303 lines
9.4 KiB
Python
303 lines
9.4 KiB
Python
import os
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os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import torchvision
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from torchvision import transforms
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from torch.utils.data import DataLoader
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import math
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import torch.nn.functional as F
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import numpy as np
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from torch.utils.tensorboard import SummaryWriter
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import datetime
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torch.manual_seed(1234)
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np.random.seed(1234)
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torch.cuda.manual_seed_all(1234)
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batch_size = 16
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lr = 0.001
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"Using device: {device}")
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transform = transforms.Compose(
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[transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))] # MNIST数据集的均值和标准差
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)
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train_dataset = torchvision.datasets.MNIST(root="./data", train=True, download=True, transform=transform)
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test_dataset = torchvision.datasets.MNIST(root="./data", train=False, download=True, transform=transform)
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train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
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test_loader = DataLoader(test_dataset, batch_size=1024, shuffle=False)
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def to_binary_tensor(input_tensor, bits):
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int_tensor = torch.round(input_tensor).clamp(0, 2**bits - 1).to(torch.int64)
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shifts = torch.arange(bits - 1, -1, -1, device=int_tensor.device)
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binary_bits = (int_tensor.unsqueeze(-1) >> shifts) & 1
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return binary_bits
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class Lut(torch.autograd.Function):
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# input [batch,count,bits]
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# weight [count,2**bits]
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@staticmethod
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def forward(ctx, input, weight):
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batch = input.shape[0]
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count = input.shape[1]
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bits = input.shape[2]
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assert int(math.log2(weight.shape[-1])) == bits
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index = 2 ** torch.arange(bits - 1, -1, -1, device=input.device)
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x = (input > 0).long()
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x = x * index
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ind = x.sum(dim=-1)
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row_indices = torch.arange(count).unsqueeze(0).expand(batch, -1)
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output = weight[row_indices, ind]
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ctx.save_for_backward(input, weight, ind)
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return output
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@staticmethod
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def backward(ctx, grad_output):
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input, weight, ind = ctx.saved_tensors
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grad_input = grad_weight = None
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batch = input.shape[0]
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count = input.shape[1]
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bits = input.shape[2]
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if ctx.needs_input_grad[1]:
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grad_weight = torch.zeros_like(weight)
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ind_p = ind.permute(1, 0)
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grad_output_p = grad_output.permute(1, 0)
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grad_weight.scatter_add_(1, ind_p, grad_output_p)
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if ctx.needs_input_grad[0]:
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row_indices = torch.arange(count).unsqueeze(0).expand(batch, -1)
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grad_input = grad_output * weight[row_indices, ind]
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grad_input = grad_input.unsqueeze(-1).repeat(1, 1, bits)
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return grad_input, grad_weight
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class SimpleCNN(nn.Module):
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def __init__(self):
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super(SimpleCNN, self).__init__()
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self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
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self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
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self.bn = nn.BatchNorm1d(320 * 4)
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self.fc1 = nn.Linear(160, 50)
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self.fc2 = nn.Linear(50, 10)
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self.pool = nn.MaxPool2d(2)
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self.relu = nn.ReLU()
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self.weight = nn.Parameter(torch.randn(160, pow(2, 8)))
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def forward(self, x):
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x = self.relu(self.pool(self.conv1(x)))
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x = self.relu((self.conv2(x)))
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x = x.view(-1, 320 * 4)
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x = self.bn(x)
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x = x.view(-1, 160, 8)
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x = Lut.apply(x, self.weight)
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x = self.relu(self.fc1(x))
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x = self.fc2(x)
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return x
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class LutGroup(nn.Module):
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def __init__(self, totalBits, groupBits=0, repeat=1):
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if not groupBits:
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groupBits = totalBits
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super(LutGroup, self).__init__()
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assert (totalBits % groupBits) == 0
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self.weight = nn.Parameter(torch.randn(repeat * totalBits, pow(2, groupBits)))
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self.totalBits = totalBits
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self.groupBits = groupBits
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self.repeat = repeat
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def forward(self, x):
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# input [ batch, totalBits ]
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# output [ batch, totalBits / groupBits * repeat ]
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batch = x.shape[0]
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x = x.view(batch, -1, self.groupBits)
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x = x.repeat(1, self.repeat, 1)
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x = Lut.apply(x, self.weight)
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return x
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class SimpleBNN(nn.Module):
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def __init__(self):
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super(SimpleBNN, self).__init__()
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# self.w = nn.Parameter(torch.randn(3, 784 * 8))
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# self.b = nn.Parameter(torch.zeros(3, 784 * 8))
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self.w = nn.Parameter(torch.randn(3, 784))
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self.b = nn.Parameter(torch.zeros(3, 784))
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self.lut2_p = LutGroup(784, 4, 2) # pool2 => 14*14*4
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self.lut3_p = LutGroup(14 * 14 * 2, 4, 2) # pool2 => 7*7*4
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self.lut4_p = LutGroup(3 * 3 * 5 * 5 * 4, 9, 2) # conv 3 => 5*5*8
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# self.lut4 = LutGroup(5 * 5 * 8, 8, 8) # conv 3 => 5*5*8
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self.lut5_p = LutGroup(3 * 3 * 3 * 3 * 8, 9, 2) # conv 3 => 3*3*8*2
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# self.lut5 = LutGroup(3 * 3 * 8 * 2, 16, 16) # conv 3 => 3*3*16
