780 lines
36 KiB
Python
780 lines
36 KiB
Python
import itertools
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from time import time
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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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from models.arch_util import ResBlock
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from models.audio.music.gpt_music2 import UpperEncoder, GptMusicLower
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from models.audio.music.music_quantizer2 import MusicQuantizer2
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from models.audio.tts.lucidrains_dvae import DiscreteVAE
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from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
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from models.diffusion.unet_diffusion import TimestepBlock
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from models.lucidrains.x_transformers import Encoder, Attention, RMSScaleShiftNorm, RotaryEmbedding, \
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FeedForward
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from trainer.networks import register_model
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from utils.util import checkpoint, print_network
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def is_latent(t):
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return t.dtype == torch.float
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def is_sequence(t):
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return t.dtype == torch.long
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class MultiGroupEmbedding(nn.Module):
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def __init__(self, tokens, groups, dim):
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super().__init__()
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self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
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def forward(self, x):
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h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
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return torch.cat(h, dim=-1)
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class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
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def forward(self, x, emb, rotary_emb):
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for layer in self:
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if isinstance(layer, TimestepBlock):
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x = layer(x, emb, rotary_emb)
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else:
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x = layer(x, rotary_emb)
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return x
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class SubBlock(nn.Module):
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def __init__(self, inp_dim, contraction_dim, heads, dropout):
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super().__init__()
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self.attn = Attention(inp_dim, out_dim=contraction_dim, heads=heads, dim_head=contraction_dim//heads, causal=False, dropout=dropout)
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self.attnorm = nn.LayerNorm(contraction_dim)
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self.ff = nn.Conv1d(inp_dim+contraction_dim, contraction_dim, kernel_size=3, padding=1)
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self.ffnorm = nn.LayerNorm(contraction_dim)
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def forward(self, x, rotary_emb):
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ah, _, _, _ = checkpoint(self.attn, x, None, None, None, None, None, rotary_emb)
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ah = F.gelu(self.attnorm(ah))
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h = torch.cat([ah, x], dim=-1)
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hf = checkpoint(self.ff, h.permute(0,2,1)).permute(0,2,1)
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hf = F.gelu(self.ffnorm(hf))
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h = torch.cat([h, hf], dim=-1)
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return h
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class ConcatAttentionBlock(TimestepBlock):
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def __init__(self, trunk_dim, contraction_dim, time_embed_dim, heads, dropout):
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super().__init__()
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self.prenorm = RMSScaleShiftNorm(trunk_dim, embed_dim=time_embed_dim, bias=False)
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self.block1 = SubBlock(trunk_dim, contraction_dim, heads, dropout)
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self.block2 = SubBlock(trunk_dim+contraction_dim*2, contraction_dim, heads, dropout)
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self.out = nn.Linear(contraction_dim*4, trunk_dim, bias=False)
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self.out.weight.data.zero_()
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def forward(self, x, timestep_emb, rotary_emb):
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h = self.prenorm(x, norm_scale_shift_inp=timestep_emb)
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h = self.block1(h, rotary_emb)
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h = self.block2(h, rotary_emb)
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h = self.out(h[:,:,x.shape[-1]:])
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return h + x
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class TransformerDiffusion(nn.Module):
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"""
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A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
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"""
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def __init__(
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self,
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prenet_channels=1024,
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prenet_layers=3,
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time_embed_dim=256,
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model_channels=1024,
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contraction_dim=256,
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num_layers=8,
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in_channels=256,
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rotary_emb_dim=32,
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input_vec_dim=1024,
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out_channels=512, # mean and variance
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num_heads=4,
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dropout=0,
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use_fp16=False,
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ar_prior=False,
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new_code_expansion=False,
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permute_codes=False,
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# Parameters for regularization.
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unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training.
