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[autoparallel] handled illegal sharding strategy in shape consistency (#1744) 

* [autoparallel] handled illegal sharding strategy in shape consistency

* polish code
pull/1745/head
Frank Lee 2022-10-20 12:06:25 +08:00 committed by GitHub
parent 88a79814fb
commit 993b8875b6
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4 changed files with 109 additions and 89 deletions
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4
colossalai/auto_parallel/tensor_shard/node_handler/conv_handler.py
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@ -3,7 +3,7 @@ from typing import Dict, List
import torch
import torch.nn.functional as F
from ..sharding_strategy import (OperationData, OperationDataType, ShardingStrategy)
from ..sharding_strategy import OperationData, OperationDataType, ShardingStrategy
from .node_handler import ModuleHandler, NodeHandler
from .registry import operator_registry
from .strategy import ConvStrategyGenerator, StrategyGenerator
@ -68,7 +68,7 @@ class ConvModuleHandler(ModuleHandler):
dim_partition_dict[1] = second_dim_partition
# re-init the sharding spec
sharding_spec.__init__(sharding_spec.device_mesh, sharding_spec.entire_shape, dim_partition_dict)
sharding_spec.__init__(sharding_spec.device_mesh, op_data.data.shape, dim_partition_dict)
return strategy

5
colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py
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@ -46,6 +46,7 @@ class NodeHandler(ABC):
# TODO: test this function when other handlers are ready
resharding_costs = {}
shape_consistency_manager = ShapeConsistencyManager()
for node in self.predecessor_node:
node_name = str(node)
@ -54,7 +55,9 @@ class NodeHandler(ABC):
assert hasattr(node, 'strategies_vector'), \
f'The predecessor node {node_name} has no strategy vector to compute the resharding cost.'
prev_strategy_vector = node.strategies_vector
prev_sharding_specs = [strategy.get_sharding_spec_by_name(node_name) for strategy in prev_strategy_vector]
prev_sharding_specs = [
prev_strategy.get_sharding_spec_by_name(node_name) for prev_strategy in prev_strategy_vector
]
# get the current sharding spec generated by this node handler
op_data = strategy.get_op_data_by_name(node_name)

20
colossalai/fx/passes/experimental/adding_shape_consistency_pass_v2.py
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@ -1,15 +1,17 @@
from ast import NodeTransformer
import torch
from typing import List
from torch.fx import symbolic_trace
from torch.fx.node import Node
from colossalai.fx.passes.split_module import split_module
from colossalai.tensor.shape_consistency import ShapeConsistencyManager
from colossalai.device.device_mesh import DeviceMesh
from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
import builtins
import operator
from ast import NodeTransformer
from copy import deepcopy
from typing import List
import torch
from torch.fx import symbolic_trace
from torch.fx.node import Node
from colossalai.device.device_mesh import DeviceMesh
from colossalai.fx.passes.split_module import split_module
from colossalai.tensor.shape_consistency import ShapeConsistencyManager
from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
shape_consistency_manager = ShapeConsistencyManager()

169
colossalai/tensor/shape_consistency.py
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@ -1,16 +1,19 @@
import torch
from dataclasses import dataclass
from colossalai.tensor.sharding_spec import ShardingSpec, _DimSpec
from colossalai.tensor.utils import all_gather_simulator, all_to_all_simulator, shard_simulator
from enum import Enum
from copy import deepcopy
from typing import Dict, List, Optional, Tuple, Union
from colossalai.context.singleton_meta import SingletonMeta
import torch.distributed as dist
import math
from functools import reduce
import operator
from copy import deepcopy
from dataclasses import dataclass
from enum import Enum
from functools import reduce
from typing import Dict, List, Optional, Tuple, Union
import torch
import torch.distributed as dist
from torch.distributed import ReduceOp
from colossalai.context.singleton_meta import SingletonMeta
from colossalai.tensor.sharding_spec import ShardingSpec, ShardingSpecException, _DimSpec
from colossalai.tensor.utils import all_gather_simulator, all_to_all_simulator, shard_simulator
from .comm_spec import *
__all__ = ['ShapeConsistencyManager', 'ShapeConsistencyOptions', 'set_shape_consistency_options']
@ -62,10 +65,10 @@ class ShapeConsistencyManager(metaclass=SingletonMeta):
def get_all_all_gather_spec(self, source_spec, orig_cost_dict):
'''
Get all valid sharding specs from source_spec with single all-gather operation, and
Get all valid sharding specs from source_spec with single all-gather operation, and
accumulate commucation cost on origin cost which will finally be used in auto sharding solver.
