distributed.tensor.rst

.. currentmodule:: torch.distributed.tensor

torch.distributed.tensor

Note

torch.distributed.tensor is currently in alpha state and under development, we are committing backward compatibility for the most APIs listed in the doc, but there might be API changes if necessary.

PyTorch DTensor (Distributed Tensor)

PyTorch DTensor offers simple and flexible tensor sharding primitives that transparently handles distributed logic, including sharded storage, operator computation and collective communications across devices/hosts. DTensor could be used to build different paralleism solutions and support sharded state_dict representation when working with multi-dimensional sharding.

Please see examples from the PyTorch native parallelism solutions that are built on top of DTensor:

• Tensor Parallel
• FSDP2

.. automodule:: torch.distributed.tensor

:class:`DTensor` follows the SPMD (single program, multiple data) programming model to empower users to write distributed program as if it's a single-device program with the same convergence property. It provides a uniform tensor sharding layout (DTensor Layout) through specifying the :class:`DeviceMesh` and :class:`Placement`:

• :class:`DeviceMesh` represents the device topology and the communicators of the cluster using an n-dimensional array.
• :class:`Placement` describes the sharding layout of the logical tensor on the :class:`DeviceMesh`. DTensor supports three types of placements: :class:`Shard`, :class:`Replicate` and :class:`Partial`.

DTensor Class APIs

.. currentmodule:: torch.distributed.tensor

:class:`DTensor` is a torch.Tensor subclass. This means once a :class:`DTensor` is created, it could be used in very similar way to torch.Tensor, including running different types of PyTorch operators as if running them in a single device, allowing proper distributed computation for PyTorch operators.

In addition to existing torch.Tensor methods, it also offers a set of additional methods to interact with torch.Tensor, redistribute the DTensor Layout to a new DTensor, get the full tensor content on all devices, etc.

.. autoclass:: DTensor
    :members:
    :member-order: bysource

DeviceMesh as the distributed communicator

.. currentmodule:: torch.distributed.device_mesh

:class:`DeviceMesh` was built from DTensor as the abstraction to describe cluster's device topology and represent multi-dimensional communicators (on top of ProcessGroup). To see the details of how to create/use a DeviceMesh, please refer to the DeviceMesh recipe.

DTensor Placement Types

.. automodule:: torch.distributed.tensor.placement_types

.. currentmodule:: torch.distributed.tensor.placement_types

DTensor supports the following types of :class:`Placement` on each :class:`DeviceMesh` dimension:

.. autoclass:: Shard
  :members:
  :undoc-members:

.. autoclass:: Replicate
  :members:
  :undoc-members:

.. autoclass:: Partial
  :members:
  :undoc-members:

.. autoclass:: Placement
  :members:
  :undoc-members:

Different ways to create a DTensor

.. currentmodule:: torch.distributed.tensor

There're three ways to construct a :class:`DTensor`:

• :meth:`distribute_tensor` creates a :class:`DTensor` from a logical or "global" torch.Tensor on each rank. This could be used to shard the leaf torch.Tensor s (i.e. model parameters/buffers and inputs).
• :meth:`DTensor.from_local` creates a :class:`DTensor` from a local torch.Tensor on each rank, which can be used to create :class:`DTensor` from a non-leaf torch.Tensor s (i.e. intermediate activation tensors during forward/backward).
• DTensor provides dedicated tensor factory functions (e.g. :meth:`empty`, :meth:`ones`, :meth:`randn`, etc.) to allow different :class:`DTensor` creations by directly specifying the :class:`DeviceMesh` and :class:`Placement`. Compare to :meth:`distribute_tensor`, this could directly materializing the sharded memory on device, instead of performing sharding after initializing the logical Tensor memory.

Create DTensor from a logical torch.Tensor

The SPMD (single program, multiple data) programming model in torch.distributed launches multiple processes (i.e. via torchrun) to execute the same program, this means that the model inside the program would be initialized on different processes first (i.e. the model might be initialized on CPU, or meta device, or directly on GPU if enough memory).

DTensor offers a :meth:`distribute_tensor` API that could shard the model weights or Tensors to DTensor s, where it would create a DTensor from the "logical" Tensor on each process. This would empower the created DTensor s to comply with the single device semantic, which is critical for numerical correctness.

.. autofunction::  distribute_tensor

Along with :meth:`distribute_tensor`, DTensor also offers a :meth:`distribute_module` API to allow easier sharding on the :class:`nn.Module` level

.. autofunction::  distribute_module

DTensor Factory Functions

DTensor also provides dedicated tensor factory functions to allow creating :class:`DTensor` directly using torch.Tensor like factory function APIs (i.e. torch.ones, torch.empty, etc), by additionally specifying the :class:`DeviceMesh` and :class:`Placement` for the :class:`DTensor` created:

.. autofunction:: zeros

.. autofunction:: ones

.. autofunction:: empty

.. autofunction:: full

.. autofunction:: rand

.. autofunction:: randn

Debugging

.. automodule:: torch.distributed.tensor.debug

.. currentmodule:: torch.distributed.tensor.debug

Logging

When launching the program, you can turn on additional logging using the TORCH_LOGS environment variable from torch._logging :

• TORCH_LOGS=+dtensor will display logging.DEBUG messages and all levels above it.
• TORCH_LOGS=dtensor will display logging.INFO messages and above.
• TORCH_LOGS=-dtensor will display logging.WARNING messages and above.

Debugging Tools

To debug the program that applied DTensor, and understand more details about what collectives happened under the hood, DTensor provides a :class:`CommDebugMode`:

.. autoclass:: CommDebugMode
    :members:
    :undoc-members:

To visualize the sharding of a DTensor that have less than 3 dimensions, DTensor provides :meth:`visualize_sharding`:

.. autofunction:: visualize_sharding

Experimental Features

DTensor also provides a set of experimental features. These features are either in prototyping stage, or the basic functionality is done and but looking for user feedbacks. Please submit a issue to PyTorch if you have feedbacks to these features.

.. automodule:: torch.distributed.tensor.experimental

.. currentmodule:: torch.distributed.tensor.experimental

.. autofunction:: context_parallel

.. autofunction:: local_map

.. autofunction:: register_sharding

.. py:module:: torch.distributed.tensor.device_mesh