JAX compatibility page upate

docs/compatibility/ml-compatibility/jax-compatibility.rst

@@ -14,17 +14,18 @@ JAX provides a NumPy-like API, which combines automatic differentiation and the
 Accelerated Linear Algebra (XLA) compiler to achieve high-performance machine
 learning at scale.
 
-JAX uses composable transformations of Python and NumPy through just-in-time (JIT) compilation,
+JAX uses composable transformations of Python and NumPy through just-in-time
-automatic vectorization, and parallelization. To learn about JAX, including profiling and
+(JIT) compilation, automatic vectorization, and parallelization. To learn about
-optimizations, see the official `JAX documentation
+JAX, including profiling and optimizations, see the official `JAX documentation
 <https://jax.readthedocs.io/en/latest/notebooks/quickstart.html>`_.
 
-ROCm support for JAX is upstreamed and users can build the official source code with ROCm
+ROCm support for JAX is upstreamed, and users can build the official source code
-support:
+with ROCm support:
 
 - ROCm JAX release:
 
-  - Offers AMD-validated and community :ref:`Docker images <jax-docker-compat>` with ROCm and JAX pre-installed.
+  - Offers AMD-validated and community :ref:`Docker images <jax-docker-compat>`
+    with ROCm and JAX preinstalled.
 
   - ROCm JAX repository: `ROCm/jax <https://github.com/ROCm/jax>`_
 
@@ -36,8 +37,8 @@ support:
   - Official JAX repository: `jax-ml/jax <https://github.com/jax-ml/jax>`_
 
   - See the `AMD GPU (Linux) installation section
-    <https://jax.readthedocs.io/en/latest/installation.html#amd-gpu-linux>`_ in the JAX
+    <https://jax.readthedocs.io/en/latest/installation.html#amd-gpu-linux>`_ in
-    documentation.
+    the JAX documentation.
 
 .. note::
 
@@ -46,6 +47,44 @@ support:
    `Community ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax-community>`_
    follow upstream JAX releases and use the latest available ROCm version.
 
+Use cases and recommendations
+================================================================================
+
+* The `nanoGPT in JAX <https://rocm.blogs.amd.com/artificial-intelligence/nanoGPT-JAX/README.html>`_
+  blog explores the implementation and training of a Generative Pre-trained
+  Transformer (GPT) model in JAX, inspired by Andrej Karpathy’s JAX-based
+  nanoGPT. Comparing how essential GPT components—such as self-attention 
+  mechanisms and optimizers—are realized in JAX and JAX, also highlights
+  JAX’s unique features.
+
+* The `Optimize GPT Training: Enabling Mixed Precision Training in JAX using
+  ROCm on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/jax-mixed-precision/README.html>`_
+  blog post provides a comprehensive guide on enhancing the training efficiency
+  of GPT models by implementing mixed precision techniques in JAX, specifically
+  tailored for AMD GPUs utilizing the ROCm platform.
+
+* The `Supercharging JAX with Triton Kernels on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/jax-triton/README.html>`_
+  blog demonstrates how to develop a custom fused dropout-activation kernel for
+  matrices using Triton, integrate it with JAX, and benchmark its performance
+  using ROCm.
+
+* The `Distributed fine-tuning with JAX on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/distributed-sft-jax/README.html>`_
+  outlines the process of fine-tuning a Bidirectional Encoder Representations
+  from Transformers (BERT)-based large language model (LLM) using JAX for a text
+  classification task. The blog post discuss techniques for parallelizing the
+  fine-tuning across multiple AMD GPUs and assess the model's performance on a
+  holdout dataset. During the fine-tuning, a BERT-base-cased transformer model
+  and the General Language Understanding Evaluation (GLUE) benchmark dataset was
+  used on a multi-GPU setup.
+
+* The `MI300X workload optimization guide <https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/workload.html>`_
+  provides detailed guidance on optimizing workloads for the AMD Instinct MI300X
+  accelerator using ROCm. The page is aimed at helping users achieve optimal
+  performance for deep learning and other high-performance computing tasks on
+  the MI300X GPU.
+
+For more use cases and recommendations, see `ROCm JAX blog posts <https://rocm.blogs.amd.com/blog/tag/jax.html>`_.
+
 .. _jax-docker-compat:
 
