281 lines
12 KiB
Python
281 lines
12 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 AttentionBlock, TimestepEmbedSequential, build_local_attention_mask
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from models.audio.music.encoders import ResEncoder16x
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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, print_network
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class SubBlock(nn.Module):
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def __init__(self, inp_dim, contraction_dim, blk_dim, heads, dropout, enable_attention_masking=False):
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super().__init__()
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self.enable_attention_masking = enable_attention_masking
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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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if self.enable_attention_masking:
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# All regions can attend to the first token, which will be the timestep embedding. Hence, fixed_region.
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self.mask = build_local_attention_mask(n=4000, l=48, fixed_region=1)
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self.mask_initialized = False
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else:
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self.mask = None
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def forward(self, x, blk_emb):
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if self.mask is not None and not self.mask_initialized:
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self.mask = self.mask.to(x.device)
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self.mask_initialized = True
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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), mask=self.mask))
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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, enable_attention_masking=False):
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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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enable_attention_masking=enable_attention_masking)
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self.block2 = SubBlock(trunk_dim+contraction_dim*2, contraction_dim, trunk_dim, heads, dropout,
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enable_attention_masking=enable_attention_masking)
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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 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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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_corner_alignment=False, # This is an interpolation parameter only provided for backwards compatibility. ALL NEW TRAINS SHOULD SET THIS TO TRUE.
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use_fp16=False,
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new_code_expansion=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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# Parameters for re-training head
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freeze_except_code_converters=False,
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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.new_code_expansion = new_code_expansion
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self.use_corner_alignment = use_corner_alignment
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self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
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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.input_converter = nn.Conv1d(input_vec_dim, model_channels, 1)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1))
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self.intg = nn.Conv1d(model_channels*2, model_channels, 1)
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self.layers = TimestepEmbedSequential(*[ConcatAttentionBlock(model_channels, contraction_dim, num_heads, dropout, enable_attention_masking=True) 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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if freeze_except_code_converters:
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for p in self.parameters():
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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for m in [self.code_converter and self.input_converter]:
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for p in m.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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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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}
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return groups
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def forward(self, x, timesteps, prior=None, conditioning_free=False):
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if conditioning_free:
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code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
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else:
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code_emb = self.input_converter(prior)
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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(x.shape[0], 1, 1),
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code_emb)
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code_emb = F.interpolate(code_emb, size=x.shape[-1], mode='nearest')
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with torch.autocast(x.device.type, enabled=self.enable_fp16):
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blk_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim)).unsqueeze(-1)
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x = self.inp_block(x)
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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)
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x = x.float()
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out = self.out(x)
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# Defensively involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
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unused_params = [self.unconditioned_embedding]
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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 TransformerDiffusionWithCheaterLatent(nn.Module):
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def __init__(self, freeze_encoder_until=None, checkpoint_encoder=True, **kwargs):
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super().__init__()
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self.internal_step = 0
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self.freeze_encoder_until = freeze_encoder_until
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self.diff = TransformerDiffusion(**kwargs)
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self.encoder = ResEncoder16x(256, 1024, 256, checkpointing_enabled=checkpoint_encoder)
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def forward(self, x, timesteps, truth_mel, conditioning_free=False):
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unused_parameters = []
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encoder_grad_enabled = self.freeze_encoder_until is not None and self.internal_step > self.freeze_encoder_until
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if not encoder_grad_enabled:
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unused_parameters.extend(list(self.encoder.parameters()))
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with torch.set_grad_enabled(encoder_grad_enabled):
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proj = self.encoder(truth_mel)
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for p in unused_parameters:
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proj = proj + p.mean() * 0
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diff = self.diff(x, timesteps, prior=proj, conditioning_free=conditioning_free)
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return diff
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def get_debug_values(self, step, __):
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self.internal_step = step
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return {}
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def get_grad_norm_parameter_groups(self):
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groups = self.diff.get_grad_norm_parameter_groups()
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groups['encoder'] = list(self.encoder.parameters())
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return groups
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def before_step(self, step):
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scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers])) + \
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list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.diff.layers]))
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# Scale back the gradients of the blkout and prenorm layers by a constant factor. These get two orders of magnitudes
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# higher gradients. Ideally we would use parameter groups, but ZeroRedundancyOptimizer makes this trickier than
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# directly fiddling with the gradients.
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for p in scaled_grad_parameters:
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if hasattr(p, 'grad') and p.grad is not None:
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p.grad *= .2
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@register_model
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def register_transformer_diffusion14(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_diffusion_14_with_cheater_latent(opt_net, opt):
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return TransformerDiffusionWithCheaterLatent(**opt_net['kwargs'])
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def test_tfd():
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clip = torch.randn(2,256,400)
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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=3, input_vec_dim=256, num_layers=12, dropout=.1)
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model(clip, ts, clip)
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def test_cheater_model():
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clip = torch.randn(2, 256, 400)
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ts = torch.LongTensor([600, 600])
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# For music:
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model = TransformerDiffusionWithCheaterLatent(in_channels=256, out_channels=512,
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model_channels=1024, contraction_dim=512, num_heads=8,
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input_vec_dim=256, num_layers=16,
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dropout=.1, new_code_expansion=True,
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)
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#diff_weights = torch.load('extracted_diff.pth')
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#model.diff.load_state_dict(diff_weights, strict=False)
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#model.encoder.load_state_dict(torch.load('../experiments/music_cheater_encoder_256.pth', map_location=torch.device('cpu')), strict=True)
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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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o = model(clip, ts, clip, conditioning_free=True)
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pg = model.get_grad_norm_parameter_groups()
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def extract_cheater_encoder(in_f, out_f):
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p = torch.load(in_f)
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out = {}
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for k, v in p.items():
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if k.startswith('encoder.'):
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out[k] = v
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torch.save(out, out_f)
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if __name__ == '__main__':
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#test_local_attention_mask()
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#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)
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test_cheater_model()
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#extract_diff('X:\\dlas\experiments\\train_music_diffusion_tfd_cheater_from_scratch\\models\\56500_generator_ema.pth', 'extracted.pth', remove_head=True)
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