02修剪标准&&方法

2.1修剪标准

2.1.1基于权重大小的修剪标准

参考上一节，对权重做绝对值按大小修剪，或者做L1/L2范数来进行修剪

2.1.2基于梯度幅度来修剪

基于前面可知，我们按照值的大小来修剪，把值小的裁剪掉了，或者说某个权重在训练过程中一直不变，直观上感觉没有那么重要。但其实这样是不对的，从梯度上来说，该权值可能初始就很低（接近合适值），所以他的更新值就会很小，而不能认为他变动小就不重要。因此我们需要将权重大小和梯度大小结合考虑，进行裁剪。最简单的一种方式是考虑乘积。

下面这段代码可以应用于硕士毕业论文中，按照你的想法来剪枝

import numpy as np
import torch

def prune_by_gradient_weight_product(model, pruning_rate):
    grad_weight_product_list = []
    for name, param in model.named_parameters():
        if 'weight' in name:
            # 计算梯度与权重的乘积
            grad_weight_product = torch.abs(param.grad * param.data)
            grad_weight_product_list.append(grad_weight_product)

    # 将所有的乘积值合并到一个张量中
    all_product_values = torch.cat([torch.flatten(x) for x in grad_weight_product_list])
    # 计算需要修剪的阈值
    threshold = np.percentile(all_product_values.cpu().detach().numpy(), pruning_rate)

    # 对权重进行修剪
    for name, param in model.named_parameters():
        if 'weight' in name:
            # 创建一个掩码，表示哪些权重应该保留
            mask = torch.where(torch.abs(param.grad * param.data) >= threshold, 1, 0)
            # 应用掩码
            param.data *= mask.float()
pruning_rate = 50
#一个全连接层，输入10的向量输出5的向量，然后是激活层，然后又是全连接层输入5的向量输出1的向量
model = torch.nn.Sequential(torch.nn.Linear(10, 5), torch.nn.ReLU(), torch.nn.Linear(5, 1))
input_tensor = torch.randn(1, 10)  # 创建一个随机输入张量
#output_tensor就是传入input_tensor，跑一个最小的前向和反向传播
output_tensor = model(input_tensor)  # 前向传递
loss = torch.sum(output_tensor)  # 定义一个虚拟损失
loss.backward()                  # 执行反向传递以计算梯度
prune_by_gradient_weight_product(model, pruning_rate)  # 对模型进行修剪

2.2修剪方法

2.2.1修剪框架

2015年提出了经典框架，训练-剪枝-微调

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
import numpy as np
# 1. 训练基础的大网络
#这里实现了一个bigmodel只构建了三个全连接层
class BigModel(nn.Module):
    def __init__(self):
        super(BigModel, self).__init__()
        self.fc1 = nn.Linear(784, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, 10)
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 准备MNIST数据集
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

def train(model, dataloader, criterion, optimizer, device='cpu', num_epochs=10):
    model.train()
    model.to(device)

    for epoch in range(num_epochs):
        running_loss = 0.0
        for batch_idx, (inputs, targets) in enumerate(dataloader):
            inputs, targets = inputs.to(device), targets.to(device)

            # 前向传播
            outputs = model(inputs.view(inputs.size(0), -1))
            loss = criterion(outputs, targets)

            # 反向传播
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            running_loss += loss.item()

        print(f"Epoch {epoch + 1}, Loss: {running_loss / len(dataloader)}")

    return model

big_model = BigModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(big_model.parameters(), lr=1e-3)
big_model = train(big_model, train_loader, criterion, optimizer, device='cuda', num_epochs=2)

# 保存训练好的大网络
torch.save(big_model.state_dict(), "big_model.pth")

# 2. 修剪大网络为小网络  <==================================
def prune_network(model, pruning_rate=0.5, method="global"):
    for name, param in model.named_parameters():
        if "weight" in name:
            tensor = param.data.cpu().numpy()
            if method == "global":
                threshold = np.percentile(abs(tensor), pruning_rate * 100)
            else:  # local pruning
                threshold = np.percentile(abs(tensor), pruning_rate * 100, axis=1, keepdims=True)
            mask = abs(tensor) > threshold
            param.data = torch.FloatTensor(tensor * mask.astype(float)).to(param.device)


big_model.load_state_dict(torch.load("big_model.pth"))
prune_network(big_model, pruning_rate=0.5, method="global") # <==================================

