mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2025-05-06 20:01:37 +08:00
修复了Onnx Attention的一个Shape错误 (#2003)
* Add files via upload * Fix Onnx Bugs * Add files via upload
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Ναρουσέ·μ·γιουμεμί·Χινακάννα
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fb69a36068
commit
deca566ab2
459
infer/lib/infer_pack/attentions_onnx.py
Normal file
459
infer/lib/infer_pack/attentions_onnx.py
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@ -0,0 +1,459 @@
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import copy
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import math
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from typing import Optional
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import numpy as np
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import torch
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from torch import nn
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from torch.nn import functional as F
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from infer.lib.infer_pack import commons, modules
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from infer.lib.infer_pack.modules import LayerNorm
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class Encoder(nn.Module):
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def __init__(
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self,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size=1,
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p_dropout=0.0,
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window_size=10,
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**kwargs
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):
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super(Encoder, self).__init__()
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = int(n_layers)
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.window_size = window_size
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self.drop = nn.Dropout(p_dropout)
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self.attn_layers = nn.ModuleList()
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self.norm_layers_1 = nn.ModuleList()
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self.ffn_layers = nn.ModuleList()
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self.norm_layers_2 = nn.ModuleList()
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for i in range(self.n_layers):
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self.attn_layers.append(
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MultiHeadAttention(
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hidden_channels,
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hidden_channels,
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n_heads,
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p_dropout=p_dropout,
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window_size=window_size,
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)
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)
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self.norm_layers_1.append(LayerNorm(hidden_channels))
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self.ffn_layers.append(
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FFN(
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hidden_channels,
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hidden_channels,
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filter_channels,
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kernel_size,
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p_dropout=p_dropout,
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)
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)
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self.norm_layers_2.append(LayerNorm(hidden_channels))
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def forward(self, x, x_mask):
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attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
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x = x * x_mask
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zippep = zip(
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self.attn_layers, self.norm_layers_1, self.ffn_layers, self.norm_layers_2
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)
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for attn_layers, norm_layers_1, ffn_layers, norm_layers_2 in zippep:
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y = attn_layers(x, x, attn_mask)
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y = self.drop(y)
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x = norm_layers_1(x + y)
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y = ffn_layers(x, x_mask)
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y = self.drop(y)
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x = norm_layers_2(x + y)
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x = x * x_mask
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return x
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class Decoder(nn.Module):
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def __init__(
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self,
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hidden_channels,
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filter_channels,
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n_heads,
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n_layers,
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kernel_size=1,
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p_dropout=0.0,
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proximal_bias=False,
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proximal_init=True,
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**kwargs
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):
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super(Decoder, self).__init__()
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self.hidden_channels = hidden_channels
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self.filter_channels = filter_channels
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self.n_heads = n_heads
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self.n_layers = n_layers
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.proximal_bias = proximal_bias
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self.proximal_init = proximal_init
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self.drop = nn.Dropout(p_dropout)
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self.self_attn_layers = nn.ModuleList()
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self.norm_layers_0 = nn.ModuleList()
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self.encdec_attn_layers = nn.ModuleList()
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self.norm_layers_1 = nn.ModuleList()
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self.ffn_layers = nn.ModuleList()
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self.norm_layers_2 = nn.ModuleList()
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for i in range(self.n_layers):
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self.self_attn_layers.append(
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MultiHeadAttention(
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hidden_channels,
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hidden_channels,
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n_heads,
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p_dropout=p_dropout,
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proximal_bias=proximal_bias,
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proximal_init=proximal_init,
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)
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)
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self.norm_layers_0.append(LayerNorm(hidden_channels))
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self.encdec_attn_layers.append(
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MultiHeadAttention(
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hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout
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)
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)
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self.norm_layers_1.append(LayerNorm(hidden_channels))
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self.ffn_layers.append(
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FFN(
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hidden_channels,
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hidden_channels,
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filter_channels,
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kernel_size,
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p_dropout=p_dropout,
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causal=True,
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)
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)
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self.norm_layers_2.append(LayerNorm(hidden_channels))
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def forward(self, x, x_mask, h, h_mask):
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"""
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x: decoder input
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h: encoder output
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"""
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self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(
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device=x.device, dtype=x.dtype
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)
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encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
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x = x * x_mask
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for i in range(self.n_layers):
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y = self.self_attn_layers[i](x, x, self_attn_mask)
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y = self.drop(y)
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x = self.norm_layers_0[i](x + y)
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y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
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y = self.drop(y)
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x = self.norm_layers_1[i](x + y)
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y = self.ffn_layers[i](x, x_mask)
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y = self.drop(y)
