tfd9 returns with some optimizations
This commit is contained in:
James Betker
parent
acfe9cf880
commit
df0cdf1a4f
361
codes/models/audio/music/transformer_diffusion9.py
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361
codes/models/audio/music/transformer_diffusion9.py
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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 ResBlock
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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
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from models.lucidrains.x_transformers import Encoder, Attention, FeedForward, RMSScaleShiftNorm, RotaryEmbedding
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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 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 DietAttentionBlock(TimestepBlock):
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def __init__(self, in_dim, dim, heads, dropout):
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super().__init__()
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self.proj = nn.Linear(in_dim, dim)
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self.proj.bias.data.zero_()
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self.rms_scale_norm = RMSScaleShiftNorm(dim, bias=False)
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self.attn = Attention(dim, heads=heads, dim_head=dim//heads, causal=False, dropout=dropout)
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self.ff = FeedForward(dim, in_dim, mult=1, dropout=dropout, zero_init_output=True)
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def forward(self, x, timestep_emb, rotary_emb):
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h = self.proj(x)
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h = self.rms_scale_norm(h, norm_scale_shift_inp=timestep_emb)
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h, _, _, _ = checkpoint(self.attn, h, None, None, None, None, None, rotary_emb)
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h = checkpoint(self.ff, h)
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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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prenet_channels=256,
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prenet_layers=3,
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model_channels=512,
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block_channels=256,
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num_layers=8,
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in_channels=256,
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rotary_emb_dim=32,
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input_vec_dim=512,
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out_channels=512, # mean and variance
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num_heads=16,
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dropout=0,
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use_fp16=False,
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ar_prior=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.prenet_channels = prenet_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, prenet_channels, 3, 1, 1)
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self.time_embed = nn.Sequential(
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linear(prenet_channels, prenet_channels),
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nn.SiLU(),
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linear(prenet_channels, block_channels),
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)
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self.ar_prior = ar_prior
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prenet_heads = prenet_channels//64
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if ar_prior:
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self.ar_input = nn.Linear(input_vec_dim, prenet_channels)
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self.ar_prior_intg = Encoder(
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dim=prenet_channels,
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depth=prenet_layers,
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heads=prenet_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=1,
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)
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else:
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self.input_converter = nn.Linear(input_vec_dim, prenet_channels)
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self.code_converter = Encoder(
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dim=prenet_channels,
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depth=prenet_layers,
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heads=prenet_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=1,
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)
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self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,prenet_channels))
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self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
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self.intg = nn.Linear(prenet_channels*2, model_channels)
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self.layers = TimestepRotaryEmbedSequential(*[DietAttentionBlock(model_channels, block_channels, num_heads, dropout) for _ in range(num_layers)])
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self.out = nn.Sequential(
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normalization(model_channels),
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nn.SiLU(),
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zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
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)
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self.debug_codes = {}
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def get_grad_norm_parameter_groups(self):
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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, prior, expected_seq_len):
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code_emb = self.ar_input(prior) if self.ar_prior else self.input_converter(prior)
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code_emb = self.ar_prior_intg(code_emb) if self.ar_prior else self.code_converter(code_emb)
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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(prior.shape[0], 1, 1),
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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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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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with torch.autocast(x.device.type, enabled=self.enable_fp16):
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blk_emb = self.time_embed(timestep_embedding(timesteps, self.prenet_channels))
