some updates

codes/models/arch_util.py

@@ -458,10 +458,12 @@ class AttentionBlock(nn.Module):
         num_head_channels=-1,
         use_new_attention_order=False,
         do_checkpoint=True,
+        do_activation=False,
     ):
         super().__init__()
         self.channels = channels
         self.do_checkpoint = do_checkpoint
+        self.do_activation = do_activation
         if num_head_channels == -1:
             self.num_heads = num_heads
         else:
@@ -492,7 +494,10 @@ class AttentionBlock(nn.Module):
     def _forward(self, x, mask=None):
         b, c, *spatial = x.shape
         x = x.reshape(b, c, -1)
-        qkv = self.qkv(self.norm(x))
+        x = self.norm(x)
+        if self.do_activation:
+            x = F.silu(x, inplace=True)
+        qkv = self.qkv(x)
         h = self.attention(qkv, mask)
         h = self.proj_out(h)
         return (x + h).reshape(b, c, *spatial)

codes/models/audio/music/gpt_music.py

@@ -3,31 +3,57 @@ from torch import nn
 import torch.nn.functional as F
 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 trainer.networks import register_model
 from utils.util import opt_get
 
 
-class GptMusic(nn.Module):
+class ConditioningEncoder(nn.Module):
+    def __init__(self,
+                 spec_dim,
+                 embedding_dim,
+                 attn_blocks=6,
+                 num_attn_heads=4):
+        super().__init__()
+        attn = []
+        self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=3, stride=2, padding=1)
+        for a in range(attn_blocks):
+            attn.append(AttentionBlock(embedding_dim, num_attn_heads, do_activation=True))
+        self.attn = nn.Sequential(*attn)
+        self.dim = embedding_dim
+
+    def forward(self, x):
+        h = self.init(x)
+        h = self.attn(h)
+        return h.mean(dim=2)
+
+
+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):
         super().__init__()
         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)
+        # 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
-        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)
         del self.upper_quantizer.up
+
+        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):
+    def forward(self, mel, conditioning):
         with torch.no_grad():
             self.target_quantizer.eval()
             codes = self.target_quantizer.get_codes(mel)
@@ -37,11 +63,17 @@ class GptMusic(nn.Module):
         upper_vector = upper_vector.permute(0,2,1)
 
         inputs = codes[:, :-1]
+        targets = codes
         upper_vector = upper_vector[:, :-1]
-        targets = codes[:, 1:]
-
         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)
+
         h = self.gpt(inputs_embeds=h, return_dict=True).last_hidden_state
 
         losses = 0
@@ -54,11 +86,11 @@ class GptMusic(nn.Module):
 
 
 @register_model
-def register_music_gpt(opt_net, opt):
-    return GptMusic(**opt_get(opt_net, ['kwargs'], {}))
+def register_music_gpt_lower(opt_net, opt):
+    return GptMusicLower(**opt_get(opt_net, ['kwargs'], {}))
 
 
 if __name__ == '__main__':
-    model = GptMusic(512, 12)
+    model = GptMusicLower(512, 12)
     mel = torch.randn(2,256,400)
-    model(mel)
+    model(mel, mel)

