None,
                 weight_type=None,
                 weight_act=None,
                 linear_attention=False,
                 cancel_skip_connection=[],
                 layer_use_relu=[],
                 feed_same=False,
                 head_mask=None,
                 layer_mask=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".

    New added args:
      use_estimator: bool, whether to use estimator API. If not, need to use tf.cond to control the dropout
      use_approximator: list, layers to be replaced by approximators.
      approximator_setting: string, parts to be approximated. e.g. HS*4_FFGeLU, HS_MLP, etc.
      layer_order: list, orders of layers.
      add_weight: string, weight the skip-connections of attention, FFN, or both.
      weight_type: string, weighting method, learn or static.
      weight_act: string, if weight_type == 'learn', the activation of the weight calculation layer.
      linear_attention: bool, whether to apply softmax in attention blocks.
      cancel_skip_connection: list, layers which needs to remove skip-connections.
      feed_same: bool, whether the input of each layer is the same.
      head_mask: list or array, heads to be masked out.
      layer_mask: list or array, layers which needs to do head masking.

    Raises:
      ValueError: The config is invalid or one of the input tensor shapes
        is invalid.
    """
        config = copy.deepcopy(config)
        if use_estimator:
            if not is_training:
                config.hidden_dropout_prob = 0.0
                config.attention_probs_dropout_prob = 0.0
        else:
            config.hidden_dropout_prob = tf.cond(is_training,
                                                 lambda: config.hidden_dropout_prob,
                                                 lambda: 0.0)
            config.attention_probs_dropout_prob = tf.cond(is_training,
                                                          lambda: config.attention_probs_dropout_prob,
                                                          lambda: 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.compat.v1.variable_scope(scope, default_name="bert"):
            with tf.compat.v1.variable_scope("embeddings"):
                # Perform embedding lookup on the word ids.
                (self.word_embed, 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, self.tok_type, self.pos_embed) = embedding_postprocessor(
                    input_tensor=self.word_embed,
                    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.compat.v1.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, self.all_head_output,
                 self.all_layer_tokens_beforeMLP, self.all_layer_tokens_afterMLP,
                 self.all_layer_attention_weight, self.all_layer_ffn_weight,
                 self.all_attention_before_dropout, self.all_layer_tokens_after_FFGeLU,
                 self.all_attention_before_layernorm, self.all_ffn_before_layernorm) = 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,
                    use_approximator=use_approximator,
                    approximator_setting=approximator_setting,
                    add_GeLU_att=add_GeLU_att,
                    layer_order=layer_order,
                    add_weight=add_weight,
                    weight_type=weight_type,
                    weight_act=weight_act,
                    linear_attention=linear_attention,
                    cancel_skip_connection=cancel_skip_connection,
                    layer_use_relu=layer_use_relu,
                    feed_same=feed_same,
                    head_mask=head_mask,
                    layer_mask=layer_mask)

            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.compat.v1.variable_scope("pooler") as vs:
                # 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):
        return self.sequence_output

    def get_all_encoder_layers(self):
        return self.all_encoder_layers

    def get_all_layer_tokens_beforeMLP(self):
        return self.all_layer_tokens_beforeMLP

    def get_all_layer_tokens_afterMLP(self):
        return self.all_layer_tokens_afterMLP

    def get_all_head_output(self):
        return self.all_head_output

    def get_all_layer_attention_weight(self):
        return self.all_layer_attention_weight

    def get_all_layer_ffn_weight(self):
        return self.all_layer_ffn_weight

    def get_in_embeds(self):
        return self.word_embed, self.tok_type, self.pos_embed

    def get_embedding_output(self):
        return self.embedding_output

    def get_embedding_table(self):
        return self.embedding_table

    def get_all_attention_before_dropout(self):
        return self.all_attention_before_dropout

    def get_all_layer_tokens_after_FFGeLU(self):
        return self.all_layer_tokens_after_FFGeLU

    def get_all_attention_before_layernorm(self):
        return self.all_attention_before_layernorm

    def get_all_ffn_before_layernorm(self):
        return self.all_ffn_before_layernorm


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)

    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
        assignment_map[name] = name_to_variable[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

    # here used keep_prob, not rate!
    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.compat.v1.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.compat.v1.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.compat.v1.assert_less_equal(seq_length, max_position_embeddings)
        with tf.control_dependencies([assert_op]):
            full_position_embeddings = tf.compat.v1.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, token_type_embeddings, position_embeddings


