scope attention in tfd13 as well

codes/models/arch_util.py

@@ -439,6 +439,32 @@ class ResBlock(nn.Module):
         return self.skip_connection(x) + h
 
 
+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 AttentionBlock(nn.Module):
     """
     An attention block that allows spatial positions to attend to each other.
@@ -492,10 +518,11 @@ 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]]
+        if mask is not None:
+            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)

codes/models/audio/music/transformer_diffusion13.py

@@ -5,7 +5,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from models.arch_util import ResBlock, TimestepEmbedSequential, AttentionBlock
+from models.arch_util import ResBlock, TimestepEmbedSequential, AttentionBlock, build_local_attention_mask
 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
@@ -29,10 +29,11 @@ class SubBlock(nn.Module):
         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)
+        self.mask = build_local_attention_mask(n=4000, l=64, fixed_region=8)
 
     def forward(self, x, blk_emb):
         blk_enc = self.blk_emb_proj(blk_emb)
-        ah = self.dropout(self.attn(torch.cat([blk_enc, x], dim=-1)))
+        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)

codes/models/audio/music/transformer_diffusion14.py

@@ -4,7 +4,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from models.arch_util import AttentionBlock, TimestepEmbedSequential
+from models.arch_util import AttentionBlock, TimestepEmbedSequential, build_local_attention_mask
 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
@@ -12,32 +12,6 @@ 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__()