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Adding a note for Getting Started with PyTorch on Intel GPUs Pull Request resolved: https://github.com/pytorch/pytorch/pull/127872 Approved by: https://github.com/svekars
340 lines
10 KiB
ReStructuredText
340 lines
10 KiB
ReStructuredText
Pytorch 2.4: Getting Started on Intel GPU
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=========================================
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The support for Intel GPUs is released alongside PyTorch v2.4.
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This release only supports build from source for Intel GPUs.
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Hardware Prerequisites
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----------------------
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.. list-table::
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:header-rows: 1
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* - Supported Hardware
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- Intel® Data Center GPU Max Series
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* - Supported OS
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- Linux
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PyTorch for Intel GPUs is compatible with Intel® Data Center GPU Max Series and only supports OS Linux with release 2.4.
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Software Prerequisites
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----------------------
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As a prerequisite, install the driver and required packages by following the `PyTorch Installation Prerequisites for Intel GPUs <https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpus.html>`_.
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Set up Environment
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------------------
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Before you begin, you need to set up the environment. This can be done by sourcing the ``setvars.sh`` script provided by the ``intel-for-pytorch-gpu-dev`` and ``intel-pti-dev`` packages.
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.. code-block::
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source ${ONEAPI_ROOT}/setvars.sh
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.. note::
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The ``ONEAPI_ROOT`` is the folder you installed your ``intel-for-pytorch-gpu-dev`` and ``intel-pti-dev`` packages. Typically, it is located at ``/opt/intel/oneapi/`` or ``~/intel/oneapi/``.
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Build from source
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-----------------
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Now we have all the required packages installed and environment acitvated. Use the following commands to install ``pytorch``, ``torchvision``, ``torchaudio`` by building from source. For more details, refer to official guides in `PyTorch from source <https://github.com/pytorch/pytorch?tab=readme-ov-file#intel-gpu-support>`_, `Vision from source <https://github.com/pytorch/vision/blob/main/CONTRIBUTING.md#development-installation>`_ and `Audio from source <https://pytorch.org/audio/main/build.linux.html>`_.
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.. code-block::
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# Get PyTorch Source Code
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git clone --recursive https://github.com/pytorch/pytorch
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cd pytorch
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git checkout main # or checkout the specific release version >= v2.4
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git submodule sync
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git submodule update --init --recursive
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# Get required packages for compilation
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conda install cmake ninja
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pip install -r requirements.txt
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# Pytorch for Intel GPUs only support Linux platform for now.
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# Install the required packages for pytorch compilation.
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conda install intel::mkl-static intel::mkl-include
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# (optional) If using torch.compile with inductor/triton, install the matching version of triton
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# Run from the pytorch directory after cloning
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# For Intel GPU support, please explicitly `export USE_XPU=1` before running command.
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USE_XPU=1 make triton
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# If you would like to compile PyTorch with new C++ ABI enabled, then first run this command:
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export _GLIBCXX_USE_CXX11_ABI=1
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# pytorch build from source
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export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
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python setup.py develop
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cd ..
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# (optional) If using torchvison.
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# Get torchvision Code
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git clone https://github.com/pytorch/vision.git
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cd vision
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git checkout main # or specific version
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python setup.py develop
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cd ..
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# (optional) If using torchaudio.
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# Get torchaudio Code
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git clone https://github.com/pytorch/audio.git
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cd audio
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pip install -r requirements.txt
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git checkout main # or specific version
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git submodule sync
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git submodule update --init --recursive
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python setup.py develop
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cd ..
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Check availability for Intel GPU
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--------------------------------
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.. note::
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Make sure the environment is properly set up by following `Environment Set up <#set-up-environment>`_ before running the code.
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To check if your Intel GPU is available, you would typically use the following code:
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.. code-block::
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import torch
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torch.xpu.is_available() # torch.xpu is the API for Intel GPU support
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If the output is ``False``, ensure that you have Intel GPU in your system and correctly follow the `PyTorch Installation Prerequisites for Intel GPUs <https://www.intel.com/content/www/us/en/developer/articles/tool/pytorch-prerequisites-for-intel-gpus.html>`_. Then, check that the PyTorch compilation is correctly finished.
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Minimum Code Change
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-------------------
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If you are migrating code from ``cuda``, you would change references from ``cuda`` to ``xpu``. For example:
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.. code-block::
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# CUDA CODE
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tensor = torch.tensor([1.0, 2.0]).to("cuda")
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# CODE for Intel GPU
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tensor = torch.tensor([1.0, 2.0]).to("xpu")
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The following points outline the support and limitations for PyTorch with Intel GPU:
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#. Both training and inference workflows are supported.
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#. Both eager mode and ``torch.compile`` is supported.
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#. Data types such as FP32, BF16, FP16, and Automatic Mixed Precision (AMP) are all supported.
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#. Models that depend on third-party components, will not be supported until PyTorch v2.5 or later.
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Examples
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--------
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This section contains usage examples for both inference and training workflows.
