flat diffusion network

codes/models/audio/tts/unet_diffusion_tts_flat.py

@@ -0,0 +1,304 @@
+import random
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import autocast
+from x_transformers import Encoder
+
+from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
+from models.diffusion.unet_diffusion import AttentionBlock, TimestepEmbedSequential, \
+    Downsample, Upsample, TimestepBlock
+from models.audio.tts.mini_encoder import AudioMiniEncoder
+from models.audio.tts.unet_diffusion_tts7 import CheckpointedXTransformerEncoder
+from scripts.audio.gen.use_diffuse_tts import ceil_multiple
+from trainer.networks import register_model
+from utils.util import checkpoint
+
+
+def is_latent(t):
+    return t.dtype == torch.float
+
+def is_sequence(t):
+    return t.dtype == torch.long
+
+
+class ResBlock(TimestepBlock):
+    def __init__(
+        self,
+        channels,
+        emb_channels,
+        dropout,
+        out_channels=None,
+        dims=2,
+        kernel_size=3,
+        efficient_config=True,
+        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(nn.Module):
+    def __init__(self, model_channels, aligned_channels, cond_channels, dropout, num_heads):
+        super().__init__()
+        self.aligned_mutation = zero_module(conv_nd(1, aligned_channels, model_channels, 1))
+        self.cond_mutation = zero_module(conv_nd(1, cond_channels, model_channels, 1))
+        self.inp_mutation = conv_nd(1, model_channels, model_channels, 1)
+        self.resblk = ResBlock(model_channels, model_channels, dropout, model_channels, dims=1, use_scale_shift_norm=True)
+        self.attn = AttentionBlock(model_channels, num_heads)
+
+    def forward(self, x, aligned, pointwise, time_emb):
+        a = self.aligned_mutation(aligned)
+        c = self.cond_mutation(pointwise.unsqueeze(-1))
+        f = self.inp_mutation(x)
+        y = self.resblk(f + c.repeat(1,1,f.shape[-1]) + F.interpolate(a, size=f.shape[-1], mode='nearest'), time_emb)
+        y = self.attn(y)
+        return y
+
+
+class DiffusionTtsFlat(nn.Module):
+    def __init__(
+            self,
+            model_channels=512,
+            num_layers=8,
+            in_channels=100,
+            in_latent_channels=512,
+            in_tokens=8193,
+            max_timesteps=4000,
+            max_positions=4000,
+            out_channels=200,  # mean and variance
+            dropout=0,
+            use_fp16=False,
+            num_heads=16,
+            # Parameters for regularization.
+            layer_drop=.1,
+            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.num_heads = num_heads
+        self.unconditioned_percentage = unconditioned_percentage
+        self.enable_fp16 = use_fp16
+        self.layer_drop = layer_drop
+
+        self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
+        self.position_embed = nn.Embedding(max_positions, model_channels)
+        self.time_embed = nn.Embedding(max_timesteps, model_channels)
+
+        # Either code_converter or latent_converter is used, depending on what type of conditioning data is fed.
+        # This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally
+        # complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive
+        # transformer network.
+        self.code_converter = nn.Sequential(
+            nn.Embedding(in_tokens, model_channels),
+            CheckpointedXTransformerEncoder(
+                needs_permute=False,
+                max_seq_len=-1,
+                use_pos_emb=False,
+                attn_layers=Encoder(
+                    dim=model_channels,
+                    depth=3,
+                    heads=num_heads,
+                    ff_dropout=dropout,
+                    attn_dropout=dropout,
+                    use_rmsnorm=True,
+                    ff_glu=True,
+                    rotary_emb_dim=True,
+                )
+            ))
+        self.latent_converter = nn.Conv1d(in_latent_channels, model_channels, 1)
+        if in_channels > 60:  # It's a spectrogram.
+            self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,model_channels,3,padding=1,stride=2),
+                                                     CheckpointedXTransformerEncoder(
+                                                         needs_permute=True,
+                                                         max_seq_len=-1,
+                                                         use_pos_emb=False,
+                                                         attn_layers=Encoder(
+                                                             dim=model_channels,
+                                                             depth=4,
+                                                             heads=num_heads,
+                                                             ff_dropout=dropout,
+                                                             attn_dropout=dropout,
+                                                             use_rmsnorm=True,
+                                                             ff_glu=True,
+                                                             rotary_emb_dim=True,
+                                                         )
+                                                     ))
+        else:
+            self.contextual_embedder = AudioMiniEncoder(1, model_channels, base_channels=32, depth=6, resnet_blocks=1,
+                                                        attn_blocks=3, num_attn_heads=8, dropout=dropout, downsample_factor=4, kernel_size=5)
+        self.conditioning_conv = nn.Conv1d(model_channels*2, model_channels, 1)
+        self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1))
+        self.conditioning_timestep_integrator = TimestepEmbedSequential(
