335 lines
13 KiB
Python
335 lines
13 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.audio.music.music_quantizer2 import MusicQuantizer2
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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, AttentionBlock, TimestepEmbedSequential
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from models.lucidrains.x_transformers import Encoder
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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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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 MultiGroupEmbedding(nn.Module):
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def __init__(self, tokens, groups, dim):
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super().__init__()
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self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
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def forward(self, x):
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h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
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return torch.cat(h, dim=-1)
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class ResBlock(TimestepBlock):
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def __init__(
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self,
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channels,
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emb_channels,
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dropout,
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out_channels=None,
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dims=2,
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kernel_size=3,
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efficient_config=False,
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use_scale_shift_norm=False,
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):
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super().__init__()
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self.channels = channels
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self.emb_channels = emb_channels
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self.dropout = dropout
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self.out_channels = out_channels or channels
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self.use_scale_shift_norm = use_scale_shift_norm
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padding = {1: 0, 3: 1, 5: 2}[kernel_size]
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eff_kernel = 1 if efficient_config else 3
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eff_padding = 0 if efficient_config else 1
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self.in_layers = nn.Sequential(
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normalization(channels),
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nn.SiLU(),
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conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding),
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)
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self.emb_layers = nn.Sequential(
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nn.SiLU(),
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linear(
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emb_channels,
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2 * self.out_channels if use_scale_shift_norm else self.out_channels,
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),
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)
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self.out_layers = nn.Sequential(
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normalization(self.out_channels),
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nn.SiLU(),
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nn.Dropout(p=dropout),
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zero_module(
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conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)
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),
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)
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if self.out_channels == channels:
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self.skip_connection = nn.Identity()
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else:
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self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding)
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def forward(self, x, emb):
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"""
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Apply the block to a Tensor, conditioned on a timestep embedding.
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:param x: an [N x C x ...] Tensor of features.
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:param emb: an [N x emb_channels] Tensor of timestep embeddings.
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:return: an [N x C x ...] Tensor of outputs.
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"""
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return checkpoint(
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self._forward, x, emb
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)
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def _forward(self, x, emb):
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h = self.in_layers(x)
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emb_out = self.emb_layers(emb).type(h.dtype)
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while len(emb_out.shape) < len(h.shape):
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emb_out = emb_out[..., None]
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if self.use_scale_shift_norm:
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out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
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scale, shift = torch.chunk(emb_out, 2, dim=1)
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h = out_norm(h) * (1 + scale) + shift
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h = out_rest(h)
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else:
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h = h + emb_out
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h = self.out_layers(h)
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return self.skip_connection(x) + h
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class DiffusionLayer(TimestepBlock):
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def __init__(self, model_channels, dropout, num_heads):
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super().__init__()
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self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True)
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self.attn = AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True)
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def forward(self, x, time_emb):
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y = self.resblk(x, time_emb)
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return self.attn(y)
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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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model_channels=512,
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prenet_layers=3,
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num_layers=8,
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in_channels=256,
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input_vec_dim=512,
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out_channels=512, # mean and variance
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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.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(model_channels, model_channels),
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nn.SiLU(),
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linear(model_channels, model_channels),
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)
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self.input_converter = nn.Linear(input_vec_dim, model_channels)
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self.code_converter = Encoder(
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dim=model_channels,
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depth=prenet_layers,
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heads=model_channels//64,
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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=1,
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)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
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self.intg = nn.Conv1d(model_channels*2, model_channels, kernel_size=1)
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self.layers = TimestepEmbedSequential(*[DiffusionLayer(model_channels, dropout, model_channels // 64) 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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groups = {
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'layers': list(self.layers.parameters()) + list(self.inp_block.parameters()),
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'code_converters': list(self.input_converter.parameters()) + list(self.code_converter.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 timestep_independent(self, codes, expected_seq_len):
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code_emb = self.input_converter(codes)
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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(codes.shape[0], 1, 1),
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code_emb)
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code_emb = self.code_converter(code_emb)
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expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
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return expanded_code_emb
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def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None, conditioning_free=False):
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if precomputed_code_embeddings is not None:
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assert codes is None and conditioning_input is None, "Do not provide precomputed embeddings and the other parameters. It is unclear what you want me to do here."
