diff_ttsg/models/components/transformer.py

# Copyright (c) 2019 NVIDIA CORPORATION. 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.

import torch
import torch.nn as nn
import torch.nn.functional as F
from conformer.conformer import Attention as RelAttention
from einops import rearrange


class PositionalEmbedding(nn.Module):
    def __init__(self, demb):
        super().__init__()
        self.demb = demb
        inv_freq = 1 / (10000 ** (torch.arange(0.0, demb, 2.0) / demb))
        self.register_buffer("inv_freq", inv_freq)

    def forward(self, pos_seq, bsz=None):
        sinusoid_inp = torch.matmul(torch.unsqueeze(pos_seq, -1), torch.unsqueeze(self.inv_freq, 0))
        pos_emb = torch.cat([sinusoid_inp.sin(), sinusoid_inp.cos()], dim=1)
        if bsz is not None:
            return pos_emb[None, :, :].expand(bsz, -1, -1)
        else:
            return pos_emb[None, :, :]


class PositionwiseConvFF(nn.Module):
    def __init__(self, d_model, d_inner, kernel_size, dropout, pre_lnorm=False):
        super().__init__()

        self.d_model = d_model
        self.d_inner = d_inner
        self.dropout = dropout

        self.CoreNet = nn.Sequential(
            nn.Conv1d(d_model, d_inner, kernel_size, 1, (kernel_size // 2)),
            nn.ReLU(),
            # nn.Dropout(dropout),  # worse convergence
            nn.Conv1d(d_inner, d_model, kernel_size, 1, (kernel_size // 2)),
            nn.Dropout(dropout),
        )
        self.layer_norm = nn.LayerNorm(d_model)
        self.pre_lnorm = pre_lnorm

    def forward(self, inp):
        return self._forward(inp)

    def _forward(self, inp):
        if self.pre_lnorm:
            # layer normalization + positionwise feed-forward
            # core_out = inp
            core_out = self.CoreNet(self.layer_norm(inp).transpose(1, 2))
            core_out = core_out.transpose(1, 2)

            # residual connection
            output = core_out + inp
        else:
            # positionwise feed-forward
            core_out = inp.transpose(1, 2)
            core_out = self.CoreNet(core_out)
            core_out = core_out.transpose(1, 2)

            # residual connection + layer normalization
            output = self.layer_norm(inp + core_out).to(inp.dtype)

        return output


class MultiHeadAttn(nn.Module):
    def __init__(
        self, n_head, d_model, d_head, dropout, rel_attention, dropatt=0.1, pre_lnorm=True, rel_window_size=10
    ):
        super().__init__()

        self.n_head = n_head
        self.d_model = d_model
        self.d_head = d_head
        self.scale = 1 / (d_head**0.5)
        self.pre_lnorm = pre_lnorm
        self.rel_attention = rel_attention
        if rel_attention:
            self.attn = RelAttention(d_model, n_head, d_head, dropout, max_pos_emb=rel_window_size)
        else:
            self.qkv_net = nn.Linear(d_model, 3 * n_head * d_head)
            self.drop = nn.Dropout(dropout)
            self.dropatt = nn.Dropout(dropatt)
            self.o_net = nn.Linear(n_head * d_head, d_model, bias=False)

        self.layer_norm = nn.LayerNorm(d_model)

    def forward(self, inp, attn_mask=None):
        return self._forward(inp, attn_mask)

    def _forward(self, inp, attn_mask=None):
        residual = inp

        if self.pre_lnorm:
            # layer normalization
            inp = self.layer_norm(inp)

        if not self.rel_attention:
            n_head, d_head = self.n_head, self.d_head

            head_q, head_k, head_v = torch.chunk(self.qkv_net(inp), 3, dim=2)
            head_q = head_q.view(inp.size(0), inp.size(1), n_head, d_head)
            head_k = head_k.view(inp.size(0), inp.size(1), n_head, d_head)
            head_v = head_v.view(inp.size(0), inp.size(1), n_head, d_head)

            q = head_q.permute(2, 0, 1, 3).reshape(-1, inp.size(1), d_head)
            k = head_k.permute(2, 0, 1, 3).reshape(-1, inp.size(1), d_head)
            v = head_v.permute(2, 0, 1, 3).reshape(-1, inp.size(1), d_head)

