ColossalAI/colossalai/auto_parallel/tensor_shard/utils/broadcast.py

161 lines
5.9 KiB
Python

from enum import Enum, auto
from typing import List
import torch
from torch.fx.node import Node
from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
CommAction,
CommType,
OperationData,
OperationDataType,
)
from colossalai.tensor.comm_spec import CollectiveCommPattern, CommSpec
from colossalai.tensor.sharding_spec import ShardingSpec
__all__ = [
'BroadcastType', 'is_broadcastable', 'get_broadcast_shape', 'recover_sharding_spec_for_broadcast_shape',
'comm_actions_for_oprands'
]
class BroadcastType(Enum):
EQUAL = auto()
PADDDING = auto()
MULTIPLE = auto()
def is_broadcastable(shape1: torch.Size, shape2: torch.Size) -> bool:
"""
Check if two shapes are broadcastable to each other.
"""
for s1, s2 in zip(shape1[::-1], shape2[::-1]):
if s1 == 1 or s2 == 1 or s1 == s2:
pass
else:
return False
return True
def get_broadcast_shape(shape1: torch.Size, shape2: torch.Size) -> List[int]:
"""
Compute the broadcast shape given two shapes.
"""
assert is_broadcastable(shape1, shape2), f'{shape1} and {shape2} are not broadcastable'
shape1_reverse = shape1[::-1]
shape2_reverse = shape2[::-1]
min_common_dim = min(len(shape1), len(shape2))
dims = []
for s1, s2 in zip(shape1_reverse, shape2_reverse):
dims.append(max(s1, s2))
# append the remaining dims
dims.extend(shape1_reverse[min_common_dim:])
dims.extend(shape2_reverse[min_common_dim:])
return dims[::-1]
def get_broadcast_dim_info(logical_shape, physical_shape):
# get the number of dimensions
logical_num_dims = len(logical_shape)
physical_num_dims = len(physical_shape)
assert logical_num_dims >= physical_num_dims, \
'The number of dimensions in the logical shape is smaller than that of the physical shape, this tensor is not broadcast!'
# track the dim and its broadcasting type
logical_dim_broadcast_info = {}
for i in range(logical_num_dims):
# get the trailing dim size
logical_dim_idx = logical_num_dims - i - 1
phyiscal_dim_idx = physical_num_dims - i - 1
logical_dim_size = logical_shape[logical_dim_idx]
if phyiscal_dim_idx >= 0:
physical_dim_size = physical_shape[phyiscal_dim_idx]
if physical_dim_size == logical_dim_size:
logical_dim_broadcast_info[logical_dim_idx] = BroadcastType.EQUAL
elif physical_dim_size == 1 and physical_dim_size != logical_dim_size:
logical_dim_broadcast_info[logical_dim_idx] = BroadcastType.MULTIPLE
else:
logical_dim_broadcast_info[logical_dim_idx] = BroadcastType.PADDDING
return logical_dim_broadcast_info
def recover_sharding_spec_for_broadcast_shape(logical_sharding_spec: ShardingSpec, logical_shape: torch.Size,
physical_shape: torch.Size) -> ShardingSpec:
"""
This function computes the sharding spec for the physical shape of a broadcast tensor.
Args:
logical_sharding_spec (ShardingSpec): the sharding spec for the broadcast tensor
logical_shape (torch.Size): logical shape is the broadcast shape of a tensor
physical_shape (torch.Size): the shape of the tensor before broadcasting
"""
# if the two shapes are the same, no broadcast occurs
# we directly return the current sharding spec
# recording the sharding dimensions removed during logical shape converting to physical one
removed_dims = []
if list(logical_shape) == list(physical_shape):
return logical_sharding_spec, removed_dims
# get the number of dimensions
logical_num_dims = len(logical_shape)
physical_num_dims = len(physical_shape)
# get the broadcast info
logical_dim_broadcast_info = get_broadcast_dim_info(logical_shape, physical_shape)
# generate the sharding spec for the physical shape
physical_dim_partition = {}
logical_dim_partition = logical_sharding_spec.dim_partition_dict
for shape_dim, mesh_dim in logical_dim_partition.items():
logical_broadcast_type = logical_dim_broadcast_info[shape_dim]
if logical_broadcast_type == BroadcastType.PADDDING or logical_broadcast_type == BroadcastType.MULTIPLE:
removed_dims.extend(mesh_dim)
else:
# get the corresponding physical dim
physical_dim = physical_num_dims - (logical_num_dims - shape_dim)
physical_dim_partition[physical_dim] = mesh_dim
physical_sharding_spec = ShardingSpec(device_mesh=logical_sharding_spec.device_mesh,
entire_shape=physical_shape,
dim_partition_dict=physical_dim_partition)
return physical_sharding_spec, removed_dims
def comm_actions_for_oprands(node: Node, removed_dims: List[int], op_data: OperationData,
sharding_spec: ShardingSpec) -> CommAction:
"""
This method is used to generate communication actions for oprands which lose information
during convert logical shape to physical shape.
"""
if len(removed_dims) == 1:
# if list length is 1, extract element from list to avoid using flatten device mesh
removed_dims = removed_dims[0]
comm_spec = CommSpec(comm_pattern=CollectiveCommPattern.IDENTITY_FWD_ALLREDUCE_BWD,
sharding_spec=sharding_spec,
logical_process_axis=removed_dims)
if op_data.type == OperationDataType.PARAM:
comm_type = CommType.HOOK
else:
comm_type = CommType.BEFORE
arg_index = -1
for index, arg in enumerate(node.args):
if op_data.name == str(arg):
arg_index = index
assert arg_index >= 0, f'op_data should be an argument of node.'
comm_action = CommAction(
comm_spec=comm_spec,
comm_type=comm_type,
arg_index=arg_index,
)
return comm_action