321 lines
15 KiB
Python
321 lines
15 KiB
Python
import itertools
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import random
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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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import torch_intermediary as ml
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from models.arch_util import ResBlock, TimestepEmbedSequential, AttentionBlock, build_local_attention_mask, cGLU, \
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RelativeQKBias
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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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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.dropout = nn.Dropout(p=dropout)
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self.attn = AttentionBlock(inp_dim, out_channels=contraction_dim, num_heads=heads)
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self.register_buffer('mask', build_local_attention_mask(n=6000, l=64), persistent=False)
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self.pos_bias = RelativeQKBias(l=64, max_positions=6000)
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ff_contract = contraction_dim//2
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self.ff1 = nn.Sequential(nn.Conv1d(inp_dim+contraction_dim, ff_contract, kernel_size=1),
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nn.GroupNorm(8, ff_contract),
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cGLU(ff_contract))
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self.ff2 = nn.Sequential(nn.Conv1d(inp_dim+contraction_dim*3//2, ff_contract, kernel_size=3, padding=1),
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nn.GroupNorm(8, ff_contract),
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cGLU(ff_contract))
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def forward(self, x):
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ah = self.dropout(self.attn(x, mask=self.mask, qk_bias=self.pos_bias(x.shape[-1])))
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h = torch.cat([ah, x], dim=1)
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hf = self.dropout(checkpoint(self.ff1, h))
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h = torch.cat([h, hf], dim=1)
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hf = self.dropout(checkpoint(self.ff2, h))
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return torch.cat([h, hf], dim=1)
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class ConcatAttentionBlock(TimestepBlock):
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def __init__(self, trunk_dim, contraction_dim, blk_dim, heads, dropout):
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super().__init__()
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self.contraction_dim = contraction_dim
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self.prenorm = nn.GroupNorm(8, trunk_dim)
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self.block1 = SubBlock(trunk_dim+blk_dim, contraction_dim, heads, dropout)
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self.block2 = SubBlock(trunk_dim+blk_dim+contraction_dim*2, contraction_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 = torch.cat([h, blk_emb.unsqueeze(-1).repeat(1,1,x.shape[-1])], dim=1)
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h = self.block1(h)
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h = self.block2(h)
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h = self.out(h[:,-self.contraction_dim*4:])
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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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hidden_dim,
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out_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, hidden_dim, kernel_size=5, stride=2)
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# nn.Embedding
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self.resolution_embedding = ml.Embedding(num_resolutions, hidden_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(hidden_dim, num_attn_heads, do_checkpoint=do_checkpointing))
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attn.append(ResBlock(hidden_dim, dims=1, checkpointing_enabled=do_checkpointing))
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self.attn = nn.Sequential(*attn)
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self.out = ml.Linear(hidden_dim, out_dim, bias=False)
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self.dim = hidden_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 self.out(h[:, :, 0])
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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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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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time_embed_dim=256,
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time_proj_dim=64,
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cond_proj_dim=256,
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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, time_proj_dim),
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)
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self.prior_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_proj_dim),
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)
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# nn.Embedding
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self.resolution_embed = ml.Embedding(resolution_steps, time_proj_dim)
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self.conditioning_encoder = ConditioningEncoder(in_channels, model_channels, cond_proj_dim, resolution_steps, num_attn_heads=model_channels//64)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,cond_proj_dim))
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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, time_proj_dim*3 + cond_proj_dim,
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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 input_to_random_resolution_and_window(self, x, ts, diffuser):
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"""
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This function MUST be applied to the target *before* noising. It returns the reduced, re-scoped target as well
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as caches an internal prior for the rescoped target which will be used in training.
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Args:
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x: Diffusion target
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"""
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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='nearest')
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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 = F.interpolate(s, scale_factor=.25, mode='nearest')
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s_prior = F.interpolate(s_prior, size=(s.shape[-1],), mode='linear', align_corners=True)
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# Now diffuse the prior randomly between the x timestep and 0.
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adv = torch.rand_like(ts.float())
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t_prior = (adv * ts).long() - 1
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# The t_prior-1 below is an important detail: it forces s_prior to be unmodified for ts=0. It also means that t_prior is not on the same timescale as ts (instead it is shifted by 1).
