This commit is contained in:
James Betker 2022-07-18 10:12:23 -06:00
parent 7a10c3fed8
commit 1b648abd7c

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@ -5,6 +5,7 @@ import torch.nn as nn
import torch.nn.functional as F
from models.diffusion.nn import timestep_embedding
from models.lucidrains.vq import VectorQuantize
from models.lucidrains.x_transformers import FeedForward, Attention, Decoder, RMSScaleShiftNorm
from trainer.networks import register_model
from utils.util import checkpoint
@ -16,55 +17,36 @@ class SelfClassifyingHead(nn.Module):
self.seq_len = seq_len
self.num_classes = classes
self.temperature = init_temperature
self.dec = Decoder(dim=dim, depth=head_depth, heads=2, ff_dropout=dropout, ff_mult=2, attn_dropout=dropout,
use_rmsnorm=True, ff_glu=True, rotary_pos_emb=True)
self.to_classes = nn.Linear(dim, classes)
self.feedback_codebooks = nn.Linear(classes, dim, bias=False)
self.codebooks = nn.Linear(classes, out_dim, bias=False)
@staticmethod
def _compute_perplexity(probs, mask=None):
if mask is not None:
mask_extended = mask.flatten()[:, None, None].expand(probs.shape)
probs = torch.where(mask_extended, probs, torch.zeros_like(probs))
marginal_probs = probs.sum(dim=0) / mask.sum()
else:
marginal_probs = probs.mean(dim=0)
perplexity = torch.exp(-torch.sum(marginal_probs * torch.log(marginal_probs + 1e-7), dim=-1)).sum()
return perplexity
self.dec = Decoder(dim=dim, depth=head_depth, heads=4, ff_dropout=dropout, ff_mult=2, attn_dropout=dropout,
use_rmsnorm=True, ff_glu=True, do_checkpointing=False)
self.quantizer = VectorQuantize(dim, classes, codebook_dim=32, use_cosine_sim=True, threshold_ema_dead_code=2,
sample_codebook_temp=init_temperature)
self.to_output = nn.Linear(dim, out_dim)
def do_ar_step(self, x, used_codes):
h = self.dec(x)
h = self.to_classes(h[:,-1])
for uc in used_codes:
mask = torch.arange(0, self.num_classes, device=x.device).unsqueeze(0).repeat(x.shape[0],1) == uc.unsqueeze(1)
h[mask] = -torch.inf
c = F.gumbel_softmax(h, tau=self.temperature, hard=self.temperature==1)\
soft_c = torch.softmax(h, dim=-1)
perplexity = self._compute_perplexity(soft_c)
return c, perplexity
h, c, _ = self.quantizer(h[:, -1], used_codes)
return h, c
def forward(self, x):
with torch.no_grad():
# Force one of the codebook weights to zero, allowing the model to "skip" any classes it chooses.
self.codebooks.weight.data[:, 0] = 0
self.quantizer._codebook.embed.data[0] = 0
# manually perform ar regression over sequence_length=self.seq_len
stack = [x]
outputs = []
results = []
codes = []
total_perplexity = 0
for i in range(self.seq_len):
nc, perp = checkpoint(functools.partial(self.do_ar_step, used_codes=codes), torch.stack(stack, dim=1))
codes.append(nc.argmax(-1))
stack.append(self.feedback_codebooks(nc))
outputs.append(self.codebooks(nc))
h, c = checkpoint(functools.partial(self.do_ar_step, used_codes=codes), torch.stack(stack, dim=1))
c_mask = c
c_mask[c==0] = -1 # Mask this out because we want code=0 to be capable of being repeated.
codes.append(c)
stack.append(h.detach()) # Detach here to avoid piling up gradients from autoregression. We really just want the gradients to flow to the selected class embeddings and the selector for those classes.
outputs.append(self.to_output(h))
results.append(torch.stack(outputs, dim=1).sum(1))
total_perplexity = total_perplexity + perp
return results, total_perplexity / self.seq_len, torch.cat(codes, dim=-1)
return results, torch.cat(codes, dim=0)
class VectorResBlock(nn.Module):
@ -83,7 +65,6 @@ class InstrumentQuantizer(nn.Module):
def __init__(self, op_dim, dim, num_classes, enc_depth, head_depth, class_seq_len=5, dropout=.1,
min_temp=1, max_temp=10, temp_decay=.999):
"""
Args:
op_dim:
dim:
@ -100,6 +81,7 @@ class InstrumentQuantizer(nn.Module):
self.op_dim = op_dim
self.proj = nn.Linear(op_dim, dim)
self.encoder = nn.ModuleList([VectorResBlock(dim, dropout) for _ in range(enc_depth)])
self.final_bn = nn.BatchNorm1d(dim)
self.heads = SelfClassifyingHead(dim, num_classes, op_dim, head_depth, class_seq_len, dropout, max_temp)
self.min_gumbel_temperature = min_temp
self.max_gumbel_temperature = max_temp
@ -117,14 +99,15 @@ class InstrumentQuantizer(nn.Module):
h = self.proj(f)
for lyr in self.encoder:
h = lyr(h)
h = self.final_bn(h.unsqueeze(-1)).squeeze(-1)
reconstructions, perplexity, codes = self.heads(h)
reconstructions, codes = self.heads(h)
reconstruction_losses = torch.stack([F.mse_loss(r.reshape(b, s, c), px) for r in reconstructions])
r_follow = torch.arange(1, reconstruction_losses.shape[0]+1, device=x.device)
reconstruction_losses = (reconstruction_losses * r_follow / r_follow.shape[0])
self.log_codes(codes)
return reconstruction_losses, perplexity
return reconstruction_losses
def log_codes(self, codes):
if self.internal_step % 5 == 0:
@ -139,13 +122,13 @@ class InstrumentQuantizer(nn.Module):
def get_debug_values(self, step, __):
if self.total_codes > 0:
return {'histogram_codes': self.codes[:self.total_codes],
'temperature': self.heads.temperature}
'temperature': self.heads.quantizer._codebook.sample_codebook_temp}
else:
return {}
def update_for_step(self, step, *args):
self.internal_step = step
self.heads.temperature = max(
self.heads.quantizer._codebook.sample_codebook_temp = max(
self.max_gumbel_temperature * self.gumbel_temperature_decay**step,
self.min_gumbel_temperature,
)