add a simple vit flavor for a new bytedance paper that proposes to break out of the traditional one residual stream architecture - "hyper-connections"

README.md

@@ -2161,4 +2161,15 @@ Coming from computer vision and new to transformers? Here are some resources tha
 }
 ```
 
+```bibtex
+@article{Zhu2024HyperConnections,
+    title   = {Hyper-Connections},
+    author  = {Defa Zhu and Hongzhi Huang and Zihao Huang and Yutao Zeng and Yunyao Mao and Banggu Wu and Qiyang Min and Xun Zhou},
+    journal = {ArXiv},
+    year    = {2024},
+    volume  = {abs/2409.19606},
+    url     = {https://api.semanticscholar.org/CorpusID:272987528}
+}
+```
+
 *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 = '1.8.9',
+  version = '1.9.0',
   license='MIT',
   description = 'Vision Transformer (ViT) - Pytorch',
   long_description = long_description,

vit_pytorch/simple_vit_with_hyper_connections.py

@@ -0,0 +1,233 @@
+"""
+ViT + Hyper-Connections + Register Tokens
+https://arxiv.org/abs/2409.19606
+"""
+
+import torch
+from torch import nn, tensor
+from torch.nn import Module, ModuleList
+
+from einops import rearrange, repeat, reduce, einsum, pack, unpack
+from einops.layers.torch import Rearrange
+
+# b - batch, h - heads, n - sequence, e - expansion rate / residual streams, d - feature dimension
+
+# helpers
+
+def pair(t):
+    return t if isinstance(t, tuple) else (t, t)
+
+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 (dim % 4) == 0, "feature dimension must be multiple of 4 for sincos emb"
+    omega = torch.arange(dim // 4) / (dim // 4 - 1)
+    omega = 1.0 / (temperature ** omega)
+
+    y = y.flatten()[:, None] * omega[None, :]
+    x = x.flatten()[:, None] * omega[None, :]
+    pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos()), dim=1)
+    return pe.type(dtype)
+
+# hyper connections
+
+class HyperConnection(Module):
+    def __init__(
+        self,
+        dim,
+        num_residual_streams,
+        layer_index
+    ):
+        """ Appendix J - Algorithm 2, Dynamic only """
+        super().__init__()
+
+        self.norm = nn.LayerNorm(dim, bias = False)
+
+        self.num_residual_streams = num_residual_streams
+        self.layer_index = layer_index
+
+        self.static_beta = nn.Parameter(torch.ones(num_residual_streams))
+
+        init_alpha0 = torch.zeros((num_residual_streams, 1))
+        init_alpha0[layer_index % num_residual_streams, 0] = 1.
+
+        self.static_alpha = nn.Parameter(torch.cat([init_alpha0, torch.eye(num_residual_streams)], dim = 1))
+
+        self.dynamic_alpha_fn = nn.Parameter(torch.zeros(dim, num_residual_streams + 1))
+        self.dynamic_alpha_scale = nn.Parameter(tensor(1e-2))
+        self.dynamic_beta_fn = nn.Parameter(torch.zeros(dim))
+        self.dynamic_beta_scale = nn.Parameter(tensor(1e-2))
+
+    def width_connection(self, residuals):
+        normed = self.norm(residuals)
+
+        wc_weight = (normed @ self.dynamic_alpha_fn).tanh()
+        dynamic_alpha = wc_weight * self.dynamic_alpha_scale
+        alpha = dynamic_alpha + self.static_alpha
+
+        dc_weight = (normed @ self.dynamic_beta_fn).tanh()
+        dynamic_beta = dc_weight * self.dynamic_beta_scale
+        beta = dynamic_beta + self.static_beta
+
+        # width connection
+        mix_h = einsum(alpha, residuals, '... e1 e2, ... e1 d -> ... e2 d')
+
+        branch_input, residuals = mix_h[..., 0, :], mix_h[..., 1:, :]
+
+        return branch_input, residuals, beta
+
+    def depth_connection(
+        self,
+        residuals,
+        branch_output,
+        beta
+    ):
+        return einsum(branch_output, beta, "b n d, b n e -> b n e d") + residuals
+
+# classes
+
+class FeedForward(Module):
+    def __init__(self, dim, hidden_dim):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.LayerNorm(dim),
+            nn.Linear(dim, hidden_dim),
+            nn.GELU(),
+            nn.Linear(hidden_dim, dim),
+        )
+    def forward(self, x):
+        return self.net(x)
+
+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 Transformer(Module):
+    def __init__(self, dim, depth, heads, dim_head, mlp_dim, num_residual_streams):
+        super().__init__()
+
+        self.num_residual_streams = num_residual_streams
+
+        self.norm = nn.LayerNorm(dim)
+        self.layers = ModuleList([])
+
+        for layer_index in range(depth):
+            self.layers.append(nn.ModuleList([
+                HyperConnection(dim, num_residual_streams, layer_index),
+                Attention(dim, heads = heads, dim_head = dim_head),
+                HyperConnection(dim, num_residual_streams, layer_index),
+                FeedForward(dim, mlp_dim)
+            ]))
+
+    def forward(self, x):
+
+        x = repeat(x, 'b n d -> b n e d', e = self.num_residual_streams)
+
+        for attn_hyper_conn, attn, ff_hyper_conn, ff in self.layers:
+
+            x, attn_res, beta = attn_hyper_conn.width_connection(x)
+
+            x = attn(x)
+
+            x = attn_hyper_conn.depth_connection(attn_res, x, beta)
+
+            x, ff_res, beta = ff_hyper_conn.width_connection(x)
+
+            x = ff(x)
+
+            x = ff_hyper_conn.depth_connection(ff_res, x, beta)
+
+        x = reduce(x, 'b n e d -> b n d', 'sum')
+
+        return self.norm(x)
+
+class SimpleViT(nn.Module):
+    def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, num_residual_streams, num_register_tokens = 4, channels = 3, dim_head = 64):
+        super().__init__()
+        image_height, image_width = pair(image_size)
+        patch_height, patch_width = pair(patch_size)
+
+        assert image_height % patch_height == 0 and image_width % patch_width == 0, '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.register_tokens = nn.Parameter(torch.randn(num_register_tokens, dim))
+
+        self.pos_embedding = posemb_sincos_2d(
+            h = image_height // patch_height,
+            w = image_width // patch_width,
+            dim = dim,
+        ) 
+
+        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, num_residual_streams)
+
+        self.pool = "mean"
+        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)
+        x += self.pos_embedding.to(x)
+
+        r = repeat(self.register_tokens, 'n d -> b n d', b = batch)
+
+        x, ps = pack([x, r], 'b * d')
+
+        x = self.transformer(x)
+
+        x, _ = unpack(x, ps, 'b * d')
+
+        x = x.mean(dim = 1)
+
+        x = self.to_latent(x)
+        return self.linear_head(x)
+
+# main
+
+if __name__ == '__main__':
+    vit = SimpleViT(
+        num_classes = 1000,
+        image_size = 256,
+        patch_size = 8,
+        dim = 1024,
+        depth = 12,
+        heads = 8,
+        mlp_dim = 2048,
+        num_residual_streams = 8
+    )
+
+    images = torch.randn(3, 3, 256, 256)
+
+    logits = vit(images)