 Docker image compatibility
@@ -57,7 +96,7 @@ Docker image compatibility
 
 AMD validates and publishes ready-made `ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax>`_
 with ROCm backends on Docker Hub. The following Docker image tags and
-associated inventories are validated for
+associated inventories represent the latest JAX version from the official Docker Hub and are validated for
 `ROCm 6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`_. Click the |docker-icon|
 icon to view the image on Docker Hub.
 
@@ -121,13 +160,12 @@ associated inventories are tested for `ROCm 6.3.2 <https://repo.radeon.com/rocm/
       - Ubuntu 22.04
       - `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
 
-Critical ROCm libraries for JAX
+Key ROCm libraries for JAX
 ================================================================================
 
-The functionality of JAX with ROCm is determined by its underlying library
+JAX functionality on ROCm is determined by its underlying library
-dependencies. These critical ROCm components affect the capabilities,
+dependencies. These ROCm components affect the capabilities, performance, and
-performance, and feature set available to developers. The versions described
+feature set available to developers.
-are available in ROCm :version:`rocm_version`.
 
 .. list-table::
     :header-rows: 1
@@ -215,10 +253,10 @@ are available in ROCm :version:`rocm_version`.
         distributed training, which involves parallel reductions or
         operations like ``jax.numpy.cumsum`` can use rocThrust.
 
-Supported and unsupported features
+Supported features
 ===============================================================================
 
-The following table maps GPU-accelerated JAX modules to their supported
+The following table maps the public JAX API modules to their supported
 ROCm and JAX versions.
 
 .. list-table::
@@ -226,8 +264,8 @@ ROCm and JAX versions.
 
     * - Module
       - Description
-      - Since JAX
+      - As of JAX
-      - Since ROCm
+      - As of ROCm
     * - ``jax.numpy``
       - Implements the NumPy API, using the primitives in ``jax.lax``.
       - 0.1.56
@@ -255,21 +293,11 @@ ROCm and JAX versions.
         devices.
       - 0.3.20
       - 5.1.0
-    * - ``jax.dlpack``
-      - For exchanging tensor data between JAX and other libraries that support the
-        DLPack standard.
-      - 0.1.57
-      - 5.0.0
     * - ``jax.distributed``
       - Enables the scaling of computations across multiple devices on a single
         machine or across multiple machines.
       - 0.1.74
       - 5.0.0
-    * - ``jax.dtypes``
-      - Provides utilities for working with and managing data types in JAX
-        arrays and computations.
-      - 0.1.66
-      - 5.0.0
     * - ``jax.image``
       - Contains image manipulation functions like resize, scale and translation.
       - 0.1.57
@@ -283,27 +311,10 @@ ROCm and JAX versions.
         array.
       - 0.1.57
       - 5.0.0
-    * - ``jax.profiler``
-      - Contains JAX’s tracing and time profiling features.
-      - 0.1.57
-      - 5.0.0
     * - ``jax.stages``
       - Contains interfaces to stages of the compiled execution process.
       - 0.3.4
       - 5.0.0
-    * - ``jax.tree``
-      - Provides utilities for working with tree-like container data structures.
-      - 0.4.26
-      - 5.6.0
-    * - ``jax.tree_util``
-      - Provides utilities for working with nested data structures, or
-        ``pytrees``.
-      - 0.1.65
-      - 5.0.0
-    * - ``jax.typing``
-      - Provides JAX-specific static type annotations.
-      - 0.3.18
-      - 5.1.0
     * - ``jax.extend``
       - Provides modules for access to JAX internal machinery module. The
         ``jax.extend`` module defines a library view of some of JAX’s internal
@@ -339,8 +350,8 @@ A SciPy-like API for scientific computing.
     :header-rows: 1
 
