每日Attention学习22——Inverted Residual RWKV
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模块出处
[arXiv 25] [link] [
](https://github.com/juntaoJianggavin/RWKV-UNet) 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 load
T_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-6
norm_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_out
padding = 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 = x
x = self.conv(x)
x = self.norm(x)
x = self.act(x)
if self.has_skip:
x = self.drop_path(x) + shortcut
return x
class 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 round
rd_channels = rd_round_fn(in_chs * rd_ratio)
act_layer = force_act_layer or act_layer
self.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 <= 1/4
B, N, C = input.shape
input = input.transpose(1, 2).reshape(B, C, patch_resolution[0], patch_resolution[1])
B, C, H, W = input.shape
output = 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):
@staticmethod
def forward(ctx, B, T, C, w, u, k, v):
ctx.B = B
ctx.T = T
ctx.C = C
assert T <= T_MAX
assert B * C % min(C, 1024) == 0
half_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
@staticmethod
def backward(ctx, gy):
B = ctx.B
T = ctx.T
C = ctx.C
assert T <= T_MAX
assert B * C % min(C, 1024) == 0
w, u, k, v = ctx.saved_tensors
gw = 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_embd
attn_sz = n_embd
self._init_weights()
self.shift_pixel = shift_pixel
if shift_pixel > 0:
self.channel_gamma = channel_gamma
else:
self.spatial_mix_k = None
self.spatial_mix_v = None
self.spatial_mix_r = None
self.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 = 0
self.receptance.scale_init = 0
self.output.scale_init = 0
def _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, xr
B, T, C = x.size()
# Use xk, xv, xr to produce k, v, r
if 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 = x
xv = x
xr = x
k = self.key(xk)
v = self.value(xv)
r = self.receptance(xr)
sr = torch.sigmoid(r)
return sr, k, v
def 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 * x
x = self.output(x)
return x
class 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_skip
if 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_s
self.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 = x
x = 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, hidde
h, 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 x
return x
if __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]
python
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