CANN模型压缩与加速技术

CANN组织链接：https://atomgit.com/cann
CANN community仓库链接：https://atomgit.com/cann/community

一、模型压缩概述

1.1 模型压缩简介

模型压缩是指通过一系列技术手段减少模型的大小和计算量，同时尽可能保持模型的精度。

1.1.1 压缩技术分类

• 量化：降低参数精度
• 剪枝：移除冗余连接
• 知识蒸馏：用小模型学习大模型
• 低秩分解：分解大矩阵为小矩阵
• 权重共享：共享相似权重

1.1.2 应用场景

• 移动端部署
• 边缘设备推理
• 实时应用
• 资源受限环境

1.2 CANN在模型压缩中的优势

• 硬件加速支持
• 量化工具链完整
• 端侧部署优化
• 多精度计算支持

二、模型量化技术

2.1 量化感知训练

import torch
import torch.nn as nn
import torch.quantization as quant

class QuantizationAwareTraining:
    def __init__(self, model, device_id=0):
        """量化感知训练"""
        self.device = torch.device(f"npu:{device_id}")
        self.model = model.to(self.device)

        # 配置量化
        self.qconfig = quant.get_default_qconfig('fbgemm')

        # 准备量化
        self.model = quant.prepare_qat(self.model, inplace=True)

    def train(self, train_loader, num_epochs=10):
        """训练量化模型"""
        self.model.train()

        optimizer = torch.optim.SGD(self.model.parameters(), lr=0.01)

        for epoch in range(num_epochs):
            for data, target in train_loader:
                data = data.to(self.device)
                target = target.to(self.device)

                # 前向传播
                output = self.model(data)
                loss = nn.CrossEntropyLoss()(output, target)

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

            print(f"Epoch {epoch+1}, Loss: {loss.item():.4f}")

    def convert_to_quantized(self):
        """转换为量化模型"""
        # 校准
        self.model.eval()

        # 转换
        quantized_model = quant.convert(self.model, inplace=True)

        return quantized_model

class CANNQuantization:
    def __init__(self, model_path, output_path):
        """CANN量化流程"""
        self.model_path = model_path
        self.output_path = output_path

    def quantize_model(self, bits=8):
        """执行量化"""
        import subprocess

        # 使用ATC工具进行量化
        cmd = [
            "atc",
            "--model=" + self.model_path,
            "--output=" + self.output_path,
            f"--input_format=NHWC",
            f"--op_select_implmode=high_performance",
            f"--optypelist_for_implmode=all",
            f"--enable_small_channel=1",
            f"--compressed_optimize=1"
        ]

        # 执行量化
        result = subprocess.run(cmd, capture_output=True, text=True)

        if result.returncode == 0:
            print(f"量化成功，输出：{self.output_path}")
        else:
            print(f"量化失败：{result.stderr}")

        return result.returncode == 0

2.2 混合精度量化

class MixedPrecisionQuantization:
    def __init__(self, model):
        """混合精度量化"""
        self.model = model

        # 定义敏感层
        self.sensitive_layers = []

        # 定义非敏感层
        self.insensitive_layers = []

    def analyze_layer_sensitivity(self, val_loader):
        """分析层敏感度"""
        sensitivity_scores = {}

        for name, module in self.model.named_modules():
            if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear):
                # 评估该层量化后的精度损失
                sensitivity = self._evaluate_layer_sensitivity(
                    name, module, val_loader
                )
                sensitivity_scores[name] = sensitivity

        # 分类敏感层和非敏感层
        threshold = 0.5
        for name, score in sensitivity_scores.items():
            if score > threshold:
                self.sensitive_layers.append(name)
            else:
                self.insensitive_layers.append(name)

        return sensitivity_scores

    def _evaluate_layer_sensitivity(self, name, module, val_loader):
        """评估单层敏感度"""
        # 保存原始权重
        original_weight = module.weight.data.clone()

