tfd14

hopefully this helps address the positional dependencies of tfd12

codes/models/arch_util.py

@@ -492,6 +492,11 @@ class AttentionBlock(nn.Module):
 
     def _forward(self, x, mask=None):
         b, c, *spatial = x.shape
+        if len(mask.shape) == 2:
+            mask = mask.unsqueeze(0).repeat(x.shape[0],1,1)
+        if mask.shape[1] != x.shape[-1]:
+            mask = mask[:, :x.shape[-1], :x.shape[-1]]
+
         x = x.reshape(b, c, -1)
         x = self.norm(x)
         if self.do_activation:
@@ -527,11 +532,10 @@ class QKVAttentionLegacy(nn.Module):
         weight = torch.einsum(
             "bct,bcs->bts", q * scale, k * scale
         )  # More stable with f16 than dividing afterwards
-        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
         if mask is not None:
-            # The proper way to do this is to mask before the softmax using -inf, but that doesn't work properly on CPUs.
+            mask = mask.repeat(self.n_heads, 1, 1)
-            mask = mask.repeat(self.n_heads, 1).unsqueeze(1)
+            weight[mask.logical_not()] = -torch.inf
-            weight = weight * mask
+        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
         a = torch.einsum("bts,bcs->bct", weight, v)
 
         return a.reshape(bs, -1, length)
@@ -564,9 +568,8 @@ class QKVAttention(nn.Module):
             (k * scale).view(bs * self.n_heads, ch, length),
         )  # More stable with f16 than dividing afterwards
         if mask is not None:
-            # The proper way to do this is to mask before the softmax using -inf, but that doesn't work properly on CPUs.
+            mask = mask.repeat(self.n_heads, 1, 1)
-            mask = mask.repeat(self.n_heads, 1).unsqueeze(1)
+            weight[mask.logical_not()] = -torch.inf
-            weight = weight * mask
         weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
         a = torch.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
         return a.reshape(bs, -1, length)