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x = self.norm_layers_2[i](x + y)
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x = x * x_mask
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return x
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class MultiHeadAttention(nn.Module):
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def __init__(
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self,
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channels,
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out_channels,
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n_heads,
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p_dropout=0.0,
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window_size=None,
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heads_share=True,
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block_length=None,
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proximal_bias=False,
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proximal_init=False,
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):
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super(MultiHeadAttention, self).__init__()
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assert channels % n_heads == 0
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self.channels = channels
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self.out_channels = out_channels
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self.n_heads = n_heads
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self.p_dropout = p_dropout
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self.window_size = window_size
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self.heads_share = heads_share
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self.block_length = block_length
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self.proximal_bias = proximal_bias
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self.proximal_init = proximal_init
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self.attn = None
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self.k_channels = channels // n_heads
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self.conv_q = nn.Conv1d(channels, channels, 1)
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self.conv_k = nn.Conv1d(channels, channels, 1)
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self.conv_v = nn.Conv1d(channels, channels, 1)
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self.conv_o = nn.Conv1d(channels, out_channels, 1)
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self.drop = nn.Dropout(p_dropout)
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if window_size is not None:
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n_heads_rel = 1 if heads_share else n_heads
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rel_stddev = self.k_channels**-0.5
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self.emb_rel_k = nn.Parameter(
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torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
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* rel_stddev
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)
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self.emb_rel_v = nn.Parameter(
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torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
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* rel_stddev
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)
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nn.init.xavier_uniform_(self.conv_q.weight)
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nn.init.xavier_uniform_(self.conv_k.weight)
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nn.init.xavier_uniform_(self.conv_v.weight)
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if proximal_init:
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with torch.no_grad():
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self.conv_k.weight.copy_(self.conv_q.weight)
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self.conv_k.bias.copy_(self.conv_q.bias)
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def forward(
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self, x: torch.Tensor, c: torch.Tensor, attn_mask: Optional[torch.Tensor] = None
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):
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q = self.conv_q(x)
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k = self.conv_k(c)
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v = self.conv_v(c)
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x, _ = self.attention(q, k, v, mask=attn_mask)
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x = self.conv_o(x)
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return x
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def attention(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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mask: Optional[torch.Tensor] = None,
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):
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# reshape [b, d, t] -> [b, n_h, t, d_k]
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b, d, t_s = key.size()
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t_t = query.size(2)
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query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
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key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
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value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
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scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
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if self.window_size is not None:
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assert (
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t_s == t_t
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), "Relative attention is only available for self-attention."
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key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
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rel_logits = self._matmul_with_relative_keys(
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query / math.sqrt(self.k_channels), key_relative_embeddings
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)
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scores_local = self._relative_position_to_absolute_position(rel_logits)
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scores = scores + scores_local
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if self.proximal_bias:
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assert t_s == t_t, "Proximal bias is only available for self-attention."
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scores = scores + self._attention_bias_proximal(t_s).to(
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device=scores.device, dtype=scores.dtype
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)
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if mask is not None:
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scores = scores.masked_fill(mask == 0, -1e4)
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if self.block_length is not None:
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assert (
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t_s == t_t
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), "Local attention is only available for self-attention."
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block_mask = (
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torch.ones_like(scores)
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.triu(-self.block_length)
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.tril(self.block_length)
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)
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scores = scores.masked_fill(block_mask == 0, -1e4)
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p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
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p_attn = self.drop(p_attn)
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output = torch.matmul(p_attn, value)
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if self.window_size is not None:
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relative_weights = self._absolute_position_to_relative_position(p_attn)
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value_relative_embeddings = self._get_relative_embeddings(
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self.emb_rel_v, t_s
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)
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output = output + self._matmul_with_relative_values(
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relative_weights, value_relative_embeddings
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)
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output = (
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output.transpose(2, 3).contiguous().view(b, d, t_t)
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) # [b, n_h, t_t, d_k] -> [b, d, t_t]
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return output, p_attn
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def _matmul_with_relative_values(self, x, y):
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"""
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x: [b, h, l, m]
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y: [h or 1, m, d]
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ret: [b, h, l, d]
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"""
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ret = torch.matmul(x, y.unsqueeze(0))
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return ret
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def _matmul_with_relative_keys(self, x, y):
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"""
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x: [b, h, l, d]
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y: [h or 1, m, d]
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ret: [b, h, l, m]
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"""
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ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
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return ret
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def _get_relative_embeddings(self, relative_embeddings, length: int):
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max_relative_position = 2 * self.window_size + 1
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# Pad first before slice to avoid using cond ops.