codes/models/audio/music/transformer_diffusion9.py

@@ -0,0 +1,334 @@
+import itertools
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from models.audio.music.music_quantizer2 import MusicQuantizer2
+from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
+from models.diffusion.unet_diffusion import TimestepBlock, AttentionBlock, TimestepEmbedSequential
+from models.lucidrains.x_transformers import Encoder
+from trainer.networks import register_model
+from utils.util import checkpoint, print_network
+
+
+def is_latent(t):
+    return t.dtype == torch.float
+
+def is_sequence(t):
+    return t.dtype == torch.long
+
+
+class MultiGroupEmbedding(nn.Module):
+    def __init__(self, tokens, groups, dim):
+        super().__init__()
+        self.m = nn.ModuleList([nn.Embedding(tokens, dim // groups) for _ in range(groups)])
+
+    def forward(self, x):
+        h = [embedding(x[:, :, i]) for i, embedding in enumerate(self.m)]
+        return torch.cat(h, dim=-1)
+
+
+class ResBlock(TimestepBlock):
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        dims=2,
+        kernel_size=3,
+        efficient_config=False,
+        use_scale_shift_norm=False,
+    ):
+        super().__init__()
+        self.channels = channels
+        self.emb_channels = emb_channels
+        self.dropout = dropout
+        self.out_channels = out_channels or channels
+        self.use_scale_shift_norm = use_scale_shift_norm
+        padding = {1: 0, 3: 1, 5: 2}[kernel_size]
+        eff_kernel = 1 if efficient_config else 3
+        eff_padding = 0 if efficient_config else 1
+
+        self.in_layers = nn.Sequential(
+            normalization(channels),
+            nn.SiLU(),
+            conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding),
+        )
+
+        self.emb_layers = nn.Sequential(
+            nn.SiLU(),
+            linear(
+                emb_channels,
+                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+            ),
+        )
+        self.out_layers = nn.Sequential(
+            normalization(self.out_channels),
+            nn.SiLU(),
+            nn.Dropout(p=dropout),
+            zero_module(
+                conv_nd(dims, self.out_channels, self.out_channels, kernel_size, padding=padding)
+            ),
+        )
+
+        if self.out_channels == channels:
+            self.skip_connection = nn.Identity()
+        else:
+            self.skip_connection = conv_nd(dims, channels, self.out_channels, eff_kernel, padding=eff_padding)
+
+    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]
+        if self.use_scale_shift_norm:
+            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+            scale, shift = torch.chunk(emb_out, 2, dim=1)
+            h = out_norm(h) * (1 + scale) + shift
+            h = out_rest(h)
+        else:
+            h = h + emb_out
+            h = self.out_layers(h)
+        return self.skip_connection(x) + h
+
+
+class DiffusionLayer(TimestepBlock):
+    def __init__(self, model_channels, dropout, num_heads):
+        super().__init__()
+        self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True)
+        self.attn = AttentionBlock(model_channels, num_heads, relative_pos_embeddings=True)
+
+    def forward(self, x, time_emb):
+        y = self.resblk(x, time_emb)
+        return self.attn(y)
+    
+
+class TransformerDiffusion(nn.Module):
+    """
+    A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
+    """
+    def __init__(
+            self,
+            model_channels=512,
+            prenet_layers=3,
+            num_layers=8,
+            in_channels=256,
+            input_vec_dim=512,
+            out_channels=512,  # mean and variance
+            dropout=0,
+            use_fp16=False,
+            # Parameters for regularization.
+            unconditioned_percentage=.1,  # This implements a mechanism similar to what is used in classifier-free training.
+    ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.model_channels = model_channels
+        self.out_channels = out_channels
+        self.dropout = dropout
+        self.unconditioned_percentage = unconditioned_percentage
+        self.enable_fp16 = use_fp16
+
+        self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
+
+        self.time_embed = nn.Sequential(
+            linear(model_channels, model_channels),
+            nn.SiLU(),
+            linear(model_channels, model_channels),
+        )
+
+        self.input_converter = nn.Linear(input_vec_dim, model_channels)
+        self.code_converter = Encoder(
+                    dim=model_channels,
+                    depth=prenet_layers,
+                    heads=model_channels//64,
+                    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.intg = nn.Conv1d(model_channels*2, model_channels, kernel_size=1)
+        self.layers = TimestepEmbedSequential(*[DiffusionLayer(model_channels, dropout, model_channels // 64) for _ in range(num_layers)])
+
+        self.out = nn.Sequential(
+            normalization(model_channels),
+            nn.SiLU(),
+            zero_module(conv_nd(1, model_channels, out_channels, 3, padding=1)),
+        )
+
+        self.debug_codes = {}
+
+    def get_grad_norm_parameter_groups(self):
+        groups = {
+            'layers': list(self.layers.parameters()) + list(self.inp_block.parameters()),
+            'code_converters': list(self.input_converter.parameters()) + list(self.code_converter.parameters()),
+            'time_embed': list(self.time_embed.parameters()),
+        }
+        return groups
+
+    def timestep_independent(self, codes, expected_seq_len):
+        code_emb = self.input_converter(codes)
+
+        # Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance.
+        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(codes.shape[0], 1, 1),
+                                   code_emb)
+        code_emb = self.code_converter(code_emb)
+
+        expanded_code_emb = F.interpolate(code_emb.permute(0,2,1), size=expected_seq_len, mode='nearest').permute(0,2,1)
