forked from mrq/tortoise-tts
225 lines
6.2 KiB
Python
225 lines
6.2 KiB
Python
from functools import partial
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import torch
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import torch.nn.functional as F
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from einops import rearrange
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from rotary_embedding_torch import RotaryEmbedding, broadcat
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from torch import nn
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import tortoise.utils.torch_intermediary as ml
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# helpers
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def exists(val):
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return val is not None
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def default(val, d):
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return val if exists(val) else d
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def cast_tuple(val, depth = 1):
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if isinstance(val, list):
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val = tuple(val)
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return val if isinstance(val, tuple) else (val,) * depth
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def max_neg_value(t):
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return -torch.finfo(t.dtype).max
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def stable_softmax(t, dim = -1, alpha = 32 ** 2):
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t = t / alpha
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t = t - torch.amax(t, dim = dim, keepdim = True).detach()
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return (t * alpha).softmax(dim = dim)
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def route_args(router, args, depth):
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routed_args = [(dict(), dict()) for _ in range(depth)]
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matched_keys = [key for key in args.keys() if key in router]
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for key in matched_keys:
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val = args[key]
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for depth, ((f_args, g_args), routes) in enumerate(zip(routed_args, router[key])):
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new_f_args, new_g_args = map(lambda route: ({key: val} if route else {}), routes)
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routed_args[depth] = ({**f_args, **new_f_args}, {**g_args, **new_g_args})
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return routed_args
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# classes
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class SequentialSequence(nn.Module):
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def __init__(self, layers, args_route = {}, layer_dropout = 0.):
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super().__init__()
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assert all(len(route) == len(layers) for route in args_route.values()), 'each argument route map must have the same depth as the number of sequential layers'
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self.layers = layers
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self.args_route = args_route
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self.layer_dropout = layer_dropout
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def forward(self, x, **kwargs):
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args = route_args(self.args_route, kwargs, len(self.layers))
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layers_and_args = list(zip(self.layers, args))
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for (f, g), (f_args, g_args) in layers_and_args:
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x = x + f(x, **f_args)
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x = x + g(x, **g_args)
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return x
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class DivideMax(nn.Module):
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def __init__(self, dim):
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super().__init__()
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self.dim = dim
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def forward(self, x):
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maxes = x.amax(dim = self.dim, keepdim = True).detach()
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return x / maxes
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# https://arxiv.org/abs/2103.17239
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class LayerScale(nn.Module):
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def __init__(self, dim, depth, fn):
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super().__init__()
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if depth <= 18:
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init_eps = 0.1
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elif depth > 18 and depth <= 24:
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init_eps = 1e-5
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else:
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init_eps = 1e-6
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scale = torch.zeros(1, 1, dim).fill_(init_eps)
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self.scale = nn.Parameter(scale)
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self.fn = fn
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def forward(self, x, **kwargs):
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return self.fn(x, **kwargs) * self.scale
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# layer norm
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class PreNorm(nn.Module):
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def __init__(self, dim, fn, sandwich = False):
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super().__init__()
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self.norm = nn.LayerNorm(dim)
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self.norm_out = nn.LayerNorm(dim) if sandwich else nn.Identity()
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self.fn = fn
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def forward(self, x, **kwargs):
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x = self.norm(x)
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x = self.fn(x, **kwargs)
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return self.norm_out(x)
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# feed forward
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class GEGLU(nn.Module):
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def forward(self, x):
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x, gates = x.chunk(2, dim = -1)
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return x * F.gelu(gates)
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class FeedForward(nn.Module):
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def __init__(self, dim, dropout = 0., mult = 4.):
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super().__init__()
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self.net = nn.Sequential(
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# nn.Linear
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ml.Linear(dim, dim * mult * 2),
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GEGLU(),
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nn.Dropout(dropout),
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# nn.Linear
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ml.Linear(dim * mult, dim)
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)
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def forward(self, x):
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return self.net(x)
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# Attention
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class Attention(nn.Module):
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def __init__(self, dim, seq_len, causal = True, heads = 8, dim_head = 64, dropout = 0.):
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super().__init__()
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inner_dim = dim_head * heads
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self.heads = heads
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self.seq_len = seq_len
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self.scale = dim_head ** -0.5
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self.causal = causal
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# nn.Linear
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self.to_qkv = ml.Linear(dim, inner_dim * 3, bias = False)
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self.to_out = nn.Sequential(
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# nn.Linear
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ml.Linear(inner_dim, dim),
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nn.Dropout(dropout)
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)
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def forward(self, x, mask = None):
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b, n, _, h, device = *x.shape, self.heads, x.device
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softmax = torch.softmax
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qkv = self.to_qkv(x).chunk(3, dim = -1)
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)
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q = q * self.scale
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dots = torch.einsum('b h i d, b h j d -> b h i j', q, k)
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mask_value = max_neg_value(dots)
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if exists(mask):
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mask = rearrange(mask, 'b j -> b () () j')
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dots.masked_fill_(~mask, mask_value)
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del mask
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if self.causal:
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i, j = dots.shape[-2:]
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mask = torch.ones(i, j, device = device).triu_(j - i + 1).bool()
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dots.masked_fill_(mask, mask_value)
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attn = softmax(dots, dim=-1)
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out = torch.einsum('b h i j, b h j d -> b h i d', attn, v)
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out = rearrange(out, 'b h n d -> b n (h d)')
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out = self.to_out(out)
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return out
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# main transformer class
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class Transformer(nn.Module):
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def __init__(
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self,
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*,
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dim,
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depth,
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seq_len,
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causal = True,
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heads = 8,
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dim_head = 64,
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ff_mult = 4,
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attn_dropout = 0.,
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ff_dropout = 0.,
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sparse_attn = False,
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sandwich_norm = False,
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):
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super().__init__()
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layers = nn.ModuleList([])
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sparse_layer = cast_tuple(sparse_attn, depth)
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for ind, sparse_attn in zip(range(depth), sparse_layer):
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attn = Attention(dim, causal = causal, seq_len = seq_len, heads = heads, dim_head = dim_head, dropout = attn_dropout)
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ff = FeedForward(dim, mult = ff_mult, dropout = ff_dropout)
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layers.append(nn.ModuleList([
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LayerScale(dim, ind + 1, PreNorm(dim, attn, sandwich = sandwich_norm)),
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LayerScale(dim, ind + 1, PreNorm(dim, ff, sandwich = sandwich_norm))
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]))
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execute_type = SequentialSequence
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route_attn = ((True, False),) * depth
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attn_route_map = {'mask': route_attn}
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self.layers = execute_type(layers, args_route = attn_route_map)
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def forward(self, x, **kwargs):
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return self.layers(x, **kwargs)
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