unet with ar prior

codes/models/audio/music/gpt_music.py

@@ -6,6 +6,7 @@ from transformers import GPT2Config, GPT2Model
 from models.arch_util import AttentionBlock
 from models.audio.music.music_quantizer import MusicQuantizer
 from models.audio.music.music_quantizer2 import MusicQuantizer2
+from models.lucidrains.x_transformers import Encoder
 from trainer.networks import register_model
 from utils.util import opt_get
 
@@ -31,34 +32,55 @@ class ConditioningEncoder(nn.Module):
 
 
 class GptMusicLower(nn.Module):
-    def __init__(self, dim, layers, num_target_vectors=512, num_target_groups=2, cv_dim=1024, num_upper_vectors=64, num_upper_groups=4):
+    def __init__(self, dim, layers, dropout=0, num_target_vectors=512, num_target_groups=2, num_upper_vectors=64, num_upper_groups=4):
         super().__init__()
+        self.internal_step = 0
         self.num_groups = num_target_groups
         self.config = GPT2Config(vocab_size=1, n_positions=8192, n_embd=dim, n_layer=layers, n_head=dim//64,
-                                 n_inner=dim*2)
-        self.target_quantizer = MusicQuantizer(inp_channels=256, inner_dim=[1024,1024,512], codevector_dim=cv_dim, codebook_size=num_target_vectors, codebook_groups=num_target_groups)
-        self.upper_quantizer = MusicQuantizer2(inp_channels=256, inner_dim=[1024,896,768,640,512,384], codevector_dim=cv_dim, codebook_size=num_upper_vectors, codebook_groups=num_upper_groups)
+                                 n_inner=dim*2, attn_pdrop=dropout, resid_pdrop=dropout, gradient_checkpointing=True, use_cache=False)
+        self.target_quantizer = MusicQuantizer2(inp_channels=256, inner_dim=[1024], codevector_dim=1024, codebook_size=256,
+                                                codebook_groups=2, max_gumbel_temperature=4, min_gumbel_temperature=.5)
+        self.upper_quantizer = MusicQuantizer2(inp_channels=256, inner_dim=[dim,
+                                                                            max(512,dim-128),
+                                                                            max(512,dim-256),
+                                                                            max(512,dim-384),
+                                                                            max(512,dim-512),
+                                                                            max(512,dim-512)], codevector_dim=dim,
+                                               codebook_size=num_upper_vectors, codebook_groups=num_upper_groups, expressive_downsamples=True)
         # Following are unused quantizer constructs we delete to avoid DDP errors (and to be efficient.. of course..)
         del self.target_quantizer.decoder
         del self.target_quantizer.up
         del self.upper_quantizer.up
+        # Freeze the target quantizer.
+        for p in self.target_quantizer.parameters():
+            p.DO_NOT_TRAIN = True
+            p.requires_grad = False
 
+        self.upper_mixer = Encoder(
+                    dim=dim,
+                    depth=4,
+                    heads=dim//64,
+                    ff_dropout=dropout,
+                    attn_dropout=dropout,
+                    use_rmsnorm=True,
+                    ff_glu=True,
+                    rotary_emb_dim=True,
+                )
         self.conditioning_encoder = ConditioningEncoder(256, dim, attn_blocks=4, num_attn_heads=dim//64)
 
         self.gpt = GPT2Model(self.config)
         del self.gpt.wte  # Unused, we'll do our own embeddings.
 
         self.embeddings = nn.ModuleList([nn.Embedding(num_target_vectors, dim // num_target_groups) for _ in range(num_target_groups)])
-        self.upper_proj = nn.Conv1d(cv_dim, dim, kernel_size=1)
         self.heads = nn.ModuleList([nn.Linear(dim, num_target_vectors) for _ in range(num_target_groups)])
 
