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- # Copyright 2020 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 pytest
-
- import mindspore as ms
- from mindspore import context, Tensor, Parameter
- from mindspore.common.api import _executor
- from mindspore.nn import Cell, TrainOneStepCell, Momentum
- from mindspore.ops import operations as P
-
-
- class Net(Cell):
- def __init__(self, weight, w2, begin, end, strides, strategy1=None, strategy2=None, is_parameter=True, mask=0):
- super().__init__()
- self.mul = P.Mul().shard(strategy1)
- self.strided_slice = P.StridedSlice(begin_mask=mask).shard(strategy2)
- if is_parameter:
- self.weight = Parameter(weight, "w1")
- else:
- self.weight = weight
- self.mul2 = P.Mul()
- self.weight2 = Parameter(w2, "w2")
- self.begin = begin
- self.end = end
- self.strides = strides
-
- def construct(self, x, b):
- out = self.strided_slice(self.weight, self.begin, self.end, self.strides)
- out = self.mul(x, out)
- out = self.mul2(out, self.weight2)
- return out
-
-
- class Net2(Cell):
- def __init__(self, weight2, begin, end, strides, strategy1=None, strategy2=None):
- super().__init__()
- self.mul = P.Mul().shard(strategy1)
- self.strided_slice = P.StridedSlice().shard(strategy2)
- self.weight2 = Parameter(weight2, "w2")
- self.begin = begin
- self.end = end
- self.strides = strides
-
- def construct(self, x, b):
- out = self.mul(x, self.weight2)
- out = self.strided_slice(out, self.begin, self.end, self.strides)
- return out
-
-
- _x = Tensor(np.ones([128, 64, 1]), dtype=ms.float32)
- _w1 = Tensor(np.ones([256, 64, 32]), dtype=ms.float32)
- _w2 = Tensor(np.ones([128, 64, 1]), dtype=ms.float32)
- _b = Tensor(np.ones([128, 64, 32]), dtype=ms.float32)
-
-
- def compile_net(net):
- context.set_context(save_graphs=True)
- optimizer = 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()
- _executor.compile(train_net, _x, _b)
- context.reset_auto_parallel_context()
-
-
- def test_stridedslice_no_fully_fetch_split_error():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
- strategy1 = ((2, 2, 2), (2, 2, 2))
- strategy2 = ((2, 2, 2),)
- net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True)
- with pytest.raises(RuntimeError):
- compile_net(net)
-
-
- def test_stridedslice_strides_no_1_split_error():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
- strategy1 = ((2, 2, 2), (2, 2, 2))
- strategy2 = ((1, 2, 2),)
- net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 2), strategy1, strategy2, is_parameter=True)
- with pytest.raises(RuntimeError):
- compile_net(net)
-
-
- def test_stridedslice_mask_no_0_split_error():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
- strategy1 = ((2, 2, 2), (2, 2, 2))
- strategy2 = ((1, 2, 2),)
- net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True, mask=1)
- with pytest.raises(RuntimeError):
- compile_net(net)
-
-
- def test_stridedslice_begin_size_smaller():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
- strategy1 = ((1, 4, 1), (1, 4, 2))
- strategy2 = ((1, 4, 2),)
- net = Net(_w1, _w2, (0, 0), (128, 64), (1, 1), strategy1, strategy2, is_parameter=True)
- compile_net(net)
-
-
- def test_stridedslice_parameter():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
- strategy1 = ((1, 4, 1), (1, 4, 2))
- strategy2 = ((1, 4, 2),)
- net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True)
- compile_net(net)
-
-
- def test_stridedslice_tensor():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
- strategy1 = ((1, 4, 1), (1, 4, 2))
- strategy2 = ((1, 4, 2),)
- net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=False)
- compile_net(net)
-
-
- def test_stridedslice_parameter_no_full_split():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
- strategy1 = ((1, 4, 1), (1, 4, 2))
- strategy2 = ((1, 2, 2),)
- net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True)
- compile_net(net)
-
-
- def test_stridedslice_output():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
- strategy1 = ((1, 8, 1), (1, 8, 1))
- strategy2 = ((1, 8, 1),)
- net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2)
- compile_net(net)
-
-
- def test_stridedslice_output_no_full_split():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
- strategy1 = ((1, 8, 1), (1, 8, 1))
- strategy2 = ((1, 4, 1),)
- net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2)
- compile_net(net)
-
-
- def test_stridedslice_no_strategy():
- context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
- strategy1 = ((1, 8, 1), (1, 8, 1))
- strategy2 = None
- net = Net2(_w2, (0, 0, 0), (128, 64, 1), (1, 1, 1), strategy1, strategy2)
- compile_net(net)
-
-
- def test_stridedslice_auto_parallel():
- context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
- net = Net2(_w2, (0, 0, 0), (32, 64, 1), (1, 1, 1))
- compile_net(net)
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