forked from mrq/DL-Art-School
269 lines
11 KiB
Python
269 lines
11 KiB
Python
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import itertools
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from time import time
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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, AttentionBlock
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from models.audio.music.gpt_music2 import UpperEncoder, GptMusicLower
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from models.audio.music.music_quantizer2 import MusicQuantizer2
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from models.audio.tts.lucidrains_dvae import DiscreteVAE
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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, RMSScaleShiftNorm, RotaryEmbedding, \
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FeedForward
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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 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 SubBlock(nn.Module):
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def __init__(self, inp_dim, contraction_dim, heads, dropout):
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super().__init__()
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self.attn = Attention(inp_dim, out_dim=contraction_dim, heads=heads, dim_head=contraction_dim//heads, causal=False, dropout=dropout)
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self.attnorm = nn.LayerNorm(contraction_dim)
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self.ff = FeedForward(inp_dim+contraction_dim, dim_out=contraction_dim, mult=2, dropout=dropout)
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self.ffnorm = nn.LayerNorm(contraction_dim)
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def forward(self, x, rotary_emb):
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ah, _, _, _ = checkpoint(self.attn, x, None, None, None, None, None, rotary_emb)
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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 = 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, time_embed_dim, cond_dim_in, cond_dim_hidden, heads, dropout):
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super().__init__()
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self.prenorm = RMSScaleShiftNorm(trunk_dim, embed_dim=time_embed_dim, bias=False)
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self.cond_project = nn.Linear(cond_dim_in, cond_dim_hidden)
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self.block1 = SubBlock(trunk_dim+cond_dim_hidden, contraction_dim, heads, dropout)
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self.block2 = SubBlock(trunk_dim+cond_dim_hidden+contraction_dim*2, contraction_dim, heads, dropout)
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self.out = nn.Linear(contraction_dim*4, trunk_dim, bias=False)
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self.out.weight.data.zero_()
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def forward(self, x, cond, timestep_emb, rotary_emb):
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h = self.prenorm(x, norm_scale_shift_inp=timestep_emb)
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cond = self.cond_project(cond)
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h = torch.cat([h, cond], dim=-1)
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h = self.block1(h, rotary_emb)
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h = self.block2(h, rotary_emb)
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h = self.out(h[:,:,x.shape[-1]+cond.shape[-1]:])
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return h + x
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class TransformerDiffusionWithPointConditioning(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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in_channels=256,
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out_channels=512, # mean and variance
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model_channels=1024,
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contraction_dim=256,
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time_embed_dim=256,
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num_layers=8,
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rotary_emb_dim=32,
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input_cond_dim=1024,
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num_heads=8,
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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.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, time_embed_dim),
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)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
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self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
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self.layers = TimestepRotaryEmbedSequential(*[ConcatAttentionBlock(model_channels,
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contraction_dim,
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time_embed_dim,
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cond_dim_in=input_cond_dim,
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cond_dim_hidden=input_cond_dim//2,
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heads=num_heads,
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dropout=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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'rotary_embeddings': list(self.rotary_embeddings.parameters()),
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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, conditioning_input, conditioning_free=False):
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unused_params = []
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if conditioning_free:
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cond = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
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else:
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cond = conditioning_input
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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((cond.shape[0], 1, 1),
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device=cond.device) < self.unconditioned_percentage
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cond = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(cond.shape[0], 1, 1), cond)
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unused_params.append(self.unconditioned_embedding)
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cond = cond.repeat(1,x.shape[-1],1)
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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))
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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]+1, x.device)
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for layer in self.layers:
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x = checkpoint(layer, x, cond, 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 ConditioningEncoder(nn.Module):
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def __init__(self,
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cond_dim,
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embedding_dim,
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attn_blocks=6,
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num_attn_heads=8,
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dropout=.1,
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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(cond_dim, embedding_dim, kernel_size=1)
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self.attn = Encoder(
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dim=embedding_dim,
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depth=attn_blocks,
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heads=num_attn_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=2,
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)
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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):
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h = self.init(x).permute(0,2,1)
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h = self.attn(h).permute(0,2,1)
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return h.mean(dim=2).unsqueeze(1)
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class TransformerDiffusionWithConditioningEncoder(nn.Module):
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def __init__(self, **kwargs):
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super().__init__()
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self.internal_step = 0
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self.diff = TransformerDiffusionWithPointConditioning(**kwargs)
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self.conditioning_encoder = ConditioningEncoder(256, kwargs['model_channels'])
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def forward(self, x, timesteps, true_cheater, conditioning_input=None, disable_diversity=False, conditioning_free=False):
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cond = self.conditioning_encoder(true_cheater)
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diff = self.diff(x, timesteps, conditioning_input=cond, 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['conditioning_encoder'] = list(self.conditioning_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_tfdpc2(opt_net, opt):
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return TransformerDiffusionWithPointConditioning(**opt_net['kwargs'])
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@register_model
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def register_tfdpc3_with_conditioning_encoder(opt_net, opt):
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return TransformerDiffusionWithConditioningEncoder(**opt_net['kwargs'])
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def test_cheater_model():
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clip = torch.randn(2, 256, 400)
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cl = 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 = TransformerDiffusionWithConditioningEncoder(model_channels=1024)
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print_network(model)
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o = model(clip, ts, cl)
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pg = model.get_grad_norm_parameter_groups()
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if __name__ == '__main__':
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test_cheater_model()
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