forked from mindspore-Ecosystem/mindspore
130 lines
4.0 KiB
Python
130 lines
4.0 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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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.common.dtype as mstype
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.parameter import Parameter, ParameterTuple
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from mindspore.nn.reinforcement._batch_read_write import BatchRead, BatchWrite
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class DstNet(nn.Cell):
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'''Dst net'''
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def __init__(self):
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super(DstNet, self).__init__()
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self.a = Parameter(Tensor(0.1, mstype.float32), name="a")
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self.dense = nn.Dense(in_channels=16, out_channels=1)
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def construct(self, data):
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d = self.dense(data)
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out = d + self.a
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return out
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class SourceNet(nn.Cell):
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'''Source net'''
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def __init__(self):
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super(SourceNet, self).__init__()
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self.a = Parameter(Tensor(0.5, mstype.float32), name="a")
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self.dense = nn.Dense(in_channels=16, out_channels=1, weight_init=0)
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def construct(self, data):
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d = self.dense(data)
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out = d + self.a
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return out
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class Write(nn.Cell):
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'''Write cell'''
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def __init__(self, dst, src):
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super(Write, self).__init__()
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self.write = BatchWrite()
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self.dst = ParameterTuple(dst.trainable_params())
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self.src = ParameterTuple(src.trainable_params())
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def construct(self):
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success = self.write(self.dst, self.src)
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return success
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class Read(nn.Cell):
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'''Read cell'''
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def __init__(self, dst, src):
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super(Read, self).__init__()
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self.read = BatchRead()
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self.dst = ParameterTuple(dst.trainable_params())
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self.src = ParameterTuple(src.trainable_params())
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def construct(self):
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success = self.read(self.dst, self.src)
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return success
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_read_write_model_gpu():
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"""
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Feature: BatchPushPull gpu TEST.
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Description: Test the batch assign.
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Expectation: success.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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dst_net = DstNet()
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source_net = SourceNet()
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dst_param = dst_net.trainable_params()
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source_param = source_net.trainable_params()
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nets = nn.CellList()
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nets.append(dst_net)
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nets.append(source_net)
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# Test read source net's params to replace dst_net's params.
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_ = Read(nets[0], nets[1])()
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assert np.allclose(dst_param[0].asnumpy(), 0.5)
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# Test write dst net's params to overwrite the source.
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dst_net2 = DstNet()
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nets[0] = dst_net2
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_ = Write(nets[1], nets[0])()
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assert np.allclose(source_param[0].asnumpy(), 0.1)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_read_write_model_cpu():
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"""
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Feature: BatchPushPull cpu TEST.
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Description: Test the batch assign.
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Expectation: success.
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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dst_net = DstNet()
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source_net = SourceNet()
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dst_param = dst_net.trainable_params()
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source_param = source_net.trainable_params()
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cpu_nets = nn.CellList()
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cpu_nets.append(dst_net)
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cpu_nets.append(source_net)
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_ = Read(cpu_nets[0], cpu_nets[1])()
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assert np.allclose(dst_param[0].asnumpy(), 0.5)
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dst_net2 = DstNet()
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cpu_nets[0] = dst_net2
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_ = Write(cpu_nets[1], cpu_nets[0])()
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assert np.allclose(source_param[0].asnumpy(), 0.1)
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