wehub-resource-sync
942 lines
26 KiB
Python
942 lines
26 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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import unittest
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import numpy as np
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from op_test import OpTest, get_device_place, is_custom_device
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import paddle
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from paddle.base import core
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paddle.enable_static()
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# ----------------- TEST OP: BitwiseAnd ----------------- #
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class TestBitwiseAnd(OpTest):
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def setUp(self):
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self.op_type = "bitwise_and"
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self.python_api = paddle.tensor.bitwise_and
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self.init_dtype()
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self.init_shape()
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self.init_bound()
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x = np.random.randint(
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self.low, self.high, self.x_shape, dtype=self.dtype
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)
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y = np.random.randint(
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self.low, self.high, self.y_shape, dtype=self.dtype
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)
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out = np.bitwise_and(x, y)
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': out}
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def test_check_output(self):
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self.check_output(
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check_cinn=True, check_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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pass
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def init_dtype(self):
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self.dtype = np.int32
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def init_shape(self):
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self.x_shape = [2, 3, 4, 5]
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self.y_shape = [2, 3, 4, 5]
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def init_bound(self):
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self.low = -100
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self.high = 100
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class TestBitwiseAnd_ZeroDim1(TestBitwiseAnd):
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def init_shape(self):
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self.x_shape = []
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self.y_shape = []
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class TestBitwiseAnd_ZeroDim2(TestBitwiseAnd):
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def init_shape(self):
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self.x_shape = [2, 3, 4, 5]
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self.y_shape = []
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class TestBitwiseAnd_ZeroDim3(TestBitwiseAnd):
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def init_shape(self):
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self.x_shape = []
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self.y_shape = [2, 3, 4, 5]
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class TestBitwiseAndUInt8(TestBitwiseAnd):
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def init_dtype(self):
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self.dtype = np.uint8
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def init_bound(self):
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self.low = 0
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self.high = 100
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class TestBitwiseAndInt8(TestBitwiseAnd):
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def init_dtype(self):
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self.dtype = np.int8
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def init_shape(self):
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self.x_shape = [4, 5]
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self.y_shape = [2, 3, 4, 5]
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class TestBitwiseAndInt16(TestBitwiseAnd):
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def init_dtype(self):
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self.dtype = np.int16
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def init_shape(self):
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self.x_shape = [2, 3, 4, 5]
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self.y_shape = [4, 1]
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class TestBitwiseAndInt64(TestBitwiseAnd):
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def init_dtype(self):
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self.dtype = np.int64
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def init_shape(self):
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self.x_shape = [1, 4, 1]
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self.y_shape = [2, 3, 4, 5]
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class TestBitwiseAndBool(TestBitwiseAnd):
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def setUp(self):
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self.op_type = "bitwise_and"
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self.python_api = paddle.tensor.bitwise_and
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self.init_shape()
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x = np.random.choice([True, False], self.x_shape)
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y = np.random.choice([True, False], self.y_shape)
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out = np.bitwise_and(x, y)
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': out}
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestElementwiseBitwiseAndOp_Stride(OpTest):
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no_need_check_grad = True
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def setUp(self):
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self.op_type = "bitwise_and"
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self.python_api = paddle.tensor.bitwise_and
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self.public_python_api = paddle.tensor.bitwise_and
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self.transpose_api = paddle.transpose
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self.as_stride_api = paddle.as_strided
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self.init_dtype()
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self.init_bound()
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self.init_input_output()
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self.inputs_stride = {
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'X': self.x,
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'Y': self.y_trans,
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}
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self.inputs = {
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'X': self.x,