     * - Module
-      - Since JAX
+      - As of JAX
-      - Since ROCm
+      - As of ROCm
     * - ``jax.scipy.cluster``
       - 0.3.11
       - 5.1.0
@@ -385,8 +396,8 @@ jax.scipy.stats module
    :header-rows: 1
 
    * - Module
-     - Since JAX
+     - As of JAX
-     - Since ROCm
+     - As of ROCm
    * - ``jax.scipy.stats.bernouli``
      - 0.1.56
      - 5.0.0
@@ -469,8 +480,8 @@ Modules for JAX extensions.
     :header-rows: 1
 
     * - Module
-      - Since JAX
+      - As of JAX
-      - Since ROCm
+      - As of ROCm
     * - ``jax.extend.ffi``
       - 0.4.30
       - 6.0.0
@@ -484,190 +495,23 @@ Modules for JAX extensions.
       - 0.4.15
       - 5.5.0
 
-jax.experimental module
--------------------------------------------------------------------------------
-
-Experimental modules and APIs.
-
-.. list-table::
-    :header-rows: 1
-
-    * - Module
-      - Since JAX
-      - Since ROCm
-    * - ``jax.experimental.checkify``
-      - 0.1.75
-      - 5.0.0
-    * - ``jax.experimental.compilation_cache.compilation_cache``
-      - 0.1.68
-      - 5.0.0
-    * - ``jax.experimental.custom_partitioning``
-      - 0.4.0
-      - 5.3.0
-    * - ``jax.experimental.jet``
-      - 0.1.56
-      - 5.0.0
-    * - ``jax.experimental.key_reuse``
-      - 0.4.26
-      - 5.6.0
-    * - ``jax.experimental.mesh_utils``
-      - 0.1.76
-      - 5.0.0
-    * - ``jax.experimental.multihost_utils``
-      - 0.3.2
-      - 5.0.0
-    * - ``jax.experimental.pallas``
-      - 0.4.15
-      - 5.5.0
-    * - ``jax.experimental.pjit``
-      - 0.1.61
-      - 5.0.0
-    * - ``jax.experimental.serialize_executable``
-      - 0.4.0
-      - 5.3.0
-    * - ``jax.experimental.shard_map``
-      - 0.4.3
-      - 5.3.0
-    * - ``jax.experimental.sparse``
-      - 0.1.75
-      - 5.0.0
-
-.. list-table::
-    :header-rows: 1
-
-    * - API
-      - Since JAX
-      - Since ROCm
-    * - ``jax.experimental.enable_x64``
-      - 0.1.60
-      - 5.0.0
-    * - ``jax.experimental.disable_x64``
-      - 0.1.60
-      - 5.0.0
-
-jax.experimental.pallas module
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Module for Pallas, a JAX extension for custom kernels.
-
-.. list-table::
-    :header-rows: 1
-
-    * - Module
-      - Since JAX
-      - Since ROCm
-    * - ``jax.experimental.pallas.mosaic_gpu``
-      - 0.4.31
-      - 6.1.3
-    * - ``jax.experimental.pallas.tpu``
-      - 0.4.15
-      - 5.5.0
-    * - ``jax.experimental.pallas.triton``
-      - 0.4.32
-      - 6.1.3
-
-jax.experimental.sparse module
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Experimental support for sparse matrix operations.
-
-.. list-table::
-    :header-rows: 1
-
-    * - Module
-      - Since JAX
-      - Since ROCm
-    * - ``jax.experimental.sparse.linalg``
-      - 0.3.15
-      - 5.2.0
-    * - ``jax.experimental.sparse.sparsify``
-      - 0.3.25
-      - ❌
-
-.. list-table::
-    :header-rows: 1
-
-    * - ``sparse`` data structure API
-      - Since JAX
-      - Since ROCm
-    * - ``jax.experimental.sparse.BCOO``
-      - 0.1.72
-      - 5.0.0
-    * - ``jax.experimental.sparse.BCSR``
-      - 0.3.20
-      - 5.1.0
-    * - ``jax.experimental.sparse.CSR``
-      - 0.1.75
-      - 5.0.0
-    * - ``jax.experimental.sparse.NM``
-      - 0.4.27
-      - 5.6.0
-    * - ``jax.experimental.sparse.COO``
-      - 0.1.75
-      - 5.0.0
-
 Unsupported JAX features
-------------------------
+--------------------------------------------------------------------------------
 
