Examples

These examples will help you get started using Intel® Extension for PyTorch* with Intel GPUs.

Prerequisites: Before running these examples, install the torchvision and transformers Python packages.

• Python examples demonstrate usage of Python APIs:

  • Training

  • Inference

• C++ examples demonstrate usage of C++ APIs

• Intel® AI Reference Models provide out-of-the-box use cases, demonstrating the performance benefits achievable with Intel Extension for PyTorch*

Python

Training

Single-Instance Training

To use Intel® Extension for PyTorch* on training, you need to make the following changes in your code:

1. Import intel_extension_for_pytorch as ipex.

2. Use the ipex.optimize function for additional performance boost, which applies optimizations against the model object, as well as an optimizer object.

3. Use Auto Mixed Precision (AMP) with BFloat16 data type.

4. Convert input tensors, loss criterion and model to XPU, as shown below:

...
import torch
import intel_extension_for_pytorch as ipex
...
model = Model()
criterion = ...
optimizer = ...
model.train()
# For Float32
model, optimizer = ipex.optimize(model, optimizer=optimizer)
# For BFloat16
model, optimizer = ipex.optimize(model, optimizer=optimizer, dtype=torch.bfloat16)
...
# For Float32
output = model(data)
...
# For BFloat16
with torch.xpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
  output = model(input)
...

Below you can find complete code examples demonstrating how to use the extension on training for different data types:

Float32

import torch
import torchvision

############# code changes ###############
import intel_extension_for_pytorch as ipex

############# code changes ###############

LR = 0.001
DOWNLOAD = True
DATA = "datasets/cifar10/"

transform = torchvision.transforms.Compose(
    [
        torchvision.transforms.Resize((224, 224)),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
    ]
)
train_dataset = torchvision.datasets.CIFAR10(
    root=DATA,
    train=True,
    transform=transform,
    download=DOWNLOAD,
)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=128)

model = torchvision.models.resnet50()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=LR, momentum=0.9)
model.train()
######################## code changes #######################
model = model.to("xpu")
criterion = criterion.to("xpu")
model, optimizer = ipex.optimize(model, optimizer=optimizer)
######################## code changes #######################

for batch_idx, (data, target) in enumerate(train_loader):
    ########## code changes ##########
    data = data.to("xpu")
    target = target.to("xpu")
    ########## code changes ##########
    optimizer.zero_grad()
    output = model(data)
    loss = criterion(output, target)
    loss.backward()
    optimizer.step()
    print(batch_idx)
torch.save(
    {
        "model_state_dict": model.state_dict(),
        "optimizer_state_dict": optimizer.state_dict(),
    },
    "checkpoint.pth",
)

print("Execution finished")

BFloat16

import torch
import torchvision

############# code changes ###############
import intel_extension_for_pytorch as ipex

############# code changes ###############

LR = 0.001
DOWNLOAD = True
DATA = "datasets/cifar10/"

transform = torchvision.transforms.Compose(
    [
        torchvision.transforms.Resize((224, 224)),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
    ]
)
train_dataset = torchvision.datasets.CIFAR10(
    root=DATA,
    train=True,
    transform=transform,
    download=DOWNLOAD,
)
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=128)

model = torchvision.models.resnet50()
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=LR, momentum=0.9)
model.train()
##################################### code changes ################################
model = model.to("xpu")
criterion = criterion.to("xpu")
model, optimizer = ipex.optimize(model, optimizer=optimizer, dtype=torch.bfloat16)
##################################### code changes ################################

for batch_idx, (data, target) in enumerate(train_loader):
    optimizer.zero_grad()
    ######################### code changes #########################
    data = data.to("xpu")
    target = target.to("xpu")
    with torch.xpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
    ######################### code changes #########################
        output = model(data)
        loss = criterion(output, target)
    loss.backward()
    optimizer.step()
    print(batch_idx)
torch.save(
    {
        "model_state_dict": model.state_dict(),
        "optimizer_state_dict": optimizer.state_dict(),
    },
    "checkpoint.pth",
)

print("Execution finished")

Inference

Get additional performance boosts for your computer vision and NLP workloads by applying the Intel® Extension for PyTorch* optimize function against your model object.

