documentation: smdmp 1.8.1 release note

doc/api/training/smd_model_parallel.rst

@@ -11,10 +11,11 @@ across multiple GPUs with minimal code changes. The library's API can be accesse
 
 .. tip::
 
-  We recommended using this API documentation with the conceptual guide at
+  We recommend that you use this API documentation along with the conceptual guide at
   `SageMaker's Distributed Model Parallel
   <http://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel.html>`_
-  in the *Amazon SageMaker developer guide*. This developer guide documentation includes:
+  in the *Amazon SageMaker developer guide*.
+  The conceptual guide includes the following topics:
 
   - An overview of model parallelism, and the library's
     `core features <https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-core-features.html>`_,
@@ -32,10 +33,11 @@ across multiple GPUs with minimal code changes. The library's API can be accesse
 
 
 .. important::
-   The model parallel library only supports training jobs using CUDA 11. When you define a PyTorch or TensorFlow
-   ``Estimator`` with ``modelparallel`` parameter ``enabled`` set to ``True``,
-   it uses CUDA 11. When you extend or customize your own training image
-   you must use a CUDA 11 base image. See
-   `Extend or Adapt A Docker Container that Contains the Model Parallel Library
-   <https://integ-docs-aws.amazon.com/sagemaker/latest/dg/model-parallel-use-api.html#model-parallel-customize-container>`__
-   for more information.
+   The model parallel library only supports SageMaker training jobs using CUDA 11.
+   Make sure you use the pre-built Deep Learning Containers.
+   If you want to extend or customize your own training image,
+   you must use a CUDA 11 base image. For more information, see `Extend a Prebuilt Docker
+   Container that Contains SageMaker's Distributed Model Parallel Library
+   <https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-sm-sdk.html#model-parallel-customize-container>`_
+   and `Create Your Own Docker Container with the SageMaker Distributed Model Parallel Library
+   <https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-sm-sdk.html#model-parallel-bring-your-own-container>`_.

doc/api/training/smd_model_parallel_release_notes/smd_model_parallel_change_log.rst

@@ -5,9 +5,68 @@ Release Notes
 New features, bug fixes, and improvements are regularly made to the SageMaker
 distributed model parallel library.
 
-SageMaker Distributed Model Parallel 1.8.0 Release Notes
+SageMaker Distributed Model Parallel 1.8.1 Release Notes
 ========================================================
 
+*Date: April. 23. 2022*
+
+**New Features**
+
+* Added support for more configurations of the Hugging Face Transformers GPT-2 and GPT-J models
+  with tensor parallelism: ``scale_attn_weights``, ``scale_attn_by_inverse_layer_idx``,
+  ``reorder_and_upcast_attn``. To learn more about these features, please refer to
+  the following model configuration classes
+  in the *Hugging Face Transformers documentation*:
+
+  * `transformers.GPT2Config <https://huggingface.co/docs/transformers/model_doc/gpt2#transformers.GPT2Config>`_
+  * `transformers.GPTJConfig <https://huggingface.co/docs/transformers/model_doc/gptj#transformers.GPTJConfig>`_
+
+* Added support for activation checkpointing of modules which pass keyword value arguments
+  and arbitrary structures in their forward methods. This helps support
+  activation checkpointing with Hugging Face Transformers models even
+  when tensor parallelism is not enabled.
+
+**Bug Fixes**
+
+* Fixed a correctness issue with tensor parallelism for GPT-J model
+  which was due to improper scaling during gradient reduction
+  for some layer normalization modules.
+* Fixed the creation of unnecessary additional processes which take up some
+  GPU memory on GPU 0 when the :class:`smp.allgather` collective is called.
+
+**Improvements**
+
+* Improved activation offloading so that activations are preloaded on a
+  per-layer basis as opposed to all activations for a micro batch earlier.
+  This not only improves memory efficiency and performance, but also makes
+  activation offloading a useful feature for non-pipeline parallelism cases.
+
+**Migration to AWS Deep Learning Containers**
+
+This version passed benchmark testing and is migrated to the following AWS Deep Learning Containers:
+
+* HuggingFace 4.17.0 DLC with PyTorch 1.10.2
+
+    .. code::
+
+      763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.10.2-transformers4.17.0-gpu-py38-cu113-ubuntu20.04
+
+
+* The binary file of this version of the library for custom container users
+
+    .. code::
+
+      https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/pytorch-1.10.0/build-artifacts/2022-04-14-03-58/smdistributed_modelparallel-1.8.1-cp38-cp38-linux_x86_64.whl
+
+
+----
+
+Release History
+===============
+
+SageMaker Distributed Model Parallel 1.8.0 Release Notes
+--------------------------------------------------------
+
 *Date: March. 23. 2022*
 
 **New Features**
@@ -32,18 +91,13 @@ This version passed benchmark testing and is migrated to the following AWS Deep
       763104351884.dkr.ecr.us-west-2.amazonaws.com/huggingface-pytorch-training:1.10.2-transformers4.17.0-gpu-py38-cu113-ubuntu20.04
 
 
-    The binary file of this version of the library for custom container users:
+* The binary file of this version of the library for custom container users
 
     .. code::
 
       https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/pytorch-1.10.0/build-artifacts/2022-03-12-00-33/smdistributed_modelparallel-1.8.0-cp38-cp38-linux_x86_64.whl
 
 
-----
-
-Release History
-===============
-
 SageMaker Distributed Model Parallel 1.7.0 Release Notes
 --------------------------------------------------------
 

doc/api/training/smp_versions/latest.rst

@@ -10,8 +10,8 @@ depending on which version of the library you need to use.
 To use the library, reference the
 **Common API** documentation alongside the framework specific API documentation.
 
-Version 1.7.0, 1.8.0 (Latest)
-=============================
+Version 1.7.0, 1.8.0, 1.8.1 (Latest)
+====================================
 
 To use the library, reference the Common API documentation alongside the framework specific API documentation.