[AI学习笔记]DeepSeek 量化压缩实战：INT8 精度保持方案详解

# 量化参数初始化示例代码
import numpy as np

# 假设 model_weights 是模型的权重参数
model_weights = np.random.randn(1000, 1000)  # 示例权重参数

# 进行小批量采样，估计权重参数的分布范围
sample_size = 100
weight_sample = model_weights[np.random.choice(model_weights.shape[0], sample_size, replace=False)]

# 计算权重参数的最小值和最大值
weight_min = np.min(weight_sample)
weight_max = np.max(weight_sample)

# 根据权重参数的范围确定量化参数
quantization_scale = weight_max - weight_min
quantization_offset = weight_min

# 量化误差补偿示例代码
# 假设 quantized_weights 是量化后的权重参数
quantized_weights = np.round((model_weights - weight_min) / quantization_scale * 255)

# 计算量化误差
quantization_error = model_weights - (quantized_weights * quantization_scale / 255 + weight_min)

# 计算补偿项
compensation_term = np.mean(quantization_error, axis=0)

# 对量化后的权重参数进行补偿
compensated_weights = quantized_weights * quantization_scale / 255 + weight_min + compensation_term

# 量化模型微调示例代码
# 假设 quantized_model 是量化后的模型
# 加载训练数据和测试数据
train_data = ...  # 训练数据
test_data = ...  # 测试数据

# 设置微调参数
learning_rate = 0.0001
optimizer = Adam(learning_rate)
batch_size = 64
epochs = 10

# 进行模型微调
for epoch in range(epochs):
    # 训练过程
    for batch in train_data.batch(batch_size):
        with tf.GradientTape() as tape:
            predictions = quantized_model(batch[0], training=True)
            loss = loss_function(batch[1], predictions)
        gradients = tape.gradient(loss, quantized_model.trainable_variables)
        optimizer.apply_gradients(zip(gradients, quantized_model.trainable_variables))
    
    # 测试过程
    test_accuracy = evaluate_model(quantized_model, test_data)
    print(f"Epoch {epoch+1}, Test Accuracy: {test_accuracy}")

# 数据预处理示例代码
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# 加载 CIFAR-10 数据集
(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()

# 对图像数据进行归一化处理
train_images = train_images.astype('float32') / 255.0
test_images = test_images.astype('float32') / 255.0

# 进行数据增强
data_augmentation = ImageDataGenerator(
    rotation_range=15,
    width_shift_range=0.1,
    height_shift_range=0.1,
    horizontal_flip=True
)

train_generator = data_augmentation.flow(train_images, train_labels, batch_size=128)

# 模型构建示例代码
from tensorflow.keras import models, layers

def build_model():
    model = models.Sequential()
    model.add(layers.Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(32, 32, 3)))
    model.add(layers.MaxPooling2D((2, 2)))
    model.add(layers.Conv2D(64, (3, 3), activation='relu', padding='same'))
    model.add(layers.MaxPooling2D((2, 2)))
    model.add(layers.Flatten())
    model.add(layers.Dense(512, activation='relu'))
    model.add(layers.Dense(10))
    return model

model = build_model()
model.summary()

# 模型训练示例代码
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.losses import SparseCategoricalCrossentropy

# 设置训练参数
learning_rate = 0.001
batch_size = 128
epochs = 50

# 编译模型
model.compile(optimizer=Adam(learning_rate),
              loss=SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# 进行模型训练
history = model.fit(train_generator, epochs=epochs, validation_data=(test_images, test_labels))

# 量化压缩示例代码
import tensorflow_model_optimization as tfmot

# 应用 INT8 量化
quantize_annotate = tfmot.quantization.keras.quantize_annotate
quantize_scope = tfmot.quantization.keras.quantize_scope

# 定义量化配置
quantize_config = tfmot.quantization.keras.Default8BitQuantizeConfig()

# 对模型进行量化注解
quantized_model = quantize_annotate(model)

# 定义量化具体实现
with quantize_scope():
    quantized_model = tfmot.quantization.keras.quantize_apply(quantized_model)

# 保存量化后的模型
quantized_model.save('quantized_model.h5')

# 模型评估示例代码
# 加载量化后的模型
from tensorflow.keras.models import load_model

quantized_model = load_model('quantized_model.h5', custom_objects={'QuantizeConfig': quantize_config})

# 评估量化后模型的性能
test_loss, test_acc = quantized_model.evaluate(test_images, test_labels, verbose=2)
print('\nQuantized model test accuracy:', test_acc)

# 如果需要，进行模型微调
# 设置微调参数
learning_rate_ft = 0.0001
optimizer_ft = Adam(learning_rate_ft)
epochs_ft = 10

# 编译量化后的模型用于微调
quantized_model.compile(optimizer=optimizer_ft,
                        loss=SparseCategoricalCrossentropy(from_logits=True),
                        metrics=['accuracy'])

# 进行模型微调
history_ft = quantized_model.fit(train_generator, epochs=epochs_ft, validation_data=(test_images, test_labels))