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cfc227ad43
reland of https://github.com/pytorch/pytorch/pull/133113 I have to create a new PR because the previous reverted PR could not either be rebased, or imported successfully :( ---- Moving DTensor to be in the public namespace, to formally add the documentation page that includes all the public APIs. This includes: * many path renames and path import fixes * a dedicated doc page without too much content yet (adding in the next PRs) * To preserve the BC for users still using the torch.distributed._tensor, I added a shim script to redirect old path calls to the new module The BC preserving is evidented by the fact that all DTensor tests are still working without changing the public imports. So it's safe to land the changes Pull Request resolved: https://github.com/pytorch/pytorch/pull/134203 Approved by: https://github.com/tianyu-l
352 lines
14 KiB
Python
352 lines
14 KiB
Python
# mypy: allow-untyped-defs
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# Copyright (c) Meta Platforms, Inc. and affiliates
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import logging
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from functools import lru_cache
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from typing import cast, List, NamedTuple, Tuple
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import torch
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import torch.distributed._functional_collectives as funcol
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import torch.distributed.tensor._api as dtensor
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from torch.distributed.tensor._dtensor_spec import DTensorSpec, TensorMeta
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from torch.distributed.tensor.device_mesh import DeviceMesh
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from torch.distributed.tensor.placement_types import (
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Partial,
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Placement,
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Replicate,
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Shard,
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)
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logger = logging.getLogger(__name__)
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class _TransformInfo(NamedTuple):
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mesh_dim: int
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src_dst_placements: Tuple[Placement, Placement]
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# logical_shape on this mesh dimension
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logical_shape: List[int]
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@lru_cache(maxsize=None)
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def _gen_transform_infos(
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src_spec: DTensorSpec,
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dst_spec: DTensorSpec,
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) -> List[_TransformInfo]:
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"""
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Generate the transform infos from the source placements to the target placements.
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To transform from source to target placement it might have multiple steps, i.e. it
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might decompose Si -> Sj into Si -> R -> Sj.
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This would detect if there're mis-aligned/nested shardings between src/dst placements.
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E.g. Suppose the redistribution to perform is (Shard(0), Shard(0)) -> (Replicate(), Shard(0)),
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in this case Shard(0) -> Shard(0) for mesh dimension 1 actually needs resharding, because in
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the former is a nested-sharding of a tensor already already sharded dimension 0, whereras
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the latter is the first sharding on tensor dimension 0.
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"""
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transform_infos: List[_TransformInfo] = []
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device_mesh = src_spec.device_mesh
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my_coordinate = device_mesh.get_coordinate()
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assert my_coordinate is not None
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# logical shape records the logic tensor shape on the mesh dimension
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# this is useful to ensure uneven sharding gets correct output shape
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initial_logical_shape = list(src_spec.shape)
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mesh_dims_to_logical_shape = [initial_logical_shape]
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if device_mesh.ndim == 1:
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# if device_mesh is 1D, redistribute is a simple direct transformation
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transform_infos.append(
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_TransformInfo(
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mesh_dim=0,
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src_dst_placements=(src_spec.placements[0], dst_spec.placements[0]),
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logical_shape=initial_logical_shape,
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)
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)
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return transform_infos
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# Handle multi-dim device mesh placement redistribution
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# First, we need to build the logical shape for each mesh dim
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# for correct allgathering uneven shards on each mesh dim (with dynamic padding)
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for i, (src, dst) in enumerate(zip(src_spec.placements, dst_spec.placements)):
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current_logical_shape = mesh_dims_to_logical_shape[i]
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if isinstance(src, Shard):
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if i < device_mesh.ndim - 1:
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# calculate and save the logical shape for this sharding
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mesh_dim_size = device_mesh.size(mesh_dim=i)
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local_shard_size, _ = src._local_shard_size_on_dim(
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current_logical_shape[src.dim],
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mesh_dim_size,
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my_coordinate[i],
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)
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new_logical_shape = list(current_logical_shape)
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new_logical_shape[src.dim] = local_shard_size
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mesh_dims_to_logical_shape.append(new_logical_shape)
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else:
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mesh_dims_to_logical_shape.append(current_logical_shape)
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# Next, we need to derive the transform infos from src to dst placements,
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# here we use a greedy search with step by step state transformations
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current_placements = list(src_spec.placements)
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target_placements = list(dst_spec.placements)
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if src_spec.num_shards > 1:
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# If src_spec have sharding, it could potentially have sharding that is misaligned with dst_spec
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# a common case of this is nested sharding (i.e. (S(0), S(0)) -> (R, S(0))).
