From c59843d7b8c5bce4a37f381ed4b37926fee6987d Mon Sep 17 00:00:00 2001 From: Phil Wang Date: Sat, 18 Mar 2023 09:41:12 -0700 Subject: [PATCH] add a version of simple vit using flash attention --- README.md | 9 ++ setup.py | 2 +- vit_pytorch/simple_flash_attn_vit.py | 176 +++++++++++++++++++++++++++ 3 files changed, 186 insertions(+), 1 deletion(-) create mode 100644 vit_pytorch/simple_flash_attn_vit.py diff --git a/README.md b/README.md index 96d673a..2832e52 100644 --- a/README.md +++ b/README.md @@ -1945,4 +1945,13 @@ Coming from computer vision and new to transformers? Here are some resources tha } ``` +```bibtex +@inproceedings{dao2022flashattention, + title = {Flash{A}ttention: Fast and Memory-Efficient Exact Attention with {IO}-Awareness}, + author = {Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{\'e}, Christopher}, + booktitle = {Advances in Neural Information Processing Systems}, + year = {2022} +} +``` + *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 69f7c9d..3e66aed 100644 --- a/setup.py +++ b/setup.py @@ -3,7 +3,7 @@ from setuptools import setup, find_packages setup( name = 'vit-pytorch', packages = find_packages(exclude=['examples']), - version = '1.1.1', + version = '1.2.0', license='MIT', description = 'Vision Transformer (ViT) - Pytorch', long_description_content_type = 'text/markdown', diff --git a/vit_pytorch/simple_flash_attn_vit.py b/vit_pytorch/simple_flash_attn_vit.py new file mode 100644 index 0000000..323c6d8 --- /dev/null +++ b/vit_pytorch/simple_flash_attn_vit.py @@ -0,0 +1,176 @@ +from collections import namedtuple +from packaging import version + +import torch +import torch.nn.functional as F +from torch import nn + +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_2d(patches, temperature = 10000, dtype = torch.float32): + _, h, w, dim, device, dtype = *patches.shape, patches.device, patches.dtype + + y, x = torch.meshgrid(torch.arange(h, device = device), torch.arange(w, device = device), indexing = 'ij') + assert (dim % 4) == 0, 'feature dimension must be multiple of 4 for sincos emb' + omega = torch.arange(dim // 4, device = device) / (dim // 4 - 1) + omega = 1. / (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) + +# main class + +class Attend(nn.Module): + def __init__(self, use_flash = False): + super().__init__() + 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' + + # determine efficient attention configs for cuda and cpu + + self.cpu_config = Config(True, True, True) + self.cuda_config = None + + if not torch.cuda.is_available() or not use_flash: + return + + device_properties = torch.cuda.get_device_properties(torch.device('cuda')) + + if device_properties.major == 8 and device_properties.minor == 0: + self.cuda_config = Config(True, False, False) + else: + self.cuda_config = Config(False, True, True) + + def flash_attn(self, q, k, v): + config = self.cuda_config if q.is_cuda else self.cpu_config + + # flash attention - https://arxiv.org/abs/2205.14135 + + with torch.backends.cuda.sdp_kernel(**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(nn.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(nn.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(nn.Module): + def __init__(self, dim, depth, heads, dim_head, mlp_dim, use_flash): + super().__init__() + self.layers = nn.ModuleList([]) + for _ in range(depth): + self.layers.append(nn.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(nn.Module): + def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, channels = 3, dim_head = 64, use_flash = True): + 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.' + + num_patches = (image_height // patch_height) * (image_width // patch_width) + 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.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, use_flash) + + self.to_latent = nn.Identity() + self.linear_head = nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, num_classes) + ) + + def forward(self, img): + *_, h, w, dtype = *img.shape, img.dtype + + x = self.to_patch_embedding(img) + pe = posemb_sincos_2d(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)