+        return expanded_code_emb
+
+    def forward(self, x, timesteps, codes=None, conditioning_input=None, precomputed_code_embeddings=None, conditioning_free=False):
+        if precomputed_code_embeddings is not None:
+            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."
+
+        unused_params = []
+        if conditioning_free:
+            code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
+            unused_params.extend(list(self.code_converter.parameters()))
+        else:
+            if precomputed_code_embeddings is not None:
+                code_emb = precomputed_code_embeddings
+            else:
+                code_emb = self.timestep_independent(codes, x.shape[-1])
+            unused_params.append(self.unconditioned_embedding)
+        code_emb = code_emb.permute(0,2,1)
+
+        blk_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
+        x = self.inp_block(x)
+        x = self.intg(torch.cat([x, code_emb], dim=1))
+        for layer in self.layers:
+            x = checkpoint(layer, x, blk_emb)
+
+        x = x.float()
+        out = self.out(x)
+
+        # Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
+        extraneous_addition = 0
+        for p in unused_params:
+            extraneous_addition = extraneous_addition + p.mean()
+        out = out + extraneous_addition * 0
+
+        return out
+
+
+class TransformerDiffusionWithQuantizer(nn.Module):
+    def __init__(self, freeze_quantizer_until=20000, **kwargs):
+        super().__init__()
+
+        self.internal_step = 0
+        self.freeze_quantizer_until = freeze_quantizer_until
+        self.diff = TransformerDiffusion(**kwargs)
+        self.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.quantizer.quantizer.temperature = self.quantizer.min_gumbel_temperature
+        del self.quantizer.up
+
+    def update_for_step(self, step, *args):
+        self.internal_step = step
+        qstep = max(0, self.internal_step - self.freeze_quantizer_until)
+        self.quantizer.quantizer.temperature = max(
+            self.quantizer.max_gumbel_temperature * self.quantizer.gumbel_temperature_decay ** qstep,
+                    self.quantizer.min_gumbel_temperature,
+                )
+
+    def forward(self, x, timesteps, truth_mel, conditioning_input, disable_diversity=False, conditioning_free=False):
+        quant_grad_enabled = self.internal_step > self.freeze_quantizer_until
+        with torch.set_grad_enabled(quant_grad_enabled):
+            proj, diversity_loss = self.quantizer(truth_mel, return_decoder_latent=True)
+            proj = proj.permute(0,2,1)
+
+        # Make sure this does not cause issues in DDP by explicitly using the parameters for nothing.
+        if not quant_grad_enabled:
+            unused = 0
+            for p in self.quantizer.parameters():
+                unused = unused + p.mean() * 0
+            proj = proj + unused
+            diversity_loss = diversity_loss * 0
+
+        diff = self.diff(x, timesteps, codes=proj, conditioning_input=conditioning_input, conditioning_free=conditioning_free)
+        if disable_diversity:
+            return diff
+        return diff, diversity_loss
+
+    def get_debug_values(self, step, __):
+        if self.quantizer.total_codes > 0:
+            return {'histogram_codes': self.quantizer.codes[:self.quantizer.total_codes]}
+        else:
+            return {}
+
+    def get_grad_norm_parameter_groups(self):
+        groups = {
+            'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])),
+            'res_layers': list(itertools.chain.from_iterable([lyr.resblk.parameters() for lyr in self.diff.layers])),
+            'quantizer_encoder': list(self.quantizer.encoder.parameters()),
+            'quant_codebook': [self.quantizer.quantizer.codevectors],
+            'out': list(self.diff.out.parameters()),
+            'x_proj': list(self.diff.inp_block.parameters()),
+            'layers': list(self.diff.layers.parameters()),
+            'code_converters': list(self.diff.input_converter.parameters()) + list(self.diff.code_converter.parameters()),
+            'time_embed': list(self.diff.time_embed.parameters()),
+        }
+        return groups
+
+
+@register_model
+def register_transformer_diffusion9(opt_net, opt):
+    return TransformerDiffusion(**opt_net['kwargs'])
+
+
+@register_model
+def register_transformer_diffusion8_with_quantizer(opt_net, opt):
+    return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
+
+
+"""
+# For TFD5
+if __name__ == '__main__':
+    clip = torch.randn(2, 256, 400)
+    aligned_sequence = torch.randn(2,100,512)
+    cond = torch.randn(2, 256, 400)
+    ts = torch.LongTensor([600, 600])
+    model = TransformerDiffusion(model_channels=3072, model_channels=1536, model_channels=1536)
+    torch.save(model, 'sample.pth')
+    print_network(model)
+    o = model(clip, ts, aligned_sequence, cond)
+"""
+
+if __name__ == '__main__':
+    clip = torch.randn(2, 256, 400)
+    cond = torch.randn(2, 256, 400)
+    ts = torch.LongTensor([600, 600])
+    model = TransformerDiffusionWithQuantizer(model_channels=1024, input_vec_dim=1024, num_layers=16, prenet_layers=6)
+    model.get_grad_norm_parameter_groups()
+
+    quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth')
+    #diff_weights = torch.load('X:\\dlas\\experiments\\train_music_diffusion_tfd5\\models\\48000_generator_ema.pth')
+    model.quantizer.load_state_dict(quant_weights, strict=False)
+    #model.diff.load_state_dict(diff_weights)
+
+    torch.save(model.state_dict(), 'sample.pth')
+    print_network(model)
+    o = model(clip, ts, clip, cond)
+

codes/models/audio/music/unet_diffusion_music_codes.py

@@ -658,7 +658,7 @@ class UNetMusicModel(nn.Module):
         emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
 
         if conditioning_free:
-            expanded_code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, x.shape[-1])
+            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()))
         else:
             code_emb = self.input_converter(y)