start a file for all attention-related modules, use attention-based upsampling in the unets in dalle-2

dalle2_pytorch/attention.py

@@ -0,0 +1,125 @@
+import torch
+from torch import nn, einsum
+import torch.nn.functional as F
+
+from einops import rearrange, repeat
+
+class LayerNormChan(nn.Module):
+    def __init__(
+        self,
+        dim,
+        eps = 1e-5
+    ):
+        super().__init__()
+        self.eps = eps
+        self.gamma = nn.Parameter(torch.ones(1, dim, 1, 1))
+
+    def forward(self, x):
+        var = torch.var(x, dim = 1, unbiased = False, keepdim = True)
+        mean = torch.mean(x, dim = 1, keepdim = True)
+        return (x - mean) / (var + self.eps).sqrt() * self.gamma
+
+# attention-based upsampling
+# from https://arxiv.org/abs/2112.11435
+
+class QueryAndAttend(nn.Module):
+    def __init__(
+        self,
+        *,
+        dim,
+        num_queries = 1,
+        dim_head = 32,
+        heads = 8,
+        window_size = 3
+    ):
+        super().__init__()
+        self.scale = dim_head ** -0.5
+        inner_dim = dim_head * heads
+        self.heads = heads
+        self.dim_head = dim_head
+        self.window_size = window_size
+        self.num_queries = num_queries
+
+        self.rel_pos_bias = nn.Parameter(torch.randn(heads, num_queries, window_size * window_size, 1, 1))
+
+        self.queries = nn.Parameter(torch.randn(heads, num_queries, dim_head))
+        self.to_kv = nn.Conv2d(dim, dim_head * 2, 1, bias = False)
+        self.to_out = nn.Conv2d(inner_dim, dim, 1, bias = False)
+
+    def forward(self, x):
+        """
+        einstein notation
+        b - batch
+        h - heads
+        l - num queries
+        d - head dimension
+        x - height
+        y - width
+        j - source sequence for attending to (kernel size squared in this case)
+        """
+
+        wsz, heads, dim_head, num_queries = self.window_size, self.heads, self.dim_head, self.num_queries
+        batch, _, height, width = x.shape
+
+        is_one_query = self.num_queries == 1
+
+        # queries, keys, values
+
+        q = self.queries * self.scale
+        k, v = self.to_kv(x).chunk(2, dim = 1)
+
+        # similarities
+
+        sim = einsum('h l d, b d x y -> b h l x y', q, k)
+        sim = rearrange(sim, 'b ... x y -> b (...) x y')
+
+        # unfold the similarity scores, with float(-inf) as padding value
+
+        mask_value = -torch.finfo(sim.dtype).max
+        sim = F.pad(sim, ((wsz // 2,) * 4), value = mask_value)
+        sim = F.unfold(sim, kernel_size = wsz)
+        sim = rearrange(sim, 'b (h l j) (x y) -> b h l j x y', h = heads, l = num_queries, x = height, y = width)
+
+        # rel pos bias
+
+        sim = sim + self.rel_pos_bias
+
+        # numerically stable attention
+
+        sim = sim - sim.amax(dim = -3, keepdim = True).detach()
+        attn = sim.softmax(dim = -3)
+
+        # unfold values
+
+        v = F.pad(v, ((wsz // 2,) * 4), value = 0.)
+        v = F.unfold(v, kernel_size = wsz)
+        v = rearrange(v, 'b (d j) (x y) -> b d j x y', d = dim_head, x = height, y = width)
+
+        # aggregate values
+
+        out = einsum('b h l j x y, b d j x y -> b l h d x y', attn, v)
+
+        # combine heads
+
+        out = rearrange(out, 'b l h d x y -> (b l) (h d) x y')
+        out = self.to_out(out)
+        out = rearrange(out, '(b l) d x y -> b l d x y', b = batch)
+
+        # return original input if one query
+
+        if is_one_query:
+            out = rearrange(out, 'b 1 ... -> b ...')
+
+        return out
+
+class QueryAttnUpsample(nn.Module):
+    def __init__(self, dim, **kwargs):
+        super().__init__()
+        self.norm = LayerNormChan(dim)
+        self.qna = QueryAndAttend(dim = dim, num_queries = 4, **kwargs)
+
+    def forward(self, x):
+        x = self.norm(x)
+        out = self.qna(x)
+        out = rearrange(out, 'b (w1 w2) c h w -> b c (h w1) (w w2)', w1 = 2, w2 = 2)
+        return out

dalle2_pytorch/dalle2_pytorch.py

@@ -17,6 +17,7 @@ from kornia.filters import gaussian_blur2d
 
 from dalle2_pytorch.tokenizer import tokenizer
 from dalle2_pytorch.vqgan_vae import NullVQGanVAE, VQGanVAE
+from dalle2_pytorch.attention import QueryAttnUpsample
 
