Added pytorch and pybuda mnist training scripts.

pybuda/test/mlir/mnist/training/mnist_linear_pybuda.py

@@ -0,0 +1,127 @@
+import torch
+from torchvision import datasets, transforms
+from torch.utils.tensorboard import SummaryWriter
+
+import pybuda
+from pybuda import (
+    CPUDevice,
+    PyTorchModule,
+)
+from utils import (
+    MNISTLinear,
+    Identity,
+    load_tb_writer,
+    load_dataset,
+)
+from pybuda.config import _get_global_compiler_config
+
+class FeedForward(torch.nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(FeedForward, self).__init__()
+        self.fc1 = torch.nn.Linear(input_size, hidden_size)
+        self.relu = torch.nn.ReLU()
+        self.fc2 = torch.nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        return x
+
+def train(loss_on_cpu=True):
+    torch.manual_seed(777)
+    transform=transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.1307,), (0.3081,)),
+        transforms.Lambda(lambda x: x.view(-1))
+        ])
+    train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
+    test_dataset = datasets.MNIST(root='./data', train=False, transform=transform, download=True)
+
+    writer = SummaryWriter()
+
+    num_epochs = 2
+    input_size = 784
+    hidden_size = 256
+    output_size = 10
+    batch_size = 3
+    learning_rate = 0.001
+    sequential = True
+
+    framework_model = FeedForward(input_size, hidden_size, output_size)
+    tt_model = pybuda.PyTorchModule(f"mnist_linear_{batch_size}", framework_model)
+    tt_optimizer = pybuda.optimizers.SGD(
+        learning_rate=learning_rate, device_params=True
+    )
+    tt0 = pybuda.TTDevice("tt0", module=tt_model, optimizer=tt_optimizer)
+
+    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)
+    # Dataset sample input
+    first_sample = test_loader.dataset[0]
+    sample_input = (first_sample[0].repeat(1, batch_size, 1),)
+    sample_target = (
+        torch.nn.functional.one_hot(torch.tensor(first_sample[1]), num_classes=output_size)
+        .float()
+        .repeat(1, batch_size, 1)
+    )
+
+    if loss_on_cpu:
+        cpu0 = CPUDevice("cpu0", module=PyTorchModule("identity", Identity()))
+        cpu0.place_loss_module(pybuda.PyTorchModule(f"loss_{batch_size}", torch.nn.CrossEntropyLoss()))
+    else:
+        tt_loss = pybuda.PyTorchModule(f"loss_{batch_size}", torch.nn.CrossEntropyLoss())
+        tt0.place_loss_module(tt_loss)
+
+    compiler_cfg = _get_global_compiler_config()
+    compiler_cfg.enable_auto_fusing = False
+
+    if not loss_on_cpu:
+        sample_target = (sample_target,)
+
+    checkpoint_queue = pybuda.initialize_pipeline(
+        training=True,
+        sample_inputs=sample_input,
+        sample_targets=sample_target,
+        _sequential=sequential,
+    )
+
+    best_accuracy = 0.0
+    best_checkpoint = None
+
+    for epoch in range(num_epochs):
+        for batch_idx, (images, labels) in enumerate(train_loader):
+
+            images = (images.unsqueeze(0),)
+            tt0.push_to_inputs(images)
+
+            targets = (
+                torch.nn.functional.one_hot(labels, num_classes=output_size)
+                .float()
+                .unsqueeze(0)
+            )          
+            if loss_on_cpu:
+                cpu0.push_to_target_inputs(targets)
+            else:
+                tt0.push_to_target_inputs(targets)
+
+            pybuda.run_forward(input_count=1, _sequential=sequential)
+            pybuda.run_backward(input_count=1, zero_grad=True, _sequential=sequential)
+            pybuda.run_optimizer(checkpoint=True, _sequential=sequential)
+
+            loss_q = pybuda.run.get_loss_queue()
+
+            step = 0
+            loss = loss_q.get()[0]
+            print(loss)
+            # while not loss_q.empty():
+            #     if loss_on_cpu:
+            #         writer.add_scalar("Loss/PyBuda/overfit", loss_q.get()[0], step)
+            #     else:
+            #         writer.add_scalar("Loss/PyBuda/overfit", loss_q.get()[0].value()[0], step)
+            #     step += 1
+
+    writer.close()
+
+if __name__ == "__main__":
+    train()

pybuda/test/mlir/mnist/training/mnist_linear_pytorch.py

@@ -0,0 +1,84 @@
+import torch
+from torchvision import datasets, transforms
+from torch.utils.tensorboard import SummaryWriter
+
+class FeedForward(torch.nn.Module):
+    def __init__(self, input_size, hidden_size, output_size):
+        super(FeedForward, self).__init__()
+        self.fc1 = torch.nn.Linear(input_size, hidden_size)
+        self.relu = torch.nn.ReLU()
+        self.fc2 = torch.nn.Linear(hidden_size, output_size)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.relu(x)
+        x = self.fc2(x)
+        return x
+
+def train():
+    torch.manual_seed(777)
+    transform=transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.1307,), (0.3081,)),
+        transforms.Lambda(lambda x: x.view(-1))
+        ])
+    train_dataset = datasets.MNIST(root='./data', train=True, transform=transform, download=True)
+    test_dataset = datasets.MNIST(root='./data', train=False, transform=transform, download=True)
+
+    writer = SummaryWriter()
+
+    num_epochs = 10
+    input_size = 784
+    hidden_size = 256
+    output_size = 10
+    model = FeedForward(input_size, hidden_size, output_size)
+
+    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
+    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
+    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=False)
+
+    best_accuracy = 0.0
+    best_checkpoint = None
+
+    for epoch in range(num_epochs):
+        for batch_idx, (images, labels) in enumerate(train_loader):
+            outputs = model(images)
+            loss = torch.nn.CrossEntropyLoss()(outputs, labels)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            if (batch_idx+1) % 100 == 0:
+                print(f'Epoch [{epoch+1}/{num_epochs}], Step [{batch_idx+1}/{len(train_loader)}], Loss: {loss.item():.4f}')
+                writer.add_scalar('Loss/train', loss.item(), epoch * len(train_loader) + batch_idx)
+
+        total_correct = 0
+        total_samples = 0
+        with torch.no_grad():
+            for images, labels in test_loader:
+                outputs = model(images)
+                _, predicted = torch.max(outputs, dim=1)
+                total_samples += labels.size(0)
+                total_correct += (predicted == labels).sum().item()
+
+        accuracy = 100.0 * total_correct / total_samples
+        print(f'Epoch [{epoch+1}/{num_epochs}], Validation Accuracy: {accuracy:.2f}%')
+
+        if accuracy > best_accuracy:
+            best_accuracy = accuracy
+            best_checkpoint = {
+                'epoch': epoch,
+                'model_state_dict': model.state_dict(),
+                'optimizer_state_dict': optimizer.state_dict(),
+                'accuracy': accuracy
+            }
+
+    if best_checkpoint is not None:
+        model.load_state_dict(best_checkpoint['model_state_dict'])
+        optimizer.load_state_dict(best_checkpoint['optimizer_state_dict'])
+        print(f'Reverted to checkpoint with highest validation accuracy: {best_checkpoint["accuracy"]:.2f}%')
+
+    writer.close()
+
+if __name__ == "__main__":
+    train()