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from copy import deepcopy
from typing import Optional, Union
import torch
from torch import nn
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
from raw_vit import ViT, Attention, FeedForward
from utils.dl.common.model import get_model_size, set_module
class KTakesAll(nn.Module):
# k means sparsity (the larger k is, the smaller model is)
def __init__(self, k):
super(KTakesAll, self).__init__()
self.k = k
def forward(self, g: torch.Tensor):
k = int(g.size(1) * self.k)
i = (-g).topk(k, 1)[1]
t = g.scatter(1, i, 0)
return t
class Abs(nn.Module):
def __init__(self):
super(Abs, self).__init__()
def forward(self, x):
return x.abs()
class SqueezeLast(nn.Module):
def __init__(self):
super(SqueezeLast, self).__init__()
def forward(self, x):
return x.squeeze(-1)
class Linear_WrappedWithFBS(nn.Module):
def __init__(self, linear: nn.Linear, r, k):
super(Linear_WrappedWithFBS, self).__init__()
self.linear = linear
# for conv: (B, C_in, H, W) -> (B, C_in) -> (B, C_out)
# for mlp in ViT: (B, #patches, D: dim of patches embedding) -> (B, D) -> (B, C_out)
self.fbs = nn.Sequential(
Rearrange('b n d -> b d n'),
Abs(),
nn.AdaptiveAvgPool1d(1),
SqueezeLast(),
nn.Linear(linear.in_features, linear.out_features // r),
nn.ReLU(),
nn.Linear(linear.out_features // r, linear.out_features),
nn.ReLU(),
KTakesAll(k)
)
self.k = k
self.cached_channel_attention = None # (batch_size, dim)
self.use_cached_channel_attention = False
def forward(self, x):
if self.use_cached_channel_attention and self.cached_channel_attention is not None:
channel_attention = self.cached_channel_attention
else:
channel_attention = self.fbs(x)
self.cached_channel_attention = channel_attention
raw_res = self.linear(x)
return channel_attention.unsqueeze(1) * raw_res
class ToQKV_WrappedWithFBS(nn.Module):
"""
This regards to_q/to_k/to_v as a whole (in fact it consists of multiple heads) and prunes it.
It seems different channels of different heads are pruned according to the input.
This is different from "removing some head" or "removing the same channels in each head".
"""
def __init__(self, to_qkv: nn.Linear, r, k):
super(ToQKV_WrappedWithFBS, self).__init__()
self.to_qkv = to_qkv
self.fbses = nn.ModuleList([nn.Sequential(
Rearrange('b n d -> b d n'),
Abs(),
nn.AdaptiveAvgPool1d(1),
SqueezeLast(),
nn.Linear(to_qkv.in_features, to_qkv.out_features // 3 // r),
nn.ReLU(),
nn.Linear(to_qkv.out_features // 3 // r, to_qkv.out_features // 3),
nn.ReLU(),
KTakesAll(k)
) for _ in range(3)])
self.k = k
self.cached_channel_attention = None
self.use_cached_channel_attention = False
def forward(self, x):
if self.use_cached_channel_attention and self.cached_channel_attention is not None:
# print('use cache')
channel_attention = self.cached_channel_attention
else:
# print('dynamic')
channel_attention = torch.cat([fbs(x) for fbs in self.fbses], dim=1)
self.cached_channel_attention = channel_attention
raw_res = self.to_qkv(x)
return channel_attention.unsqueeze(1) * raw_res
def boost_raw_vit_by_fbs(raw_vit: ViT, r, k):
raw_vit = deepcopy(raw_vit)
raw_vit_model_size = get_model_size(raw_vit, True)
# set_module(raw_vit.to_patch_embedding, '2', Linear_WrappedWithFBS(raw_vit.to_patch_embedding[2], r, k))
for attn, ff in raw_vit.transformer.layers:
attn = attn.fn
ff = ff.fn
set_module(attn, 'to_qkv', ToQKV_WrappedWithFBS(attn.to_qkv, r, k))
set_module(ff.net, '0', Linear_WrappedWithFBS(ff.net[0], r, k))
boosted_vit_model_size = get_model_size(raw_vit, True)
print(f'boost_raw_vit_by_fbs() | model size from {raw_vit_model_size:.3f}MB to {boosted_vit_model_size:.3f}MB '