Float32

Imperative Mode

Resnet50

import torch
import torchvision.models as models

############# code changes ###############
import intel_extension_for_pytorch as ipex

############# code changes ###############

model = models.resnet50(weights="ResNet50_Weights.DEFAULT")
model.eval()
data = torch.rand(1, 3, 224, 224)

######## code changes #######
model = model.to("xpu")
data = data.to("xpu")
model = ipex.optimize(model)
######## code changes #######

with torch.no_grad():
    model(data)

print("Execution finished")

BERT

import torch
from transformers import BertModel

############# code changes ###############
import intel_extension_for_pytorch as ipex

############# code changes ###############

model = BertModel.from_pretrained("bert-base-uncased")
model.eval()

vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])

######## code changes #######
model = model.to("xpu")
data = data.to("xpu")
model = ipex.optimize(model)
######## code changes #######

with torch.no_grad():
    model(data)

print("Execution finished")

TorchScript Mode

We recommend using Intel® Extension for PyTorch* with TorchScript for further optimizations.

Resnet50

import torch
import torchvision.models as models

############# code changes ###############
import intel_extension_for_pytorch as ipex

############# code changes ###############

model = models.resnet50(weights="ResNet50_Weights.DEFAULT")
model.eval()
data = torch.rand(1, 3, 224, 224)

######## code changes #######
model = model.to("xpu")
data = data.to("xpu")
model = ipex.optimize(model)
######## code changes #######

with torch.no_grad():
    d = torch.rand(1, 3, 224, 224)
    ##### code changes #####
    d = d.to("xpu")
    ##### code changes #####
    model = torch.jit.trace(model, d)
    model = torch.jit.freeze(model)

    model(data)

print("Execution finished")

BERT

import torch
from transformers import BertModel

############# code changes ###############
import intel_extension_for_pytorch as ipex

############# code changes ###############

model = BertModel.from_pretrained("bert-base-uncased")
model.eval()

vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])

######## code changes #######
model = model.to("xpu")
data = data.to("xpu")
model = ipex.optimize(model)
######## code changes #######

with torch.no_grad():
    d = torch.randint(vocab_size, size=[batch_size, seq_length])
    ##### code changes #####
    d = d.to("xpu")
    ##### code changes #####
    model = torch.jit.trace(model, (d,), strict=False)
    model = torch.jit.freeze(model)

    model(data)

print("Execution finished")

BFloat16

The optimize function works for both Float32 and BFloat16 data type. For BFloat16 data type, set the dtype parameter to torch.bfloat16. We recommend using Auto Mixed Precision (AMP) with BFloat16 data type.

Imperative Mode

Resnet50

import torch
import torchvision.models as models

############# code changes ###############
import intel_extension_for_pytorch as ipex

############# code changes ###############

model = models.resnet50(weights="ResNet50_Weights.DEFAULT")
model.eval()
data = torch.rand(1, 3, 224, 224)

#################### code changes #################
model = model.to("xpu")
data = data.to("xpu")
model = ipex.optimize(model, dtype=torch.bfloat16)
#################### code changes #################

with torch.no_grad():
    ############################# code changes #####################
    with torch.xpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
    ############################ code changes ######################
        model(data)

print("Execution finished")

BERT

import torch
from transformers import BertModel

############# code changes ###############
import intel_extension_for_pytorch as ipex

############# code changes ###############

model = BertModel.from_pretrained("bert-base-uncased")
model.eval()

vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])

#################### code changes #################
model = model.to("xpu")
data = data.to("xpu")
model = ipex.optimize(model, dtype=torch.bfloat16)
#################### code changes #################

with torch.no_grad():
    ########################### code changes ########################
    with torch.xpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
    ########################### code changes ########################
        model(data)

print("Execution finished")

TorchScript Mode

We recommend using Intel® Extension for PyTorch* with TorchScript for further optimizations.