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# Parameters for re-training head
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freeze_except_code_converters=False,
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):
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super().__init__()
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.prenet_channels = prenet_channels
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self.time_embed_dim = time_embed_dim
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self.out_channels = out_channels
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self.dropout = dropout
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self.unconditioned_percentage = unconditioned_percentage
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self.enable_fp16 = use_fp16
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self.new_code_expansion = new_code_expansion
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self.permute_codes = permute_codes
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self.inp_block = conv_nd(1, in_channels, prenet_channels, 3, 1, 1)
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self.time_embed = nn.Sequential(
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linear(time_embed_dim, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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)
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self.ar_prior = ar_prior
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prenet_heads = prenet_channels//64
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if ar_prior:
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self.ar_input = nn.Linear(input_vec_dim, prenet_channels)
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self.ar_prior_intg = Encoder(
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dim=prenet_channels,
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depth=prenet_layers,
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heads=prenet_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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zero_init_branch_output=True,
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ff_mult=1,
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)
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else:
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self.input_converter = nn.Linear(input_vec_dim, prenet_channels)
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self.code_converter = Encoder(
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dim=prenet_channels,
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depth=prenet_layers,
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heads=prenet_heads,
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ff_dropout=dropout,
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attn_dropout=dropout,
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use_rmsnorm=True,
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ff_glu=True,
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rotary_pos_emb=True,
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zero_init_branch_output=True,
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ff_mult=1,
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)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,prenet_channels))
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self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
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self.intg = nn.Linear(prenet_channels*2, model_channels)
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self.layers = TimestepRotaryEmbedSequential(*[ConcatAttentionBlock(model_channels, contraction_dim, time_embed_dim, num_heads, dropout) for _ in range(num_layers)])
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self.out = nn.Sequential(
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normalization(model_channels),
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nn.SiLU(),
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zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
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)
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if freeze_except_code_converters:
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for p in self.parameters():
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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if hasattr(self, 'ar_input'):
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for m in [self.ar_input and self.ar_prior_intg]:
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for p in m.parameters():
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del p.DO_NOT_TRAIN
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p.requires_grad = True
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if hasattr(self, 'code_converter'):
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for m in [self.code_converter and self.input_converter]:
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for p in m.parameters():
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del p.DO_NOT_TRAIN
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p.requires_grad = True
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self.debug_codes = {}
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def get_grad_norm_parameter_groups(self):
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attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.layers]))
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attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.layers]))
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ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.layers]))
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ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.layers]))
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blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.layers]))
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groups = {
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'prenorms': list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.layers])),
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'blk1_attention_layers': attn1,
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'blk2_attention_layers': attn2,
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'attention_layers': attn1 + attn2,
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'blk1_ff_layers': ff1,
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'blk2_ff_layers': ff2,
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'ff_layers': ff1 + ff2,
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'block_out_layers': blkout_layers,
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'rotary_embeddings': list(self.rotary_embeddings.parameters()),
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'out': list(self.out.parameters()),
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'x_proj': list(self.inp_block.parameters()),
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'layers': list(self.layers.parameters()),
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#'code_converters': list(self.input_converter.parameters()) + list(self.code_converter.parameters()),
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'time_embed': list(self.time_embed.parameters()),
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}
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return groups
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def timestep_independent(self, prior, expected_seq_len):
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if self.new_code_expansion:
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prior = F.interpolate(prior.permute(0,2,1), size=expected_seq_len, mode='linear').permute(0,2,1)
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code_emb = self.ar_input(prior) if self.ar_prior else self.input_converter(prior)
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code_emb = self.ar_prior_intg(code_emb) if self.ar_prior else self.code_converter(code_emb)
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# Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
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if self.training and self.unconditioned_percentage > 0:
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unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
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device=code_emb.device) < self.unconditioned_percentage
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code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(prior.shape[0], 1, 1),
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code_emb)
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if not self.new_code_expansion:
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code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
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return code_emb
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def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None, conditioning_free=False):
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if precomputed_code_embeddings is not None:
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assert codes is None and conditioning_input is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
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if self.permute_codes:
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codes = codes.permute(0,2,1)
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unused_params = []
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if conditioning_free:
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code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
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else:
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if precomputed_code_embeddings is not None:
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code_emb = precomputed_code_embeddings
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else:
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code_emb = self.timestep_independent(codes, x.shape[-1])
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unused_params.append(self.unconditioned_embedding)
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with torch.autocast(x.device.type, enabled=self.enable_fp16):
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blk_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim))
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x = self.inp_block(x).permute(0,2,1)
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rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
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x = self.intg(torch.cat([x, code_emb], dim=-1))
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for layer in self.layers:
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x = checkpoint(layer, x, blk_emb, rotary_pos_emb)
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x = x.float().permute(0,2,1)
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out = self.out(x)
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# Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
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extraneous_addition = 0
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for p in unused_params:
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extraneous_addition = extraneous_addition + p.mean()
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out = out + extraneous_addition * 0
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return out
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class TransformerDiffusionWithQuantizer(nn.Module):
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def __init__(self, quantizer_dims=[1024], quantizer_codebook_size=256, quantizer_codebook_groups=2,
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freeze_quantizer_until=20000, **kwargs):
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super().__init__()
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self.internal_step = 0
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self.freeze_quantizer_until = freeze_quantizer_until
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self.diff = TransformerDiffusion(**kwargs)
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self.quantizer = MusicQuantizer2(inp_channels=kwargs['in_channels'], inner_dim=quantizer_dims,
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codevector_dim=quantizer_dims[0], codebook_size=quantizer_codebook_size,
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codebook_groups=quantizer_codebook_groups, max_gumbel_temperature=4,
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min_gumbel_temperature=.5)
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self.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature
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del self.quantizer.up
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def update_for_step(self, step, *args):
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self.internal_step = step
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qstep = max(0, self.internal_step - self.freeze_quantizer_until)
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self.quantizer.quantizer.temperature = max(
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self.quantizer.max_gumbel_temperature * self.quantizer.gumbel_temperature_decay ** qstep,
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self.quantizer.min_gumbel_temperature,
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)
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def forward(self, x, timesteps, truth_mel, conditioning_input=None, disable_diversity=False, conditioning_free=False):
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quant_grad_enabled = self.internal_step > self.freeze_quantizer_until
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with torch.set_grad_enabled(quant_grad_enabled):
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proj, diversity_loss = self.quantizer(truth_mel, return_decoder_latent=True)
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proj = proj.permute(0,2,1)
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# Make sure this does not cause issues in DDP by explicitly using the parameters for nothing.
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if not quant_grad_enabled:
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unused = 0
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for p in self.quantizer.parameters():
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unused = unused + p.mean() * 0
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proj = proj + unused
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diversity_loss = diversity_loss * 0
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diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
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if disable_diversity:
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return diff
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return diff, diversity_loss
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def get_debug_values(self, step, __):
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if self.quantizer.total_codes > 0:
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return {'histogram_quant_codes': self.quantizer.codes[:self.quantizer.total_codes],
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'gumbel_temperature': self.quantizer.quantizer.temperature}
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else:
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return {}
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def get_grad_norm_parameter_groups(self):
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attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.diff.layers]))
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attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.diff.layers]))
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ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.diff.layers]))
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ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.diff.layers]))
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blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers]))
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groups = {
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'prenorms': list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.diff.layers])),
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'blk1_attention_layers': attn1,
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'blk2_attention_layers': attn2,
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'attention_layers': attn1 + attn2,
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'blk1_ff_layers': ff1,
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'blk2_ff_layers': ff2,
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'ff_layers': ff1 + ff2,
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'block_out_layers': blkout_layers,
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'quantizer_encoder': list(self.quantizer.encoder.parameters()),
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'quant_codebook': [self.quantizer.quantizer.codevectors],
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'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
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'out': list(self.diff.out.parameters()),
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'x_proj': list(self.diff.inp_block.parameters()),
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'layers': list(self.diff.layers.parameters()),
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'code_converters': list(self.diff.input_converter.parameters()) + list(self.diff.code_converter.parameters()),
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'time_embed': list(self.diff.time_embed.parameters()),
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}
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return groups
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def before_step(self, step):
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scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers])) + \
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list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.diff.layers]))
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# Scale back the gradients of the blkout and prenorm layers by a constant factor. These get two orders of magnitudes
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# higher gradients. Ideally we would use parameter groups, but ZeroRedundancyOptimizer makes this trickier than
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# directly fiddling with the gradients.
