forked from mrq/DL-Art-School
273 lines
11 KiB
Python
273 lines
11 KiB
Python
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.audio.music.music_quantizer import MusicQuantizer
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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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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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# 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, prenet_channels),
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)
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prenet_heads = prenet_channels//64
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self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, prenet_channels // 2, 3, padding=1, stride=2),
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nn.Conv1d(prenet_channels//2, prenet_channels,3,padding=1,stride=2))
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self.conditioning_encoder = Encoder(
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dim=prenet_channels,
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depth=4,
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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.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=3,
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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.cond_intg = nn.Linear(prenet_channels*2, model_channels)
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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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'contextual_embedder': list(self.conditioning_embedder.parameters()),
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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, codes, conditioning_input, expected_seq_len):
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cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
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cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
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code_emb = self.input_converter(codes)
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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(codes.shape[0], 1, 1),
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code_emb)
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code_emb = self.code_converter(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, cond_emb
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def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None,
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precomputed_cond_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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cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
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cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
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unused_params.extend(list(self.code_converter.parameters()))
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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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cond_emb = precomputed_cond_embeddings
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else:
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code_emb, cond_emb = self.timestep_independent(codes, conditioning_input, x.shape[-1])
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unused_params.append(self.unconditioned_embedding)
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blk_emb = torch.cat([self.time_embed(timestep_embedding(timesteps, self.prenet_channels)), cond_emb], dim=-1)
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blk_emb = self.cond_intg(blk_emb)
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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, **kwargs):
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super().__init__()
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self.diff = TransformerDiffusion(**kwargs)
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from models.audio.mel2vec import ContrastiveTrainingWrapper
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self.m2v = MusicQuantizer(inp_channels=256, inner_dim=2048, codevector_dim=1024)
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self.m2v.quantizer.temperature = self.m2v.min_gumbel_temperature
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del self.m2v.up
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self.codes = torch.zeros((3000000,), dtype=torch.long)
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self.internal_step = 0
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self.code_ind = 0
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self.total_codes = 0
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def update_for_step(self, step, *args):
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self.internal_step = step
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self.m2v.quantizer.temperature = max(
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self.m2v.max_gumbel_temperature * self.m2v.gumbel_temperature_decay**step,
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self.m2v.min_gumbel_temperature,
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)
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def forward(self, x, timesteps, truth_mel, conditioning_input, conditioning_free=False):
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proj = self.m2v(truth_mel, return_decoder_latent=True).permute(0,2,1)
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return self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input,
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conditioning_free=conditioning_free)
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def get_debug_values(self, step, __):
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if self.total_codes > 0:
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return {'histogram_codes': self.m2v.codes[:self.m2v.total_codes]}
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else:
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return {}
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@register_model
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def register_transformer_diffusion7(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_diffusion7_with_quantizer(opt_net, opt):
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return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
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"""
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# For TFD5
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if __name__ == '__main__':
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clip = torch.randn(2, 256, 400)
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aligned_sequence = torch.randn(2,100,512)
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cond = torch.randn(2, 256, 400)
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ts = torch.LongTensor([600, 600])
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model = TransformerDiffusion(model_channels=3072, block_channels=1536, prenet_channels=1536)
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torch.save(model, 'sample.pth')
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print_network(model)
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o = model(clip, ts, aligned_sequence, cond)
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"""
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if __name__ == '__main__':
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clip = torch.randn(2, 256, 400)
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cond = torch.randn(2, 256, 400)
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ts = torch.LongTensor([600, 600])
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model = TransformerDiffusionWithQuantizer(model_channels=2048, block_channels=1024, prenet_channels=1024, input_vec_dim=2048, num_layers=16)
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quant_weights = torch.load('X:\\dlas\\experiments\\train_music_quant\\models\\1000_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.m2v.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, cond)
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