Resnet50

import torch
import torchvision.models as models

############# code changes ###############
import intel_extension_for_pytorch as ipex

############# code changes ###############

model = models.resnet50(weights="ResNet50_Weights.DEFAULT")
model.eval()
data = torch.rand(1, 3, 224, 224)

#################### code changes #################
model = model.to("xpu")
data = data.to("xpu")
model = ipex.optimize(model, dtype=torch.bfloat16)
#################### code changes #################

with torch.no_grad():
    d = torch.rand(1, 3, 224, 224)
    ############################# code changes #####################
    d = d.to("xpu")
    with torch.xpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
    ############################# code changes #####################
        model = torch.jit.trace(model, d)
        model = torch.jit.freeze(model)
        model(data)

print("Execution finished")

BERT

import torch
from transformers import BertModel

############# code changes ###############
import intel_extension_for_pytorch as ipex

############# code changes ###############

model = BertModel.from_pretrained("bert-base-uncased")
model.eval()

vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])

#################### code changes #################
model = model.to("xpu")
data = data.to("xpu")
model = ipex.optimize(model, dtype=torch.bfloat16)
#################### code changes #################

with torch.no_grad():
    d = torch.randint(vocab_size, size=[batch_size, seq_length])
    ############################# code changes #####################
    d = d.to("xpu")
    with torch.xpu.amp.autocast(enabled=True, dtype=torch.bfloat16):
    ############################# code changes #####################
        model = torch.jit.trace(model, (d,), strict=False)
        model = torch.jit.freeze(model)

        model(data)

print("Execution finished")

Float16

The optimize function works for both Float32 and Float16 data type. For Float16 data type, set the dtype parameter to torch.float16. We recommend using Auto Mixed Precision (AMP) with Float16 data type.

Imperative Mode

Resnet50

import torch
import torchvision.models as models

############# code changes ###############
import intel_extension_for_pytorch as ipex

############# code changes ###############

model = models.resnet50(weights="ResNet50_Weights.DEFAULT")
model.eval()
data = torch.rand(1, 3, 224, 224)

#################### code changes ################
model = model.to("xpu")
data = data.to("xpu")
model = ipex.optimize(model, dtype=torch.float16)
#################### code changes ################

with torch.no_grad():
    ############################# code changes #####################
    with torch.xpu.amp.autocast(enabled=True, dtype=torch.float16):
    ############################# code changes #####################
        model(data)

print("Execution finished")

BERT

import torch
from transformers import BertModel

############# code changes ###############
import intel_extension_for_pytorch as ipex

############# code changes ###############

model = BertModel.from_pretrained("bert-base-uncased")
model.eval()

vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])

#################### code changes ################
model = model.to("xpu")
data = data.to("xpu")
model = ipex.optimize(model, dtype=torch.float16)
#################### code changes ################

with torch.no_grad():
    ############################# code changes #####################
    with torch.xpu.amp.autocast(enabled=True, dtype=torch.float16):
    ############################# code changes #####################
        model(data)

print("Execution finished")

TorchScript Mode

We recommend using Intel® Extension for PyTorch* with TorchScript for further optimizations.

Resnet50

import torch
import torchvision.models as models

############# code changes ###############
import intel_extension_for_pytorch as ipex

############# code changes ###############

model = models.resnet50(weights="ResNet50_Weights.DEFAULT")
model.eval()
data = torch.rand(1, 3, 224, 224)

#################### code changes ################
model = model.to("xpu")
data = data.to("xpu")
model = ipex.optimize(model, dtype=torch.float16)
#################### code changes ################

with torch.no_grad():
    d = torch.rand(1, 3, 224, 224)
    ############################# code changes #####################
    d = d.to("xpu")
    with torch.xpu.amp.autocast(enabled=True, dtype=torch.float16):
    ############################# code changes #####################
        model = torch.jit.trace(model, d)
        model = torch.jit.freeze(model)
        model(data)

print("Execution finished")

BERT

import torch
from transformers import BertModel

############# code changes ###############
import intel_extension_for_pytorch as ipex

############# code changes ###############

model = BertModel.from_pretrained("bert-base-uncased")
model.eval()

vocab_size = model.config.vocab_size
batch_size = 1
seq_length = 512
data = torch.randint(vocab_size, size=[batch_size, seq_length])

