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62 lines
2.6 KiB
62 lines
2.6 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import mindspore as ms
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from mindspore import context, Tensor, Parameter
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from mindspore.common.api import _executor
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from mindspore.nn import Cell, TrainOneStepCell, Momentum
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from mindspore.ops import operations as P
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from mindspore.common.initializer import initializer
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class Net(Cell):
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def __init__(self, strategy1=None, strategy2=None, strategy3=None):
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super().__init__()
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self.gatherv2 = P.GatherV2().set_strategy(strategy1)
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self.gatherv2.add_prim_attr("manual_split", ((1, 0), (7, 1)))
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self.mul = P.Mul().set_strategy(strategy2)
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self.reshape = P.Reshape()
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self.matmul = P.MatMul().set_strategy(strategy3)
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self.matmul.add_prim_attr("forward_reduce_scatter", True)
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self.param = Parameter(initializer("ones", (8, 64), ms.float32), name="gatherv2_param")
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self.mul_weight = Parameter(initializer("ones", (2, 4, 64), ms.float32), name="mul_weight")
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self.matmul_weight = Parameter(initializer("ones", (256, 16), ms.float32), name="matmul_weight")
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def construct(self, x, b):
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out = self.gatherv2(self.param, x, 0)
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out = self.mul(out, self.mul_weight)
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out = self.reshape(out, (2, 256))
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out = self.matmul(out, self.matmul_weight)
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return out
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_x = Tensor(np.ones([2, 4]), dtype=ms.int32)
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_b = Tensor(np.ones([64, 8]), dtype=ms.float32)
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def compile_net(net):
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_auto_parallel()
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_executor.compile(train_net, _x, _b)
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context.reset_auto_parallel_context()
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def test_neg_data_parallel():
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context.set_context(save_graphs=True)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=2, global_rank=0)
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strategy1 = ((2, 1), (1, 2))
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strategy2 = ((1, 2, 1), (1, 2, 1))
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strategy3 = ((1, 2), (2, 1))
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net = Net(strategy1, strategy2, strategy3)
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compile_net(net)
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