tfdpc2

codes/models/audio/music/tfdpc_v1.py

@@ -80,7 +80,7 @@ class ConcatAttentionBlock(TimestepBlock):
         return h[:, 1:] + x
 
 
-class TransformerDiffusion(nn.Module):
+class TransformerDiffusionWithPointConditioning(nn.Module):
     """
     A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
     """
@@ -214,7 +214,7 @@ class TransformerDiffusionWithConditioningEncoder(nn.Module):
     def __init__(self, **kwargs):
         super().__init__()
         self.internal_step = 0
-        self.diff = TransformerDiffusion(**kwargs)
+        self.diff = TransformerDiffusionWithPointConditioning(**kwargs)
         self.conditioning_encoder = ConditioningEncoder(256, kwargs['model_channels'])
 
     def forward(self, x, timesteps, true_cheater, conditioning_input=None, disable_diversity=False, conditioning_free=False):
@@ -243,8 +243,8 @@ class TransformerDiffusionWithConditioningEncoder(nn.Module):
 
 
 @register_model
-def register_transformer_diffusion_with_point_conditioning(opt_net, opt):
-    return TransformerDiffusion(**opt_net['kwargs'])
+def register_tfdpc(opt_net, opt):
+    return TransformerDiffusionWithPointConditioning(**opt_net['kwargs'])
 
