Release Notes v1.14.0

New Features and Enhancements - 1.14.0

The following documentation and packages correspond to the latest software release version from Intel Gaudi: 1.14.0-493. We recommend using the latest release where possible to stay aligned with performance improvements and updated model coverage. Please refer to the Installation Guide for further details.

Important Note: With the acquisition of Habana® Labs by Intel®, the Habana and Gaudi brands have changed to Intel and Intel Gaudi® accelerator or Intel Gaudi AI accelerator. To make this transition as easy as possible for developers and customers, many of the brand references in developer literature remain unchanged. Specifically, no code has changed. You will see continued reference to Habana and HPU in the code and code examples.

General

  • Removed Ubuntu 20.04 support.

  • Added Ubuntu 22.04 and TencentOS support to Gaudi 2.

PyTorch

TensorFlow

  • Upgraded to TensorFlow v2.15.0.

  • tensorflow-io is no longer installed on Intel Gaudi dockers.

  • Starting with v1.15.0, support for TensorFlow will no longer be available.

Known Issues and Limitations - 1.14.0

PyTorch

  • To bypass a performance issue in Linux kernel version >= 5.9 (e.g. Ubuntu 22.04), the intel_idle driver must be disabled by adding intel_idle.max_cstate=0 to the kernel command line.

  • Support for torch.compile is in early stage. Models may not work (due to missing OPs implementation) or performance may be affected.

  • Support for Eager mode is in early stages. Models may not work (due to missing OPs implementation) or performance may be affected. The functionality of Eager mode as a subset of Lazy mode can be emulated by using PT_HPU_MAX_COMPOUND_OP_SIZE environment variable and limiting cluster sizes to 1. See Eager Mode.

  • Model checkpointing for ResNet50 in torch.compile mode is broken. This will be fixed in the next release.

  • Timing events where enable_timing=True may not provide accurate timing information.

  • Handling Dynamic shapes can be initiated by setting the PT_HPU_ENABLE_REFINE_DYNAMIC_SHAPES flag. This is disabled by default. For best performance, users should follow the guidance on how to work with Dynamic Shapes in the Handling Dynamic Shapes document.

  • Graphs displayed in TensorBoard have some minor limitations, eg. operator’s assigned device is displayed as “unknown device” when it is scheduled to HPU.

  • HPU tensor strides might not match that of CPU as tensor storage is managed differently. Reference to tensor storage (such as torch.as_strided) should take into account the input tensor strides explicitly. It is recommended to use other view functions instead of torch.as_strided. For further details, see Tensor Views and TORCH.AS_STRIDED.

  • Weights sharing:

    • Weights can be shared among two or more layers using PyTorch with Gaudi only if they are created inside the module. For more details, refer to Weight Sharing.

    • Weights are not shared with operators outside of the PyTorch library (i.e. PyBind11 functions).

  • User-defined attributes in HPU torch.nn.Parameter are not preserved after torch.nn.Parameter is assigned with a CPU tensor.

  • Python API habana_frameworks.torch.hpu.current_device() returns 0 regardless of the actual device being used.

  • For torch.nn.Parameter which is not created inside torch.nn.Module:

    • When two torch.nn.Parameter are on CPU storage and referencing the same parameter, the connection will be lost if one of them is moved to HPU.

    • Assigning a CPU tensor to HPU torch.nn.Parameter is not supported.

  • Saving metrics to a file configured using Runtime Environment Variables is not supported for workloads spawned via torch.multiprocessing.

  • Using torch.device(hpu:x) - (for example, as model.to) - where x is rank > 0 may lead to memory leaks. Instead, always use torch.device(hpu) to access the current rank.

TensorFlow

  • When using TF dataset cache feature where the dataset size is large, setting hugepage for host memory may be required. Refer to SSD_ResNet34 Model Reference for instructions on setting hugepage.

  • Users need to convert models to TensorFlow2 if they are currently based on TensorFlow V1. TF XLA compiler option is currently not supported.

  • Control flow ops such as tf.cond and tf.while_loop are currently not supported on Gaudi and will fall back on CPU for execution.

  • Eager mode feature in TensorFlow2 is not supported and must be disabled to run TensorFlow models on Gaudi. To disable Eager mode, see Creating a TensorFlow Example.

  • Distributed training with tf.distribute is enabled only with HPUStrategy. Other TensorFlow built-in distribution strategies such as MirroredStrategy, MultiWorkerMirroredStrategy, CentralStorageStrategy, ParameterServerStrategy are not supported.

  • (Gaudi 2) In rare cases, when a hardware accelerated media loader is used, a segmentation fault occurs when closing TensorFlow after training is completed. This may happen due to an error in the order of which the Python interpreter unloads the modules. This issue does not affect training results.