# 保存修剪后的模型
torch.save(big_model.state_dict(), "pruned_model.pth")

# 3. 以低的学习率做微调
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(big_model.parameters(), lr=1e-4) # <==================================
finetuned_model = train(big_model, train_loader, criterion, optimizer, device='cuda', num_epochs=10)

# 保存微调后的模型
torch.save(finetuned_model.state_dict(), "finetuned_pruned_model.pth")


# Epoch 1, Loss: 0.2022465198550985
# Epoch 2, Loss: 0.08503768096334421
# Epoch 1, Loss: 0.03288614955859935
# Epoch 2, Loss: 0.021574671817958347
# Epoch 3, Loss: 0.015933904873507806

2018年提出了边训练边剪枝，在每完成一个epoch之后就进行剪枝，剪枝只是置0，下一次依然会更新

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
import numpy as np

class BigModel(nn.Module):
    def __init__(self):
        super(BigModel, self).__init__()
        self.fc1 = nn.Linear(784, 512)
        self.fc2 = nn.Linear(512, 256)
        self.fc3 = nn.Linear(256, 10)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

def prune_network(model, pruning_rate=0.5, method="global"):
    for name, param in model.named_parameters():
        if "weight" in name:
            tensor = param.data.cpu().numpy()
            if method == "global":
                threshold = np.percentile(abs(tensor), pruning_rate * 100)
            else:  # local pruning
                threshold = np.percentile(abs(tensor), pruning_rate * 100, axis=1, keepdims=True)
            mask = abs(tensor) > threshold
            param.data = torch.FloatTensor(tensor * mask.astype(float)).to(param.device)

def train_with_pruning(model, dataloader, criterion, optimizer, device='cpu', num_epochs=10, pruning_rate=0.5):
    model.train()
    model.to(device)

    for epoch in range(num_epochs):
        running_loss = 0.0
        for batch_idx, (inputs, targets) in enumerate(dataloader):
            inputs, targets = inputs.to(device), targets.to(device)

            # 前向传播
            outputs = model(inputs.view(inputs.size(0), -1))
            loss = criterion(outputs, targets)

            # 反向传播
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            running_loss += loss.item()

        print(f"Epoch {epoch + 1}, Loss: {running_loss / len(dataloader)}")

        # 在每个 epoch 结束后进行剪枝
        prune_network(model, pruning_rate, method="global") # <================================== just prune the weights ot 0 but still allow them to grow back by optimizer.step()

    return model

big_model = BigModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(big_model.parameters(), lr=1e-3)
big_model = train_with_pruning(big_model, train_loader, criterion, optimizer, device='cuda', num_epochs=10, pruning_rate=0.1)

# 保存训练好的模型
torch.save(big_model.state_dict(), "trained_with_pruning_model.pth")

直接remove剪枝，优点是可以减少模型的计算量和内存使用。可以通过减少网络容量来防止过拟合。

缺点是可能会降低网络的表示能力，导致性能下降。需要对网络结构进行改变，这可能会增加实现和微调的复杂性

# train phase

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
import numpy as np

# 1. Train a large base network
class BigModel(nn.Module):
    def __init__(self):
        super(BigModel, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 16, kernel_size=3, padding=1)
        self.fc = nn.Linear(16 * 28 * 28, 10)

        # Initialize l1norm as a parameter and register as buffer
        self.conv1_l1norm = nn.Parameter(torch.Tensor(32), requires_grad=False)
        self.conv2_l1norm = nn.Parameter(torch.Tensor(16), requires_grad=False)
        self.register_buffer('conv1_l1norm_buffer', self.conv1_l1norm)
        self.register_buffer('conv2_l1norm_buffer', self.conv2_l1norm)
        

    def forward(self, x):
        x = torch.relu(self.conv1(x))
        self.conv1_l1norm.data = torch.sum(torch.abs(self.conv1.weight.data), dim=(1, 2, 3))
        
        x = torch.relu(self.conv2(x))
        self.conv2_l1norm.data = torch.sum(torch.abs(self.conv2.weight.data), dim=(1, 2, 3))
        
