import itertools from time import time import torch import torch.nn as nn import torch.nn.functional as F from models.arch_util import ResBlock, AttentionBlock from models.audio.music.gpt_music2 import UpperEncoder, GptMusicLower from models.audio.music.music_quantizer2 import MusicQuantizer2 from models.audio.tts.lucidrains_dvae import DiscreteVAE from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear from models.diffusion.unet_diffusion import TimestepBlock from models.lucidrains.x_transformers import Encoder, Attention, RMSScaleShiftNorm, RotaryEmbedding, \ FeedForward from trainer.networks import register_model from utils.util import checkpoint, print_network def is_latent(t): return t.dtype == torch.float def is_sequence(t): return t.dtype == torch.long class MultiGroupEmbedding(nn.Module): def __init__(self, tokens, groups, dim): super().__init__() self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)]) def forward(self, x): h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)] return torch.cat(h, dim=-1) class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock): def forward(self, x, emb, rotary_emb): for layer in self: if isinstance(layer, TimestepBlock): x = layer(x, emb, rotary_emb) else: x = layer(x, rotary_emb) return x class SubBlock(nn.Module): def __init__(self, inp_dim, contraction_dim, heads, dropout): super().__init__() self.attn = Attention(inp_dim, out_dim=contraction_dim, heads=heads, dim_head=contraction_dim//heads, causal=False, dropout=dropout) self.attnorm = nn.LayerNorm(contraction_dim) self.ff = FeedForward(inp_dim+contraction_dim, dim_out=contraction_dim, mult=2, dropout=dropout) self.ffnorm = nn.LayerNorm(contraction_dim) def forward(self, x, rotary_emb): ah, _, _, _ = checkpoint(self.attn, x, None, None, None, None, None, rotary_emb) ah = F.gelu(self.attnorm(ah)) h = torch.cat([ah, x], dim=-1) hf = 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, time_embed_dim, heads, dropout): super().__init__() self.prenorm = RMSScaleShiftNorm(trunk_dim, embed_dim=time_embed_dim, bias=False) self.block1 = SubBlock(trunk_dim, contraction_dim, heads, dropout) self.block2 = SubBlock(trunk_dim+contraction_dim*2, contraction_dim, heads, dropout) self.out = nn.Linear(contraction_dim*4, trunk_dim, bias=False) self.out.weight.data.zero_() def forward(self, x, conditioning, timestep_emb, rotary_emb): h = self.prenorm(x, norm_scale_shift_inp=timestep_emb) h = torch.cat([conditioning, h], dim=1) h = self.block1(h, rotary_emb) h = self.block2(h, rotary_emb) h = self.out(h[:,:,x.shape[-1]:]) return h[:, 1:] + x class TransformerDiffusionWithPointConditioning(nn.Module): """ A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way? """ def __init__( self, in_channels=256, out_channels=512, # mean and variance model_channels=1024, contraction_dim=256, time_embed_dim=256, num_layers=8, rotary_emb_dim=32, input_cond_dim=1024, num_heads=8, dropout=0, use_fp16=False, # Parameters for regularization. unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training. ): 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.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, time_embed_dim), ) self.conditioner = nn.Linear(input_cond_dim, model_channels) if input_cond_dim != model_channels else nn.Identity() self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels)) self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim) self.layers = TimestepRotaryEmbedSequential(*[ConcatAttentionBlock(model_channels, contraction_dim, time_embed_dim, num_heads, dropout) 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)), ) self.debug_codes = {} 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, 'rotary_embeddings': list(self.rotary_embeddings.parameters()), '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, conditioning_input, conditioning_free=False): unused_params = [] if conditioning_free: cond = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1) else: cond = self.conditioner(conditioning_input) # 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((cond.shape[0], 1, 1), device=cond.device) < self.unconditioned_percentage cond = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(cond.shape[0], 1, 1), cond) unused_params.append(self.unconditioned_embedding) with torch.autocast(x.device.type, enabled=self.enable_fp16): blk_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim)) x = self.inp_block(x).permute(0,2,1) rotary_pos_emb = self.rotary_embeddings(x.shape[1]+1, x.device) for layer in self.layers: x = checkpoint(layer, x, cond, blk_emb, rotary_pos_emb) x = x.float().permute(0,2,1) out = self.out(x) # Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors. extraneous_addition = 0 for p in unused_params: extraneous_addition = extraneous_addition + p.mean() out = out + extraneous_addition * 0 return out class ConditioningEncoder(nn.Module): def __init__(self, cond_dim, embedding_dim, attn_blocks=6, num_attn_heads=8, do_checkpointing=False): super().__init__() attn = [] self.init = nn.Conv1d(cond_dim, embedding_dim, kernel_size=1) for a in range(attn_blocks): attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=do_checkpointing)) self.attn = nn.Sequential(*attn) self.dim = embedding_dim self.do_checkpointing = do_checkpointing def forward(self, x): h = self.init(x) h = self.attn(h) return h.mean(dim=2).unsqueeze(1) class TransformerDiffusionWithConditioningEncoder(nn.Module): def __init__(self, **kwargs): super().__init__() self.internal_step = 0 self.diff = TransformerDiffusionWithPointConditioning(**kwargs) self.conditioning_encoder = ConditioningEncoder(256, kwargs['model_channels']) def forward(self, x, timesteps, true_cheater, conditioning_input=None, disable_diversity=False, conditioning_free=False): cond = self.conditioning_encoder(true_cheater) diff = self.diff(x, timesteps, conditioning_input=cond, 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['conditioning_encoder'] = list(self.conditioning_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_tfdpc(opt_net, opt): return TransformerDiffusionWithPointConditioning(**opt_net['kwargs']) @register_model def register_tfdpc_with_conditioning_encoder(opt_net, opt): return TransformerDiffusionWithConditioningEncoder(**opt_net['kwargs']) def test_cheater_model(): clip = torch.randn(2, 256, 400) cl = torch.randn(2, 1, 400) ts = torch.LongTensor([600, 600]) # For music: model = TransformerDiffusionWithConditioningEncoder(model_channels=1024) print_network(model) o = model(clip, ts, cl) pg = model.get_grad_norm_parameter_groups() if __name__ == '__main__': test_cheater_model()