forked from mrq/tortoise-tts
367 lines
13 KiB
Python
367 lines
13 KiB
Python
import functools
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchaudio
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from tortoise.models.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
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def zero_module(module):
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"""
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Zero out the parameters of a module and return it.
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"""
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for p in module.parameters():
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p.detach().zero_()
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return module
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class GroupNorm32(nn.GroupNorm):
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def forward(self, x):
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return super().forward(x.float()).type(x.dtype)
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def normalization(channels):
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"""
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Make a standard normalization layer.
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:param channels: number of input channels.
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:return: an nn.Module for normalization.
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"""
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groups = 32
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if channels <= 16:
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groups = 8
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elif channels <= 64:
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groups = 16
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while channels % groups != 0:
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groups = int(groups / 2)
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assert groups > 2
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return GroupNorm32(groups, channels)
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class QKVAttentionLegacy(nn.Module):
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"""
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A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
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"""
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def __init__(self, n_heads):
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super().__init__()
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self.n_heads = n_heads
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def forward(self, qkv, mask=None, rel_pos=None):
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"""
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Apply QKV attention.
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:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
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:return: an [N x (H * C) x T] tensor after attention.
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"""
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bs, width, length = qkv.shape
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assert width % (3 * self.n_heads) == 0
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ch = width // (3 * self.n_heads)
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q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
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scale = 1 / math.sqrt(math.sqrt(ch))
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weight = torch.einsum(
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"bct,bcs->bts", q * scale, k * scale
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) # More stable with f16 than dividing afterwards
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if rel_pos is not None:
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weight = rel_pos(weight.reshape(bs, self.n_heads, weight.shape[-2], weight.shape[-1])).reshape(bs * self.n_heads, weight.shape[-2], weight.shape[-1])
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weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
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if mask is not None:
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# The proper way to do this is to mask before the softmax using -inf, but that doesn't work properly on CPUs.
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mask = mask.repeat(self.n_heads, 1).unsqueeze(1)
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weight = weight * mask
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a = torch.einsum("bts,bcs->bct", weight, v)
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return a.reshape(bs, -1, length)
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class AttentionBlock(nn.Module):
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"""
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An attention block that allows spatial positions to attend to each other.
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Originally ported from here, but adapted to the N-d case.
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https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
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"""
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def __init__(
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self,
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channels,
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num_heads=1,
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num_head_channels=-1,
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do_checkpoint=True,
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relative_pos_embeddings=False,
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):
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super().__init__()
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self.channels = channels
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self.do_checkpoint = do_checkpoint
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if num_head_channels == -1:
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self.num_heads = num_heads
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else:
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assert (
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channels % num_head_channels == 0
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), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
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self.num_heads = channels // num_head_channels
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self.norm = normalization(channels)
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self.qkv = nn.Conv1d(channels, channels * 3, 1)
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# split heads before split qkv
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self.attention = QKVAttentionLegacy(self.num_heads)
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self.proj_out = zero_module(nn.Conv1d(channels, channels, 1))
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if relative_pos_embeddings:
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self.relative_pos_embeddings = RelativePositionBias(scale=(channels // self.num_heads) ** .5, causal=False, heads=num_heads, num_buckets=32, max_distance=64)
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else:
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self.relative_pos_embeddings = None
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def forward(self, x, mask=None):
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b, c, *spatial = x.shape
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x = x.reshape(b, c, -1)
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qkv = self.qkv(self.norm(x))
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h = self.attention(qkv, mask, self.relative_pos_embeddings)
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h = self.proj_out(h)
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return (x + h).reshape(b, c, *spatial)
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class Upsample(nn.Module):
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"""
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An upsampling layer with an optional convolution.
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:param channels: channels in the inputs and outputs.
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:param use_conv: a bool determining if a convolution is applied.
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"""
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def __init__(self, channels, use_conv, out_channels=None, factor=4):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.factor = factor
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if use_conv:
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ksize = 5
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pad = 2
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self.conv = nn.Conv1d(self.channels, self.out_channels, ksize, padding=pad)
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def forward(self, x):
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assert x.shape[1] == self.channels
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x = F.interpolate(x, scale_factor=self.factor, mode="nearest")
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if self.use_conv:
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x = self.conv(x)
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return x
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class Downsample(nn.Module):
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"""
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A downsampling layer with an optional convolution.
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:param channels: channels in the inputs and outputs.
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:param use_conv: a bool determining if a convolution is applied.
