optimize(infer): move attentions into rvc

infer/lib/infer_pack/attentions.py

@@ -1,432 +0,0 @@
-import math
-from typing import Optional
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from infer.lib.infer_pack.modules import LayerNorm
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        window_size=10,
-    ):
-        super(Encoder, self).__init__()
-        self.hidden_channels = hidden_channels
-        self.filter_channels = filter_channels
-        self.n_heads = n_heads
-        self.n_layers = int(n_layers)
-        self.kernel_size = kernel_size
-        self.p_dropout = p_dropout
-        self.window_size = window_size
-
-        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,
-                    p_dropout=p_dropout,
-                    window_size=window_size,
-                )
-            )
-            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 __call__(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
-        return super().__call__(x, x_mask)
-
-    def forward(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
-        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        zippep = zip(
-            self.attn_layers, self.norm_layers_1, self.ffn_layers, self.norm_layers_2
-        )
-        for attn_layers, norm_layers_1, ffn_layers, norm_layers_2 in zippep:
-            y = attn_layers(x, x, attn_mask)
-            y = self.drop(y)
-            x = norm_layers_1(x + y)
-
-            y = ffn_layers(x, x_mask)
-            y = self.drop(y)
-            x = norm_layers_2(x + y)
-        x = x * x_mask
-        return x
-
-
-"""
-class Decoder(nn.Module):
-    def __init__(
-        self,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size=1,
-        p_dropout=0.0,
-        proximal_bias=False,
-        proximal_init=True,
-    ):
-        super(Decoder, 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.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-
-        self.drop = nn.Dropout(p_dropout)
-        self.self_attn_layers = nn.ModuleList()
-        self.norm_layers_0 = nn.ModuleList()
-        self.encdec_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.self_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels,
-                    hidden_channels,
-                    n_heads,
-                    p_dropout=p_dropout,
-                    proximal_bias=proximal_bias,
-                    proximal_init=proximal_init,
-                )
-            )
-            self.norm_layers_0.append(LayerNorm(hidden_channels))
-            self.encdec_attn_layers.append(
-                MultiHeadAttention(
-                    hidden_channels, hidden_channels, n_heads, 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,
-                    causal=True,
-                )
-            )
-            self.norm_layers_2.append(LayerNorm(hidden_channels))
-
-    def forward(self, x, x_mask, h, h_mask):
-        # x: decoder input
-        # h: encoder output
-        self_attn_mask = utils.subsequent_mask(x_mask.size(2)).to(
-            device=x.device, dtype=x.dtype
-        )
-        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
-        x = x * x_mask
-        for i in range(self.n_layers):
-            y = self.self_attn_layers[i](x, x, self_attn_mask)
-            y = self.drop(y)
-            x = self.norm_layers_0[i](x + y)
-
-            y = self.encdec_attn_layers[i](x, h, encdec_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 MultiHeadAttention(nn.Module):
-    def __init__(
-        self,
-        channels,
-        out_channels,
-        n_heads,
-        p_dropout=0.0,
-        window_size=None,
-        heads_share=True,
-        block_length=None,
-        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.p_dropout = p_dropout
-        self.window_size = window_size
-        self.heads_share = heads_share
-        self.block_length = block_length
-        self.proximal_bias = proximal_bias
-        self.proximal_init = proximal_init
-        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)
-        self.conv_o = nn.Conv1d(channels, out_channels, 1)
-        self.drop = nn.Dropout(p_dropout)
-
-        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
-            )
-
-        nn.init.xavier_uniform_(self.conv_q.weight)
