exercise attention expertise

README.md

@@ -134,10 +134,10 @@ video_as_images_out = unet(video, enable_time = False)
 
 ## Todo
 
+- [x] give attention the best positional embeddings research has to offer
+
 - [ ] make sure dalle2-pytorch can accept `SpaceTimeUnet` for training
-- [ ] give attention the best positional embeddings research has to offer
 - [ ] soup up the attention
-- [ ] offer a function, similar to how MosaicML's approach, that automatically rigs a 2d-unet from dalle2-pytorch to be 3d
 - [ ] consider learned exponential moving average across time from https://github.com/lucidrains/Mega-pytorch
 
 ## Citations

make_a_video_pytorch/make_a_video.py

@@ -70,6 +70,61 @@ def FeedForward(dim, mult = 4):
         nn.Linear(inner_dim, bias = False)
     )
 
+# best relative positional encoding
+
+class ContinuousPositionBias(nn.Module):
+    """ from https://arxiv.org/abs/2111.09883 """
+
+    def __init__(
+        self,
+        *,
+        dim,
+        heads,
+        num_dims = 1,
+        layers = 2,
+        log_dist = True,
+        cache_rel_pos = False
+    ):
+        super().__init__()
+        self.num_dims = num_dims
+        self.log_dist = log_dist
+
+        self.net = nn.ModuleList([])
+        self.net.append(nn.Sequential(nn.Linear(self.num_dims, dim), nn.SiLU()))
+
+        for _ in range(layers - 1):
+            self.net.append(nn.Sequential(nn.Linear(dim, dim), nn.SiLU()))
+
+        self.net.append(nn.Linear(dim, heads))
+
+        self.cache_rel_pos = cache_rel_pos
+        self.register_buffer('rel_pos', None, persistent = False)
+
+    @property
+    def device(self):
+        return next(self.parameters()).device
+
+    def forward(self, *dimensions):
+        device = self.device
+
+        if not exists(self.rel_pos) or not self.cache_rel_pos:
+            positions = [torch.arange(d, device = device) for d in dimensions]
+            grid = torch.stack(torch.meshgrid(*positions, indexing = 'ij'))
+            grid = rearrange(grid, 'c ... -> (...) c')
+            rel_pos = rearrange(grid, 'i c -> i 1 c') - rearrange(grid, 'j c -> 1 j c')
+
+            if self.log_dist:
+                rel_pos = torch.sign(rel_pos) * torch.log(rel_pos.abs() + 1)
+
+            self.register_buffer('rel_pos', rel_pos, persistent = False)
+
+        rel_pos = self.rel_pos.float()
+
+        for layer in self.net:
+            rel_pos = layer(rel_pos)
+
+        return rearrange(rel_pos, 'i j h -> h i j')
+
 # helper classes
 
 class Attention(nn.Module):
@@ -92,7 +147,12 @@ def __init__(
 
         nn.init.zeros_(self.to_out.weight.data) # identity with skip connection
 
-    def forward(self, x):
+    def forward(
+        self,
+        x,
+        rel_pos_bias = None
+    ):
+
         x = self.norm(x)
 
         q, k, v = self.to_q(x), *self.to_kv(x).chunk(2, dim = -1)
@@ -103,6 +163,9 @@ def forward(self, x):
 
         sim = einsum('b h i d, b h j d -> b h i j', q, k)
 
+        if exists(rel_pos_bias):
+            sim = sim + rel_pos_bias
+
         attn = sim.softmax(dim = -1)
 
         out = einsum('b h i j, b h j d -> b h i d', attn, v)
@@ -175,7 +238,10 @@ def __init__(
     ):
         super().__init__()
         self.spatial_attn = Attention(dim = dim, dim_head = dim_head, heads = heads)
+        self.spatial_rel_pos_bias = ContinuousPositionBias(dim = dim // 2, heads = heads, num_dims = 2)
+
         self.temporal_attn = Attention(dim = dim, dim_head = dim_head, heads = heads)
+        self.temporal_rel_pos_bias = ContinuousPositionBias(dim = dim // 2, heads = heads, num_dims = 1)
 
     def forward(
         self,
@@ -191,7 +257,9 @@ def forward(
         else:
             x = rearrange(x, 'b c h w -> b (h w) c')
 
-        x = self.spatial_attn(x) + x
+        space_rel_pos_bias = self.spatial_rel_pos_bias(h, w)
+
+        x = self.spatial_attn(x, rel_pos_bias = space_rel_pos_bias) + x
 
         if is_video:
             x = rearrange(x, '(b f) (h w) c -> b c f h w', b = b, h = h, w = w)
@@ -203,7 +271,9 @@ def forward(
 
         x = rearrange(x, 'b c f h w -> (b h w) f c')
 
-        x = self.temporal_attn(x) + x
+        time_rel_pos_bias = self.temporal_rel_pos_bias(x.shape[1])
+
+        x = self.temporal_attn(x, rel_pos_bias = time_rel_pos_bias) + x
 
         x = rearrange(x, '(b h w) f c -> b c f h w', w = w, h = h)
 

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'make-a-video-pytorch',
   packages = find_packages(exclude=[]),
-  version = '0.0.7',
+  version = '0.0.8',
   license='MIT',
   description = 'Make-A-Video - Pytorch',
   author = 'Phil Wang',