Merge branch 'main' into guangyey/accelerator4

Author: svekars

.jenkins/validate_tutorials_built.py

@@ -25,7 +25,6 @@
     "intermediate_source/mnist_train_nas",  # used by ax_multiobjective_nas_tutorial.py
     "intermediate_source/fx_conv_bn_fuser",
     "intermediate_source/_torch_export_nightly_tutorial",  # does not work on release
-    "intermediate_source/transformer_building_blocks",  # does not work on release
     "advanced_source/super_resolution_with_onnxruntime",
     "advanced_source/usb_semisup_learn", # fails with CUDA OOM error, should try on a different worker
     "prototype_source/fx_graph_mode_ptq_dynamic",

beginner_source/basics/buildmodel_tutorial.py

@@ -32,17 +32,10 @@
 #############################################
 # Get Device for Training
 # -----------------------
-# We want to be able to train our model on a hardware accelerator like the GPU or MPS,
-# if available. Let's check to see if `torch.cuda <https://pytorch.org/docs/stable/notes/cuda.html>`_
-# or `torch.backends.mps <https://pytorch.org/docs/stable/notes/mps.html>`_ are available, otherwise we use the CPU.
-
-device = (
-    "cuda"
-    if torch.cuda.is_available()
-    else "mps"
-    if torch.backends.mps.is_available()
-    else "cpu"
-)
+# We want to be able to train our model on an `accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__
+# such as CUDA, MPS, MTIA, or XPU. If the current accelerator is available, we will use it. Otherwise, we use the CPU.
+
+device = torch.accelerator.current_accelerator().type if torch.accelerator.is_available() else "cpu"
 print(f"Using {device} device")
 
 ##############################################

en-wordlist.txt

@@ -159,6 +159,7 @@ Minifier
 MobileNet
 ModelABC
 MPS
+MTIA
 Mypy
 NAS
 NCCL