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for p in scaled_grad_parameters:
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if hasattr(p, 'grad') and p.grad is not None:
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p.grad *= .2
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class TransformerDiffusionWithARPrior(nn.Module):
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def __init__(self, freeze_diff=False, **kwargs):
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super().__init__()
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self.internal_step = 0
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from models.audio.music.gpt_music import GptMusicLower
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self.ar = GptMusicLower(dim=512, layers=12)
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for p in self.ar.parameters():
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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self.diff = TransformerDiffusion(ar_prior=True, **kwargs)
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if freeze_diff:
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for p in self.diff.parameters():
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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for p in list(self.diff.ar_prior_intg.parameters()) + list(self.diff.ar_input.parameters()):
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del p.DO_NOT_TRAIN
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p.requires_grad = True
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def get_grad_norm_parameter_groups(self):
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groups = {
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'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])),
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'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])),
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'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
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'out': list(self.diff.out.parameters()),
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'x_proj': list(self.diff.inp_block.parameters()),
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'layers': list(self.diff.layers.parameters()),
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'ar_prior_intg': list(self.diff.ar_prior_intg.parameters()),
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'time_embed': list(self.diff.time_embed.parameters()),
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}
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return groups
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def forward(self, x, timesteps, truth_mel, disable_diversity=False, conditioning_input=None, conditioning_free=False):
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with torch.no_grad():
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prior = self.ar(truth_mel, conditioning_input, return_latent=True)
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diff = self.diff(x, timesteps, prior, conditioning_free=conditioning_free)
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return diff
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class TransformerDiffusionWithPretrainedVqvae(nn.Module):
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def __init__(self, vqargs, **kwargs):
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super().__init__()
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self.internal_step = 0
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self.diff = TransformerDiffusion(**kwargs)
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self.quantizer = DiscreteVAE(**vqargs)
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self.quantizer = self.quantizer.eval()
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for p in self.quantizer.parameters():
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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def forward(self, x, timesteps, truth_mel, conditioning_input=None, disable_diversity=False, conditioning_free=False):
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with torch.no_grad():
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reconstructed, proj = self.quantizer.infer(truth_mel)
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proj = proj.permute(0,2,1)
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diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
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return diff
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def get_debug_values(self, step, __):
|
|
if self.quantizer.total_codes > 0:
|
|
return {'histogram_quant_codes': self.quantizer.codes[:self.quantizer.total_codes]}
|
|
else:
|
|
return {}
|
|
|
|
def get_grad_norm_parameter_groups(self):
|
|
attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.diff.layers]))
|
|
attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.diff.layers]))
|
|
ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.diff.layers]))
|
|
ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.diff.layers]))
|
|
blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers]))
|
|
groups = {
|
|
'prenorms': list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.diff.layers])),
|
|
'blk1_attention_layers': attn1,
|
|
'blk2_attention_layers': attn2,
|
|
'attention_layers': attn1 + attn2,
|
|
'blk1_ff_layers': ff1,
|
|
'blk2_ff_layers': ff2,
|
|
'ff_layers': ff1 + ff2,
|
|
'block_out_layers': blkout_layers,
|
|
'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
|
|
'out': list(self.diff.out.parameters()),
|
|
'x_proj': list(self.diff.inp_block.parameters()),
|
|
'layers': list(self.diff.layers.parameters()),
|
|
#'code_converters': list(self.diff.input_converter.parameters()) + list(self.diff.code_converter.parameters()),
|
|
'time_embed': list(self.diff.time_embed.parameters()),
|
|
}
|
|
return groups
|
|
|
|
def before_step(self, step):
|
|
scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers])) + \
|
|
list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.diff.layers]))