        # 量化权重
        quantized_weight = self._quantize_weight(module.weight.data)

        # 替换为量化权重
        module.weight.data = quantized_weight

        # 评估精度
        original_acc = self._evaluate_model(val_loader)

        # 恢复原始权重
        module.weight.data = original_weight

        # 计算精度下降
        quantized_acc = self._evaluate_model(val_loader)
        sensitivity = original_acc - quantized_acc

        return sensitivity

    def _quantize_weight(self, weight):
        """量化权重"""
        # 线性量化
        scale = weight.abs().max() / 127
        quantized = torch.round(weight / scale).clamp(-127, 127)

        return quantized * scale

    def _evaluate_model(self, val_loader):
        """评估模型"""
        self.model.eval()
        correct = 0
        total = 0

        with torch.no_grad():
            for data, target in val_loader:
                output = self.model(data)
                _, predicted = torch.max(output.data, 1)
                total += target.size(0)
                correct += (predicted == target).sum().item()

        accuracy = correct / total
        return accuracy

    def apply_mixed_precision(self):
        """应用混合精度"""
        for name, module in self.model.named_modules():
            if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear):
                if name in self.insensitive_layers:
                    # 应用INT8量化
                    module.weight.data = self._quantize_weight(module.weight.data)
                else:
                    # 保持FP16
                    module.weight.data = module.weight.data.half()

        return self.model

2.3 动态量化

class DynamicQuantization:
    def __init__(self, model, device_id=0):
        """动态量化"""
        self.device = torch.device(f"npu:{device_id}")
        self.model = model.to(self.device)

    def quantize_dynamic(self, qconfig_spec=None):
        """动态量化"""
        if qconfig_spec is None:
            # 默认量化Linear和LSTM层
            qconfig_spec = {
                nn.Linear: quant.get_default_qconfig('fbgemm'),
                nn.LSTM: quant.get_default_qconfig('fbgemm')
            }

        # 动态量化
        quantized_model = quant.quantize_dynamic(
            self.model,
            qconfig_spec,
            dtype=torch.qint8
        )

        return quantized_model

    def compare_model_size(self, quantized_model):
        """比较模型大小"""
        # 计算原始模型大小
        original_size = sum(
            param.numel() * param.element_size()
            for param in self.model.parameters()
        )

        # 计算量化模型大小
        quantized_size = sum(
            param.numel() * param.element_size()
            for param in quantized_model.parameters()
        )

        compression_ratio = original_size / quantized_size

        print(f"原始模型大小: {original_size / 1024 / 1024:.2f} MB")
        print(f"量化模型大小: {quantized_size / 1024 / 1024:.2f} MB")
        print(f"压缩比: {compression_ratio:.2f}x")

        return compression_ratio

三、模型剪枝技术

3.1 结构化剪枝

class StructuredPruning:
    def __init__(self, model, sparsity=0.5):
        """结构化剪枝"""
        self.model = model
        self.sparsity = sparsity

        # 记录mask
        self.masks = {}

    def calculate_importance_scores(self, data_loader):
        """计算重要性分数"""
        importance_scores = {}

        # 收集激活值
        activation = {}

        def get_activation(name):
            def hook(model, input, output):
                activation[name] = output.detach()
            return hook

        # 注册hook
        hooks = []
        for name, module in self.model.named_modules():
            if isinstance(module, nn.Conv2d):
                hook = module.register_forward_hook(
                    get_activation(name)
                )
                hooks.append(hook)

        # 前向传播
        self.model.eval()
        with torch.no_grad():
            for data, _ in data_loader:
                _ = self.model(data)

        # 移除hook
        for hook in hooks:
            hook.remove()

        # 计算分数
        for name, module in self.model.named_modules():
            if isinstance(module, nn.Conv2d):
                # 使用L1范数作为重要性
                score = torch.abs(module.weight).sum(dim=(1, 2, 3))
                importance_scores[name] = score

        return importance_scores

    def prune_channels(self, importance_scores):
        """剪枝通道"""
        for name, module in self.model.named_modules():
            if name in importance_scores:
                scores = importance_scores[name]

                # 确定要剪枝的通道
                num_channels = int(len(scores) * self.sparsity)
                _, indices = torch.topk(scores, num_channels)

