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# mypy: allow-untyped-defs
from dataclasses import dataclass
from typing import cast, List, Optional, TYPE_CHECKING, Union
import torch
import torch.distributed as dist
import torch.distributed._shard.sharded_tensor.metadata as sharded_tensor_meta
import torch.distributed.distributed_c10d as distributed_c10d
from torch.distributed._shard._utils import narrow_tensor
from torch.distributed._shard.metadata import ShardMetadata
from torch.distributed._shard.sharded_tensor.shard import Shard
from torch.distributed._shard.sharded_tensor.utils import (
_parse_and_validate_remote_device,
)
from ._internals import get_chunked_dim_size, get_split_size
from .api import ShardingSpec
if TYPE_CHECKING:
# Only include ShardedTensor when do type checking, exclude it
# from run-time to resolve circular dependency.
from torch.distributed._shard.sharded_tensor import ShardedTensor
@dataclass
class ChunkShardingSpec(ShardingSpec):
"""
This is a type of PlacementSpec that defines the placement as being sharded
across multiple devices. In particular, it represents sharding a Tensor
along a single dimension into equal chunks (similar to :meth:`torch.chunk`).
The semantics of how a tensor is partitioned is inline with
:meth:`torch.chunk`, where ``dim`` in torch.chunk corresponds to the
specified ``dim`` and ``chunks`` in torch.chunk is the number of elements
in the placement specified.
Args:
dim (int or str):
The dimension to shard on, could be an integer representing the
dimension or a string in case of named tensors where dimensions are
named. Note that named tensor support is not added yet.
placement(List[Union[_remote_device, str]]):
Specifies the placement of each shard of the Tensor. The size of
the list represents the number of shards to be created. This could
be a list of
:class:`torch.distributed._remote_device`'s. This list
could also contain a string which represents remote
device as accepted by
:class:`torch.distributed._remote_device`
"""
ShardingDim = Union[int, str]
dim: ShardingDim
placements: List[Union[torch.distributed._remote_device, str]]
def __post_init__(self):
self._verify_dim(self.dim)
for i, remote_device in enumerate(self.placements):
if not isinstance(remote_device, torch.distributed._remote_device):
self.placements[i] = torch.distributed._remote_device(remote_device)
@staticmethod
def _verify_dim(dim):
# Validate the sharding spec.
# TODO: support named dimension
if isinstance(dim, str):
raise NotImplementedError(
"ChunkShardingSpec does not support named dimension yet!"
)
if not isinstance(dim, int):
raise ValueError(f"Sharding dim needs to be an integer, found: {dim}")
def build_metadata(
self,
tensor_sizes: torch.Size,
tensor_properties: sharded_tensor_meta.TensorProperties,
) -> sharded_tensor_meta.ShardedTensorMetadata:
tensor_num_dim = len(tensor_sizes)
self._verify_dim(self.dim)
if self.dim >= tensor_num_dim or self.dim < -tensor_num_dim: # type: ignore[operator]
raise ValueError(f"Invalid sharding dim: {self.dim}")
shards_metadata = []
sharding_dim_size = tensor_sizes[self.dim] # type: ignore[index]
chunks = len(self.placements)
split_size = get_split_size(sharding_dim_size, chunks)
for idx, placement in enumerate(self.placements):
# generate ShardMetadata for each placement device
chunked_dim_size = get_chunked_dim_size(sharding_dim_size, split_size, idx)
shard_size = list(tensor_sizes)
current_offsets = [0] * tensor_num_dim
current_offsets[self.dim] = split_size * idx # type: ignore[index]
shard_size[self.dim] = chunked_dim_size # type: ignore[index]
shard_metadata = ShardMetadata(
shard_offsets=current_offsets,
shard_sizes=shard_size,
placement=placement,
)
shards_metadata.append(shard_metadata)
return sharded_tensor_meta.ShardedTensorMetadata(
shards_metadata, tensor_sizes, tensor_properties
)
def shard(
self, tensor: torch.Tensor, src_rank: int = 0, process_group=None
) -> "ShardedTensor":
"""
Args:
src_rank: group rank relative to ``process_group``
N.B. If ``process_group`` is None, ``src_rank`` is a global rank.