|
|
# Scale back the gradients of the blkout and prenorm layers by a constant factor. These get two orders of magnitudes
|
|
# higher gradients. Ideally we would use parameter groups, but ZeroRedundancyOptimizer makes this trickier than
|
|
# directly fiddling with the gradients.
|
|
for p in scaled_grad_parameters:
|
|
p.grad *= .2
|
|
|
|
|
|
class TransformerDiffusionWithMultiPretrainedVqvae(nn.Module):
|
|
def __init__(self, num_vaes=4, vqargs={}, **kwargs):
|
|
super().__init__()
|
|
|
|
self.internal_step = 0
|
|
self.diff = TransformerDiffusion(**kwargs)
|
|
self.quantizers = nn.ModuleList([DiscreteVAE(**vqargs).eval() for _ in range(num_vaes)])
|
|
for p in self.quantizers.parameters():
|
|
p.DO_NOT_TRAIN = True
|
|
p.requires_grad = False
|
|
|
|
def forward(self, x, timesteps, truth_mel, conditioning_input=None, disable_diversity=False, conditioning_free=False):
|
|
with torch.no_grad():
|
|
proj = []
|
|
partition_size = truth_mel.shape[1] // len(self.quantizers)
|
|
for i, q in enumerate(self.quantizers):
|
|
mel_partition = truth_mel[:, i*partition_size:(i+1)*partition_size]
|
|
_, p = q.infer(mel_partition)
|
|
proj.append(p.permute(0,2,1))
|
|
proj = torch.cat(proj, dim=-1)
|
|
|
|
diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
|
|
return diff
|
|
|
|
def get_debug_values(self, step, __):
|
|
if self.quantizers[0].total_codes > 0:
|
|
dbgs = {}
|
|
for i in range(len(self.quantizers)):
|
|
dbgs[f'histogram_quant{i}_codes'] = self.quantizers[i].codes[:self.quantizers[i].total_codes]
|
|
return dbgs
|
|
else:
|
|
return {}
|
|
|
|
def get_grad_norm_parameter_groups(self):
|
|
attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.diff.layers]))
|
|
attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.diff.layers]))
|
|
ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.diff.layers]))
|
|
ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.diff.layers]))
|
|
blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers]))
|
|
groups = {
|
|
'prenorms': list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.diff.layers])),
|
|
'blk1_attention_layers': attn1,
|
|
'blk2_attention_layers': attn2,
|
|
'attention_layers': attn1 + attn2,
|
|
'blk1_ff_layers': ff1,
|
|
'blk2_ff_layers': ff2,
|
|
'ff_layers': ff1 + ff2,
|
|
'block_out_layers': blkout_layers,
|
|
'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
|
|
'out': list(self.diff.out.parameters()),
|
|
'x_proj': list(self.diff.inp_block.parameters()),
|
|
'layers': list(self.diff.layers.parameters()),
|
|
'code_converters': list(self.diff.input_converter.parameters()) + list(self.diff.code_converter.parameters()),
|
|
'time_embed': list(self.diff.time_embed.parameters()),
|
|
}
|
|
return groups
|
|
|
|
def before_step(self, step):
|
|
scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers])) + \
|
|
list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.diff.layers]))