 
-    def forward(self, mel, conditioning):
+    def forward(self, mel, conditioning, return_latent=False):
         with torch.no_grad():
             self.target_quantizer.eval()
             codes = self.target_quantizer.get_codes(mel)
         upper_vector, upper_diversity = self.upper_quantizer(mel, return_decoder_latent=True)
-        upper_vector = self.upper_proj(upper_vector)
+        upper_vector = self.upper_mixer(upper_vector.permute(0,2,1)).permute(0,2,1)  # Allow the upper vector to fully attend to itself (the whole thing is a prior.)
         upper_vector = F.interpolate(upper_vector, size=codes.shape[1], mode='linear')
         upper_vector = upper_vector.permute(0,2,1)
 
@@ -68,21 +90,49 @@ class GptMusicLower(nn.Module):
         h = [embedding(inputs[:, :, i]) for i, embedding in enumerate(self.embeddings)]
         h = torch.cat(h, dim=-1) + upper_vector
 
-        # Stick the conditioning embedding on the front of the input sequence.
-        # The transformer will learn how to integrate it.
-        # This statement also serves to pre-pad the inputs by one token, which is the basis of the next-token-prediction task. IOW: this is the "START" token.
-        cond_emb = self.conditioning_encoder(conditioning).unsqueeze(1)
-        h = torch.cat([cond_emb, h], dim=1)
+        with torch.autocast(mel.device.type):
+            # Stick the conditioning embedding on the front of the input sequence.
+            # The transformer will learn how to integrate it.
+            # This statement also serves to pre-pad the inputs by one token, which is the basis of the next-token-prediction task. IOW: this is the "START" token.
+            cond_emb = self.conditioning_encoder(conditioning).unsqueeze(1)
+            h = torch.cat([cond_emb, h], dim=1)
 
-        h = self.gpt(inputs_embeds=h, return_dict=True).last_hidden_state
+            h = self.gpt(inputs_embeds=h, return_dict=True).last_hidden_state
 
-        losses = 0
-        for i, head in enumerate(self.heads):
-            logits = head(h).permute(0,2,1)
-            loss = F.cross_entropy(logits, targets[:,:,i])
-            losses = losses + loss
+            if return_latent:
+                return h.float()
 
-        return losses / self.num_groups
+            losses = 0
+            for i, head in enumerate(self.heads):
+                logits = head(h).permute(0,2,1)
+                loss = F.cross_entropy(logits, targets[:,:,i])
+                losses = losses + loss
+
+        return losses / self.num_groups, upper_diversity
+
+    def get_grad_norm_parameter_groups(self):
+        groups = {
+            'gpt': list(self.gpt.parameters()),
+            'conditioning': list(self.conditioning_encoder.parameters()),
+            'upper_mixer': list(self.upper_mixer.parameters()),
+            'upper_quant_down': list(self.upper_quantizer.down.parameters()),
+            'upper_quant_encoder': list(self.upper_quantizer.encoder.parameters()),
+            'upper_quant_codebook': [self.upper_quantizer.quantizer.codevectors],
+        }
+        return groups
+
+    def get_debug_values(self, step, __):
+        if self.upper_quantizer.total_codes > 0:
+            return {'histogram_upper_codes': self.upper_quantizer.codes[:self.upper_quantizer.total_codes]}
+        else:
+            return {}
+
+    def update_for_step(self, step, *args):
+        self.internal_step = step
+        self.upper_quantizer.quantizer.temperature = max(
+                    self.upper_quantizer.max_gumbel_temperature * self.upper_quantizer.gumbel_temperature_decay**self.internal_step,
+                    self.upper_quantizer.min_gumbel_temperature,
+                )
 
 
 @register_model
@@ -91,6 +141,15 @@ def register_music_gpt_lower(opt_net, opt):
 
 
 if __name__ == '__main__':
+    from models.audio.music.transformer_diffusion8 import TransformerDiffusionWithQuantizer
+    base_diff = TransformerDiffusionWithQuantizer(in_channels=256, out_channels=512, model_channels=2048, block_channels=1024,
+                                                  prenet_channels=1024, prenet_layers=6, num_layers=16, input_vec_dim=1024,
+                                                  dropout=.1, unconditioned_percentage=0, freeze_quantizer_until=6000)
+    base_diff.load_state_dict(torch.load('x:/dlas/experiments/train_music_diffusion_tfd8/models/28000_generator.pth', map_location=torch.device('cpu')))
+
     model = GptMusicLower(512, 12)
+    model.target_quantizer.load_state_dict(base_diff.quantizer.state_dict(), strict=False)
+    torch.save(model.state_dict(), "sample.pth")
     mel = torch.randn(2,256,400)
     model(mel, mel)
+    model.get_grad_norm_parameter_groups()