-The following are GPU-accelerated JAX features not currently supported by
+The following GPU-accelerated JAX features are not supported by ROCm for
-ROCm.
+the listed supported JAX versions.
 
 .. list-table::
     :header-rows: 1
 
     * - Feature
       - Description
-      - Since JAX
     * - Mixed Precision with TF32
       - Mixed precision with TF32 is used for matrix multiplications,
         convolutions, and other linear algebra operations, particularly in
         deep learning workloads like CNNs and transformers.
-      - 0.2.25
-    * - RNN support
-      - Currently only LSTM with double bias is supported with float32 input
-        and weight.
-      - 0.3.25
     * - XLA int4 support
       - 4-bit integer (int4) precision in the XLA compiler.
-      - 0.4.0
+    * - MOSAIC (GPU)
-    * - ``jax.experimental.sparsify``
+      - Mosaic is a library of kernel-building abstractions for JAX's Pallas system
-      - Converts a dense matrix to a sparse matrix representation.
+      - Not Supported
-      - Experimental
-
-Use cases and recommendations
-================================================================================
-
-* The `nanoGPT in JAX <https://rocm.blogs.amd.com/artificial-intelligence/nanoGPT-JAX/README.html>`_
-  blog explores the implementation and training of a Generative Pre-trained
-  Transformer (GPT) model in JAX, inspired by Andrej Karpathy’s PyTorch-based
-  nanoGPT. By comparing how essential GPT components—such as self-attention
-  mechanisms and optimizers—are realized in PyTorch and JAX, also highlight
-  JAX’s unique features.
-
-* The `Optimize GPT Training: Enabling Mixed Precision Training in JAX using
-  ROCm on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/jax-mixed-precision/README.html>`_
-  blog post provides a comprehensive guide on enhancing the training efficiency
-  of GPT models by implementing mixed precision techniques in JAX, specifically
-  tailored for AMD GPUs utilizing the ROCm platform.
-
-* The `Supercharging JAX with Triton Kernels on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/jax-triton/README.html>`_
-  blog demonstrates how to develop a custom fused dropout-activation kernel for
-  matrices using Triton, integrate it with JAX, and benchmark its performance
-  using ROCm.
-
-* The `Distributed fine-tuning with JAX on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/distributed-sft-jax/README.html>`_
-  outlines the process of fine-tuning a Bidirectional Encoder Representations
-  from Transformers (BERT)-based large language model (LLM) using JAX for a text
-  classification task. The blog post discuss techniques for parallelizing the
-  fine-tuning across multiple AMD GPUs and assess the model's performance on a
-  holdout dataset. During the fine-tuning, a BERT-base-cased transformer model
-  and the General Language Understanding Evaluation (GLUE) benchmark dataset was
-  used on a multi-GPU setup.
-
-* The `MI300X workload optimization guide <https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/workload.html>`_
-  provides detailed guidance on optimizing workloads for the AMD Instinct MI300X
-  accelerator using ROCm. The page is aimed at helping users achieve optimal
-  performance for deep learning and other high-performance computing tasks on
-  the MI300X GPU.
-
-For more use cases and recommendations, see `ROCm JAX blog posts <https://rocm.blogs.amd.com/blog/tag/jax.html>`_.