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This reverts commit7522ca55f1. Reverted https://github.com/pytorch/pytorch/pull/94786 on behalf of https://github.com/huydhn due to Sorry for reverting your PR, but the doc failure looks related and they are also failing in trunk7522ca55f1
61 lines
2.2 KiB
ReStructuredText
61 lines
2.2 KiB
ReStructuredText
.. role:: hidden
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:class: hidden-section
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Tensor Parallelism - torch.distributed.tensor.parallel
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======================================================
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Tensor Parallelism(TP) is built on top of DistributedTensor(DTensor) and
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provides several Parallelism styles: Rowwise, Colwise and Pairwise Parallelism.
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.. warning ::
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Tensor Parallelism APIs are experimental and subject to change.
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The entrypoint to parallelize your ``nn.Module`` using Tensor Parallelism is:
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.. automodule:: torch.distributed.tensor.parallel
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.. currentmodule:: torch.distributed.tensor.parallel
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.. autofunction:: parallelize_module
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Tensor Parallelism supports the following parallel styles:
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.. autoclass:: torch.distributed.tensor.parallel.style.RowwiseParallel
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:members:
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.. autoclass:: torch.distributed.tensor.parallel.style.ColwiseParallel
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:members:
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.. autoclass:: torch.distributed.tensor.parallel.style.PairwiseParallel
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:members:
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Since Tensor Parallelism is built on top of DTensor, we need to specify the
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input and output placement of the module with DTensors so it can expectedly
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interacts with the module before and after. The followings are functions
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used for input/output preparation:
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.. currentmodule:: torch.distributed.tensor.parallel.style
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.. autofunction:: make_input_replicate_1d
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.. autofunction:: make_input_shard_1d
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.. autofunction:: make_input_shard_1d_last_dim
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.. autofunction:: make_output_replicate_1d
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.. autofunction:: make_output_tensor
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.. autofunction:: make_output_shard_1d
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Currently, there are some constraints which makes it hard for the `nn.MultiheadAttention`
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module to work out of box for Tensor Parallelism, so we built this multihead_attention
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module for Tensor Parallelism users. Also, in ``parallelize_module``, we automatically
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swap ``nn.MultiheadAttention`` to this custom module when specifying ``PairwiseParallel``.
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.. autoclass:: torch.distributed.tensor.parallel.multihead_attention_tp.TensorParallelMultiheadAttention
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:members:
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We also enabled 2D parallelism to integrate with ``FullyShardedDataParallel``.
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Users just need to call the following API explicitly:
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.. currentmodule:: torch.distributed.tensor.parallel.fsdp
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.. autofunction:: is_available
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