add the proposed jumbo vit from Fuller et al. of Carleton University

README.md

@@ -2172,4 +2172,13 @@ Coming from computer vision and new to transformers? Here are some resources tha
 }
 ```
 
+```bibtex
+@inproceedings{Fuller2025SimplerFV,
+    title   = {Simpler Fast Vision Transformers with a Jumbo CLS Token},
+    author  = {Anthony Fuller and Yousef Yassin and Daniel G. Kyrollos and Evan Shelhamer and James R. Green},
+    year    = {2025},
+    url     = {https://api.semanticscholar.org/CorpusID:276557720}
+}
+```
+
 *I visualise a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.* — Claude Shannon

setup.py

@@ -6,7 +6,7 @@ with open('README.md') as f:
 setup(
   name = 'vit-pytorch',
   packages = find_packages(exclude=['examples']),
-  version = '1.9.2',
+  version = '1.10.1',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   long_description = long_description,

vit_pytorch/jumbo_vit.py

@@ -0,0 +1,204 @@
+# Simpler Fast Vision Transformers with a Jumbo CLS Token
+# https://arxiv.org/abs/2502.15021
+
+import torch
+from torch import nn
+from torch.nn import Module, ModuleList
+
+from einops import rearrange, repeat, reduce, pack, unpack
+from einops.layers.torch import Rearrange
+
+# helpers
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+def divisible_by(num, den):
+    return (num % den) == 0
+
+def posemb_sincos_2d(h, w, dim, temperature: int = 10000, dtype = torch.float32):
+    y, x = torch.meshgrid(torch.arange(h), torch.arange(w), indexing="ij")
+    assert divisible_by(dim, 4), "feature dimension must be multiple of 4 for sincos emb"
+
+    omega = torch.arange(dim // 4) / (dim // 4 - 1)
+    omega = temperature ** -omega
+
+    y = y.flatten()[:, None] * omega[None, :]
+    x = x.flatten()[:, None] * omega[None, :]
+    pos_emb = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim=1)
+
+    return pos_emb.type(dtype)
+
+# classes
+
+def FeedForward(dim, mult = 4.):
+    hidden_dim = int(dim * mult)
+    return nn.Sequential(
+        nn.LayerNorm(dim),
+        nn.Linear(dim, hidden_dim),
+        nn.GELU(),
+        nn.Linear(hidden_dim, dim),
+    )
+
+class Attention(Module):
+    def __init__(self, dim, heads = 8, dim_head = 64):
+        super().__init__()
+        inner_dim = dim_head *  heads
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+        self.norm = nn.LayerNorm(dim)
+
+        self.attend = nn.Softmax(dim = -1)
+
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
+        self.to_out = nn.Linear(inner_dim, dim, bias = False)
+
+    def forward(self, x):
+        x = self.norm(x)
+
+        qkv = self.to_qkv(x).chunk(3, dim = -1)
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)
+
+        dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
+
+        attn = self.attend(dots)
+
+        out = torch.matmul(attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)')
+        return self.to_out(out)
+
+class JumboViT(Module):
+    def __init__(
+        self,
+        *,
+        image_size,
+        patch_size,
+        num_classes,
+        dim,
+        depth,
+        heads,
+        mlp_dim,
+        num_jumbo_cls = 1,  # differing from paper, allow for multiple jumbo cls, so one could break it up into 2 jumbo cls tokens with 3x the dim, as an example
+        jumbo_cls_k = 6,    # they use a CLS token with this factor times the dimension - 6 was the value they settled on
+        jumbo_ff_mult = 2,  # expansion factor of the jumbo cls token feedforward
+        channels = 3,
+        dim_head = 64
+    ):
+        super().__init__()
+        image_height, image_width = pair(image_size)
+        patch_height, patch_width = pair(patch_size)
+
+        assert divisible_by(image_height, patch_height) and divisible_by(image_width, patch_width), 'Image dimensions must be divisible by the patch size.'