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'Y': self.y,
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}
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self.outputs = {'Out': self.out}
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def init_dtype(self):
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self.dtype = np.int32
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def test_check_output(self):
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place = get_device_place()
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self.check_strided_forward = True
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self.check_output_with_place(
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place,
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)
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.randint(
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self.low, self.high, [13, 17], dtype=self.dtype
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)
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self.y = np.random.randint(
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self.low, self.high, [13, 17], dtype=self.dtype
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)
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self.out = np.bitwise_and(self.x, self.y)
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self.perm = [1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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def init_bound(self):
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self.low = -100
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self.high = 100
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def test_check_grad(self):
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pass
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class TestElementwiseBitwiseAndOp_Stride1(TestElementwiseBitwiseAndOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.randint(
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self.low, self.high, [20, 2, 13, 17], dtype=self.dtype
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)
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self.y = np.random.randint(
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self.low, self.high, [20, 2, 13, 17], dtype=self.dtype
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)
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self.out = np.bitwise_and(self.x, self.y)
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self.perm = [0, 1, 3, 2]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseBitwiseAndOp_Stride2(TestElementwiseBitwiseAndOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.randint(
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self.low, self.high, [20, 2, 13, 17], dtype=self.dtype
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)
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self.y = np.random.randint(
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self.low, self.high, [20, 2, 13, 17], dtype=self.dtype
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)
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self.out = np.bitwise_and(self.x, self.y)
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self.perm = [0, 2, 1, 3]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseBitwiseAndOp_Stride3(TestElementwiseBitwiseAndOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.randint(
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self.low, self.high, [20, 2, 13, 17], dtype=self.dtype
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)
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self.y = np.random.randint(
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self.low, self.high, [20, 2, 13, 1], dtype=self.dtype
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)
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self.out = np.bitwise_and(self.x, self.y)
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self.perm = [0, 1, 3, 2]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseBitwiseAndOp_Stride4(TestElementwiseBitwiseAndOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.randint(
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self.low, self.high, [1, 2, 13, 17], dtype=self.dtype
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)
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self.y = np.random.randint(
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self.low, self.high, [20, 2, 13, 1], dtype=self.dtype
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)
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self.out = np.bitwise_and(self.x, self.y)
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self.perm = [1, 0, 2, 3]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseBitwiseAndOp_Stride5(TestElementwiseBitwiseAndOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "as_stride"
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self.x = np.random.randint(
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self.low, self.high, [23, 10, 1, 17], dtype=self.dtype
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)
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self.y = np.random.randint(
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self.low, self.high, [23, 2, 13, 20], dtype=self.dtype
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)
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self.y_trans = self.y
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self.y = self.y[:, 0:1, :, 0:1]
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self.out = np.bitwise_and(self.x, self.y)
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self.shape_param = [23, 1, 13, 1]
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self.stride_param = [520, 260, 20, 1]
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class TestElementwiseBitwiseAndOp_Stride_ZeroDim1(
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TestElementwiseBitwiseAndOp_Stride
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):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.randint(self.low, self.high, [], dtype=self.dtype)
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self.y = np.random.randint(
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self.low, self.high, [13, 17], dtype=self.dtype
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)
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self.out = np.bitwise_and(self.x, self.y)
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self.perm = [1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseBitwiseAndOp_Stride_ZeroSize1(
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TestElementwiseBitwiseAndOp_Stride
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):
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def init_data(self):
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self.strided_input_type = "transpose"
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self.x = np.random.rand(1, 0, 2).astype('float32')
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self.y = np.random.rand(3, 0, 1).astype('float32')
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self.out = np.bitwise_and(self.x, self.y)
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self.perm = [2, 1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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# ----------------- TEST OP: BitwiseOr ------------------ #
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class TestBitwiseOr(OpTest):
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def setUp(self):
