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85 lines
2.9 KiB
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85 lines
2.9 KiB
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.. meta::
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:description: Learn how to migrate your frontend code from cuDNN to hipDNN.
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:keywords: hipDNN, ROCm, migrate, cuDNN
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.. _migrate-cudnn:
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********************************************
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Migrate NVIDIA CUDA cuDNN projects to hipDNN
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********************************************
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This topic demonstrates how to migrate a NVIDIA CUDA cuDNN project to hipDNN.
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Before you begin, ensure hipDNN (ROCm) is installed. See :ref:`prerequisites` for more information.
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Here's a minimal example of a hipDNN project in ``CMakeLists.txt``:
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.. code:: cmake
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project(my_app LANGUAGES CXX)
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set(CMAKE_CXX_STANDARD 17)
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find_package(hipdnn_frontend CONFIG REQUIRED)
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add_executable(my_app main.cpp)
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target_link_libraries(my_app PRIVATE hipdnn_frontend)
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.. tip::
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See `Working examples in the Porting Guide <https://github.com/ROCm/rocm-libraries/blob/develop/projects/hipdnn/docs/PortingGuide.md#working-examples>`_ for ported code samples.
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Key differences between cuDNN and hipDNN
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========================================
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This table provides a high-level overview of the differences between cuDNN and hipDNN:
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.. list-table::
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:widths: 3 3 3
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:header-rows: 1
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* - Aspect
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- cuDNN frontend
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- hipDNN frontend
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* - Namespace
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- ``cudnn_frontend``
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- ``hipdnn_frontend``
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* - Handle creation
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- ``cudnnCreate(&handle)``
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- ``hipdnnCreate(&handle``
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* - Handle destruction
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- ``cudnnDestroy(handle)``
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- ``hipdnnDestroy(handle)``
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* - Heuristics modes
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- All cuDNN heuristic modes
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- Currently only ``HeurMode_t::FALLBACK``
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* - Operation support
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- All cuDNN operations
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- See :ref:`plugin-support` for more information.
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* - Device memory utility
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- ``Surface<type>``
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- ``MigratableMemory<type>``
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* - Device memory access
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- ``Surface<type>::devPtr``
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- ``MigratableMemory<type>::deviceData()``
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See the :ref:`dimension-layouts` section of the Operation Support doccument for operation-specifc hipDNN tensor layout details.
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Troubleshooting
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===============
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Error: Missing Heuristic modes A and B
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--------------------------------------
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The heuristic implementation in hipDNN is not implemented.
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To fix the problem, use a combination of ``graph::get_ranked_engine_ids()`` and ``graph::set_preferred_engine_id_ext()`` if you need more detailed control over engine selection.
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Error: Different memory utilities for allocating device memory
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--------------------------------------------------------------
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The memory utilities are typically consumer dependent and written on an as-needed basis.
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cuDNN provides a surface utility for their samples, for example.
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To fix the issue, use ``MigratableMemory<type>``, a utility that can automatically migrate data between the host and device (it also works as a stand-in).
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If you want to manage dims/strides more carefully, use the ``Tensor`` utility class. Both of these classes can be found in the ``hipdnn_data_sdk::utilities`` namespace.
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