import math import torch from torch import nn from torch.nn import functional as F from modules.commons.layers import Embedding def convert_pad_shape(pad_shape): l = pad_shape[::-1] pad_shape = [item for sublist in l for item in sublist] return pad_shape def shift_1d(x): x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1] return x def sequence_mask(length, max_length=None): if max_length is None: max_length = length.max() x = torch.arange(max_length, dtype=length.dtype, device=length.device) return x.unsqueeze(0) < length.unsqueeze(1) class Encoder(nn.Module): def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=None, block_length=None, pre_ln=False, **kwargs): super().__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.block_length = block_length self.pre_ln = pre_ln self.drop = nn.Dropout(p_dropout) self.attn_layers = nn.ModuleList() self.norm_layers_1 = nn.ModuleList() self.ffn_layers = nn.ModuleList() self.norm_layers_2 = nn.ModuleList() for i in range(self.n_layers): self.attn_layers.append( MultiHeadAttention(hidden_channels, hidden_channels, n_heads, window_size=window_size, p_dropout=p_dropout, block_length=block_length)) 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)) if pre_ln: self.last_ln = LayerNorm(hidden_channels) def forward(self, x, x_mask, attn_mask=1): if isinstance(attn_mask, torch.Tensor): attn_mask = attn_mask[:, None] attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1) * attn_mask for i in range(self.n_layers): x = x * x_mask x_ = x if self.pre_ln: x = self.norm_layers_1[i](x) y = self.attn_layers[i](x, x, attn_mask) y = self.drop(y) x = x_ + y if not self.pre_ln: x = self.norm_layers_1[i](x) x_ = x if self.pre_ln: x = self.norm_layers_2[i](x) y = self.ffn_layers[i](x, x_mask) y = self.drop(y) x = x_ + y if not self.pre_ln: x = self.norm_layers_2[i](x) if self.pre_ln: x = self.last_ln(x) x = x * x_mask return x class MultiHeadAttention(nn.Module): def __init__(self, channels, out_channels, n_heads, window_size=None, heads_share=True, p_dropout=0., block_length=None, proximal_bias=False, proximal_init=False): super().__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.block_length = block_length self.proximal_bias = proximal_bias self.p_dropout = p_dropout self.attn = None self.k_channels = channels // n_heads self.conv_q = nn.Conv1d(channels, channels, 1) self.conv_k = nn.Conv1d(channels, channels, 1) self.conv_v = 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 = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev) self.conv_o = nn.Conv1d(channels, out_channels, 1) self.drop = nn.Dropout(p_dropout) nn.init.xavier_uniform_(self.conv_q.weight) 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) 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): # reshape [b, d, t] -> [b, n_h, t, d_k] 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) if self.block_length is not None: block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length) scores = scores * block_mask + -1e4 * (1 - block_mask) p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s] 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) # [b, n_h, t_t, d_k] -> [b, d, t_t] return output, p_attn def _matmul_with_relative_values(self, x, y): """ x: [b, h, l, m] y: [h or 1, m, d] ret: [b, h, l, d] """ ret = torch.matmul(x, y.unsqueeze(0)) return ret def _matmul_with_relative_keys(self, x, y): """ x: [b, h, l, d] y: [h or 1, m, d] ret: [b, h, l, m] """ ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1)) return ret def _get_relative_embeddings(self, relative_embeddings, length): max_relative_position = 2 * self.window_size + 1 # Pad first before slice to avoid using cond ops. 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 = F.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): """ x: [b, h, l, 2*l-1] ret: [b, h, l, l] """ batch, heads, length, _ = x.size() # Concat columns of pad to shift from relative to absolute indexing. x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])) # Concat extra elements so to add up to shape (len+1, 2*len-1). x_flat = x.view([batch, heads, length * 2 * length]) x_flat = F.pad(x_flat, convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])) # Reshape and slice out the padded elements. 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): """ x: [b, h, l, l] ret: [b, h, l, 2*l-1] """ batch, heads, length, _ = x.size() # padd along column x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])) x_flat = x.view([batch, heads, -1]) # add 0's in the beginning that will skew the elements after reshape x_flat = F.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): """Bias for self-attention to encourage attention to close positions. Args: length: an integer scalar. Returns: a Tensor with shape [1, 1, length, 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(nn.Module): def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None): super().__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.activation = activation self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size // 2) self.conv_2 = nn.Conv1d(filter_channels, out_channels, 1) self.drop = nn.Dropout(p_dropout) def forward(self, x, x_mask): x = self.conv_1(x * x_mask) if self.activation == "gelu": x = x * torch.sigmoid(1.702 * x) else: x = torch.relu(x) x = self.drop(x) x = self.conv_2(x * x_mask) return x * x_mask class LayerNorm(nn.Module): def __init__(self, channels, eps=1e-4): super().__init__() self.channels = channels self.eps = eps self.gamma = nn.Parameter(torch.ones(channels)) self.beta = 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(nn.Module): def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout): super().__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 assert n_layers > 1, "Number of layers should be larger than 0." self.conv_layers = nn.ModuleList() self.norm_layers = nn.ModuleList() self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) self.norm_layers.append(LayerNorm(hidden_channels)) self.relu_drop = nn.Sequential( nn.ReLU(), nn.Dropout(p_dropout)) for _ in range(n_layers - 1): self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size // 2)) self.norm_layers.append(LayerNorm(hidden_channels)) self.proj = nn.Conv1d(hidden_channels, out_channels, 1) self.proj.weight.data.zero_() self.proj.bias.data.zero_() def forward(self, x, x_mask): x_org = x for i in range(self.n_layers): x = self.conv_layers[i](x * x_mask) x = self.norm_layers[i](x) x = self.relu_drop(x) x = x_org + self.proj(x) return x * x_mask class RelTransformerEncoder(nn.Module): def __init__(self, n_vocab, out_channels, hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout=0.0, window_size=4, block_length=None, in_channels=None, prenet=True, pre_ln=True, ): super().__init__() self.n_vocab = n_vocab self.out_channels = out_channels 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.block_length = block_length self.prenet = prenet if n_vocab > 0: self.emb = Embedding(n_vocab, hidden_channels, padding_idx=0) if prenet: if in_channels is None: in_channels = hidden_channels self.pre = ConvReluNorm(in_channels, in_channels, in_channels, kernel_size=5, n_layers=3, p_dropout=0) if in_channels is not None and in_channels != hidden_channels: self.encoder_inp_proj = nn.Conv1d(in_channels, hidden_channels, 1) self.encoder = Encoder( hidden_channels, filter_channels, n_heads, n_layers, kernel_size, p_dropout, window_size=window_size, block_length=block_length, pre_ln=pre_ln, ) def forward(self, x, x_mask=None, other_embeds=0, attn_mask=1): if self.n_vocab > 0: x_lengths = (x > 0).long().sum(-1) x = self.emb(x) * math.sqrt(self.hidden_channels) # [b, t, h] else: x_lengths = (x.abs().sum(-1) > 0).long().sum(-1) x = x + other_embeds x = torch.transpose(x, 1, -1) # [b, h, t] x_mask = torch.unsqueeze(sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype) if self.prenet: x = self.pre(x, x_mask) self.prenet_out = x.transpose(1, 2) if hasattr(self, 'encoder_inp_proj'): x = self.encoder_inp_proj(x) * x_mask x = self.encoder(x, x_mask, attn_mask) return x.transpose(1, 2)