                # 创建mask
                mask = torch.zeros_like(scores).bool()
                mask[indices] = True
                self.masks[name] = mask

        return self.masks

    def apply_pruning(self):
        """应用剪枝"""
        for name, module in self.model.named_modules():
            if name in self.masks:
                mask = self.masks[name]
                # 零化不重要的通道
                module.weight.data[~mask, :, :, :] = 0

                # 如果有bias也要处理
                if module.bias is not None:
                    module.bias.data[~mask] = 0

        return self.model

    def fine_tune(self, train_loader, num_epochs=5):
        """微调剪枝后的模型"""
        optimizer = torch.optim.SGD(self.model.parameters(), lr=0.01)

        for epoch in range(num_epochs):
            self.model.train()
            for data, target in train_loader:
                output = self.model(data)
                loss = nn.CrossEntropyLoss()(output, target)

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

            print(f"Fine-tune epoch {epoch+1}, Loss: {loss.item():.4f}")

3.2 非结构化剪枝

class UnstructuredPruning:
    def __init__(self, model, sparsity=0.5):
        """非结构化剪枝"""
        self.model = model
        self.sparsity = sparsity

    def prune_by_magnitude(self):
        """基于幅度的剪枝"""
        for name, param in self.model.named_parameters():
            if 'weight' in name:
                # 计算阈值
                weight_abs = param.data.abs()
                threshold = torch.quantile(weight_abs.flatten(), self.sparsity)

                # 创建mask
                mask = weight_abs > threshold

                # 应用剪枝
                param.data = param.data * mask.float()

        return self.model

    def prune_iteratively(self, train_loader, num_iterations=10):
        """迭代式剪枝"""
        for iteration in range(num_iterations):
            # 计算当前稀疏度
            current_sparsity = self._calculate_sparsity()

            # 计算目标稀疏度
            target_sparsity = self.sparsity * (iteration + 1) / num_iterations

            # 剪枝到目标稀疏度
            self._prune_to_sparsity(target_sparsity)

            # 微调
            self._fine_tune(train_loader, num_epochs=1)

            print(f"Iteration {iteration+1}, Sparsity: {current_sparsity:.2%}")

        return self.model

    def _calculate_sparsity(self):
        """计算当前稀疏度"""
        total_params = 0
        zero_params = 0

        for param in self.model.parameters():
            total_params += param.numel()
            zero_params += (param.data == 0).sum().item()

        sparsity = zero_params / total_params
        return sparsity

    def _prune_to_sparsity(self, target_sparsity):
        """剪枝到目标稀疏度"""
        for name, param in self.model.named_parameters():
            if 'weight' in name:
                # 计算阈值
                weight_abs = param.data.abs()
                threshold = torch.quantile(
                    weight_abs.flatten(),
                    target_sparsity
                )

                # 创建mask
                mask = weight_abs > threshold

                # 应用剪枝
                param.data = param.data * mask.float()

    def _fine_tune(self, train_loader, num_epochs=1):
        """微调"""
        optimizer = torch.optim.SGD(self.model.parameters(), lr=0.01)

        for epoch in range(num_epochs):
            self.model.train()
            for data, target in train_loader:
                output = self.model(data)
                loss = nn.CrossEntropyLoss()(output, target)

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

3.3 渐进式剪枝

class ProgressivePruning:
    def __init__(self, model, final_sparsity=0.9, num_iterations=30):
        """渐进式剪枝"""
        self.model = model
        self.final_sparsity = final_sparsity
        self.num_iterations = num_iterations

        # 初始化mask
        self.masks = {}
        for name, param in self.model.named_parameters():
            if 'weight' in name:
                self.masks[name] = torch.ones_like(param.data)

    def get_sparsity_schedule(self, iteration):
        """获取稀疏度调度"""
        if iteration < self.num_iterations * 0.2:
            # 初始阶段：快速增加稀疏度
            progress = iteration / (self.num_iterations * 0.2)
            sparsity = self.final_sparsity * progress
        elif iteration < self.num_iterations * 0.8:
            # 中间阶段：线性增加
            progress = (iteration - self.num_iterations * 0.2) / (self.num_iterations * 0.6)
            sparsity = self.final_sparsity * (0.2 + 0.6 * progress)
        else:
            # 最后阶段：保持
            sparsity = self.final_sparsity

        return sparsity

    def prune_step(self, train_loader, iteration):
        """执行一步剪枝"""
        # 获取目标稀疏度
        target_sparsity = self.get_sparsity_schedule(iteration)

