Witllm/binary/mnist.py

import os

os.environ["CUDA_LAUNCH_BLOCKING"] = "1"


import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader
import math
import torch.nn.functional as F
import numpy as np

torch.manual_seed(1234)
np.random.seed(1234)
torch.cuda.manual_seed_all(1234)


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

transform = transforms.Compose(
    [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]  # MNIST数据集的均值和标准差
)

train_dataset = torchvision.datasets.MNIST(root="./data", train=True, download=True, transform=transform)
test_dataset = torchvision.datasets.MNIST(root="./data", train=False, download=True, transform=transform)

train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=1024, shuffle=False)


def to_binary_tensor(input_tensor, bits):
    int_tensor = torch.round(input_tensor).clamp(0, 2**bits - 1).to(torch.int64)
    shifts = torch.arange(bits - 1, -1, -1, device=int_tensor.device)
    binary_bits = (int_tensor.unsqueeze(-1) >> shifts) & 1
    return binary_bits


class MyLut(torch.autograd.Function):
    @staticmethod
    def forward(ctx, input, weight):
        batch = input.shape[0]
        count = input.shape[1]
        bits = input.shape[2]
        assert int(math.log2(weight.shape[-1])) == bits

        index = 2 ** torch.arange(bits - 1, -1, -1, device=input.device)
        x = (input > 0).long()
        x = x * index
        ind = x.sum(dim=-1)

        row_indices = torch.arange(count).unsqueeze(0).expand(batch, -1)
        output = weight[row_indices, ind]

        ctx.save_for_backward(input, weight, ind)
        return output

    @staticmethod
    def backward(ctx, grad_output):
        input, weight, ind = ctx.saved_tensors
        grad_input = grad_weight = None

        batch = input.shape[0]
        count = input.shape[1]
        bits = input.shape[2]

        if ctx.needs_input_grad[1]:
            grad_weight = torch.zeros_like(weight)
            ind_p = ind.permute(1, 0)
            grad_output_p = grad_output.permute(1, 0)
            grad_weight.scatter_add_(1, ind_p, grad_output_p)

        if ctx.needs_input_grad[0]:
            row_indices = torch.arange(count).unsqueeze(0).expand(batch, -1)
            grad_input = grad_output * weight[row_indices, ind]
            grad_input = grad_input.unsqueeze(-1).repeat(1, 1, bits)

        return grad_input, grad_weight


class SimpleCNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.bn = nn.BatchNorm1d(320 * 4)
        self.fc1 = nn.Linear(160, 50)
        self.fc2 = nn.Linear(50, 10)
        self.pool = nn.MaxPool2d(2)
        self.relu = nn.ReLU()
        self.weight = nn.Parameter(torch.randn(160, pow(2, 8)))

    def forward(self, x):
        x = self.relu(self.pool(self.conv1(x)))
        x = self.relu((self.conv2(x)))
        x = x.view(-1, 320 * 4)
        x = self.bn(x)
        x = x.view(-1, 160, 8)

        x = MyLut.apply(x, self.weight)

        x = self.relu(self.fc1(x))
        x = self.fc2(x)
        return x


class Lut(nn.Module):
    def __init__(self, bits):
        super(Lut, self).__init__()
        self.weight = nn.Parameter(torch.randn(pow(2, bits)))
        self.bias = nn.Parameter(torch.randn(pow(2, bits)))
        self.index = torch.pow(2, torch.arange(bits))
        self.bits = bits

    def forward(self, x):

        x = MyLut.apply(x, self.weight, self.bias)

        # tmp = torch.where(x > 0, torch.ones_like(x), torch.zeros_like(x))
        # x = tmp + x - x.detach()

        xx = (x > 0).float()
        x = xx + (x - x.detach())

        # print(xx.requires_grad)
        # print(xx.grad_fn)
        x = x * (self.index.to(x.device))
        x = torch.sum(x, dim=-1)

        w = torch.gather(self.weight, 0, x.long())
        b = torch.gather(self.weight, 0, x.long())
        x = w * x + b