#################### code changes ################
model = model.to("xpu")
data = data.to("xpu")
model = ipex.optimize(model, dtype=torch.float16)
#################### code changes ################

with torch.no_grad():
    d = torch.randint(vocab_size, size=[batch_size, seq_length])
    ############################# code changes #####################
    d = d.to("xpu")
    with torch.xpu.amp.autocast(enabled=True, dtype=torch.float16):
    ############################# code changes #####################
        model = torch.jit.trace(model, (d,), strict=False)
        model = torch.jit.freeze(model)

        model(data)

print("Execution finished")

INT8

We recommend using TorchScript for INT8 model because it has wider support for models. TorchScript mode also auto-enables our optimizations. For TorchScript INT8 model, inserting observer and model quantization is achieved through prepare_jit and convert_jit separately. Calibration process is required for collecting statistics from real data. After conversion, optimizations such as operator fusion would be auto-enabled.

import os
import torch
from torch.jit._recursive import wrap_cpp_module
from torch.quantization.quantize_jit import (
    convert_jit,
    prepare_jit,
)

#################### code changes ####################
import intel_extension_for_pytorch as ipex

######################################################

##### Example Model #####
import torchvision.models as models

model = models.resnet50(weights="ResNet50_Weights.DEFAULT")
model.eval()
model = model.to("xpu")

with torch.no_grad():
    data = torch.rand(1, 3, 224, 224)
    data = data.to("xpu")
    modelJit = torch.jit.trace(model, data)
#########################

qconfig = torch.quantization.QConfig(
    activation=torch.quantization.observer.MinMaxObserver.with_args(
        qscheme=torch.per_tensor_symmetric, reduce_range=False, dtype=torch.quint8
    ),
    weight=torch.quantization.default_weight_observer,
)
modelJit = prepare_jit(modelJit, {"": qconfig}, True)

##### Example Dataloader #####
import torchvision

DOWNLOAD = True
DATA = "datasets/cifar10/"

transform = torchvision.transforms.Compose(
    [
        torchvision.transforms.Resize((224, 224)),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
    ]
)
train_dataset = torchvision.datasets.CIFAR10(
    root=DATA,
    train=True,
    transform=transform,
    download=DOWNLOAD,
)
calibration_data_loader = torch.utils.data.DataLoader(
    dataset=train_dataset, batch_size=128
)

for batch_idx, (d, target) in enumerate(calibration_data_loader):
    print(f"calibrated on batch {batch_idx} out of {len(calibration_data_loader)}")
    d = d.to("xpu")
    modelJit(d)
##############################

modelJit = convert_jit(modelJit, True)

data = torch.rand(1, 3, 224, 224)
data = data.to("xpu")
modelJit(data)

print("Execution finished")

torch.xpu.optimize

The torch.xpu.optimize function is an alternative to ipex.optimize in Intel® Extension for PyTorch*, and provides identical usage for XPU devices only. The motivation for adding this alias is to unify the coding style in user scripts base on torch.xpu modular. Refer to the example below for usage.

import torch
import torchvision.models as models

############# code changes #########
import intel_extension_for_pytorch

############# code changes #########

model = models.resnet50(weights="ResNet50_Weights.DEFAULT")
model.eval()
data = torch.rand(1, 3, 224, 224)

model = model.to(memory_format=torch.channels_last)
data = data.to(memory_format=torch.channels_last)

########## code changes #########
model = model.to("xpu")
data = data.to("xpu")
model = torch.xpu.optimize(model)
########## code changes #########

with torch.no_grad():
    model(data)

print("Execution finished")

C++

To work with libtorch, the PyTorch C++ library, Intel® Extension for PyTorch* provides its own C++ dynamic library. The C++ library only handles inference workloads, such as service deployment. For regular development, use the Python interface. Unlike using libtorch, no specific code changes are required. Compilation follows the recommended methodology with CMake. Detailed instructions can be found in the PyTorch tutorial.

During compilation, Intel optimizations will be activated automatically after the C++ dynamic library of Intel® Extension for PyTorch* is linked.