codes/models/audio/music/music_quantizer2.py

@@ -11,13 +11,25 @@ from utils.util import checkpoint, ceil_multiple, print_network
 
 
 class Downsample(nn.Module):
-    def __init__(self, chan_in, chan_out):
+    def __init__(self, chan_in, chan_out, norm=False, act=False, stride_down=False):
         super().__init__()
-        self.conv = nn.Conv1d(chan_in, chan_out, kernel_size=3, padding=1)
+        self.interpolate = not stride_down
+        if stride_down:
+            self.conv = nn.Conv1d(chan_in, chan_out, kernel_size=3, padding=1, stride=2)
+        else:
+            self.conv = nn.Conv1d(chan_in, chan_out, kernel_size=3, padding=1)
+        if norm:
+            self.norm = nn.GroupNorm(8, chan_out)
+        self.act = act
 
     def forward(self, x):
-        x = F.interpolate(x, scale_factor=.5, mode='linear')
+        if self.interpolate:
+            x = F.interpolate(x, scale_factor=.5, mode='linear')
         x = self.conv(x)
+        if hasattr(self, 'norm'):
+            x = self.norm(x)
+        if self.act:
+            x = F.silu(x, inplace=True)
         return x
 
 
@@ -153,7 +165,9 @@ class Wav2Vec2GumbelVectorQuantizer(nn.Module):
 class MusicQuantizer2(nn.Module):
     def __init__(self, inp_channels=256, inner_dim=1024, codevector_dim=1024, down_steps=2,
                  max_gumbel_temperature=2.0, min_gumbel_temperature=.5, gumbel_temperature_decay=.999995,
-                 codebook_size=16, codebook_groups=4):
+                 codebook_size=16, codebook_groups=4,
+                 # Downsample args:
+                 expressive_downsamples=False):
         super().__init__()
         if not isinstance(inner_dim, list):
             inner_dim = [inner_dim // 2 ** x for x in range(down_steps+1)]
@@ -172,7 +186,8 @@ class MusicQuantizer2(nn.Module):
             self.up = nn.Conv1d(inner_dim[0], inp_channels, kernel_size=3, padding=1)
         elif down_steps == 2:
             self.down = nn.Sequential(nn.Conv1d(inp_channels, inner_dim[-1], kernel_size=3, padding=1),
-                                      *[Downsample(inner_dim[-i], inner_dim[-i-1]) for i in range(1,len(inner_dim))])
+                                      *[Downsample(inner_dim[-i], inner_dim[-i-1], norm=expressive_downsamples, act=expressive_downsamples,
+                                                   stride_down=expressive_downsamples) for i in range(1,len(inner_dim))])
             self.up = nn.Sequential(*[Upsample(inner_dim[i], inner_dim[i+1]) for i in range(len(inner_dim)-1)] +
                                     [nn.Conv1d(inner_dim[-1], inp_channels, kernel_size=3, padding=1)])
 
@@ -190,14 +205,11 @@ class MusicQuantizer2(nn.Module):
         self.code_ind = 0
         self.total_codes = 0
 
-    def get_codes(self, mel, project=False):
-        proj = self.m2v.input_blocks(mel).permute(0,2,1)
-        _, proj = self.m2v.projector(proj)
-        if project:
-            proj, _ = self.quantizer(proj)
-            return proj
-        else:
-            return self.quantizer.get_codes(proj)
+    def get_codes(self, mel):
+        h = self.down(mel)
+        h = self.encoder(h)
+        h = self.enc_norm(h.permute(0,2,1))
+        return self.quantizer.get_codes(h)
 
     def forward(self, mel, return_decoder_latent=False):
         orig_mel = mel

codes/models/audio/music/unet_diffusion_music_codes.py

@@ -10,6 +10,7 @@ import torch.nn.functional as F
 import torchvision  # For debugging, not actually used.
 from x_transformers.x_transformers import RelativePositionBias
 