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# In those cases, we first traverse from inner placement to outer placement
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# to detect misaligned shardings and properly replicate nested sharding first.
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for mesh_dim in reversed(range(len(current_placements))):
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current = current_placements[mesh_dim]
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target = target_placements[mesh_dim]
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# If target is not Shard, we can directly redistribute since we are traversing from innner
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# to outer placements here
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if isinstance(target, Shard):
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# If target is Shard, check for nested sharding on the tensor dim BEFORE the current mesh_dim
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shard_dim = target.dim
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current_mesh_sharding, target_mesh_sharding = [], []
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for i, (s, p) in enumerate(zip(current_placements, target_placements)):
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if i >= mesh_dim:
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break
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if s.is_shard(shard_dim):
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current_mesh_sharding.append(i)
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if p.is_shard(shard_dim):
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target_mesh_sharding.append(i)
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if current_mesh_sharding != target_mesh_sharding:
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# if current/target_placements have misaligned sharding on the tensor dim BEFORE the current
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# mesh_dim, we need to replicate the tensor on the mesh dim first to clear the nested sharding
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target = Replicate()
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if current != target:
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transform_infos.append(
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_TransformInfo(
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mesh_dim=mesh_dim,
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src_dst_placements=(current, target),
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logical_shape=mesh_dims_to_logical_shape[mesh_dim],
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)
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)
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current_placements[mesh_dim] = target
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# We always traverse from outer placement to inner placement to collect the remaining
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# needed transform infos (i.e. the replication from nested sharding might need to further
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# perform resharding to Shard again)
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for mesh_dim, (current, target) in enumerate(
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zip(current_placements, target_placements)
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):
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if current != target:
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transform_infos.append(
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_TransformInfo(
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mesh_dim=mesh_dim,
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src_dst_placements=(current, target),
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logical_shape=mesh_dims_to_logical_shape[mesh_dim],
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)
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)
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current_placements[mesh_dim] = target
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return transform_infos
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def redistribute_local_tensor(
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local_tensor: torch.Tensor,
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current_spec: DTensorSpec,
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target_spec: DTensorSpec,
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*,
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async_op: bool = False,
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is_backward: bool = False,
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) -> torch.Tensor:
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"""
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This redistribute the local tensor (torch.Tensor) from the current DTensorSpec to
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the target DTensorSpec, which involves the necessary collective calls to transform
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the local shard of the DTensor from its current spec to the target spec.
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"""
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if current_spec.mesh != target_spec.mesh:
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# TODO: alltoall/permute reshuffling to change device_mesh if they are not the same
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raise NotImplementedError("Cross device mesh comm not supported yet!")
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new_local_tensor = None
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device_mesh = current_spec.mesh
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my_coordinate = device_mesh.get_coordinate()
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if my_coordinate is None:
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# if rank is not part of mesh, we skip redistribute and simply return local_tensor,
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# which should be an empty tensor
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return local_tensor
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transform_infos = _gen_transform_infos(current_spec, target_spec)
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for transform_info in transform_infos:
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i = transform_info.mesh_dim
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current, target = transform_info.src_dst_placements
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num_chunks = device_mesh.size(mesh_dim=i)
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if current == target:
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# short cut, just use the original local tensor
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new_local_tensor = local_tensor
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continue
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logger.debug("redistribute from %s to %s on mesh dim %s", current, target, i)
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if target.is_replicate():
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# Case 1: target is Replicate
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if current.is_partial():
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partial_spec = cast(Partial, current)
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new_local_tensor = partial_spec._reduce_value(
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local_tensor, device_mesh, i
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)
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elif current.is_shard():
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current_placement = cast(Shard, current)
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new_local_tensor = current_placement._to_replicate_tensor(
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local_tensor, device_mesh, i, transform_info.logical_shape
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)
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else:
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raise RuntimeError(
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f"redistribute from {current} to {target} not supported yet"
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)
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elif target.is_shard():
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# Case 2: target is Shard
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target_placement = cast(Shard, target)
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target_dim = target_placement.dim
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if current.is_partial():
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partial_spec = cast(Partial, current)
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new_local_tensor = partial_spec._reduce_shard_value(
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local_tensor, device_mesh, i, target_placement
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)
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elif current.is_replicate():
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# split the tensor and return the corresponding cloned local shard
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new_local_tensor = target_placement._replicate_to_shard(
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local_tensor, device_mesh, i, my_coordinate[i]
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)
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else:
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assert (
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current.is_shard()
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), f"Current placement should be shard but found {current}"
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shard_spec = cast(Shard, current)
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if shard_spec.dim != target_placement.dim:
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new_local_tensor = shard_spec._to_new_shard_dim(
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local_tensor,
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device_mesh,
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i,
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transform_info.logical_shape,
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target_placement.dim,
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)
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elif target.is_partial():
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if current.is_replicate():
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partial_spec = cast(Partial, target)
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# skip the replicate to partial transformation when we are in backward pass
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# In this case we keep the grad as replicate, this is because we don't
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# want to convert the replicated gradients back to partial, although
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# that's logically conform with the same layout, converting the gradients
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# back to partial is actually useless as you would have to do reduce later
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# which would be more expensive than keeping it replicate! For this reason,
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# we keep the replicate grad here.
