Merge branch 'main' into captum-fix

Author: QasimKhan5x

Diff for: beginner_source/data_loading_tutorial.py

@@ -165,9 +165,7 @@ def __getitem__(self, idx):
 
 fig = plt.figure()
 
-for i in range(len(face_dataset)):
-    sample = face_dataset[i]
-
+for i, sample in enumerate(face_dataset):
     print(i, sample['image'].shape, sample['landmarks'].shape)
 
     ax = plt.subplot(1, 4, i + 1)
@@ -268,8 +266,8 @@ def __call__(self, sample):
         h, w = image.shape[:2]
         new_h, new_w = self.output_size
 
-        top = np.random.randint(0, h - new_h)
-        left = np.random.randint(0, w - new_w)
+        top = np.random.randint(0, h - new_h + 1)
+        left = np.random.randint(0, w - new_w + 1)
 
         image = image[top: top + new_h,
                       left: left + new_w]
@@ -294,7 +292,7 @@ def __call__(self, sample):
 
 ######################################################################
 # .. note::
-#     In the example above, `RandomCrop` uses an external library's random number generator 
+#     In the example above, `RandomCrop` uses an external library's random number generator
 #     (in this case, Numpy's `np.random.int`). This can result in unexpected behavior with `DataLoader`
 #     (see `here <https://pytorch.org/docs/stable/notes/faq.html#my-data-loader-workers-return-identical-random-numbers>`_).
 #     In practice, it is safer to stick to PyTorch's random number generator, e.g. by using `torch.randint` instead.
@@ -356,9 +354,7 @@ def __call__(self, sample):
                                                ToTensor()
                                            ]))
 
-for i in range(len(transformed_dataset)):
-    sample = transformed_dataset[i]
-
+for i, sample in enumerate(transformed_dataset):
     print(i, sample['image'].size(), sample['landmarks'].size())
 
     if i == 3:

Diff for: beginner_source/transfer_learning_tutorial.py

@@ -46,7 +46,7 @@
 import matplotlib.pyplot as plt
 import time
 import os
-import copy
+from tempfile import TemporaryDirectory
 
 cudnn.benchmark = True
 plt.ion()   # interactive mode
@@ -146,67 +146,71 @@ def imshow(inp, title=None):
 def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
     since = time.time()
 
-    best_model_wts = copy.deepcopy(model.state_dict())
-    best_acc = 0.0
-
-    for epoch in range(num_epochs):
-        print(f'Epoch {epoch}/{num_epochs - 1}')
-        print('-' * 10)
-
-        # Each epoch has a training and validation phase
-        for phase in ['train', 'val']:
-            if phase == 'train':
-                model.train()  # Set model to training mode
-            else:
-                model.eval()   # Set model to evaluate mode
-
-            running_loss = 0.0
-            running_corrects = 0
-
-            # Iterate over data.
-            for inputs, labels in dataloaders[phase]:
-                inputs = inputs.to(device)
-                labels = labels.to(device)
-
-                # zero the parameter gradients
-                optimizer.zero_grad()
-
-                # forward
-                # track history if only in train
-                with torch.set_grad_enabled(phase == 'train'):
-                    outputs = model(inputs)
-                    _, preds = torch.max(outputs, 1)
-                    loss = criterion(outputs, labels)
-
-                    # backward + optimize only if in training phase
-                    if phase == 'train':
-                        loss.backward()
-                        optimizer.step()
-
-                # statistics
-                running_loss += loss.item() * inputs.size(0)
-                running_corrects += torch.sum(preds == labels.data)
-            if phase == 'train':
-                scheduler.step()
-
-            epoch_loss = running_loss / dataset_sizes[phase]
-            epoch_acc = running_corrects.double() / dataset_sizes[phase]
-
-            print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
-
-            # deep copy the model
-            if phase == 'val' and epoch_acc > best_acc:
-                best_acc = epoch_acc
-                best_model_wts = copy.deepcopy(model.state_dict())
-
-        print()
-
-    time_elapsed = time.time() - since
-    print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
-    print(f'Best val Acc: {best_acc:4f}')
-
-    # load best model weights
-    model.load_state_dict(best_model_wts)
+    # Create a temporary directory to save training checkpoints
+    with TemporaryDirectory() as tempdir:
+        best_model_params_path = os.path.join(tempdir, 'best_model_params.pt')
+    
+        torch.save(model.state_dict(), best_model_params_path)
+        best_acc = 0.0
+
+        for epoch in range(num_epochs):
+            print(f'Epoch {epoch}/{num_epochs - 1}')
+            print('-' * 10)
+
+            # Each epoch has a training and validation phase
+            for phase in ['train', 'val']:
+                if phase == 'train':
+                    model.train()  # Set model to training mode
+                else:
+                    model.eval()   # Set model to evaluate mode
+
+                running_loss = 0.0
+                running_corrects = 0
+
+                # Iterate over data.
+                for inputs, labels in dataloaders[phase]:
+                    inputs = inputs.to(device)
+                    labels = labels.to(device)
+
+                    # zero the parameter gradients
+                    optimizer.zero_grad()
+
+                    # forward
+                    # track history if only in train
+                    with torch.set_grad_enabled(phase == 'train'):
+                        outputs = model(inputs)
+                        _, preds = torch.max(outputs, 1)
+                        loss = criterion(outputs, labels)
+
+                        # backward + optimize only if in training phase
+                        if phase == 'train':
+                            loss.backward()
+                            optimizer.step()
+
+                    # statistics
+                    running_loss += loss.item() * inputs.size(0)
+                    running_corrects += torch.sum(preds == labels.data)
+                if phase == 'train':
+                    scheduler.step()
+
+                epoch_loss = running_loss / dataset_sizes[phase]
+                epoch_acc = running_corrects.double() / dataset_sizes[phase]
+
+                print(f'{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}')
+
+                # deep copy the model
+                if phase == 'val' and epoch_acc > best_acc:
+                    best_acc = epoch_acc
+                    torch.save(model.state_dict(), best_model_params_path)
+
+            print()
+
+        time_elapsed = time.time() - since
+        print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s')
+        print(f'Best val Acc: {best_acc:4f}')
+
+        # load best model weights
+        model.load_state_dict(torch.load(best_model_params_path))
     return model
 
