GPU的单精度半精度双精度的区别 单精度和半精度的区别

作者：李长安。

1 混合精度训练

混合精度训练最初是在论文Mixed Precision Training中被踢出，该论文对混合精度训练进行了详细的阐述，并对其实现进行了讲解，有兴趣的同学可以看看这篇论文。

1.1半精度与单精度

半精度（也被称为FP16）对比高精度的FP32与FP64降低了神经网络的显存占用，使得我们可以训练部署更大的网络，并且FP16在数据转换时比FP32或者FP64更节省时间。

单精度（也被称为32-bit）是通用的浮点数格式（在C扩展语言中表示为float），64-bit被称为双精度（double）。

如图所示，我们能够很直观的看到半精度的存储空间是单精度存储空间的一半。

1.2为什么使用混合精度训练

混合精度训练，指代的是单精度 float和半精度 float16 混合训练。

float16和float相比恰里，总结下来就是两个原因：内存占用更少，计算更快。

内存占用更少：这个是显然可见的，通用的模型 fp16 占用的内存只需原来的一半。memory-bandwidth 减半所带来的好处：

模型占用的内存更小，训练的时候可以用更大的batchsize。

模型训练时，通信量（特别是多卡，或者多机多卡）大幅减少，大幅减少等待时间，加快数据的流通。

计算更快：目前的不少GPU都有针对 fp16 的计算进行优化。论文指出：在近期的GPU中，半精度的计算吞吐量可以是单精度的 2-8 倍；

损失控制原理：

2 实验设计

本次实验主要从两个方面进行测试，分别在精度和速度两个部分进行对比。实验中采用ResNet-18作为测试对象，使用的数据集为美食数据集，共五种类别。

# 解压数据集
!cd data/data64280/ && unzip -q trainset.zip

2.1数据集预处理

import pandas as pd
import numpy as np
import os
all_file_dir = 'data/data64280/trainset'
img_list = []
label_list = []
label_id = 0
class_list = [c for c in os.listdir(all_file_dir) if os.path.isdir(os.path.join(all_file_dir, c))]
for class_dir in class_list:
 image_path_pre = os.path.join(all_file_dir, class_dir)
 for img in os.listdir(image_path_pre):
 img_list.append(os.path.join(image_path_pre, img))
 label_list.append(label_id)
 label_id += 1
img_df = pd.DataFrame(img_list)
label_df = pd.DataFrame(label_list)
img_df.columns = ['images']
label_df.columns = ['label']
df = pd.concat([img_df, label_df], axis=1)
df = df.reindex(np.random.permutation(df.index))
df.to_csv('food_data.csv', index=0)
import pandas as pd
# 读取数据
df = pd.read_csv('food_data.csv')
image_path_list = df['images'].values
label_list = df['label'].values
# 划分训练集和校验集
all_size = len(image_path_list)
train_size = int(all_size * 0.8)
train_image_path_list = image_path_list[:train_size]
train_label_list = label_list[:train_size]
val_image_path_list = image_path_list[train_size:]
val_label_list = label_list[train_size:]

2.2自定义数据集

import numpy as np
from PIL import Image
from paddle.io import Dataset
import paddle.vision.transforms as T
import paddle as pd
class MyDataset(Dataset):
 """
 步骤一：继承paddle.io.Dataset类
    """
 def __init__(self, image, label, transform=None):
 """
 步骤二：实现构造函数，定义数据读取方式，划分训练和测试数据集
        """
 super(MyDataset, self).__init__()
 imgs = image
        labels = label
 self.labels = labels
 self.imgs = imgs
 self.transform = transform
 # self.loader = loader
 def __getitem__(self, index): # 这个方法是必须要有的，用于按照索引读取每个元素的具体内容
 fn = self.imgs
        label = self.labels
 # fn是图片path #fn和label分别获得imgs[index]也即是刚才每行中word[0]和word[1]的信息
 for im,la in zip(fn, label):
 img = Image.open(im)
 img = img.convert("RGB")
 img = np.array(img).astype('float32') / 255.0
            label = np.array([la]).astype(dtype='int64')
 # 按照路径读取图片
 if self.transform is not None:
 img = self.transform(img)
 # 数据标签转换为Tensor
 return img, label
 # return回哪些内容，那么我们在训练时循环读取每个batch时，就能获得哪些内容
 # **********************************  #使用__len__()初始化一些需要传入的参数及数据集的调用**********************
 def __len__(self):
 # 这个函数也必须要写，它返回的是数据集的长度，也就是多少张图片，要和loader的长度作区分
 return len(self.imgs)