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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], x.device)
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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, 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 TransformerDiffusionWithQuantizer(nn.Module):
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def __init__(self, quantizer_dims=[1024], 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=kwargs['in_channels'], inner_dim=quantizer_dims,
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codevector_dim=quantizer_dims[0], 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=None, 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_quant_codes': self.quantizer.codes[:self.quantizer.total_codes],
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'gumbel_temperature': self.quantizer.quantizer.temperature}
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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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'ff_layers': list(itertools.chain.from_iterable([lyr.ff.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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'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
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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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class TransformerDiffusionWithARPrior(nn.Module):
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def __init__(self, freeze_diff=False, **kwargs):
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super().__init__()
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self.internal_step = 0
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from models.audio.music.gpt_music import GptMusicLower
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self.ar = GptMusicLower(dim=512, layers=12)
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for p in self.ar.parameters():
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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self.diff = TransformerDiffusion(ar_prior=True, **kwargs)
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if freeze_diff:
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for p in self.diff.parameters():
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p.DO_NOT_TRAIN = True
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p.requires_grad = False
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for p in list(self.diff.ar_prior_intg.parameters()) + list(self.diff.ar_input.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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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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'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])),
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'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
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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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'ar_prior_intg': list(self.diff.ar_prior_intg.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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def forward(self, x, timesteps, truth_mel, disable_diversity=False, conditioning_input=None, conditioning_free=False):
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with torch.no_grad():
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prior = self.ar(truth_mel, conditioning_input, return_latent=True)
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diff = self.diff(x, timesteps, prior, conditioning_free=conditioning_free)
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return diff
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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_diffusion9_with_quantizer(opt_net, opt):
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return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
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@register_model
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def register_transformer_diffusion9_with_ar_prior(opt_net, opt):
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return TransformerDiffusionWithARPrior(**opt_net['kwargs'])
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def test_quant_model():
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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(in_channels=256, model_channels=3072, block_channels=1536,
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prenet_channels=1024, num_heads=12,
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input_vec_dim=1024, num_layers=24, 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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model.quantizer.load_state_dict(quant_weights, strict=False)
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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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def test_ar_model():
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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 = TransformerDiffusionWithARPrior(model_channels=3072, block_channels=1536, prenet_channels=1536,
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input_vec_dim=512, num_layers=24, prenet_layers=6, freeze_diff=True,
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unconditioned_percentage=.4)
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model.get_grad_norm_parameter_groups()
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ar_weights = torch.load('D:\\dlas\\experiments\\train_music_gpt\\models\\44500_generator_ema.pth')
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model.ar.load_state_dict(ar_weights, strict=True)
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diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd8\\models\\47500_generator_ema.pth')
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pruned_diff_weights = {}
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for k,v in diff_weights.items():
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if k.startswith('diff.'):
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pruned_diff_weights[k.replace('diff.', '')] = v
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model.diff.load_state_dict(pruned_diff_weights, strict=False)
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torch.save(model.state_dict(), 'sample.pth')
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model(clip, ts, cond, conditioning_input=cond)
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if __name__ == '__main__':
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test_quant_model()
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@ -1,330 +0,0 @@
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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 torch import autocast
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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 AttentionBlock, TimestepEmbedSequential, \
