also fix examples

experimental/torch_xla2/examples/basic_training.py

@@ -51,7 +51,8 @@ def matplotlib_imshow(img, one_channel=False):
         plt.imshow(npimg, cmap="Greys")
     else:
         plt.imshow(np.transpose(npimg, (1, 2, 0)))
-
+#torch_xla2.env.config.debug_print_each_op = True
+#torch_xla2.env.config.debug_mixed_tensor = True
 dataiter = iter(training_loader)
 images, labels = next(dataiter)
 
@@ -80,15 +81,15 @@ def forward(self, x):
         return x
 
 
-model = GarmentClassifier()
+model = GarmentClassifier().to('jax')
 
 loss_fn = torch.nn.CrossEntropyLoss()
 
 # NB: Loss functions expect data in batches, so we're creating batches of 4
 # Represents the model's confidence in each of the 10 classes for a given input
-dummy_outputs = torch.rand(4, 10)
+dummy_outputs = torch.rand(4, 10, device='jax')
 # Represents the correct class among the 10 being tested
-dummy_labels = torch.tensor([1, 5, 3, 7])
+dummy_labels = torch.tensor([1, 5, 3, 7], device='jax')
 
 print(dummy_outputs)
 print(dummy_labels)
@@ -110,6 +111,8 @@ def train_one_epoch(epoch_index, tb_writer=None):
         # Every data instance is an input + label pair
         # NEW: Move model to XLA device
         inputs, labels = data
+        inputs = inputs.to('jax')
+        labels = labels.to('jax')
 
         # Zero your gradients for every batch!
         optimizer.zero_grad()
@@ -162,7 +165,9 @@ def train_one_epoch(epoch_index, tb_writer=None):
     # Disable gradient computation and reduce memory consumption.
     with torch.no_grad():
         for i, vdata in enumerate(validation_loader):
-          # NOTE: move to XLA device
+          vinputs, vlabels = vdata
+          vinputs = vinputs.to('jax')
+          vlabels = vlabels.to('jax')
           voutputs = model(vinputs)  # call model's forward
           vloss = loss_fn(voutputs, vlabels)
           running_vloss += vloss
@@ -172,15 +177,11 @@ def train_one_epoch(epoch_index, tb_writer=None):
 
     # Log the running loss averaged per batch
     # for both training and validation
-    writer.add_scalars('Training vs. Validation Loss',
-                    { 'Training' : avg_loss, 'Validation' : avg_vloss },
-                    epoch_number + 1)
-    writer.flush()
-
-    # Track best performance, and save the model's state
-    if avg_vloss < best_vloss:
-        best_vloss = avg_vloss
-        model_path = 'model_{}_{}'.format(timestamp, epoch_number)
-        torch.save(model.state_dict(), model_path)
+
+    # # Track best performance, and save the model's state
+    # if avg_vloss < best_vloss:
+    #     best_vloss = avg_vloss
+    #     model_path = 'model_{}_{}'.format(timestamp, epoch_number)
+    #     torch.save(model.state_dict(), model_path)
 
     epoch_number += 1

experimental/torch_xla2/torch_xla2/tensor.py

@@ -248,6 +248,7 @@ def _name_of_func(func):
   torch.rand,
   torch.randint,
   torch.full,
+  torch.as_tensor,
 }