249 lines
10 KiB
Python
249 lines
10 KiB
Python
import itertools
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from random import randrange
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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, TimestepEmbedSequential, AttentionBlock
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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 trainer.networks import register_model
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from utils.util import checkpoint
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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 SubBlock(nn.Module):
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def __init__(self, inp_dim, contraction_dim, blk_dim, heads, dropout):
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super().__init__()
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self.dropout = nn.Dropout(p=dropout)
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self.blk_emb_proj = nn.Conv1d(blk_dim, inp_dim, 1)
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self.attn = AttentionBlock(inp_dim, out_channels=contraction_dim, num_heads=heads)
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self.attnorm = nn.GroupNorm(8, 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.GroupNorm(8, contraction_dim)
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def forward(self, x, blk_emb):
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blk_enc = self.blk_emb_proj(blk_emb)
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ah = self.dropout(self.attn(torch.cat([blk_enc, x], dim=-1)))
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ah = ah[:,:,blk_emb.shape[-1]:] # Strip off the blk_emb and re-align with x.
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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 = self.dropout(checkpoint(self.ff, h))
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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, heads, dropout):
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super().__init__()
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self.prenorm = nn.GroupNorm(8, trunk_dim)
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self.block1 = SubBlock(trunk_dim, contraction_dim, trunk_dim, heads, dropout)
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self.block2 = SubBlock(trunk_dim+contraction_dim*2, contraction_dim, trunk_dim, heads, dropout)
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self.out = nn.Conv1d(contraction_dim*4, trunk_dim, kernel_size=1, bias=False)
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self.out.weight.data.zero_()
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def forward(self, x, blk_emb):
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h = self.prenorm(x)
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h = self.block1(h, blk_emb)
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h = self.block2(h, blk_emb)
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h = self.out(h[:,x.shape[1]:])
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return h + x
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class ConditioningEncoder(nn.Module):
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def __init__(self,
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spec_dim,
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embedding_dim,
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num_resolutions,
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attn_blocks=6,
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num_attn_heads=4,
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do_checkpointing=False):
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super().__init__()
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attn = []
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self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=5, stride=2)
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self.resolution_embedding = nn.Embedding(num_resolutions, embedding_dim)
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self.resolution_embedding.weight.data.mul(.1) # Reduces the relative influence of this embedding from the start.
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for a in range(attn_blocks):
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attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_checkpoint=do_checkpointing))
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attn.append(ResBlock(embedding_dim, dims=1, checkpointing_enabled=do_checkpointing))
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self.attn = nn.Sequential(*attn)
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self.dim = embedding_dim
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self.do_checkpointing = do_checkpointing
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def forward(self, x, resolution):
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h = self.init(x) + self.resolution_embedding(resolution).unsqueeze(-1)
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h = self.attn(h)
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return h[:, :, :6]
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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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time_embed_dim=256,
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resolution_steps=8,
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max_window=384,
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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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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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# 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.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.resolution_steps = resolution_steps
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self.max_window = max_window
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self.preprocessed = None
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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, model_channels),
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)
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self.resolution_embed = nn.Embedding(resolution_steps, model_channels)
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self.conditioning_encoder = ConditioningEncoder(in_channels, model_channels, resolution_steps, num_attn_heads=model_channels//64)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
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self.inp_block = conv_nd(1, in_channels+input_vec_dim, model_channels, 3, 1, 1)
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self.layers = TimestepEmbedSequential(*[ConcatAttentionBlock(model_channels, contraction_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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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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'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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'time_embed': list(self.time_embed.parameters()),
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'resolution_embed': list(self.resolution_embed.parameters()),
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}
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return groups
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def input_to_random_resolution_and_window(self, x, x_prior):
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assert x.shape == x_prior.shape, f'{x.shape} {x_prior.shape}'
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resolution = randrange(0, self.resolution_steps)
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resolution_scale = 2 ** resolution
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s = F.interpolate(x, scale_factor=1/resolution_scale, mode='linear', align_corners=True)
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s_prior = F.interpolate(x_prior, scale_factor=1/resolution_scale, mode='linear', align_corners=True)
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s_diff = s.shape[-1] - self.max_window
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if s_diff > 1:
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start = randrange(0, s_diff)
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s = s[:,:,start:start+self.max_window]
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s_prior = x_prior[:,:,start:start+self.max_window]
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s_prior = F.interpolate(s_prior, scale_factor=.25, mode='linear', align_corners=True)
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s_prior = F.interpolate(s_prior, size=(s.shape[-1],), mode='linear', align_corners=True)
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self.preprocessed = (s_prior, torch.tensor([resolution] * x.shape[0], dtype=torch.long, device=x.device))
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return s
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def forward(self, x, timesteps, x_prior=None, resolution=None, conditioning_input=None, conditioning_free=False):
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unused_params = []
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conditioning_input = x_prior if conditioning_input is None else conditioning_input
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h = x
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if resolution is None:
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assert self.preprocessed is not None, 'Preprocessing function not called.'
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h = x
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h_sub, resolution = self.preprocessed
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self.preprocessed = None
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else:
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h_sub = F.interpolate(x_prior, scale_factor=4, mode='linear', align_corners=True)
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assert h.shape == h_sub.shape, f'{h.shape} {h_sub.shape}'
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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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MIN_COND_LEN = 200
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MAX_COND_LEN = 1200
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if self.training and conditioning_input.shape[-1] > MAX_COND_LEN:
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clen = randrange(MIN_COND_LEN, MAX_COND_LEN)
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gap = conditioning_input.shape[-1] - clen
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cstart = randrange(0, gap)
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conditioning_input = conditioning_input[:,:,cstart:cstart+clen]
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code_emb = self.conditioning_encoder(conditioning_input, resolution)
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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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time_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim))
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res_emb = self.resolution_embed(resolution)
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blk_emb = torch.cat([time_emb.unsqueeze(-1), res_emb.unsqueeze(-1), code_emb], dim=-1)
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h = torch.cat([h, h_sub], dim=1)
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h = self.inp_block(h)
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for layer in self.layers:
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h = checkpoint(layer, h, blk_emb)
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h = h.float()
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out = self.out(h)
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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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@register_model
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def register_transformer_diffusion13(opt_net, opt):
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return TransformerDiffusion(**opt_net['kwargs'])
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def test_tfd():
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clip = torch.randn(2,256,10336)
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cond = torch.randn(2,256,10336)
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ts = torch.LongTensor([600, 600])
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model = TransformerDiffusion(in_channels=256, model_channels=1024, contraction_dim=512,
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num_heads=512//64, input_vec_dim=256, num_layers=12, dropout=.1)
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for k in range(100):
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x = model.input_to_random_resolution_and_window(clip, x_prior=clip)
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model(x, ts, clip)
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if __name__ == '__main__':
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test_tfd()
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