Release notes for the SageMaker model parallelism library

Fixed an ImportError issue that occurred when using older training scripts with SMP v2.6.0. This fixes the backward incompatibility with SMP v2.6.0.

Added a DeprecationWarning for torch.sagemaker.distributed.fsdp.checkpoint. This module will be deprecated and removed in SMP v2.7.0. If you're currently using torch.sagemaker.distributed.fsdp.checkpoint in your code, you should plan to update your scripts before the release of SMP v2.7.0 to avoid issues in the future.

Fixed a backward compatibility issue identified in SMP v2.6.0. This issue was related to the deprecation of the USE_PG_WITH_UTIL checkpoint method in SMP v2.6.0, which broke backward compatibility with previous versions of training scripts. To resolve this issue, re-run your PyTorch training jobs to pick up the latest SMP container packaged with SMP v2.6.1.

SMP Docker container for PyTorch v2.4.1 with CUDA v12.1

658645717510.dkr.ecr.<us-west-2>.amazonaws.com/smdistributed-modelparallel:2.4.1-gpu-py311-cu121

Pre-installed packages

https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/smp-v2/

Added support for the following LLM model configurations. You can start using Context parallelism and Tensor parallelism.

Added Tensor parallelism support for the following Mixtral model configurations.

Added support for an AllGather-based context parallelism implementation that utilizes the AllGather communication collective to obtain the full sequence of key-and-value tensors. Available implementations are p2p and all_gather. The p2p implementation utilizes peer-to-peer send-receive calls for key-and-value (KV) tensor accumulation during the attention computation, running asynchronously and allowing communication to overlap with computation. On the other hand, the all_gather implementation employs the AllGather communication collective operation for KV tensor accumulation. To learn how to apply these context parallelism implementation, see Context parallelism.

Added support for tuning the Rotary Position Embedding (RoPE) theta value.

Fixed a bug where Rotary Position Embedding (RoPE) isn’t properly initialized during pre-training when delayed parameter is enabled.

Transformer Engine does not currently support context parallelism or FP8 with sliding window attention enabled. Thus, the SMP version of Mistral transformers don’t support context parallelism or FP8 training when sliding window configuration is set to a non-null value.

Upgraded PyTorch to v2.4.1

Upgraded Megatron to v0.8.0

Upgraded the TransformerEngine library to v1.10

Upgraded Transformers to v4.44.2

Upgraded cuDNN to v9.4.0.58

SMP Docker container for PyTorch v2.4.1 with CUDA v12.1

658645717510.dkr.ecr.<us-west-2>.amazonaws.com/smdistributed-modelparallel:2.4.1-gpu-py311-cu121

Pre-installed packages

https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/smp-v2/

Added support for mixed-precision training using FP8 data format on P5 instances for the Mixtral model.

Added support for Context parallelism for the following model configurations.

Added support for saving checkpoints asynchronously. To learn more, see Checkpointing using SMP.

Resolved an issue that caused unexpectedly high initial loss during Llama fine-tuning when loading a pre-trained model checkpoint and utilizing tensor parallelism.

To use activation checkpointing for Mixtral with FP8 mixed precision, you will need to checkpoint the attention and expert layers separately. For an example of setting it up properly, see the example training script in the Amazon SageMaker Examples repository.

The balanced load balancing type in the MoE configuration (torch.sagemaker.moe.moe_config.MoEConfig) is currently incompatible with activation checkpointing.

With context parallelism, GPT-NeoX shows performance regression in both pre-training and fine-tuning.

For GPT-NeoX on P4 instances, directly loading weights from a delayed parameter initialized transformed model into a Hugging Face transformer model leads to a loss mismatch on the first step.

Upgraded the FlashAttention library to v2.5.8

Upgraded the Transformer Engine library to v1.8

SMP Docker container for PyTorch v2.3.1 with CUDA v12.1

658645717510.dkr.ecr.<region>.amazonaws.com/smdistributed-modelparallel:2.3.1-gpu-py311-cu121

For a complete list of supported regions, see AWS Regions.

Pre-installed packages

https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/smp-v2/

Fixed a bug that causes incorrect logit shapes when labels are not passed in the forward pass while using the SMP Transformer.

Added support for PyTorch v2.3.1.

Added support for Python v3.11.

Added support for the Hugging Face Transformers library v4.40.1.

Discontinued support for Python v3.10.

Discontinued support for the Hugging Face Transformers library versions before v4.40.1.

Included a patch to toggle saving de-duplicated tensors on different ranks. To learn more, see the discussion threadin the PyTorch GitHub repository.

There is a known issue that the loss might spike and then resume at a higher loss value while fine-tuning Llama-3 70B with tensor parallelism.

Upgraded the SMDDP library to v2.3.0.

Upgraded the NCCL library to v2.21.5.

Upgraded the EFA software to v1.32.0.

Discontinued the installation of the Torch Distributed Experimental (torchdistX) library.