The example code below works for all data types.

Basic Usage

Download and Install cppsdk

Ensure you have download and install cppsdk in the installation page before compiling the cpp code.

1. Go to installation page

2. Select the desired Platform & Version & OS

3. In the package part, select cppsdk

4. Follow the instructions in the cppsdk installation page to download and install cppsdk into libtorch.

example-app.cpp

#include <torch/script.h>
#include <iostream>
#include <memory>

int main(int argc, const char* argv[]) {
  torch::jit::script::Module module;
  try {
    module = torch::jit::load(argv[1]);
  }
  catch (const c10::Error& e) {
    std::cerr << "error loading the model\n";
    return -1;
  }
  module.to(at::kXPU);

  std::vector<torch::jit::IValue> inputs;
  torch::Tensor input = torch::rand({1, 3, 224, 224}).to(at::kXPU);
  inputs.push_back(input);

  at::Tensor output = module.forward(inputs).toTensor();
  std::cout << output.slice(/*dim=*/1, /*start=*/0, /*end=*/5) << std::endl;
  std::cout << "Execution finished" << std::endl;

  return 0;
}

CMakeLists.txt

cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
project(example-app)

find_package(IPEX REQUIRED)

set(target example-app)
add_executable(${target} example-app.cpp)
target_link_libraries(${target} ${TORCH_IPEX_LIBRARIES})

set_property(TARGET ${target} PROPERTY CXX_STANDARD 17)

Command for compilation

$ cd examples/gpu/inference/cpp/example-app
$ mkdir build
$ cd build
$ CC=icx CXX=icpx cmake -DCMAKE_PREFIX_PATH=<LIBPYTORCH_PATH> ..
$ make

The <LIBPYTORCH_PATH> is the absolute path of libtorch we install at the first step.

If Found IPEX is shown as dynamic library paths, the extension was linked into the binary. This can be verified with the Linux command ldd.

The value of x, y, z in the following log will change depending on the version you choose.

$ CC=icx CXX=icpx cmake -DCMAKE_PREFIX_PATH=/workspace/libtorch ..
-- The C compiler identification is IntelLLVM 202x.y.z
-- The CXX compiler identification is IntelLLVM 202x.y.z
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /workspace/intel/oneapi/compiler/202x.y.z/linux/bin/icx - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /workspace/intel/oneapi/compiler/202x.y.z/linux/bin/icpx - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
-- Looking for pthread.h
-- Looking for pthread.h - found
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD
-- Performing Test CMAKE_HAVE_LIBC_PTHREAD - Success
-- Found Threads: TRUE
-- Found Torch: /workspace/libtorch/lib/libtorch.so
-- Found IPEX: /workspace/libtorch/lib/libintel-ext-pt-cpu.so;/workspace/libtorch/lib/libintel-ext-pt-gpu.so
-- Configuring done
-- Generating done
-- Build files have been written to: examples/gpu/inference/cpp/example-app/build

