remove unuse test

tests/ut/python/parallel/test_attention.py

@@ -1,149 +0,0 @@
-# Copyright 2019 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
-import mindspore.nn as nn
-from mindspore import Tensor
-from mindspore import context
-from mindspore.common.api import _executor
-from mindspore.context import set_auto_parallel_context
-from mindspore.ops import composite as C
-from mindspore.ops import operations as P
-from mindspore.common.initializer import initializer
-from mindspore.common.parameter import Parameter
-from tests.ut.python.ops.test_math_ops import VirtualLoss
-
-
-grad_all = C.GradOperation(get_all=True)
-
-
-class NetWithLoss(nn.Cell):
-    def __init__(self, network):
-        super(NetWithLoss, self).__init__()
-        self.loss = VirtualLoss()
-        self.network = network
-
-    def construct(self, x):
-        predict = self.network(x)
-        return self.loss(predict)
-
-
-class GradWrap(nn.Cell):
-    def __init__(self, network):
-        super(GradWrap, self).__init__()
-        self.network = network
-
-    def construct(self, x):
-        return grad_all(self.network)(x)
-
-
-def compile_net(net, x):
-    net.set_auto_parallel()
-    _executor.compile(net, x)
-
-
-class Net(nn.Cell):
-    def __init__(self, strategy1, strategy2, strategy3, strategy4, strategy5):
-        super().__init__()
-        self.query_w = Parameter(initializer(
-            "normal", [8, 16], ms.float32), name='query')
-        self.query = P.MatMul().shard(strategy1)
-
-        self.key_w = Parameter(initializer(
-            "normal", [8, 16], ms.float32), name='key')
-        self.key = P.MatMul().shard(strategy2)
-
-        self.value_w = Parameter(initializer(
-            "normal", [8, 16], ms.float32), name='value')
-        self.value = P.MatMul().shard(strategy3)
-
-        self.score = P.MatMul().shard(strategy4)
-        self.context = P.MatMul().shard(strategy5)
-        self.transpose1 = P.Transpose()
-        self.transpose2 = P.Transpose()
-        self.relu = P.ReLU()
-
-    def construct(self, x):
-        q = self.query(x, self.query_w)
-        k = self.key(x, self.key_w)
-        v = self.value(x, self.value_w)
-
-        k = self.transpose1(k, (1, 0))
-        s = self.score(q, k)
-
-        v = self.transpose2(v, (1, 0))
-        c = self.context(v, s)
-        out = self.relu(c)
-
-        return out
-
-
-def test_self_attention_standalone():
-    set_auto_parallel_context(device_num=8, global_rank=0)
-    context.set_auto_parallel_context(parallel_mode="stand_alone")
-    net = GradWrap(NetWithLoss(
-        Net(None, None, None, None, None)))
-
-    x = Tensor(np.ones([32, 8]), dtype=ms.float32)
-
-    compile_net(net, x)
-
-
-def test_self_attention_semi():
-    set_auto_parallel_context(device_num=8, global_rank=0)
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
-
-    strategy1 = ((2, 2), (2, 2))
-    strategy2 = ((2, 2), (2, 2))
-    strategy3 = ((2, 2), (2, 2))
-    strategy4 = ((2, 4), (4, 1))
-    strategy5 = ((2, 1), (1, 4))
-
-    net = GradWrap(NetWithLoss(
-        Net(strategy1, strategy2, strategy3, strategy4, strategy5)))
-
-    x = Tensor(np.ones([32, 8]), dtype=ms.float32)
-
-    compile_net(net, x)
-
-
-def test_self_attention_dp():
-    set_auto_parallel_context(device_num=8, global_rank=0)
-    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
-
-    strategy1 = ((8, 1), (1, 1))
-    strategy2 = ((8, 1), (1, 1))
-    strategy3 = ((8, 1), (1, 1))
-    strategy4 = ((8, 1), (1, 1))
-    strategy5 = ((8, 1), (1, 1))
-
-    net = GradWrap(NetWithLoss(
-        Net(strategy1, strategy2, strategy3, strategy4, strategy5)))
-
-    x = Tensor(np.ones([32, 8]), dtype=ms.float32)
-
-    compile_net(net, x)
-
-
-def test_self_attention_auto():
-    set_auto_parallel_context(device_num=8, global_rank=0)
-    context.set_auto_parallel_context(parallel_mode="auto_parallel")
-    net = GradWrap(NetWithLoss(
-        Net(None, None, None, None, None)))
-
-    x = Tensor(np.ones([32, 8]), dtype=ms.float32)
-
-    compile_net(net, x)