-        nn.init.xavier_uniform_(self.conv_k.weight)
-        nn.init.xavier_uniform_(self.conv_v.weight)
-        if proximal_init:
-            with torch.no_grad():
-                self.conv_k.weight.copy_(self.conv_q.weight)
-                self.conv_k.bias.copy_(self.conv_q.bias)
-
-    def forward(
-        self, x: torch.Tensor, c: torch.Tensor, attn_mask: Optional[torch.Tensor] = None
-    ):
-        q = self.conv_q(x)
-        k = self.conv_k(c)
-        v = self.conv_v(c)
-
-        x, _ = self.attention(q, k, v, mask=attn_mask)
-
-        x = self.conv_o(x)
-        return x
-
-    def attention(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        mask: Optional[torch.Tensor] = None,
-    ):
-        # reshape [b, d, t] -> [b, n_h, t, d_k]
-        b, d, t_s = key.size()
-        t_t = 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 / math.sqrt(self.k_channels), key.transpose(-2, -1))
-        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 / math.sqrt(self.k_channels), key_relative_embeddings
-            )
-            scores_local = self._relative_position_to_absolute_position(rel_logits)
-            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:
-                assert (
-                    t_s == t_t
-                ), "Local attention is only available for self-attention."
-                block_mask = (
-                    torch.ones_like(scores)
-                    .triu(-self.block_length)
-                    .tril(self.block_length)
-                )
-                scores = scores.masked_fill(block_mask == 0, -1e4)
-        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: int):
-        max_relative_position = 2 * self.window_size + 1
-        # Pad first before slice to avoid using cond ops.
-        pad_length: int = 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,
-                [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, [0, 1, 0, 0, 0, 0, 0, 0], )
-
-        # 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, [0, length - 1, 0, 0, 0, 0])
-
-        # 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, [0, length - 1, 0, 0, 0, 0, 0, 0])
-        x_flat = x.view([batch, heads, (length**2) + (length * (length - 1))])
-        # add 0's in the beginning that will skew the elements after reshape
-        x_flat = F.pad(x_flat, [length, 0, 0, 0, 0, 0])
-        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
-        return x_final
-
-    def _attention_bias_proximal(self, length: int):
-        """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=0.0,
-        activation: str = None,
-        causal=False,
-    ):
-        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.activation = activation
-        self.causal = causal
-        self.is_activation = True if activation == "gelu" else False
-        # if causal:
-        #     self.padding = self._causal_padding
-        # else:
-        #     self.padding = self._same_padding
-
-        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
-        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
-        self.drop = nn.Dropout(p_dropout)
-
-    def padding(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
-        if self.causal:
-            padding = self._causal_padding(x * x_mask)
-        else:
-            padding = self._same_padding(x * x_mask)
-        return padding
-
-    def forward(self, x: torch.Tensor, x_mask: torch.Tensor):
-        x = self.conv_1(self.padding(x, x_mask))
-        if self.is_activation:
-            x = x * torch.sigmoid(1.702 * x)
-        else:
-            x = torch.relu(x)
-        x = self.drop(x)
-
-        x = self.conv_2(self.padding(x, x_mask))
-        return x * x_mask
-
-    def _causal_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l: int = self.kernel_size - 1
-        pad_r: int = 0
-        # padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, [pad_l, pad_r, 0, 0, 0, 0])
-        return x
-
-    def _same_padding(self, x):
-        if self.kernel_size == 1:
-            return x
-        pad_l: int = (self.kernel_size - 1) // 2
-        pad_r: int = self.kernel_size // 2
-        # padding = [[0, 0], [0, 0], [pad_l, pad_r]]
-        x = F.pad(x, [pad_l, pad_r, 0, 0, 0, 0])
-        return x

