Parallelism config + TP + HSDP + BYODM (Bring Your Own Device Mesh) #3682
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SalmanMohammadi
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Jul 16, 2025
SalmanMohammadi
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Jul 16, 2025
SalmanMohammadi
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Jul 16, 2025
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July 21, 2025 12:13
SalmanMohammadi
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winglian
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winglian
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S1ro1
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SunMarc
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Jul 29, 2025
src/accelerate/accelerator.py
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| clip_context_manager = implicit_replication | ||
| else: | ||
| clip_context_manager = contextlib.nullcontext |
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nice ! We will be able to clean up a bit trainer code after that
src/accelerate/utils/dataclasses.py
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| def build_device_mesh(self, device_type: str): | ||
| mesh = self.get_mesh() | ||
| if not len(list(mesh)): | ||
| return |
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yeah we should probably raise an error but tbh we don't really need to deal with this case
examples/fsdp2/README.md
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| This folder contains examples of using FSDP2 with Accelerate, utilizing extra methods to improve training speed, performance or accuracy. | |||
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| ### FSDP2 + ND Parallelism | |||
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| With `AccelerateDistributedConfig`, you can use 🤗 accelerate to train with n-dimensional parallelism. Script `nd_parallel.py` showcases just how you can do it. We enable you to configure 3 different parallel dimensions: | |||
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You mean ParallelConfig no ?
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| With `AccelerateDistributedConfig`, you can use 🤗 accelerate to train with n-dimensional parallelism. Script `nd_parallel.py` showcases just how you can do it. We enable you to configure 3 different parallel dimensions: | |
| With `ParallelConfig`, you can use 🤗 accelerate to train with n-dimensional parallelism. Script `nd_parallel.py` showcases just how you can do it. We enable you to configure 3 different parallel dimensions: |
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I think we have both because of the duplicate config upstream in transformers - but it would be good to clarify which to use.
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It will be better to use the one from accelerate
SunMarc
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Jul 30, 2025
| <Tip> | ||
| Only use TP intra-node - therefore max TP size you should need is 8, you can also lower this as FSDP (`--dp-shard-size`) can be faster on smaller models with | ||
| shorter sequence lengths. If you still cannot fit into memory, utilize `--dp-shard-size` as much as you can. Then to scale up to utilize all your GPUs, fill the rest | ||
| with `--dp-replicate-size`. This is only a general guideline, you can (and should) experiment with different parallelism configurations to find the best one for your model and hardware. You can learn more about the general strategies for parallelism in our [blog](TODO) or if you wanna dive deep, read the [Ultra-Scale Playbook](https://huggingface.co/spaces/nanotron/ultrascale-playbook). |
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Well the blog isn't ready, so we kind of need to keep the todo there haha (we'll finish before release)
src/accelerate/accelerator.py
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| def parallelism_config(self) -> ParallelismConfig | None: | ||
| return self.state.parallelism_config |
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| syntax only works with py3.10 but we still need to support py3.9
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we will drop py3.9 in october btw !
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What does this PR do?
Building on #3651
Dependencies:
ParallelismConfigin HFTrainerFixes # (issue)
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