+from models.audio.music.gpt_music import GptMusicLower
 from models.audio.music.music_quantizer import MusicQuantizer
 from models.diffusion.fp16_util import convert_module_to_f16, convert_module_to_f32
 from models.diffusion.nn import (
@@ -451,6 +452,7 @@ class UNetMusicModel(nn.Module):
         attention_resolutions,
         dropout=0,
         channel_mult=(1, 2, 4, 8),
+        ar_prior=False,
         conv_resample=True,
         dims=2,
         num_classes=None,
@@ -483,6 +485,7 @@ class UNetMusicModel(nn.Module):
         self.num_head_channels = num_head_channels
         self.num_heads_upsample = num_heads_upsample
         self.unconditioned_percentage = unconditioned_percentage
+        self.ar_prior = ar_prior
 
         time_embed_dim = model_channels * 4
         self.time_embed = nn.Sequential(
@@ -491,8 +494,9 @@ class UNetMusicModel(nn.Module):
             linear(time_embed_dim, time_embed_dim),
         )
 
-        self.input_converter = nn.Linear(input_vec_dim, model_channels)
-        self.code_converter = Encoder(
+        if self.ar_prior:
+            self.ar_input = nn.Linear(input_vec_dim, model_channels)
+            self.ar_prior_intg = Encoder(
                     dim=model_channels,
                     depth=4,
                     heads=num_heads,
@@ -504,6 +508,20 @@ class UNetMusicModel(nn.Module):
                     zero_init_branch_output=True,
                     ff_mult=1,
                 )
+        else:
+            self.input_converter = nn.Linear(input_vec_dim, model_channels)
+            self.code_converter = 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,
+                        zero_init_branch_output=True,
+                        ff_mult=1,
+                    )
         self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels))
         self.x_processor = conv_nd(dims, in_channels, model_channels, 3, padding=1)
 
@@ -659,15 +677,18 @@ class UNetMusicModel(nn.Module):
 
         if conditioning_free:
             expanded_code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1).permute(0,2,1)
-            unused_params.extend(list(self.code_converter.parameters()) + list(self.input_converter.parameters()))
+            if self.ar_prior:
+                unused_params.extend(list(self.ar_input.parameters()) + list(self.ar_prior_intg.parameters()))
+            else:
+                unused_params.extend(list(self.input_converter.parameters()) + list(self.code_converter.parameters()))
         else:
-            code_emb = self.input_converter(y)
+            code_emb = self.ar_input(y) if self.ar_prior else self.input_converter(y)
             if self.training and self.unconditioned_percentage > 0:
                 unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1),
                                                    device=code_emb.device) < self.unconditioned_percentage
                 code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(y.shape[0], 1, 1),
                                        code_emb)
-            code_emb = self.code_converter(code_emb)
+            code_emb = self.ar_prior_intg(code_emb) if self.ar_prior else self.code_converter(code_emb)
             expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=x.shape[-1], mode='nearest')
 
         h = x.type(self.dtype)
@@ -740,23 +761,60 @@ class UNetMusicModelWithQuantizer(nn.Module):
             return {}
 
 
+class UNetMusicModelARPrior(nn.Module):
+    def __init__(self, freeze_unet=False, **kwargs):
+        super().__init__()
+
+        self.internal_step = 0
+        self.ar = GptMusicLower(dim=512, layers=12)
+        for p in self.ar.parameters():
+            p.DO_NOT_TRAIN = True
+            p.requires_grad = False
+
+        self.diff = UNetMusicModel(ar_prior=True, **kwargs)
+        if freeze_unet:
+            for p in self.diff.parameters():
+                p.DO_NOT_TRAIN = True
+                p.requires_grad = False
+            for p in list(self.diff.ar_prior_intg.parameters()) + list(self.diff.ar_input.parameters()):
+                del p.DO_NOT_TRAIN
+                p.requires_grad = True
+
+    def forward(self, x, timesteps, truth_mel, disable_diversity=False, conditioning_input=None, conditioning_free=False):
+        with torch.no_grad():
+            prior = self.ar(truth_mel, conditioning_input, return_latent=True)
+
+        diff = self.diff(x, timesteps, prior, conditioning_free=conditioning_free)
+        return diff
+
+
 @register_model
 def register_unet_diffusion_music_codes(opt_net, opt):
     return UNetMusicModelWithQuantizer(**opt_net['args'])
 
