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[autoparallel] handled illegal strategy in node handler (#1743)

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* [autoparallel] handled illegal strategy in node handler

* polish code
This commit is contained in:
Frank Lee 2022-10-19 17:08:52 +08:00 committed by GitHub
parent 30874f1692
commit 88a79814fb
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8 changed files with 89 additions and 82 deletions
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22
colossalai/auto_parallel/tensor_shard/deprecated/solver.py
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@ -1,18 +1,20 @@
import warnings
import time
import numpy as np
import multiprocessing
from torch.fx.node import Node
from torch.fx.graph import Graph
from .graph_analysis import GraphAnalyser
from .cost_graph import CostGraph
from .strategies_constructor import StrategiesConstructor
import time
import warnings
from typing import Dict
import numpy as np
from torch.fx.graph import Graph
from torch.fx.node import Node
from .constants import INFINITY_COST
from .cost_graph import CostGraph
from .graph_analysis import GraphAnalyser
from .strategies_constructor import StrategiesConstructor
try:
import pulp
from pulp import LpVariable, LpProblem, LpMinimize, lpSum, lpDot, LpStatus
from pulp import LpMinimize, LpProblem, LpStatus, LpVariable, lpDot, lpSum
except:
warnings.warn(f'please install the pulp')

16
colossalai/auto_parallel/tensor_shard/node_handler/node_handler.py
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@ -1,10 +1,16 @@
from abc import ABC, abstractmethod
from typing import Dict, List, Union
import torch
from torch.fx.node import Node
from colossalai.auto_parallel.tensor_shard.sharding_strategy import (OperationData, ShardingStrategy, StrategiesVector,
TrainCycleItem)
from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
OperationData,
ShardingStrategy,
StrategiesVector,
TrainCycleItem,
)
from colossalai.auto_parallel.tensor_shard.utils import check_sharding_spec_validity
from colossalai.device.device_mesh import DeviceMesh
from colossalai.tensor.shape_consistency import ShapeConsistencyManager
@ -98,6 +104,12 @@ class NodeHandler(ABC):
self.strategies_vector.extend(post_processed_strategies)
# validating the correctness of the sharding strategy
for strategy in self.strategies_vector:
for op_data, sharding_spec in strategy.sharding_specs.items():
if op_data.data is not None and isinstance(op_data.data, torch.Tensor):
check_sharding_spec_validity(sharding_spec, op_data.data)
return self.strategies_vector
def post_process(self, strategy: ShardingStrategy) -> Union[ShardingStrategy, List[ShardingStrategy]]:

6
colossalai/auto_parallel/tensor_shard/node_handler/strategy/batch_norm_generator.py
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@ -3,7 +3,7 @@ import operator
from functools import reduce
from typing import List
from colossalai.auto_parallel.tensor_shard.sharding_strategy import (MemoryCost, ShardingStrategy, TrainCycleItem)
from colossalai.auto_parallel.tensor_shard.sharding_strategy import MemoryCost, ShardingStrategy, TrainCycleItem
from colossalai.tensor.shape_consistency import CollectiveCommPattern
from .strategy_generator import StrategyGenerator
@ -31,8 +31,8 @@ class BatchNormStrategyGenerator(StrategyGenerator):
For BatchNorm3d, the dim of input data should be 5([N, C, H, W, D]).
'''
input_op_data = self.op_data['input']
assert input_op_data.dim() in (3, 4,
5), f'We suppose the dim of input fed into conv op should in range of [3, 5].'
assert input_op_data.data.dim() in (
3, 4, 5), f'We suppose the dim of input fed into conv op should in range of [3, 5].'
def update_compute_cost(self, strategy: ShardingStrategy):
'''