codes/models/audio/music/transformer_diffusion14.py

@@ -0,0 +1,303 @@
+import itertools
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from models.arch_util import AttentionBlock, TimestepEmbedSequential
+from models.audio.music.encoders import ResEncoder16x
+from models.diffusion.nn import timestep_embedding, normalization, zero_module, conv_nd, linear
+from models.diffusion.unet_diffusion import TimestepBlock
+from trainer.networks import register_model
+from utils.util import checkpoint, print_network
+
+
+def build_local_attention_mask(n, l, fixed_region):
+    """
+    Builds an attention mask that focuses attention on local region
+    Includes provisions for a "fixed_region" at the start of the sequence where full attention weights will be applied.
+    Args:
+        n: Size of returned matrix (maximum sequence size)
+        l: Size of local context (uni-directional, e.g. the total context is l*2)
+        fixed_region: The number of sequence elements at the start of the sequence that get full attention.
+    Returns:
+        A mask that can be applied to AttentionBlock to achieve local attention.
+    """
+    assert l*2 < n, f'Local context must be less than global context. {l}, {n}'
+    o = torch.arange(0,n)
+    c = o.unsqueeze(-1).repeat(1,n)
+    r = o.unsqueeze(0).repeat(n,1)
+    localized = ((-(r-c).abs())+l).clamp(0,l-1) / (l-1)
+    localized[:fixed_region] = 1
+    localized[:, :fixed_region] = 1
+    mask = localized > 0
+    return mask
+
+
+def test_local_attention_mask():
+    print(build_local_attention_mask(9,4,1))
+
+
+class SubBlock(nn.Module):
+    def __init__(self, inp_dim, contraction_dim, blk_dim, heads, dropout, enable_attention_masking=False):
+        super().__init__()
+        self.enable_attention_masking = enable_attention_masking
+        self.dropout = nn.Dropout(p=dropout)
+        self.blk_emb_proj = nn.Conv1d(blk_dim, inp_dim, 1)
+        self.attn = AttentionBlock(inp_dim, out_channels=contraction_dim, num_heads=heads)
+        self.attnorm = nn.GroupNorm(8, contraction_dim)
+        self.ff = nn.Conv1d(inp_dim+contraction_dim, contraction_dim, kernel_size=3, padding=1)
+        self.ffnorm = nn.GroupNorm(8, contraction_dim)
+        if self.enable_attention_masking:
+            # All regions can attend to the first token, which will be the timestep embedding. Hence, fixed_region.
+            self.mask = build_local_attention_mask(n=2000, l=48, fixed_region=1)
+        else:
+            self.mask = None
+
+    def forward(self, x, blk_emb):
+        if self.mask is not None:
+            self.mask = self.mask.to(x.device)
+        blk_enc = self.blk_emb_proj(blk_emb)
+        ah = self.dropout(self.attn(torch.cat([blk_enc, x], dim=-1), mask=self.mask))
+        ah = ah[:,:,blk_emb.shape[-1]:]  # Strip off the blk_emb and re-align with x.
+        ah = F.gelu(self.attnorm(ah))
+        h = torch.cat([ah, x], dim=1)
+        hf = self.dropout(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, heads, dropout, enable_attention_masking=False):
+        super().__init__()
+        self.prenorm = nn.GroupNorm(8, trunk_dim)
+        self.block1 = SubBlock(trunk_dim, contraction_dim, trunk_dim, heads, dropout,
+                               enable_attention_masking=enable_attention_masking)
+        self.block2 = SubBlock(trunk_dim+contraction_dim*2, contraction_dim, trunk_dim, heads, dropout,
+                               enable_attention_masking=enable_attention_masking)
+        self.out = nn.Conv1d(contraction_dim*4, trunk_dim, kernel_size=1, bias=False)
+        self.out.weight.data.zero_()
+
+    def forward(self, x, blk_emb):
+        h = self.prenorm(x)
+        h = self.block1(h, blk_emb)
+        h = self.block2(h, blk_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,
+            time_embed_dim=256,
+            model_channels=1024,
+            contraction_dim=256,
+            num_layers=8,
+            in_channels=256,
+            input_vec_dim=1024,
+            out_channels=512,  # mean and variance
+            num_heads=4,
+            dropout=0,
+            use_corner_alignment=False,  # This is an interpolation parameter only provided for backwards compatibility. ALL NEW TRAINS SHOULD SET THIS TO TRUE.
+            use_fp16=False,
+            new_code_expansion=False,
+            # Parameters for regularization.
+            unconditioned_percentage=.1,  # This implements a mechanism similar to what is used in classifier-free training.
+            # Parameters for re-training head
+            freeze_except_code_converters=False,
+    ):
+        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.new_code_expansion = new_code_expansion
+        self.use_corner_alignment = use_corner_alignment
+        self.inp_block = conv_nd(1, in_channels, model_channels, 3, 1, 1)
+
+        self.time_embed = nn.Sequential(
+            linear(time_embed_dim, time_embed_dim),
+            nn.SiLU(),
+            linear(time_embed_dim, model_channels),
+        )
+
+        self.input_converter = nn.Conv1d(input_vec_dim, model_channels, 1)
+        self.unconditioned_embedding = nn.Parameter(torch.randn(1,model_channels,1))
+        self.intg = nn.Conv1d(model_channels*2, model_channels, 1)
+        self.layers = TimestepEmbedSequential(*[ConcatAttentionBlock(model_channels, contraction_dim, num_heads, dropout, enable_attention_masking=True) 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)),