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s_prior_diffused = diffuser.q_sample(s_prior, t_prior-1, torch.randn_like(s_prior), allow_negatives=True)
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self.preprocessed = (s_prior_diffused, t_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, prior_timesteps=None, x_prior=None, resolution=None, conditioning_input=None, conditioning_free=False):
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"""
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Predicts the previous diffusion timestep of x, given a partially diffused low-resolution prior and a conditioning
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input.
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All parameters are optional because during training, input_to_random_resolution_and_window is used by a training
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harness to preformat the inputs and fill in the parameters as state variables.
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Args:
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x: Prediction prior.
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timesteps: Number of timesteps x has been diffused for.
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prior_timesteps: Number of timesteps x_prior has been diffused for. Must be <= timesteps for each batch element. If nothing is specified, then [0] is assumed, e.g. a fully diffused prior.
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x_prior: A low-resolution prior that guides the model.
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resolution: Integer indicating the operating resolution level. '0' is the highest resolution.
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conditioning_input: A semi-related (un-aligned) conditioning input which is used to guide diffusion. Similar to a class input, but hooked to a learned conditioning encoder.
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conditioning_free: Whether or not to ignore the conditioning input.
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"""
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conditioning_input = x_prior if conditioning_input is None else conditioning_input
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if resolution is None:
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# This is assumed to be training.
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assert self.preprocessed is not None, 'Preprocessing function not called.'
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assert x_prior is None, 'Provided prior will not be used, instead preprocessing output will be used.'
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x_prior, prior_timesteps, resolution = self.preprocessed
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self.preprocessed = None
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else:
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assert x.shape[-1] > x_prior.shape[-1] * 3.9, f'{x.shape} {x_prior.shape}'
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if prior_timesteps is None:
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# This is taken to mean a fully diffused prior was given.
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prior_timesteps = torch.tensor([0], device=x.device) # Assuming batch_size=1 for inference.
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x_prior = F.interpolate(x_prior, size=(x.shape[-1],), mode='linear', align_corners=True)
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assert torch.all(timesteps - prior_timesteps >= 0), f'Prior timesteps should always be lower (more resolved) than input timesteps. {timesteps}, {prior_timesteps}'
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if conditioning_free:
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code_emb = self.unconditioned_embedding.repeat(x.shape[0], 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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# Mask out the conditioning input and x_prior inputs 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((x.shape[0], 1), device=x.device) < self.unconditioned_percentage
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code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(code_emb.shape[0], 1), code_emb)
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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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prior_time_emb = self.prior_time_embed(timestep_embedding(prior_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, prior_time_emb, res_emb, code_emb], dim=1)
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h = torch.cat([x, x_prior], 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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# 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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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.ff1.parameters() for lyr in self.layers] +
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[lyr.block1.ff2.parameters() for lyr in self.layers]))
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ff2 = list(itertools.chain.from_iterable([lyr.block2.ff1.parameters() for lyr in self.layers] +
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[lyr.block2.ff2.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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'prior_time_embed': list(self.prior_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 before_step(self, step):
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scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.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_diffusion13(opt_net, opt):
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return TransformerDiffusion(**opt_net['kwargs'])
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def test_tfd():
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from models.diffusion.respace import SpacedDiffusion
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from models.diffusion.respace import space_timesteps
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from models.diffusion.gaussian_diffusion import get_named_beta_schedule
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diffuser = SpacedDiffusion(use_timesteps=space_timesteps(4000, [4000]), model_mean_type='epsilon',
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model_var_type='learned_range', loss_type='mse',
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betas=get_named_beta_schedule('linear', 4000))
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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([0, 0])
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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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unconditioned_percentage=.6)
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model.get_grad_norm_parameter_groups()
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for k in range(100):
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x = model.input_to_random_resolution_and_window(clip, ts, diffuser)
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model(x, ts, conditioning_input=cond)
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def remove_conditioning(sd_path):
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sd = torch.load(sd_path)
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del sd['unconditioned_embedding']
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torch.save(sd, sd_path.replace('.pth', '') + '_fixed.pth')
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if __name__ == '__main__':
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#remove_conditioning('X:\\dlas\\experiments\\train_music_diffusion_multilevel_sr_pre\\models\\12500_generator.pth')
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test_tfd()
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