python GPU多进程 pytorch多进程

文章目录

• 导包
• 准备训练数据（同单进程）
• 构造模型（同单进程）
• 构造训练代码（同单进程）
• 开始训练（这里设置多进程！）
• 验证多进程下参数的有效性
• 完整的代码

导包

import torch.multiprocessing as mp
import torch
import torch.nn as nn
import torch.utils.data as Data

这里使用torch.multiprocessing构造多进程，与python的多进程神似

准备训练数据（同单进程）

这里使用随机数生成训练数据，就不用下载什么数据了

def get_train_data():
    """得到训练数据，这里使用随机数生成训练数据，由此导致最终结果并不好"""

    def get_tensor_from_pd(dataframe_series) -> torch.Tensor:
        return torch.tensor(data=dataframe_series.values)

    import numpy as np
    import pandas as pd
    from sklearn import preprocessing
    # 生成训练数据x并做归一化后，构造成dataframe格式，再转换为tensor格式
    df = pd.DataFrame(data=preprocessing.MinMaxScaler().fit_transform(np.random.randint(0, 10, size=(2000, 300))))
    y = pd.Series(list(range(2000)))
    return get_tensor_from_pd(df).float(), get_tensor_from_pd(y).float()

构造模型（同单进程）

这里使用一个LSTM的AutoEncoder，可以换成任何你想要训练的模型哈~，多进程与模型是什么无关

class LstmFcAutoEncoder(nn.Module):
    def __init__(self, input_layer=300, hidden_layer=100, batch_size=20):
        super(LstmFcAutoEncoder, self).__init__()

        self.input_layer = input_layer
        self.hidden_layer = hidden_layer
        self.batch_size = batch_size

        self.encoder_lstm = nn.LSTM(self.input_layer, self.hidden_layer, batch_first=True)
        self.encoder_fc = nn.Linear(self.hidden_layer, self.hidden_layer)
        self.decoder_lstm = nn.LSTM(self.hidden_layer, self.input_layer, batch_first=True)
        self.decoder_fc = nn.Linear(self.hidden_layer, self.hidden_layer)
        self.relu = nn.ReLU()

    def forward(self, input_x):
        input_x = input_x.view(len(input_x), 1, -1)
        # encoder
        encoder_lstm, (n, c) = self.encoder_lstm(input_x,
                                                 # shape: (n_layers, batch, hidden_size)
                                                 (torch.zeros(1, self.batch_size, self.hidden_layer),
                                                  torch.zeros(1, self.batch_size, self.hidden_layer)))
        encoder_fc = self.encoder_fc(encoder_lstm)
        encoder_out = self.relu(encoder_fc)
        # decoder
        decoder_fc = self.relu(self.decoder_fc(encoder_out))
        decoder_lstm, (n, c) = self.decoder_lstm(decoder_fc,
                                                 (torch.zeros(1, 20, self.input_layer),
                                                  torch.zeros(1, 20, self.input_layer)))
        return decoder_lstm.squeeze()

构造训练代码（同单进程）

由于多个进程之间是隔离的，因此除了模型参数会共享，其余都不共享：损失函数、优化器、迭代次数不共享

def train(model, data_loader, loss_function, optimizer, epochs):
    for i in range(epochs):
        for seq, labels in data_loader:
            optimizer.zero_grad()
            y_pred = model(seq).squeeze()  # 压缩维度：得到输出，并将维度为1的去除
            single_loss = loss_function(y_pred, seq)
            single_loss.backward()
            optimizer.step()

开始训练（这里设置多进程！）

if __name__ == '__main__':
    model = LstmFcAutoEncoder()
    x, y = get_train_data()

这里将data构造为DataLoader，当然不构造也可以

train_loader = Data.DataLoader(
        dataset=Data.TensorDataset(x, y),  # 封装进Data.TensorDataset()类的数据，可以为任意维度
        batch_size=20,  # 每块的大小
        shuffle=True,  # 要不要打乱数据 (打乱比较好)
        num_workers=3,  # 多进程（multiprocess）来读数据
    )

# 常规训练三件套
    loss_function = nn.MSELoss()  # loss
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)  # 优化器
    epochs = 150

开始多进程的设置：

# 多进程开始
    num_processes = 4  # 设置4个进程
    # NOTE: this is required for the ``fork`` method to work
    model.share_memory()
    processes = []
    for rank in range(num_processes):
        # 4 个进程，每个进程epoch为150，也就是说其实迭代了 4*150 = 600 次 !!!
        p = mp.Process(target=train, args=(model, train_loader, loss_function, optimizer, epochs))
        p.start()
        processes.append(p)
    for p in processes:
        p.join()