+        )
+
+        if freeze_except_code_converters:
+            for p in self.parameters():
+                p.DO_NOT_TRAIN = True
+                p.requires_grad = False
+            for m in [self.code_converter and self.input_converter]:
+                for p in m.parameters():
+                    del p.DO_NOT_TRAIN
+                    p.requires_grad = True
+
+    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,
+            '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, prior=None, conditioning_free=False):
+        if conditioning_free:
+            code_emb = self.unconditioned_embedding.repeat(x.shape[0], x.shape[-1], 1)
+        else:
+            code_emb = self.input_converter(prior)
+
+            # 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)
+
+            code_emb = F.interpolate(code_emb, size=x.shape[-1], mode='nearest')
+
+        with torch.autocast(x.device.type, enabled=self.enable_fp16):
+            blk_emb = self.time_embed(timestep_embedding(timesteps, self.time_embed_dim)).unsqueeze(-1)
+            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)
+
+        # Defensively involve probabilistic or possibly unused parameters in loss so we don't get DDP errors.
+        unused_params = [self.unconditioned_embedding]
+        extraneous_addition = 0
+        for p in unused_params:
+            extraneous_addition = extraneous_addition + p.mean()
+        out = out + extraneous_addition * 0
+
+        return out
+
+
+class TransformerDiffusionWithCheaterLatent(nn.Module):
+    def __init__(self, freeze_encoder_until=None, checkpoint_encoder=True, **kwargs):
+        super().__init__()
+        self.internal_step = 0
+        self.freeze_encoder_until = freeze_encoder_until
+        self.diff = TransformerDiffusion(**kwargs)
+        self.encoder = ResEncoder16x(256, 1024, 256, checkpointing_enabled=checkpoint_encoder)
+
+    def forward(self, x, timesteps, truth_mel, conditioning_free=False):
+        unused_parameters = []
+        encoder_grad_enabled = self.freeze_encoder_until is not None and self.internal_step > self.freeze_encoder_until
+        if not encoder_grad_enabled:
+            unused_parameters.extend(list(self.encoder.parameters()))
+        with torch.set_grad_enabled(encoder_grad_enabled):
+            proj = self.encoder(truth_mel)
+
+        for p in unused_parameters:
+            proj = proj + p.mean() * 0
+
+        diff = self.diff(x, timesteps, prior=proj, 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['encoder'] = list(self.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_transformer_diffusion14(opt_net, opt):
+    return TransformerDiffusion(**opt_net['kwargs'])
+
+
+@register_model
+def register_transformer_diffusion_14_with_cheater_latent(opt_net, opt):
+    return TransformerDiffusionWithCheaterLatent(**opt_net['kwargs'])
+
+
+def test_tfd():
+    clip = torch.randn(2,256,400)
+    ts = torch.LongTensor([600, 600])
+    model = TransformerDiffusion(in_channels=256, model_channels=1024, contraction_dim=512,
+                                              num_heads=3, input_vec_dim=256, num_layers=12, dropout=.1)
+    model(clip, ts, clip)
+
+
+def test_cheater_model():
+    clip = torch.randn(2, 256, 400)
+    ts = torch.LongTensor([600, 600])
+
+    # For music:
+    model = TransformerDiffusionWithCheaterLatent(in_channels=256, out_channels=512,
+                                                    model_channels=1024, contraction_dim=512, num_heads=8,
+                                                    input_vec_dim=256, num_layers=16,
+                                                    dropout=.1, new_code_expansion=True,
+                                              )
+    #diff_weights = torch.load('extracted_diff.pth')
+    #model.diff.load_state_dict(diff_weights, strict=False)
+    #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)
+    o = model(clip, ts, clip)
+    pg = model.get_grad_norm_parameter_groups()
+
+
+def extract_cheater_encoder(in_f, out_f):
+    p = torch.load(in_f)
+    out = {}
+    for k, v in p.items():
+        if k.startswith('encoder.'):
+            out[k] = v
+    torch.save(out, out_f)
+
+
+if __name__ == '__main__':
+    #test_local_attention_mask()
+    extract_cheater_encoder('X:\\dlas\\experiments\\train_music_diffusion_tfd_and_cheater\\models\\104500_generator_ema.pth', 'X:\\dlas\\experiments\\tfd12_self_learned_cheater_enc.pth', True)
+    test_cheater_model()
+    #extract_diff('X:\\dlas\experiments\\train_music_diffusion_tfd_cheater_from_scratch\\models\\56500_generator_ema.pth', 'extracted.pth', remove_head=True)

codes/trainer/injectors/gaussian_diffusion_injector.py

@@ -80,7 +80,7 @@ class GaussianDiffusionInjector(Injector):
     def forward(self, state):
         gen = self.env['generators'][self.opt['generator']]
         hq = state[self.input]
-        assert hq.max() < 1 or hq.min() > -1, f"Attempting to train gaussian diffusion on un-normalized inputs. This won't work, silly! {hq.min()}  {hq.max()}"
+        assert hq.max() < 1.000001 or hq.min() > -1.00001, f"Attempting to train gaussian diffusion on un-normalized inputs. This won't work, silly! {hq.min()}  {hq.max()}"
 
         with autocast(enabled=self.env['opt']['fp16']):
             if not gen.training or (self.deterministic_timesteps_every != 0 and self.env['step'] % self.deterministic_timesteps_every == 0):