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feat: add support for tensor parallel training workflow with accelerate (#34194)

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* feat: add support for tensor parallel flow using accelerate

Signed-off-by: Mehant Kammakomati <mehant.kammakomati2@ibm.com>

* fix: add tp degree to env variable

Signed-off-by: Mehant Kammakomati <mehant.kammakomati2@ibm.com>

* fix: add version check for accelerate to allow TP

Signed-off-by: Mehant Kammakomati <mehant.kammakomati2@ibm.com>

* docs: tensor parallelism

Signed-off-by: Mehant Kammakomati <mehant.kammakomati2@ibm.com>

* nit: rename plugin name

Signed-off-by: Mehant Kammakomati <mehant.kammakomati2@ibm.com>

* fix: guard accelerate version before allow tp

Signed-off-by: Mehant Kammakomati <mehant.kammakomati2@ibm.com>

* docs: add more docs and updates related to TP

Signed-off-by: Mehant Kammakomati <mehant.kammakomati2@ibm.com>

---------

Signed-off-by: Mehant Kammakomati <mehant.kammakomati2@ibm.com>
Co-authored-by: Marc Sun <57196510+SunMarc@users.noreply.github.com>
This commit is contained in:
Mehant Kammakomati
2025-02-18 18:35:46 +05:30
committed by GitHub
parent e6cc410d5b
commit c3ba53303b
8 changed files with 133 additions and 6 deletions
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7
docs/source/en/perf_train_gpu_many.md
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@@ -450,12 +450,13 @@ Implementations:
- [parallelformers](https://github.com/tunib-ai/parallelformers) (only inference at the moment)
- [SageMaker](https://arxiv.org/abs/2111.05972) - this is a proprietary solution that can only be used on AWS.
- [OSLO](https://github.com/tunib-ai/oslo) has the tensor parallelism implementation based on the Transformers.
- [`transformers` integration](main_classes/trainer) tensor parallelism is available through tp_size attribute for models having `base_tp_plan`. Further you can look at [example usage](perf_infer_gpu_multi)
SageMaker combines TP with DP for a more efficient processing.
🤗 Transformers status:
- core: not yet implemented in the core
- but if you want inference [parallelformers](https://github.com/tunib-ai/parallelformers) provides this support for most of our models. So until this is implemented in the core you can use theirs. And hopefully training mode will be supported too.
- core: uses PyTorch 2 APIs to support tensor parallelism to models having base_tp_plan in their respective config classes.
- Alternatively, you can as well try [parallelformers](https://github.com/tunib-ai/parallelformers) that provides this support for most of our models. Training mode with TP is as well supported natively in transformers.
- Deepspeed-Inference also supports our BERT, GPT-2, and GPT-Neo models in their super-fast CUDA-kernel-based inference mode, see more [here](https://www.deepspeed.ai/tutorials/inference-tutorial/)
🤗 Accelerate integrates with [TP from Megatron-LM](https://huggingface.co/docs/accelerate/v0.23.0/en/usage_guides/megatron_lm).
@@ -535,7 +536,7 @@ Important papers:
- [Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model](
https://arxiv.org/abs/2201.11990)
🤗 Transformers status: not yet implemented, since we have no PP and TP.
🤗 Transformers status: not yet implemented, since we have no PP.
## FlexFlow