fix memory issues del tensors

examples/train_multigpu.py

@@ -38,6 +38,7 @@ def main():
         ]
     )
 
+    print("Loading data")
     train_data, test_data = norch.norchvision.datasets.MNIST.splits(transform=transform, target_transform=target_transform)
     distributed_sampler = DistributedSampler(dataset=train_data, num_replicas=world_size, rank=rank)
     train_loader = norch.utils.data.DataLoader(train_data, batch_size=BATCH_SIZE, sampler=distributed_sampler)
@@ -58,6 +59,7 @@ def main():
             
             return out
 
+    print("Creating model")
     model = MyModel().to(device)
     model = DistributedDataParallel(model)
     criterion = nn.CrossEntropyLoss()
@@ -67,8 +69,15 @@ def main():
     print(f"Starting training on Rank {rank}/{world_size}\n\n")
 
     for epoch in range(epochs):    
+        
+        avg_loss = 0  
+        num_steps = 0  
+
         for idx, batch in enumerate(train_loader):
 
+            if idx % 100 == 0 and rank == 0:
+                print(f"Epoch: {epoch}/{epochs} - Step: {idx} / {len(train_loader)}")
+
             inputs, target = batch
 
             inputs = inputs.to(device)
@@ -83,10 +92,14 @@ def main():
             loss.backward()
 
             optimizer.step()
-        
+
+            avg_loss += loss[0]
+            num_steps += 1
+
+        avg_loss = avg_loss / num_steps
         if rank == 0:
-            print(f'Epoch [{epoch + 1}/{epochs}], Loss: {loss[0]:.4f}')
-            loss_list.append(loss[0])
+            print(f'Epoch [{epoch + 1}/{epochs}], Loss: {avg_loss:.4f}')
+            loss_list.append(avg_loss)
 
 if __name__ == "__main__":
     main()

examples/train_nn_mnist.ipynb

@@ -139,7 +139,11 @@
     "optimizer = optim.SGD(model.parameters(), lr=0.01)\n",
     "loss_list = []\n",
     "\n",
-    "for epoch in range(epochs):    \n",
+    "for epoch in range(epochs):  \n",
+    "    \n",
+    "    avg_loss = 0  \n",
+    "    num_steps = 0\n",
+    "    \n",
     "    for idx, batch in enumerate(train_loader):\n",
     "\n",
     "        inputs, target = batch\n",
@@ -157,8 +161,12 @@
     "\n",
     "        optimizer.step()\n",
     "\n",
-    "    print(f'Epoch [{epoch + 1}/{epochs}], Loss: {loss[0]:.4f}')\n",
-    "    loss_list.append(loss[0])"
+    "        avg_loss += loss[0]\n",
+    "        num_steps += 1\n",
+    "\n",
+    "    avg_loss = avg_loss / num_steps\n",
+    "    print(f'Epoch [{epoch + 1}/{epochs}], Loss: {avg_loss:.4f}')\n",
+    "    loss_list.append(avg_loss)"
    ]
   },
   {

examples/train_singlegpu.py

@@ -24,6 +24,8 @@ def main():
         ]
     )
 
+
+    print("Loading data")
     train_data, test_data = norch.norchvision.datasets.MNIST.splits(transform=transform, target_transform=target_transform)
     train_loader = norch.utils.data.DataLoader(train_data, batch_size=BATCH_SIZE)
 
@@ -43,14 +45,23 @@ def main():
             
             return out
 
+    print("Creating model")
     model = MyModel().to(device)
     criterion = nn.CrossEntropyLoss()
     optimizer = optim.SGD(model.parameters(), lr=0.01)
     loss_list = []
 
-    for epoch in range(epochs):    
+    print("Starting training")
+    for epoch in range(epochs): 
+
+        avg_loss = 0  
+        num_steps = 0  
+        
         for idx, batch in enumerate(train_loader):
 
+            if idx % 100 == 0:
+                print(f"Epoch: {epoch}/{epochs} - Step: {idx} / {len(train_loader)}")
+
             inputs, target = batch
 
             inputs = inputs.to(device)
@@ -64,6 +75,13 @@ def main():
             loss.backward()
 
             optimizer.step()
+        
+            avg_loss += loss[0]
+            num_steps += 1
+
+        avg_loss = avg_loss / num_steps
+        print(f'Epoch [{epoch + 1}/{epochs}], Loss: {avg_loss:.4f}')
+        loss_list.append(avg_loss)
 
 
 if __name__ == "__main__":

norch/csrc/tensor.cpp

@@ -26,23 +26,29 @@ extern "C" {
         tensor->device = strdup(device);
         tensor->strides = (int*)malloc(ndim * sizeof(int));
         tensor->shape = (int*)malloc(ndim * sizeof(int));
-        tensor->data = (float*)malloc(tensor->size * sizeof(float));
 
-        if (tensor->device == NULL || tensor->strides == NULL || tensor->shape == NULL || tensor->data == NULL) {
+        if (tensor->device == NULL || tensor->strides == NULL || tensor->shape == NULL) {
             fprintf(stderr, "Memory allocation failed\n");
             exit(1);
         }
 
         memcpy(tensor->shape, shape, tensor->ndim * sizeof(int));
         strcpy(tensor->device, device);
-        memcpy(tensor->data, data, tensor->size * sizeof(float));
-
+        
+        if (strcmp(tensor->device, "cpu") == 0) {
+            tensor->data = (float*)malloc(tensor->size * sizeof(float));
+            memcpy(tensor->data, data, tensor->size * sizeof(float));
+        } else {
+            //already allocated on gpu
+            tensor->data = data;
+        }
+            
         int stride = 1;
         for (int i = ndim - 1; i >= 0; i--) {
             tensor->strides[i] = stride;
             stride *= shape[i];
         }
-        
+                
         return tensor;
     }
 

norch/tensor.py

@@ -46,13 +46,16 @@ class Tensor:
             Tensor._C.create_tensor.argtypes = [ctypes.POINTER(ctypes.c_float), ctypes.POINTER(ctypes.c_int), ctypes.c_int, ctypes.c_char_p]
             Tensor._C.create_tensor.restype = ctypes.POINTER(CTensor)
             
-
             self.tensor = Tensor._C.create_tensor(
                 self.data_ctype,
                 self.shape_ctype,
                 self.ndim_ctype,
                 self.device_ctype
             )
+
+            del self.data_ctype
+            del self.shape_ctype
+            del self.device_ctype
         
         else:
             self.tensor = None,