2.3训练准备

import paddle
from paddle.metric import Accuracy
import warnings
warnings.filterwarnings("ignore")
import paddle.vision.transforms as T
transform = T.Compose([
 T.Resize([224, 224]),
 T.ToTensor(),
 # T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
 # T.Transpose(),
])
train_dataset = MyDataset(image=train_image_path_list, label=train_label_list ,transform=transform)
train_loader = paddle.io.DataLoader(train_dataset, places=paddle.CPUPlace(), batch_size=16, shuffle=True)
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import Conv2D, BatchNorm, Linear, Dropout
from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D
from paddle.nn.initializer import Uniform
import math
__all__ = ["ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"]
class ConvBNLayer(nn.Layer):
 def __init__(self,
 num_channels,
 num_filters,
 filter_size,
                 stride=1,
                 groups=1,
                 act=None,
                 name=None,
 data_format="NCHW"):
 super(ConvBNLayer, self).__init__()
 self._conv = Conv2D(
 in_channels=num_channels,
 out_channels=num_filters,
 kernel_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
 weight_attr=ParamAttr(name=name + "_weights"),
 bias_attr=False,
 data_format=data_format)
 if name == "conv1":
 bn_name = "bn_" + name
 else:
 bn_name = "bn" + name[3:]
 self._batch_norm = BatchNorm(
 num_filters,
            act=act,
 param_attr=ParamAttr(name=bn_name + "_scale"),
 bias_attr=ParamAttr(bn_name + "_offset"),
 moving_mean_name=bn_name + "_mean",
 moving_variance_name=bn_name + "_variance",
 data_layout=data_format)
 def forward(self, inputs):
        y = self._conv(inputs)
        y = self._batch_norm(y)
 return y
class BottleneckBlock(nn.Layer):
 def __init__(self,
 num_channels,
 num_filters,
                 stride,
                 shortcut=True,
                 name=None,
 data_format="NCHW"):
 super(BottleneckBlock, self).__init__()
 self.conv0 = ConvBNLayer(
 num_channels=num_channels,
 num_filters=num_filters,
 filter_size=1,
            act="relu",
            name=name + "_branch2a",
 data_format=data_format)
 self.conv1 = ConvBNLayer(
 num_channels=num_filters,
 num_filters=num_filters,
 filter_size=3,
            stride=stride,
            act="relu",
            name=name + "_branch2b",
 data_format=data_format)
 self.conv2 = ConvBNLayer(
 num_channels=num_filters,
 num_filters=num_filters * 4,
 filter_size=1,
            act=None,
            name=name + "_branch2c",
 data_format=data_format)
 if not shortcut:
 self.short = ConvBNLayer(
 num_channels=num_channels,
 num_filters=num_filters * 4,
 filter_size=1,
                stride=stride,
                name=name + "_branch1",
 data_format=data_format)
 self.shortcut = shortcut
 self._num_channels_out = num_filters * 4
 def forward(self, inputs):
        y = self.conv0(inputs)
        conv1 = self.conv1(y)
        conv2 = self.conv2(conv1)
 if self.shortcut:
            short = inputs
 else:
            short = self.short(inputs)
        y = paddle.add(x=short, y=conv2)
        y = F.relu(y)
 return y
class BasicBlock(nn.Layer):
 def __init__(self,
 num_channels,
 num_filters,
                 stride,
                 shortcut=True,
                 name=None,
 data_format="NCHW"):
 super(BasicBlock, self).__init__()
 self.stride = stride
 self.conv0 = ConvBNLayer(
 num_channels=num_channels,
 num_filters=num_filters,
 filter_size=3,
            stride=stride,
            act="relu",
            name=name + "_branch2a",
 data_format=data_format)
 self.conv1 = ConvBNLayer(
 num_channels=num_filters,
 num_filters=num_filters,
 filter_size=3,
            act=None,
            name=name + "_branch2b",
