366 lines
15 KiB
Python
366 lines
15 KiB
Python
import itertools
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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.music_quantizer2 import MusicQuantizer2
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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, FeedForward, RMSScaleShiftNorm, RotaryEmbedding
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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 DietAttentionBlock(TimestepBlock):
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def __init__(self, in_dim, dim, heads, dropout):
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super().__init__()
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self.rms_scale_norm = RMSScaleShiftNorm(in_dim)
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self.proj = nn.Linear(in_dim, dim)
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self.attn = Attention(dim, heads=heads, causal=False, dropout=dropout)
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self.ff = FeedForward(dim, in_dim, mult=1, dropout=dropout, zero_init_output=True)
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def forward(self, x, timestep_emb, rotary_emb):
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h = self.rms_scale_norm(x, norm_scale_shift_inp=timestep_emb)
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h = self.proj(h)
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h, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb)
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h = checkpoint(self.ff, h)
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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=256,
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prenet_layers=3,
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model_channels=512,
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block_channels=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=512,
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out_channels=512, # mean and variance
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dropout=0,
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use_fp16=False,
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ar_prior=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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):
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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.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.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(prenet_channels, prenet_channels),
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nn.SiLU(),
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linear(prenet_channels, model_channels),
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)
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prenet_heads = prenet_channels//64
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self.ar_prior = ar_prior
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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(*[DietAttentionBlock(model_channels, block_channels, block_channels // 64, 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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self.debug_codes = {}
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def get_grad_norm_parameter_groups(self):
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groups = {
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'layers': list(self.layers.parameters()) + list(self.inp_block.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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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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expanded_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 expanded_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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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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blk_emb = self.time_embed(timestep_embedding(timesteps, self.prenet_channels))
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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, freeze_quantizer_until=20000, quantizer_dims=[1024], no_reconstruction=True, **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], checkpoint=False,
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codebook_size=256, codebook_groups=2,
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max_gumbel_temperature=4, min_gumbel_temperature=.5)
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self.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature
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if no_reconstruction:
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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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mse, diversity_loss, proj = self.quantizer(truth_mel, return_decoder_latent=True)
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proj = proj.permute(0,2,1)
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quant_grad_enabled = self.internal_step > self.freeze_quantizer_until
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if not quant_grad_enabled:
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proj = proj.detach()
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# Make sure this does not cause issues in DDP by explicitly using the parameters for nothing.
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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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diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input,
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conditioning_free=conditioning_free)
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if disable_diversity:
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return diff
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if mse is None:
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return diff, diversity_loss
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return diff, diversity_loss, mse
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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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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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'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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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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@register_model
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def register_transformer_diffusion8(opt_net, opt):
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return TransformerDiffusion(**opt_net['kwargs'])
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@register_model
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def register_transformer_diffusion8_with_quantizer(opt_net, opt):
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return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
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@register_model
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def register_transformer_diffusion8_with_ar_prior(opt_net, opt):
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return TransformerDiffusionWithARPrior(**opt_net['kwargs'])
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def test_quant_model():
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clip = torch.randn(2, 100, 401)
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ts = torch.LongTensor([600, 600])
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model = TransformerDiffusionWithQuantizer(in_channels=100, out_channels=200, quantizer_dims=[1024,768,512,384],
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model_channels=2048, block_channels=1024, prenet_channels=1024,
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input_vec_dim=1024, num_layers=16, prenet_layers=6,
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no_reconstruction=False)
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#model.get_grad_norm_parameter_groups()
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#quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth')
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#diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd5\\models\\48000_generator_ema.pth')
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#model.quantizer.load_state_dict(quant_weights, strict=False)
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#model.diff.load_state_dict(diff_weights)
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#torch.save(model.state_dict(), 'sample.pth')
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print_network(model)
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o = model(clip, ts, clip)
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def test_ar_model():
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clip = torch.randn(2, 256, 401)
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cond = torch.randn(2, 256, 400)
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ts = torch.LongTensor([600, 600])
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model = TransformerDiffusionWithARPrior(model_channels=2048, block_channels=1024, prenet_channels=1024,
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input_vec_dim=512, num_layers=16, prenet_layers=6, freeze_diff=True,
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unconditioned_percentage=.4)
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model.get_grad_norm_parameter_groups()
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ar_weights = torch.load('D:\\dlas\\experiments\\train_music_gpt\\models\\44500_generator_ema.pth')
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model.ar.load_state_dict(ar_weights, strict=True)
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diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd8\\models\\47500_generator_ema.pth')
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pruned_diff_weights = {}
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for k,v in diff_weights.items():
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if k.startswith('diff.'):
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pruned_diff_weights[k.replace('diff.', '')] = v
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model.diff.load_state_dict(pruned_diff_weights, strict=False)
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torch.save(model.state_dict(), 'sample.pth')
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model(clip, ts, cond, conditioning_input=cond)
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if __name__ == '__main__':
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test_quant_model()
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