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Downsample, Upsample, TimestepBlock
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from models.lucidrains.x_transformers import Encoder
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from scripts.audio.gen.use_diffuse_tts import ceil_multiple
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from trainer.networks import register_model
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from utils.util import checkpoint
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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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):
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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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padding = 1 if kernel_size == 3 else 2
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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, 1, padding=0),
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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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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, 1)
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||||
|
||||
def forward(self, x, emb):
|
||||
"""
|
||||
Apply the block to a Tensor, conditioned on a timestep embedding.
|
||||
|
||||
:param x: an [N x C x ...] Tensor of features.
|
||||
:param emb: an [N x emb_channels] Tensor of timestep embeddings.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
return checkpoint(
|
||||
self._forward, x, emb
|
||||
)
|
||||
|
||||
def _forward(self, x, emb):
|
||||
h = self.in_layers(x)
|
||||
emb_out = self.emb_layers(emb).type(h.dtype)
|
||||
while len(emb_out.shape) < len(h.shape):
|
||||
emb_out = emb_out[..., None]
|
||||
h = h + emb_out
|
||||
h = self.out_layers(h)
|
||||
return self.skip_connection(x) + h
|
||||
|
||||
|
||||
class DiffusionTts(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
model_channels,
|
||||
in_channels=100,
|
||||
num_tokens=256,
|
||||
out_channels=200, # mean and variance
|
||||
dropout=0,
|
||||
# m 1, 2, 4, 8
|
||||
block_channels= (512,640, 768,1024),
|
||||
num_res_blocks= (3, 3, 3, 3),
|
||||
token_conditioning_resolutions=(2,4,8),
|
||||
attention_resolutions=(2,4,8),
|
||||
conv_resample=True,
|
||||
dims=1,
|
||||
use_fp16=False,
|
||||
kernel_size=3,
|
||||
scale_factor=2,
|
||||
num_heads=None,
|
||||
time_embed_dim_multiplier=4,
|
||||
nil_guidance_fwd_proportion=.15,
|
||||
):
|
||||
super().__init__()
|
||||
|
||||
self.in_channels = in_channels
|
||||
self.model_channels = model_channels
|
||||
self.out_channels = out_channels
|
||||
self.attention_resolutions = attention_resolutions
|
||||
self.dropout = dropout
|
||||
self.conv_resample = conv_resample
|
||||
self.dtype = torch.float16 if use_fp16 else torch.float32
|
||||
self.dims = dims
|
||||
self.nil_guidance_fwd_proportion = nil_guidance_fwd_proportion
|
||||
self.mask_token_id = num_tokens
|
||||
num_heads = model_channels // 64 if num_heads is None else num_heads
|
||||
|
||||
padding = 1 if kernel_size == 3 else 2
|
||||
|
||||
time_embed_dim = model_channels * time_embed_dim_multiplier
|
||||
self.time_embed = nn.Sequential(
|
||||
linear(model_channels, time_embed_dim),
|
||||
nn.SiLU(),
|
||||
linear(time_embed_dim, time_embed_dim),
|
||||
)
|
||||
|
||||
self.code_embedding = nn.Embedding(num_tokens+1, model_channels)
|
||||
self.conditioning_embedder = nn.Sequential(nn.Conv1d(in_channels, model_channels // 2, 3, padding=1, stride=2),
|
||||
nn.Conv1d(model_channels//2, model_channels,3,padding=1,stride=2))
|
||||
self.conditioning_encoder = Encoder(
|
||||
dim=model_channels,
|
||||
depth=4,
|
||||
heads=num_heads,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rmsnorm=True,
|
||||
ff_glu=True,
|
||||
rotary_pos_emb=True,
|
||||
)
|
||||
|
||||
self.codes_encoder = Encoder(
|
||||
dim=model_channels,
|
||||
depth=8,
|
||||
heads=num_heads,
|
||||
ff_dropout=dropout,
|
||||
attn_dropout=dropout,
|
||||
use_rms_scaleshift_norm=True,
|
||||
ff_glu=True,
|
||||
rotary_pos_emb=True,
|
||||
zero_init_branch_output=True,
|
||||
)
|
||||
|
||||
self.input_blocks = nn.ModuleList(
|
||||
[
|
||||
TimestepEmbedSequential(
|
||||
conv_nd(dims, in_channels, model_channels, kernel_size, padding=padding)
|
||||
)
|
||||
]
|
||||
)
|
||||
token_conditioning_blocks = []
|
||||
self._feature_size = model_channels
|
||||
input_block_chans = [model_channels]
|
||||
ch = model_channels
|
||||
ds = 1
|
||||
|
||||
for level, (blk_chan, num_blocks) in enumerate(zip(block_channels, num_res_blocks)):
|
||||
if ds in token_conditioning_resolutions:
|
||||
token_conditioning_block = nn.Conv1d(model_channels, ch, 1)
|
||||
token_conditioning_block.weight.data *= .02
|
||||
self.input_blocks.append(token_conditioning_block)
|
||||
token_conditioning_blocks.append(token_conditioning_block)
|
||||
|
||||
for _ in range(num_blocks):
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=blk_chan,
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
)
|
||||
]
|
||||
ch = blk_chan
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads,
|
||||
)
|
||||
)
|
||||
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
input_block_chans.append(ch)
|
||||
if level != len(block_channels) - 1:
|
||||
out_ch = ch
|
||||
self.input_blocks.append(
|
||||
TimestepEmbedSequential(
|
||||
Downsample(
|
||||
ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor, ksize=1, pad=0
|
||||
)
|
||||
)
|
||||
)
|
||||
ch = out_ch
|
||||
input_block_chans.append(ch)
|
||||
ds *= 2
|
||||
self._feature_size += ch
|
||||
|
||||
self.middle_block = TimestepEmbedSequential(
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
),
|
||||
AttentionBlock(
|
||||
ch,
|
||||
num_heads=num_heads,
|
||||
),
|
||||
ResBlock(
|
||||
ch,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
dims=dims,
|
||||
),
|
||||
)
|
||||
self._feature_size += ch
|
||||
|
||||
self.output_blocks = nn.ModuleList([])
|
||||
for level, (blk_chan, num_blocks) in list(enumerate(zip(block_channels, num_res_blocks)))[::-1]:
|
||||
for i in range(num_blocks + 1):
|
||||
ich = input_block_chans.pop()
|
||||
layers = [
|
||||
ResBlock(
|
||||
ch + ich,
|
||||
time_embed_dim,
|
||||
dropout,
|
||||
out_channels=blk_chan,
|
||||
dims=dims,
|
||||
kernel_size=kernel_size,
|
||||
)
|
||||
]
|
||||
ch = blk_chan
|
||||
if ds in attention_resolutions:
|
||||
layers.append(
|
||||
AttentionBlock(
|
||||
ch,
|
||||
)
|
||||
)
|
||||
if level and i == num_blocks:
|
||||
out_ch = ch
|
||||
layers.append(
|
||||
Upsample(ch, conv_resample, dims=dims, out_channels=out_ch, factor=scale_factor)
|
||||
)
|
||||
ds //= 2
|
||||
self.output_blocks.append(TimestepEmbedSequential(*layers))
|
||||
self._feature_size += ch
|
||||
|
||||
self.out = nn.Sequential(
|
||||
normalization(ch),
|
||||
nn.SiLU(),
|
||||
zero_module(conv_nd(dims, model_channels, out_channels, kernel_size, padding=padding)),
|
||||
)
|
||||
|
||||
def forward(self, x, timesteps, codes, conditioning_input=None):
|
||||
"""
|
||||
Apply the model to an input batch.
