每日Attention学习22——Inverted Residual RWKV

模块出处

[arXiv 25] [link] [code] RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation

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模块名称

Inverted Residual RWKV (IR-RWKV)

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模块作用

用于vision的RWKV结构

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模块结构

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模块代码

注：cpp扩展请参考作者原仓库

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
from timm.layers.activations import *
from functools import partial
from timm.layers import DropPath, create_act_layer, LayerType
from typing import Callable, Dict, Optional, Type
from torch.utils.cpp_extension import loadT_MAX = 1024
inplace = True
wkv_cuda = load(name="wkv", sources=["cuda/wkv_op.cpp", "cuda/wkv_cuda.cu"],verbose=True, extra_cuda_cflags=['-res-usage', '--maxrregcount 60', '--use_fast_math', '-O3', '-Xptxas -O3', f'-DTmax={T_MAX}'])def get_norm(norm_layer='in_1d'):eps = 1e-6norm_dict = {'none': nn.Identity,'in_1d': partial(nn.InstanceNorm1d, eps=eps),'in_2d': partial(nn.InstanceNorm2d, eps=eps),'in_3d': partial(nn.InstanceNorm3d, eps=eps),'bn_1d': partial(nn.BatchNorm1d, eps=eps),'bn_2d': partial(nn.BatchNorm2d, eps=eps),# 'bn_2d': partial(nn.SyncBatchNorm, eps=eps),'bn_3d': partial(nn.BatchNorm3d, eps=eps),'gn': partial(nn.GroupNorm, eps=eps),'ln_1d': partial(nn.LayerNorm, eps=eps),# 'ln_2d': partial(LayerNorm2d, eps=eps),}return norm_dict[norm_layer]def get_act(act_layer='relu'):act_dict = {'none': nn.Identity,'sigmoid': Sigmoid,'swish': Swish,'mish': Mish,'hsigmoid': HardSigmoid,'hswish': HardSwish,'hmish': HardMish,'tanh': Tanh,'relu': nn.ReLU,'relu6': nn.ReLU6,'prelu': PReLU,'gelu': GELU,'silu': nn.SiLU}return act_dict[act_layer]class ConvNormAct(nn.Module):def __init__(self, dim_in, dim_out, kernel_size, stride=1, dilation=1, groups=1, bias=False,skip=False, norm_layer='bn_2d', act_layer='relu', inplace=True, drop_path_rate=0.):super(ConvNormAct, self).__init__()self.has_skip = skip and dim_in == dim_outpadding = math.ceil((kernel_size - stride) / 2)self.conv = nn.Conv2d(dim_in, dim_out, kernel_size, stride, padding, dilation, groups, bias)self.norm = get_norm(norm_layer)(dim_out)self.act = get_act(act_layer)(inplace=inplace)self.drop_path = DropPath(drop_path_rate) if drop_path_rate else nn.Identity()def forward(self, x):shortcut = xx = self.conv(x)x = self.norm(x)x = self.act(x)if self.has_skip:x = self.drop_path(x) + shortcutreturn xclass SE(nn.Module):def __init__(self,in_chs: int,rd_ratio: float = 0.25,rd_channels: Optional[int] = None,act_layer: LayerType = nn.ReLU,gate_layer: LayerType = nn.Sigmoid,force_act_layer: Optional[LayerType] = None,rd_round_fn: Optional[Callable] = None,):super(SE, self).