forked from mindspore-Ecosystem/mindspore
296 lines
11 KiB
Python
296 lines
11 KiB
Python
# Copyright 2020-2021 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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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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from mindspore.ops import functional as F
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from mindspore.ops.functional import vmap
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class OpNetWrapper(nn.Cell):
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"""OpNetWrapper"""
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def __init__(self, op):
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"""__init__"""
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super(OpNetWrapper, self).__init__()
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self.op = op
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def construct(self, *inputs):
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"""construct"""
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return self.op(*inputs)
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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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@pytest.mark.parametrize('shape', [(2,), (4, 5), (3, 4, 5, 6), (3, 4, 5, 6, 2)])
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@pytest.mark.parametrize('dtype', [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64])
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def test_bitwise_and(shape, dtype):
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"""
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Feature: BitwiseAnd cpu kernel.
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Description: test the rightness of BitwiseAnd cpu kernel.
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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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op = P.BitwiseAnd()
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op_wrapper = OpNetWrapper(op)
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prop = 100 if np.random.random() > 0.5 else -100
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x_np = (np.random.randn(*shape) * prop).astype(dtype)
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y_np = (np.random.randn(*shape) * prop).astype(dtype)
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outputs = op_wrapper(Tensor(x_np), Tensor(y_np))
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outputs_functional = F.bitwise_and(Tensor(x_np), Tensor(y_np))
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expect = np.bitwise_and(x_np, y_np)
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assert np.allclose(outputs.asnumpy(), expect)
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assert np.allclose(outputs_functional.asnumpy(), expect)
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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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@pytest.mark.parametrize('shape', [(2,), (4, 5), (3, 4, 5, 6), (3, 4, 5, 6, 2)])
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@pytest.mark.parametrize('dtype', [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64])
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def test_bitwise_or(shape, dtype):
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"""
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Feature: BitwiseOr cpu kernel.
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Description: test the rightness of BitwiseOr cpu kernel.
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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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op = P.BitwiseOr()
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op_wrapper = OpNetWrapper(op)
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prop = 100 if np.random.random() > 0.5 else -100
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x_np = (np.random.randn(*shape) * prop).astype(dtype)
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y_np = (np.random.randn(*shape) * prop).astype(dtype)
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outputs = op_wrapper(Tensor(x_np), Tensor(y_np))
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outputs_functional = F.bitwise_or(Tensor(x_np), Tensor(y_np))
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expect = np.bitwise_or(x_np, y_np)
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assert np.allclose(outputs.asnumpy(), expect)
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assert np.allclose(outputs_functional.asnumpy(), expect)
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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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@pytest.mark.parametrize('shape', [(2,), (4, 5), (3, 4, 5, 6), (3, 4, 5, 6, 2)])
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@pytest.mark.parametrize('dtype', [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64])
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def test_bitwise_xor(shape, dtype):
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"""
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Feature: BitwiseXor cpu kernel.
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Description: test the rightness of BitwiseXor cpu kernel.
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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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op = P.BitwiseXor()
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op_wrapper = OpNetWrapper(op)
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prop = 100 if np.random.random() > 0.5 else -100
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x_np = (np.random.randn(*shape) * prop).astype(dtype)
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y_np = (np.random.randn(*shape) * prop).astype(dtype)
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outputs = op_wrapper(Tensor(x_np), Tensor(y_np))
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outputs_functional = F.bitwise_xor(Tensor(x_np), Tensor(y_np))
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expect = np.bitwise_xor(x_np, y_np)
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assert np.allclose(outputs.asnumpy(), expect)
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assert np.allclose(outputs_functional.asnumpy(), expect)
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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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@pytest.mark.parametrize('op', [P.BitwiseAnd(), P.BitwiseOr(), P.BitwiseXor()])
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@pytest.mark.parametrize('dtype', [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64])
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def test_bitwise_vmap(op, dtype):
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"""
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Feature: Bitwise cpu kernel.
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Description: test the rightness of Bitwise vmap feature.
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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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def test_add(x, y):
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return op(x, y)
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x = Tensor(np.array([[1, 2], [3, 4], [5, 6]]).astype(dtype))
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y = Tensor(np.array([[-3, -2, -1], [3, 2, 1]]).astype(dtype))
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outputs = vmap(test_add, in_axes=(0, 1), out_axes=0)(x, y)
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x_manual = np.array([[1, 2], [3, 4], [5, 6]]).astype(dtype)
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y_manual = np.array([[-3, 3], [-2, 2], [-1, 1]]).astype(dtype)
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def manually_batched(xs, ws):
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output = []
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for i in range(xs.shape[0]):
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output.append(test_add(Tensor(xs[i]), Tensor(ws[i])).asnumpy())
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return np.stack(output)
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expect = manually_batched(x_manual, y_manual)
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assert np.allclose(outputs.asnumpy(), expect)
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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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@pytest.mark.parametrize('dtype', [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64])
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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@pytest.mark.parametrize('shape', [(2,), (4, 5), (3, 4, 5, 6), (3, 4, 5, 6, 2)])
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def test_bitwise_and_tensor_interface(dtype, mode, shape):
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"""
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Feature: BitwiseAnd cpu kernel.
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Description: test the rightness of BitwiseAnd tensor interface.
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Expectation: Success.