+@register_model
+def register_unet_diffusion_music_ar_prior(opt_net, opt):
+    return UNetMusicModelARPrior(**opt_net['args'])
+
 
 if __name__ == '__main__':
-    clip = torch.randn(2, 256, 782)
-    cond = torch.randn(2, 256, 782)
+    clip = torch.randn(2, 256, 300)
+    cond = torch.randn(2, 256, 300)
     ts = torch.LongTensor([600, 600])
-    model = UNetMusicModelWithQuantizer(in_channels=256, out_channels=512, model_channels=1024, num_res_blocks=3, input_vec_dim=1024,
-                                        attention_resolutions=(2,4), channel_mult=(1,1.5,2), dims=1,
-                                        use_scale_shift_norm=True, dropout=.1, num_heads=16, unconditioned_percentage=.4)
+    model = UNetMusicModelARPrior(in_channels=256, out_channels=512, model_channels=640, num_res_blocks=3, input_vec_dim=512,
+                                  attention_resolutions=(2,4), channel_mult=(1,2,3), dims=1,
+                                  use_scale_shift_norm=True, dropout=.1, num_heads=8, unconditioned_percentage=.4, freeze_unet=True)
     print_network(model)
 
-    quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant\\models\\18000_generator_ema.pth')
-    model.m2v.load_state_dict(quant_weights, strict=False)
+    ar_weights = torch.load('D:\\dlas\\experiments\\train_music_gpt\\models\\44500_generator_ema.pth')
+    model.ar.load_state_dict(ar_weights, strict=True)
+    diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_unet_music\\models\\55500_generator_ema.pth')
+    pruned_diff_weights = {}
+    for k,v in diff_weights.items():
+        if k.startswith('diff.'):
+            pruned_diff_weights[k.replace('diff.', '')] = v
+    model.diff.load_state_dict(pruned_diff_weights, strict=False)
     torch.save(model.state_dict(), 'sample.pth')
 
-    model(clip, ts, cond)
+    model(clip, ts, cond, cond)
 

codes/train.py

@@ -339,7 +339,7 @@ class Trainer:
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_music_quant.yml')
+    parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_music_gpt.yml')
     parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
     args = parser.parse_args()
     opt = option.parse(args.opt, is_train=True)

codes/trainer/eval/music_diffusion_fid.py

@@ -201,13 +201,13 @@ class MusicDiffusionFid(evaluator.Evaluator):
 
 
 if __name__ == '__main__':
-    diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_diffusion_tfd5_quant\\train_music_diffusion_tfd5_quant.yml', 'generator',
+    diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_diffusion_tfd_quant7.yml', 'generator',
                                        also_load_savepoint=False,
-                                       load_path='X:\\dlas\\experiments\\train_music_diffusion_tfd5_quant\\models\\40500_generator_ema.pth'
+                                       load_path='X:\\dlas\\experiments\\train_music_diffusion_unet_music\\models\\46500_generator_ema.pth'
                                        ).cuda()
     opt_eval = {'path': 'Y:\\split\\yt-music-eval', 'diffusion_steps': 100,
                 'conditioning_free': True, 'conditioning_free_k': 1,
                 'diffusion_schedule': 'linear', 'diffusion_type': 'from_codes_quant'}
-    env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 560, 'device': 'cuda', 'opt': {}}
+    env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 561, 'device': 'cuda', 'opt': {}}
     eval = MusicDiffusionFid(diffusion, opt_eval, env)
     print(eval.perform_eval())