documentation: rewording

Author: mchoi8739

doc/api/training/smd_data_parallel_release_notes/smd_data_parallel_change_log.rst

@@ -14,12 +14,16 @@ SageMaker Distributed Data Parallel 1.6.0 Release Notes
 
 **New Features**
 
-* New SMDDP Collectives support for the SageMaker model parallelism library’s sharded data parallelism operating AllGather. For more information, see `Sharded data parallelism with SMDDP Collectives <https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-extended-features-pytorch-sharded-data-parallelism.html#model-parallel-extended-features-pytorch-sharded-data-parallelism-smddp-collectives>`_ in the Amazon SageMaker Developer Guide.
-* Added support for Amazon EC2 ml.p4de.24xlarge instances.
+* New optimized SMDDP AllGather collective to complement the sharded data parallelism technique
+  in the SageMaker model parallelism library. For more information, see `Sharded data parallelism with SMDDP Collectives
+  <https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-extended-features-pytorch-sharded-data-parallelism.html#model-parallel-extended-features-pytorch-sharded-data-parallelism-smddp-collectives>`_
+  in the *Amazon SageMaker Developer Guide*.
+* Added support for Amazon EC2 ``ml.p4de.24xlarge`` instances. You can run data parallel training jobs
+  on ``ml.p4de.24xlarge`` instances with the SageMaker data parallelism library’s AllReduce collective.
 
 **Improvements**
 
-* Improved general performance of the SMDDP AllReduce collective communication operation.
+* General performance improvements of the SMDDP AllReduce collective communication operation.
 
 **Migration to AWS Deep Learning Containers**
 

doc/api/training/smd_model_parallel_release_notes/smd_model_parallel_change_log.rst

@@ -13,26 +13,25 @@ SageMaker Distributed Model Parallel 1.13.0 Release Notes
 
 **New Features**
 
-* Sharded data parallelism now supports a new backend for collectives, SMDDP. For supported scenarios
-  this is used by default for AllGather. For more information, see
+* Sharded data parallelism now supports a new backend for collectives called *SMDDP Collectives*.
+  For supported scenarios, SMDDP Collectives are on by default for the AllGather operation.
+  For more information, see
   `Sharded data parallelism with SMDDP Collectives
   <https://docs.aws.amazon.com/sagemaker/latest/dg/model-parallel-extended-features-pytorch-sharded-data-parallelism.html#model-parallel-extended-features-pytorch-sharded-data-parallelism-smddp-collectives>`_
-  in the Amazon SageMaker Developer Guide.
+  in the *Amazon SageMaker Developer Guide*.
 * Introduced FlashAttention for DistributedTransformer to improve memory usage and computational
   performance of models such as GPT2, GPTNeo, GPTJ, GPTNeoX, BERT, and RoBERTa.
 
 **Bug Fixes**
 
-* Fixed initialization of lm_head in DistributedTransformer to use a provided range
+* Fixed initialization of ``lm_head`` in DistributedTransformer to use a provided range
   for initialization, when weights are not tied with the embeddings.
 
 **Improvements**
 
 * When a module has no parameters, we have introduced an optimization to execute
   such a module on the same rank as its parent during pipeline parallelism.
 
-
-
 **Migration to AWS Deep Learning Containers**
 
 This version passed benchmark testing and is migrated to the following AWS Deep Learning Containers (DLC):