Use torch.accelerator API in Fast Neural Style example

`torch.accelerator` API allows to abstract some of the accelerator specifics
in the user scripts. This commit modifies Fast Neural Style example with the mentioned API.

Things to note:
* Commit modifies example command line by replacing accelerator specific
  flags (`--cuda`, `--mps`, `--xpu`) with the --accel
* `torch.accelerator` API appeared in pytorch 2.6, so commit binds example to this torch version
* Commit introduces `USE_ACCEL` flag for the test `*.sh` script. At the moment this
  flag is just an alias for `USE_CUDA`. In the future it should replace `USE_CUDA` once all
  examples will be converted to use `torch.accelerator` API.

Signed-off-by: Dmitry Rogozhkin <[email protected]>

Author: dvrogozh

fast_neural_style/README.md

@@ -19,31 +19,29 @@ The program is written in Python, and uses [pytorch](http://pytorch.org/), [scip
 Stylize image
 
 ```
-python neural_style/neural_style.py eval --content-image </path/to/content/image> --model </path/to/saved/model> --output-image </path/to/output/image> --cuda 0
+python neural_style/neural_style.py eval --content-image </path/to/content/image> --model </path/to/saved/model> --output-image </path/to/output/image> --accel
 ```
 
 - `--content-image`: path to content image you want to stylize.
 - `--model`: saved model to be used for stylizing the image (eg: `mosaic.pth`)
 - `--output-image`: path for saving the output image.
 - `--content-scale`: factor for scaling down the content image if memory is an issue (eg: value of 2 will halve the height and width of content-image)
-- `--cuda 0|1`: set it to 1 for running on GPU, 0 for CPU.
-- `--mps`: use MPS device backend.
-- `--xpu`: use XPU device backend.
+- `--accel`: use accelerator
 
 Train model
 
 ```bash
-python neural_style/neural_style.py train --dataset </path/to/train-dataset> --style-image </path/to/style/image> --save-model-dir </path/to/save-model/folder> --epochs 2 --cuda 1
+python neural_style/neural_style.py train --dataset </path/to/train-dataset> --style-image </path/to/style/image> --save-model-dir </path/to/save-model/folder> --epochs 2 --accel
 ```
 
 There are several command line arguments, the important ones are listed below
 
 - `--dataset`: path to training dataset, the path should point to a folder containing another folder with all the training images. I used COCO 2014 Training images dataset [80K/13GB] [(download)](https://cocodataset.org/#download).
 - `--style-image`: path to style-image.
 - `--save-model-dir`: path to folder where trained model will be saved.
-- `--cuda 0|1`: set it to 1 for running on GPU, 0 for CPU.
-- `--mps`: use MPS device backend.
-- `--xpu`: use XPU device backend.
+- `--accel`: use accelerator.
+
+If `--accel` argument is given, pytorch will search for available hardware acceleration device and attempt to use it. This example is known to work on CUDA, MPS and XPU devices.
 
 Refer to `neural_style/neural_style.py` for other command line arguments. For training new models you might have to tune the values of `--content-weight` and `--style-weight`. The mosaic style model shown above was trained with `--content-weight 1e5` and `--style-weight 1e10`. The remaining 3 models were also trained with similar order of weight parameters with slight variation in the `--style-weight` (`5e10` or `1e11`).
 

fast_neural_style/neural_style/neural_style.py

@@ -29,16 +29,12 @@ def check_paths(args):
 
 
 def train(args):
-    if args.cuda:
-        device = torch.device("cuda")
-    elif args.mps:
-        device = torch.device("mps")
-    elif args.xpu:
-        device = torch.device("xpu")
+    if args.accel:
+        device = torch.accelerator.current_accelerator()
     else:
         device = torch.device("cpu")
 
-    print("Device to use: ", device)
+    print(f"Using device: {device}")
 
     np.random.seed(args.seed)
     torch.manual_seed(args.seed)
@@ -129,10 +125,12 @@ def train(args):
 
 
 def stylize(args):
-    device = torch.device("cuda" if args.cuda else "cpu")
-    device = torch.device("xpu" if args.xpu else "cpu")
+    if args.accel:
+        device = torch.accelerator.current_accelerator()
+    else:
+        device = torch.device("cpu")
 