|
||||
|
||||
:param x: an [N x C x ...] Tensor of inputs.
|
||||
:param timesteps: a 1-D batch of timesteps.
|
||||
:param codes: an aligned text input.
|
||||
:return: an [N x C x ...] Tensor of outputs.
|
||||
"""
|
||||
with autocast(x.device.type):
|
||||
orig_x_shape = x.shape[-1]
|
||||
cm = ceil_multiple(x.shape[-1], 16)
|
||||
if cm != 0:
|
||||
pc = (cm-x.shape[-1])/x.shape[-1]
|
||||
x = F.pad(x, (0,cm-x.shape[-1]))
|
||||
codes = F.pad(codes, (0, int(pc * codes.shape[-1])))
|
||||
|
||||
hs = []
|
||||
time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
|
||||
|
||||
# Mask out guidance tokens for un-guided diffusion.
|
||||
if self.training and self.nil_guidance_fwd_proportion > 0:
|
||||
token_mask = torch.rand(codes.shape, device=codes.device) < self.nil_guidance_fwd_proportion
|
||||
codes = torch.where(token_mask, self.mask_token_id, codes)
|
||||
code_emb = self.code_embedding(codes).permute(0, 2, 1)
|
||||
cond_emb = self.conditioning_embedder(conditioning_input).permute(0,2,1)
|
||||
cond_emb = self.conditioning_encoder(cond_emb)[:, 0]
|
||||
code_emb = self.codes_encoder(code_emb.permute(0,2,1), norm_scale_shift_inp=cond_emb).permute(0,2,1)
|
||||
|
||||
first = True
|
||||
time_emb = time_emb.float()
|
||||
h = x
|
||||
for k, module in enumerate(self.input_blocks):
|
||||
if isinstance(module, nn.Conv1d):
|
||||
h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest')
|
||||
h = h + h_tok
|
||||
else:
|
||||
with autocast(x.device.type, enabled=not first):
|
||||
# First block has autocast disabled to allow a high precision signal to be properly vectorized.
|
||||
h = module(h, time_emb)
|
||||
hs.append(h)
|
||||
first = False
|
||||
h = self.middle_block(h, time_emb)
|
||||
for module in self.output_blocks:
|
||||
h = torch.cat([h, hs.pop()], dim=1)
|
||||
h = module(h, time_emb)
|
||||
|
||||
# Last block also has autocast disabled for high-precision outputs.
|
||||
h = h.float()
|
||||
out = self.out(h)
|
||||
return out[:, :, :orig_x_shape]
|
||||
|
||||
|
||||
@register_model
|
||||
def register_diffusion_tts10(opt_net, opt):
|
||||
return DiffusionTts(**opt_net['kwargs'])
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
clip = torch.randn(2, 100, 500).cuda()
|
||||
tok = torch.randint(0,256, (2,230)).cuda()
|
||||
cond = torch.randn(2, 100, 300).cuda()
|
||||
ts = torch.LongTensor([600, 600]).cuda()
|
||||
model = DiffusionTts(512).cuda()
|
||||
print(sum(p.numel() for p in model.parameters()) / 1000000)
|
||||
model(clip, ts, tok, cond)
|
||||
|
||||
|
|
@ -352,12 +352,12 @@ class RMSNorm(nn.Module):
|
|||
|
||||
|
||||
class RMSScaleShiftNorm(nn.Module):
|
||||
def __init__(self, dim, eps=1e-8):
|
||||
def __init__(self, dim, eps=1e-8, bias=True):
|
||||
super().__init__()
|
||||
self.scale = dim ** -0.5
|
||||
self.eps = eps
|
||||
self.g = nn.Parameter(torch.ones(dim))
|
||||
self.scale_shift_process = nn.Linear(dim, dim * 2)
|
||||
self.scale_shift_process = nn.Linear(dim, dim * 2, bias=bias)
|
||||
|
||||
def forward(self, x, norm_scale_shift_inp):
|
||||
norm = torch.norm(x, dim=-1, keepdim=True) * self.scale
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user