|
|
# Scale back the gradients of the blkout and prenorm layers by a constant factor. These get two orders of magnitudes
|
|
# higher gradients. Ideally we would use parameter groups, but ZeroRedundancyOptimizer makes this trickier than
|
|
# directly fiddling with the gradients.
|
|
for p in scaled_grad_parameters:
|
|
if hasattr(p, 'grad') and p.grad is not None:
|
|
p.grad *= .2
|
|
|
|
|
|
class TransformerDiffusionWithCheaterLatent(nn.Module):
|
|
def __init__(self, freeze_encoder_until=50000, **kwargs):
|
|
super().__init__()
|
|
self.internal_step = 0
|
|
self.freeze_encoder_until = freeze_encoder_until
|
|
self.diff = TransformerDiffusion(**kwargs)
|
|
self.encoder = UpperEncoder(256, 1024, 256)
|
|
self.encoder = self.encoder.eval()
|
|
|
|
def forward(self, x, timesteps, truth_mel, conditioning_input=None, disable_diversity=False, conditioning_free=False):
|
|
unused_parameters = []
|
|
encoder_grad_enabled = self.internal_step > self.freeze_encoder_until
|
|
if not encoder_grad_enabled:
|
|
unused_parameters.extend(list(self.encoder.parameters()))
|
|
with torch.set_grad_enabled(encoder_grad_enabled):
|
|
proj = self.encoder(truth_mel).permute(0,2,1)
|
|
|
|
for p in unused_parameters:
|
|
proj = proj + p.mean() * 0
|
|
|
|
diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
|
|
return diff
|
|
|
|
def get_debug_values(self, step, __):
|
|
self.internal_step = step
|
|
return {}
|
|
|
|
def get_grad_norm_parameter_groups(self):
|
|
attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.diff.layers]))
|
|
attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.diff.layers]))
|
|
ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.diff.layers]))
|
|
ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.diff.layers]))
|
|
blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers]))
|
|
groups = {
|
|
'prenorms': list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.diff.layers])),
|
|
'blk1_attention_layers': attn1,
|
|
'blk2_attention_layers': attn2,
|
|
'attention_layers': attn1 + attn2,
|
|
'blk1_ff_layers': ff1,
|
|
'blk2_ff_layers': ff2,
|
|
'ff_layers': ff1 + ff2,
|
|
'block_out_layers': blkout_layers,
|
|
'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
|
|
'out': list(self.diff.out.parameters()),
|
|
'x_proj': list(self.diff.inp_block.parameters()),
|
|
'layers': list(self.diff.layers.parameters()),
|
|
'code_converters': list(self.diff.input_converter.parameters()) + list(self.diff.code_converter.parameters()),
|
|
'time_embed': list(self.diff.time_embed.parameters()),
|
|
'encoder': list(self.encoder.parameters()),
|
|
}
|
|
return groups
|
|
|
|
def before_step(self, step):
|
|
scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers])) + \
|
|
list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.diff.layers]))
|
|
# Scale back the gradients of the blkout and prenorm layers by a constant factor. These get two orders of magnitudes
|
|
# higher gradients. Ideally we would use parameter groups, but ZeroRedundancyOptimizer makes this trickier than
|
|
# directly fiddling with the gradients.
|
|
for p in scaled_grad_parameters:
|
|
if hasattr(p, 'grad') and p.grad is not None:
|
|
p.grad *= .2
|
|
|
|
|
|
@register_model
|
|
def register_transformer_diffusion12(opt_net, opt):
|
|
return TransformerDiffusion(**opt_net['kwargs'])
|
|
|
|
|
|
@register_model
|
|
def register_transformer_diffusion12_with_quantizer(opt_net, opt):
|
|
return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