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x

# Training function
def train(model, dataloader, criterion, optimizer, device='gpu', num_epochs=10):
    model.train()
    model.to(device)

    for epoch in range(num_epochs):
        running_loss = 0.0
        for batch_idx, (inputs, targets) in enumerate(dataloader):
            inputs, targets = inputs.to(device), targets.to(device)

            # Forward propagation
            outputs = model(inputs)
            loss = criterion(outputs, targets)

            # Backpropagation
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            running_loss += loss.item()
            # print(f"Loss: {running_loss / len(dataloader)}")
            
        print(f"Epoch {epoch + 1}, Loss: {running_loss / len(dataloader)}")

    return model


if __name__ == "__main__":
    # Prepare the MNIST dataset
    transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
    train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform)
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

    big_model = BigModel()
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(big_model.parameters(), lr=1e-3)
    big_model = train(big_model, train_loader, criterion, optimizer, device='cuda', num_epochs=3)

    # Save the trained big network
    torch.save(big_model.state_dict(), "big_model.pth")
    
    # Set the input shape of the model
    dummy_input = torch.randn(1, 1, 28, 28).to("cuda")

    # Export the model to ONNX format
    torch.onnx.export(big_model, dummy_input, "big_model.onnx")

# (/home/coeyliang/miniconda3) coeyliang_pruning> python train.py 
# Epoch 1, Loss: 0.14482067066501939
# Epoch 2, Loss: 0.05070804020739657
# Epoch 3, Loss: 0.03378467213614771

剪枝部分，下面这份代码，对conv1修剪第一个通道，对conv2修剪第2个通道

#初始化一个空列表，用于存储二维卷积的层的L1范数
l1norms_for_local_threshold = []
for name, m in model.name_modules:
    if isinstance(m, nn.Conv2d):
        #为当前模块的L1范数创建一个名称
        l1norm_buffer_name = f"{name}_l1norm_buffer"
        #使用getattr函数从模型中获取该名称对应的属性。这个属性应该是当前模块的L1范数
        l1norm = getattr(model, l1norm_buffer_name)
        
        l1norms_for_local_threshold.append(l1norm)
#排序并且只取value，然后确定阈值，这里的0.5用于确定阈值
T_conv1 = torch.sort(l1norms_for_local_threshold[0])[0][int(len(l1norms_for_local_threshold[0]) * 0.5)]
#下面就是剪掉
#先取出来，方便后面操作
conv1 = model.conv1 #[32*1*3*3]
conv2 = model.conv2 #[16*32*3*3]
conv1_l1norm_buffer = model.conv1_l1norm_buffer
conv2_l1norm_buffer = model.conv2_l1norm_buffer
#比T_conv1大的留下
keep_idxs = torch.where(conv1_l1norm_buffer >= T_conv1)[0]
k = len(keep_idxs)

conv1.weight.data = conv1.weight.data[keep_idxs]
conv1.bias.data = conv1.bias.data[keep_idxs]
conv1_l1norm_buffer.data = conv1_l1norm_buffer.data[keep_idxs]
conv1.out_channels = k

_, keep_idxs = torch.topk(conv2_l1norm_buffer, k)

#注意这里要塞到第2个通道，所以是[:, keep_idxs]
conv2.weight.data = conv2.weight.data[:,keep_idxs]
conv2_l1norm_buffer.data = conv2_l1norm_buffer.data[keep_idxs]
conv2.in_channels = k

torch.save(model.state_dict(), "pruned_model.pth")
dummy_input = torch.randn(1, 1, 28, 28)
torch.onnx.export(model, dummy_input, "pruned_model.onnx")

#后面是finetune略

2.3.1稀疏训练

2018年提出，步骤分为

• 初始化一个带有随机mask的网络
• 训练这个pruned network 一个epoch
• 去掉一些权重较小的一些weights（或者不满足自定义条件的weights）
• 重新生成(regrow)同样数量的random weights