        # 计算梯度
        self.model.train()
        data, target = next(iter(train_loader))
        output = self.model(data)
        loss = nn.CrossEntropyLoss()(output, target)
        loss.backward()

        # 更新mask
        for name, param in self.model.named_parameters():
            if 'weight' in name and name in self.masks:
                # 计算重要性
                importance = param.data.abs() * param.grad.data.abs()

                # 更新mask
                threshold = torch.quantile(
                    importance.flatten(),
                    target_sparsity
                )
                new_mask = (importance > threshold).float()

                # 应用mask
                self.masks[name] = new_mask
                param.data = param.data * new_mask

        return target_sparsity

    def train_with_pruning(self, train_loader, num_epochs=90):
        """带剪枝的训练"""
        optimizer = torch.optim.SGD(self.model.parameters(), lr=0.1)

        iteration = 0
        for epoch in range(num_epochs):
            for data, target in train_loader:
                # 剪枝步骤
                if iteration < self.num_iterations:
                    sparsity = self.prune_step(train_loader, iteration)

                    if iteration % 10 == 0:
                        print(f"Iteration {iteration}, Sparsity: {sparsity:.2%}")

                    iteration += 1

                # 训练步骤
                self.model.train()
                output = self.model(data)
                loss = nn.CrossEntropyLoss()(output, target)

                optimizer.zero_grad()
                loss.backward()

                # 应用mask到梯度
                for name, param in self.model.named_parameters():
                    if 'weight' in name and name in self.masks:
                        param.grad.data *= self.masks[name]

                optimizer.step()

            print(f"Epoch {epoch+1}, Loss: {loss.item():.4f}")

        return self.model

四、知识蒸馏

4.1 基础知识蒸馏

class KnowledgeDistillation:
    def __init__(self, teacher_model, student_model, temperature=5.0, alpha=0.5):
        """知识蒸馏"""
        self.teacher_model = teacher_model
        self.student_model = student_model
        self.temperature = temperature
        self.alpha = alpha

        # 冻结教师模型
        for param in self.teacher_model.parameters():
            param.requires_grad = False

        self.teacher_model.eval()

    def distillation_loss(self, student_output, teacher_output, target):
        """蒸馏损失"""
        # 软标签损失
        soft_loss = nn.KLDivLoss(reduction='batchmean')(
            nn.functional.log_softmax(student_output / self.temperature, dim=1),
            nn.functional.softmax(teacher_output / self.temperature, dim=1)
        ) * (self.temperature ** 2)

        # 硬标签损失
        hard_loss = nn.CrossEntropyLoss()(student_output, target)

        # 组合损失
        loss = self.alpha * soft_loss + (1 - self.alpha) * hard_loss

        return loss

    def train_student(self, train_loader, num_epochs=10):
        """训练学生模型"""
        optimizer = torch.optim.Adam(self.student_model.parameters(), lr=0.001)

        for epoch in range(num_epochs):
            self.student_model.train()

            total_loss = 0
            for data, target in train_loader:
                # 教师模型前向传播
                with torch.no_grad():
                    teacher_output = self.teacher_model(data)

                # 学生模型前向传播
                student_output = self.student_model(data)

                # 计算蒸馏损失
                loss = self.distillation_loss(
                    student_output,
                    teacher_output,
                    target
                )