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unused_params = []
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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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unused_params.extend(list(self.code_converter.parameters()))
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else:
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if precomputed_code_embeddings is not None:
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code_emb = precomputed_code_embeddings
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else:
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code_emb = self.timestep_independent(codes, x.shape[-1])
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unused_params.append(self.unconditioned_embedding)
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code_emb = code_emb.permute(0,2,1)
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blk_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
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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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# 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 TransformerDiffusionWithQuantizer(nn.Module):
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def __init__(self, freeze_quantizer_until=20000, **kwargs):
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super().__init__()
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self.internal_step = 0
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self.freeze_quantizer_until = freeze_quantizer_until
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self.diff = TransformerDiffusion(**kwargs)
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self.quantizer = MusicQuantizer2(inp_channels=256, inner_dim=[1024], codevector_dim=1024, codebook_size=256,
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codebook_groups=2, max_gumbel_temperature=4, min_gumbel_temperature=.5)
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self.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature
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del self.quantizer.up
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def update_for_step(self, step, *args):
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self.internal_step = step
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qstep = max(0, self.internal_step - self.freeze_quantizer_until)
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self.quantizer.quantizer.temperature = max(
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self.quantizer.max_gumbel_temperature * self.quantizer.gumbel_temperature_decay ** qstep,
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self.quantizer.min_gumbel_temperature,
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)
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def forward(self, x, timesteps, truth_mel, conditioning_input, disable_diversity=False, conditioning_free=False):
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quant_grad_enabled = self.internal_step > self.freeze_quantizer_until
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with torch.set_grad_enabled(quant_grad_enabled):
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proj, diversity_loss = self.quantizer(truth_mel, return_decoder_latent=True)
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proj = proj.permute(0,2,1)
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# Make sure this does not cause issues in DDP by explicitly using the parameters for nothing.
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if not quant_grad_enabled:
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unused = 0
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for p in self.quantizer.parameters():
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unused = unused + p.mean() * 0
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proj = proj + unused
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diversity_loss = diversity_loss * 0
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diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
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if disable_diversity:
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return diff
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return diff, diversity_loss
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def get_debug_values(self, step, __):
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if self.quantizer.total_codes > 0:
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return {'histogram_codes': self.quantizer.codes[:self.quantizer.total_codes]}
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else:
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return {}
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def get_grad_norm_parameter_groups(self):
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groups = {
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'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])),
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'res_layers': list(itertools.chain.from_iterable([lyr.resblk.parameters() for lyr in self.diff.layers])),
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'quantizer_encoder': list(self.quantizer.encoder.parameters()),
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'quant_codebook': [self.quantizer.quantizer.codevectors],
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'out': list(self.diff.out.parameters()),
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'x_proj': list(self.diff.inp_block.parameters()),
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'layers': list(self.diff.layers.parameters()),
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'code_converters': list(self.diff.input_converter.parameters()) + list(self.diff.code_converter.parameters()),
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'time_embed': list(self.diff.time_embed.parameters()),
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}
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return groups
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@register_model
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def register_transformer_diffusion9(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_diffusion8_with_quantizer(opt_net, opt):
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return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
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"""
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# For TFD5
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if __name__ == '__main__':
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clip = torch.randn(2, 256, 400)
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aligned_sequence = torch.randn(2,100,512)
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cond = torch.randn(2, 256, 400)
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ts = torch.LongTensor([600, 600])
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model = TransformerDiffusion(model_channels=3072, model_channels=1536, model_channels=1536)
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torch.save(model, 'sample.pth')
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print_network(model)
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o = model(clip, ts, aligned_sequence, cond)
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"""
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if __name__ == '__main__':
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clip = torch.randn(2, 256, 400)
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cond = torch.randn(2, 256, 400)
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ts = torch.LongTensor([600, 600])
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model = TransformerDiffusionWithQuantizer(model_channels=1024, input_vec_dim=1024, num_layers=16, prenet_layers=6)
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model.get_grad_norm_parameter_groups()
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quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth')
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#diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd5\\models\\48000_generator_ema.pth')
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model.quantizer.load_state_dict(quant_weights, strict=False)
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#model.diff.load_state_dict(diff_weights)
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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, cond)
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