import itertools import torch import torch.nn as nn import torch.nn.functional as F from models.arch_util import AttentionBlock, TimestepEmbedSequential, build_local_attention_mask from models.audio.music.encoders import ResEncoder16x from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear from models.diffusion.unet_diffusion import TimestepBlock from trainer.networks import register_model from utils.util import checkpoint, print_network class SubBlock(nn.Module): def __init__(self, inp_dim, contraction_dim, blk_dim, heads, dropout, enable_attention_masking=False): super().__init__() self.enable_attention_masking = enable_attention_masking self.dropout = nn.Dropout(p=dropout) self.blk_emb_proj = nn.Conv1d(blk_dim, inp_dim, 1) self.attn = AttentionBlock(inp_dim, out_channels=contraction_dim, num_heads=heads) self.attnorm = nn.GroupNorm(8, contraction_dim) self.ff = nn.Conv1d(inp_dim+contraction_dim, contraction_dim, kernel_size=3, padding=1) self.ffnorm = nn.GroupNorm(8, contraction_dim) if self.enable_attention_masking: # All regions can attend to the first token, which will be the timestep embedding. Hence, fixed_region. self.mask = build_local_attention_mask(n=4000, l=48, fixed_region=1) self.mask_initialized = False else: self.mask = None def forward(self, x, blk_emb): if self.mask is not None and not self.mask_initialized: self.mask = self.mask.to(x.device) self.mask_initialized = True blk_enc = self.blk_emb_proj(blk_emb) ah = self.dropout(self.attn(torch.cat([blk_enc, x], dim=-1), mask=self.mask)) ah = ah[:,:,blk_emb.shape[-1]:] # Strip off the blk_emb and re-align with x. ah = F.gelu(self.attnorm(ah)) h = torch.cat([ah, x], dim=1) hf = self.dropout(checkpoint(self.ff, h)) hf = F.gelu(self.ffnorm(hf)) h = torch.cat([h, hf], dim=1) return h class ConcatAttentionBlock(TimestepBlock): def __init__(self, trunk_dim, contraction_dim, heads, dropout, enable_attention_masking=False): super().__init__() self.prenorm = nn.GroupNorm(8, trunk_dim) self.block1 = SubBlock(trunk_dim, contraction_dim, trunk_dim, heads, dropout, enable_attention_masking=enable_attention_masking) self.block2 = SubBlock(trunk_dim+contraction_dim*2, contraction_dim, trunk_dim, heads, dropout, enable_attention_masking=enable_attention_masking) self.out = nn.Conv1d(contraction_dim*4, trunk_dim, kernel_size=1, bias=False) self.out.weight.data.zero_() def forward(self, x, blk_emb): h = self.prenorm(x) h = self.block1(h, blk_emb) h = self.block2(h, blk_emb) h = self.out(h[:,x.shape[1]:]) return h + x class TransformerDiffusion(nn.Module): """ A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way? """ def __init__( self, time_embed_dim=256, model_channels=1024, contraction_dim=256, num_layers=8, in_channels=256, input_vec_dim=1024, out_channels=512, # mean and variance num_heads=4, dropout=0, use_corner_alignment=False, # This is an interpolation parameter only provided for backwards compatibility. ALL NEW TRAINS SHOULD SET THIS TO TRUE. use_fp16=False, new_code_expansion=False, # Parameters for regularization. unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training. # Parameters for re-training head freeze_except_code_converters=False, ): super().__init__() self.in_channels = in_channels self.model_channels = model_channels self.time_embed_dim = time_embed_dim self.out_channels = out_channels self.dropout = dropout self.unconditioned_percentage = unconditioned_percentage self.enable_fp16 = use_fp16 self.new_code_expansion = new_code_expansion self.use_corner_alignment = use_corner_alignment self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1) self.time_embed = nn.Sequential( linear(time_embed_dim, time_embed_dim), nn.SiLU(), linear(time_embed_dim, model_channels), ) self.input_converter = nn.Conv1d(input_vec_dim, model_channels, 1) self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1)) self.intg = nn.Conv1d(model_channels*2, model_channels, 1) self.layers = TimestepEmbedSequential(*[ConcatAttentionBlock(model_channels, contraction_dim, num_heads, dropout, enable_attention_masking=True) for _ in range(num_layers)]) self.out = nn.Sequential( normalization(model_channels), nn.SiLU(), zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)), ) if