 data_format=data_format)
 if not shortcut:
 self.short = ConvBNLayer(
 num_channels=num_channels,
 num_filters=num_filters,
 filter_size=1,
                stride=stride,
                name=name + "_branch1",
 data_format=data_format)
 self.shortcut = shortcut
 def forward(self, inputs):
        y = self.conv0(inputs)
        conv1 = self.conv1(y)
 if self.shortcut:
            short = inputs
 else:
            short = self.short(inputs)
        y = paddle.add(x=short, y=conv1)
        y = F.relu(y)
 return y
class ResNet(nn.Layer):
 def __init__(self, layers=50, class_dim=1000, input_image_channel=3, data_format="NCHW"):
 super(ResNet, self).__init__()
 self.layers = layers
 self.data_format = data_format
 self.input_image_channel = input_image_channel
 supported_layers = [18, 34, 50, 101, 152]
 assert layers in supported_layers, \
 "supported layers are {} but input layer is {}".format(
 supported_layers, layers)
 if layers == 18:
            depth = [2, 2, 2, 2]
 elif layers == 34 or layers == 50:
            depth = [3, 4, 6, 3]
 elif layers == 101:
            depth = [3, 4, 23, 3]
 elif layers == 152:
            depth = [3, 8, 36, 3]
 num_channels = [64, 256, 512,
 1024] if layers >= 50 else [64, 64, 128, 256]
 num_filters = [64, 128, 256, 512]
 self.conv = ConvBNLayer(
 num_channels=self.input_image_channel,
 num_filters=64,
 filter_size=7,
            stride=2,
            act="relu",
            name="conv1",
 data_format=self.data_format)
 self.pool2d_max = MaxPool2D(
 kernel_size=3,
            stride=2, 
            padding=1,
 data_format=self.data_format)
 self.block_list = []
 if layers >= 50:
 for block in range(len(depth)):
                shortcut = False
 for i in range(depth[block]):
 if layers in [101, 152] and block == 2:
 if i == 0:
 conv_name = "res" + str(block + 2) + "a"
 else:
 conv_name = "res" + str(block + 2) + "b" + str(i)
 else:
 conv_name = "res" + str(block + 2) + chr(97 + i)
 bottleneck_block = self.add_sublayer(
 conv_name,
 BottleneckBlock(
 num_channels=num_channels[block]
 if i == 0 else num_filters[block] * 4,
 num_filters=num_filters[block],
                            stride=2 if i == 0 and block != 0 else 1,
                            shortcut=shortcut,
                            name=conv_name,
 data_format=self.data_format))
 self.block_list.append(bottleneck_block)
                    shortcut = True
 else:
 for block in range(len(depth)):
                shortcut = False
 for i in range(depth[block]):
 conv_name = "res" + str(block + 2) + chr(97 + i)
 basic_block = self.add_sublayer(
 conv_name,
 BasicBlock(
 num_channels=num_channels[block]
 if i == 0 else num_filters[block],
 num_filters=num_filters[block],
                            stride=2 if i == 0 and block != 0 else 1,
                            shortcut=shortcut,
                            name=conv_name,
 data_format=self.data_format))
 self.block_list.append(basic_block)
                    shortcut = True
 self.pool2d_avg = AdaptiveAvgPool2D(1, data_format=self.data_format)
 self.pool2d_avg_channels = num_channels[-1] * 2
 stdv = 1.0 / math.sqrt(self.pool2d_avg_channels * 1.0)
 self.out = Linear(
 self.pool2d_avg_channels,
 class_dim,
 weight_attr=ParamAttr(
                initializer=Uniform(-stdv, stdv), name="fc_0.w_0"),
 bias_attr=ParamAttr(name="fc_0.b_0"))
 def forward(self, inputs):
        y = self.conv(inputs)
        y = self.pool2d_max(y)
 for block in self.block_list:
            y = block(y)
        y = self.pool2d_avg(y)
        y = paddle.reshape(y, shape=[-1, self.pool2d_avg_channels])
        y = self.out(y)
 return y
def ResNet18(**args):
    model = ResNet(layers=18, **args)
 return model