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"""
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def __init__(self, channels, use_conv, out_channels=None, factor=4, ksize=5, pad=2):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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stride = factor
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if use_conv:
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self.op = nn.Conv1d(
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self.channels, self.out_channels, ksize, stride=stride, padding=pad
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)
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else:
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assert self.channels == self.out_channels
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self.op = nn.AvgPool1d(kernel_size=stride, stride=stride)
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def forward(self, x):
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assert x.shape[1] == self.channels
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return self.op(x)
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class ResBlock(nn.Module):
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def __init__(
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self,
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channels,
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dropout,
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out_channels=None,
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use_conv=False,
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use_scale_shift_norm=False,
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up=False,
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down=False,
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kernel_size=3,
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):
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super().__init__()
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self.channels = channels
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self.dropout = dropout
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.use_scale_shift_norm = use_scale_shift_norm
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padding = 1 if kernel_size == 3 else 2
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self.in_layers = nn.Sequential(
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normalization(channels),
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nn.SiLU(),
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nn.Conv1d(channels, self.out_channels, kernel_size, padding=padding),
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)
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self.updown = up or down
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if up:
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self.h_upd = Upsample(channels, False)
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self.x_upd = Upsample(channels, False)
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elif down:
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self.h_upd = Downsample(channels, False)
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self.x_upd = Downsample(channels, False)
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else:
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self.h_upd = self.x_upd = nn.Identity()
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self.out_layers = nn.Sequential(
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normalization(self.out_channels),
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nn.SiLU(),
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nn.Dropout(p=dropout),
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zero_module(
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nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding)
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),
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)
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if self.out_channels == channels:
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self.skip_connection = nn.Identity()
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elif use_conv:
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self.skip_connection = nn.Conv1d(
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channels, self.out_channels, kernel_size, padding=padding
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)
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else:
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self.skip_connection = nn.Conv1d(channels, self.out_channels, 1)
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def forward(self, x):
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if self.updown:
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in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
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h = in_rest(x)
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h = self.h_upd(h)
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x = self.x_upd(x)
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h = in_conv(h)
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else:
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h = self.in_layers(x)
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h = self.out_layers(h)
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return self.skip_connection(x) + h
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class AudioMiniEncoder(nn.Module):
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def __init__(self,
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spec_dim,
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embedding_dim,
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base_channels=128,
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depth=2,
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resnet_blocks=2,
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attn_blocks=4,
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num_attn_heads=4,
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dropout=0,
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downsample_factor=2,
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kernel_size=3):
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super().__init__()
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self.init = nn.Sequential(
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nn.Conv1d(spec_dim, base_channels, 3, padding=1)
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)
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ch = base_channels
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res = []
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for l in range(depth):
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for r in range(resnet_blocks):
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res.append(ResBlock(ch, dropout, kernel_size=kernel_size))
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res.append(Downsample(ch, use_conv=True, out_channels=ch*2, factor=downsample_factor))
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ch *= 2
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self.res = nn.Sequential(*res)
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self.final = nn.Sequential(
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normalization(ch),
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nn.SiLU(),
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nn.Conv1d(ch, embedding_dim, 1)
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)
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attn = []
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for a in range(attn_blocks):
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attn.append(AttentionBlock(embedding_dim, num_attn_heads,))
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self.attn = nn.Sequential(*attn)
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self.dim = embedding_dim
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def forward(self, x):
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h = self.init(x)
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h = self.res(h)
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h = self.final(h)
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h = self.attn(h)
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return h[:, :, 0]
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class TorchMelSpectrogram(nn.Module):
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def __init__(self, filter_length=1024, hop_length=256, win_length=1024, n_mel_channels=80, mel_fmin=0, mel_fmax=8000,
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sampling_rate=22050, normalize=False, mel_norm_file='data/mel_norms.pth'):
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super().__init__()
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# These are the default tacotron values for the MEL spectrogram.
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self.filter_length = filter_length
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self.hop_length = hop_length
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self.win_length = win_length
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self.n_mel_channels = n_mel_channels
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self.mel_fmin = mel_fmin
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self.mel_fmax = mel_fmax
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self.sampling_rate = sampling_rate
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self.mel_stft = torchaudio.transforms.MelSpectrogram(n_fft=self.filter_length, hop_length=self.hop_length,
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win_length=self.win_length, power=2, normalized=normalize,
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sample_rate=self.sampling_rate, f_min=self.mel_fmin,
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f_max=self.mel_fmax, n_mels=self.n_mel_channels,
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norm="slaney")
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self.mel_norm_file = mel_norm_file
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if self.mel_norm_file is not None:
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self.mel_norms = torch.load(self.mel_norm_file)
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else:
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self.mel_norms = None
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def forward(self, inp):
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if len(inp.shape) == 3: # Automatically squeeze out the channels dimension if it is present (assuming mono-audio)
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inp = inp.squeeze(1)
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assert len(inp.shape) == 2
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self.mel_stft = self.mel_stft.to(inp.device)
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mel = self.mel_stft(inp)
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# Perform dynamic range compression
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mel = torch.log(torch.clamp(mel, min=1e-5))
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if self.mel_norms is not None:
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self.mel_norms = self.mel_norms.to(mel.device)
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mel = mel / self.mel_norms.unsqueeze(0).unsqueeze(-1)
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return mel
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class CheckpointedLayer(nn.Module):
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"""
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Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses
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checkpoint for all other args.
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"""
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def __init__(self, wrap):
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super().__init__()
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self.wrap = wrap
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def forward(self, x, *args, **kwargs):
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for k, v in kwargs.items():
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assert not (isinstance(v, torch.Tensor) and v.requires_grad) # This would screw up checkpointing.
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partial = functools.partial(self.wrap, **kwargs)
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return torch.utils.checkpoint.checkpoint(partial, x, *args)
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class CheckpointedXTransformerEncoder(nn.Module):
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"""
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Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid
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to channels-last that XTransformer expects.
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"""
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def __init__(self, needs_permute=True, exit_permute=True, checkpoint=True, **xtransformer_kwargs):
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super().__init__()
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self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs)
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self.needs_permute = needs_permute
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self.exit_permute = exit_permute
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if not checkpoint:
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return
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for i in range(len(self.transformer.attn_layers.layers)):
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n, b, r = self.transformer.attn_layers.layers[i]
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self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r])
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def forward(self, x, **kwargs):
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if self.needs_permute:
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x = x.permute(0,2,1)
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h = self.transformer(x, **kwargs)
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if self.exit_permute:
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h = h.permute(0,2,1)
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return h |