infer/lib/infer_pack/models.py

@@ -2,104 +2,19 @@ import math
 import logging
 from typing import Optional, Tuple, List
 
-from rvc import utils
-
-logger = logging.getLogger(__name__)
-
 import torch
 from torch import nn
 from torch.nn import Conv1d, Conv2d, ConvTranspose1d
 from torch.nn import functional as F
 from torch.nn.utils import remove_weight_norm, spectral_norm, weight_norm
-from infer.lib.infer_pack import attentions, modules
-from rvc.utils import get_padding, call_weight_data_normal_if_Conv
+from infer.lib.infer_pack import modules
+
+from rvc.utils import get_padding, call_weight_data_normal_if_Conv, sequence_mask, slice_on_last_dim, rand_slice_segments_on_last_dim
+from rvc.encoders import TextEncoder
 
 has_xpu = bool(hasattr(torch, "xpu") and torch.xpu.is_available())
 
 
-class TextEncoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        hidden_channels,
-        filter_channels,
-        n_heads,
-        n_layers,
-        kernel_size,
-        p_dropout,
-        f0=True,
-    ):
-        super(TextEncoder, self).__init__()
-        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 = float(p_dropout)
-        self.emb_phone = nn.Linear(in_channels, hidden_channels)
-        self.lrelu = nn.LeakyReLU(0.1, inplace=True)
-        if f0 == True:
-            self.emb_pitch = nn.Embedding(256, hidden_channels)  # pitch 256
-        self.encoder = attentions.Encoder(
-            hidden_channels,
-            filter_channels,
-            n_heads,
-            n_layers,
-            kernel_size,
-            float(p_dropout),
-        )
-        self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
-
-    def __call__(
-        self,
-        phone: torch.Tensor,
-        pitch: torch.Tensor,
-        lengths: torch.Tensor,
-        # skip_head: Optional[torch.Tensor] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        return super().__call__(
-            phone,
-            pitch,
-            lengths,
-            # skip_head=skip_head,
-        )
-
-    def forward(
-        self,
-        phone: torch.Tensor,
-        pitch: torch.Tensor,
-        lengths: torch.Tensor,
-        # skip_head: Optional[torch.Tensor] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-        if pitch is None:
-            x = self.emb_phone(phone)
-        else:
-            x = self.emb_phone(phone) + self.emb_pitch(pitch)
-        x = x * math.sqrt(self.hidden_channels)  # [b, t, h]
-        x = self.lrelu(x)
-        x = torch.transpose(x, 1, -1)  # [b, h, t]
-        x_mask = torch.unsqueeze(
-            utils.sequence_mask(
-                lengths,
-                x.size(2),
-            ),
-            1,
-        ).to(x.dtype)
-        x = self.encoder(x * x_mask, x_mask)
-        """
-        if skip_head is not None:
-            assert isinstance(skip_head, torch.Tensor)
-            head = int(skip_head.item())
-            x = x[:, :, head:]
-            x_mask = x_mask[:, :, head:]
-        """
-        stats: torch.Tensor = self.proj(x) * x_mask
-        m, logs = torch.split(stats, self.out_channels, dim=1)
-        return m, logs, x_mask
-
-
 class ResidualCouplingBlock(nn.Module):
     def __init__(
         self,
@@ -205,11 +120,7 @@ class PosteriorEncoder(nn.Module):
         self, x: torch.Tensor, x_lengths: torch.Tensor, g: Optional[torch.Tensor] = None
     ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
         x_mask = torch.unsqueeze(
-            utils.sequence_mask(
-                x_lengths,
-                x.size(2),
-            ),
-            1,
+            sequence_mask(x_lengths, x.size(2)), 1,
         ).to(x.dtype)
         x = self.pre(x) * x_mask
         x = self.enc(x, x_mask, g=g)
@@ -728,12 +639,6 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
             inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
         )
         self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        logger.debug(
-            "gin_channels: "
-            + str(gin_channels)
-            + ", self.spk_embed_dim: "
-            + str(self.spk_embed_dim)
-        )
 
