forked from mindspore-Ecosystem/mindspore
110 lines
3.9 KiB
Python
110 lines
3.9 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.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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import mindspore.common.dtype as mstype
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from mindspore.ops import composite as C
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from mindspore.nn.reinforcement._tensors_queue import TensorsQueue
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class TensorsQueueNet(nn.Cell):
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def __init__(self, dtype, shapes, size=0, name="q"):
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super(TensorsQueueNet, self).__init__()
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self.tq = TensorsQueue(dtype, shapes, size, name)
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def construct(self, grads):
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self.tq.put(grads)
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self.tq.put(grads)
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size_before = self.tq.size()
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ans = self.tq.pop()
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size_after = self.tq.size()
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self.tq.clear()
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self.tq.close()
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return ans, size_before, size_after
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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=4, 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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@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_tensorsqueue_gpu():
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"""
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Feature: TensorsQueue gpu TEST.
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Description: Test the function write, read, stack, clear, close in both graph and pynative mode.
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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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input_data = Tensor(np.arange(8).reshape(2, 4), mstype.float32)
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net = SourceNet()
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weight = ParameterTuple(net.trainable_params())
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grad = C.GradOperation(get_by_list=True, sens_param=False)
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_ = net(input_data)
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grads = grad(net, weight)(input_data)
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shapes = []
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for i in grads:
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shapes.append(i.shape)
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tq = TensorsQueueNet(dtype=mstype.float32, shapes=shapes, size=5, name="tq")
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ans, size_before, size_after = tq(grads)
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assert np.allclose(size_before.asnumpy(), 2)
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assert np.allclose(size_after.asnumpy(), 1)
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assert np.allclose(ans[0].asnumpy(), 2.0)
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assert np.allclose(ans[1].asnumpy(), [[4.0, 6.0, 8.0, 10.0]])
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assert np.allclose(ans[2].asnumpy(), [2.0])
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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_tensorsqueue_cpu():
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"""
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Feature: TensorsQueue cpu TEST.
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Description: Test the function write, read, stack, clear, close in both graph and pynative mode.
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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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data = Tensor(np.arange(8).reshape(2, 4), mstype.float32)
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net = SourceNet()
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weight = ParameterTuple(net.trainable_params())
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grad = C.GradOperation(get_by_list=True, sens_param=False)
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_ = net(data)
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grads = grad(net, weight)(data)
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shapes = []
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for i in grads:
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shapes.append(i.shape)
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tq_cpu = TensorsQueueNet(dtype=mstype.float32, shapes=shapes, size=5, name="tq")
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ans, size_before, size_after = tq_cpu(grads)
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assert np.allclose(ans[0].asnumpy(), 2.0)
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assert np.allclose(ans[1].asnumpy(), [[4.0, 6.0, 8.0, 10.0]])
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assert np.allclose(ans[2].asnumpy(), [2.0])
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assert np.allclose(size_before.asnumpy(), 2)
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assert np.allclose(size_after.asnumpy(), 1)
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