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self.lut6_p = LutGroup(3 * 3 * 16, 9, 5) # conv 3 => 80
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self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
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self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
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self.fc1 = nn.Linear(320, 50)
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self.fc2 = nn.Linear(50, 10)
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self.pool = nn.MaxPool2d(2)
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self.relu = nn.ReLU()
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def forward(self, x):
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batch = x.shape[0]
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x = x.view(batch, -1)
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# 变换x [-0.5:0.5] 到 0-255，然后按照二进制展开成8个值
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# x = (x * 256 + 128).clamp(0, 255).to(torch.uint8)
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# xx = torch.arange(7, -1, -1).to(x.device)
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# bits = (x.unsqueeze(-1) >> xx) & 1
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# x = bits.view(batch, -1)
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# x = x.float() - 0.5
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# x = (x > 0).float()
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# q = x * self.w[0] + self.b[0]
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# k = x * self.w[1] + self.b[1]
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# v = x * self.w[2] + self.b[2]
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# q = q.view(batch, -1, 1)
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# k = k.view(batch, 1, -1)
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# v = v.view(batch, -1, 1)
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# kq = q @ k
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# kqv = kq @ v
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# kqv = kqv.view(batch, -1, 8)
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# x = kqv
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#########################
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# # x = (x > 0) << xx
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# # x = x.sum(2)
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# # x = x.view(batch, 1, 28, 28)
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# # x = (x - 128.0) / 256.0
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# x = (x > 0).float()
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# x = x.view(batch, 1, 28, 28)
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# x = self.relu(self.pool(self.conv1(x)))
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# x = self.relu(self.pool((self.conv2(x))))
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# x = x.view(-1, 320)
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# x = self.relu(self.fc1(x))
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# x = self.fc2(x)
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#########################
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x = x.view(batch, 1, 28, 28)
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x = F.unfold(x, kernel_size=2, dilation=1, padding=0, stride=2)
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x = x.view(batch, -1)
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x = self.lut2_p(x)
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x = x.view(batch, 2, 14, 14)
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x = F.unfold(x, kernel_size=2, dilation=1, padding=0, stride=2)
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x = x.view(batch, -1)
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x = self.lut3_p(x)
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x = x.view(batch, 4, 7, 7)
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x = F.unfold(x, kernel_size=3, dilation=1, padding=0, stride=1)
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x = x.view(batch, -1)
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x = self.lut4_p(x)
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# x = self.lut4(x)
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x = x.view(batch, 8, 5, 5)
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x = F.unfold(x, kernel_size=3, dilation=1, padding=0, stride=1)
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x = x.view(batch, -1)
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x = self.lut5_p(x)
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# x = self.lut5(x)
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x = x.view(batch, 16, 3, 3)
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x = F.unfold(x, kernel_size=3, dilation=1, padding=0, stride=1)
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x = x.view(batch, -1)
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x = self.lut6_p(x)
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# xx = 2 ** torch.arange(7, -1, -1).to(x.device)
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x = x.view(batch, -1, 8)
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# x = x * xx
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# x = (x - 128.0) / 256.0
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x = x.sum(2)
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return x
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torch.autograd.set_detect_anomaly(True)
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# model = SimpleCNN().to(device)
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model = SimpleBNN().to(device)
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criterion = nn.CrossEntropyLoss()
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optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
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current_time = datetime.datetime.now().strftime("%m%d-%H%M%S")
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writer = SummaryWriter(f"log/{current_time}")
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hparam_dict = {"lr": lr, "batch_size": batch_size}
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writer.add_hparams(hparam_dict, {}, run_name=f"./")
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def train(epoch):
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model.train()
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for batch_idx, (data, target) in enumerate(train_loader):
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data, target = data.to(device), target.to(device)
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optimizer.zero_grad()
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output = model(data)
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# output = output * 1.0
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# output = F.softmax(output, dim=1)
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# print(output.requires_grad)
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# print(output.grad_fn)
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loss = criterion(output, target)
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loss.backward()
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optimizer.step()
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writer.add_scalar("loss", loss, epoch)
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if batch_idx % 512 == 0:
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print(
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f"Train Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)} "
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f"({100. * batch_idx / len(train_loader):.0f}%)]\tLoss: {loss.item():.6f}"
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)
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def test(epoch):
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model.eval()
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test_loss = 0
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correct = 0
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with torch.no_grad():
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for data, target in test_loader:
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data, target = data.to(device), target.to(device)
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output = model(data)
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test_loss += criterion(output, target).item()
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pred = output.argmax(dim=1, keepdim=True)
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correct += pred.eq(target.view_as(pred)).sum().item()
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test_loss /= len(test_loader.dataset)
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accuracy = 100.0 * correct / len(test_loader.dataset)
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writer.add_scalar("accuracy", accuracy, epoch)
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print(
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f"\nTest set: Average loss: {test_loss:.4f}, Accuracy: {correct}/{len(test_loader.dataset)} "
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f"({accuracy:.0f}%)\n"
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)
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for epoch in range(1, 300):
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train(epoch)
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test(epoch)
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# torch.save(model.state_dict(), "mnist_cnn.pth")
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print("Model saved to mnist_cnn.pth")
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writer.close()
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