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Rework the diet blocks a bit

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James Betker 2022-06-11 08:28:10 -06:00
parent 0dd3883662
commit 36b5e89a69
1 changed files with 15 additions and 8 deletions
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23
codes/models/audio/music/transformer_diffusion9.py
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@ -44,15 +44,21 @@ class DietAttentionBlock(TimestepBlock):
def __init__(self, in_dim, dim, heads, dropout): def __init__(self, in_dim, dim, heads, dropout):
super().__init__() super().__init__()
self.proj = nn.Linear(in_dim, dim, bias=False) self.proj = nn.Linear(in_dim, dim, bias=False)
self.rms_scale_norm = RMSScaleShiftNorm(dim, bias=False) self.prenorm = nn.LayerNorm(dim)
self.attn = Attention(dim, heads=heads, dim_head=dim//heads, causal=False, dropout=dropout) self.attn = Attention(dim, heads=heads, dim_head=dim//heads, causal=False, dropout=dropout)
self.ff = FeedForward(dim, in_dim, mult=2, dropout=dropout, zero_init_output=True) 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)
def forward(self, x, timestep_emb, rotary_emb): def forward(self, x, timestep_emb, rotary_emb):
h = self.proj(x) h = self.proj(x)
h = self.rms_scale_norm(h, norm_scale_shift_inp=timestep_emb) h = self.prenorm(h)
h, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb) ah, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb)
h = checkpoint(self.ff, h) 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 return h + x
@ -247,7 +253,7 @@ class TransformerDiffusionWithQuantizer(nn.Module):
def get_grad_norm_parameter_groups(self): def get_grad_norm_parameter_groups(self):
groups = { groups = {
'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])), '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])), '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])),
'quantizer_encoder': list(self.quantizer.encoder.parameters()), 'quantizer_encoder': list(self.quantizer.encoder.parameters()),
'quant_codebook': [self.quantizer.quantizer.codevectors], 'quant_codebook': [self.quantizer.quantizer.codevectors],
'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()), 'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
@ -322,7 +328,8 @@ def test_quant_model():
ts = torch.LongTensor([600, 600]) ts = torch.LongTensor([600, 600])
model = TransformerDiffusionWithQuantizer(in_channels=256, model_channels=2048, block_channels=1024, model = TransformerDiffusionWithQuantizer(in_channels=256, model_channels=2048, block_channels=1024,
prenet_channels=1024, num_heads=8, prenet_channels=1024, num_heads=8,
input_vec_dim=1024, num_layers=20, prenet_layers=6) input_vec_dim=1024, num_layers=20, prenet_layers=6,
dropout=.1)
model.get_grad_norm_parameter_groups() model.get_grad_norm_parameter_groups()
quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth') quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth')
@ -338,7 +345,7 @@ def test_ar_model():
cond = torch.randn(2, 256, 400) cond = torch.randn(2, 256, 400)
ts = torch.LongTensor([600, 600]) ts = torch.LongTensor([600, 600])
model = TransformerDiffusionWithARPrior(model_channels=2048, block_channels=1024, prenet_channels=1536, model = TransformerDiffusionWithARPrior(model_channels=2048, block_channels=1024, prenet_channels=1536,
input_vec_dim=512, num_layers=24, prenet_layers=6, freeze_diff=True, input_vec_dim=512, num_layers=16, prenet_layers=6, freeze_diff=True,
unconditioned_percentage=.4) unconditioned_percentage=.4)
model.get_grad_norm_parameter_groups() model.get_grad_norm_parameter_groups()