-    print("Device to use: ", device)
+    print(f"Using device: {device}")
 
     content_image = utils.load_image(args.content_image, scale=args.content_scale)
     content_transform = transforms.Compose([
@@ -212,8 +210,8 @@ def main():
                                   help="size of training images, default is 256 X 256")
     train_arg_parser.add_argument("--style-size", type=int, default=None,
                                   help="size of style-image, default is the original size of style image")
-    train_arg_parser.add_argument("--cuda", type=int, required=True,
-                                  help="set it to 1 for running on GPU, 0 for CPU")
+    train_arg_parser.add_argument('--accel', action='store_true',
+                                  help='use accelerator')
     train_arg_parser.add_argument("--seed", type=int, default=42,
                                   help="random seed for training")
     train_arg_parser.add_argument("--content-weight", type=float, default=1e5,
@@ -226,10 +224,6 @@ def main():
                                   help="number of images after which the training loss is logged, default is 500")
     train_arg_parser.add_argument("--checkpoint-interval", type=int, default=2000,
                                   help="number of batches after which a checkpoint of the trained model will be created")
-    train_arg_parser.add_argument('--mps', action='store_true',
-                                  help='enable macOS GPU training')
-    train_arg_parser.add_argument('--xpu', action='store_true',
-                                  help='enable Intel XPU training')
 
     eval_arg_parser = subparsers.add_parser("eval", help="parser for evaluation/stylizing arguments")
     eval_arg_parser.add_argument("--content-image", type=str, required=True,
@@ -240,28 +234,21 @@ def main():
                                  help="path for saving the output image")
     eval_arg_parser.add_argument("--model", type=str, required=True,
                                  help="saved model to be used for stylizing the image. If file ends in .pth - PyTorch path is used, if in .onnx - Caffe2 path")
-    eval_arg_parser.add_argument("--cuda", type=int, default=False,
-                                 help="set it to 1 for running on cuda, 0 for CPU")
     eval_arg_parser.add_argument("--export_onnx", type=str,
                                  help="export ONNX model to a given file")
-    eval_arg_parser.add_argument('--mps', action='store_true',
-                                 help='enable macOS GPU evaluation')
-    eval_arg_parser.add_argument('--xpu', action='store_true',
-                                 help='enable Intel XPU evaluation')
-
+    eval_arg_parser.add_argument('--accel', action='store_true',
+                                 help='use accelerator')
 
     args = main_arg_parser.parse_args()
 
     if args.subcommand is None:
         print("ERROR: specify either train or eval")
         sys.exit(1)
-    if args.cuda and not torch.cuda.is_available():
-        print("ERROR: cuda is not available, try running on CPU")
+    if args.accel and not torch.accelerator.is_available():
+        print("ERROR: accelerator is not available, try running on CPU")
         sys.exit(1)
-    if not args.mps and torch.backends.mps.is_available():
-        print("WARNING: mps is available, run with --mps to enable macOS GPU")
-    if not args.xpu and torch.xpu.is_available():
-        print("WARNING: XPU is available, run with --xpu to enable Intel XPU")
+    if not args.accel and torch.accelerator.is_available():
+        print("WARNING: accelerator is available, run with --accel to enable it")
 
     if args.subcommand == "train":
         check_paths(args)

fast_neural_style/requirements.txt

@@ -1,3 +1,3 @@
 numpy
-torch
+torch>=2.6
 torchvision

run_python_examples.sh

@@ -13,6 +13,12 @@
 # To test examples on CUDA accelerator, run as:
 #   USE_CUDA=True ./run_python_examples.sh
 #
+# To test examples on hardware accelerator (CUDA, MPS, XPU, etc.), run as:
+#   USE_ACCEL=True ./run_python_examples.sh
+# NOTE: USE_ACCEL relies on torch.accelerator API and not all examples are converted
+# to use it at the moment. Thus, expect failures using this flag on non-CUDA accelerators
+# and consider to run examples one by one.
+#
 # Script requires uv to be installed. When executed, script will install prerequisites from
 # `requirements.txt` for each example. If ran within activated virtual environment (uv venv,
 # python -m venv, conda) this might reinstall some of the packages. To change pip installation
@@ -27,17 +33,24 @@
 BASE_DIR="$(pwd)/$(dirname $0)"
 source $BASE_DIR/utils.sh
 
+# TODO: Leave only USE_ACCEL and drop USE_CUDA once all examples will be converted
+# to torch.accelerator API. For now, just add USE_ACCEL as an alias for USE_CUDA.
+if [ -n "$USE_ACCEL" ]; then
+  USE_CUDA=$USE_ACCEL
+fi
 USE_CUDA=${USE_CUDA:-False}
 case $USE_CUDA in
   "True")
     echo "using cuda"
     CUDA=1
     CUDA_FLAG="--cuda"
+    ACCEL_FLAG="--accel"
     ;;
   "False")
     echo "not using cuda"
     CUDA=0
     CUDA_FLAG=""
+    ACCEL_FLAG=""
     ;;
   "")
     exit 1;
@@ -56,7 +69,7 @@ function fast_neural_style() {
   test -d "saved_models" || { error "saved models not found"; return; }
 
   echo "running fast neural style model"
-  uv run neural_style/neural_style.py eval --content-image images/content-images/amber.jpg --model saved_models/candy.pth --output-image images/output-images/amber-candy.jpg --cuda $CUDA --mps || error "neural_style.py failed"
+  uv run neural_style/neural_style.py eval --content-image images/content-images/amber.jpg --model saved_models/candy.pth --output-image images/output-images/amber-candy.jpg $ACCEL_FLAG || error "neural_style.py failed"
 }
 
 function imagenet() {