takedown test_auto_parallel_adasum.py to ensure stability, again

tests/ut/python/parallel/test_auto_parallel_adasum.py

@@ -1,134 +0,0 @@
-# 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)