forked from mindspore-Ecosystem/mindspore
141 lines
4.9 KiB
Python
141 lines
4.9 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 pytest
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import numpy as np
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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from mindspore import Tensor, context
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from mindspore.ops.functional import vmap
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from mindspore.ops import functional as F
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from mindspore.common.api import ms_function
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context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
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class CeluTEST(nn.Cell):
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def __init__(self, alpha):
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super(CeluTEST, self).__init__()
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self.celu = P.CeLU(alpha)
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def construct(self, x):
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return self.celu(x)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.parametrize("data_type", [np.float32, np.float16])
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def test_celu_op(data_type):
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"""
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Feature: Celu cpu kernel
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Description: test the celu alpha = 1.0.
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Expectation: match to np benchmark.
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"""
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error = 1e-3
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celu = CeluTEST(1.)
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x = Tensor(np.array([-2.0, -1.0, 1.0, 2.0]).astype(data_type))
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expect = np.array([-0.8646, -0.6321, 1., 2.]).astype(data_type)
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context.set_context(mode=context.GRAPH_MODE)
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output = celu(x)
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print(output)
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np.testing.assert_allclose(output.asnumpy(), expect, rtol=error)
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context.set_context(mode=context.PYNATIVE_MODE)
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output = celu(x)
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print(output)
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np.testing.assert_allclose(output.asnumpy(), expect, rtol=error)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.parametrize("data_type", [np.float32, np.float16])
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def test_celu_func(data_type):
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"""
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Feature: Celu cpu kernel
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Description: test the celu alpha = 1.0.
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Expectation: match to np benchmark.
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"""
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error = 1e-3
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x = Tensor(np.array([-2.0, -1.0, 1.0, 2.0]).astype(data_type))
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expect = np.array([-0.86468184, -0.6321212, 1., 2.]).astype(data_type)
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context.set_context(mode=context.GRAPH_MODE)
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output = F.celu(x, 1.0)
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print(output)
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np.testing.assert_allclose(output.asnumpy(), expect, rtol=error)
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context.set_context(mode=context.PYNATIVE_MODE)
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output = F.celu(x, 1.0)
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print(output)
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np.testing.assert_allclose(output.asnumpy(), expect, rtol=error)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.parametrize("data_type", [np.float32, np.float16])
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def test_celu_tensor(data_type):
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"""
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Feature: Celu gpu kernel
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Description: test the celu alpha = 1.0.
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Expectation: match to np benchmark.
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"""
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error = 1e-3
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x = Tensor(np.array([-2.0, -1.0, 1.0, 2.0]).astype(data_type))
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expect = np.array([-0.86468184, -0.6321212, 1., 2.]).astype(data_type)
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context.set_context(mode=context.GRAPH_MODE)
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output = x.celu(1.0)
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print(output)
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np.testing.assert_allclose(output.asnumpy(), expect, rtol=error)
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context.set_context(mode=context.PYNATIVE_MODE)
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output = x.celu(1.0)
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print(output)
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np.testing.assert_allclose(output.asnumpy(), expect, rtol=error)
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_gpu_training
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def test_celu_vmap():
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"""
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Feature: celu gpu kernel.
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Description: test celu vmap feature.
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Expectation: Success.
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"""
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error = 1e-3
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def cal_celu(x):
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return P.CeLU(1.0)(x)
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x = Tensor(np.array([[-2.0, -1.0, 1.0, 2.0], [-2.0, -1.0, 1.0, 2.0], [-2.0, -1.0, 1.0, 2.0],
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[-2.0, -1.0, 1.0, 2.0], [-2.0, -1.0, 1.0, 2.0], [-2.0, -1.0, 1.0, 2.0],
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[-2.0, -1.0, 1.0, 2.0], [-2.0, -1.0, 1.0, 2.0]]).astype(np.float32))
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expect = np.array([[-0.86468184, -0.6321212, 1., 2.], [-0.86468184, -0.6321212, 1., 2.],
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[-0.86468184, -0.6321212, 1., 2.], [-0.86468184, -0.6321212, 1., 2.],
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[-0.86468184, -0.6321212, 1., 2.], [-0.86468184, -0.6321212, 1., 2.],
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[-0.86468184, -0.6321212, 1., 2.], [-0.86468184, -0.6321212, 1., 2.]]).astype(np.float32)
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vmap_celu = vmap(cal_celu, in_axes=(0), out_axes=0)
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output = vmap_celu(x)
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np.testing.assert_allclose(output.asnumpy(), expect, rtol=error)
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@ms_function
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def manually_batched(xs):
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output = []
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for i in range(xs.shape[0]):
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output.append(cal_celu(xs[i]))
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return F.stack(output)
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expect_m = manually_batched(x)
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np.testing.assert_allclose(output.asnumpy(), expect_m.asnumpy(), rtol=error)
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