|
|
|
|
|
|
@register_model
|
|
def register_transformer_diffusion12_with_ar_prior(opt_net, opt):
|
|
return TransformerDiffusionWithARPrior(**opt_net['kwargs'])
|
|
|
|
|
|
@register_model
|
|
def register_transformer_diffusion_12_with_pretrained_vqvae(opt_net, opt):
|
|
return TransformerDiffusionWithPretrainedVqvae(**opt_net['kwargs'])
|
|
|
|
|
|
@register_model
|
|
def register_transformer_diffusion_12_with_multi_vqvae(opt_net, opt):
|
|
return TransformerDiffusionWithMultiPretrainedVqvae(**opt_net['kwargs'])
|
|
|
|
|
|
@register_model
|
|
def register_transformer_diffusion_12_with_cheater_latent(opt_net, opt):
|
|
return TransformerDiffusionWithCheaterLatent(**opt_net['kwargs'])
|
|
|
|
|
|
def test_tfd():
|
|
clip = torch.randn(2,256,400)
|
|
ts = torch.LongTensor([600, 600])
|
|
model = TransformerDiffusion(in_channels=256, model_channels=1024, contraction_dim=512,
|
|
prenet_channels=1024, num_heads=3, permute_codes=True,
|
|
input_vec_dim=256, num_layers=12, prenet_layers=4,
|
|
dropout=.1)
|
|
model(clip, ts, clip)
|
|
|
|
def test_quant_model():
|
|
clip = torch.randn(2, 256, 400)
|
|
ts = torch.LongTensor([600, 600])
|
|
|
|
# For music:
|
|
model = TransformerDiffusionWithQuantizer(in_channels=256, model_channels=1536, contraction_dim=768,
|
|
prenet_channels=1024, num_heads=10,
|
|
input_vec_dim=1024, num_layers=24, prenet_layers=4,
|
|
dropout=.1)
|
|
quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth')
|
|
model.quantizer.load_state_dict(quant_weights, strict=False)
|
|
torch.save(model.state_dict(), 'sample.pth')
|
|
|
|
print_network(model)
|
|
o = model(clip, ts, clip)
|
|
pg = model.get_grad_norm_parameter_groups()
|
|
t = 0
|
|
for k, vs in pg.items():
|
|
s = 0
|
|
for v in vs:
|
|
m = 1
|
|
for d in v.shape:
|
|
m *= d
|
|
s += m
|
|
t += s
|
|
print(k, s/1000000)
|
|
print(t)
|
|
|
|
|
|
def test_vqvae_model():
|
|
clip = torch.randn(2, 100, 400)
|
|
cond = torch.randn(2,80,400)
|
|
ts = torch.LongTensor([600, 600])
|
|
|
|
# For music:
|
|
model = TransformerDiffusionWithPretrainedVqvae(in_channels=100, out_channels=200,
|
|
model_channels=1024, contraction_dim=512,
|
|
prenet_channels=1024, num_heads=8,
|
|
input_vec_dim=512, num_layers=12, prenet_layers=6, ar_prior=True,
|
|
dropout=.1, vqargs= {
|
|
'positional_dims': 1, 'channels': 80,
|
|
'hidden_dim': 512, 'num_resnet_blocks': 3, 'codebook_dim': 512, 'num_tokens': 8192,
|
|
'num_layers': 2, 'record_codes': True, 'kernel_size': 3, 'use_transposed_convs': False,
|
|
}
|
|
)
|
|
quant_weights = torch.load('D:\\dlas\\experiments\\retrained_dvae_8192_clips.pth')
|
|
model.quantizer.load_state_dict(quant_weights, strict=True)
|
|
torch.save(model.state_dict(), 'sample.pth')
|
|
|
|
print_network(model)
|
|
o = model(clip, ts, cond)
|
|
pg = model.get_grad_norm_parameter_groups()
|
|
|
|
"""
|
|
with torch.no_grad():
|
|
proj = torch.randn(2, 100, 512).cuda()
|
|
clip = clip.cuda()
|
|
ts = ts.cuda()
|
|
start = time()
|
|
model = model.cuda().eval()
|
|
model.diff.enable_fp16 = True
|
|
ti = model.diff.timestep_independent(proj, clip.shape[2])
|
|
for k in range(100):
|
|
model.diff(clip, ts, precomputed_code_embeddings=ti)
|
|
print(f"Elapsed: {time()-start}")
|
|
"""
|
|
|
|
|
|
def test_multi_vqvae_model():
|
|
clip = torch.randn(2, 256, 400)
|
|
cond = torch.randn(2,256,400)
|
|
ts = torch.LongTensor([600, 600])
|
|
|
|
# For music:
|
|
model = TransformerDiffusionWithMultiPretrainedVqvae(in_channels=256, out_channels=512,