 # use x-clip
 
@@ -692,7 +693,7 @@ class DiffusionPrior(nn.Module):
 # decoder
 
 def Upsample(dim):
-    return nn.ConvTranspose2d(dim, dim, 4, 2, 1)
+    return QueryAttnUpsample(dim)
 
 def Downsample(dim):
     return nn.Conv2d(dim, dim, 4, 2, 1)

dalle2_pytorch/vqgan_vae.py

@@ -13,6 +13,8 @@ import torchvision
 
 from einops import rearrange, reduce, repeat
 
+from dalle2_pytorch.attention import QueryAttnUpsample
+
 # constants
 
 MList = nn.ModuleList
@@ -243,111 +245,6 @@ class ResBlock(nn.Module):
     def forward(self, x):
         return self.net(x) + x
 
-# attention-based upsampling
-# from https://arxiv.org/abs/2112.11435
-
-class QueryAndAttend(nn.Module):
-    def __init__(
-        self,
-        *,
-        dim,
-        num_queries = 1,
-        dim_head = 32,
-        heads = 8,
-        window_size = 3
-    ):
-        super().__init__()
-        self.scale = dim_head ** -0.5
-        inner_dim = dim_head * heads
-        self.heads = heads
-        self.dim_head = dim_head
-        self.window_size = window_size
-        self.num_queries = num_queries
-
-        self.rel_pos_bias = nn.Parameter(torch.randn(heads, num_queries, window_size * window_size, 1, 1))
-
-        self.queries = nn.Parameter(torch.randn(heads, num_queries, dim_head))
-        self.to_kv = nn.Conv2d(dim, dim_head * 2, 1, bias = False)
-        self.to_out = nn.Conv2d(inner_dim, dim, 1, bias = False)
-
-    def forward(self, x):
-        """
-        einstein notation
-        b - batch
-        h - heads
-        l - num queries
-        d - head dimension
-        x - height
-        y - width
-        j - source sequence for attending to (kernel size squared in this case)
-        """
-
-        wsz, heads, dim_head, num_queries = self.window_size, self.heads, self.dim_head, self.num_queries
-        batch, _, height, width = x.shape
-
-        is_one_query = self.num_queries == 1
-
-        # queries, keys, values
-
-        q = self.queries * self.scale
-        k, v = self.to_kv(x).chunk(2, dim = 1)
-
-        # similarities
-
-        sim = einsum('h l d, b d x y -> b h l x y', q, k)
-        sim = rearrange(sim, 'b ... x y -> b (...) x y')
-
-        # unfold the similarity scores, with float(-inf) as padding value
-
-        mask_value = -torch.finfo(sim.dtype).max
-        sim = F.pad(sim, ((wsz // 2,) * 4), value = mask_value)
-        sim = F.unfold(sim, kernel_size = wsz)
-        sim = rearrange(sim, 'b (h l j) (x y) -> b h l j x y', h = heads, l = num_queries, x = height, y = width)
-
-        # rel pos bias
-
-        sim = sim + self.rel_pos_bias
-
-        # numerically stable attention
-
-        sim = sim - sim.amax(dim = -3, keepdim = True).detach()
-        attn = sim.softmax(dim = -3)
-
-        # unfold values
-
-        v = F.pad(v, ((wsz // 2,) * 4), value = 0.)
-        v = F.unfold(v, kernel_size = wsz)
-        v = rearrange(v, 'b (d j) (x y) -> b d j x y', d = dim_head, x = height, y = width)
-
-        # aggregate values
-
-        out = einsum('b h l j x y, b d j x y -> b l h d x y', attn, v)
-
-        # combine heads
-
-        out = rearrange(out, 'b l h d x y -> (b l) (h d) x y')
-        out = self.to_out(out)
-        out = rearrange(out, '(b l) d x y -> b l d x y', b = batch)
-
-        # return original input if one query
-
-        if is_one_query:
-            out = rearrange(out, 'b 1 ... -> b ...')
-
-        return out
-
-class QueryAttnUpsample(nn.Module):
-    def __init__(self, dim, **kwargs):
-        super().__init__()
-        self.norm = LayerNormChan(dim)
-        self.qna = QueryAndAttend(dim = dim, num_queries = 4, **kwargs)
-
-    def forward(self, x):
-        x = self.norm(x)
-        out = self.qna(x)
-        out = rearrange(out, 'b (w1 w2) c h w -> b c (h w1) (w w2)', w1 = 2, w2 = 2)
-        return out
-
 # vqgan attention layer
 class VQGanAttention(nn.Module):
     def __init__(

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.0.44',
+  version = '0.0.45',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',