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83 lines
3.0 KiB
83 lines
3.0 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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from mindspore import Tensor
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from mindspore.ops import operations as P
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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_topk():
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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x_np = np.random.rand(3, 4).astype(np.float32)
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k = 4
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ms_output = P.TopK(True)(Tensor(x_np), k)
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np_output = np.sort(x_np, axis=-1)[..., ::-1][..., 0:k]
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assert np.allclose(ms_output[0].asnumpy(), np_output)
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x_np = np.random.rand(3, 4).astype(np.float32)
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k = 4
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ms_output = P.TopK(False)(Tensor(x_np), k)
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assert np.allclose(ms_output[0].asnumpy(), x_np)
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x_np = np.random.rand(2, 3, 4).astype(np.float32)
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k = 2
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ms_output = P.TopK(True)(Tensor(x_np), k)
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np_output = np.sort(x_np, axis=-1)[..., ::-1][..., 0:k]
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assert np.allclose(ms_output[0].asnumpy(), np_output)
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x_np = np.random.rand(512, 1024).astype(np.float32)
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k = 512
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ms_output = P.TopK(True)(Tensor(x_np), k)
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np_output = np.sort(x_np, axis=-1)[..., ::-1][..., 0:k]
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assert np.allclose(ms_output[0].asnumpy(), np_output)
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# sorted elements num greater than max thread per block
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x_np = np.random.rand(512, 2048).astype(np.float32)
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k = 1
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ms_output = P.TopK(True)(Tensor(x_np), k)
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np_output = np.sort(x_np, axis=-1)[..., ::-1][..., 0:k]
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assert np.allclose(ms_output[0].asnumpy(), np_output)
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x_np = np.random.rand(512, 2048).astype(np.float32)
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k = 2048
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ms_output = P.TopK(True)(Tensor(x_np), k)
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np_output = np.sort(x_np, axis=-1)[..., ::-1][..., 0:k]
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assert np.allclose(ms_output[0].asnumpy(), np_output)
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# sorted elements num greater than max share memory per block
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x_np = np.random.rand(512, 40960).astype(np.float32)
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k = 1
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ms_output = P.TopK(True)(Tensor(x_np), k)
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np_output = np.sort(x_np, axis=-1)[..., ::-1][..., 0:k]
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assert np.allclose(ms_output[0].asnumpy(), np_output)
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x_np = np.random.rand(512, 40960).astype(np.float32)
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k = 40960
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ms_output = P.TopK(True)(Tensor(x_np), k)
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np_output = np.sort(x_np, axis=-1)[..., ::-1][..., 0:k]
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assert np.allclose(ms_output[0].asnumpy(), np_output)
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x_np = np.random.rand(512, 40960).astype(np.float32)
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k = 40960
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ms_output = P.TopK(False)(Tensor(x_np), k)
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assert np.allclose(ms_output[0].asnumpy(), x_np)
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