forked from mindspore-Ecosystem/mindspore
93 lines
3.1 KiB
Python
93 lines
3.1 KiB
Python
# Copyright 2022 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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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 _cell_graph_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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class DependNet(Cell):
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def __init__(self, mul_weight, strategy):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy)
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self.cast = P.Cast()
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self.sigmoid = P.Sigmoid()
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self.relu = P.ReLU()
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self.weight = Parameter(mul_weight, "w1")
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self.denpend_tensor = Tensor(0.0, dtype=ms.float32)
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self.depend = P.Depend()
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def construct(self, x):
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u = self.relu(self.denpend_tensor)
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weight = self.depend(self.weight, u)
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out = self.matmul(self.cast(x, ms.float16), self.cast(weight, ms.float16))
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out = self.sigmoid(out)
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out = self.relu(out)
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return out
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class LoadNet(Cell):
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def __init__(self, mul_weight, strategy):
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super().__init__()
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self.matmul = P.MatMul().shard(strategy)
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self.cast = P.Cast()
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self.sigmoid = P.Sigmoid()
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self.relu = P.ReLU()
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self.weight = Parameter(mul_weight, "w1")
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def construct(self, x):
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out = self.matmul(self.cast(x, ms.float16), self.cast(self.weight, ms.float16))
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out = self.sigmoid(out)
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out = self.relu(out)
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return out
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_x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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_w1 = Tensor(np.ones([32, 64]), 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_train()
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_cell_graph_executor.compile(train_net, _x)
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context.reset_auto_parallel_context()
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def test_mirror_insert1():
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"""
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Feature: Test find parameter and insert mirror before Depend.
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Description: simulate auto_monad case: param->Depend
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Expectation: Successful graph compilation.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy = ((8, 1), (1, 1))
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net = DependNet(_w1, strategy)
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compile_net(net)
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def test_mirror_insert2():
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"""
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Feature: Test find parameter and insert mirror before Load.
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Description: simulate auto_monad case: param->Load
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Expectation: Successful graph compilation.
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy = ((8, 1), (1, 1))
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net = LoadNet(_w1, strategy)
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compile_net(net)
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