2.4训练过程定义

import paddle
import numpy
import paddle.nn.functional as F
import time
def train(model):
 model.train()
    epochs = 5
 optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
 # 用Adam作为优化函数
 for epoch in range(epochs):
 for batch_id, data in enumerate(train_loader()):
 x_data = data[0]
 y_data = data[1]
 # print(y_data)
            predicts = model(x_data)
            loss = F.cross_entropy(predicts, y_data)
 # 计算损失
            acc = paddle.metric.accuracy(predicts, y_data, k=2)
 loss.backward()
 if batch_id % 10 == 0:
 print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(), acc.numpy()))
 optim.step()
 optim.clear_grad()
import paddle
import numpy
import paddle.nn.functional as F
import time
def train_amp(model):
 model.train()
    epochs = 5
 optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
 # 用Adam作为优化函数
 for epoch in range(epochs):
 for batch_id, data in enumerate(train_loader()):
 x_data = data[0].astype('float16')
 y_data = data[1]
            scaler = paddle.amp.GradScaler(init_loss_scaling=1024)
 with paddle.amp.auto_cast():
                predicts = model(x_data)
                loss = F.cross_entropy(predicts, y_data)
            scaled = scaler.scale(loss) # scale the loss
 scaled.backward() # do backward
            acc = paddle.metric.accuracy(predicts, y_data, k=2)
 if batch_id % 10 == 0:
 print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, loss.numpy(),
 acc.numpy()))
 optim.step()
 optim.clear_grad()

2.5开启训练

此部分，分别对两种训练方式进行对比，主要关注模型的训练速度

model = ResNet18(class_dim=2)
strat = time.time()
train(model)
end = time.time()
print('no_amp:', end-strat)
epoch: 0, batch_id: 0, loss is: [0.21116894], acc is: [1.]
epoch: 1, batch_id: 0, loss is: [0.00010776], acc is: [1.]
epoch: 2, batch_id: 0, loss is: [2.5868081e-05], acc is: [1.]
epoch: 3, batch_id: 0, loss is: [1.442422e-05], acc is: [1.]
epoch: 4, batch_id: 0, loss is: [1.1086402e-05], acc is: [1.]
no_amp: 740.6813971996307
strat1 = time.time()
train_amp(model)
end1 = time.time()
print('with amp:', end1-strat1)
epoch: 0, batch_id: 0, loss is: [0.512834], acc is: [1.]
epoch: 1, batch_id: 0, loss is: [0.00025519], acc is: [1.]
epoch: 2, batch_id: 0, loss is: [5.9364465e-05], acc is: [1.]
epoch: 3, batch_id: 0, loss is: [3.2305197e-05], acc is: [1.]
epoch: 4, batch_id: 0, loss is: [2.4556812e-05], acc is: [1.]
with amp: 740.9603228569031

3 总结

对于本次实验，由于迭代轮数较少，只迭代了5次，故时间上的优势没有体现出来，大家有兴趣的可以增加迭代次数，或者换更深的网络进行测试。

从训练的结果来看，使用混合精度训练，其loss值是高于未使用混合精度训练模型的。