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

                total_loss += loss.item()

            avg_loss = total_loss / len(train_loader)
            print(f"Epoch {epoch+1}, Loss: {avg_loss:.4f}")

        return self.student_model

    def evaluate_student(self, val_loader):
        """评估学生模型"""
        self.student_model.eval()
        correct = 0
        total = 0

        with torch.no_grad():
            for data, target in val_loader:
                output = self.student_model(data)
                _, predicted = torch.max(output.data, 1)
                total += target.size(0)
                correct += (predicted == target).sum().item()

        accuracy = correct / total
        return accuracy

4.2 特征蒸馏

class FeatureDistillation:
    def __init__(self, teacher_model, student_model, feature_layers):
        """特征蒸馏"""
        self.teacher_model = teacher_model
        self.student_model = student_model
        self.feature_layers = feature_layers

        # 冻结教师模型
        for param in self.teacher_model.parameters():
            param.requires_grad = False

        self.teacher_model.eval()

        # 注册hook获取特征
        self.teacher_features = {}
        self.student_features = {}

        self._register_hooks()

    def _register_hooks(self):
        """注册hook"""
        def get_feature_hook(features_dict, layer_name):
            def hook(module, input, output):
                features_dict[layer_name] = output.detach()
            return hook

        # 为教师模型注册hook
        for name, module in self.teacher_model.named_modules():
            if name in self.feature_layers:
                module.register_forward_hook(
                    get_feature_hook(self.teacher_features, name)
                )

        # 为学生模型注册hook
        for name, module in self.student_model.named_modules():
            if name in self.feature_layers:
                module.register_forward_hook(
                    get_feature_hook(self.student_features, name)
                )

    def feature_loss(self):
        """特征损失"""
        total_loss = 0

        for layer_name in self.feature_layers:
            teacher_feat = self.teacher_features[layer_name]
            student_feat = self.student_features[layer_name]

            # 调整尺寸
            if teacher_feat.shape != student_feat.shape:
                student_feat = nn.functional.adaptive_avg_pool2d(
                    student_feat,
                    teacher_feat.shape[2:]
                )

            # MSE损失
            loss = nn.MSELoss()(student_feat, teacher_feat)
            total_loss += loss

        return total_loss / len(self.feature_layers)

    def train_with_feature_distillation(self, train_loader, num_epochs=10, lambda_feat=0.5):
        """带特征蒸馏的训练"""
        optimizer = torch.optim.Adam(self.student_model.parameters(), lr=0.001)

        for epoch in range(num_epochs):
            self.student_model.train()

            total_loss = 0
            for data, target in train_loader:
                # 清空特征缓存
                self.teacher_features.clear()
                self.student_features.clear()

                # 教师模型前向传播
                with torch.no_grad():
                    _ = self.teacher_model(data)

                # 学生模型前向传播
                student_output = self.student_model(data)

                # 计算损失
                task_loss = nn.CrossEntropyLoss()(student_output, target)
                feat_loss = self.feature_loss()

                loss = (1 - lambda_feat) * task_loss + lambda_feat * feat_loss

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

                total_loss += loss.item()

            avg_loss = total_loss / len(train_loader)
            print(f"Epoch {epoch+1}, Loss: {avg_loss:.4f}")

        return self.student_model

五、低秩分解

5.1 SVD分解

class LowRankDecomposition:
    def __init__(self, model):
        """低秩分解"""
        self.model = model

    def decompose_conv2d(self, conv_layer, rank):
        """分解卷积层"""
        # 获取权重
        weight = conv_layer.weight.data  # (out_channels, in_channels, kH, kW)

        out_channels, in_channels, kH, kW = weight.shape

        # 重塑为2D矩阵
        weight_2d = weight.view(out_channels, -1)  # (out_channels, in_channels*kH*kW)

        # SVD分解
        U, S, V = torch.svd(weight_2d)

        # 截断到指定秩
        U_r = U[:, :rank]  # (out_channels, rank)
        S_r = S[:rank]     # (rank,)
        V_r = V[:, :rank]  # (in_channels*kH*kW, rank)

        # 创建新的卷积层
        # 第一个卷积：降维
        conv1 = nn.Conv2d(
            in_channels,
            rank,
            kernel_size=(kH, kW),
            stride=conv_layer.stride,
            padding=conv_layer.padding,
            bias=False
        )

