.. _introduce-pytorch-2-7: Introducing PyTorch 2.7 Support =============================== .. contents:: Table of contents :local: :depth: 2 What are we introducing? ------------------------ Starting with the :ref:`Neuron 2.24 ` release, customers can now upgrade to PyTorch NeuronX (``torch-neuronx``) with specific support for PyTorch version 2.7. :ref:`setup-torch-neuronx` is updated to include installation instructions for PyTorch NeuronX 2.7 for Amazon Linux 2023 and Ubuntu 22.04. Note that PyTorch NeuronX 2.7 is supported on Python 3.9, 3.10, and 3.11. Review :ref:`migration guide ` for possible changes to training scripts. No code changes are required for inference scripts. .. _how-pytorch-2.7-different: How is PyTorch NeuronX 2.7 different compared to PyTorch NeuronX 2.5? --------------------------------------------------------------------- PyTorch NeuronX 2.7 uses Torch-XLA v2.7 and PyTorch v2.7 which have C++11 ABI enabled by default. Additionally, Torch-XLA v2.7 includes a fix for the training performance issue https://github.com/pytorch/xla/issues/9037. See `Torch-XLA 2.7 release `__ for a full list. See :ref:`migrate_to_pytorch_2.7` for changes needed to use PyTorch NeuronX 2.7. .. note:: GSPMD and Torch Dynamo (torch.compile) support in Neuron will be available in a future release. .. _install_pytorch_neuron_2.7: How can I install PyTorch NeuronX 2.7? -------------------------------------------- To install PyTorch NeuronX 2.7, follow the :ref:`setup-torch-neuronx` guides for Amazon Linux 2023 and Ubuntu 22.04 AMI. Refer to the Neuron Multi-Framework DLAMI :ref:`setup guide ` for Ubuntu 22.04 with a pre-installed virtual environment for PyTorch NeuronX 2.7 that you can use to get started. PyTorch NeuronX 2.7 can be installed using the following: .. code:: python -m pip install --upgrade neuronx-cc==2.* torch-neuronx==2.7.* torchvision .. note:: PyTorch NeuronX 2.7 is currently available for Python 3.9, 3.10, 3.11. .. _migrate_to_pytorch_2.7: Migrate your application to PyTorch 2.7 --------------------------------------- First, install the PyTorch NeuronX 2.7 as described above in :ref:`installation guide ` Migrating training scripts ^^^^^^^^^^^^^^^^^^^^^^^^^^ To migrate the training scripts from PyTorch NeuronX 2.5/2.6 to PyTorch NeuronX 2.7, implement the following changes: .. note:: ``xm`` below refers to ``torch_xla.core.xla_model``, ``xr`` refers to ``torch_xla.runtime``, and ``xmp`` refers to ``torch_xla.distributed.xla_multiprocessing`` * The environment variables ``XLA_DOWNCAST_BF16`` and ``XLA_USE_BF16`` are deprecated (warnings are shown when used) and will be removed in an upcoming release. Switch to automatic mixed-precision or use ``model.to(torch.bfloat16)`` command to convert model to BF16 format. (see :ref:`migration_from_xla_downcast_bf16`) * The functions ``xm.xrt_world_size()``, ``xm.get_ordinal()``, and ``xm.get_local_ordinal()`` are deprecated and removed so there are errors when used. Switch to ``xr.world_size()``, ``xr.global_ordinal()``, and ``xr.local_ordinal()`` respectively as replacements. * The default behavior of ``torch.load`` parameter ``weights_only`` is changed from ``False`` to ``True``. Setting ``weights_only`` to ``True`` may cause issues with pickling custom objects. * If using ``xmp.spawn``, the ``nprocs`` argument is limited to 1 or None since v2.1. Previously, passing a value > 1 would result in a warning. In torch-xla 2.6+, passing a value > 1 will result in an error with an actionable message to use ``NEURON_NUM_DEVICES`` to set the number of NeuronCores to use. See :ref:`v2.6 migration guide ` for additional changes needed if you are migrating from PyTorch NeuronX 2.5. See :ref:`v2.5 migration guide ` for additional changes needed if you are migrating from PyTorch NeuronX 2.1. Migrating inference scripts ^^^^^^^^^^^^^^^^^^^^^^^^^^^ There are no code changes required in the inference scripts. Troubleshooting and Known Issues -------------------------------- Using the latest torch-xla v2.7 may result in increase in host memory usage ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Using the latest torch-xla v2.7 may result in an increase in host memory usage compared to torch-xla v2.6. In one example, LLama2 pretraining with ZeRO1 and sequence length 16k could see an increase of 1.6% in host memory usage. TypeError: AdamW.__init__() got an unexpected keyword argument 'decoupled_weight_decay' ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ AdamW now has an additional argument “decoupled_weight_decay” which defaults to False. If you get “TypeError: AdamW.__init__() got an unexpected keyword argument 'decoupled_weight_decay'” with NeuronX Distributed, update to the latest version. Tensor split on second dimension of 2D array not working ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Currently, when using the tensor split operation on a 2D array in the second dimension, the resulting tensors do not contain the expected data (https://github.com/pytorch/xla/issues/8640). The workaround is to set ``XLA_DISABLE_FUNCTIONALIZATION=0``. Another workaround is to use ``torch.tensor_split``. Lower BERT pretraining performance when switch to using ``model.to(torch.bfloat16)`` ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Currently, BERT pretraining performance is approximately 11% lower when switching to using ``model.to(torch.bfloat16)`` as part of migration away from the deprecated environment variable ``XLA_DOWNCAST_BF16`` due to https://github.com/pytorch/xla/issues/8545. As a workaround to recover the performance, you can set ``XLA_DOWNCAST_BF16=1``, which will still work in torch-neuronx 2.5 and 2.7 although there will be end-of-support warnings (as noted below). Warning "XLA_DOWNCAST_BF16 will be deprecated after the 2.6 release, please downcast your model directly" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Environment variables ``XLA_DOWNCAST_BF16`` and ``XLA_USE_BF16`` are deprecated (warnings are shown when