 

Diff for: beginner_source/transformer_tutorial.py

@@ -103,6 +103,15 @@ def generate_square_subsequent_mask(sz: int) -> Tensor:
 # positional encodings have the same dimension as the embeddings so that
 # the two can be summed. Here, we use ``sine`` and ``cosine`` functions of
 # different frequencies.
+# The ``div_term`` in the code is calculated as 
+# ``torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))``. 
+# This calculation is based on the original Transformer paper’s formulation 
+# for positional encoding. The purpose of this calculation is to create 
+# a range of values that decrease exponentially. 
+# This allows the model to learn to attend to positions based on their relative distances.
+# The ``math.log(10000.0)`` term in the exponent represents the maximum effective 
+# input length (in this case, ``10000``). Dividing this term by ``d_model`` scales 
+# the values to be within a reasonable range for the exponential function. 
 #
 
 class PositionalEncoding(nn.Module):

Diff for: intermediate_source/char_rnn_generation_tutorial.py

@@ -278,7 +278,7 @@ def train(category_tensor, input_line_tensor, target_line_tensor):
 
     rnn.zero_grad()
 
-    loss = 0
+    loss = torch.Tensor([0]) # you can also just simply use ``loss = 0``
 
     for i in range(input_line_tensor.size(0)):
         output, hidden = rnn(category_tensor, input_line_tensor[i], hidden)

Diff for: intermediate_source/dynamic_quantization_bert_tutorial.rst

@@ -255,6 +255,9 @@ model before and after the dynamic quantization.
         torch.manual_seed(seed)
     set_seed(42)
 
+    # Initialize a global random number generator
+    global_rng = random.Random()
+
 
 2.2 Load the fine-tuned BERT model
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -525,6 +528,21 @@ We can serialize and save the quantized model for the future use using
 
 .. code:: python
 
+    def ids_tensor(shape, vocab_size, rng=None, name=None):
+        #  Creates a random int32 tensor of the shape within the vocab size
+        if rng is None:
+            rng = global_rng
+
+        total_dims = 1
+        for dim in shape:
+            total_dims *= dim
+
+        values = []
+        for _ in range(total_dims):
+            values.append(rng.randint(0, vocab_size - 1))
+
+        return torch.tensor(data=values, dtype=torch.long, device='cpu').view(shape).contiguous()
+
     input_ids = ids_tensor([8, 128], 2)
     token_type_ids = ids_tensor([8, 128], 2)
     attention_mask = ids_tensor([8, 128], vocab_size=2)