        # 第二个卷积：升维
        conv2 = nn.Conv2d(
            rank,
            out_channels,
            kernel_size=1,
            stride=1,
            padding=0,
            bias=(conv_layer.bias is not None)
        )

        # 设置权重
        conv1.weight.data = V_r.view(rank, in_channels, kH, kW)
        conv2.weight.data = (U_r * S_r).view(out_channels, rank, 1, 1)

        if conv_layer.bias is not None:
            conv2.bias.data = conv_layer.bias.data

        return nn.Sequential(conv1, conv2)

    def decompose_linear(self, linear_layer, rank):
        """分解线性层"""
        # 获取权重
        weight = linear_layer.weight.data  # (out_features, in_features)

        out_features, in_features = weight.shape

        # SVD分解
        U, S, V = torch.svd(weight)

        # 截断到指定秩
        U_r = U[:, :rank]
        S_r = S[:rank]
        V_r = V[:, :rank]

        # 创建新的线性层
        linear1 = nn.Linear(in_features, rank, bias=False)
        linear2 = nn.Linear(rank, out_features, bias=(linear_layer.bias is not None))

        # 设置权重
        linear1.weight.data = V_r.t()
        linear2.weight.data = (U_r * S_r).t()

        if linear_layer.bias is not None:
            linear2.bias.data = linear_layer.bias.data

        return nn.Sequential(linear1, linear2)

    def apply_decomposition(self, rank_ratio=0.5):
        """应用分解"""
        for name, module in list(self.model.named_children()):
            if isinstance(module, nn.Conv2d):
                # 计算秩
                rank = int(min(module.weight.shape) * rank_ratio)

                # 分解
                decomposed = self.decompose_conv2d(module, rank)

                # 替换
                setattr(self.model, name, decomposed)

            elif isinstance(module, nn.Linear):
                # 计算秩
                rank = int(min(module.weight.shape) * rank_ratio)

                # 分解
                decomposed = self.decompose_linear(module, rank)

                # 替换
                setattr(self.model, name, decomposed)

        return self.model

    def fine_tune(self, train_loader, num_epochs=5):
        """微调分解后的模型"""
        optimizer = torch.optim.SGD(self.model.parameters(), lr=0.01)

        for epoch in range(num_epochs):
            self.model.train()
            for data, target in train_loader:
                output = self.model(data)
                loss = nn.CrossEntropyLoss()(output, target)

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

            print(f"Fine-tune epoch {epoch+1}, Loss: {loss.item():.4f}")

5.2 CP分解

class CPDecomposition:
    def __init__(self, model):
        """CP分解"""
        self.model = model

    def decompose_conv2d_cp(self, conv_layer, rank):
        """CP分解卷积层"""
        weight = conv_layer.weight.data
        out_channels, in_channels, kH, kW = weight.shape

        # 重塑为矩阵
        weight_matrix = weight.view(out_channels, -1)

        # 使用CP分解
        # 这里简化为逐通道分解
        A = torch.randn(out_channels, rank)
        B = torch.randn(in_channels, rank)
        C = torch.randn(kH * kW, rank)

        # 优化分解（简化版，实际应该使用ALS等算法）
        for _ in range(100):
            # 更新A
            BC = torch.mm(B.t(), C.t())
            BC_t_BC = torch.mm(BC, BC.t())
            W_BC = torch.mm(weight_matrix, BC)
            A = torch.mm(W_BC, torch.inverse(BC_t_BC))

            # 更新B
            AC = torch.mm(A.t(), C.t())
            AC_t_AC = torch.mm(AC, AC.t())
            W_AC = torch.mm(weight_matrix.t(), A)
            B = torch.mm(W_AC, torch.inverse(AC_t_AC))

            # 更新C
            AB = torch.mm(A.t(), B.t())
            AB_t_AB = torch.mm(AB, AB.t())
            W_reshaped = weight_matrix.t().contiguous().view(-1, out_channels)
            AB_W = torch.mm(AB, W_reshaped.t())
            C = torch.mm(AB_W.t(), torch.inverse(AB_t_AB))

