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:

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 DeviceMesh and Placement:

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

DTensor Class APIs¶

DTensor is a torch.Tensor subclass. This means once a 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.

class torch.distributed.tensor.DTensor(local_tensor, spec, *, requires_grad)¶

DTensor (Distributed Tensor) is a subclass of torch.Tensor that provides single-device like abstraction to program with multi-device torch.Tensor. It describes the distributed tensor sharding layout (DTensor Layout) through the DeviceMesh and following types of Placement:

Shard: Tensor sharded on the tensor dimension dim on the devices of the DeviceMesh dimension
Replicate: Tensor replicated on the devices of the DeviceMesh dimension
Partial: Tensor is pending reduction on the devices of the DeviceMesh dimension

When calling PyTorch operators, DTensor overrides the PyTorch operators to perform sharded computation and issue communications whenever necessary. Along with the operator computation, DTensor will transform or propagate the placements (DTensor Layout) properly (based on the operator semantic itself) and generate new DTensor outputs.

To ensure numerical correctness of the DTensor sharded computation when calling PyTorch operators, DTensor requires every Tensor argument of the operator be DTensor.

Return type: DTensor

property device_mesh: DeviceMesh¶: The DeviceMesh attribute that associates with this DTensor object.

Note

device_mesh is a read-only property, it can not be set.

static from_local(local_tensor, device_mesh=None, placements=None, *, run_check=False, shape=None, stride=None)[source]¶

Create a DTensor from a local torch.Tensor on each rank according to the device_mesh and placements specified.

Parameters

local_tensor (torch.Tensor) – local torch.Tensor on each rank.
device_mesh (DeviceMesh, optional) – DeviceMesh to place the tensor, if not specified, must be called under a DeviceMesh context manager, default: None
placements (List[Placement], optional) – the placements that describes how to place the local torch.Tensor on DeviceMesh, must have the same number of elements as device_mesh.ndim.

Keyword Arguments

run_check (bool, optional) – at a cost of extra communications, perform sanity check across ranks to check each local tensor’s meta information to ensure correctness. If have Replicate in placements, the data on first rank of the device mesh dimension will be broadcasted to other ranks. default: False
shape (torch.Size, optional) – A List of int which specifies the size of DTensor which build on top of local_tensor. Note this needs to be provided if the shape of local_tensor are different across the ranks. If not provided, shape will be computed assuming the given distributed tensor is evenly sharded across ranks. default: None
stride (tuple, optional) – A List of int which specifies the stride of DTensor. If not provided, stride will be computed assuming the given distributed tensor is evenly sharded across ranks. default: None

Returns

A DTensor object

Return type

DTensor

Note

When run_check=False, it is the user’s responsibility to ensure the local tensor passed in is correct across ranks (i.e. the tensor is sharded for the Shard(dim) placement or replicated for the Replicate() placement). If not, the behavior of the created DTensor is undefined.

Note

from_local is differentiable, the requires_grad of the created DTensor object will depend on if local_tensor requires_grad or not.

full_tensor(*, grad_placements=None)[source]¶

Return the full tensor of this DTensor. It will perform necessary collectives to gather the local tensors from other ranks in its DeviceMesh and concatenate them together. It’s a syntatic sugar of the following code:

dtensor.redistribute(placements=[Replicate()] * mesh.ndim).to_local()

Keyword Arguments: grad_placements (List[Placement], optional) – the placements describes the future layout of any gradient layout of the full Tensor returned from this function. full_tensor converts DTensor to a full torch.Tensor and the returned torch.tensor might not be used as the original replicated DTensor layout later in the code. This argument is the hint that user can give to autograd in case the gradient layout of the returned tensor does not match the original replicated DTensor layout. If not specified, we will assume the gradient layout of the full tensor be replicated.
Returns: A torch.Tensor object that represents the full tensor of this DTensor.
Return type: Tensor

Note

full_tensor is differentiable.

property placements: Tuple[Placement, ...]¶: The placements attribute of this DTensor that describes the layout of this DTensor on the its DeviceMesh.

Note

placements is a read-only property, it can not be set.

redistribute(device_mesh=None, placements=None, *, async_op=False)[source]¶

redistribute performs necessary collective operations that redistribute the current DTensor from its current placements to a new placements, or from is current DeviceMesh to a new DeviceMesh. i.e. we can turn a Sharded DTensor to a Replicated DTensor by specifying a Replicate placement for each dimension of the DeviceMesh.