+
+        patch_dim = channels * patch_height * patch_width
+
+        self.to_patch_embedding = nn.Sequential(
+            Rearrange("b c (h p1) (w p2) -> b (h w) (p1 p2 c)", p1 = patch_height, p2 = patch_width),
+            nn.LayerNorm(patch_dim),
+            nn.Linear(patch_dim, dim),
+            nn.LayerNorm(dim),
+        )
+
+        self.pos_embedding = posemb_sincos_2d(
+            h = image_height // patch_height,
+            w = image_width // patch_width,
+            dim = dim,
+        ) 
+
+        jumbo_cls_dim = dim * jumbo_cls_k
+
+        self.jumbo_cls_token = nn.Parameter(torch.zeros(num_jumbo_cls, jumbo_cls_dim))
+
+        jumbo_cls_to_tokens = Rearrange('b n (k d) -> b (n k) d', k = jumbo_cls_k)
+        self.jumbo_cls_to_tokens = jumbo_cls_to_tokens
+
+        self.norm = nn.LayerNorm(dim)
+        self.layers = ModuleList([])
+
+        # attention and feedforwards
+
+        self.jumbo_ff = nn.Sequential(
+            Rearrange('b (n k) d -> b n (k d)', k = jumbo_cls_k),
+            FeedForward(jumbo_cls_dim, int(jumbo_cls_dim * jumbo_ff_mult)), # they use separate parameters for the jumbo feedforward, weight tied for parameter efficient
+            jumbo_cls_to_tokens
+        )
+
+        for _ in range(depth):
+            self.layers.append(ModuleList([
+                Attention(dim, heads = heads, dim_head = dim_head),
+                FeedForward(dim, mlp_dim),
+            ]))
+
+        self.to_latent = nn.Identity()
+
+        self.linear_head = nn.Linear(dim, num_classes)
+
+    def forward(self, img):
+
+        batch, device = img.shape[0], img.device
+
+        x = self.to_patch_embedding(img)
+
+        # pos embedding
+
+        pos_emb = self.pos_embedding.to(device, dtype = x.dtype)
+
+        x = x + pos_emb
+
+        # add cls tokens
+
+        cls_tokens = repeat(self.jumbo_cls_token, 'nj d -> b nj d', b = batch)
+
+        jumbo_tokens = self.jumbo_cls_to_tokens(cls_tokens)
+
+        x, cls_packed_shape = pack([jumbo_tokens, x], 'b * d')
+
+        # attention and feedforwards
+
+        for layer, (attn, ff) in enumerate(self.layers, start = 1):
+            is_last = layer == len(self.layers)
+
+            x = attn(x) + x
+
+            # jumbo feedforward
+
+            jumbo_cls_tokens, x = unpack(x, cls_packed_shape, 'b * d')
+
+            x = ff(x) + x
+            jumbo_cls_tokens = self.jumbo_ff(jumbo_cls_tokens) + jumbo_cls_tokens
+
+            if is_last:
+                continue
+
+            x, _ = pack([jumbo_cls_tokens, x], 'b * d')
+
+        pooled = reduce(jumbo_cls_tokens, 'b n d -> b d', 'mean')
+
+        # normalization and project to logits
+
+        embed = self.norm(pooled)
+
+        embed = self.to_latent(embed)
+        logits = self.linear_head(embed)
+        return logits
+
+# copy pasteable file
+
+if __name__ == '__main__':
+
+    v = JumboViT(
+        num_classes = 1000,
+        image_size = 64,
+        patch_size = 8,
+        dim = 16,
+        depth = 2,
+        heads = 2,
+        mlp_dim = 32,
+        jumbo_cls_k = 3,
+        jumbo_ff_mult = 2,
+    )
+
+    images = torch.randn(1, 3, 64, 64)
+
+    logits = v(images)
+    assert logits.shape == (1, 1000)