Add scheduling to quantizer, enable cudnn_benchmarking to be disabled

codes/models/diffusion/diffusion_dvae.py

@@ -1,5 +1,7 @@
 from models.diffusion.fp16_util import convert_module_to_f32, convert_module_to_f16
+from models.diffusion.gaussian_diffusion import get_named_beta_schedule
 from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
+from models.diffusion.respace import SpacedDiffusion, space_timesteps
 from models.diffusion.unet_diffusion import AttentionPool2d, AttentionBlock, ResBlock, TimestepEmbedSequential, \
     Downsample, Upsample
 import torch
@@ -253,7 +255,8 @@ class DiffusionDVAE(nn.Module):
         )
 
     def get_debug_values(self, step, __):
-        return {'histogram_codes': self.codes}
+        # Note: this is very poor design, but quantizer.get_temperature not only retrieves the temperature, it also updates the step and thus it is extremely important that this function get called regularly.
+        return {'histogram_codes': self.codes, 'quantizer_temperature': self.quantizer.get_temperature(step)}
 
     @torch.no_grad()
     @eval_decorator

codes/models/vqvae/gumbel_quantizer.py

@@ -3,22 +3,54 @@ import torch.nn as nn
 import torch.nn.functional as F
 from torch import einsum
 
+from utils.weight_scheduler import LinearDecayWeightScheduler
+
 
 class GumbelQuantizer(nn.Module):
-    def __init__(self, inp_dim, codebook_dim, num_tokens, straight_through=False, temperature=.9):
+    def __init__(self, inp_dim, codebook_dim, num_tokens, straight_through=False):
         super().__init__()
         self.to_logits = nn.Conv1d(inp_dim, num_tokens, 1)
         self.codebook = nn.Embedding(num_tokens, codebook_dim)
         self.straight_through = straight_through
-        self.temperature = temperature
+        self.temperature_scheduler = LinearDecayWeightScheduler(10, 5000, .9, 2000)
+        self.step = 0
+
+    def get_temperature(self, step):
+        self.step = step  # VERY POOR DESIGN. WHEN WILL HE EVER LEARN???
+        return self.temperature_scheduler.get_weight_for_step(step)
 
     def embed_code(self, codes):
         return self.codebook(codes)
 
+    def gumbel_softmax(self, logits, tau, dim, hard):
+        gumbels = torch.rand_like(logits)
+        gumbels = -torch.log(-torch.log(gumbels + 1e-8) + 1e-8)
+        logits = (logits + gumbels) / tau  # ~Gumbel(logits,tau)
+        y_soft = F.softmax(logits, dim=dim)
+
+        if hard:
+            index = y_soft.max(dim, keepdim=True)[1]
+            y_hard = torch.zeros_like(logits, memory_format=torch.legacy_contiguous_format).scatter_(dim, index, 1.0)
+            ret = y_hard - y_soft.detach() + y_soft
+        else:
+            ret = y_soft
+        return ret
+
     def forward(self, h):
         h = h.permute(0,2,1)
         logits = self.to_logits(h)
-        logits = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=self.straight_through)
+        logits = self.gumbel_softmax(logits, tau=self.temperature_scheduler.get_weight_for_step(self.step), dim=1, hard=self.straight_through)
         codes = logits.argmax(dim=1).flatten(1)
         sampled = einsum('b n l, n d -> b d l', logits, self.codebook.weight)
-        return sampled.permute(0,2,1), 0, codes
+        return sampled.permute(0,2,1), 0, codes
+
+if __name__ == '__main__':
+    from models.diffusion.diffusion_dvae import DiscreteDecoder
+    j =  torch.randn(8,40,1024)
+    m = GumbelQuantizer(1024, 1024, 4096)
+    m2 = DiscreteDecoder(1024, (512, 256), 2)
+    l=m2(m(j)[0].permute(0,2,1))
+    mean = 0
+    for ls in l:
+        mean = mean + ls.mean()
+    mean.backward()

codes/scripts/audio/preparation/save_mels_to_disk.py

@@ -1,4 +1,5 @@
 import argparse
+import os
 
 import numpy
 import torch
@@ -19,10 +20,12 @@ def main():
     mel_inj = MelSpectrogramInjector({'in':'in', 'out':'out'}, {})
     audio_loader = AudioAdapter.default()
     for e, wav_file in enumerate(tqdm(files)):
-        if e < 272583:
+        if e < 0:
             continue
         print(f"Processing {wav_file}..")
         outfile = f'{wav_file}.npz'
+        if os.path.exists(outfile):
+            continue
 
         try:
             wave, sample_rate = audio_loader.load(wav_file, sample_rate=22050)

codes/train.py

@@ -95,7 +95,7 @@ class Trainer:
         seed += self.rank  # Different multiprocessing instances should behave differently.
         util.set_random_seed(seed)
 
-        torch.backends.cudnn.benchmark = True
+        torch.backends.cudnn.benchmark = opt_get(opt, ['cuda_benchmarking_enabled'], True)
         # torch.backends.cudnn.deterministic = True
         if opt_get(opt, ['anomaly_detection'], False):
             torch.autograd.set_detect_anomaly(True)

codes/utils/weight_scheduler.py

@@ -55,11 +55,11 @@ def get_scheduler_for_opt(opt):
 # Do some testing.
 if __name__ == "__main__":
     #sched = SinusoidalWeightScheduler(1, .1, 50, 10)
-    sched = LinearDecayWeightScheduler(1, 150, .1, 20)
+    sched = LinearDecayWeightScheduler(10, 5000, .9, 2000)
 
     x = []
     y = []
-    for s in range(200):
+    for s in range(8000):
         x.append(s)
         y.append(sched.get_weight_for_step(s))
     plt.plot(x, y)