When redistributing from current to the new placements on one device mesh dimension, we will perform the following operations including communication collective or local operation:

Shard(dim) -> Replicate(): all_gather
Shard(src_dim) -> Shard(dst_dim): all_to_all
Replicate() -> Shard(dim): local chunking (i.e. torch.chunk)
Partial() -> Replicate(): all_reduce
Partial() -> Shard(dim): reduce_scatter

redistribute would correctly figure out the necessary redistribute steps for DTensors that are created either on 1-D or N-D DeviceMesh.

Parameters

device_mesh (DeviceMesh, optional) – DeviceMesh to place the DTensor. If not specified, it would use the current DTensor’s DeviceMesh. default: None
placements (List[Placement], optional) – the new placements that describes how to place the DTensor into the DeviceMesh, must have the same number of elements as device_mesh.ndim. default: replicate on all mesh dimensions

Keyword Arguments

async_op (bool, optional) – whether to perform the DTensor redistribute operation asynchronously or not. Default: False

Returns

A DTensor object

Return type

DTensor

Note

redistribute is differentiable, which means user do not need to worry about the backward formula of the redistribute operation.

Note

redistribute currently only supports redistributing DTensor on the same DeviceMesh, Please file an issue if you need to redistribute DTensor to different DeviceMesh.

to_local(*, grad_placements=None)[source]¶

Get the local tensor of this DTensor on its current rank. For sharding it returns a local shard of the logical tensor view, for replication it returns the replica on its current rank.

Keyword Arguments: grad_placements (List[Placement], optional) – the placements describes the future layout of any gradient layout of the Tensor returned from this function. to_local converts DTensor to local tensor and the returned local tensor might not be used as the original DTensor layout later in the code. This argument is the hint that user can give to autograd in case the gradient layout of the returned tensor does not match the original DTensor layout. If not specified, we will assume the gradient layout remains the same as the original DTensor and use that for gradient computation.
Returns: A torch.Tensor or AsyncCollectiveTensor object. it represents the local tensor on its current rank. When an AsyncCollectiveTensor object is returned, it means the local tensor is not ready yet (i.e. communication is not finished). In this case, user needs to call wait to wait the local tensor to be ready.
Return type: Tensor

Note

to_local is differentiable, the requires_grad of the local tensor returned will depend on if the DTensor requires_grad or not.

DeviceMesh as the distributed communicator¶

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¶

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

class torch.distributed.tensor.placement_types.Shard(dim)[source]¶

The Shard(dim) placement describes the DTensor sharding on tensor dimension dim over a corresponding DeviceMesh dimension, where each rank on the DeviceMesh dimension only holds a shard/piece of the global Tensor. The Shard(dim) placement follows the torch.chunk(dim) semantic, where the last few shards on the DeviceMesh dimension might be empty when the tensor dimension is not evenly divisble on the DeviceMesh dimension. The Shard placement can be used by all DTensor APIs (i.e. distribute_tensor, from_local, etc.)

Parameters: dim (int) – The tensor dimension that describes the DTensor is sharded over its corresponding DeviceMesh dimension.

Warning

sharding on a tensor dimension where the tensor dimension size is not evenly divisible on a DeviceMesh dimension is currently experimental and subject to change.

dim: int¶

class torch.distributed.tensor.placement_types.Replicate[source]¶

The Replicate() placement describes the DTensor replicating on a corresponding DeviceMesh dimension, where each rank on the DeviceMesh dimension holds a replica of the global Tensor. The Replicate placement can be used by all DTensor APIs (i.e. distribute_tensor, DTensor.from_local, etc.)

class torch.distributed.tensor.placement_types.Partial(reduce_op='sum')[source]¶

The Partial(reduce_op) placement describes the DTensor that is pending reduction on a specified DeviceMesh dimension, where each rank on the DeviceMesh dimension holds the partial value of the global Tensor. User can redistribute the Partial DTensor to a Replicate or Shard(dim) placement on the specified DeviceMesh dimension using redistribute, which would trigger necessary communication operations under the hood (i.e. allreduce, reduce_scatter).

Parameters: reduce_op (str, optional) – The reduction op to be used for the partial DTensor to produce Replicated/Sharded DTensor. Only element-wise reduction operations are supported, including: “sum”, “avg”, “product”, “max”, “min”, default: “sum”.

