Add binary/mnist.py.
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Colin
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db97131caf
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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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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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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=16, 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 MyLut(torch.autograd.Function):
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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 = MyLut.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 Lut(nn.Module):
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def __init__(self, bits):
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super(Lut, self).__init__()
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self.weight = nn.Parameter(torch.randn(pow(2, bits)))
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self.bias = nn.Parameter(torch.randn(pow(2, bits)))
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self.index = torch.pow(2, torch.arange(bits))
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self.bits = bits
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def forward(self, x):
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x = MyLut.apply(x, self.weight, self.bias)
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# tmp = torch.where(x > 0, torch.ones_like(x), torch.zeros_like(x))
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# x = tmp + x - x.detach()
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xx = (x > 0).float()
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x = xx + (x - x.detach())
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# print(xx.requires_grad)
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# print(xx.grad_fn)
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x = x * (self.index.to(x.device))
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x = torch.sum(x, dim=-1)
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w = torch.gather(self.weight, 0, x.long())
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b = torch.gather(self.weight, 0, x.long())
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x = w * x + b
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# tmp = torch.where(x > 0, torch.ones_like(x), torch.zeros_like(x))
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# x = tmp + x - x.detach()
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xx = (x > 0).float()
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x = xx + (x - x.detach())
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x = x.view(-1, 1)
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return x
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class LutGroup(nn.Module):
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def __init__(self, bits, subbits):
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super(LutGroup, self).__init__()
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assert (bits % subbits) == 0
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self.lutlist = nn.ModuleList([Lut(subbits) for _ in range(int(bits / subbits))])
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self.bits = bits
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self.subbits = subbits
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def forward(self, x):
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ll = len(self.lutlist)
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tmp = torch.empty((x.shape[0], 0), dtype=x.dtype, device=x.device)
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start = 0
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end = self.subbits
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for i in range(ll):
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tx = self.lutlist[i](x[:, start:end])
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tmp = torch.cat((tmp, tx), dim=1)
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start += self.subbits
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end += self.subbits
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return tmp
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class LutParallel(nn.Module):
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def __init__(self, bits, number):
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super(LutParallel, self).__init__()
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self.lutlist = nn.ModuleList([Lut(bits) for _ in range(number)])
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self.bits = bits
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self.number = number
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def forward(self, x):
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tmp = torch.empty((x.shape[0], 0), dtype=x.dtype, device=x.device)
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for i in range(self.number):
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tx = self.lutlist[i](x)
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tmp = torch.cat((tmp, tx), dim=1)
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return tmp
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class SimpleBNN(nn.Module):
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def __init__(self):
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super(SimpleCNN, 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.lut1 = LutGroup(784 * 8, 8)
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self.lut2 = LutGroup(784, 8)
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self.lut3 = LutGroup(98, 14)
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self.lut4 = LutParallel(7, 10)
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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(8).to(x.device)
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bits = (x.unsqueeze(-1) >> xx) & 1
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bits = bits.view(batch, -1)
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x = bits
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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)
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kqv = x
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x = self.lut1(kqv)
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x = self.lut2(x)
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x = self.lut3(x)
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x = self.lut4(x)
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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.SGD(model.parameters(), lr=0.01)
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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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if batch_idx % 100 == 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():
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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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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, 30):
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train(epoch)
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test()
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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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