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codes/models/audio/music/transformer_diffusion11.py

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+import itertools
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from models.arch_util import ResBlock
+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
+from models.lucidrains.x_transformers import Encoder, Attention, RMSScaleShiftNorm, RotaryEmbedding, \
+    FeedForward
+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 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 = nn.Conv1d(inp_dim+contraction_dim, contraction_dim, kernel_size=3, padding=1)
+        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.permute(0,2,1)).permute(0,2,1)
+        hf = F.gelu(self.ffnorm(hf))
+        h = torch.cat([h, hf], dim=-1)
+        return h
+
+
+class ConcatAttentionBlock(TimestepBlock):
+    def __init__(self, trunk_dim, heads, dropout):
+        super().__init__()
+        contraction_dim = trunk_dim // 4
+        self.prenorm = RMSScaleShiftNorm(trunk_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.block3 = SubBlock(trunk_dim+contraction_dim*4, contraction_dim, heads, dropout)
+        self.out = nn.Linear(contraction_dim*6, 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.block3(h, rotary_emb)
+        h = self.out(h[:,:,x.shape[-1]:])
+        return h + x
+
+
+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,
+            prenet_channels=256,
+            prenet_layers=3,
+            model_channels=512,
+            num_layers=8,
+            in_channels=256,
+            rotary_emb_dim=32,
+            input_vec_dim=512,
+            out_channels=512,  # mean and variance
+            num_heads=16,
+            dropout=0,
+            use_fp16=False,
+            ar_prior=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.prenet_channels = prenet_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, prenet_channels, 3, 1, 1)
+
+        self.time_embed = nn.Sequential(
+            linear(prenet_channels, prenet_channels),
+            nn.SiLU(),
+            linear(prenet_channels, model_channels),
+        )
+
+        self.ar_prior = ar_prior
+        prenet_heads = prenet_channels//64
+        if ar_prior:
+            self.ar_input = nn.Linear(input_vec_dim, prenet_channels)
+            self.ar_prior_intg = Encoder(
+                    dim=prenet_channels,
+                    depth=prenet_layers,
+                    heads=prenet_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,
+                )
+        else:
+            self.input_converter = nn.Linear(input_vec_dim, prenet_channels)
+            self.code_converter = Encoder(
+                        dim=prenet_channels,
+                        depth=prenet_layers,
+                        heads=prenet_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,prenet_channels))
+        self.rotary_embeddings = RotaryEmbedding(rotary_emb_dim)
+        self.intg = nn.Linear(prenet_channels*2, model_channels)
+        self.layers = TimestepRotaryEmbedSequential(*[ConcatAttentionBlock(model_channels, 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):
+        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, prior, expected_seq_len):
+        code_emb = self.ar_input(prior) if self.ar_prior else self.input_converter(prior)
+        code_emb = self.ar_prior_intg(code_emb) if self.ar_prior else self.code_converter(code_emb)
+
+        # 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(prior.shape[0], 1, 1),
+                                   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)
+        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)
+
+        with torch.autocast(x.device.type, enabled=self.enable_fp16):
+            blk_emb = self.time_embed(timestep_embedding(timesteps, self.prenet_channels))
+            x = self.inp_block(x).permute(0,2,1)
+
+            rotary_pos_emb = self.rotary_embeddings(x.shape[1], x.device)
+            x = self.intg(torch.cat([x, code_emb], dim=-1))
+            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 TransformerDiffusionWithQuantizer(nn.Module):
+    def __init__(self, quantizer_dims=[1024], 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=kwargs['in_channels'], inner_dim=quantizer_dims,
+                                         codevector_dim=quantizer_dims[0], 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=None, 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_quant_codes': self.quantizer.codes[:self.quantizer.total_codes],
+                    'gumbel_temperature': self.quantizer.quantizer.temperature}
+        else:
+            return {}
+
+    def get_grad_norm_parameter_groups(self):