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self.op_type = "bitwise_or"
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self.python_api = paddle.tensor.bitwise_or
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self.init_dtype()
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self.init_shape()
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self.init_bound()
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x = np.random.randint(
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self.low, self.high, self.x_shape, dtype=self.dtype
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)
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y = np.random.randint(
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self.low, self.high, self.y_shape, dtype=self.dtype
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)
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out = np.bitwise_or(x, y)
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': out}
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def test_check_output(self):
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self.check_output(
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check_cinn=True, check_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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pass
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def init_dtype(self):
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self.dtype = np.int32
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def init_shape(self):
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self.x_shape = [2, 3, 4, 5]
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self.y_shape = [2, 3, 4, 5]
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def init_bound(self):
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self.low = -100
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self.high = 100
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class TestBitwiseOr_ZeroDim1(TestBitwiseOr):
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def init_shape(self):
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self.x_shape = []
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self.y_shape = []
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class TestBitwiseOr_ZeroDim2(TestBitwiseOr):
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def init_shape(self):
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self.x_shape = [2, 3, 4, 5]
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self.y_shape = []
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class TestBitwiseOr_ZeroDim3(TestBitwiseOr):
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def init_shape(self):
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self.x_shape = []
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self.y_shape = [2, 3, 4, 5]
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class TestBitwiseOrUInt8(TestBitwiseOr):
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def init_dtype(self):
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self.dtype = np.uint8
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def init_bound(self):
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self.low = 0
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self.high = 100
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class TestBitwiseOrInt8(TestBitwiseOr):
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def init_dtype(self):
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self.dtype = np.int8
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def init_shape(self):
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self.x_shape = [4, 5]
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self.y_shape = [2, 3, 4, 5]
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class TestBitwiseOrInt16(TestBitwiseOr):
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def init_dtype(self):
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self.dtype = np.int16
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def init_shape(self):
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self.x_shape = [2, 3, 4, 5]
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self.y_shape = [4, 1]
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class TestBitwiseOrInt64(TestBitwiseOr):
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def init_dtype(self):
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self.dtype = np.int64
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def init_shape(self):
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self.x_shape = [1, 4, 1]
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self.y_shape = [2, 3, 4, 5]
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class TestBitwiseOrBool(TestBitwiseOr):
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def setUp(self):
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self.op_type = "bitwise_or"
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self.python_api = paddle.tensor.bitwise_or
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self.init_shape()
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x = np.random.choice([True, False], self.x_shape)
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y = np.random.choice([True, False], self.y_shape)
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out = np.bitwise_or(x, y)
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self.inputs = {'X': x, 'Y': y}
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self.outputs = {'Out': out}
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestElementwiseBitwiseOrOp_Stride(OpTest):
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no_need_check_grad = True
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def setUp(self):
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self.op_type = "bitwise_or"
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self.python_api = paddle.tensor.bitwise_or
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self.public_python_api = paddle.tensor.bitwise_or
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self.transpose_api = paddle.transpose
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self.as_stride_api = paddle.as_strided
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self.init_dtype()
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self.init_bound()
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self.init_input_output()
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self.inputs_stride = {
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'X': self.x,
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'Y': self.y_trans,
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}
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self.inputs = {
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'X': self.x,
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'Y': self.y,
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}
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self.outputs = {'Out': self.out}
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def init_dtype(self):
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self.dtype = np.int32
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def test_check_output(self):
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place = get_device_place()
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self.check_strided_forward = True
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self.check_output_with_place(
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place,
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)
