SSAN_encoder.py

#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""Transformer encoder."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from functools import partial

import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid.layers import core


def multi_head_attention(queries,
                         keys,
                         values,
                         attn_bias,
                         structure_mask,
                         with_ent_structure,
                         d_key,
                         d_value,
                         d_model,
                         n_head=1,
                         dropout_rate=0.,
                         cache=None,
                         param_initializer=None,
                         name='multi_head_att'):
    """
    Multi-Head Attention. Note that attn_bias is added to the logit before
    computing softmax activiation to mask certain selected positions so that
    they will not considered in attention weights.
    """
    keys = queries if keys is None else keys
    values = keys if values is None else values

    if not (len(queries.shape) == len(keys.shape) == len(values.shape) == 3):
        raise ValueError(
            "Inputs: quries, keys and values should all be 3-D tensors.")

    def __compute_qkv(queries, keys, values, n_head, d_key, d_value):
        """
        Add linear projection to queries, keys, and values.
        """
        q = layers.fc(input=queries,
                      size=d_key * n_head,
                      num_flatten_dims=2,
                      param_attr=fluid.ParamAttr(
                          name=name + '_query_fc.w_0',
                          initializer=param_initializer),
                      bias_attr=name + '_query_fc.b_0')
        k = layers.fc(input=keys,
                      size=d_key * n_head,
                      num_flatten_dims=2,
                      param_attr=fluid.ParamAttr(
                          name=name + '_key_fc.w_0',
                          initializer=param_initializer),
                      bias_attr=name + '_key_fc.b_0')
        v = layers.fc(input=values,
                      size=d_value * n_head,
                      num_flatten_dims=2,
                      param_attr=fluid.ParamAttr(
                          name=name + '_value_fc.w_0',
                          initializer=param_initializer),
                      bias_attr=name + '_value_fc.b_0')

        return q, k, v

    def __split_heads(x, n_head):
        """
        Reshape the last dimension of inpunt tensor x so that it becomes two
        dimensions and then transpose. Specifically, input a tensor with shape
        [bs, max_sequence_length, n_head * hidden_dim] then output a tensor
        with shape [bs, n_head, max_sequence_length, hidden_dim].
        """
        hidden_size = x.shape[-1]
        # The value 0 in shape attr means copying the corresponding dimension
        # size of the input as the output dimension size.
        reshaped = layers.reshape(
            x=x, shape=[0, 0, n_head, hidden_size // n_head], inplace=True)

        # permuate the dimensions into:
        # [batch_size, n_head, max_sequence_len, hidden_size_per_head]
        return layers.transpose(x=reshaped, perm=[0, 2, 1, 3])

    def __combine_heads(x):
        """
        Transpose and then reshape the last two dimensions of inpunt tensor x
        so that it becomes one dimension, which is reverse to __split_heads.
        """
        if len(x.shape) == 3: return x
        if len(x.shape) != 4:
            raise ValueError("Input(x) should be a 4-D Tensor.")

        trans_x = layers.transpose(x, perm=[0, 2, 1, 3])
        # The value 0 in shape attr means copying the corresponding dimension
        # size of the input as the output dimension size.
        return layers.reshape(
            x=trans_x,
            shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]],
            inplace=True)

    def scaled_dot_product_attention(q, k, v, attn_bias, biaffine_transformation, biaffine_transformation_bias,
                                     structure_mask, with_ent_structure, d_key, dropout_rate):
        """
        Scaled Dot-Product Attention
        """
        scaled_q = layers.scale(x=q, scale=d_key**-0.5)
        product = layers.matmul(x=scaled_q, y=k, transpose_y=True)

        if with_ent_structure:
            # TRANSFORMATION
            # 1.reshape input
            # q: [bs, n_head, seq, hidden] -> [bs, 1, n_head, seq, hidden] -> [bs, 5, n_head, seq, hidden]
            # -> [5, n_head, bs, seq, hidden] -> [5, n_head, bs * seq, hidden]
            # transformation: [dependencies(5), n_head, hidden, hidden]
            # k: [bs, n_head, seq, hidden] -> [bs, 1, n_head, seq, hidden]
            q_ = layers.unsqueeze(scaled_q, [1])
            q_ = layers.expand(q_, [1, biaffine_transformation.shape[0], 1, 1, 1])
            q_ = layers.transpose(q_, perm=[1, 2, 0, 3, 4])
            q_ = layers.reshape(q_, shape=[0, 0, -1, biaffine_transformation.shape[3]], inplace=True)
            k_ = layers.unsqueeze(k, [1])
            k_ = layers.expand(k_, [1, biaffine_transformation.shape[0], 1, 1, 1])