19
colossalai/auto_parallel/tensor_shard/utils/__init__.py
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@ -1,12 +1,17 @@
from .broadcast import (BroadcastType, get_broadcast_shape, is_broadcastable, recover_sharding_spec_for_broadcast_shape)
from .broadcast import BroadcastType, get_broadcast_shape, is_broadcastable, recover_sharding_spec_for_broadcast_shape
from .factory import generate_resharding_costs, generate_sharding_spec
from .misc import ignore_sharding_exception
from .sharding import (enumerate_all_possible_1d_sharding, enumerate_all_possible_2d_sharding, generate_sharding_size,
switch_partition_dim, update_partition_dim)
from .misc import check_sharding_spec_validity, ignore_sharding_exception
from .sharding import (
enumerate_all_possible_1d_sharding,
enumerate_all_possible_2d_sharding,
generate_sharding_size,
switch_partition_dim,
update_partition_dim,
)
__all__ = [
'BroadcastType', 'get_broadcast_shape', 'is_broadcastable', 'recover_sharding_spec_for_broadcast_shape',
'generate_resharding_costs', 'generate_sharding_spec', 'ignore_sharding_exception', 'switch_partition_dim',
'update_partition_dim', 'enumerate_all_possible_1d_sharding', 'enumerate_all_possible_2d_sharding',
'generate_sharding_size'
'generate_resharding_costs', 'generate_sharding_spec', 'ignore_sharding_exception', 'check_sharding_spec_validity'
'switch_partition_dim', 'update_partition_dim', 'enumerate_all_possible_1d_sharding',
'enumerate_all_possible_2d_sharding', 'generate_sharding_size'
]

38
colossalai/auto_parallel/tensor_shard/utils/misc.py
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@ -1,7 +1,9 @@
import functools
import torch
from colossalai.logging import get_dist_logger
from colossalai.tensor.sharding_spec import ShardingSpecException
from colossalai.tensor.sharding_spec import ShardingSpec, ShardingSpecException
__all__ = ['ignore_sharding_exception']
@ -29,3 +31,37 @@ def ignore_sharding_exception(func):
return None
return wrapper
def check_sharding_spec_validity(sharding_spec: ShardingSpec, tensor: torch.Tensor):
"""
This function checks whether the ShardingSpec is valid for the physical tensor.
This check includes 2 items:
1. the sharding spec covers all dimensions of the physical tensor
2. the sharding spec for each dimension is divisible by the number of devices.
#
"""
# make sure all dims are covered in sharding spec
sharding_len = len(sharding_spec.sharding_sequence)
tensor_num_dim = tensor.dim()
num_devices_in_col = sharding_spec.device_mesh.mesh_shape[0]
num_devices_in_row = sharding_spec.device_mesh.mesh_shape[1]
assert sharding_len == tensor_num_dim, \
f'The ShardingSpec ({sharding_spec.sharding_sequence}) is created for {sharding_len}-dimension tensor, but the given tensor is {tensor_num_dim}-dimension ({tensor.shape}).'
# make sure the sharding is valid for each dim
for i in range(tensor_num_dim):
dim_size = tensor.shape[i]
dim_spec = sharding_spec.sharding_sequence[i]
if str(dim_spec).startswith('S'):
devices_str = str(dim_spec).lstrip('S')
num_devices = 1
if '0' in devices_str:
num_devices *= num_devices_in_col
if '1' in devices_str:
num_devices *= num_devices_in_row
assert dim_size >= num_devices and dim_size % num_devices == 0, \
f'The dimension at index {i} has value {dim_size}, but it is sharded over {num_devices} devices.'

37
tests/test_auto_parallel/test_tensor_shard/test_node_handler/common.py
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@ -1,37 +0,0 @@
import torch
from colossalai.tensor.sharding_spec import ShardingSpec
def is_sharding_spec_valid(sharding_spec: ShardingSpec, tensor: torch.Tensor):
"""
This function checks whether the ShardingSpec is valid for the physical tensor.
This check includes 2 items:
1. the sharding spec covers all dimensions of the physical tensor
2. the sharding spec for each dimension is divisible by the number of devices.
#
"""
# make sure all dims are covered in sharding spec
sharding_len = len(sharding_spec.sharding_sequence)
tensor_num_dim = tensor.dim()
num_devices_in_col = sharding_spec.device_mesh.mesh_shape[0]
num_devices_in_row = sharding_spec.device_mesh.mesh_shape[1]
assert sharding_len == tensor_num_dim, \
f'The ShardingSpec ({sharding_spec.sharding_sequence}) is created for {sharding_len}-dimension tensor, but the given tensor is {tensor_num_dim}-dimension ({tensor.shape}).'
# make sure the sharding is valid for each dim
for i in range(tensor_num_dim):
dim_size = tensor.shape[i]
dim_spec = sharding_spec.sharding_sequence[i]
if str(dim_spec).startswith('S'):
devices_str = str(dim_spec).lstrip('S')
num_devices = 1
if '0' in devices_str:
num_devices *= num_devices_in_col
if '1' in devices_str:
num_devices *= num_devices_in_row
assert dim_size >= num_devices and dim_size % num_devices == 0, \
f'The dimension at index {i} has value {dim_size}, but it is sharded over {num_devices} devices.'