        # 创建分解后的层
        conv1 = nn.Conv2d(in_channels, rank, (kH, kW),
                         stride=conv_layer.stride,
                         padding=conv_layer.padding,
                         bias=False)

        conv2 = nn.Conv2d(rank, out_channels, 1,
                         stride=1,
                         padding=0,
                         bias=(conv_layer.bias is not None))

        # 设置权重
        conv1.weight.data = C.view(rank, kH, kW, in_channels).permute(3, 0, 1, 2)
        conv2.weight.data = A.t().view(out_channels, rank, 1, 1)

        if conv_layer.bias is not None:
            conv2.bias.data = conv_layer.bias.data

        return nn.Sequential(conv1, conv2)

    def apply_cp_decomposition(self, rank=32):
        """应用CP分解"""
        for name, module in list(self.model.named_children()):
            if isinstance(module, nn.Conv2d) and module.kernel_size > (1, 1):
                decomposed = self.decompose_conv2d_cp(module, rank)
                setattr(self.model, name, decomposed)

        return self.model

六、模型融合与优化

6.1 层融合

class LayerFusion:
    def __init__(self, model):
        """层融合"""
        self.model = model

    def fuse_conv_bn(self, conv, bn):
        """融合卷积和批归一化"""
        # 获取参数
        w_conv = conv.weight.data
        b_conv = conv.bias.data if conv.bias is not None else torch.zeros(conv.out_channels)

        w_bn = bn.weight.data
        b_bn = bn.bias.data
        running_mean = bn.running_mean.data
        running_var = bn.running_var.data
        eps = bn.eps
        momentum = bn.momentum

        # 计算融合参数
        sigma = torch.sqrt(running_var + eps)
        gamma = w_bn / sigma
        beta = b_bn - w_bn * running_mean / sigma

        # 融合权重
        w_fused = w_conv * gamma.view(-1, 1, 1, 1)
        b_fused = b_conv * gamma + beta

        # 创建新的卷积层
        fused_conv = nn.Conv2d(
            conv.in_channels,
            conv.out_channels,
            conv.kernel_size,
            stride=conv.stride,
            padding=conv.padding,
            bias=True
        )

        # 设置融合后的权重
        fused_conv.weight.data = w_fused
        fused_conv.bias.data = b_fused

        return fused_conv

    def apply_fusion(self):
        """应用融合"""
        modules = list(self.model.named_children())

        for i, (name, module) in enumerate(modules):
            # 检查是否是Conv+BN序列
            if isinstance(module, nn.Conv2d):
                # 查看下一个模块
                if i + 1 < len(modules):
                    next_name, next_module = modules[i + 1]
                    if isinstance(next_module, nn.BatchNorm2d):
                        # 融合
                        fused = self.fuse_conv_bn(module, next_module)

                        # 替换
                        setattr(self.model, name, fused)

                        # 删除BN层
                        delattr(self.model, next_name)

        return self.model

6.2 常量折叠

class ConstantFolding:
    def __init__(self, model, example_input):
        """常量折叠"""
        self.model = model
        self.example_input = example_input

    def fold_constants(self):
        """折叠常量"""
        # 使用JIT跟踪
        self.model.eval()

        with torch.no_grad():
            traced_model = torch.jit.trace(self.model, self.example_input)

        # 优化
        optimized_model = torch.jit.optimize_for_inference(traced_model)

        return optimized_model

    def simplify_graph(self):
        """简化计算图"""
        from torch.fx import symbolic_trace

        # 符号跟踪
        self.model.eval()
        graph_module = symbolic_trace(self.model)

        # 简化
        # 这里可以添加各种简化规则
        # 例如：消除死代码、常量传播等

        return graph_module

七、总结

CANN为模型压缩与加速提供了完整的解决方案，从量化到剪枝，从知识蒸馏到低秩分解，都可以高效实现。通过合理的压缩策略，可以在保证精度的前提下大幅提升推理效率。

关键点：

• 量化降低精度
• 剪枝移除冗余
• 知识蒸馏传递知识
• 低秩分解减少参数
• 层融合优化计算

参考资料

• CANN模型优化指南
• CANN community仓库
• 模型压缩最佳实践