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,
                    linear_attention=False,
                    head_mask=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)  # [B * F, hidden_size]
    to_tensor_2d = reshape_to_matrix(to_tensor)  # [B * T, hidden_size]

    # `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) if not linear_attention else 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])

    # For masking out the head, here we need to zero out the specific head
    if head_mask is not None:
        def mask_fn(ts, h):
            multipler = tf.cond(tf.reduce_any(tf.equal(h, 0)),
                                lambda: tf.zeros(shape=[batch_size, from_seq_length, 1, size_per_head]),
                                lambda: tf.ones(shape=[batch_size, from_seq_length, 1, size_per_head]))
            for i in range(1, num_attention_heads):
                cur = tf.cond(tf.reduce_any(tf.equal(h, i)),
                              lambda: tf.zeros(shape=[batch_size, from_seq_length, 1, size_per_head]),
                              lambda: tf.ones(shape=[batch_size, from_seq_length, 1, size_per_head]))
                multipler = tf.concat([multipler, cur], axis=-2)
            # left = tf.ones(shape=[batch_size, from_seq_length, h, size_per_head])
            # right = tf.ones(shape=[batch_size, from_seq_length, num_attention_heads - h - 1, size_per_head])
            # masked_head = tf.zeros(shape=[batch_size, from_seq_length, 1, size_per_head])
            # multipler = tf.concat([left, masked_head, right], axis=-2)
            return tf.multiply(ts, multipler)

        context_layer = tf.cond(tf.reduce_any(tf.equal(head_mask, -1)),
                                lambda: context_layer,
                                lambda: mask_fn(context_layer, head_mask))

    context_layer_out = context_layer

    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, attention_probs, context_layer_out, value_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,
                      use_approximator=[],
                      approximator_setting=None,
                      add_GeLU_att=False,
                      layer_order=[],
                      add_weight=None,
                      weight_type=None,
                      weight_act=None,
                      linear_attention=False,
                      cancel_skip_connection=[],
                      layer_use_relu=[],
                      feed_same=False,
                      head_mask=None,
                      layer_mask=None):
    """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, seq_length, hidden_size]
    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)  # [batch_size * seq_length, hidden_size]

    all_layer_outputs = []  # list, [batch_size, seq_length, hidden_size] * n_layers
    all_layer_tokens_beforeMLP = []  # list, [batch_size, seq_length, hidden_size] * n_layers
    all_layer_tokens_afterMLP = []  # list, [batch_size, seq_length, hidden_size] * n_layers
    all_layer_tokens_after_FFGeLU = []  # list, [batch_size, seq_length, hidden_size] * n_layers
    all_head_output = []  # list, [batch_size, seq_length, num_head, hidden_size_per_head] * n_layers
    all_attention_before_dropout = []  # list, [batch_size, seq_length, hidden_size] * n_layers
    all_attention_before_layernorm = []
    all_ffn_before_layernorm = []

    all_layer_attention_weight = []
    all_layer_ffn_weight = []

    layers = range(num_hidden_layers) if not layer_order else layer_order
    for layer_idx in layers:
        with tf.compat.v1.variable_scope("layer_%d" % layer_idx, reuse=tf.AUTO_REUSE):
            layer_input = prev_output

            ##############################################################
            # Linear layer with hidden size, replacing Self_Attention_FF #
            ##############################################################
            if layer_idx not in use_approximator:
                with tf.compat.v1.variable_scope("attention"):
                    attention_heads = []
                    with tf.compat.v1.variable_scope("self"):

                        # This is for head masking experiments
                        # condition on current layer idx
                        head = head_mask
                        if layer_mask is not None:
                            head = tf.cond(tf.reduce_any(tf.equal(layer_mask, layer_idx)),
                                           lambda: head_mask,
                                           lambda: -1)

                        # attention_head, [batch_size * seq_length, hidden_size]
                        # attn_probs, [batch_size, num_head, seq_length, seq_length]
                        # head_output, [batch_size, seq_length, num_head, hidden_size_per_head]
                        # value_layer, [batch_size, num_head, seq_length, hidden_size_per_head]
                        attention_head, attn_probs, head_output, value_layer = 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,
                            linear_attention=linear_attention,
                            head_mask=head)
                        attention_heads.append(attention_head)

                    attention_output = None  # [batch_size * seq_length, hidden_size]
                    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)

                    ##HERE TO RETURN HEAD
                    all_head_output.append(head_output)