Note

The Partial placement can be generated as a result of the DTensor operators, and can only be used by the DTensor.from_local API.

reduce_op: str = 'sum'¶

class torch.distributed.tensor.placement_types.Placement[source]¶

The base class for the Placement type, where it describes how a DTensor is placed onto the DeviceMesh. Placement and DeviceMesh together could describe the DTensor Layout. It is the base class of the three main DTensor Placement types: Shard, Replicate, and Partial.

This class is not meant to be used directly, mainly served as a typing stub.

is_partial()[source]¶

Return type: bool

is_replicate()[source]¶

Return type: bool

is_shard(dim=None)[source]¶

Return type: bool

Different ways to create a DTensor¶

There’re three ways to construct a DTensor:

distribute_tensor() creates a 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).
DTensor.from_local() creates a DTensor from a local torch.Tensor on each rank, which can be used to create DTensor from a non-leaf torch.Tensor s (i.e. intermediate activation tensors during forward/backward).
DTensor provides dedicated tensor factory functions (e.g. empty(), ones(), randn(), etc.) to allow different DTensor creations by directly specifying the DeviceMesh and Placement. Compare to 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 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.

torch.distributed.tensor.distribute_tensor(tensor, device_mesh=None, placements=None)¶

Distribute a leaf torch.Tensor (i.e. nn.Parameter/buffers) to the device_mesh according to the placements specified. The rank of device_mesh and placements must be the same. The tensor to distribute is the logical or “global” tensor, and the API would use the tensor from first rank of the DeviceMesh dimension as the source of truth to perserve the single-device semantic. If you want to construct a DTensor in the middle of the Autograd computation, please use DTensor.from_local() instead.

Parameters

tensor (torch.Tensor) – torch.Tensor to be distributed. Note that if you want to shard a tensor on a dimension that is not evenly divisible by the number of devices in that mesh dimension, we use torch.chunk semantic to shard the tensor and scatter the shards. The uneven sharding behavior is experimental and subject to change.
device_mesh (DeviceMesh, optional) – DeviceMesh to distribute the tensor, if not specified, must be called under a DeviceMesh context manager, default: None
placements (List[Placement], optional) – the placements that describes how to place the tensor on DeviceMesh, must have the same number of elements as device_mesh.ndim. If not specified, we will by default replicate the tensor across the device_mesh from the first rank of each dimension of the device_mesh.

Returns

A DTensor or XLAShardedTensor object.

Return type

DTensor

Note

When initialize the DeviceMesh with the xla device_type, distribute_tensor return XLAShardedTensor instead. see this issue for more details. The XLA integration is experimental and subject to change.

Along with distribute_tensor(), DTensor also offers a distribute_module() API to allow easier sharding on the nn.Module level

torch.distributed.tensor.distribute_module(module, device_mesh=None, partition_fn=None, input_fn=None, output_fn=None)¶

This function expose three functions to control the parameters/inputs/outputs of the module:

1. To perform sharding on the module before runtime execution by specifying the partition_fn (i.e. allow user to convert Module parameters to DTensor parameters according to the partition_fn specified). 2. To control the inputs or outputs of the module during runtime execution by specifying the input_fn and output_fn. (i.e. convert the input to DTensor, convert the output back to torch.Tensor)

Parameters

module (nn.Module) – user module to be partitioned.
device_mesh (DeviceMesh) – the device mesh to place the module.
partition_fn (Callable) – the function to partition parameters (i.e. shard certain parameters across the device_mesh). If partition_fn is not specified, by default we replicate all module parameters of module across the mesh.
input_fn (Callable) – specify the input distribution, i.e. could control how the input of the module is sharded. input_fn will be installed as a module forward_pre_hook (pre forward hook).
output_fn (Callable) – specify the output distribution, i.e. could control how the output is sharded, or convert it back to torch.Tensor. output_fn will be installed as a module forward_hook (post forward hook).

Returns

A module that contains parameters/buffers that are all DTensor s.

Return type

Module

Note

When initialize the DeviceMesh with the xla device_type, distribute_module return nn.Module with PyTorch/XLA SPMD annotated parameters. See this issue for more details. The XLA integration is experimental and subject to change.

DTensor Factory Functions¶

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

torch.distributed.tensor.zeros(*size, requires_grad=False, dtype=None, layout=torch.strided, device_mesh=None, placements=None)¶

Returns a DTensor filled with the scalar value 0.

