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This commit is contained in:
James Betker 2022-06-11 11:17:16 -06:00
parent 36b5e89a69
commit 00b9f332ee
1 changed files with 13 additions and 14 deletions
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codes/models/audio/music/transformer_diffusion9.py
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@ -4,11 +4,11 @@ import torch
import torch.nn as nn
import torch.nn.functional as F
from models.arch_util import ResBlock
from models.audio.music.music_quantizer2 import MusicQuantizer2
from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
from models.diffusion.unet_diffusion import TimestepBlock
from models.lucidrains.x_transformers import Encoder, Attention, FeedForward, RMSScaleShiftNorm, RotaryEmbedding
from models.lucidrains.x_transformers import Encoder, Attention, RMSScaleShiftNorm, RotaryEmbedding, \
FeedForward
from trainer.networks import register_model
from utils.util import checkpoint, print_network
@ -44,22 +44,20 @@ class DietAttentionBlock(TimestepBlock):
def __init__(self, in_dim, dim, heads, dropout):
super().__init__()
self.proj = nn.Linear(in_dim, dim, bias=False)
self.prenorm = nn.LayerNorm(dim)
self.prenorm = RMSScaleShiftNorm(dim, bias=False)
self.attn = Attention(dim, heads=heads, dim_head=dim//heads, causal=False, dropout=dropout)
self.attnorm = RMSScaleShiftNorm(dim, bias=False)
self.ff1 = FeedForward(dim, mult=2, dropout=dropout)
self.midnorm = RMSScaleShiftNorm(dim, bias=False)
self.ff2 = FeedForward(dim, in_dim, mult=1, dropout=dropout, zero_init_output=True)
self.attnorm = nn.LayerNorm(dim*2)
self.ff = FeedForward(dim*2, in_dim, mult=1, dropout=dropout)
self.exit_mult = nn.Parameter(torch.zeros(1,1,in_dim))
def forward(self, x, timestep_emb, rotary_emb):
h = self.proj(x)
h = self.prenorm(h)
h = self.prenorm(h, norm_scale_shift_inp=timestep_emb)
ah, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb)
h = F.gelu(self.attnorm(h, norm_scale_shift_inp=timestep_emb))
h = checkpoint(self.ff1, ah + h) + h
h = F.gelu(self.midnorm(h, norm_scale_shift_inp=timestep_emb))
h = checkpoint(self.ff2, h)
return h + x
h = torch.cat([ah, h], dim=-1)
h = F.gelu(self.attnorm(h))
h = checkpoint(self.ff, h)
return h * self.exit_mult
class TransformerDiffusion(nn.Module):
@ -253,7 +251,7 @@ class TransformerDiffusionWithQuantizer(nn.Module):
def get_grad_norm_parameter_groups(self):
groups = {
'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])),
'ff_layers': list(itertools.chain.from_iterable([lyr.ff1.parameters() for lyr in self.diff.layers])) + list(itertools.chain.from_iterable([lyr.ff2.parameters() for lyr in self.diff.layers])),
'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])),
'quantizer_encoder': list(self.quantizer.encoder.parameters()),
'quant_codebook': [self.quantizer.quantizer.codevectors],
'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
@ -290,6 +288,7 @@ class TransformerDiffusionWithARPrior(nn.Module):
groups = {
'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])),
'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])),
'exit_mults': list([lyr.ff.exit_mult for lyr in self.diff.layers]),
'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
'out': list(self.diff.out.parameters()),
'x_proj': list(self.diff.inp_block.parameters()),