Update binary mnist.
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Colin
parent
9194595716
commit
f1124bc3b1
162
binary/mnist.py
162
binary/mnist.py
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@ -19,6 +19,8 @@ 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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# 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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@ -39,7 +41,9 @@ def to_binary_tensor(input_tensor, bits):
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return binary_bits
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class MyLut(torch.autograd.Function):
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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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@ -100,131 +104,125 @@ class SimpleCNN(nn.Module):
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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 = 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 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.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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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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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.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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self.weight = nn.Parameter(torch.randn(number, pow(2, bits)))
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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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x = x.unsqueeze(1).repeat(1, self.number, 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(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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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.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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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.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(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 = (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 = 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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x = (x > 0).float()
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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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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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x = self.lut1(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.view(batch, 1, 28, 28)
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# x = (x > 0).float()
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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 > 0).float()
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x = x.view(batch, 196, 4)
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# x = self.lut1(x)
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x = self.lut2(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 = 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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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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# 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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optimizer = torch.optim.SGD(model.parameters(), lr=0.001)
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def train(epoch):
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