        # tmp = torch.where(x > 0, torch.ones_like(x), torch.zeros_like(x))
        # x = tmp + x - x.detach()

        xx = (x > 0).float()
        x = xx + (x - x.detach())

        x = x.view(-1, 1)
        return x


class LutGroup(nn.Module):
    def __init__(self, bits, subbits):
        super(LutGroup, self).__init__()
        assert (bits % subbits) == 0
        self.lutlist = nn.ModuleList([Lut(subbits) for _ in range(int(bits / subbits))])
        self.bits = bits
        self.subbits = subbits

    def forward(self, x):
        ll = len(self.lutlist)
        tmp = torch.empty((x.shape[0], 0), dtype=x.dtype, device=x.device)
        start = 0
        end = self.subbits
        for i in range(ll):
            tx = self.lutlist[i](x[:, start:end])
            tmp = torch.cat((tmp, tx), dim=1)
            start += self.subbits
            end += self.subbits
        return tmp


class LutParallel(nn.Module):
    def __init__(self, bits, number):
        super(LutParallel, self).__init__()
        self.lutlist = nn.ModuleList([Lut(bits) for _ in range(number)])
        self.bits = bits
        self.number = number

    def forward(self, x):
        tmp = torch.empty((x.shape[0], 0), dtype=x.dtype, device=x.device)
        for i in range(self.number):
            tx = self.lutlist[i](x)
            tmp = torch.cat((tmp, tx), dim=1)
        return tmp


class SimpleBNN(nn.Module):
    def __init__(self):
        super(SimpleCNN, self).__init__()
        self.w = nn.Parameter(torch.randn(3, 784 * 8))
        self.b = nn.Parameter(torch.zeros(3, 784 * 8))
        self.lut1 = LutGroup(784 * 8, 8)
        self.lut2 = LutGroup(784, 8)
        self.lut3 = LutGroup(98, 14)
        self.lut4 = LutParallel(7, 10)

    def forward(self, x):
        batch = x.shape[0]
        x = x.view(batch, -1)

        # 变换x [-0.5:0.5] 到 0-255，然后按照二进制展开成8个值
        x = (x * 256 + 128).clamp(0, 255).to(torch.uint8)
        xx = torch.arange(8).to(x.device)
        bits = (x.unsqueeze(-1) >> xx) & 1
        bits = bits.view(batch, -1)

        x = bits
        # q = x * self.w[0] + self.b[0]
        # k = x * self.w[1] + self.b[1]
        # v = x * self.w[2] + self.b[2]

        # q = q.view(batch, -1, 1)
        # k = k.view(batch, 1, -1)
        # v = v.view(batch, -1, 1)
        # kq = q @ k
        # kqv = kq @ v
        # kqv = kqv.view(batch, -1)
        kqv = x

        x = self.lut1(kqv)
        x = self.lut2(x)
        x = self.lut3(x)
        x = self.lut4(x)
        return x


torch.autograd.set_detect_anomaly(True)
model = SimpleCNN().to(device)
# model = SimpleBNN().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)


def train(epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        # output = output * 1.0
        # output = F.softmax(output, dim=1)

        # print(output.requires_grad)
        # print(output.grad_fn)

        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

        if batch_idx % 100 == 0:
            print(
                f"Train Epoch: {epoch} [{batch_idx * len(data)}/{len(train_loader.dataset)} "
                f"({100. * batch_idx / len(train_loader):.0f}%)]\tLoss: {loss.item():.6f}"
            )


def test():
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    accuracy = 100.0 * correct / len(test_loader.dataset)
    print(
        f"\nTest set: Average loss: {test_loss:.4f}, Accuracy: {correct}/{len(test_loader.dataset)} "
        f"({accuracy:.0f}%)\n"
    )


for epoch in range(1, 30):
    train(epoch)
    test()

# torch.save(model.state_dict(), "mnist_cnn.pth")
print("Model saved to mnist_cnn.pth")