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pad_length: int = max(length - (self.window_size + 1), 0)
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slice_start_position = max((self.window_size + 1) - length, 0)
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slice_end_position = slice_start_position + 2 * length - 1
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if pad_length > 0:
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padded_relative_embeddings = F.pad(
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relative_embeddings,
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# commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
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[0, 0, pad_length, pad_length, 0, 0],
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)
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else:
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padded_relative_embeddings = relative_embeddings
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used_relative_embeddings = padded_relative_embeddings[
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:, slice_start_position:slice_end_position
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]
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return used_relative_embeddings
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def _relative_position_to_absolute_position(self, x):
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"""
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x: [b, h, l, 2*l-1]
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ret: [b, h, l, l]
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"""
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batch, heads, length, _ = x.size()
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# Concat columns of pad to shift from relative to absolute indexing.
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x = F.pad(
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x,
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# commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]])
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[0, 1, 0, 0, 0, 0, 0, 0],
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)
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# Concat extra elements so to add up to shape (len+1, 2*len-1).
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x_flat = x.view([batch, heads, length * 2 * length])
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x_flat = F.pad(
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x_flat,
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# commons.convert_pad_shape([[0, 0], [0, 0], [0, int(length) - 1]])
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[0, length - 1, 0, 0, 0, 0],
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)
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# Reshape and slice out the padded elements.
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x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
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:, :, :length, length - 1 :
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]
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return x_final
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def _absolute_position_to_relative_position(self, x):
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"""
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x: [b, h, l, l]
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ret: [b, h, l, 2*l-1]
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"""
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batch, heads, length, _ = x.size()
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# padd along column
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x = F.pad(
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x,
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# commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, int(length) - 1]])
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[0, length - 1, 0, 0, 0, 0, 0, 0],
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)
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x_flat = x.view([batch, heads, (length**2) + (length * (length - 1))])
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# add 0's in the beginning that will skew the elements after reshape
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x_flat = F.pad(
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x_flat,
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# commons.convert_pad_shape([[0, 0], [0, 0], [int(length), 0]])
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[length, 0, 0, 0, 0, 0],
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)
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x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
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return x_final
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def _attention_bias_proximal(self, length: int):
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"""Bias for self-attention to encourage attention to close positions.
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Args:
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length: an integer scalar.
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Returns:
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a Tensor with shape [1, 1, length, length]
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"""
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r = torch.arange(length, dtype=torch.float32)
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diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
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return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
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class FFN(nn.Module):
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def __init__(
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self,
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in_channels,
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out_channels,
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filter_channels,
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kernel_size,
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p_dropout=0.0,
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activation: str = None,
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causal=False,
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):
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super(FFN, self).__init__()
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.filter_channels = filter_channels