 
 @register_model

codes/models/audio/music/tfdpc_v2.py

@@ -0,0 +1,260 @@
+import itertools
+from time import time
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from models.arch_util import ResBlock, AttentionBlock
+from models.audio.music.gpt_music2 import UpperEncoder, GptMusicLower
+from models.audio.music.music_quantizer2 import MusicQuantizer2
+from models.audio.tts.lucidrains_dvae import DiscreteVAE
+from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
+from models.diffusion.unet_diffusion import TimestepBlock
+from models.lucidrains.x_transformers import Encoder, Attention, RMSScaleShiftNorm, RotaryEmbedding, \
+    FeedForward
+from trainer.networks import register_model
+from utils.util import checkpoint, print_network
+
+
+class TimestepRotaryEmbedSequential(nn.Sequential, TimestepBlock):
+    def forward(self, x, emb, rotary_emb):
+        for layer in self:
+            if isinstance(layer, TimestepBlock):
+                x = layer(x, emb, rotary_emb)
+            else:
+                x = layer(x, rotary_emb)
+        return x
+
+
+class SubBlock(nn.Module):
+    def __init__(self, inp_dim, contraction_dim, heads, dropout):
+        super().__init__()
+        self.attn = Attention(inp_dim, out_dim=contraction_dim, heads=heads, dim_head=contraction_dim//heads, causal=False, dropout=dropout)
+        self.attnorm = nn.LayerNorm(contraction_dim)
+        self.ff = FeedForward(inp_dim+contraction_dim, dim_out=contraction_dim, mult=2, dropout=dropout)
+        self.ffnorm = nn.LayerNorm(contraction_dim)
+
+    def forward(self, x, rotary_emb):
+        ah, _, _, _ = checkpoint(self.attn, x, None, None, None, None, None, rotary_emb)
+        ah = F.gelu(self.attnorm(ah))
+        h = torch.cat([ah, x], dim=-1)
+        hf = checkpoint(self.ff, h)
+        hf = F.gelu(self.ffnorm(hf))
+        h = torch.cat([h, hf], dim=-1)
+        return h
+
+
+class ConcatAttentionBlock(TimestepBlock):
+    def __init__(self, trunk_dim, contraction_dim, time_embed_dim, heads, dropout):
+        super().__init__()
+        self.prenorm = RMSScaleShiftNorm(trunk_dim, embed_dim=time_embed_dim, bias=False)
+        self.block1 = SubBlock(trunk_dim, contraction_dim, heads, dropout)
+        self.block2 = SubBlock(trunk_dim+contraction_dim*2, contraction_dim, heads, dropout)
+        self.out = nn.Linear(contraction_dim*4, trunk_dim, bias=False)
+        self.out.weight.data.zero_()
+
+    def forward(self, x, timestep_emb, rotary_emb):
+        h = self.prenorm(x, norm_scale_shift_inp=timestep_emb)
+        h = self.block1(h, rotary_emb)
+        h = self.block2(h, rotary_emb)
+        h = self.out(h[:,:,x.shape[-1]:])
+        return h + x
+
+
+class TransformerDiffusionWithPointConditioning(nn.Module):
+    """
+    A diffusion model composed entirely of stacks of transformer layers. Why would you do it any other way?
+    """
+    def __init__(
+            self,
+            in_channels=256,
+            out_channels=512,  # mean and variance
+            model_channels=1024,
+            contraction_dim=256,
+            time_embed_dim=256,
+            num_layers=8,
+            rotary_emb_dim=32,
+            input_cond_dim=1024,
+            num_heads=8,
+            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.time_embed_dim = time_embed_dim
+        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//2, 3, 1, 1)
+
+        self.time_embed = nn.Sequential(
+            linear(time_embed_dim, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, time_embed_dim),
+        )
+
+        self.conditioner = nn.Linear(input_cond_dim, model_channels//2)
+        self.unconditioned_embedding = nn.Parameter(torch.randn(1,1,model_channels//2))
+        self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
+        self.layers = TimestepRotaryEmbedSequential(*[ConcatAttentionBlock(model_channels, contraction_dim, time_embed_dim, num_heads, dropout) 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):
+        attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.layers]))
+        attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.layers]))
+        ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.layers]))
+        ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.layers]))
+        blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.layers]))
+        groups = {
+            'prenorms': list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.layers])),
+            'blk1_attention_layers': attn1,
+            'blk2_attention_layers': attn2,
+            'attention_layers': attn1 + attn2,
+            'blk1_ff_layers': ff1,
+            'blk2_ff_layers': ff2,
+            'ff_layers': ff1 + ff2,
+            'block_out_layers': blkout_layers,
+            'rotary_embeddings': list(self.rotary_embeddings.parameters()),
+            'out': list(self.out.parameters()),
+            'x_proj': list(self.inp_block.parameters()),
+            'layers': list(self.layers.parameters()),
+            'time_embed': list(self.time_embed.parameters()),
+        }
+        return groups
+
+    def forward(self, x, timesteps, conditioning_input, conditioning_free=False):
+        unused_params = []
+        if conditioning_free:
+            cond = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
+        else:
+            cond = self.conditioner(conditioning_input)
+            # 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((cond.shape[0], 1, 1),
+                                                   device=cond.device) < self.unconditioned_percentage
+                cond = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(cond.shape[0], 1, 1), cond)
+            unused_params.append(self.unconditioned_embedding)
+
+        with torch.autocast(x.device.type, enabled=self.enable_fp16):
+            blk_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim))
+            x = self.inp_block(x).permute(0,2,1)
+            x = torch.cat([x, cond.repeat(1,x.shape[1],1)], dim=-1)
+
+            rotary_pos_emb = self.rotary_embeddings(x.shape[1]+1, x.device)
+            for layer in self.layers:
+                x = checkpoint(layer, x, blk_emb, rotary_pos_emb)
+
+        x = x.float().permute(0,2,1)
+        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 ConditioningEncoder(nn.Module):
+    def __init__(self,
+                 cond_dim,
+                 embedding_dim,
+                 attn_blocks=6,
+                 num_attn_heads=8,
+                 dropout=.1,
+                 do_checkpointing=False):
+        super().__init__()
+        attn = []
+        self.init = nn.Conv1d(cond_dim, embedding_dim, kernel_size=1)
+        self.attn = Encoder(
+                dim=embedding_dim,
+                depth=attn_blocks,
+                heads=num_attn_heads,
+                ff_dropout=dropout,
+                attn_dropout=dropout,
+                use_rmsnorm=True,
+                ff_glu=True,
+                rotary_pos_emb=True,
+                zero_init_branch_output=True,
+                ff_mult=2,
+            )
+        self.dim = embedding_dim
+        self.do_checkpointing = do_checkpointing
+
+    def forward(self, x):
+        h = self.init(x).permute(0,2,1)
+        h = self.attn(h).permute(0,2,1)
+        return h.mean(dim=2).unsqueeze(1)
+
+
+class TransformerDiffusionWithConditioningEncoder(nn.Module):
+    def __init__(self, **kwargs):
+        super().__init__()
+        self.internal_step = 0
+        self.diff = TransformerDiffusionWithPointConditioning(**kwargs)
+        self.conditioning_encoder = ConditioningEncoder(256, kwargs['model_channels'])
+
+    def forward(self, x, timesteps, true_cheater, conditioning_input=None, disable_diversity=False, conditioning_free=False):
+        cond = self.conditioning_encoder(true_cheater)
+        diff = self.diff(x, timesteps, conditioning_input=cond, conditioning_free=conditioning_free)
+        return diff
+
+    def get_debug_values(self, step, __):
+        self.internal_step = step
+        return {}
+
+    def get_grad_norm_parameter_groups(self):
+        groups = self.diff.get_grad_norm_parameter_groups()
+        groups['conditioning_encoder'] = list(self.conditioning_encoder.parameters())
+        return groups
+
+    def before_step(self, step):
+        scaled_grad_parameters = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers])) + \
+                                 list(itertools.chain.from_iterable([lyr.prenorm.parameters() for lyr in self.diff.layers]))
+        # Scale back the gradients of the blkout and prenorm layers by a constant factor. These get two orders of magnitudes
+        # higher gradients. Ideally we would use parameter groups, but ZeroRedundancyOptimizer makes this trickier than
+        # directly fiddling with the gradients.
+        for p in scaled_grad_parameters:
+            if hasattr(p, 'grad') and p.grad is not None:
+                p.grad *= .2
+
+
+@register_model
+def register_tfdpc2(opt_net, opt):
+    return TransformerDiffusionWithPointConditioning(**opt_net['kwargs'])
+
+
+@register_model
+def register_tfdpc2_with_conditioning_encoder(opt_net, opt):
+    return TransformerDiffusionWithConditioningEncoder(**opt_net['kwargs'])
+
+
+def test_cheater_model():
+    clip = torch.randn(2, 256, 400)
+    cl = torch.randn(2, 256, 400)
+    ts = torch.LongTensor([600, 600])
+
+    # For music:
+    model = TransformerDiffusionWithConditioningEncoder(model_channels=1024)
+    print_network(model)
+    o = model(clip, ts, cl)
+    pg = model.get_grad_norm_parameter_groups()
+
+
+if __name__ == '__main__':
+    test_cheater_model()

