icefall/egs/librispeech/ASR/conformer_ctc2/attention.py

# Copyright    2022  Xiaomi Corp.        (author: Quandong Wang)
#
# See ../../../../LICENSE for clarification regarding multiple 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.

from typing import Optional, Tuple

import torch
import torch.nn as nn
from scaling import ScaledLinear
from torch import Tensor
from torch.nn.init import xavier_normal_


class MultiheadAttention(nn.Module):
    r"""Allows the model to jointly attend to information
    from different representation subspaces.
    See `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_.

    .. math::
        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O

    where :math:`head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)`.

    Args:
        embed_dim: Total dimension of the model.
        num_heads: Number of parallel attention heads. Note that ``embed_dim`` will be split
            across ``num_heads`` (i.e. each head will have dimension ``embed_dim // num_heads``).
        dropout: Dropout probability on ``attn_output_weights``. Default: ``0.0`` (no dropout).
        bias: If specified, adds bias to input / output projection layers. Default: ``True``.
        add_bias_kv: If specified, adds bias to the key and value sequences at dim=0. Default: ``False``.
        add_zero_attn: If specified, adds a new batch of zeros to the key and value sequences at dim=1.
            Default: ``False``.
        kdim: Total number of features for keys. Default: ``None`` (uses ``kdim=embed_dim``).
        vdim: Total number of features for values. Default: ``None`` (uses ``vdim=embed_dim``).
        batch_first: If ``True``, then the input and output tensors are provided
            as (batch, seq, feature). Default: ``False`` (seq, batch, feature).

    Examples::

        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
    """
    __constants__ = ["batch_first"]
    bias_k: Optional[torch.Tensor]
    bias_v: Optional[torch.Tensor]

    def __init__(
        self,
        embed_dim,
        num_heads,
        dropout=0.0,
        bias=True,
        add_bias_kv=False,
        add_zero_attn=False,
        kdim=None,
        vdim=None,
        batch_first=False,
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super(MultiheadAttention, self).__init__()
        self.embed_dim = embed_dim
        self.kdim = kdim if kdim is not None else embed_dim
        self.vdim = vdim if vdim is not None else embed_dim
        self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim

        self.num_heads = num_heads
        self.dropout = dropout
        self.batch_first = batch_first
        self.head_dim = embed_dim // num_heads
        assert (
            self.head_dim * num_heads == self.embed_dim
        ), "embed_dim must be divisible by num_heads"

        if self._qkv_same_embed_dim is False:
            self.q_proj_weight = ScaledLinear(embed_dim, embed_dim, bias=bias)
            self.k_proj_weight = ScaledLinear(self.kdim, embed_dim, bias=bias)
            self.v_proj_weight = ScaledLinear(self.vdim, embed_dim, bias=bias)
            self.register_parameter("in_proj_weight", None)
        else:
            self.in_proj_weight = ScaledLinear(embed_dim, 3 * embed_dim, bias=bias)
            self.register_parameter("q_proj_weight", None)
            self.register_parameter("k_proj_weight", None)
            self.register_parameter("v_proj_weight", None)

        if not bias:
            self.register_parameter("in_proj_bias", None)

        self.out_proj = ScaledLinear(embed_dim, embed_dim, bias=bias)

        if add_bias_kv:
            self.bias_k = nn.Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs))
            self.bias_v = nn.Parameter(torch.empty((1, 1, embed_dim), **factory_kwargs))
        else:
            self.bias_k = self.bias_v = None

        self.add_zero_attn = add_zero_attn

        self._reset_parameters()

    def _reset_parameters(self):
        if self.bias_k is not None:
            xavier_normal_(self.bias_k)
        if self.bias_v is not None:
            xavier_normal_(self.bias_v)

    def __setstate__(self, state):
        # Support loading old MultiheadAttention checkpoints generated by v1.1.0
        if "_qkv_same_embed_dim" not in state:
            state["_qkv_same_embed_dim"] = True

        super(MultiheadAttention, self).__setstate__(state)

