models/svc/transformer/conformer.py

# Copyright (c) 2023 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.

import math
import torch
import numpy as np
import torch.nn as nn
from utils.util import convert_pad_shape


class BaseModule(torch.nn.Module):
    def __init__(self):
        super(BaseModule, self).__init__()

    @property
    def nparams(self):
        """
        Returns number of trainable parameters of the module.
        """
        num_params = 0
        for name, param in self.named_parameters():
            if param.requires_grad:
                num_params += np.prod(param.detach().cpu().numpy().shape)
        return num_params

    def relocate_input(self, x: list):
        """
        Relocates provided tensors to the same device set for the module.
        """
        device = next(self.parameters()).device
        for i in range(len(x)):
            if isinstance(x[i], torch.Tensor) and x[i].device != device:
                x[i] = x[i].to(device)
        return x


class LayerNorm(BaseModule):
    def __init__(self, channels, eps=1e-4):
        super(LayerNorm, self).__init__()
        self.channels = channels
        self.eps = eps

        self.gamma = torch.nn.Parameter(torch.ones(channels))
        self.beta = torch.nn.Parameter(torch.zeros(channels))

    def forward(self, x):
        n_dims = len(x.shape)
        mean = torch.mean(x, 1, keepdim=True)
        variance = torch.mean((x - mean) ** 2, 1, keepdim=True)

        x = (x - mean) * torch.rsqrt(variance + self.eps)

        shape = [1, -1] + [1] * (n_dims - 2)
        x = x * self.gamma.view(*shape) + self.beta.view(*shape)
        return x


class ConvReluNorm(BaseModule):
    def __init__(
        self,
        in_channels,
        hidden_channels,
        out_channels,
        kernel_size,
        n_layers,
        p_dropout,
        eps=1e-5,
    ):
        super(ConvReluNorm, self).__init__()
        self.in_channels = in_channels
        self.hidden_channels = hidden_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.n_layers = n_layers
        self.p_dropout = p_dropout
        self.eps = eps

        self.conv_layers = torch.nn.ModuleList()
        self.conv_layers.append(
            torch.nn.Conv1d(
                in_channels, hidden_channels, kernel_size, padding=kernel_size // 2
            )
        )
        self.relu_drop = torch.nn.Sequential(
            torch.nn.ReLU(), torch.nn.Dropout(p_dropout)
        )
        for _ in range(n_layers - 1):
            self.conv_layers.append(
                torch.nn.Conv1d(
                    hidden_channels,
                    hidden_channels,
                    kernel_size,
                    padding=kernel_size // 2,
                )
            )
        self.proj = torch.nn.Conv1d(hidden_channels, out_channels, 1)
        self.proj.weight.data.zero_()
        self.proj.bias.data.zero_()

    def forward(self, x, x_mask):
        for i in range(self.n_layers):
            x = self.conv_layers[i](x * x_mask)
            x = self.instance_norm(x, x_mask)
            x = self.relu_drop(x)
        x = self.proj(x)
        return x * x_mask

    def instance_norm(self, x, mask, return_mean_std=False):
        mean, std = self.calc_mean_std(x, mask)
        x = (x - mean) / std
        if return_mean_std:
            return x, mean, std
        else:
            return x

    def calc_mean_std(self, x, mask=None):
        x = x * mask
        B, C = x.shape[:2]
        mn = x.view(B, C, -1).mean(-1)
        sd = (x.view(B, C, -1).var(-1) + self.eps).sqrt()
        mn = mn.view(B, C, *((len(x.shape) - 2) * [1]))
        sd = sd.view(B, C, *((len(x.shape) - 2) * [1]))
        return mn, sd


class MultiHeadAttention(BaseModule):
    def __init__(
        self,
        channels,
        out_channels,
        n_heads,
        window_size=None,
        heads_share=True,
        p_dropout=0.0,
        proximal_bias=False,
        proximal_init=False,
    ):
        super(MultiHeadAttention, self).__init__()
        assert channels % n_heads == 0