codes/models/audio/music/transformer_diffusion12.py

@@ -748,21 +748,14 @@ def test_cheater_model():
 
     # For music:
     model = TransformerDiffusionWithCheaterLatent(in_channels=256, out_channels=512,
-                                                    model_channels=1536, contraction_dim=768,
-                                                    prenet_channels=1024, num_heads=12,
-                                                    input_vec_dim=256, num_layers=20, prenet_layers=6,
+                                                    model_channels=1024, contraction_dim=512,
+                                                    prenet_channels=1024, num_heads=8,
+                                                    input_vec_dim=256, num_layers=16, prenet_layers=6,
                                                     dropout=.1, new_code_expansion=True,
                                               )
     #diff_weights = torch.load('extracted_diff.pth')
     #model.diff.load_state_dict(diff_weights, strict=False)
-    cheater_ar_weights = torch.load('X:\\dlas\\experiments\\train_music_gpt_cheater\\models\\60000_generator_ema.pth')
-    cheater_ar = GptMusicLower(dim=1024, encoder_out_dim=256, layers=16, fp16=False, num_target_vectors=8192, num_vaes=4,
-                          vqargs= {'positional_dims': 1, 'channels': 64,
-            'hidden_dim': 512, 'num_resnet_blocks': 3, 'codebook_dim': 512, 'num_tokens': 8192,
-            'num_layers': 0, 'record_codes': True, 'kernel_size': 3, 'use_transposed_convs': False,
-                                                })
-    cheater_ar.load_state_dict(cheater_ar_weights)
-    model.encoder.load_state_dict(cheater_ar.upper_encoder.state_dict(), strict=True)
+    model.encoder.load_state_dict(torch.load('../experiments/music_cheater_encoder_256.pth', map_location=torch.device('cpu')), strict=True)
     torch.save(model.state_dict(), 'sample.pth')
 
     print_network(model)
@@ -783,4 +776,4 @@ def extract_diff(in_f, out_f, remove_head=False):
 
 if __name__ == '__main__':
     #extract_diff('X:\\dlas\\experiments\\train_music_diffusion_tfd12\\models\\41000_generator_ema.pth', 'extracted_diff.pth', True)
-    test_tfd()
+    test_cheater_model()