Parameters

size (int...) – a sequence of integers defining the shape of the output DTensor. Can be a variable number of arguments or a collection like a list or tuple. E.g.: zeros(1,2,3..) or zeros([1,2,3..]) or zeros((1,2,3..))

Keyword Arguments

requires_grad (bool, optional) – If autograd should record operations on the returned DTensor. Default: False.
dtype (torch.dtype, optional) – the desired data type of returned DTensor. Default: if None, uses a global default (see torch.set_default_dtype()).
layout (torch.layout, optional) – the desired layout of returned DTensor. Default: torch.strided.
device_mesh – DeviceMesh type, contains the mesh info of ranks
placements – a sequence of Placement type: Shard, Replicate

Returns

A DTensor object on each rank

Return type

DTensor

torch.distributed.tensor.ones(*size, dtype=None, layout=torch.strided, requires_grad=False, device_mesh=None, placements=None)¶

Returns a DTensor filled with the scalar value 1, with the shape defined by the variable argument size.

Parameters

size (int...) – a sequence of integers defining the shape of the output DTensor. Can be a variable number of arguments or a collection like a list or tuple. E.g.: ones(1,2,3..) or ones([1,2,3..]) or ones((1,2,3..))

Keyword Arguments

dtype (torch.dtype, optional) – the desired data type of returned DTensor. Default: if None, uses a global default (see torch.set_default_dtype()).
layout (torch.layout, optional) – the desired layout of returned DTensor. Default: torch.strided.
requires_grad (bool, optional) – If autograd should record operations on the returned DTensor. Default: False.
device_mesh – DeviceMesh type, contains the mesh info of ranks
placements – a sequence of Placement type: Shard, Replicate

Returns

A DTensor object on each rank

Return type

DTensor

torch.distributed.tensor.empty(*size, dtype=None, layout=torch.strided, requires_grad=False, device_mesh=None, placements=None)¶

Returns a DTensor filled with uninitialized data. The shape of the DTensor is defined by the variable argument size.

Parameters

size (int...) – a sequence of integers defining the shape of the output DTensor. Can be a variable number of arguments or a collection like a list or tuple. E.g.: empty(1,2,3..) or empty([1,2,3..]) or empty((1,2,3..))

Keyword Arguments

dtype (torch.dtype, optional) – the desired data type of returned DTensor. Default: if None, uses a global default (see torch.set_default_dtype()). layout (torch.layout, optional): the desired layout of returned DTensor. Default: torch.strided.
requires_grad (bool, optional) – If autograd should record operations on the returned DTensor. Default: False.
device_mesh – DeviceMesh type, contains the mesh info of ranks
placements – a sequence of Placement type: Shard, Replicate

Returns

A DTensor object on each rank

Return type

DTensor

torch.distributed.tensor.full(size, fill_value, *, dtype=None, layout=torch.strided, requires_grad=False, device_mesh=None, placements=None)¶

Returns a DTensor filled with fill_value according to device_mesh and placements, with the shape defined by the argument size.

Parameters

size (int...) – a sequence of integers defining the shape of the output DTensor. Can be a variable number of arguments or a collection like a list or tuple. E.g.: ones(1,2,3..) or ones([1,2,3..]) or ones((1,2,3..))
fill_value (Scalar) – the value to fill the output tensor with.

Keyword Arguments

dtype (torch.dtype, optional) – the desired data type of returned DTensor. Default: if None, uses a global default (see torch.set_default_dtype()).
layout (torch.layout, optional) – the desired layout of returned DTensor. Default: torch.strided.
requires_grad (bool, optional) – If autograd should record operations on the returned DTensor. Default: False.
device_mesh – DeviceMesh type, contains the mesh info of ranks.
placements – a sequence of Placement type: Shard, Replicate

Returns

A DTensor object on each rank

Return type

DTensor

torch.distributed.tensor.rand(*size, requires_grad=False, dtype=None, layout=torch.strided, device_mesh=None, placements=None)¶

Returns a DTensor filled with random numbers from a uniform distribution on the interval [0, 1). The shape of the tensor is defined by the variable argument size.

Parameters

size (int...) – a sequence of integers defining the shape of the output DTensor. Can be a variable number of arguments or a collection like a list or tuple. E.g.: ones(1,2,3..) or ones([1,2,3..]) or ones((1,2,3..))

