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315 lines
11 KiB
315 lines
11 KiB
# Copyright 2020 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.ops import operations as P
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class SquaredDifference(nn.Cell):
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def __init__(self):
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super(SquaredDifference, self).__init__()
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self.squaredDiff = P.SquaredDifference()
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def construct(self, x, y):
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return self.squaredDiff(x, y)
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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_nobroadcast_f16():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.uniform(10, 20, (3, 4, 5, 2)).astype(np.float16)
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input_y = np.random.uniform(40, 50, (3, 4, 5, 2)).astype(np.float16)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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assert np.all(output == expect)
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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_nobroadcast_f32():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(3, 4, 5, 2).astype(np.float32)
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input_y = np.random.rand(3, 4, 5, 2).astype(np.float32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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assert np.all(output == expect)
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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_nobroadcast_int32():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(3, 4, 5, 2).astype(np.int32)
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input_y = np.random.rand(3, 4, 5, 2).astype(np.int32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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assert np.all(output == expect)
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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_broadcast_int32():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(1, 4, 1, 2).astype(np.int32)
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input_y = np.random.rand(3, 1, 5, 1).astype(np.int32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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assert np.all(output == expect)
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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_broadcast_f32():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(1, 4, 1, 2).astype(np.float32)
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input_y = np.random.rand(3, 1, 5, 1).astype(np.float32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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assert np.all(output == expect)
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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_broadcast_f16():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(1, 4, 1, 2).astype(np.float16)
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input_y = np.random.rand(3, 1, 5, 1).astype(np.float16)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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assert np.all(output == expect)
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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_broadcast_bool():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(1, 4, 1, 2).astype(np.bool)
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input_y = np.random.uniform(10, 20, (3, 1, 5, 1)).astype(np.float32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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error = np.ones(shape=np.array(output.shape, dtype=int))*1.0e-6
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double_check = np.abs(output-expect)/expect
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assert np.all(double_check < error)
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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_nobroadcast_bool():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(3, 4, 5, 2).astype(np.bool)
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input_y = np.random.rand(3, 4, 5, 2).astype(np.float32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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error = np.ones(shape=np.array(output.shape, dtype=int))*1.0e-6
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double_check = np.abs(output-expect)/expect
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assert np.all(double_check < error)
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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_broadcast_int32_f16():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(1, 4, 1, 2).astype(np.int32)
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input_y = np.random.uniform(10, 20, (3, 1, 5, 1)).astype(np.float16)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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error = np.ones(shape=np.array(output.shape, dtype=int))*1.0e-3
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double_check = np.abs(output-expect)/expect
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assert np.all(double_check < error)
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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_broadcast_int32_f32():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(1, 4, 1, 2).astype(np.int32)
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input_y = np.random.uniform(10, 20, (3, 1, 5, 1)).astype(np.float32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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error = np.ones(shape=np.array(output.shape, dtype=int))*1.0e-6
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double_check = np.abs(output-expect)/expect
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assert np.all(double_check < error)
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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_nobroadcast_int32_f16():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(2, 4, 3, 2).astype(np.int32)
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input_y = np.random.uniform(10, 20, (2, 4, 3, 2)).astype(np.float16)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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error = np.ones(shape=np.array(output.shape, dtype=int))*1.0e-3
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double_check = np.abs(output-expect)/expect
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assert np.all(double_check < error)
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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_nobroadcast_int32_f32():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(2, 4, 3, 2).astype(np.int32)
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input_y = np.random.uniform(10, 20, (2, 4, 3, 2)).astype(np.float32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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error = np.ones(shape=np.array(output.shape, dtype=int))*1.0e-6
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double_check = np.abs(output-expect)/expect
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assert np.all(double_check < error)
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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_broadcast_f32_scalar_tensor():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(2).astype(np.float32)
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input_y = np.random.rand(3, 1, 5, 1).astype(np.float32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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assert np.all(output == expect)
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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_broadcast_f32_tensor_tensor():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(1, 2).astype(np.float32)
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input_y = np.random.rand(3, 1, 5, 1).astype(np.float32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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assert np.all(output == expect)
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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_broadcast_f32_tensor_tensor_dim_over_7():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(1, 2).astype(np.float32)
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input_y = np.random.rand(3, 1, 5, 1, 3, 4, 2, 1).astype(np.float32)
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try:
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net(Tensor(input_x), Tensor(input_y))
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except RuntimeError:
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assert True
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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_broadcast_f32_tensor_tensor_cannot_brocast():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.rand(5, 3).astype(np.float32)
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input_y = np.random.rand(3, 1, 5, 1, 3, 4, 2).astype(np.float32)
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try:
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net(Tensor(input_x), Tensor(input_y))
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except ValueError:
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assert True
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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_broadcast_int_f32_precision():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.randint(20, 30, (1, 2)).astype(np.int32)
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input_y = np.random.rand(3, 1, 5, 1).astype(np.float32)
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output = net(Tensor(input_x), Tensor(input_y)).asnumpy()
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diff = input_x-input_y
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expect = diff*diff
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error = np.ones(shape=np.array(output.shape, dtype=int))*1.0e-3
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double_thousand = np.abs(output-expect)/expect
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assert np.all(double_thousand < error)
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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_broadcast_type_error():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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np.random.seed(42)
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net = SquaredDifference()
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input_x = np.random.randint(20, 30, (1, 2)).astype(np.bool)
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input_y = np.random.rand(3, 1, 5, 1).astype(np.bool)
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try:
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net(Tensor(input_x), Tensor(input_y))
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except TypeError:
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assert True
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