freeze_except_code_converters: for p in self.parameters(): p.DO_NOT_TRAIN = True p.requires_grad = False for m in [self.code_converter and self.input_converter]: for p in m.parameters(): del p.DO_NOT_TRAIN p.requires_grad = True def get_grad_norm_parameter_groups(self): attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.layers])) attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.layers])) ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.layers])) ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.layers])) blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.layers])) groups = { 'prenorms': list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.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, 'out': list(self.out.parameters()), 'x_proj': list(self.inp_block.parameters()), 'layers': list(self.layers.parameters()), 'time_embed': list(self.time_embed.parameters()), } return groups def forward(self, x, timesteps, prior=None, conditioning_free=False): if conditioning_free: code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1]) else: code_emb = self.input_converter(prior) # Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance. if self.training and self.unconditioned_percentage > 0: unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1), device=code_emb.device) < self.unconditioned_percentage code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(x.shape[0], 1, 1), code_emb) code_emb = F.interpolate(code_emb, size=x.shape[-1], mode='nearest') with torch.autocast(x.device.type, enabled=self.enable_fp16): blk_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim)).unsqueeze(-1) x = self.inp_block(x) x = self.intg(torch.cat([x, code_emb], dim=1)) for layer in self.layers: x = checkpoint(layer, x, blk_emb) x = x.float() out = self.out(x) # Defensively involve probabilistic or possibly unused parameters in loss so we don't get DDP errors. unused_params = [self.unconditioned_embedding] extraneous_addition = 0 for p in unused_params: extraneous_addition = extraneous_addition + p.mean() out = out + extraneous_addition * 0 return out class TransformerDiffusionWithCheaterLatent(nn.Module): def __init__(self, freeze_encoder_until=None, checkpoint_encoder=True, **kwargs): super().__init__() self.internal_step = 0 self.freeze_encoder_until = freeze_encoder_until self.diff = TransformerDiffusion(**kwargs) self.encoder = ResEncoder16x(256, 1024, 256, checkpointing_enabled=checkpoint_encoder) def forward(self, x, timesteps, truth_mel, conditioning_free=False): unused_parameters = [] encoder_grad_enabled = self.freeze_encoder_until is not None and 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) for p in unused_parameters: proj = proj + p.mean() * 0 diff = self.diff(x, timesteps, prior=proj, conditioning_free=conditioning_free) return diff def get_debug_values(self, step, __): self.internal_step = step return {} def get_grad_norm_parameter_groups(self): groups = self.diff.get_grad_norm_parameter_groups() groups['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_diffusion14(opt_net, opt): return TransformerDiffusion(**opt_net['kwargs']) @register_model def register_transformer_diffusion_14_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, num_heads=3, input_vec_dim=256, num_layers=12, dropout=.1) model(clip, ts, clip) 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, num_heads=8, input_vec_dim=256, num_layers=16, 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) o = model(clip, ts, clip, conditioning_free=True) pg = model.get_grad_norm_parameter_groups() def extract_cheater_encoder(in_f, out_f): p = torch.load(in_f) out = {} for k, v in p.items(): if k.startswith('encoder.'): out[k] = v torch.save(out, out_f) if __name__ == '__main__': #test_local_attention_mask() #extract_cheater_encoder('X:\\dlas\\experiments\\train_music_diffusion_tfd_and_cheater\\models\\104500_generator_ema.pth', 'X:\\dlas\\experiments\\tfd12_self_learned_cheater_enc.pth', True) test_cheater_model() #extract_diff('X:\\dlas\experiments\\train_music_diffusion_tfd_cheater_from_scratch\\models\\56500_generator_ema.pth', 'extracted.pth', remove_head=True)