__init__()if rd_channels is None:rd_round_fn = rd_round_fn or roundrd_channels = rd_round_fn(in_chs * rd_ratio)act_layer = force_act_layer or act_layerself.conv_reduce = nn.Conv2d(in_chs, rd_channels, 1, bias=True)self.act1 = create_act_layer(act_layer, inplace=True)self.conv_expand = nn.Conv2d(rd_channels, in_chs, 1, bias=True)self.gate = create_act_layer(gate_layer)def forward(self, x):x_se = x.mean((2, 3), keepdim=True)x_se = self.conv_reduce(x_se)x_se = self.act1(x_se)x_se = self.conv_expand(x_se)return x * self.gate(x_se)def q_shift(input, shift_pixel=1, gamma=1/4, patch_resolution=None):assert gamma <= 1/4B, N, C = input.shapeinput = input.transpose(1, 2).reshape(B, C, patch_resolution[0], patch_resolution[1])B, C, H, W = input.shapeoutput = torch.zeros_like(input)output[:, 0:int(C*gamma), :, shift_pixel:W] = input[:, 0:int(C*gamma), :, 0:W-shift_pixel]output[:, int(C*gamma):int(C*gamma*2), :, 0:W-shift_pixel] = input[:, int(C*gamma):int(C*gamma*2), :, shift_pixel:W]output[:, int(C*gamma*2):int(C*gamma*3), shift_pixel:H, :] = input[:, int(C*gamma*2):int(C*gamma*3), 0:H-shift_pixel, :]output[:, int(C*gamma*3):int(C*gamma*4), 0:H-shift_pixel, :] = input[:, int(C*gamma*3):int(C*gamma*4), shift_pixel:H, :]output[:, int(C*gamma*4):, ...] = input[:, int(C*gamma*4):, ...]return output.flatten(2).transpose(1, 2)def RUN_CUDA(B, T, C, w, u, k, v):return WKV.apply(B, T, C, w.cuda(), u.cuda(), k.cuda(), v.cuda())class WKV(torch.autograd.Function):@staticmethoddef forward(ctx, B, T, C, w, u, k, v):ctx.B = Bctx.T = Tctx.C = Cassert T <= T_MAXassert B * C % min(C, 1024) == 0half_mode = (w.dtype == torch.half)bf_mode = (w.dtype == torch.bfloat16)ctx.save_for_backward(w, u, k, v)w = w.float().contiguous()u = u.float().contiguous()k = k.float().contiguous()v = v.float().contiguous()y = torch.empty((B, T, C), device='cuda', memory_format=torch.contiguous_format)wkv_cuda.forward(B, T, C, w, u, k, v, y)if half_mode:y = y.half()elif bf_mode:y = y.bfloat16()return y@staticmethoddef backward(ctx, gy):B = ctx.BT = ctx.TC = ctx.Cassert T <= T_MAXassert B * C % min(C, 1024) == 0w, u, k, v = ctx.saved_tensorsgw = torch.zeros((B, C), device='cuda').contiguous()gu = torch.zeros((B, C), device='cuda').contiguous()gk = torch.zeros((B, T, C), device='cuda').contiguous()gv = torch.zeros((B, T, C), device='cuda').contiguous()half_mode = (w.dtype == torch.half)bf_mode = (w.dtype == torch.bfloat16)wkv_cuda.backward(B, T, C,w.float().contiguous(),u.float().contiguous(),k.float().contiguous(),v.float().contiguous(),gy.float().contiguous(),gw, gu, gk, gv)if half_mode:gw = torch.sum(gw.half(), dim=0)gu = torch.sum(gu.half(), dim=0)return (None, None, None, gw.half(), gu.half(), gk.half(), gv.half())elif bf_mode:gw = torch.sum(gw.bfloat16(), dim=0)gu = torch.sum(gu.bfloat16(), dim=0)return (None, None, None, gw.bfloat16(), gu.bfloat16(), gk.bfloat16(), gv.bfloat16())else:gw = torch.sum(gw, dim=0)gu = torch.sum(gu, dim=0)return (None, None, None, gw, gu, gk, gv)class VRWKV_SpatialMix(nn.Module):def __init__(self, n_embd, channel_gamma=1/4, shift_pixel=1):super().__init__()self.n_embd = n_embdattn_sz = n_embdself._init_weights()self.shift_pixel = shift_pixelif shift_pixel > 0:self.channel_gamma = channel_gammaelse:self.spatial_mix_k = Noneself.spatial_mix_v = Noneself.spatial_mix_r = Noneself.key = nn.Linear(n_embd, attn_sz, bias=False)self.value = nn.Linear(n_embd, attn_sz, bias=False)self.receptance = nn.Linear(n_embd, attn_sz, bias=False)self.key_norm = nn.LayerNorm(n_embd)self.output = nn.Linear(attn_sz, n_embd, bias=False)self.key.scale_init = 0self.receptance.scale_init = 0self.output.scale_init = 0def _init_weights(self):self.spatial_decay = nn.Parameter(torch.zeros(self.n_embd))self.spatial_first = nn.Parameter(torch.zeros(self.n_embd))self.spatial_mix_k = nn.Parameter(torch.ones([1, 1, self.n_embd]) * 0.5)self.spatial_mix_v = nn.Parameter(torch.ones([1, 1, self.n_embd]) * 0.5)self.spatial_mix_r = nn.Parameter(torch.ones([1, 1, self.n_embd]) * 0.5)def jit_func(self, x, patch_resolution):# Mix x with the previous timestep to produce xk, xv, xrB, T, C = x.size()# Use xk, xv, xr to produce k, v, rif self.shift_pixel > 0:xx = q_shift(x, self.shift_pixel, self.channel_gamma, patch_resolution)xk = x * self.spatial_mix_k + xx * (1 - self.spatial_mix_k)xv = x * self.spatial_mix_v + xx * (1 - self.spatial_mix_v)xr = x * self.spatial_mix_r + xx * (1 - self.spatial_mix_r)else:xk = xxv = xxr = xk = self.key(xk)v = self.value(xv)r = self.receptance(xr)sr = torch.sigmoid(r)return sr, k, vdef forward(self, x, patch_resolution=None):B, T, C = x.size()sr, k, v = self.jit_func(x, patch_resolution)x = RUN_CUDA(B, T, C, self.spatial_decay / T, self.spatial_first / T, k, v)x = self.key_norm(x)x = sr * xx = self.output(x)return xclass iR_RWKV(nn.Module):def __init__(self, dim_in, dim_out, norm_in=True, has_skip=True, exp_ratio=1.0, norm_layer='bn_2d',act_layer='relu', dw_ks=3, stride=1, dilation=1, se_ratio=0.0,attn_s=True, drop_path=0., drop=0.,img_size=224, channel_gamma=1/4, shift_pixel=1):super().__init__()self.norm = get_norm(norm_layer)(dim_in) if norm_in else nn.Identity()dim_mid = int(dim_in * exp_ratio)self.ln1 = nn.LayerNorm(dim_mid)self.conv = ConvNormAct(dim_in, dim_mid, kernel_size=1)self.has_skip = (dim_in == dim_out and stride == 1) and has_skipif attn_s==True:self.att = VRWKV_SpatialMix(dim_mid, channel_gamma, shift_pixel)self.se = SE(dim_mid, rd_ratio=se_ratio, act_layer=get_act(act_layer)) if se_ratio > 0.0 else nn.Identity()self.proj_drop = nn.Dropout(drop)self.proj = ConvNormAct(dim_mid, dim_out, kernel_size=1, norm_layer='none', act_layer='none', inplace=inplace)self.drop_path = DropPath(drop_path) if drop_path else nn.Identity()self.attn_s=attn_sself.conv_local = ConvNormAct(dim_mid, dim_mid, kernel_size=dw_ks, stride=stride, dilation=dilation, groups=dim_mid, norm_layer='bn_2d', act_layer='silu', inplace=inplace)def forward(self, x):shortcut = xx = self.norm(x)x = self.conv(x)if self.attn_s:B, hidden, H, W = x.size()patch_resolution = (H,  W)x = x.view(B, hidden, -1)  # (B, hidden, H*W) = (B, C, N)x = x.permute(0, 2, 1)x = x + self.drop_path(self.ln1(self.att(x, patch_resolution)))B, n_patch, hidden = x.size()  # reshape from (B, n_patch, hidden) to (B, h, w, hiddeh, w = int(np.sqrt(n_patch)), int(np.sqrt(n_patch))x = x.permute(0, 2, 1)x = x.contiguous().view(B, hidden, h, w)x = x + self.se(self.conv_local(x)) if self.has_skip else self.se(self.conv_local(x))x = self.proj_drop(x)x = self.proj(x)x = (shortcut + self.drop_path(x)) if self.has_skip else xreturn xif __name__ == '__main__':x = torch.randn([1, 64, 11, 11]).cuda()ir_rwkv = iR_RWKV(dim_in=64, dim_out=64).cuda()out = ir_rwkv(x)print(out.shape)  # [1, 64, 11, 11]

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