            # 2.implement matmul
            # q * transformation: [5, n_head, bs * seq, hidden]
            # q * transformation: [5, n_head, bs * seq, hidden] -> [5, n_head, bs, seq, hidden]
            # -> [bs, dependencies(5), n_head, seq, hidden]
            # q * transformation * k: [bs, dependencies(5), n_head, seq, seq]
            structured_bias = layers.matmul(x=q_, y=biaffine_transformation)
            structured_bias = layers.reshape(structured_bias, shape=[0, 0, -1, k_.shape[3], k_.shape[4]], inplace=True)
            structured_bias = layers.transpose(structured_bias, perm=[2, 0, 1, 3, 4])
            structured_bias = layers.matmul(x=structured_bias, y=k_, transpose_y=True)

            structured_bias = layers.elementwise_add(structured_bias, biaffine_transformation_bias, axis=1)

            # mask & apply
            structured_bias = structured_bias * structure_mask
            structured_bias = layers.reduce_sum(structured_bias, dim=1)
            product += structured_bias

        if attn_bias:
            product += attn_bias
        weights = layers.softmax(product)
        if dropout_rate:
            weights = layers.dropout(
                weights,
                dropout_prob=dropout_rate,
                dropout_implementation="upscale_in_train",
                is_test=False)
        out = layers.matmul(weights, v)
        return out

    q, k, v = __compute_qkv(queries, keys, values, n_head, d_key, d_value)

    if cache is not None:  # use cache and concat time steps
        # Since the inplace reshape in __split_heads changes the shape of k and
        # v, which is the cache input for next time step, reshape the cache
        # input from the previous time step first.
        k = cache["k"] = layers.concat(
            [layers.reshape(
                cache["k"], shape=[0, 0, d_model]), k], axis=1)
        v = cache["v"] = layers.concat(
            [layers.reshape(
                cache["v"], shape=[0, 0, d_model]), v], axis=1)

    q = __split_heads(q, n_head)
    k = __split_heads(k, n_head)
    v = __split_heads(v, n_head)

    biaffine_transformation = layers.create_parameter([5, n_head, d_key, d_key], core.VarDesc.VarType.FP32,
                                                      name=name + '_biaffine_transformation', attr=None,
                                                      is_bias=False, default_initializer=param_initializer)
    biaffine_transformation_bias = layers.create_parameter([5, n_head], core.VarDesc.VarType.FP32,
                                                      name=name + '_biaffine_transformation_bias', attr=None,
                                                      is_bias=False, default_initializer=param_initializer)

    ctx_multiheads = scaled_dot_product_attention(q, k, v, attn_bias,
                                                  biaffine_transformation, biaffine_transformation_bias,
                                                  structure_mask, with_ent_structure, d_key, dropout_rate)

    out = __combine_heads(ctx_multiheads)

    # Project back to the model size.
    proj_out = layers.fc(input=out,
                         size=d_model,
                         num_flatten_dims=2,
                         param_attr=fluid.ParamAttr(
                             name=name + '_output_fc.w_0',
                             initializer=param_initializer),
                         bias_attr=name + '_output_fc.b_0')
    return proj_out


def positionwise_feed_forward(x,
                              d_inner_hid,
                              d_hid,
                              dropout_rate,
                              hidden_act,
                              param_initializer=None,
                              name='ffn'):
    """
    Position-wise Feed-Forward Networks.
    This module consists of two linear transformations with a ReLU activation
    in between, which is applied to each position separately and identically.
    """
    hidden = layers.fc(input=x,
                       size=d_inner_hid,
                       num_flatten_dims=2,
                       act=hidden_act,
                       param_attr=fluid.ParamAttr(
                           name=name + '_fc_0.w_0',
                           initializer=param_initializer),
                       bias_attr=name + '_fc_0.b_0')
    if dropout_rate:
        hidden = layers.dropout(
            hidden,
            dropout_prob=dropout_rate,
            dropout_implementation="upscale_in_train",
            is_test=False)
    out = layers.fc(input=hidden,
                    size=d_hid,
                    num_flatten_dims=2,
                    param_attr=fluid.ParamAttr(
                        name=name + '_fc_1.w_0', initializer=param_initializer),
                    bias_attr=name + '_fc_1.b_0')
    return out