        self.channels = channels
        self.out_channels = out_channels
        self.n_heads = n_heads
        self.window_size = window_size
        self.heads_share = heads_share
        self.proximal_bias = proximal_bias
        self.p_dropout = p_dropout
        self.attn = None

        self.k_channels = channels // n_heads
        self.conv_q = torch.nn.Conv1d(channels, channels, 1)
        self.conv_k = torch.nn.Conv1d(channels, channels, 1)
        self.conv_v = torch.nn.Conv1d(channels, channels, 1)
        if window_size is not None:
            n_heads_rel = 1 if heads_share else n_heads
            rel_stddev = self.k_channels**-0.5
            self.emb_rel_k = torch.nn.Parameter(
                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
                * rel_stddev
            )
            self.emb_rel_v = torch.nn.Parameter(
                torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
                * rel_stddev
            )
        self.conv_o = torch.nn.Conv1d(channels, out_channels, 1)
        self.drop = torch.nn.Dropout(p_dropout)

        torch.nn.init.xavier_uniform_(self.conv_q.weight)
        torch.nn.init.xavier_uniform_(self.conv_k.weight)
        if proximal_init:
            self.conv_k.weight.data.copy_(self.conv_q.weight.data)
            self.conv_k.bias.data.copy_(self.conv_q.bias.data)
        torch.nn.init.xavier_uniform_(self.conv_v.weight)

    def forward(self, x, c, attn_mask=None):
        q = self.conv_q(x)
        k = self.conv_k(c)
        v = self.conv_v(c)

        x, self.attn = self.attention(q, k, v, mask=attn_mask)

        x = self.conv_o(x)
        return x

    def attention(self, query, key, value, mask=None):
        b, d, t_s, t_t = (*key.size(), query.size(2))
        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)

        scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
        if self.window_size is not None:
            assert (
                t_s == t_t
            ), "Relative attention is only available for self-attention."
            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
            rel_logits = self._matmul_with_relative_keys(query, key_relative_embeddings)
            rel_logits = self._relative_position_to_absolute_position(rel_logits)
            scores_local = rel_logits / math.sqrt(self.k_channels)
            scores = scores + scores_local
        if self.proximal_bias:
            assert t_s == t_t, "Proximal bias is only available for self-attention."
            scores = scores + self._attention_bias_proximal(t_s).to(
                device=scores.device, dtype=scores.dtype
            )
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e4)
        p_attn = torch.nn.functional.softmax(scores, dim=-1)
        p_attn = self.drop(p_attn)
        output = torch.matmul(p_attn, value)
        if self.window_size is not None:
            relative_weights = self._absolute_position_to_relative_position(p_attn)
            value_relative_embeddings = self._get_relative_embeddings(
                self.emb_rel_v, t_s
            )
            output = output + self._matmul_with_relative_values(
                relative_weights, value_relative_embeddings
            )
        output = output.transpose(2, 3).contiguous().view(b, d, t_t)
        return output, p_attn

    def _matmul_with_relative_values(self, x, y):
        ret = torch.matmul(x, y.unsqueeze(0))
        return ret

    def _matmul_with_relative_keys(self, x, y):
        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
        return ret

    def _get_relative_embeddings(self, relative_embeddings, length):
        pad_length = max(length - (self.window_size + 1), 0)
        slice_start_position = max((self.window_size + 1) - length, 0)
        slice_end_position = slice_start_position + 2 * length - 1
        if pad_length > 0:
            padded_relative_embeddings = torch.nn.functional.pad(
                relative_embeddings,
                convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
            )
        else:
            padded_relative_embeddings = relative_embeddings
        used_relative_embeddings = padded_relative_embeddings[
            :, slice_start_position:slice_end_position
        ]
        return used_relative_embeddings

    def _relative_position_to_absolute_position(self, x):
        batch, heads, length, _ = x.size()
        x = torch.nn.functional.pad(
            x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])
        )
        x_flat = x.view([batch, heads, length * 2 * length])
        x_flat = torch.nn.functional.pad(
            x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
        )
        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
            :, :, :length, length - 1 :
        ]
        return x_final

