diff --git a/README.md b/README.md index d61842f..2c08d62 100644 --- a/README.md +++ b/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 diff --git a/setup.py b/setup.py index 4a80dbc..773c51e 100644 --- a/setup.py +++ b/setup.py @@ -6,7 +6,7 @@ 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, diff --git a/vit_pytorch/simple_vit_with_hyper_connections.py b/vit_pytorch/simple_vit_with_hyper_connections.py new file mode 100644 index 0000000..f0a4b50 --- /dev/null +++ b/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, + branch_output, + residuals, + 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(x, attn_res, beta) + + x, ff_res, beta = ff_hyper_conn.width_connection(x) + + x = ff(x) + + x = ff_hyper_conn.depth_connection(x, ff_res, 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)