$ ldd example-app
        ...
        libtorch.so => /workspace/libtorch/lib/libtorch.so (0x00007fd5bb927000)
        libc10.so => /workspace/libtorch/lib/libc10.so (0x00007fd5bb895000)
        libtorch_cpu.so => /workspace/libtorch/lib/libtorch_cpu.so (0x00007fd5a44d8000)
        libintel-ext-pt-cpu.so => /workspace/libtorch/lib/libintel-ext-pt-cpu.so (0x00007fd5a1a1b000)
        libintel-ext-pt-gpu.so => /workspace/libtorch/lib/libintel-ext-pt-gpu.so (0x00007fd5862b0000)
        ...
        libmkl_intel_lp64.so.2 => /workspace/intel/oneapi/mkl/202x.y.z/lib/intel64/libmkl_intel_lp64.so.2 (0x00007fd584ab0000)
        libmkl_core.so.2 => /workspace/intel/oneapi/mkl/202x.y.z/lib/intel64/libmkl_core.so.2 (0x00007fd5806cc000)
        libmkl_gnu_thread.so.2 => /workspace/intel/oneapi/mkl/202x.y.z/lib/intel64/libmkl_gnu_thread.so.2 (0x00007fd57eb1d000)
        libmkl_sycl.so.3 => /workspace/intel/oneapi/mkl/202x.y.z/lib/intel64/libmkl_sycl.so.3 (0x00007fd55512c000)
        libOpenCL.so.1 => /workspace/intel/oneapi/compiler/202x.y.z/linux/lib/libOpenCL.so.1 (0x00007fd55511d000)
        libsvml.so => /workspace/intel/oneapi/compiler/202x.y.z/linux/compiler/lib/intel64_lin/libsvml.so (0x00007fd553b11000)
        libirng.so => /workspace/intel/oneapi/compiler/202x.y.z/linux/compiler/lib/intel64_lin/libirng.so (0x00007fd553600000)
        libimf.so => /workspace/intel/oneapi/compiler/202x.y.z/linux/compiler/lib/intel64_lin/libimf.so (0x00007fd55321b000)
        libintlc.so.5 => /workspace/intel/oneapi/compiler/202x.y.z/linux/compiler/lib/intel64_lin/libintlc.so.5 (0x00007fd553a9c000)
        libsycl.so.6 => /workspace/intel/oneapi/compiler/202x.y.z/linux/lib/libsycl.so.6 (0x00007fd552f36000)
        ...

Use SYCL code

Using SYCL code in an C++ application is also possible. The example below shows how to invoke SYCL codes. You need to explicitly pass -fsycl into CMAKE_CXX_FLAGS.

example-usm.cpp

#include <iostream>
#include <memory>
#include <torch/script.h>
#include <ipex.h>
#include <CL/sycl.hpp>

using namespace sycl;
using namespace xpu::dpcpp;

int main(int argc, const char* argv[]) {
  torch::jit::script::Module module;
  try {
    module = torch::jit::load(argv[1]);
  }
  catch (const c10::Error& e) {
    std::cerr << "error loading the model\n";
    return -1;
  }
  std::cout << "load model done " << std::endl;
  module.to(at::kXPU);

  std::vector<torch::jit::IValue> inputs;
  // fetch sycl queue from c10::Stream on XPU device.
  auto device_type = c10::DeviceType::XPU;
  c10::impl::VirtualGuardImpl impl(device_type);
  c10::Stream xpu_stream = impl.getStream(c10::Device(device_type));
  auto& sycl_queue = xpu::get_queue_from_stream(xpu_stream);
  float *input_ptr = malloc_device<float>(224 * 224 * 3, sycl_queue);
  auto input = fromUSM(input_ptr, at::ScalarType::Float, {1, 3, 224, 224}, c10::nullopt, -1).to(at::kXPU);
  std::cout << "input tensor created from usm " << std::endl;
  inputs.push_back(input);

  at::IValue output = module.forward(inputs);
  torch::Tensor output_tensor;
  output_tensor = output.toTensor();
  std::cout << output_tensor.slice(/*dim=*/1, /*start=*/0, /*end=*/5) << std::endl;
  std::cout << "Execution finished" << std::endl;

  return 0;
}

CMakeLists.txt

cmake_minimum_required(VERSION 3.0 FATAL_ERROR)
project(example-usm)

find_package(IPEX REQUIRED)

set(target example-usm)
add_executable(${target} example-usm.cpp)
target_link_libraries(${target} ${TORCH_IPEX_LIBRARIES})
list(APPEND CMAKE_CXX_FLAGS "-fsycl")

set_property(TARGET ${target} PROPERTY CXX_STANDARD 17)

Customize DPC++ kernels

Intel® Extension for PyTorch* provides its C++ dynamic library to allow users to implement custom DPC++ kernels to run on the XPU device. Refer to the DPC++ extension for details.

Intel® AI Reference Models

Use cases that have already been optimized by Intel engineers are available at Intel® AI Reference Models (former Model Zoo). A number of PyTorch use cases for benchmarking are also available in the Use Cases section. Models verified on Intel GPUs are marked in the Model Documentation column. You can get performance benefits out-of-the-box by simply running scripts in the Intel® AI Reference Models.