5
tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_conv_handler.py
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@ -1,11 +1,10 @@
import torch
import torch.nn as nn
from colossalai.auto_parallel.tensor_shard.node_handler.conv_handler import (ConvFunctionHandler, ConvModuleHandler)
from colossalai.auto_parallel.tensor_shard.sharding_strategy import (OperationData, OperationDataType, StrategiesVector)
from colossalai.auto_parallel.tensor_shard.node_handler.conv_handler import ConvFunctionHandler, ConvModuleHandler
from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
from colossalai.device.device_mesh import DeviceMesh
from colossalai.fx import ColoGraphModule, ColoTracer
from colossalai.fx.tracer.meta_patch.patched_module import linear
def test_conv_module_handler():

24
tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_linear_handler.py
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@ -1,13 +1,15 @@
import torch
import torch.nn as nn
from colossalai.auto_parallel.tensor_shard.node_handler.dot_handler import (LinearFunctionHandler, LinearModuleHandler)
from colossalai.auto_parallel.tensor_shard.sharding_strategy import (OperationData, OperationDataType, ShardingStrategy,
StrategiesVector)
from colossalai.auto_parallel.tensor_shard.node_handler.dot_handler import LinearFunctionHandler, LinearModuleHandler
from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
OperationData,
OperationDataType,
ShardingStrategy,
StrategiesVector,
)
from colossalai.device.device_mesh import DeviceMesh
from colossalai.fx import ColoGraphModule, ColoTracer
from tests.test_auto_parallel.test_tensor_shard.test_node_handler.common import \
is_sharding_spec_valid
def test_linear_module_handler():
@ -92,12 +94,6 @@ def test_linear_module_handler():
bias_sharding_spec = strategy.get_sharding_spec_by_name('bias')
output_sharding_spec = strategy.get_sharding_spec_by_name('_0')
# make sure the sharding spec is valid
is_sharding_spec_valid(input_sharding_spec, torch.rand(2, 2, 4, 16))
is_sharding_spec_valid(weight_sharding_spec, model.get_parameter('0.weight'))
is_sharding_spec_valid(bias_sharding_spec, model.get_parameter('0.bias'))
is_sharding_spec_valid(output_sharding_spec, torch.rand([2, 2, 4, 32]))
# make sure the sharding matches across different operation data
assert input_sharding_spec.sharding_sequence[:-1] == output_sharding_spec.sharding_sequence[:-1]
assert weight_sharding_spec.sharding_sequence[1] == input_sharding_spec.sharding_sequence[-1]
@ -182,12 +178,6 @@ def test_linear_function_handler():
bias_sharding_spec = strategy.get_sharding_spec_by_name('bias')
output_sharding_spec = strategy.get_sharding_spec_by_name('linear')
# make sure the sharding spec is valid
is_sharding_spec_valid(input_sharding_spec, torch.rand(2, 2, 4, 16))
is_sharding_spec_valid(weight_sharding_spec, model.get_parameter('weight'))
is_sharding_spec_valid(bias_sharding_spec, model.get_parameter('bias'))
is_sharding_spec_valid(output_sharding_spec, torch.rand([2, 2, 4, 32]))
# make sure the sharding matches across different operation data
assert input_sharding_spec.sharding_sequence[:-1] == output_sharding_spec.sharding_sequence[:-1]
assert weight_sharding_spec.sharding_sequence[1] == input_sharding_spec.sharding_sequence[-1]