|
|
model_channels=1024, contraction_dim=512,
|
|
prenet_channels=1024, num_heads=8,
|
|
input_vec_dim=2048, num_layers=12, prenet_layers=6,
|
|
dropout=.1, vqargs= {
|
|
'positional_dims': 1, 'channels': 64,
|
|
'hidden_dim': 512, 'num_resnet_blocks': 3, 'codebook_dim': 512, 'num_tokens': 8192,
|
|
'num_layers': 0, 'record_codes': True, 'kernel_size': 3, 'use_transposed_convs': False,
|
|
}, num_vaes=4,
|
|
)
|
|
quants = ['X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_low\\models\\7500_generator.pth',
|
|
'X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_mid_low\\models\\11000_generator.pth',
|
|
'X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_mid_high\\models\\11500_generator.pth',
|
|
'X:\\dlas\\experiments\\music_vqvaes\\train_lrdvae_music_high\\models\\11500_generator.pth']
|
|
for i, qfile in enumerate(quants):
|
|
quant_weights = torch.load(qfile)
|
|
model.quantizers[i].load_state_dict(quant_weights, strict=True)
|
|
torch.save(model.state_dict(), 'sample.pth')
|
|
|
|
print_network(model)
|
|
o = model(clip, ts, cond)
|
|
pg = model.get_grad_norm_parameter_groups()
|
|
model.diff.get_grad_norm_parameter_groups()
|
|
|
|
|
|
def test_ar_model():
|
|
clip = torch.randn(2, 256, 400)
|
|
cond = torch.randn(2, 256, 400)
|
|
ts = torch.LongTensor([600, 600])
|
|
model = TransformerDiffusionWithARPrior(model_channels=2048, prenet_channels=1536,
|
|
input_vec_dim=512, num_layers=16, prenet_layers=6, freeze_diff=True,
|
|
unconditioned_percentage=.4)
|
|
model.get_grad_norm_parameter_groups()
|
|
|
|
ar_weights = torch.load('D:\\dlas\\experiments\\train_music_gpt\\models\\44500_generator_ema.pth')
|
|
model.ar.load_state_dict(ar_weights, strict=True)
|
|
diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd8\\models\\47500_generator_ema.pth')
|
|
pruned_diff_weights = {}
|
|
for k,v in diff_weights.items():
|
|
if k.startswith('diff.'):
|
|
pruned_diff_weights[k.replace('diff.', '')] = v
|
|
model.diff.load_state_dict(pruned_diff_weights, strict=False)
|
|
torch.save(model.state_dict(), 'sample.pth')
|
|
|
|
model(clip, ts, cond, conditioning_input=cond)
|
|
|
|
|
|
def test_cheater_model():
|
|
clip = torch.randn(2, 256, 400)
|
|
ts = torch.LongTensor([600, 600])
|
|
|
|
# For music:
|
|
model = TransformerDiffusionWithCheaterLatent(in_channels=256, out_channels=512,
|
|
model_channels=1024, contraction_dim=512,
|
|
prenet_channels=1024, num_heads=8,
|
|
input_vec_dim=256, num_layers=16, prenet_layers=6,
|
|
dropout=.1, new_code_expansion=True,
|
|
)
|
|
#diff_weights = torch.load('extracted_diff.pth')
|
|
#model.diff.load_state_dict(diff_weights, strict=False)
|
|
model.encoder.load_state_dict(torch.load('../experiments/music_cheater_encoder_256.pth', map_location=torch.device('cpu')), strict=True)
|
|
torch.save(model.state_dict(), 'sample.pth')
|
|
|
|
print_network(model)
|
|
o = model(clip, ts, clip)
|
|
pg = model.get_grad_norm_parameter_groups()
|
|
|
|
|
|
def extract_diff(in_f, out_f, remove_head=False):
|
|
p = torch.load(in_f)
|
|
out = {}
|
|
for k, v in p.items():
|
|
if k.startswith('diff.'):
|
|
if remove_head and (k.startswith('diff.input_converter') or k.startswith('diff.code_converter')):
|
|
continue
|
|
out[k.replace('diff.', '')] = v
|
|
torch.save(out, out_f)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
#extract_diff('X:\\dlas\\experiments\\train_music_diffusion_tfd12\\models\\41000_generator_ema.pth', 'extracted_diff.pth', True)
|
|
test_cheater_model()
|