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self.kernel_size = kernel_size
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self.p_dropout = p_dropout
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self.activation = activation
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self.causal = causal
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self.is_activation = True if activation == "gelu" else False
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# if causal:
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# self.padding = self._causal_padding
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# else:
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# self.padding = self._same_padding
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self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
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self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
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self.drop = nn.Dropout(p_dropout)
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def padding(self, x: torch.Tensor, x_mask: torch.Tensor) -> torch.Tensor:
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if self.causal:
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padding = self._causal_padding(x * x_mask)
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else:
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padding = self._same_padding(x * x_mask)
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return padding
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def forward(self, x: torch.Tensor, x_mask: torch.Tensor):
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x = self.conv_1(self.padding(x, x_mask))
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if self.is_activation:
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x = x * torch.sigmoid(1.702 * x)
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else:
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x = torch.relu(x)
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x = self.drop(x)
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x = self.conv_2(self.padding(x, x_mask))
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return x * x_mask
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def _causal_padding(self, x):
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if self.kernel_size == 1:
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return x
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pad_l: int = self.kernel_size - 1
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pad_r: int = 0
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# padding = [[0, 0], [0, 0], [pad_l, pad_r]]
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x = F.pad(
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x,
|
||||
# commons.convert_pad_shape(padding)
|
||||
[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,
|
||||
# commons.convert_pad_shape(padding)
|
||||
[pad_l, pad_r, 0, 0, 0, 0],
|
||||
)
|
||||
return x
|
||||
@ -11,8 +11,9 @@ from torch.nn import AvgPool1d, 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, commons, modules
|
||||
from infer.lib.infer_pack import commons, modules
|
||||
from infer.lib.infer_pack.commons import get_padding, init_weights
|
||||
import infer.lib.infer_pack.attentions_onnx as attentions
|
||||
|
||||
has_xpu = bool(hasattr(torch, "xpu") and torch.xpu.is_available())
|
||||
|
||||
@ -636,15 +637,15 @@ class SynthesizerTrnMsNSFsidM(nn.Module):
|
||||
# self.hop_length = hop_length#
|
||||
self.spk_embed_dim = spk_embed_dim
|
||||
self.enc_p = TextEncoder(
|
||||
encoder_dim,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
float(p_dropout),
|
||||
)
|
||||
encoder_dim,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
float(p_dropout),
|
||||
)
|
||||
self.dec = GeneratorNSF(
|
||||
inter_channels,
|
||||
resblock,
|
||||
@ -676,6 +677,7 @@ class SynthesizerTrnMsNSFsidM(nn.Module):
|
||||
+ ", self.spk_embed_dim: "
|
||||
+ str(self.spk_embed_dim)
|
||||
)
|
||||
self.speaker_map = None
|
||||
|
||||
def remove_weight_norm(self):
|
||||
self.dec.remove_weight_norm()
|
||||
|
||||
@ -25,7 +25,7 @@ def export_onnx(ModelPath, ExportedPath):
|
||||
output_names = [
|
||||
"audio",
|
||||
]
|
||||
# net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出
|
||||
#net_g.construct_spkmixmap() #多角色混合轨道导出
|
||||
torch.onnx.export(
|
||||
net_g,
|
||||
(
|
||||
@ -44,8 +44,8 @@ def export_onnx(ModelPath, ExportedPath):
|
||||
"rnd": [2],
|
||||
},
|
||||
do_constant_folding=False,
|
||||
opset_version=18,
|
||||
verbose=True,
|
||||
opset_version=17,
|
||||
verbose=False,
|
||||
input_names=input_names,
|
||||
output_names=output_names,
|
||||
)
|
||||
|
||||
@ -1,54 +1,3 @@
|
||||
import torch
|
||||
from infer.lib.infer_pack.models_onnx import SynthesizerTrnMsNSFsidM
|
||||
from infer.modules.onnx.export import export_onnx
|
||||
|
||||
if __name__ == "__main__":
|
||||
MoeVS = True # 模型是否为MoeVoiceStudio（原MoeSS）使用
|
||||
|
||||
ModelPath = "Shiroha/shiroha.pth" # 模型路径
|
||||
ExportedPath = "model.onnx" # 输出路径
|
||||
encoder_dim = 256 # encoder_dim
|
||||
cpt = torch.load(ModelPath, map_location="cpu")
|
||||
cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
|
||||
print(*cpt["config"])
|
||||
|
||||
test_phone = torch.rand(1, 200, encoder_dim) # hidden unit
|
||||
test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用）
|
||||
test_pitch = torch.randint(size=(1, 200), low=5, high=255) # 基频（单位赫兹）
|
||||
test_pitchf = torch.rand(1, 200) # nsf基频
|
||||
test_ds = torch.LongTensor([0]) # 说话人ID
|
||||
test_rnd = torch.rand(1, 192, 200) # 噪声（加入随机因子）
|
||||
|
||||
device = "cpu" # 导出时设备（不影响使用模型）
|
||||
|
||||
net_g = SynthesizerTrnMsNSFsidM(
|
||||
*cpt["config"], is_half=False, encoder_dim=encoder_dim
|
||||
) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
|
||||
net_g.load_state_dict(cpt["weight"], strict=False)
|
||||
input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
|
||||
output_names = [
|
||||
"audio",
|
||||
]
|
||||
# net_g.construct_spkmixmap(n_speaker) 多角色混合轨道导出
|
||||
torch.onnx.export(
|
||||
net_g,
|
||||
(
|
||||
test_phone.to(device),
|
||||
test_phone_lengths.to(device),
|
||||
test_pitch.to(device),
|
||||
test_pitchf.to(device),
|
||||
test_ds.to(device),
|
||||
test_rnd.to(device),
|
||||
),
|
||||
ExportedPath,
|
||||
dynamic_axes={
|
||||
"phone": [1],
|
||||
"pitch": [1],
|
||||
"pitchf": [1],
|
||||
"rnd": [2],
|
||||
},
|
||||
do_constant_folding=False,
|
||||
opset_version=18,
|
||||
verbose=False,
|
||||
input_names=input_names,
|
||||
output_names=output_names,
|
||||
)
|
||||
export_onnx("pt/Justin Bieber.pth" ,"pt/TestRvc_Rvc.onnx")
|
||||
Loading…
x
Reference in New Issue
Block a user