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!30466 takedown test_auto_parallel_adasum.py to ensure stability, again 

Merge pull request !30466 from yanghaoran/master
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i-robot 2022-02-23 09:15:58 +00:00 committed by Gitee
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tests/ut/python/parallel/test_auto_parallel_adasum.py
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# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import mindspore as ms
from mindspore import context, Tensor, Parameter
from mindspore.common.api import _cell_graph_executor
from mindspore.nn import Cell, TrainOneStepCell, Momentum, AdaSumByDeltaWeightWrapCell, AdaSumByGradWrapCell
from mindspore.ops import operations as P
class Net(Cell):
def __init__(self, strategy1=None, strategy2=None, strategy3=None):
super().__init__()
self.mul = P.Mul().shard(strategy1)
self.matmul = P.MatMul().shard(strategy2)
self.gather = P.Gather().shard(strategy3)
self.reduce_sum = P.ReduceSum()
self.mul_weight = Parameter(Tensor(np.ones([64, 32]), dtype=ms.float32), "w1")
self.matmul_weight = Parameter(Tensor(np.ones([32, 32]), dtype=ms.float32), "w2")
self.embedding_table = Parameter(Tensor(np.ones([64, 32]), dtype=ms.float32), "embedding_table")
def construct(self, x, b):
out = self.gather(self.embedding_table, x, 0)
out = self.matmul(out, self.matmul_weight)
out = self.mul(out, self.mul_weight)
out = out + b
return self.reduce_sum(out)
_x = Tensor(np.ones([64]), dtype=ms.int32)
_b = Tensor(np.ones([64, 32]), dtype=ms.float32)
def compile_net(net, by_grad=True):
if by_grad:
optimizer = AdaSumByGradWrapCell(Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9))
else:
optimizer = AdaSumByDeltaWeightWrapCell(Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9))
train_net = TrainOneStepCell(net, optimizer)
train_net.set_auto_parallel()
train_net.set_train()
_cell_graph_executor.compile(train_net, _x, _b)
context.reset_auto_parallel_context()
def test_auto_parallel_adasum1():
"""
Feature: adasum in auto parallel.
Description: verify adasum by mul/matmul/gather, rank0, dp, mp, not_full_dp
Expectation: compile done without error.
"""
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
mul_strategy1 = ((8, 4), (8, 4))
matmul_strategy2 = ((8, 1), (1, 1))
gather_strategy3 = ((1, 1), (32,))
net = Net(mul_strategy1, matmul_strategy2, gather_strategy3)
compile_net(net)
def test_auto_parallel_adasum2():
"""
Feature: adasum in auto parallel.
Description: verify adasum by mul/matmul/gather, rank0, dp, mp, not_full_dp
Expectation: compile done without error.
"""
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
mul_strategy1 = ((8, 4), (8, 4))
matmul_strategy2 = ((8, 1), (1, 1))
gather_strategy3 = ((1, 1), (32,))
net = Net(mul_strategy1, matmul_strategy2, gather_strategy3)
compile_net(net, by_grad=False)
def test_auto_parallel_adasum3():
"""
Feature: adasum in auto parallel.
Description: verify adasum by mul/matmul/gather, rank0, mix_dp_mp, mp
Expectation: compile done without error.
"""
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
mul_strategy1 = ((8, 4), (8, 4))
matmul_strategy2 = ((8, 4), (4, 1))
gather_strategy3 = ((32, 1), (1,))
net = Net(mul_strategy1, matmul_strategy2, gather_strategy3)
compile_net(net)
def test_auto_parallel_adasum4():
"""
Feature: adasum in auto parallel.
Description: verify adasum by mul/matmul/gather, rank0, mix_dp_mp, mp
Expectation: compile done without error.
"""
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
mul_strategy1 = ((8, 4), (8, 4))
matmul_strategy2 = ((8, 4), (4, 1))
gather_strategy3 = ((32, 1), (1,))
net = Net(mul_strategy1, matmul_strategy2, gather_strategy3)
compile_net(net, by_grad=False)
def test_auto_parallel_adasum5():
"""
Feature: adasum in auto parallel.
Description: verify adasum by mul/matmul/gather, rank16, dp, mp, not_full_dp
Expectation: compile done without error.
"""
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=16)
mul_strategy1 = ((8, 4), (8, 4))
matmul_strategy2 = ((8, 1), (1, 1))
gather_strategy3 = ((1, 1), (32,))
net = Net(mul_strategy1, matmul_strategy2, gather_strategy3)
compile_net(net)
def test_auto_parallel_adasum6():
"""
Feature: adasum in auto parallel.
Description: verify adasum by mul/matmul/gather, rank16, dp, mp, not_full_dp
Expectation: compile done without error.
"""
context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=16)
mul_strategy1 = ((8, 4), (8, 4))
matmul_strategy2 = ((8, 1), (1, 1))
gather_strategy3 = ((1, 1), (32,))
net = Net(mul_strategy1, matmul_strategy2, gather_strategy3)
compile_net(net, by_grad=False)