如果需要尽情压榨cpu的效率，可以修改为：

from multiprocessing import cpu_count

    num_processes = cpu_count()

验证多进程下参数的有效性

1. 打开控制台看cpu是否多进程（当然是啦）
2. 验证参数：在调试时打断点（建议打到61行：optimizer.step()），用一个进程迭代几轮，然后看其他每个进程的模型的参数：

print(model.encoder_fc.state_dict()) # 看model的encoder_fc层的参数

完整的代码

import torch.multiprocessing as mp

import torch
import torch.nn as nn
import torch.utils.data as Data


def get_train_data():
    """得到训练数据，这里使用随机数生成训练数据，由此导致最终结果并不好"""

    def get_tensor_from_pd(dataframe_series) -> torch.Tensor:
        return torch.tensor(data=dataframe_series.values)

    import numpy as np
    import pandas as pd
    from sklearn import preprocessing
    # 生成训练数据x并做归一化后，构造成dataframe格式，再转换为tensor格式
    df = pd.DataFrame(data=preprocessing.MinMaxScaler().fit_transform(np.random.randint(0, 10, size=(2000, 300))))
    y = pd.Series(list(range(2000)))
    return get_tensor_from_pd(df).float(), get_tensor_from_pd(y).float()


class LstmFcAutoEncoder(nn.Module):
    def __init__(self, input_layer=300, hidden_layer=100, batch_size=20):
        super(LstmFcAutoEncoder, self).__init__()

        self.input_layer = input_layer
        self.hidden_layer = hidden_layer
        self.batch_size = batch_size

        self.encoder_lstm = nn.LSTM(self.input_layer, self.hidden_layer, batch_first=True)
        self.encoder_fc = nn.Linear(self.hidden_layer, self.hidden_layer)
        self.decoder_lstm = nn.LSTM(self.hidden_layer, self.input_layer, batch_first=True)
        self.decoder_fc = nn.Linear(self.hidden_layer, self.hidden_layer)
        self.relu = nn.ReLU()

    def forward(self, input_x):
        input_x = input_x.view(len(input_x), 1, -1)
        # encoder
        encoder_lstm, (n, c) = self.encoder_lstm(input_x,
                                                 # shape: (n_layers, batch, hidden_size)
                                                 (torch.zeros(1, self.batch_size, self.hidden_layer),
                                                  torch.zeros(1, self.batch_size, self.hidden_layer)))
        encoder_fc = self.encoder_fc(encoder_lstm)
        encoder_out = self.relu(encoder_fc)
        # decoder
        decoder_fc = self.relu(self.decoder_fc(encoder_out))
        decoder_lstm, (n, c) = self.decoder_lstm(decoder_fc,
                                                 (torch.zeros(1, 20, self.input_layer),
                                                  torch.zeros(1, 20, self.input_layer)))
        return decoder_lstm.squeeze()


def train(model, data_loader, loss_function, optimizer, epochs):
    for i in range(epochs):
        for seq, labels in data_loader:
            optimizer.zero_grad()
            y_pred = model(seq).squeeze()  # 压缩维度：得到输出，并将维度为1的去除
            single_loss = loss_function(y_pred, seq)
            single_loss.backward()
            optimizer.step()


if __name__ == '__main__':
    model = LstmFcAutoEncoder()
    x, y = get_train_data()
    train_loader = Data.DataLoader(
        dataset=Data.TensorDataset(x, y),  # 封装进Data.TensorDataset()类的数据，可以为任意维度
        batch_size=20,  # 每块的大小
        shuffle=True,  # 要不要打乱数据 (打乱比较好)
        num_workers=3,  # 多进程（multiprocess）来读数据
    )
    # 常规训练三件套
    loss_function = nn.MSELoss()  # loss
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)  # 优化器
    epochs = 150
    # 多进程开始
    # from multiprocessing import cpu_count
    #
    # num_processes = cpu_count()
    num_processes = 4  # 4个进程
    # NOTE: this is required for the ``fork`` method to work
    model.share_memory()
    processes = []
    for rank in range(num_processes):
        # 4 个进程，每个进程epoch为150，也就是说其实迭代了 4*150 = 600 次 !!!
        p = mp.Process(target=train, args=(model, train_loader, loss_function, optimizer, epochs))
        p.start()
        processes.append(p)
    for p in processes:
        p.join()