E2 e

README.md

@@ -81,13 +81,13 @@ sudo apt-get install python3 python3-pip
 (Please make all cuda dependencies installed before pytorck!!!)
 
 ```shell
-pip3 install torch torchvision torchaudio
+pip3 install torch==1.13.0 torchvision torchaudio
 ```
 
 (w/o CUDA)
 
 ```shell
-pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
+pip3 install torch==1.13.0 torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu
 ```
 
 #### (Optional) Pytorch with Cuda backend on jetson

benchmark/torchscripts/E2E_Minist.py

@@ -0,0 +1,146 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision.datasets as datasets
+import torchvision.transforms as transforms
+import os
+import CoOccurringFD
+# Define your custom matrix multiplication function
+def my_matmul(x, y):
+    rows,cols=x.shape
+    if cols>20:
+        sketchSize=cols/10
+    else:
+        sketchSize=10
+    # Your implementation here
+    return CoOccurringFD.FDAMM(x,y,int(sketchSize))
+
+# Define a custom Linear layer that uses your custom matrix multiplication function
+class CustomLinear(nn.Module):
+    def __init__(self, in_features, out_features, bias=True):
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        self.weight = nn.Parameter(torch.Tensor(out_features, in_features))
+        if bias:
+            self.bias = nn.Parameter(torch.Tensor(out_features))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_parameters()
+    def mySqrt(self,a:float):
+        y = torch.sqrt(torch.tensor(a, dtype=torch.float32))
+        return y.item()
+    def reset_parameters(self):
+        nn.init.kaiming_uniform_(self.weight, a=self.mySqrt(5.0))
+        if self.bias is not None:
+            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / self.mySqrt(fan_in)
+            nn.init.uniform_(self.bias, -bound, bound)
+
+    def forward(self, input):
+        # Use your custom matrix multiplication function instead of torch.matmul
+        output = my_matmul(input, self.weight.t())
+        if self.bias is not None:
+            output += self.bias
+        return output
+
+# Define your neural network architecture
+class MyNet(nn.Module):
+    def __init__(self):
+        super(MyNet, self).__init__()
+        self.fc1 = nn.Linear(784, 128)
+        self.fc2 = nn.Linear(128, 128)
+        self.fc3 = nn.Linear(128, 10)
+    def forward(self, x):
+        x = x.view(-1, 784)
+        x = nn.functional.relu(self.fc1(x))
+        x = self.fc2(x)
+        x = nn.functional.relu(self.fc3(x))
+        return x
+def testNN(net,test_loader):
+        #first, load parameters
+        pretrained_params = torch.load('pretrained_model.pt')
+        custom_params = net.state_dict()
+
+        for name in custom_params:
+            if name in pretrained_params:
+                custom_params[name] = pretrained_params[name]
+        net.load_state_dict(custom_params)
+        correct = 0
+        total = 0
+        #then, run test
+        net2=net
+        for data in test_loader:
+            images, labels = data
+            outputs = net2(images)
+            _, predicted = torch.max(outputs.data, 1)
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()
+            print(f"Accuracy on test set: {correct / total}")
+        print(f"Accuracy on test set: {correct / total}")
+        return correct / total
+def main():
+    device='cuda'
+    # Load the MNIST dataset
+    train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
+    test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())
+
+    # Set up the data loaders
+    batch_size = 64
+    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
+
+    # Train the neural network using the default Linear layers
+    net = MyNet()
+
+    if os.path.exists('pretrained_model.pt'):
+        print('find pretrained model, run test')
+        print('first run default version')
+        accuracy0=testNN(net,test_loader)
+        print('then run coocuuring 1 version')
+        # Replace the Linear layers with your custom Linear layers and load the pre-trained weights
+        net.fc1 = CustomLinear(784, 128)
+        accuracy1=testNN(net,test_loader)
+        print('next run coocuuring 2 version')
+        net.fc1=nn.Linear(784,128)
+        net.fc2 = CustomLinear(128, 128)
+        accuracy2=testNN(net,test_loader)
+        print('finally run coocuuring 3 version')
+        net.fc2 = nn.Linear(128, 128)
+        net.fc3 = CustomLinear(128, 10)
+        accuracy3=testNN(net,test_loader)
+        print('default accuracy=',accuracy0)
+        print('co-occuring 1 accuracy=',accuracy1)
+        print('co-occuring  2 accuracy=',accuracy2)
+        print('co-occuring  3 accuracy=',accuracy3)
+
+
+    else:
+        print('build pretrain model first')
+        criterion = nn.CrossEntropyLoss()
+        optimizer = optim.SGD(net.parameters(), lr=0.1)
+        net=net.to(device)
+        for epoch in range(10):
+            running_loss = 0.0
+            for i, data in enumerate(train_loader, 0):
+                inputs, labels = data
+                inputs=inputs.to(device)
+                labels=labels.to(device)
+                optimizer.zero_grad()
+                outputs = net(inputs)
+                loss = criterion(outputs, labels)
+                loss.backward()
+                optimizer.step()
+                running_loss += loss.item()
+            print(f"Epoch {epoch+1}: loss = {running_loss / len(train_loader)}")
+        net=net.to('cpu')
+        # Save the pre-trained model
+        torch.save(net.state_dict(), 'pretrained_model.pt')
+
+
+
+
+
+# Evaluate
+if __name__ == '__main__':
+    main()

commit.sh

@@ -1,4 +1,4 @@
-BRANCH=matrixLoader
+BRANCH=E2E
 git init
 git checkout -b $BRANCH
 git add .

commit_info

@@ -1,2 +1,3 @@
-1. fix doxygen bugs
+1. fix readme typo of troch install
+2. add an experimental E2E machine learning example at benchmark/torchscripts/E2E_Minsit.py