used). Switch to automatic mixed-precision or use ``model.to(torch.bfloat16)`` command to cast model to BF16. (see :ref:`migration_from_xla_downcast_bf16`) AttributeError: `_. Also set ``use_reentrant=True`` while calling the torch_xla checkpoint function. Failure to do so will lead to ``XLA currently does not support use_reentrant==False`` error. For more details on checkpointing, refer the `documentation `_. Error ``Attempted to access the data pointer on an invalid python storage`` when using HF Trainer API ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ While using HuggingFace Transformers Trainer API to train (i.e. :ref:`HuggingFace Trainer API fine-tuning tutorial`), you may see the error "Attempted to access the data pointer on an invalid python storage". This is a known `issue `_ and has been fixed in the version ``4.37.3`` of HuggingFace Transformers. ``ImportError: libcrypt.so.1: cannot open shared object file: No such file or directory`` on Amazon Linux 2023 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ torch-xla version 2.5+ now requires the ``libcrypt.so.1`` shared library. Currently, Amazon Linux 2023 includes ``libcrypt.so.2`` shared library by default so you may see `ImportError: libcrypt.so.1: cannot open shared object file: No such file or directory`` when using torch-neuronx 2.1+ on Amazon Linux 2023. To install ``libcrypt.so.1`` on Amazon Linux 2023, run the following installation command (see also https://github.com/amazonlinux/amazon-linux-2023/issues/182 for more context): .. code:: sudo dnf install libxcrypt-compat ``FileNotFoundError: [Errno 2] No such file or directory: 'libneuronpjrt-path'`` Failure ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ In PyTorch 2.7, users might face the error shown below due to incompatible ``libneuronxla`` and ``torch-neuronx`` versions being installed. .. code:: FileNotFoundError: [Errno 2] No such file or directory: 'libneuronpjrt-path' Check that the version of ``libneuronxla`` that supports PyTorch NeuronX 2.7 is ``2.2.*``. If not, then uninstall ``libneuronxla`` using ``pip uninstall libneuronxla`` and then reinstall the packages following the installation guide :ref:`installation guide ` ``Input dimension should be either 1 or equal to the output dimension it is broadcasting into`` or ``IndexError: index out of range`` error during Neuron Parallel Compile ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ When running Neuron Parallel Compile with HF Trainer API, you may see the errors ``Status: INVALID_ARGUMENT: Input dimension should be either 1 or equal to the output dimension it is broadcasting into`` or ``IndexError: index out of range`` in Accelerator's ``pad_across_processes`` function. This is due to data-dependent operations in evaluation metrics computation. Data-dependent operations would result in undefined behavior with Neuron Parallel Compile trial execution (execute empty graphs with zero outputs). To work around this error, disable compute_metrics when NEURON_EXTRACT_GRAPHS_ONLY is set to 1: .. code:: python compute_metrics=None if os.environ.get("NEURON_EXTRACT_GRAPHS_ONLY") else compute_metrics Compiler assertion error when running Stable Diffusion training ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ With PyTorch 2.7 (torch-neuronx), you may encounter the following compiler assertion error with Stable Diffusion training when gradient accumulation is enabled. This will be fixed in an upcoming release. For now, if you want to run Stable Diffusion training, disable gradient accumulation in torch-neuronx 2.7 by keeping the `default gradient accumulation steps of 1 `__. .. code:: bash ERROR 222163 [NeuronAssert]: Assertion failure in usr/lib/python3.9/concurrent/futures/process.py at line 239 with exception: too many partition dims! {{0,+,960}[10],+,10560}[10] Frequently Asked Questions (FAQ) -------------------------------- Do I need to recompile my models with PyTorch 2.7? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Yes. Do I need to update my scripts for PyTorch 2.7? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ See the :ref:`migration guide ` What environment variables will be changed with PyTorch NeuronX 2.7 ? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The environment variables ``XLA_DOWNCAST_BF16`` and ``XLA_USE_BF16`` are deprecated (warnings are shown when used). Switch to automatic mixed-precision or use ``model.to(torch.bfloat16)`` command to cast model to BF16. (see :ref:`migration_from_xla_downcast_bf16`) What features will be missing with PyTorch NeuronX 2.7? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ PyTorch NeuronX 2.7 has all of the supported features in PyTorch NeuronX 2.6, with known issues listed above, and unsupported features as listed in :ref:`pytorch_rn`. Can I use Neuron Distributed and Transformers Neuron libraries with PyTorch NeuronX 2.7? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Yes, NeuronX Distributed and Transformers NeuronX are supported by PyTorch NeuronX 2.7. AWS Neuron Reference for NeMo Megatron has reached end-of-support in release 2.23. Can I still use PyTorch 2.6 version? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ PyTorch 2.6 is supported since release 2.23. Can I still use PyTorch 2.5 version? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ PyTorch 2.5 is supported for releases 2.21 to 2.24 and will reach end-of-life in a future release. Additionally, the CVE `CVE-2025-32434 `_ affects PyTorch version 2.5. We recommend upgrading to the new version of Torch-NeuronX by following :ref:`setup-torch-neuronx`. Can I still use PyTorch 2.1 version? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ PyTorch 2.1 is supported for release 2.21 and has reached end-of-life in release 2.22. Additionally, the CVEs `CVE-2024-31583 `_ and `CVE-2024-31580 `_ affect PyTorch versions 2.1 and earlier. We recommend upgrading to the new version of Torch-NeuronX by following :ref:`setup-torch-neuronx`.