     def remove_weight_norm(self):
         self.dec.remove_weight_norm()
@@ -783,9 +688,9 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
         m_p, logs_p, x_mask = self.enc_p(phone, pitch, phone_lengths)
         z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
         z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = utils.rand_slice_segments_on_last_dim(z, y_lengths, self.segment_size)
+        z_slice, ids_slice = rand_slice_segments_on_last_dim(z, y_lengths, self.segment_size)
         # print(-1,pitchf.shape,ids_slice,self.segment_size,self.hop_length,self.segment_size//self.hop_length)
-        pitchf = utils.slice_on_last_dim(pitchf, ids_slice, self.segment_size)
+        pitchf = slice_on_last_dim(pitchf, ids_slice, self.segment_size)
         # print(-2,pitchf.shape,z_slice.shape)
         o = self.dec(z_slice, pitchf, g=g)
         return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
@@ -962,12 +867,6 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
             inter_channels, hidden_channels, 5, 1, 3, gin_channels=gin_channels
         )
         self.emb_g = nn.Embedding(self.spk_embed_dim, gin_channels)
-        logger.debug(
-            "gin_channels: "
-            + str(gin_channels)
-            + ", self.spk_embed_dim: "
-            + str(self.spk_embed_dim)
-        )
 
     def remove_weight_norm(self):
         self.dec.remove_weight_norm()
@@ -1007,7 +906,7 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
         m_p, logs_p, x_mask = self.enc_p(phone, None, phone_lengths)
         z, m_q, logs_q, y_mask = self.enc_q(y, y_lengths, g=g)
         z_p = self.flow(z, y_mask, g=g)
-        z_slice, ids_slice = utils.rand_slice_segments_on_last_dim(z, y_lengths, self.segment_size)
+        z_slice, ids_slice = rand_slice_segments_on_last_dim(z, y_lengths, self.segment_size)
         o = self.dec(z_slice, g=g)
         return o, ids_slice, x_mask, y_mask, (z, z_p, m_p, logs_p, m_q, logs_q)
 

infer/lib/infer_pack/models_onnx.py

@@ -1,13 +1,14 @@
 import torch
 from torch import nn
 
-from .attentions import (
-    TextEncoder,
+from .models import (
     ResidualCouplingBlock,
     PosteriorEncoder,
     GeneratorNSF,
 )
 
+from rvc.encoders import TextEncoder
+
 
 class SynthesizerTrnMsNSFsidM(nn.Module):
     def __init__(

infer/lib/infer_pack/modules.py

@@ -1,89 +1,19 @@
-import copy
 import math
 from typing import Optional, Tuple
 
-import numpy as np
-import scipy
 import torch
 from torch import nn
-from torch.nn import AvgPool1d, Conv1d, Conv2d, ConvTranspose1d
+from torch.nn import Conv1d
 from torch.nn import functional as F
 from torch.nn.utils import remove_weight_norm, weight_norm
 
-from rvc import utils
-from rvc.utils import get_padding, call_weight_data_normal_if_Conv
+from rvc.utils import get_padding, call_weight_data_normal_if_Conv, activate_add_tanh_sigmoid_multiply
 from rvc.transforms import piecewise_rational_quadratic_transform
+from rvc.norms import LayerNorm
 
 LRELU_SLOPE = 0.1
 
 
-class LayerNorm(nn.Module):
-    def __init__(self, channels, eps=1e-5):
-        super(LayerNorm, self).__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):
-        x = x.transpose(1, -1)
-        x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
-        return x.transpose(1, -1)
-
-
-class ConvReluNorm(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        hidden_channels,
-        out_channels,
-        kernel_size,
-        n_layers,
-        p_dropout,
-    ):
-        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 = float(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(float(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 DDSConv(nn.Module):
     """
     Dialted and Depth-Separable Convolution
@@ -203,7 +133,7 @@ class WN(torch.nn.Module):
             else:
                 g_l = torch.zeros_like(x_in)
 
-            acts = utils.activate_add_tanh_sigmoid_multiply(x_in, g_l, self.hidden_channels)
+            acts = activate_add_tanh_sigmoid_multiply(x_in, g_l, self.hidden_channels)
             acts = self.drop(acts)
 
             res_skip_acts = res_skip_layer(acts)