For the all-gather operation, we just care about the S dimension.
Argument:
source_spec(ShardingSpec): the ShardingSpec of the source_spec.
orig_cost(float): the original communication cost before this operation.
@ -82,12 +85,12 @@ class ShapeConsistencyManager(metaclass=SingletonMeta):
shape_consistency_manager = ShapeConsistencyManager()
rst_dict = shape_consistency_manager.get_all_all_gather_spec(sharding_spec, 0)
print(rst_dict)
Output:
{DistSpec:
shard_sequence: R,S1,R
device_mesh_shape: (4, 4): 0, DistSpec:
shard_sequence: S0,R,R
{DistSpec:
shard_sequence: R,S1,R
device_mesh_shape: (4, 4): 0, DistSpec:
shard_sequence: S0,R,R
device_mesh_shape: (4, 4): 0}
'''
valid_spec_dict = {}
@ -120,20 +123,23 @@ class ShapeConsistencyManager(metaclass=SingletonMeta):
cost_dict = comm_spec.get_comm_cost()
# generate new sharding spec
new_sharding_spec = ShardingSpec(source_spec.device_mesh,
source_spec.entire_shape,
dim_partition_dict=new_dim_partition_dict)
for phase, cost in cost_dict.items():
cost_dict[phase] = cost + orig_cost_dict[phase]
valid_spec_dict[new_sharding_spec] = (comm_spec, cost_dict)
try:
new_sharding_spec = ShardingSpec(source_spec.device_mesh,
source_spec.entire_shape,
dim_partition_dict=new_dim_partition_dict)
for phase, cost in cost_dict.items():
cost_dict[phase] = cost + orig_cost_dict[phase]
valid_spec_dict[new_sharding_spec] = (comm_spec, cost_dict)
except ShardingSpecException:
pass
return valid_spec_dict
def get_all_all_to_all_spec(self, source_spec, orig_cost_dict):
'''
Get all valid sharding specs from source_spec with single all-to-all operation, and
Get all valid sharding specs from source_spec with single all-to-all operation, and
accumulate commucation cost on origin cost which will finally be used in auto sharding solver.
For the all-to-all operation, we just care about the pairs containing S dimension.
Argument:
source_spec(ShardingSpec): the ShardingSpec of the source_spec.
orig_cost(float): the original communication cost before this operation.
@ -150,14 +156,14 @@ class ShapeConsistencyManager(metaclass=SingletonMeta):
shape_consistency_manager = ShapeConsistencyManager()
rst_dict = shape_consistency_manager.get_all_all_to_all_spec(sharding_spec, 0)
print(rst_dict)
Output:
{DistSpec:
shard_sequence: S01,R,R
device_mesh_shape: (4, 4): 0, DistSpec:
shard_sequence: R,S1,S0
device_mesh_shape: (4, 4): 0, DistSpec:
shard_sequence: S0,R,S1
{DistSpec:
shard_sequence: S01,R,R
device_mesh_shape: (4, 4): 0, DistSpec:
shard_sequence: R,S1,S0
device_mesh_shape: (4, 4): 0, DistSpec:
shard_sequence: S0,R,S1
device_mesh_shape: (4, 4): 0}
'''
valid_spec_dict = {}
@ -223,20 +229,24 @@ class ShapeConsistencyManager(metaclass=SingletonMeta):
new_dim_partition_dict.pop(b_index)
# generate new sharding spec
new_sharding_spec = ShardingSpec(source_spec.device_mesh,
source_spec.entire_shape,
dim_partition_dict=new_dim_partition_dict)
for phase, cost in cost_dict.items():
cost_dict[phase] = cost + orig_cost_dict[phase]
valid_spec_dict[new_sharding_spec] = (comm_spec, cost_dict)
try:
new_sharding_spec = ShardingSpec(source_spec.device_mesh,
source_spec.entire_shape,
dim_partition_dict=new_dim_partition_dict)
for phase, cost in cost_dict.items():
cost_dict[phase] = cost + orig_cost_dict[phase]
valid_spec_dict[new_sharding_spec] = (comm_spec, cost_dict)
except ShardingSpecException:
pass
return valid_spec_dict
def get_all_shard_spec(self, source_spec, orig_cost_dict):
'''
Get all valid sharding specs from source_spec with single shard operation, and
Get all valid sharding specs from source_spec with single shard operation, and
accumulate commucation cost on origin cost which will finally be used in auto sharding solver.