如下，我们拿到一个网络，考虑如何将其变成一个稀疏训练。

# raw net
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader


# Define the network architecture
class SparseNet(nn.Module):
    def __init__(self):
        super(SparseNet, self).__init__()
        self.fc1 = nn.Linear(784, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = x.view(-1, 784)
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x


# Load MNIST dataset
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)

# Initialize the network, loss function, and optimizer
sparsity_rate = 0.5
model = SparseNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

# Training loop
n_epochs = 10
for epoch in range(n_epochs):
    running_loss = 0.0
    for batch_idx, (inputs, targets) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        
        # print(f"Loss: {running_loss / (batch_idx+1)}")
    
    print(f"Epoch {epoch+1}/{n_epochs}, Loss: {running_loss / (batch_idx+1)}")

下图展示了稀疏训练，选出部分将其置0（对应图上红色），绿色是保留项（这只是前人的设计，后续有更好的）

下面是2018年这篇文章的实现

# sparse net
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

# Define the network architecture
class SparseNet(nn.Module):
    def __init__(self, sparsity_rate, mutation_rate = 0.5):
        super(SparseNet, self).__init__()
        self.fc1 = nn.Linear(784, 128)
        self.fc2 = nn.Linear(128, 10)
        self.sparsity_rate = sparsity_rate
        self.mutation_rate = mutation_rate
        self.initialize_masks() # <== 1.initialize a network with random mask
   
    def forward(self, x):
        x = x.view(-1, 784)
        x = x @ (self.fc1.weight * self.mask1.to(x.device)).T + self.fc1.bias
        x = torch.relu(x)
        x = x @ (self.fc2.weight * self.mask2.to(x.device)).T + self.fc2.bias
        return x

    def initialize_masks(self):
        self.mask1 = self.create_mask(self.fc1.weight, self.sparsity_rate)
        self.mask2 = self.create_mask(self.fc2.weight, self.sparsity_rate)

    def create_mask(self, weight, sparsity_rate):
        k = int(sparsity_rate * weight.numel())
        _, indices = torch.topk(weight.abs().view(-1), k, largest=False)
        mask = torch.ones_like(weight, dtype=bool)
        mask.view(-1)[indices] = False
        return mask  # <== 1.initialize a network with random mask

    def update_masks(self):
        self.mask1 = self.mutate_mask(self.fc1.weight, self.mask1, self.mutation_rate)
        self.mask2 = self.mutate_mask(self.fc2.weight, self.mask2, self.mutation_rate)
        
    def mutate_mask(self, weight, mask, mutation_rate=0.5): # weight and mask: 2d shape
        # Find the number of elements in the mask that are True
        num_true = torch.count_nonzero(mask)

        # Compute the number of elements to mutate
        mutate_num = int(mutation_rate * num_true)
        
        # 3) pruning a certain amount of weights of lower magnitude
        true_indices_2d = torch.where(mask == True) # index the 2d mask where is true
        true_element_1d_idx_prune = torch.topk(weight[true_indices_2d], mutate_num, largest=False)[1]
        
        for i in true_element_1d_idx_prune:
            mask[true_indices_2d[0][i], true_indices_2d[1][i]] = False
        
        # 4) regrowing the same amount of random weights.
        # Get the indices of the False elements in the mask
        false_indices = torch.nonzero(~mask)

        # Randomly select n indices from the false_indices tensor
        random_indices = torch.randperm(false_indices.shape[0])[:mutate_num]

        # the elemnt to be regrow
        regrow_indices = false_indices[random_indices]
        for regrow_idx in regrow_indices:
            mask[tuple(regrow_idx)] = True
        
        return mask


# Set the device to CUDA if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load MNIST dataset and move to the device
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])

train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)

sparsity_rate = 0.5
model = SparseNet(sparsity_rate).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)

n_epochs = 10
for epoch in range(n_epochs):
    running_loss = 0.0
    for batch_idx, (inputs, targets) in enumerate(train_loader):
        # Move the data to the device
        inputs, targets = inputs.to(device), targets.to(device)

        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        # print(f"Loss: {running_loss / (batch_idx+1)}")

    # Update masks
    model.update_masks() # generate a new mask based on the updated weights

    print(f"Epoch {epoch+1}/{n_epochs}, Loss: {running_loss / (batch_idx+1)}")