109 lines
4.1 KiB
Python
109 lines
4.1 KiB
Python
# Copyright 2021 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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""" test_pynative_heterogeneous """
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import numpy as np
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import pytest
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from mindspore import context, Tensor
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from mindspore.nn import Cell
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import mindspore.ops as ops
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class MulRelu(Cell):
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def __init__(self):
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super(MulRelu, self).__init__()
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self.relu1 = ops.ReLU()
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self.relu2 = ops.ReLU()
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self.mul = ops.Mul()
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def construct(self, inp1, inp2):
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x1 = self.relu1(inp1)
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x2 = self.relu2(inp2)
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y = self.mul(x1, x2)
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return y
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_heterogeneous_default_ascend_prim_cpu():
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"""
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Feature: PyNative heterogeneous.
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Description: Default device target is Ascend, the relu1 set to CPU.
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Expectation: The output of device is equal to the output of heterogeneous.
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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net = MulRelu()
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inp1 = Tensor(np.random.randn(2, 2).astype(np.float32))
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inp2 = Tensor(np.random.randn(2, 2).astype(np.float32))
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output_device = net(inp1, inp2)
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net.relu1.set_device("CPU")
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output_heter = net(inp1, inp2)
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assert np.allclose(output_device.asnumpy(), output_heter.asnumpy(), 1e-6, 1e-6)
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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def test_heterogeneous_default_cpu_prim_ascend():
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"""
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Feature: PyNative heterogeneous.
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Description: Default device target is CPU, the relu1 set to Ascend.
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Expectation: The output of device is equal to the output of heterogeneous.
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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net = MulRelu()
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inp1 = Tensor(np.random.randn(2, 2).astype(np.float32))
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inp2 = Tensor(np.random.randn(2, 2).astype(np.float32))
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output_device = net(inp1, inp2)
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net.relu1.set_device("Ascend")
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output_heter = net(inp1, inp2)
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assert np.allclose(output_device.asnumpy(), output_heter.asnumpy(), 1e-6, 1e-6)
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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_heterogeneous_default_gpu_prim_cpu():
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"""
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Feature: PyNative heterogeneous.
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Description: Default device target is GPU, the relu1 set to CPU.
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Expectation: The output of device is equal to the output of heterogeneous.
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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net = MulRelu()
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inp1 = Tensor(np.random.randn(2, 2).astype(np.float32))
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inp2 = Tensor(np.random.randn(2, 2).astype(np.float32))
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output_device = net(inp1, inp2)
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net.relu1.set_device("CPU")
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output_heter = net(inp1, inp2)
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assert np.allclose(output_device.asnumpy(), output_heter.asnumpy(), 1e-6, 1e-6)
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_heterogeneous_default_cpu_prim_gpu():
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"""
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Feature: PyNative heterogeneous.
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Description: Default device target is CPU, the relu1 set to GPU.
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Expectation: The output of device is equal to the output of heterogeneous.
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"""
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context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
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net = MulRelu()
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inp1 = Tensor(np.random.randn(2, 2).astype(np.float32))
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inp2 = Tensor(np.random.randn(2, 2).astype(np.float32))
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output_device = net(inp1, inp2)
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net.relu1.set_device("GPU")
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output_heter = net(inp1, inp2)
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assert np.allclose(output_device.asnumpy(), output_heter.asnumpy(), 1e-6, 1e-6)
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