SMP Docker container for PyTorch v2.3.1 with CUDA v12.1

658645717510.dkr.ecr.us-west-2.amazonaws.com/smdistributed-modelparallel:2.3.1-gpu-py311-cu121

Pre-installed packages

https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/smp-v2/

Fixed an ImportError issue when using moe_load_balancing=balanced in torch.sagemaker.moe.moe_config.MoEConfig for expert parallelism.

Fixed a fine-tuning issue where the torch.sagemaker.transform call raised KeyError when load_state_dict_from_rank0 is enabled.

Fixed an out-of-memory (OOM) error raised when loading large Mixture of Experts (MoE) models, such as Mixtral 8x22B, for fine-tuning.

SMP Docker container for PyTorch v2.2.0 with CUDA v12.1

658645717510.dkr.ecr.us-west-2.amazonaws.com/smdistributed-modelparallel:2.2.0-gpu-py310-cu121

Added a new core feature, expert parallelism, to support Mixture of Experts transformer models. To learn more, see Expert parallelism.

SMP Docker container for PyTorch v2.2.0 with CUDA v12.1

658645717510.dkr.ecr.us-west-2.amazonaws.com/smdistributed-modelparallel:2.2.0-gpu-py310-cu121

Added support for FP8 training of the following Hugging Face transformer models on P5 instances with Transformer Engine integration:

Fixed a bug where tensors were not guaranteed to be contiguous before the AllGather collective call during tensor parallelism training.

Added support for PyTorch v2.2.0.

Upgraded the SMDDP library to v2.2.0.

Upgraded the FlashAttention library to v2.3.3.

Upgraded the NCCL library to v2.19.4.

Discontinued support for Transformer Engine versions before v1.2.0.

The SMP Activation offloading feature currently does not work. Use the native PyTorch activation offloading instead.

Included a patch to fix the performance regression discussed in the issue thread at https://github.com/pytorch/pytorch/issues/117748 in the PyTorch GitHub repository.

SMP Docker container for PyTorch v2.2.0 with CUDA v12.1

658645717510.dkr.ecr.us-west-2.amazonaws.com/smdistributed-modelparallel:2.2.0-gpu-py310-cu121

Added support for PyTorch v2.1.2.

Discontinued support for Hugging Face Transformers v4.31.0.

An issue is discovered that fine-tuning of the Hugging Face Llama 2 model with attn_implementation=flash_attention_2 and FSDP causes the model to diverge. For reference, see the issue ticket in the Hugging Face Transformers GitHub repository. To avoid the divergence issue, use attn_implementation=sdpa. Alternatively, use the SMP transformer model implementation by setting up use_smp_implementation=True.

SMP Docker container for PyTorch v2.1.2 with CUDA v12.1

658645717510.dkr.ecr.us-west-2.amazonaws.com/smdistributed-modelparallel:2.1.2-gpu-py310-cu121

https://sagemaker-distributed-model-parallel.s3.us-west-2.amazonaws.com/smp-v2/

A new torch.sagemaker package, entirely revamped from the previous smdistributed.modelparallel.torch package in SMP v1.x.

Support for PyTorch 2.0.1.

Support for PyTorch FSDP.

Tensor parallelism implementation by integrating with the Transformer Engine library.

Support for both SageMaker Training and SageMaker HyperPod.

SMP v2 revamped the APIs entirely and provides the torch.sagemaker package. Mostly, you only need to initialize with the torch.sagemaker.init() module and pass model parallel configuration parameters. With this new package, you can significantly simplify code modifications in your training script. To learn more about adapting your training script to use SMP v2, see Use the SageMaker model parallelism library v2.

If you've used SMP v1 for training Hugging Face Transformer models and want to reuse the models in SMP v2, see Upgrade from SMP v1 to SMP v2.

For PyTorch FSDP training, you should use SMP v2.

Activation checkpointing currently only works with the following wrapping policies with FSDP.

To use Activation offloading, FSDP activation checkpointing type must be REENTRANT.

When running with tensor parallel enabled with the sharded data parallel degree set to 1, you must use backend = nccl. The smddp backend option is not supported in this scenario.

Transformer Engine is required to use PyTorch with the SMP library even when not using tensor parallelism.

Starting from this release, the documentation for the SageMaker model parallelism library is fully available in this Amazon SageMaker Developer Guide. In favor of this complete developer guide for SMP v2 in the Amazon SageMaker Developer Guide, the additional reference for SMP v1.x in the SageMaker Python SDK documentation is deprecated. If you still need the documentation for SMP v1.x, the developer guide for SMP v1.x is available at (Archived) SageMaker model parallelism library v1.x, and the SMP Python library v1.x reference is available in the SageMaker Python SDK v2.199.0 documentation.

Discontinued support for TensorFlow.

There is no pipeline parallelism support in SMP v2.

There is no support for the DeepSpeed library in favor of native PyTorch FSDP.

SMP Docker container for PyTorch v2.0.1 with CUDA v12.1

658645717510.dkr.ecr.us-west-2.amazonaws.com/smdistributed-modelparallel:2.0.1-gpu-py310-cu121