pytorch/docs/source/distributed.tensor.md

:::{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 parallelism 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: from_local, to_local, full_tensor, redistribute, device_mesh, placements
    :member-order: groupwise
    :special-members: __create_chunk_list__

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:

(create_dtensor)=

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

Random Operations

DTensor provides distributed RNG functionality to ensure that random operations on sharded tensors get unique values, and random operations on replicated tensors get the same values. This system requires that all participating ranks (e.g. SPMD ranks) start out using the same generator state before each dtensor random operation is performed, and if this is true, it ensures they all end up at the same state after each dtensor random operation completes. There is no communication performed during random operations to synchronize RNG states.

Operators that accept a generator kwarg will utilize the user-passed generator, if passed, or the default generator for the device otherwise. Whichever generator is used, it will be advanced after the DTensor operation. It is valid to use the same generator for both DTensor and non-DTensor operations, but care must be taken to ensure the non-DTensor operations advance the generator state equally on all ranks if so.

When using DTensor together with Pipeline Parallelism, ranks for each pipeline stage should use a distinct seed, and ranks within a pipeline stage should use the same seed.

DTensor's RNG infra is based on the philox based RNG algorithm, and supports any philox based backend (cuda, and other cuda-like devices), but unfortunately does not yet support the CPU backend.

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

% modules that are missing docs, add the doc later when necessary

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

Mixed Tensor and DTensor operations

So you got the following error message.

got mixed torch.Tensor and DTensor, need to convert all
torch.Tensor to DTensor before calling distributed operators!

There are two cases.

Case 1: this is user error

The most common way to run into this error is to create a regular Tensor (using a factory function) and then perform a Tensor-DTensor operation, like the following:

tensor = torch.arange(10)
return tensor + dtensor

We disallow mixed Tensor-DTensor operations: if the input to any operations (e.g. torch.add) is a DTensor, then all Tensor inputs must be DTensors. This is because the semantics are ambiguous. We don't know if tensor is the same across ranks or if it is different so we ask that the user figure out how to construct a DTensor with accurate placements from tensor.

If each rank does have the same tensor, then please construct a replicated DTensor:

tensor = torch.arange(10)
tensor = DTensor.from_local(tensor, placements=(Replicate(),))
return tensor + dtensor

If you wanted to create a DTensor with shards, below is how to do it. Semantically this means that your Tensor data is split between the shards and that operations act on the "full stacked data".

tensor = torch.full([], RANK)
tensor = DTensor.from_local(tensor, placements=(Shard(0),))
return tensor + dtensor

There are other things you may wish to do with your tensor beyond these situations (these are not the only two options!).

Case 2: the error came from PyTorch framework code

Sometimes the problem is that PyTorch framework code attempts to perform mixed Tensor-DTensor operations. These are bugs in PyTorch, please file an issue so that we can fix them.

On the user side, the only thing you can do is to avoid using the operation that caused the issue and file a bug report.

For PyTorch Developers: one approach of fixing this is to rewrite PyTorch framework code to avoid mixed Tensor-DTensor code (like in the previous section).

For PyTorch Developers: the second approach is to turn on DTensor implicit replication inside the right places in PyTorch framework code. When on, any mixed Tensor-DTensor operations will assume that the non-DTensors can be replicated. Please be careful when using this as it can lead to silent incorrectness.

• Turning on implicit replication in Python
• Turning on implicit replication in C++