f'(↑ {((boosted_vit_model_size - raw_vit_model_size) / raw_vit_model_size * 100):.2f}%)')
return raw_vit
def set_boosted_vit_sparsity(boosted_vit: ViT, sparsity: float):
for attn, ff in boosted_vit.transformer.layers:
attn = attn.fn
ff = ff.fn
q_features = attn.to_qkv.to_qkv.out_features // 3
if (q_features - int(q_features * sparsity)) % attn.heads != 0:
# tune sparsity to ensure #unpruned channel % num_heads == 0
# so that the pruning seems to reduce the dim_head of each head
tuned_sparsity = 1. - int((q_features - int(q_features * sparsity)) / attn.heads) * attn.heads / q_features
print(f'set_boosted_vit_sparsity() | tune sparsity from {sparsity} to {tuned_sparsity}')
sparsity = tuned_sparsity
attn.to_qkv.k = sparsity
for fbs in attn.to_qkv.fbses:
fbs[-1].k = sparsity
ff.net[0].k = sparsity
ff.net[0].fbs[-1].k = sparsity
def set_boosted_vit_inference_via_cached_channel_attentions(boosted_vit: ViT):
for attn, ff in boosted_vit.transformer.layers:
attn = attn.fn
ff = ff.fn
assert attn.to_qkv.cached_channel_attention is not None
assert ff.net[0].cached_channel_attention is not None
attn.to_qkv.use_cached_channel_attention = True
ff.net[0].use_cached_channel_attention = True
def set_boosted_vit_dynamic_inference(boosted_vit: ViT):
for attn, ff in boosted_vit.transformer.layers:
attn = attn.fn
ff = ff.fn
attn.to_qkv.use_cached_channel_attention = False
ff.net[0].use_cached_channel_attention = False
class StaticFBS(nn.Module):
def __init__(self, static_channel_attention):
super(StaticFBS, self).__init__()
assert static_channel_attention.dim() == 2 and static_channel_attention.size(0) == 1
self.static_channel_attention = nn.Parameter(static_channel_attention, requires_grad=False) # (1, dim)
def forward(self, x):
return x * self.static_channel_attention.unsqueeze(1)
def extract_surrogate_vit_via_cached_channel_attn(boosted_vit: ViT):
boosted_vit = deepcopy(boosted_vit)
raw_vit_model_size = get_model_size(boosted_vit, True)
def get_unpruned_indexes_from_channel_attn(channel_attn: torch.Tensor, k):
assert channel_attn.size(0) == 1, 'use A representative sample to generate channel attentions'
res = channel_attn[0].nonzero(as_tuple=True)[0] # should be one-dim
return res
for attn, ff in boosted_vit.transformer.layers:
attn = attn.fn
ff_w_norm = ff
ff = ff_w_norm.fn
# prune to_qkv
to_qkv = attn.to_qkv
to_q_unpruned_indexes = get_unpruned_indexes_from_channel_attn(
to_qkv.cached_channel_attention[:, 0: to_qkv.cached_channel_attention.size(1) // 3],
to_qkv.k
)
to_q_unpruned_indexes_w_offset = to_q_unpruned_indexes
to_k_unpruned_indexes = get_unpruned_indexes_from_channel_attn(
to_qkv.cached_channel_attention[:, to_qkv.cached_channel_attention.size(1) // 3: to_qkv.cached_channel_attention.size(1) // 3 * 2],
to_qkv.k
)
to_k_unpruned_indexes_w_offset = to_k_unpruned_indexes + to_qkv.cached_channel_attention.size(1) // 3
to_v_unpruned_indexes = get_unpruned_indexes_from_channel_attn(
to_qkv.cached_channel_attention[:, to_qkv.cached_channel_attention.size(1) // 3 * 2: ],
to_qkv.k
)
to_v_unpruned_indexes_w_offset = to_v_unpruned_indexes + to_qkv.cached_channel_attention.size(1) // 3 * 2
assert to_q_unpruned_indexes.size(0) == to_k_unpruned_indexes.size(0) == to_v_unpruned_indexes.size(0)
to_qkv_unpruned_indexes = torch.cat([to_q_unpruned_indexes_w_offset, to_k_unpruned_indexes_w_offset, to_v_unpruned_indexes_w_offset])
new_to_qkv = nn.Linear(to_qkv.to_qkv.in_features, to_qkv_unpruned_indexes.size(0), to_qkv.to_qkv.bias is not None)
new_to_qkv.weight.data.copy_(to_qkv.to_qkv.weight.data[to_qkv_unpruned_indexes])
if to_qkv.to_qkv.bias is not None:
new_to_qkv.bias.data.copy_(to_qkv.to_qkv.bias.data[to_qkv_unpruned_indexes])
