Merge branch 'master' into patch-1

Author: holly1238

advanced_source/cpp_extension.rst

@@ -1101,7 +1101,7 @@ on it:
     const int threads = 1024;
     const dim3 blocks((state_size + threads - 1) / threads, batch_size);
 
-    AT_DISPATCH_FLOATING_TYPES(X.type(), "lltm_forward_cuda", ([&] {
+    AT_DISPATCH_FLOATING_TYPES(X.type(), "lltm_backward_cuda", ([&] {
       lltm_cuda_backward_kernel<scalar_t><<<blocks, threads>>>(
           d_old_cell.packed_accessor32<scalar_t,2,torch::RestrictPtrTraits>(),
           d_gates.packed_accessor32<scalar_t,3,torch::RestrictPtrTraits>(),

advanced_source/super_resolution_with_onnxruntime.py

@@ -145,7 +145,7 @@ def _initialize_weights(self):
                   do_constant_folding=True,  # whether to execute constant folding for optimization
                   input_names = ['input'],   # the model's input names
                   output_names = ['output'], # the model's output names
-                  dynamic_axes={'input' : {0 : 'batch_size'},    # variable lenght axes
+                  dynamic_axes={'input' : {0 : 'batch_size'},    # variable length axes
                                 'output' : {0 : 'batch_size'}})
 
 ######################################################################

beginner_source/dcgan_faces_tutorial.py

@@ -610,10 +610,10 @@ def forward(self, input):
         output = netD(fake.detach()).view(-1)
         # Calculate D's loss on the all-fake batch
         errD_fake = criterion(output, label)
-        # Calculate the gradients for this batch
+        # Calculate the gradients for this batch, accumulated (summed) with previous gradients
         errD_fake.backward()
         D_G_z1 = output.mean().item()
-        # Add the gradients from the all-real and all-fake batches
+        # Compute error of D as sum over the fake and the real batches
         errD = errD_real + errD_fake
         # Update D
         optimizerD.step()

intermediate_source/reinforcement_q_learning.py

@@ -134,7 +134,7 @@ def __len__(self):
 
 
 ######################################################################
-# Now, let's define our model. But first, let quickly recap what a DQN is.
+# Now, let's define our model. But first, let's quickly recap what a DQN is.
 #
 # DQN algorithm
 # -------------