For the sharding operation, we just care about legal sharding dimensions.
Argument:
source_spec(ShardingSpec): the ShardingSpec of the source_spec.
orig_cost(float): the original communication cost before this operation.
@ -253,14 +263,14 @@ class ShapeConsistencyManager(metaclass=SingletonMeta):
shape_consistency_manager = ShapeConsistencyManager()
rst_dict = shape_consistency_manager.get_all_shard_spec(sharding_spec, 0)
print(rst_dict)
Output:
{DistSpec:
shard_sequence: S01,R,R
device_mesh_shape: (4, 4): 0, DistSpec:
shard_sequence: S0,S1,R
device_mesh_shape: (4, 4): 0, DistSpec:
shard_sequence: S0,R,S1
{DistSpec:
shard_sequence: S01,R,R
device_mesh_shape: (4, 4): 0, DistSpec:
shard_sequence: S0,S1,R
device_mesh_shape: (4, 4): 0, DistSpec:
shard_sequence: S0,R,S1
device_mesh_shape: (4, 4): 0}
'''
valid_spec_dict = {}
@ -275,6 +285,7 @@ class ShapeConsistencyManager(metaclass=SingletonMeta):
return valid_spec_dict
tensor_dims = len(source_spec.entire_shape)
for index in range(tensor_dims):
if index not in source_spec.dim_partition_dict:
shard_list_list = shard_simulator((index, []), legal_sharding_dims)
@ -300,23 +311,26 @@ class ShapeConsistencyManager(metaclass=SingletonMeta):
cost_dict = comm_spec.get_comm_cost()
# generate new sharding spec
new_sharding_spec = ShardingSpec(source_spec.device_mesh,
source_spec.entire_shape,
dim_partition_dict=new_dim_partition_dict)
for phase, cost in cost_dict.items():
cost_dict[phase] = cost + orig_cost_dict[phase]
valid_spec_dict[new_sharding_spec] = (comm_spec, cost_dict)
try:
new_sharding_spec = ShardingSpec(source_spec.device_mesh,
source_spec.entire_shape,
dim_partition_dict=new_dim_partition_dict)
for phase, cost in cost_dict.items():
cost_dict[phase] = cost + orig_cost_dict[phase]
valid_spec_dict[new_sharding_spec] = (comm_spec, cost_dict)
except ShardingSpecException:
pass
return valid_spec_dict
def get_all_one_step_transform_spec(self, source_spec, orig_cost_dict):
'''
Get all valid sharding specs from source_spec with one step transform, and
Get all valid sharding specs from source_spec with one step transform, and
accumulate commucation cost on origin cost which will finally be used in auto sharding solver.
Note:
all-gather will eliminate a sharding dimension, all-to-all will keep sharding dimension same as before,
and shard will add a sharding dimension. Therefore, the result of above operations are mutual exclusive,
we could safely put them together.
Argument:
source_spec(ShardingSpec): the ShardingSpec of the source_spec.
orig_cost(float): the original communication cost before this operation.
@ -343,7 +357,7 @@ class ShapeConsistencyManager(metaclass=SingletonMeta):
Repeat above steps until the source spec transform to target spec.
During finding the transform path, commucation cost will be accumulated, and it
will be finally used in auto parallel solver.
will be finally used in auto parallel solver.
Additionally, to avoid repeating the path search in runtime, we cached all solved path
in auto parallel strategy building time, which could handle most of cases in runtime.