set_module(attn, 'to_qkv', nn.Sequential(new_to_qkv, StaticFBS(to_qkv.cached_channel_attention[:, to_qkv_unpruned_indexes])))
# prune to_out
to_out = attn.to_out[0]
new_to_out = nn.Linear(to_v_unpruned_indexes.size(0), to_out.out_features, to_out.bias is not None)
new_to_out.weight.data.copy_(to_out.weight.data[:, to_v_unpruned_indexes])
if to_out.bias is not None:
new_to_out.bias.data.copy_(to_out.bias.data)
set_module(attn, 'to_out', new_to_out)
ff_0 = ff.net[0]
ff_0_unpruned_indexes = get_unpruned_indexes_from_channel_attn(ff_0.cached_channel_attention, ff_0.k)
new_ff_0 = nn.Linear(ff_0.linear.in_features, ff_0_unpruned_indexes.size(0), ff_0.linear.bias is not None)
new_ff_0.weight.data.copy_(ff_0.linear.weight.data[ff_0_unpruned_indexes])
if ff_0.linear.bias is not None:
new_ff_0.bias.data.copy_(ff_0.linear.bias.data[ff_0_unpruned_indexes])
set_module(ff.net, '0', nn.Sequential(new_ff_0, StaticFBS(ff_0.cached_channel_attention[:, ff_0_unpruned_indexes])))
ff_1 = ff.net[3]
new_ff_1 = nn.Linear(ff_0_unpruned_indexes.size(0), ff_1.out_features, ff_1.bias is not None)
new_ff_1.weight.data.copy_(ff_1.weight.data[:, ff_0_unpruned_indexes])
if ff_1.bias is not None:
new_ff_1.bias.data.copy_(ff_1.bias.data)
set_module(ff.net, '3', new_ff_1)
pruned_vit_model_size = get_model_size(boosted_vit, True)
print(f'extract_surrogate_vit_via_cached_channel_attn() | model size from {raw_vit_model_size:.3f}MB to {pruned_vit_model_size:.3f}MB '
f'({(pruned_vit_model_size / raw_vit_model_size * 100):.2f}%)')
return boosted_vit
if __name__ == '__main__':
from utils.dl.common.env import set_random_seed
set_random_seed(1)
def verify(vit, sparsity=0.8):
vit.eval()
with torch.no_grad():
r = torch.rand((1, 3, 224, 224))
print(vit(r).size())
# print(vit)
boosted_vit = boost_raw_vit_by_fbs(vit, r=32, k=sparsity)
set_boosted_vit_sparsity(boosted_vit, sparsity)
# print(boosted_vit)
with torch.no_grad():
r = torch.rand((1, 3, 224, 224))
print(boosted_vit(r).size())
# set_boosted_vit_inference_via_cached_channel_attentions(boosted_vit)
r = torch.rand((1, 3, 224, 224))
boosted_vit.eval()
with torch.no_grad():
o1 = boosted_vit(r)
pruned_vit = extract_surrogate_vit_via_cached_channel_attn(boosted_vit)
pruned_vit.eval()
with torch.no_grad():
o2 = pruned_vit(r)
print('output diff (should be tiny): ', ((o1 - o2) ** 2).sum())
# print(pruned_vit)
# print(pruned_vit)
# vit_b_16 = ViT(
# image_size = 224,
# patch_size = 16,
# num_classes = 1000,
# dim = 768, # encoder layer/attention input/output size (Hidden Size D in the paper)
# depth = 12,
# heads = 12, # (Heads in the paper)
# dim_head = 64, # attention hidden size (seems be default, never change this)
# mlp_dim = 3072, # mlp layer hidden size (MLP size in the paper)
# dropout = 0.,
# emb_dropout = 0.
# )
# verify(vit_b_16)
vit_l_16 = ViT(
image_size = 224,
patch_size = 16,
num_classes = 1000,
dim = 1024, # encoder layer/attention input/output size (Hidden Size D in the paper)
depth = 24,
heads = 16, # (Heads in the paper)
dim_head = 64, # attention hidden size (seems be default, never change this)
mlp_dim = 4096, # mlp layer hidden size (MLP size in the paper)
dropout = 0.,
emb_dropout = 0.
)
verify(vit_l_16, 0.98)
# vit_h_16 = ViT(
# image_size = 224,
# patch_size = 16,
# num_classes = 1000,
# dim = 1280, # encoder layer/attention input/output size (Hidden Size D in the paper)
# depth = 32,
# heads = 16, # (Heads in the paper)
# dim_head = 64, # attention hidden size (seems be default, never change this)
# mlp_dim = 5120, # mlp layer hidden size (MLP size in the paper)
# dropout = 0.,
# emb_dropout = 0.
# )
# verify(vit_h_16) |