142 lines
4.3 KiB
Python
142 lines
4.3 KiB
Python
# ported from: Originally ported from: https://github.com/neonbjb/tortoise-tts
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import math
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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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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 conv_nd(dims, *args, **kwargs):
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if dims == 1:
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return nn.Conv1d(*args, **kwargs)
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elif dims == 2:
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return nn.Conv2d(*args, **kwargs)
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elif dims == 3:
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return nn.Conv3d(*args, **kwargs)
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raise ValueError(f"unsupported dimensions: {dims}")
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def normalization(channels):
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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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def zero_module(module):
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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 QKVAttention(nn.Module):
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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, qk_bias=0):
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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("bct,bcs->bts", q * scale, k * scale) # More stable with f16 than dividing afterwards
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weight = weight + qk_bias
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if mask is not None:
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mask = mask.repeat(self.n_heads, 1, 1)
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weight[mask.logical_not()] = -torch.inf
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weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
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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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"""An attention block that allows spatial positions to attend to each other."""
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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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out_channels=None,
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do_activation=False,
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):
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super().__init__()
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self.channels = channels
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out_channels = channels if out_channels is None else out_channels
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self.do_activation = do_activation
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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 = conv_nd(1, channels, out_channels * 3, 1)
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self.attention = QKVAttention(self.num_heads)
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self.x_proj = nn.Identity() if out_channels == channels else conv_nd(1, channels, out_channels, 1)
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self.proj_out = zero_module(conv_nd(1, out_channels, out_channels, 1))
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def forward(self, x, mask=None, qk_bias=0):
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b, c, *spatial = x.shape
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if mask is not None:
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if len(mask.shape) == 2:
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mask = mask.unsqueeze(0).repeat(x.shape[0], 1, 1)
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if mask.shape[1] != x.shape[-1]:
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mask = mask[:, : x.shape[-1], : x.shape[-1]]
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x = x.reshape(b, c, -1)
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x = self.norm(x)
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if self.do_activation:
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x = F.silu(x, inplace=True)
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qkv = self.qkv(x)
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h = self.attention(qkv, mask=mask, qk_bias=qk_bias)
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h = self.proj_out(h)
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xp = self.x_proj(x)
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return (xp + h).reshape(b, xp.shape[1], *spatial)
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class ConditioningEncoder(nn.Module):
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def __init__(
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self,
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spec_dim,
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embedding_dim,
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attn_blocks=6,
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num_attn_heads=4,
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):
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super().__init__()
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attn = []
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self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=1)
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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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"""
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x: (b, 80, s)
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"""
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h = self.init(x)
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h = self.attn(h)
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return h
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