@ -361,30 +375,30 @@ class ShapeConsistencyManager(metaclass=SingletonMeta):
Example:
dim_partition_source = {1: [0, 1]}
dim_partition_target = {0: [0, 1]}
# DistSpec:
# shard_sequence: R,S01,R
# DistSpec:
# shard_sequence: R,S01,R
# device_mesh_shape: (4, 4)
sharding_spec_source = ShardingSpec(device_mesh, entire_shape, dim_partition_source)
# DistSpec:
# shard_sequence: S01,R,R
# DistSpec:
# shard_sequence: S01,R,R
# device_mesh_shape: (4, 4)
sharding_spec_target = ShardingSpec(device_mesh, entire_shape, dim_partition_target)
transform_path, comm_action_sequence, total_cost = shape_consistency_manager.shape_consistency(sharding_spec_source, sharding_spec_target)
print(f'transform_path: {transform_path}')
print(f'comm_action_sequence: {comm_action_sequence}')
print(f'total_cost: {total_cost}')
output:
transform_path: [DistSpec:
shard_sequence: R,S01,R
device_mesh_shape: (4, 4), DistSpec:
shard_sequence: R,S0,R
device_mesh_shape: (4, 4), DistSpec:
shard_sequence: S0,R,R
device_mesh_shape: (4, 4), DistSpec:
shard_sequence: S01,R,R
transform_path: [DistSpec:
shard_sequence: R,S01,R
device_mesh_shape: (4, 4), DistSpec:
shard_sequence: R,S0,R
device_mesh_shape: (4, 4), DistSpec:
shard_sequence: S0,R,R
device_mesh_shape: (4, 4), DistSpec:
shard_sequence: S01,R,R
device_mesh_shape: (4, 4)]
comm_action_sequence: [CommSpec:(comm_pattern:allgather, gather_dim:1, logical_process_axis:1),
comm_action_sequence: [CommSpec:(comm_pattern:allgather, gather_dim:1, logical_process_axis:1),
CommSpec:(comm_pattern:all2all, gather_dim:1, shard_dim:0, logical_process_axis: 0),
CommSpec:(comm_pattern:shard, shard_dim:0, logical_process_axis:1)]
total_cost: 12294.402000000002
@ -403,6 +417,7 @@ class ShapeConsistencyManager(metaclass=SingletonMeta):
return (transform_path, comm_action_sequence, total_cost_dict)
temp_sharding_spec = source_spec
transform_path.append(temp_sharding_spec)
# To avoid dead loop, the loop will break after MAX_TRANSFORM_STEPS transforms
while total_steps <= MAX_TRANSFORM_STEPS:
@ -437,13 +452,13 @@ class ShapeConsistencyManager(metaclass=SingletonMeta):
def apply(self, tensor_with_sharding_spec, target_spec):
'''
Apply target_spec to tensor with source sharding spec, the transform path is generated by the
Apply target_spec to tensor with source sharding spec, the transform path is generated by the
shape_consistency method.
Argument:
tensor_with_sharding_spec (torch.Tensor): a tensor with source sharding spec to be transformed to the target spec.
target_spec (ShardingSpec): The tensor transform processes will be directed by the target_spec.
Example:
physical_mesh_id = torch.arange(0, 4)
mesh_shape = (2, 2)
@ -459,7 +474,7 @@ class ShapeConsistencyManager(metaclass=SingletonMeta):
# shard_sequence: S0,R
# device_mesh_shape: (2, 2)
sharding_spec_source = ShardingSpec(device_mesh, entire_shape, dim_partition_source)
# DistSpec:
# shard_sequence: R,S0
# device_mesh_shape: (2, 2)
@ -481,13 +496,13 @@ class ShapeConsistencyManager(metaclass=SingletonMeta):
tensor_to_comm.sharding_spec = sharding_spec_source
shape_consistency_manager.apply(tensor_to_comm, sharding_spec_target)
print(tensor_to_comm)
Output in rank0 and rank2:
tensor([[0.],
[0.],
[2.],
[2.]])
Output in rank1 and rank3:
tensor([[1.],
[1.],
@ -505,4 +520,4 @@ class ShapeConsistencyManager(metaclass=SingletonMeta):
for comm_spec in comm_action_sequence:
comm_spec.covert_spec_to_action(tensor)
tensor.sharding_spec = target_spec
return tensor
return tensor