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Inference Examples
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^^^^^^^^^^^^^^^^^^
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Here is a few inference workflow examples.
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Inference with FP32
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"""""""""""""""""""
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.. code-block::
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import torch
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import torchvision.models as models
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model = models.resnet50(weights="ResNet50_Weights.DEFAULT")
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model.eval()
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data = torch.rand(1, 3, 224, 224)
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######## code changes #######
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model = model.to("xpu")
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data = data.to("xpu")
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######## code changes #######
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with torch.no_grad():
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model(data)
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print("Execution finished")
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Inference with AMP
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""""""""""""""""""
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.. code-block::
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import torch
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import torchvision.models as models
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model = models.resnet50(weights="ResNet50_Weights.DEFAULT")
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model.eval()
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data = torch.rand(1, 3, 224, 224)
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#################### code changes #################
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model = model.to("xpu")
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data = data.to("xpu")
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#################### code changes #################
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with torch.no_grad():
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d = torch.rand(1, 3, 224, 224)
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############################# code changes #####################
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d = d.to("xpu")
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# set dtype=torch.bfloat16 for BF16
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with torch.autocast(device_type="xpu", dtype=torch.float16, enabled=True):
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############################# code changes #####################
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model(data)
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print("Execution finished")
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Inference with ``torch.compile``
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""""""""""""""""""""""""""""""""
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.. code-block::
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import torch
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import torchvision.models as models
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model = models.resnet50(weights="ResNet50_Weights.DEFAULT")
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model.eval()
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data = torch.rand(1, 3, 224, 224)
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ITERS = 10
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######## code changes #######
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model = model.to("xpu")
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data = data.to("xpu")
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######## code changes #######
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model = torch.compile(model)
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for i in range(ITERS):
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with torch.no_grad():
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model(data)
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print("Execution finished")
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Training Examples
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^^^^^^^^^^^^^^^^^
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Here is a few training workflow examples.
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Train with FP32
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"""""""""""""""
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.. code-block::
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import torch
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import torchvision
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LR = 0.001
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DOWNLOAD = True
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DATA = "datasets/cifar10/"
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transform = torchvision.transforms.Compose(
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[
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torchvision.transforms.Resize((224, 224)),
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torchvision.transforms.ToTensor(),
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torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
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]
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)
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train_dataset = torchvision.datasets.CIFAR10(
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root=DATA,
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train=True,
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transform=transform,
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download=DOWNLOAD,
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)
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train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=128)
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model = torchvision.models.resnet50()
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criterion = torch.nn.CrossEntropyLoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=LR, momentum=0.9)
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model.train()
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######################## code changes #######################
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model = model.to("xpu")
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criterion = criterion.to("xpu")
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######################## code changes #######################
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for batch_idx, (data, target) in enumerate(train_loader):
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########## code changes ##########
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data = data.to("xpu")
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target = target.to("xpu")
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########## code changes ##########
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optimizer.zero_grad()
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output = model(data)
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loss = criterion(output, target)
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loss.backward()
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optimizer.step()
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print(batch_idx)
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torch.save(
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{
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"model_state_dict": model.state_dict(),
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"optimizer_state_dict": optimizer.state_dict(),
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},
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"checkpoint.pth",
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)
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print("Execution finished")
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Train with AMP
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""""""""""""""
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.. code-block::
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import torch
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import torchvision
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LR = 0.001
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DOWNLOAD = True
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DATA = "datasets/cifar10/"
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use_amp=True
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transform = torchvision.transforms.Compose(
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[
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torchvision.transforms.Resize((224, 224)),
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torchvision.transforms.ToTensor(),
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torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
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]
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)
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train_dataset = torchvision.datasets.CIFAR10(
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root=DATA,
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train=True,
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transform=transform,
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download=DOWNLOAD,
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)
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train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=128)
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model = torchvision.models.resnet50()
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criterion = torch.nn.CrossEntropyLoss()
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optimizer = torch.optim.SGD(model.parameters(), lr=LR, momentum=0.9)
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scaler = torch.amp.GradScaler(enabled=use_amp)
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model.train()
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######################## code changes #######################
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model = model.to("xpu")
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criterion = criterion.to("xpu")
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######################## code changes #######################
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for batch_idx, (data, target) in enumerate(train_loader):
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########## code changes ##########
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data = data.to("xpu")
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target = target.to("xpu")
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########## code changes ##########
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# set dtype=torch.bfloat16 for BF16
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with torch.autocast(device_type="xpu", dtype=torch.float16, enabled=use_amp):
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output = model(data)
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loss = criterion(output, target)
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scaler.scale(loss).backward()
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scaler.step(optimizer)
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scaler.update()
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optimizer.zero_grad()
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print(batch_idx)
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torch.save(
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{
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"model_state_dict": model.state_dict(),
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"optimizer_state_dict": optimizer.state_dict(),
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},
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"checkpoint.pth",
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)
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print("Execution finished")
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