def pre_post_process_layer(prev_out, out, process_cmd, dropout_rate=0.,
                           name=''):
    """
    Add residual connection, layer normalization and droput to the out tensor
    optionally according to the value of process_cmd.
    This will be used before or after multi-head attention and position-wise
    feed-forward networks.
    """
    for cmd in process_cmd:
        if cmd == "a":  # add residual connection
            out = out + prev_out if prev_out else out
        elif cmd == "n":  # add layer normalization
            out_dtype = out.dtype
            if out_dtype == fluid.core.VarDesc.VarType.FP16:
                out = layers.cast(x=out, dtype="float32")
            out = layers.layer_norm(
                out,
                begin_norm_axis=len(out.shape) - 1,
                param_attr=fluid.ParamAttr(
                    name=name + '_layer_norm_scale',
                    initializer=fluid.initializer.Constant(1.)),
                bias_attr=fluid.ParamAttr(
                    name=name + '_layer_norm_bias',
                    initializer=fluid.initializer.Constant(0.)))
            if out_dtype == fluid.core.VarDesc.VarType.FP16:
                out = layers.cast(x=out, dtype="float16")
        elif cmd == "d":  # add dropout
            if dropout_rate:
                out = layers.dropout(
                    out,
                    dropout_prob=dropout_rate,
                    dropout_implementation="upscale_in_train",
                    is_test=False)
    return out


pre_process_layer = partial(pre_post_process_layer, None)
post_process_layer = pre_post_process_layer


def encoder_layer(enc_input,
                  attn_bias,
                  structure_mask,
                  with_ent_structure,
                  n_head,
                  d_key,
                  d_value,
                  d_model,
                  d_inner_hid,
                  prepostprocess_dropout,
                  attention_dropout,
                  relu_dropout,
                  hidden_act,
                  preprocess_cmd="n",
                  postprocess_cmd="da",
                  param_initializer=None,
                  name=''):
    """The encoder layers that can be stacked to form a deep encoder.
    This module consits of a multi-head (self) attention followed by
    position-wise feed-forward networks and both the two components companied
    with the post_process_layer to add residual connection, layer normalization
    and droput.
    """
    attn_output = multi_head_attention(
        pre_process_layer(
            enc_input,
            preprocess_cmd,
            prepostprocess_dropout,
            name=name + '_pre_att'),
        None,
        None,
        attn_bias,
        structure_mask,
        with_ent_structure,
        d_key,
        d_value,
        d_model,
        n_head,
        attention_dropout,
        param_initializer=param_initializer,
        name=name + '_multi_head_att')
    attn_output = post_process_layer(
        enc_input,
        attn_output,
        postprocess_cmd,
        prepostprocess_dropout,
        name=name + '_post_att')
    ffd_output = positionwise_feed_forward(
        pre_process_layer(
            attn_output,
            preprocess_cmd,
            prepostprocess_dropout,
            name=name + '_pre_ffn'),
        d_inner_hid,
        d_model,
        relu_dropout,
        hidden_act,
        param_initializer=param_initializer,
        name=name + '_ffn')
    return post_process_layer(
        attn_output,
        ffd_output,
        postprocess_cmd,
        prepostprocess_dropout,
        name=name + '_post_ffn')


def encoder(enc_input,
            attn_bias,
            structure_mask,
            with_ent_structure,
            n_layer,
            n_head,
            d_key,
            d_value,
            d_model,
            d_inner_hid,
            prepostprocess_dropout,
            attention_dropout,
            relu_dropout,
            hidden_act,
            preprocess_cmd="n",
            postprocess_cmd="da",
            param_initializer=None,
            name=''):
    """
    The encoder is composed of a stack of identical layers returned by calling
    encoder_layer.
    """
    for i in range(n_layer):
        enc_output = encoder_layer(
            enc_input,
            attn_bias,
            structure_mask,
            with_ent_structure,
            n_head,
            d_key,
            d_value,
            d_model,
            d_inner_hid,
            prepostprocess_dropout,
            attention_dropout,
            relu_dropout,
            hidden_act,
            preprocess_cmd,
            postprocess_cmd,
            param_initializer=param_initializer,
            name=name + '_layer_' + str(i))
        enc_input = enc_output
    enc_output = pre_process_layer(
        enc_output, preprocess_cmd, prepostprocess_dropout, name="post_encoder")

    return enc_output