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new_local_tensor = (
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partial_spec._partition_value(local_tensor, device_mesh, i)
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if not is_backward
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else local_tensor
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)
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elif current.is_shard():
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if not is_backward:
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raise RuntimeError(
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f"redistribute from {current} to {target} not supported yet"
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)
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# for backward shard -> partial, we just need to convert the shard to replicate
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current_placement = cast(Shard, current)
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new_local_tensor = current_placement._to_replicate_tensor(
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local_tensor, device_mesh, i, transform_info.logical_shape
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)
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else:
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# partial -> partial no op, should never hit
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new_local_tensor = local_tensor
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assert new_local_tensor is not None
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local_tensor = new_local_tensor
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assert new_local_tensor is not None, "redistribute failed!"
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if not async_op and isinstance(new_local_tensor, funcol.AsyncCollectiveTensor):
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new_local_tensor = new_local_tensor.wait()
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return new_local_tensor
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class Redistribute(torch.autograd.Function):
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@staticmethod
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def forward( # type: ignore[override]
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# pyre-fixme[2]: Parameter must be annotated.
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ctx,
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input: "dtensor.DTensor",
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device_mesh: DeviceMesh,
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placements: Tuple[Placement, ...],
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async_op: bool = False,
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):
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current_spec = input._spec
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ctx.current_spec = current_spec
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ctx.async_op = async_op
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if current_spec.placements != placements:
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target_spec = DTensorSpec(
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device_mesh, placements, tensor_meta=input._spec.tensor_meta
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)
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local_tensor = input._local_tensor
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output = redistribute_local_tensor(
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local_tensor, current_spec, target_spec, async_op=async_op
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)
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else:
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# use the same local tensor if placements are the same.
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output = input._local_tensor
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target_spec = current_spec
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return dtensor.DTensor(
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output,
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target_spec,
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requires_grad=input.requires_grad,
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)
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@staticmethod
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def backward(ctx, grad_output: "dtensor.DTensor"): # type: ignore[override]
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previous_spec = ctx.current_spec
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current_spec = grad_output._spec
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async_op = ctx.async_op
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local_tensor = grad_output._local_tensor
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output = redistribute_local_tensor(
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local_tensor,
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current_spec,
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previous_spec,
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async_op=async_op,
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is_backward=True,
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)
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# normalize the target placement to replicate if it is partial
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normalized_placements: List[Placement] = []
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for previous_placement in previous_spec.placements:
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if previous_placement.is_partial():
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# keep target placement to replicate instead of partial in this case
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normalized_placements.append(Replicate())
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else:
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normalized_placements.append(previous_placement)
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spec = DTensorSpec(
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previous_spec.device_mesh,
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tuple(normalized_placements),
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tensor_meta=TensorMeta(
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shape=grad_output.shape,
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stride=grad_output.stride(),
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dtype=grad_output.dtype,
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),
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)
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output_dtensor = dtensor.DTensor(
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output,
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spec,
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requires_grad=grad_output.requires_grad,
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)
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return (
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output_dtensor,
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None,
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None,
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None,
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)
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