                    # Run a linear projection of `hidden_size` then add a residual with `layer_input`.
                    att_ff_activation = intermediate_act_fn if add_GeLU_att else None
                    with tf.compat.v1.variable_scope("output"):
                        # [batch_size * seq_length, hidden_size]
                        attention_output = tf.layers.dense(
                            attention_output,
                            hidden_size,
                            activation=att_ff_activation,
                            kernel_initializer=create_initializer(initializer_range))

            else:
                with tf.compat.v1.variable_scope("non-linearity"):
                    attention_output = tf.layers.dense(
                        layer_input,
                        hidden_size,
                        kernel_initializer=create_initializer(initializer_range))

            with tf.compat.v1.variable_scope("attention"):
                with tf.compat.v1.variable_scope("output"):

                    all_attention_before_dropout.append(attention_output)

                    attention_output = dropout(attention_output, hidden_dropout_prob)

                    attention_weight = 1. if layer_idx not in cancel_skip_connection else 0.
                    if add_weight in ['attention', 'all']:
                        if weight_type == 'learn':
                            # weights depend on layer_input
                            with tf.compat.v1.variable_scope("attention_weight"):
                                # [batch_size * seq_length, 1]
                                attention_weight = tf.layers.dense(
                                    layer_input,
                                    1,
                                    activation=weight_act,
                                    kernel_initializer=create_initializer(initializer_range))
                        elif weight_type == 'static':
                            # static learnable weights
                            attention_weight = tf.Variable(initial_value=tf.constant(1.),
                                                           trainable=True,
                                                           name="attention_weight",
                                                           dtype=tf.float32)
                    all_layer_attention_weight.append(attention_weight)

                    all_attention_before_layernorm.append(attention_output + layer_input * attention_weight)

                    attention_output = layer_norm(attention_output + layer_input * attention_weight)

            ##RETURN EMBEEDDING BEFORE MLP
            all_layer_tokens_beforeMLP.append(attention_output)

            if layer_idx in layer_use_relu:
                intermediate_act_fn = tf.nn.relu

            # The activation is only applied to the "intermediate" hidden layer.
            with tf.compat.v1.variable_scope("intermediate"):
                # [batch_size, seq_length, intermediate_size]
                intermediate_output = tf.layers.dense(
                    attention_output,
                    intermediate_size,
                    activation=intermediate_act_fn,
                    kernel_initializer=create_initializer(initializer_range))

            all_layer_tokens_after_FFGeLU.append(intermediate_output)

            # Down-project back to `hidden_size` then add the residual.
            with tf.compat.v1.variable_scope("output"):
                # [batch_size, seq_length, hidden_size]
                layer_output = tf.layers.dense(
                    intermediate_output,
                    hidden_size,
                    kernel_initializer=create_initializer(initializer_range))
            # ######MLP######
            ##RETURN EMBEEDDING AFTER MLP
            all_layer_tokens_afterMLP.append(layer_output)

            with tf.compat.v1.variable_scope("output"):
                layer_output = dropout(layer_output, hidden_dropout_prob)

                ffn_weight = 1. if layer_idx not in cancel_skip_connection else 0.
                if add_weight in ["ffn", "all"]:
                    if weight_type == 'learn':
                        # weights depend on attention_output
                        with tf.compat.v1.variable_scope("ffn_weight"):
                            # [batch_size * seq_length, 1]
                            ffn_weight = tf.layers.dense(
                                attention_output,
                                1,
                                activation=weight_act,
                                kernel_initializer=create_initializer(initializer_range))
                    elif weight_type == 'static':
                        # static learnable weights
                        ffn_weight = tf.Variable(initial_value=tf.constant(1.),
                                                 trainable=True,
                                                 name="ffn_weight",
                                                 dtype=tf.float32)
                all_layer_ffn_weight.append(ffn_weight)

                all_ffn_before_layernorm.append(layer_output + attention_output * ffn_weight)

                layer_output = layer_norm(layer_output + attention_output * ffn_weight)  # [batch_size * seq_length, hidden_size]

            if not feed_same:
                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, all_head_output, all_layer_tokens_beforeMLP, all_layer_tokens_afterMLP, \
               all_layer_attention_weight, all_layer_ffn_weight, all_attention_before_dropout, \
               all_layer_tokens_after_FFGeLU, all_attention_before_layernorm, all_ffn_before_layernorm
    else:
        final_output = reshape_from_matrix(prev_output, input_shape)
        return final_output, None, None, None, None, None, None, None, None, None


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)))