+        attn1 = list(itertools.chain.from_iterable([lyr.block1.attn.parameters() for lyr in self.diff.layers]))
+        attn2 = list(itertools.chain.from_iterable([lyr.block2.attn.parameters() for lyr in self.diff.layers]))
+        attn3 = list(itertools.chain.from_iterable([lyr.block3.attn.parameters() for lyr in self.diff.layers]))
+        ff1 = list(itertools.chain.from_iterable([lyr.block1.ff.parameters() for lyr in self.diff.layers]))
+        ff2 = list(itertools.chain.from_iterable([lyr.block2.ff.parameters() for lyr in self.diff.layers]))
+        ff3 = list(itertools.chain.from_iterable([lyr.block3.ff.parameters() for lyr in self.diff.layers]))
+        blkout_layers = list(itertools.chain.from_iterable([lyr.out.parameters() for lyr in self.diff.layers]))
+        groups = {
+            'blk1_attention_layers': attn1,
+            'blk2_attention_layers': attn2,
+            'blk2_attention_layers': attn3,
+            'attention_layers': attn1 + attn2 + attn3,
+            'blk1_ff_layers': ff1,
+            'blk2_ff_layers': ff2,
+            'blk3_ff_layers': ff2,
+            'ff_layers': ff1 + ff2 + ff3,
+            'block_out_layers': blkout_layers,
+            'quantizer_encoder': list(self.quantizer.encoder.parameters()),
+            'quant_codebook': [self.quantizer.quantizer.codevectors],
+            'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
+            '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
+
+
+class TransformerDiffusionWithARPrior(nn.Module):
+    def __init__(self, freeze_diff=False, **kwargs):
+        super().__init__()
+
+        self.internal_step = 0
+        from models.audio.music.gpt_music import GptMusicLower
+        self.ar = GptMusicLower(dim=512, layers=12)
+        for p in self.ar.parameters():
+            p.DO_NOT_TRAIN = True
+            p.requires_grad = False
+
+        self.diff = TransformerDiffusion(ar_prior=True, **kwargs)
+        if freeze_diff:
+            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 get_grad_norm_parameter_groups(self):
+        groups = {
+            'attention_layers': list(itertools.chain.from_iterable([lyr.attn.parameters() for lyr in self.diff.layers])),
+            'ff_layers': list(itertools.chain.from_iterable([lyr.ff.parameters() for lyr in self.diff.layers])),
+            'rotary_embeddings': list(self.diff.rotary_embeddings.parameters()),
+            'out': list(self.diff.out.parameters()),
+            'x_proj': list(self.diff.inp_block.parameters()),
+            'layers': list(self.diff.layers.parameters()),
+            'ar_prior_intg': list(self.diff.ar_prior_intg.parameters()),
+            'time_embed': list(self.diff.time_embed.parameters()),
+        }
+        return groups
+
+    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_transformer_diffusion11(opt_net, opt):
+    return TransformerDiffusion(**opt_net['kwargs'])
+
+
+@register_model
+def register_transformer_diffusion11_with_quantizer(opt_net, opt):
+    return TransformerDiffusionWithQuantizer(**opt_net['kwargs'])
+
+
+@register_model
+def register_transformer_diffusion11_with_ar_prior(opt_net, opt):
+    return TransformerDiffusionWithARPrior(**opt_net['kwargs'])
+
+
+def test_quant_model():
+    clip = torch.randn(2, 256, 400)
+    cond = torch.randn(2, 256, 400)
+    ts = torch.LongTensor([600, 600])
+    model = TransformerDiffusionWithQuantizer(in_channels=256, model_channels=1024,
+                                              prenet_channels=1024, num_heads=4,
+                                              input_vec_dim=1024, num_layers=20, prenet_layers=6,
+                                              dropout=.1)
+
+    quant_weights = torch.load('D:\\dlas\\experiments\\train_music_quant_r4\\models\\5000_generator.pth')
+    model.quantizer.load_state_dict(quant_weights, strict=False)
+
+    torch.save(model.state_dict(), 'sample.pth')
+    print_network(model)
+    o = model(clip, ts, clip, cond)
+    model.get_grad_norm_parameter_groups()
+
+
+def test_ar_model():
+    clip = torch.randn(2, 256, 400)
+    cond = torch.randn(2, 256, 400)
+    ts = torch.LongTensor([600, 600])
+    model = TransformerDiffusionWithARPrior(model_channels=2048, prenet_channels=1536,
+                                            input_vec_dim=512, num_layers=16, prenet_layers=6, freeze_diff=True,
+                                            unconditioned_percentage=.4)
+    model.get_grad_norm_parameter_groups()
+
+    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_tfd8\\models\\47500_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, conditioning_input=cond)
+
+
+
+if __name__ == '__main__':
+    test_quant_model()