            attn_score = torch.bmm(q, k.transpose(1, 2))
            attn_score.mul_(self.scale)

            if attn_mask is not None:
                attn_mask = attn_mask.unsqueeze(1).to(attn_score.dtype)
                attn_mask = attn_mask.repeat(n_head, attn_mask.size(2), 1)
                attn_score.masked_fill_(attn_mask.to(torch.bool), -float("inf"))

            attn_prob = F.softmax(attn_score, dim=2)
            attn_prob = self.dropatt(attn_prob)
            attn_vec = torch.bmm(attn_prob, v)

            attn_vec = attn_vec.view(n_head, inp.size(0), inp.size(1), d_head)
            attn_vec = attn_vec.permute(1, 2, 0, 3).contiguous().view(inp.size(0), inp.size(1), n_head * d_head)

            # linear projection
            attn_out = self.o_net(attn_vec)
            attn_out = self.drop(attn_out)
        else:
            attn_out = self.attn(inp, mask=attn_mask)

        if self.pre_lnorm:
            # residual connection
            output = residual + attn_out
        else:
            # residual connection + layer normalization
            output = self.layer_norm(residual + attn_out)

        output = output.to(attn_out.dtype)

        return output


class TransformerLayer(nn.Module):
    def __init__(self, n_head, d_model, d_head, d_inner, kernel_size, dropout, **kwargs):
        super().__init__()

        self.dec_attn = MultiHeadAttn(n_head, d_model, d_head, dropout, **kwargs)
        self.pos_ff = PositionwiseConvFF(d_model, d_inner, kernel_size, dropout, pre_lnorm=kwargs.get("pre_lnorm"))

    def forward(self, dec_inp, mask=None):
        output = self.dec_attn(dec_inp, attn_mask=~mask.squeeze(2))
        output *= mask
        output = self.pos_ff(output)
        output *= mask
        return output


class FFTransformer(nn.Module):
    def __init__(
        self,
        n_layer,
        n_head,
        hidden_channels,
        d_head,
        d_inner,
        kernel_size,
        dropout,
        dropatt,
        dropemb=0.0,
        embed_input=False,
        n_embed=None,
        d_embed=None,
        padding_idx=0,
        pre_lnorm=True,
        rel_attention=True,
        rel_window_size=10,
    ):
        super().__init__()
        self.d_model = hidden_channels
        self.n_head = n_head
        self.d_head = d_head
        self.padding_idx = padding_idx

        if embed_input:
            self.word_emb = nn.Embedding(n_embed, d_embed or hidden_channels, padding_idx=self.padding_idx)
        else:
            self.word_emb = None

        self.rel_attention = rel_attention

        if not rel_attention:
            self.pos_emb = PositionalEmbedding(self.d_model)

        self.drop = nn.Dropout(dropemb)
        self.layers = nn.ModuleList()

        for _ in range(n_layer):
            self.layers.append(
                TransformerLayer(
                    n_head,
                    hidden_channels,
                    d_head,
                    d_inner,
                    kernel_size,
                    dropout,
                    dropatt=dropatt,
                    pre_lnorm=pre_lnorm,
                    rel_attention=rel_attention,
                    rel_window_size=rel_window_size,
                )
            )

    def forward(self, dec_inp, mask=None, conditioning=0):
        inp = dec_inp.transpose(1, 2)
        mask = mask.bool().squeeze(1).unsqueeze(2)
        # if self.word_emb is None:
        #     inp = dec_inp
        #     mask = sequence_mask(seq_lens, inp.shape[1], device=seq_lens.device, dtype=seq_lens.dtype).unsqueeze(2)
        # else:
        #     inp = self.word_emb(dec_inp)
        #     # [bsz x L x 1]
        #     mask = (dec_inp != self.padding_idx).unsqueeze(2)

        if not self.rel_attention:
            pos_seq = torch.arange(inp.size(1), device=inp.device).to(inp.dtype)
            pos_emb = self.pos_emb(pos_seq) * mask
        else:
            pos_emb = 0

        out = self.drop(inp + pos_emb + conditioning)

        for layer in self.layers:
            out = layer(out, mask=mask)

        # out = self.drop(out)
        return rearrange(out, "b l h -> b h l")