Keyword Arguments

dtype (torch.dtype, optional) – the desired data type of returned DTensor. Default: if None, uses a global default (see torch.set_default_dtype()).
layout (torch.layout, optional) – the desired layout of returned DTensor. Default: torch.strided.
requires_grad (bool, optional) – If autograd should record operations on the returned DTensor. Default: False.
device_mesh – DeviceMesh type, contains the mesh info of ranks.
placements – a sequence of Placement type: Shard, Replicate

Returns

A DTensor object on each rank

Return type

DTensor

torch.distributed.tensor.randn(*size, requires_grad=False, dtype=None, layout=torch.strided, device_mesh=None, placements=None)¶

Returns a DTensor filled with random numbers from a normal distribution with mean 0 and variance 1. The shape of the tensor is defined by the variable argument size.

Parameters

size (int...) – a sequence of integers defining the shape of the output DTensor. Can be a variable number of arguments or a collection like a list or tuple. E.g.: ones(1,2,3..) or ones([1,2,3..]) or ones((1,2,3..))

Keyword Arguments

dtype (torch.dtype, optional) – the desired data type of returned DTensor. Default: if None, uses a global default (see torch.set_default_dtype()).
layout (torch.layout, optional) – the desired layout of returned DTensor. Default: torch.strided.
requires_grad (bool, optional) – If autograd should record operations on the returned DTensor. Default: False.
device_mesh – DeviceMesh type, contains the mesh info of ranks.
placements – a sequence of Placement type: Shard, Replicate

Returns

A DTensor object on each rank

Return type

DTensor

Debugging¶

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 CommDebugMode:

class torch.distributed.tensor.debug.CommDebugMode¶

CommDebugMode is a context manager that counts the number of functional collectives within its context. It does this using a TorchDispatchMode.

Example usage

mod = ...
comm_mode = CommDebugMode()
with comm_mode:
    mod.sum().backward()
print(comm_mode.get_comm_counts())

generate_comm_debug_tracing_table(noise_level=3)[source]¶

Generates detailed table displaying operations and collective tracing information on a module level. Amount of information is dependent on noise_level

prints module-level collective counts
prints dTensor operations not included in trivial operations, module information
prints operations not included in trivial operations
prints all operations

generate_json_dump(file_name='comm_mode_log.json', noise_level=3)[source]¶: Creates json file used to build browser visual 0. prints module-level collective counts 1. prints dTensor operations not included in trivial operations 2. prints operations not included in trivial operations 3. prints all operations

get_comm_counts()[source]¶

Returns the communication counts as a dictionary.

Returns: The communication counts as a dictionary.
Return type: Dict[Any, int]

get_parameter_info()[source]¶

Return type: Dict[str, Dict[str, Any]]

get_sharding_info()[source]¶

Return type: Dict[str, Dict[str, Any]]

get_total_counts()[source]¶

Return type: int

log_comm_debug_tracing_table_to_file(file_name='comm_mode_log.txt', noise_level=3)[source]¶: Alternative to console CommDebugMode output, writes to file specified by the user

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

torch.distributed.tensor.debug.visualize_sharding(dtensor, header='')¶: Visualizes sharding in the terminal for DTensor that are 1D or 2D.

Note

This requires the tabulate package. No sharding info will be printed for empty tensors

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.

torch.distributed.tensor.experimental.local_map(func, out_placements, in_placements=None, device_mesh=None, *, redistribute_inputs=False)¶

local_map() is an experimental API that allows users to pass DTensor s to a function that is written to be applied on torch.Tensor s. It is done by extracting the local components of DTensor, call the function, and wrap the outputs to DTensor according to the out_placements.

Parameters

func (Callable) – the function to be applied on each local shard of DTensor s.
out_placements (Union[PlacementType, Tuple[PlacementType, …]]) – the desired placements of the DTensor s in func’s flattened output. If the flattened output is a single value, the out_placements should be of type PlacementType. Otherwise if the flattened output has multiple values, the out_placements should be a tuple of PlacementType values 1:1 mapping to the flattened output. Besides, for Tensor output, we use PlacementType as its placements (a Tuple[Placement] value). For non-Tensor output, the PlacementType should be None. Note that the only exception is when no DTensor argument is passed in. In this case, even if out_placements is not None, the result function should ignore the desired placements because the function is not running with DTensor s.
in_placements (Tuple[PlacementType, …], optional) – the required placements of the DTensor s in the flattened inputs of func. If in_placements is specified, local_map() would examine whether the placements of each DTensor argument is the same as the required placements or not. If the placements are not the same and redistribute_inputs is False, an exception will be raised. Otherwise if redistribute_inputs is True, the argument will be first redistributed to the required sharding placements before passing its local tensor to func. The only exception is when required placements are not None and the argument is a torch.Tensor. In this case, the placements examination will be skipped and the argument will be directly passed to func. If in_placements is None, no placements examination will be performed. Default: None
device_mesh (DeviceMesh, optional) – the device mesh that all the DTensor s are placed on. If not specified, this will be inferred from the input DTensor s’ device mesh. local_map requires every DTensor s to be placed on the same device mesh. Default: None.
redistribute_inputs (bool, optional) – the bool value indicating whether to reshard the input DTensor s when their placements are different from the required input placements. If this value is False and some DTensor input has a different placement, an exception will be raised. Default: False.

