diff --git a/setup.py b/setup.py index bdd1c8b..a704c7d 100644 --- a/setup.py +++ b/setup.py @@ -6,7 +6,7 @@ setup( name = 'vit-pytorch', packages = find_packages(exclude=['examples']), - version = '1.6.7', + version = '1.6.8', license='MIT', description = 'Vision Transformer (ViT) - Pytorch', long_description=long_description, diff --git a/vit_pytorch/simple_flash_attn_vit_3d.py b/vit_pytorch/simple_flash_attn_vit_3d.py new file mode 100644 index 0000000..8381c4a --- /dev/null +++ b/vit_pytorch/simple_flash_attn_vit_3d.py @@ -0,0 +1,171 @@ +from packaging import version +from collections import namedtuple + +import torch +from torch import nn +import torch.nn.functional as F +from torch.nn import Module, ModuleList + +from einops import rearrange +from einops.layers.torch import Rearrange + +# constants + +Config = namedtuple('FlashAttentionConfig', ['enable_flash', 'enable_math', 'enable_mem_efficient']) + +# helpers + +def pair(t): + return t if isinstance(t, tuple) else (t, t) + +def posemb_sincos_3d(patches, temperature = 10000, dtype = torch.float32): + _, f, h, w, dim, device, dtype = *patches.shape, patches.device, patches.dtype + + z, y, x = torch.meshgrid( + torch.arange(f, device = device), + torch.arange(h, device = device), + torch.arange(w, device = device), + indexing = 'ij') + + fourier_dim = dim // 6 + + omega = torch.arange(fourier_dim, device = device) / (fourier_dim - 1) + omega = 1. / (temperature ** omega) + + z = z.flatten()[:, None] * omega[None, :] + y = y.flatten()[:, None] * omega[None, :] + x = x.flatten()[:, None] * omega[None, :] + + pe = torch.cat((x.sin(), x.cos(), y.sin(), y.cos(), z.sin(), z.cos()), dim = 1) + + pe = F.pad(pe, (0, dim - (fourier_dim * 6))) # pad if feature dimension not cleanly divisible by 6 + return pe.type(dtype) + +# main class + +class Attend(Module): + def __init__(self, use_flash = False, config: Config = Config(True, True, True)): + super().__init__() + self.config = config + self.use_flash = use_flash + assert not (use_flash and version.parse(torch.__version__) < version.parse('2.0.0')), 'in order to use flash attention, you must be using pytorch 2.0 or above' + + def flash_attn(self, q, k, v): + # flash attention - https://arxiv.org/abs/2205.14135 + + with torch.backends.cuda.sdp_kernel(**self.config._asdict()): + out = F.scaled_dot_product_attention(q, k, v) + + return out + + def forward(self, q, k, v): + n, device, scale = q.shape[-2], q.device, q.shape[-1] ** -0.5 + + if self.use_flash: + return self.flash_attn(q, k, v) + + # similarity + + sim = einsum("b h i d, b j d -> b h i j", q, k) * scale + + # attention + + attn = sim.softmax(dim=-1) + + # aggregate values + + out = einsum("b h i j, b j d -> b h i d", attn, v) + + return out + +# 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, use_flash = True): + super().__init__() + inner_dim = dim_head * heads + self.heads = heads + self.scale = dim_head ** -0.5 + self.norm = nn.LayerNorm(dim) + + self.attend = Attend(use_flash = use_flash) + + 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) + + out = self.attend(q, k, 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, use_flash): + super().__init__() + self.layers = ModuleList([]) + for _ in range(depth): + self.layers.append(ModuleList([ + Attention(dim, heads = heads, dim_head = dim_head, use_flash = use_flash), + FeedForward(dim, mlp_dim) + ])) + + def forward(self, x): + for attn, ff in self.layers: + x = attn(x) + x + x = ff(x) + x + + return x + +class SimpleViT(Module): + def __init__(self, *, image_size, image_patch_size, frames, frame_patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64, use_flash_attn = True): + super().__init__() + image_height, image_width = pair(image_size) + patch_height, patch_width = pair(image_patch_size) + + assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.' + assert frames % frame_patch_size == 0, 'Frames must be divisible by the frame patch size' + + num_patches = (image_height // patch_height) * (image_width // patch_width) * (frames // frame_patch_size) + patch_dim = channels * patch_height * patch_width * frame_patch_size + + self.to_patch_embedding = nn.Sequential( + Rearrange('b c (f pf) (h p1) (w p2) -> b f h w (p1 p2 pf c)', p1 = patch_height, p2 = patch_width, pf = frame_patch_size), + nn.LayerNorm(patch_dim), + nn.Linear(patch_dim, dim), + nn.LayerNorm(dim), + ) + + self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, use_flash_attn) + + self.to_latent = nn.Identity() + self.linear_head = nn.Linear(dim, num_classes) + + def forward(self, video): + *_, h, w, dtype = *video.shape, video.dtype + + x = self.to_patch_embedding(video) + pe = posemb_sincos_3d(x) + x = rearrange(x, 'b ... d -> b (...) d') + pe + + x = self.transformer(x) + x = x.mean(dim = 1) + + x = self.to_latent(x) + return self.linear_head(x)