    def _absolute_position_to_relative_position(self, x):
        batch, heads, length, _ = x.size()
        x = torch.nn.functional.pad(
            x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
        )
        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
        x_flat = torch.nn.functional.pad(
            x_flat, convert_pad_shape([[0, 0], [0, 0], [length, 0]])
        )
        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
        return x_final

    def _attention_bias_proximal(self, length):
        r = torch.arange(length, dtype=torch.float32)
        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)


class FFN(BaseModule):
    def __init__(
        self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0.0
    ):
        super(FFN, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.filter_channels = filter_channels
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout

        self.conv_1 = torch.nn.Conv1d(
            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
        )
        self.conv_2 = torch.nn.Conv1d(
            filter_channels, out_channels, kernel_size, padding=kernel_size // 2
        )
        self.drop = torch.nn.Dropout(p_dropout)

    def forward(self, x, x_mask):
        x = self.conv_1(x * x_mask)
        x = torch.relu(x)
        x = self.drop(x)
        x = self.conv_2(x * x_mask)
        return x * x_mask


class Encoder(BaseModule):
    def __init__(
        self,
        hidden_channels,
        filter_channels,
        n_heads=2,
        n_layers=6,
        kernel_size=3,
        p_dropout=0.1,
        window_size=4,
        **kwargs
    ):
        super(Encoder, self).__init__()
        self.hidden_channels = hidden_channels
        self.filter_channels = filter_channels
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.window_size = window_size

        self.drop = torch.nn.Dropout(p_dropout)
        self.attn_layers = torch.nn.ModuleList()
        self.norm_layers_1 = torch.nn.ModuleList()
        self.ffn_layers = torch.nn.ModuleList()
        self.norm_layers_2 = torch.nn.ModuleList()
        for _ in range(self.n_layers):
            self.attn_layers.append(
                MultiHeadAttention(
                    hidden_channels,
                    hidden_channels,
                    n_heads,
                    window_size=window_size,
                    p_dropout=p_dropout,
                )
            )
            self.norm_layers_1.append(LayerNorm(hidden_channels))
            self.ffn_layers.append(
                FFN(
                    hidden_channels,
                    hidden_channels,
                    filter_channels,
                    kernel_size,
                    p_dropout=p_dropout,
                )
            )
            self.norm_layers_2.append(LayerNorm(hidden_channels))

    def forward(self, x, x_mask):
        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
        for i in range(self.n_layers):
            x = x * x_mask
            y = self.attn_layers[i](x, x, attn_mask)
            y = self.drop(y)
            x = self.norm_layers_1[i](x + y)
            y = self.ffn_layers[i](x, x_mask)
            y = self.drop(y)
            x = self.norm_layers_2[i](x + y)
        x = x * x_mask
        return x


class Conformer(BaseModule):
    def __init__(self, cfg):
        super().__init__()
        self.cfg = cfg
        self.n_heads = self.cfg.n_heads
        self.n_layers = self.cfg.n_layers
        self.hidden_channels = self.cfg.input_dim
        self.filter_channels = self.cfg.filter_channels
        self.output_dim = self.cfg.output_dim
        self.dropout = self.cfg.dropout

        self.conformer_encoder = Encoder(
            self.hidden_channels,
            self.filter_channels,
            n_heads=self.n_heads,
            n_layers=self.n_layers,
            kernel_size=3,
            p_dropout=self.dropout,
            window_size=4,
        )
        self.projection = nn.Conv1d(self.hidden_channels, self.output_dim, 1)

    def forward(self, x, x_mask):
        """
        Args:
            x: (N, seq_len, input_dim)
        Returns:
            output: (N, seq_len, output_dim)
        """
        # (N, seq_len, d_model)
        x = x.transpose(1, 2)
        x_mask = x_mask.transpose(1, 2)
        output = self.conformer_encoder(x, x_mask)
        # (N, seq_len, output_dim)
        output = self.projection(output)
        output = output.transpose(1, 2)
        return output