Returns

A Callable that applies func to each local shard of the input DTensor and returns a DTensor constructed from the return value of func.

Raises

AssertionError – If the input DTensor is not placed on the same device mesh, or if they are placed on a different device mesh than the device_mesh argument passed in.
AssertionError – For any non-DTensor output, we require its corresponding output placement in out_placements be None. An AssertionError will be raised if this is not the case.
ValueError – If redistribute_inputs=False but the input DTensor needs a redistribution according to in_placements.

Example

>>> def mm_allreduce_forward(device_mesh, W, X):
>>>     partial_sum_tensor = torch.mm(W, X)
>>>     reduced_tensor = funcol.all_reduce(partial_sum_tensor, "sum", device_mesh)
>>>     return reduced_tensor
>>>
>>> W = torch.randn(12, 8, requires_grad=False)
>>> X = torch.randn(8, 16, requires_grad=False)
>>> Y = torch.mm(W, X)
>>> row_wise = [Shard(0)]  # row-wise sharding placements on 1-d mesh
>>> col_wise = [Shard(1)]  # col-wise sharding placements on 1-d mesh
>>>
>>> # local_mm_allreduce_forward is the function wrapped with DTensor/Tensor convertion
>>> local_mm_allreduce_forward = local_map(
>>>     mm_allreduce_forward,
>>>     out_placements=[Replicate()],
>>>     in_placements=[col_wise, row_wise],
>>>     device_mesh=device_mesh,
>>> )
>>>
>>> W_dt = distribute_tensor(W, device_mesh, (col_wise))  # col-wisely sharded W tensor
>>> X_dt = distribute_tensor(X, device_mesh, (row_wise))  # row-wisely sharded X tensor
>>> Y_dt = local_mm_allreduce_forward(device_mesh, W_dt, X_dt)  # apply local_mm_allreduce_forward to DTensors

Note

This API is currently experimental and subject to change

torch.distributed.tensor.experimental.register_sharding(op)¶

register_sharding() is an experimental API that allows users to register sharding strategies for an operator when the tensor inputs and outputs are DTensor. It can be useful when: (1) there doesn’t exist a default sharding strategy for op, e.g. when op is a custom operator that is not supported by DTensor; (2) when users would like to overwrite default sharding strategies of existing operators.

Parameters: op (Union[OpOverload, List[OpOverload]]) – An op or a list of ops to register the customized sharding function.
Returns: A function decorator which can be used to wrap a function that defines the sharding strategy for the operator specified in op. The defined sharding strategy will be registered to DTensor and will override the default sharding strategy if DTensor has already implemented the operator. The customized sharding function takes the same inputs as the original op (except that if an arg is a torch.Tensor, it will be replaced by a tensor-like object that DTensor uses internally). The function should return a sequence of 2-tuples, each specifying acceptable output placements and its corresponding intput placements.

Example

>>> @register_sharding(aten._softmax.default)
>>> def custom_softmax_sharding(x, dim, half_to_float):
>>>     softmax_dim = dim if dim >= 0 else dim + x.ndim
>>>     acceptable_shardings = []
>>>
>>>     all_replicate = ([Replicate()], [Replicate(), None, None])
>>>     acceptable_shardings.append(all_replicate)
>>>
>>>     for sharding_dim in range(x.ndim):
>>>         if sharding_dim != softmax_dim:
>>>             all_sharded = (
>>>                 [Shard(sharding_dim)],
>>>                 [Shard(sharding_dim), None, None],
>>>             )
>>>             acceptable_shardings.append(all_sharded)
>>>
>>>     return acceptable_shardings

Note

This API is currently experimental and subject to change

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