    def forward(
        self,
        query: Tensor,
        key: Tensor,
        value: Tensor,
        key_padding_mask: Optional[Tensor] = None,
        need_weights: bool = True,
        attn_mask: Optional[Tensor] = None,
    ) -> Tuple[Tensor, Optional[Tensor]]:
        r"""
        Args:
            query: Query embeddings of shape :math:`(L, N, E_q)` when ``batch_first=False`` or :math:`(N, L, E_q)`
                when ``batch_first=True``, where :math:`L` is the target sequence length, :math:`N` is the batch size,
                and :math:`E_q` is the query embedding dimension ``embed_dim``. Queries are compared against
                key-value pairs to produce the output. See "Attention Is All You Need" for more details.
            key: Key embeddings of shape :math:`(S, N, E_k)` when ``batch_first=False`` or :math:`(N, S, E_k)` when
                ``batch_first=True``, where :math:`S` is the source sequence length, :math:`N` is the batch size, and
                :math:`E_k` is the key embedding dimension ``kdim``. See "Attention Is All You Need" for more details.
            value: Value embeddings of shape :math:`(S, N, E_v)` when ``batch_first=False`` or :math:`(N, S, E_v)` when
                ``batch_first=True``, where :math:`S` is the source sequence length, :math:`N` is the batch size, and
                :math:`E_v` is the value embedding dimension ``vdim``. See "Attention Is All You Need" for more details.
            key_padding_mask: If specified, a mask of shape :math:`(N, S)` indicating which elements within ``key``
                to ignore for the purpose of attention (i.e. treat as "padding"). Binary and byte masks are supported.
                For a binary mask, a ``True`` value indicates that the corresponding ``key`` value will be ignored for
                the purpose of attention. For a byte mask, a non-zero value indicates that the corresponding ``key``
                value will be ignored.
            need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``.
                Default: ``True``.
            attn_mask: If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape
                :math:`(L, S)` or :math:`(N\cdot\text{num\_heads}, L, S)`, where :math:`N` is the batch size,
                :math:`L` is the target sequence length, and :math:`S` is the source sequence length. A 2D mask will be
                broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch.
                Binary, byte, and float masks are supported. For a binary mask, a ``True`` value indicates that the
                corresponding position is not allowed to attend. For a byte mask, a non-zero value indicates that the
                corresponding position is not allowed to attend. For a float mask, the mask values will be added to
                the attention weight.

        Outputs:
            - **attn_output** - Attention outputs of shape :math:`(L, N, E)` when ``batch_first=False`` or
              :math:`(N, L, E)` when ``batch_first=True``, where :math:`L` is the target sequence length, :math:`N` is
              the batch size, and :math:`E` is the embedding dimension ``embed_dim``.
            - **attn_output_weights** - Attention output weights of shape :math:`(N, L, S)`, where :math:`N` is the batch
              size, :math:`L` is the target sequence length, and :math:`S` is the source sequence length. Only returned
              when ``need_weights=True``.
        """
        if self.batch_first:
            query, key, value = [x.transpose(1, 0) for x in (query, key, value)]

        if not self._qkv_same_embed_dim:
            q_proj_weight = (
                self.q_proj_weight.get_weight()
                if self.q_proj_weight is not None
                else None
            )
            k_proj_weight = (
                self.k_proj_weight.get_weight()
                if self.k_proj_weight is not None
                else None
            )
            v_proj_weight = (
                self.v_proj_weight.get_weight()
                if self.v_proj_weight is not None
                else None
            )
            (
                attn_output,
                attn_output_weights,
            ) = nn.functional.multi_head_attention_forward(
                query,
                key,
                value,
                self.embed_dim,
                self.num_heads,
                self.in_proj_weight.get_weight(),
                self.in_proj_weight.get_bias(),
                self.bias_k,
                self.bias_v,
                self.add_zero_attn,
                self.dropout,
                self.out_proj.get_weight(),
                self.out_proj.get_bias(),
                training=self.training,
                key_padding_mask=key_padding_mask,
                need_weights=need_weights,
                attn_mask=attn_mask,
                use_separate_proj_weight=True,
                q_proj_weight=q_proj_weight,
                k_proj_weight=k_proj_weight,
                v_proj_weight=v_proj_weight,
            )
        else:
            (
                attn_output,
                attn_output_weights,
            ) = nn.functional.multi_head_attention_forward(
                query,
                key,
                value,
                self.embed_dim,
                self.num_heads,
                self.in_proj_weight.get_weight(),
                self.in_proj_weight.get_bias(),
                self.bias_k,
                self.bias_v,
                self.add_zero_attn,
                self.dropout,
                self.out_proj.get_weight(),
                self.out_proj.get_bias(),
                training=self.training,
                key_padding_mask=key_padding_mask,
                need_weights=need_weights,
                attn_mask=attn_mask,
            )
        if self.batch_first:
            return attn_output.transpose(1, 0), attn_output_weights
        else:
            return attn_output, attn_output_weights