"""
# relative imports to avoid circular dependency
from torch.distributed._shard.sharded_tensor import ShardedTensor
tensor_properties = sharded_tensor_meta.TensorProperties(
dtype=tensor.dtype,
layout=tensor.layout,
requires_grad=tensor.requires_grad,
memory_format=torch.contiguous_format,
pin_memory=tensor.is_pinned(),
)
current_rank = dist.get_rank(process_group)
current_global_rank = dist.get_rank()
tensor_meta = self.build_metadata(tensor.size(), tensor_properties)
local_shards = []
local_tensor = None
local_metadata = None
tensors_to_scatter = cast(
List[Optional[torch.Tensor]],
[None] * dist.get_world_size(process_group),
)
sharding_dim_size = tensor.size()[self.dim] # type: ignore[index]
chunks = len(self.placements)
split_size = get_split_size(sharding_dim_size, chunks)
scatter_shape = list(tensor.size())
scatter_shape[self.dim] = split_size # type: ignore[index]
for shard_meta in tensor_meta.shards_metadata:
remote_global_rank, device = _parse_and_validate_remote_device(
process_group, shard_meta.placement
)
if current_rank == src_rank:
# Reshape to get shard for this rank and we don't want autograd
# recording here for the narrow op and 'local_shard' should be a
# leaf variable in the autograd graph.
narrowed_tensor = narrow_tensor(tensor, shard_meta)
if shard_meta.shard_sizes[self.dim] < split_size: # type: ignore[index]
# for the last shard that might be smaller to other shards
# resize the narrowed tensor to the same size and use it for
# the scatter collective as dist.scatter requires same size
# inputs on every rank
tensor_to_scatter = (
narrowed_tensor.detach().clone().resize_(scatter_shape)
)
else:
tensor_to_scatter = narrowed_tensor.detach().clone().contiguous()
tensors_to_scatter[
dist.get_group_rank(process_group, remote_global_rank)
] = tensor_to_scatter
if current_global_rank == remote_global_rank:
local_tensor = torch.empty(
scatter_shape,
dtype=tensor.dtype,
layout=tensor.layout,
device=device,
)
local_metadata = shard_meta
# each rank should have local_tensor and local_metadata initialized if we build
# the metadata list in a correct way.
assert local_tensor is not None
assert local_metadata is not None
# Scatter the shards to all ranks in the pg
# scatter takes the global rank as ``src``
src_for_scatter = src_rank
if (
process_group is not None
and process_group is not distributed_c10d._get_default_group()
):
src_for_scatter = distributed_c10d.get_global_rank(
process_group, src_for_scatter
)
tensors_to_scatter_: Optional[List[torch.Tensor]] = None
if current_rank == src_rank:
tensors_to_scatter_ = []
for t in tensors_to_scatter:
assert isinstance(t, torch.Tensor)
tensors_to_scatter_.append(t)
dist.scatter(
local_tensor,
scatter_list=tensors_to_scatter_,
src=src_for_scatter,
group=process_group,
)
if list(local_tensor.size()) != local_metadata.shard_sizes:
# detach again after receiving to ensure local shards remain a leaf node
local_tensor = local_tensor.resize_(local_metadata.shard_sizes).detach()
# Sync requires_grad to local_shard.
local_tensor.requires_grad = tensor.requires_grad
local_shards.append(Shard(tensor=local_tensor, metadata=local_metadata))
st = ShardedTensor._init_from_local_shards_and_global_metadata(
local_shards, tensor_meta, process_group=process_group
)
# Manually set sharding_spec
st._sharding_spec = self
return st
|
