From 90ed2df1a77c5bc4a6c5fceed4b8ed61867f7bf8 Mon Sep 17 00:00:00 2001
From: "Yu, Guangye" <guangye.yu@intel.com>
Date: Tue, 26 Nov 2024 16:03:44 +0000
Subject: [PATCH] [2/N] Refine beginner tutorial by accelerator api

---
 beginner_source/basics/quickstart_tutorial.py | 14 ++++----------
 beginner_source/basics/tensorqs_tutorial.py   | 16 ++++++++--------
 2 files changed, 12 insertions(+), 18 deletions(-)

diff --git a/beginner_source/basics/quickstart_tutorial.py b/beginner_source/basics/quickstart_tutorial.py
index df7628081b..5cce8dcfe9 100644
--- a/beginner_source/basics/quickstart_tutorial.py
+++ b/beginner_source/basics/quickstart_tutorial.py
@@ -84,16 +84,10 @@
 # To define a neural network in PyTorch, we create a class that inherits
 # from `nn.Module <https://pytorch.org/docs/stable/generated/torch.nn.Module.html>`_. We define the layers of the network
 # in the ``__init__`` function and specify how data will pass through the network in the ``forward`` function. To accelerate
-# operations in the neural network, we move it to the GPU or MPS if available.
-
-# Get cpu, gpu or mps device for training.
-device = (
-    "cuda"
-    if torch.cuda.is_available()
-    else "mps"
-    if torch.backends.mps.is_available()
-    else "cpu"
-)
+# operations in the neural network, we move it to the `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")
 
 # Define model
diff --git a/beginner_source/basics/tensorqs_tutorial.py b/beginner_source/basics/tensorqs_tutorial.py
index 70a966d9f8..30e05cb10d 100644
--- a/beginner_source/basics/tensorqs_tutorial.py
+++ b/beginner_source/basics/tensorqs_tutorial.py
@@ -99,20 +99,20 @@
 # Operations on Tensors
 # ~~~~~~~~~~~~~~~~~~~~~~~
 #
-# Over 100 tensor operations, including arithmetic, linear algebra, matrix manipulation (transposing,
+# Over 1200 tensor operations, including arithmetic, linear algebra, matrix manipulation (transposing,
 # indexing, slicing), sampling and more are
 # comprehensively described `here <https://pytorch.org/docs/stable/torch.html>`__.
 #
-# Each of these operations can be run on the GPU (at typically higher speeds than on a
-# CPU). If you’re using Colab, allocate a GPU by going to Runtime > Change runtime type > GPU.
+# Each of these operations can be run on the CPU and `Accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__
+# such as CUDA, MPS, MTIA, or XPU. If you’re using Colab, allocate an accelerator by going to Runtime > Change runtime type > GPU.
 #
-# By default, tensors are created on the CPU. We need to explicitly move tensors to the GPU using
-# ``.to`` method (after checking for GPU availability). Keep in mind that copying large tensors
+# By default, tensors are created on the CPU. We need to explicitly move tensors to the accelerator using
+# ``.to`` method (after checking for accelerator availability). Keep in mind that copying large tensors
 # across devices can be expensive in terms of time and memory!
 
-# We move our tensor to the GPU if available
-if torch.cuda.is_available():
-    tensor = tensor.to("cuda")
+# We move our tensor to the current accelerator if available
+if torch.accelerator.is_available():
+    tensor = tensor.to(torch.accelerator.current_accelerator())
 
 
 ######################################################################