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"""
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context.set_context(mode=mode, device_target='CPU')
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prop = 100 if np.random.random() > 0.5 else -100
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x_np = (np.random.randn(*shape) * prop).astype(dtype)
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y_np = (np.random.randn(*shape) * prop).astype(dtype)
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outputs = Tensor(x_np).bitwise_and(Tensor(y_np))
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expect = np.bitwise_and(x_np, y_np)
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assert np.allclose(outputs.asnumpy(), expect)
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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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@pytest.mark.parametrize('dtype', [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64])
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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@pytest.mark.parametrize('shape', [(2,), (4, 5), (3, 4, 5, 6), (3, 4, 5, 6, 2)])
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def test_bitwise_or_tensor_interface(dtype, mode, shape):
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"""
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Feature: BitwiseOr cpu kernel.
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Description: test the rightness of BitwiseOr tensor interface.
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Expectation: Success.
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"""
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context.set_context(mode=mode, device_target='CPU')
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prop = 100 if np.random.random() > 0.5 else -100
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x_np = (np.random.randn(*shape) * prop).astype(dtype)
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y_np = (np.random.randn(*shape) * prop).astype(dtype)
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outputs = Tensor(x_np).bitwise_or(Tensor(y_np))
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expect = np.bitwise_or(x_np, y_np)
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assert np.allclose(outputs.asnumpy(), expect)
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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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@pytest.mark.parametrize('dtype', [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64])
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@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
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@pytest.mark.parametrize('shape', [(2,), (4, 5), (3, 4, 5, 6), (3, 4, 5, 6, 2)])
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def test_bitwise_xor_tensor_interface(dtype, mode, shape):
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"""
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Feature: BitwiseXor cpu kernel.
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Description: test the rightness of BitwiseXor tensor interface.
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Expectation: Success.
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"""
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context.set_context(mode=mode, device_target='CPU')
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prop = 100 if np.random.random() > 0.5 else -100
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x_np = (np.random.randn(*shape) * prop).astype(dtype)
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y_np = (np.random.randn(*shape) * prop).astype(dtype)
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outputs = Tensor(x_np).bitwise_xor(Tensor(y_np))
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expect = np.bitwise_xor(x_np, y_np)
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assert np.allclose(outputs.asnumpy(), expect)
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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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@pytest.mark.parametrize('xshape', [(2, 3)])
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@pytest.mark.parametrize('yshape', [(1, 1), (1, 3), (2, 1)])
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@pytest.mark.parametrize('dtype', [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64])
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def test_bitwise_and_broadcast(xshape, yshape, dtype):
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"""
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Feature: BitwiseAnd cpu kernel.
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Description: test the rightness of BitwiseAnd cpu kernel broadcast.
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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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op = P.BitwiseAnd()
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op_wrapper = OpNetWrapper(op)
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prop = 100 if np.random.random() > 0.5 else -100
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x_np = (np.random.randn(*xshape) * prop).astype(dtype)
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y_np = (np.random.randn(*yshape) * prop).astype(dtype)
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outputs = op_wrapper(Tensor(x_np), Tensor(y_np))
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outputs_functional = F.bitwise_and(Tensor(x_np), Tensor(y_np))
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expect = np.bitwise_and(x_np, y_np)
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assert np.allclose(outputs.asnumpy(), expect)
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assert np.allclose(outputs_functional.asnumpy(), expect)
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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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@pytest.mark.parametrize('xshape', [(2, 3)])
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@pytest.mark.parametrize('yshape', [(1, 1), (1, 3), (2, 1)])
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@pytest.mark.parametrize('dtype', [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64])
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def test_bitwise_or_broadcast(xshape, yshape, dtype):
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"""
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Feature: BitwiseOr cpu kernel.
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Description: test the rightness of BitwiseOr cpu kernel broadcast.
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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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op = P.BitwiseOr()
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op_wrapper = OpNetWrapper(op)
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prop = 100 if np.random.random() > 0.5 else -100
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x_np = (np.random.randn(*xshape) * prop).astype(dtype)
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y_np = (np.random.randn(*yshape) * prop).astype(dtype)
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outputs = op_wrapper(Tensor(x_np), Tensor(y_np))
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outputs_functional = F.bitwise_or(Tensor(x_np), Tensor(y_np))
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expect = np.bitwise_or(x_np, y_np)
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assert np.allclose(outputs.asnumpy(), expect)
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assert np.allclose(outputs_functional.asnumpy(), expect)
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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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@pytest.mark.parametrize('xshape', [(2, 3)])
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@pytest.mark.parametrize('yshape', [(1, 1), (1, 3), (2, 1)])
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@pytest.mark.parametrize('dtype', [np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64])
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def test_bitwise_xor_broadcast(xshape, yshape, dtype):
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"""
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Feature: BitwiseXor cpu kernel.
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Description: test the rightness of BitwiseXor cpu kernel broadcast.
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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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op = P.BitwiseXor()
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op_wrapper = OpNetWrapper(op)
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prop = 100 if np.random.random() > 0.5 else -100
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x_np = (np.random.randn(*xshape) * prop).astype(dtype)
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y_np = (np.random.randn(*yshape) * prop).astype(dtype)
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outputs = op_wrapper(Tensor(x_np), Tensor(y_np))
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outputs_functional = F.bitwise_xor(Tensor(x_np), Tensor(y_np))
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expect = np.bitwise_xor(x_np, y_np)
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assert np.allclose(outputs.asnumpy(), expect)
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assert np.allclose(outputs_functional.asnumpy(), expect)
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