Update mnist.
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Colin
parent
f1124bc3b1
commit
f98a951b58
140
binary/mnist.py
140
binary/mnist.py
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@ -12,15 +12,17 @@ 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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# device = "cpu"
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# torch.set_num_threads(16)
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print(f"Using device: {device}")
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transform = transforms.Compose(
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@ -30,7 +32,7 @@ transform = transforms.Compose(
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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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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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@ -112,28 +114,22 @@ class SimpleCNN(nn.Module):
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class LutGroup(nn.Module):
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def __init__(self, bits, subbits):
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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 (bits % subbits) == 0
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self.weight = nn.Parameter(torch.randn(bits, pow(2, subbits)))
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self.bits = bits
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self.subbits = subbits
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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.subbits)
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x = Lut.apply(x, self.weight)
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return x
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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.number = number
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self.weight = nn.Parameter(torch.randn(number, pow(2, bits)))
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def forward(self, x):
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x = x.unsqueeze(1).repeat(1, self.number, 1)
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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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@ -147,11 +143,13 @@ class SimpleBNN(nn.Module):
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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.lut1 = LutGroup(784 * 8, 8)
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self.lut2 = LutGroup(784, 4)
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self.lut3 = LutGroup(196, 4)
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self.lut4 = LutGroup(49, 7)
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self.lut5 = LutParallel(7, 10)
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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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@ -171,28 +169,28 @@ class SimpleBNN(nn.Module):
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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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# 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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# 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) << 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.view(batch, 1, 28, 28)
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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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@ -202,19 +200,39 @@ class SimpleBNN(nn.Module):
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#########################
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x = (x > 0).float()
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x = x.view(batch, 196, 4)
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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 = self.lut1(x)
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x = self.lut2(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(-1, 28, 7)
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x = x.permute(0, 2, 1)
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x = x.reshape(-1, 28 * 7)
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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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x = self.lut3(x)
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x = self.lut4(x)
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x = self.lut5(x)
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return x
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@ -222,7 +240,12 @@ 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=0.001)
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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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@ -240,15 +263,16 @@ def train(epoch):
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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 % 100 == 0:
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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():
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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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@ -262,15 +286,17 @@ def test():
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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, 30):
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for epoch in range(1, 300):
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train(epoch)
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test()
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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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