+                    ResBlock(model_channels, model_channels, dropout, out_channels=model_channels, dims=1, kernel_size=1, use_scale_shift_norm=True),
+                    AttentionBlock(model_channels, num_heads=num_heads),
+                    ResBlock(model_channels, model_channels, dropout, out_channels=model_channels, dims=1, kernel_size=1, use_scale_shift_norm=True),
+                    AttentionBlock(model_channels, num_heads=num_heads),
+                    ResBlock(model_channels, model_channels, dropout, out_channels=model_channels, dims=1, kernel_size=1, use_scale_shift_norm=True),
+        )
+
+        self.layers = nn.ModuleList([DiffusionLayer(model_channels, model_channels, model_channels, dropout, num_heads) 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)),
+        )
+
+    def get_grad_norm_parameter_groups(self):
+        groups = {
+            'minicoder': list(self.contextual_embedder.parameters()),
+            'layers': list(self.layers),
+        }
+        return groups
+
+    def forward(self, x, timesteps, aligned_conditioning, conditioning_input, lr_input=None, conditioning_free=False):
+        """
+        Apply the model to an input batch.
+
+        :param x: an [N x C x ...] Tensor of inputs.
+        :param timesteps: a 1-D batch of timesteps.
+        :param aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced.
+        :param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded.
+        :param lr_input: for super-sampling models, a guidance audio clip at a lower sampling rate.
+        :param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered.
+        :return: an [N x C x ...] Tensor of outputs.
+        """
+        # Shuffle aligned_latent to BxCxS format
+        if is_latent(aligned_conditioning):
+            aligned_conditioning = aligned_conditioning.permute(0, 2, 1)
+
+        # Note: this block does not need to repeated on inference, since it is not timestep-dependent or x-dependent.
+        unused_params = []
+        if conditioning_free:
+            code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1)
+        else:
+            unused_params.append(self.unconditioned_embedding)
+            cond_emb = self.contextual_embedder(conditioning_input)
+            if len(cond_emb.shape) == 3:  # Just take the first element.
+                cond_emb = cond_emb[:, :, 0]
+            if is_latent(aligned_conditioning):
+                code_emb = self.latent_converter(aligned_conditioning)
+                unused_params.extend(list(self.code_converter.parameters()))
+            else:
+                code_emb = self.code_converter(aligned_conditioning)
+                unused_params.extend(list(self.latent_converter.parameters()))
+            cond_emb_spread = cond_emb.unsqueeze(-1).repeat(1, 1, code_emb.shape[-1])
+            code_emb = self.conditioning_conv(torch.cat([cond_emb_spread, code_emb], dim=1))
+        # 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(x.shape[0], 1, 1),
+                                   code_emb)
+
+        # Everything after this comment is timestep dependent.
+        x = self.inp_block(x)
+        pos_emb = self.position_embed(torch.arange(0, x.shape[-1], device=x.device)).unsqueeze(0).repeat(x.shape[0],1,1).permute(0,2,1)
+        x = x + pos_emb
+        time_emb = self.time_embed(timesteps)
+        code_emb = self.conditioning_timestep_integrator(code_emb, time_emb)
+        for i, lyr in enumerate(self.layers):
+            # Do layer drop where applicable. Do not drop first and last layers.
+            if self.layer_drop > 0 and i != 0 and i != (len(self.layers)-1) and random.random() < self.layer_drop:
+                unused_params.extend(list(lyr.parameters()))
+            else:
+                # First and last blocks will have autocast disabled for improved precision.
+                with autocast(x.device.type, enabled=self.enable_fp16 and i != 0):
+                    x = lyr(x, code_emb, cond_emb, time_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
+
+
+@register_model
+def register_diffusion_tts_flat(opt_net, opt):
+    return DiffusionTtsFlat(**opt_net['kwargs'])
+
+
+if __name__ == '__main__':
+    clip = torch.randn(2, 100, 400)
+    aligned_latent = torch.randn(2,388,512)
+    aligned_sequence = torch.randint(0,8192,(2,388))
+    cond = torch.randn(2, 100, 400)
+    ts = torch.LongTensor([600, 600])
+    model = DiffusionTtsFlat(512, layer_drop=.3)
+    # Test with latent aligned conditioning
+    o = model(clip, ts, aligned_latent, cond)
+    # Test with sequence aligned conditioning
+    o = model(clip, ts, aligned_sequence, cond)
+

codes/trainer/ExtensibleTrainer.py

@@ -327,7 +327,13 @@ class ExtensibleTrainer(BaseModel):
                         else:
                             pgroups = {f'{name}_all_parameters': list(model.parameters())}
                     for name in pgroups.keys():
-                        grad_norms[name] = torch.norm(torch.stack([torch.norm(p.grad.detach(), 2) for p in pgroups[name]]), 2)
+                        stacked_grads = []
+                        for p in pgroups[name]:
+                            if hasattr(p, 'grad') and p.grad is not None:
+                                stacked_grads.append(torch.norm(p.grad.detach(), 2))
+                        if not stacked_grads:
+                            continue
+                        grad_norms[name] = torch.norm(torch.stack(stacked_grads), 2)
                         if distributed.is_available() and distributed.is_initialized():
                             # Gather the metric from all devices if in a distributed setting.
                             distributed.all_reduce(grad_norms[name], op=distributed.ReduceOp.SUM)