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.randint(
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self.low, self.high, [13, 17], dtype=self.dtype
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)
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self.y = np.random.randint(
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self.low, self.high, [13, 17], dtype=self.dtype
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)
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self.out = np.bitwise_or(self.x, self.y)
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self.perm = [1, 0]
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self.y_trans = np.transpose(self.y, self.perm)
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def init_bound(self):
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self.low = -100
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self.high = 100
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def test_check_grad(self):
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pass
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class TestElementwiseBitwiseOrOp_Stride1(TestElementwiseBitwiseOrOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.randint(
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self.low, self.high, [20, 2, 13, 17], dtype=self.dtype
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)
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self.y = np.random.randint(
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self.low, self.high, [20, 2, 13, 17], dtype=self.dtype
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)
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self.out = np.bitwise_or(self.x, self.y)
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self.perm = [0, 1, 3, 2]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseBitwiseOrOp_Stride2(TestElementwiseBitwiseOrOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.randint(
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self.low, self.high, [20, 2, 13, 17], dtype=self.dtype
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)
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self.y = np.random.randint(
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self.low, self.high, [20, 2, 13, 17], dtype=self.dtype
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)
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self.out = np.bitwise_or(self.x, self.y)
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self.perm = [0, 2, 1, 3]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseBitwiseOrOp_Stride3(TestElementwiseBitwiseOrOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.randint(
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self.low, self.high, [20, 2, 13, 17], dtype=self.dtype
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)
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self.y = np.random.randint(
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self.low, self.high, [20, 2, 13, 1], dtype=self.dtype
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)
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self.out = np.bitwise_or(self.x, self.y)
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self.perm = [0, 1, 3, 2]
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self.y_trans = np.transpose(self.y, self.perm)
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class TestElementwiseBitwiseOrOp_Stride4(TestElementwiseBitwiseOrOp_Stride):
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.randint(
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self.low, self.high, [1, 2, 13, 17], dtype=self.dtype
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)
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self.y = np.random.randint(
|
|
self.low, self.high, [20, 2, 13, 1], dtype=self.dtype
|
|
)
|
|
self.out = np.bitwise_or(self.x, self.y)
|
|
self.perm = [1, 0, 2, 3]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseBitwiseOrOp_Stride5(TestElementwiseBitwiseOrOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "as_stride"
|
|
self.x = np.random.randint(
|
|
self.low, self.high, [23, 10, 1, 17], dtype=self.dtype
|
|
)
|
|
self.y = np.random.randint(
|
|
self.low, self.high, [23, 2, 13, 20], dtype=self.dtype
|
|
)
|
|
self.y_trans = self.y
|
|
self.y = self.y[:, 0:1, :, 0:1]
|
|
self.out = np.bitwise_or(self.x, self.y)
|
|
self.shape_param = [23, 1, 13, 1]
|
|
self.stride_param = [520, 260, 20, 1]
|
|
|
|
|
|
class TestElementwiseBitwiseOrOp_Stride_ZeroDim1(
|
|
TestElementwiseBitwiseOrOp_Stride
|
|
):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(self.low, self.high, [], dtype=self.dtype)
|
|
self.y = np.random.randint(
|
|
self.low, self.high, [13, 17], dtype=self.dtype
|
|
)
|
|
self.out = np.bitwise_or(self.x, self.y)
|
|
self.perm = [1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseBitwiseOrOp_Stride_ZeroSize1(
|
|
TestElementwiseBitwiseOrOp_Stride
|
|
):
|
|
def init_data(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.rand(1, 0, 2).astype('float32')
|
|
self.y = np.random.rand(3, 0, 1).astype('float32')
|
|
self.out = np.bitwise_or(self.x, self.y)
|
|
self.perm = [2, 1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
# ----------------- TEST OP: BitwiseXor ---------------- #
|
|
class TestBitwiseXor(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "bitwise_xor"
|
|
self.python_api = paddle.tensor.bitwise_xor
|
|
|
|
self.init_dtype()
|
|
self.init_shape()
|
|
self.init_bound()
|
|
|
|
x = np.random.randint(
|
|
self.low, self.high, self.x_shape, dtype=self.dtype
|
|
)
|
|
y = np.random.randint(
|
|
self.low, self.high, self.y_shape, dtype=self.dtype
|
|
)
|
|
out = np.bitwise_xor(x, y)
|
|
|
|
self.inputs = {'X': x, 'Y': y}
|
|
self.outputs = {'Out': out}
|
|
|
|
def test_check_output(self):
|
|
self.check_output(
|
|
check_cinn=True, check_pir=True, check_symbol_infer=False
|
|
)
|
|
|
|
def test_check_grad(self):
|
|
pass
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.int32
|
|
|
|
def init_shape(self):
|
|
self.x_shape = [2, 3, 4, 5]
|
|
self.y_shape = [2, 3, 4, 5]
|
|
|
|
def init_bound(self):
|
|
self.low = -100
|
|
self.high = 100
|
|
|
|
|
|
class TestBitwiseXor_ZeroDim1(TestBitwiseXor):
|
|
def init_shape(self):
|
|
self.x_shape = []
|
|
self.y_shape = []
|
|
|
|
|
|
class TestBitwiseXor_ZeroDim2(TestBitwiseXor):
|
|
def init_shape(self):
|
|
self.x_shape = [2, 3, 4, 5]
|
|
self.y_shape = []
|
|
|
|
|
|
class TestBitwiseXor_ZeroDim3(TestBitwiseXor):
|
|
def init_shape(self):
|
|
self.x_shape = []
|
|
self.y_shape = [2, 3, 4, 5]
|
|
|
|
|
|
class TestBitwiseXorUInt8(TestBitwiseXor):
|
|
def init_dtype(self):
|
|
self.dtype = np.uint8
|
|
|
|
def init_bound(self):
|
|
self.low = 0
|
|
self.high = 100
|
|
|
|
|
|
class TestBitwiseXorInt8(TestBitwiseXor):
|
|
def init_dtype(self):
|
|
self.dtype = np.int8
|
|
|
|
def init_shape(self):
|
|
self.x_shape = [4, 5]
|
|
self.y_shape = [2, 3, 4, 5]
|
|
|
|
|
|
class TestBitwiseXorInt16(TestBitwiseXor):
|
|
def init_dtype(self):
|
|
self.dtype = np.int16
|
|
|
|
def init_shape(self):
|
|
self.x_shape = [2, 3, 4, 5]
|
|
self.y_shape = [4, 1]
|
|
|
|
|
|
class TestBitwiseXorInt64(TestBitwiseXor):
|
|
def init_dtype(self):
|
|
self.dtype = np.int64
|
|
|
|
def init_shape(self):
|
|
self.x_shape = [1, 4, 1]
|
|
self.y_shape = [2, 3, 4, 5]
|
|
|
|
|
|
class TestBitwiseXorBool(TestBitwiseXor):
|
|
def setUp(self):
|
|
self.op_type = "bitwise_xor"
|
|
self.python_api = paddle.tensor.bitwise_xor
|
|
|
|
self.init_shape()
|
|
|
|
x = np.random.choice([True, False], self.x_shape)
|
|
y = np.random.choice([True, False], self.y_shape)
|
|
out = np.bitwise_xor(x, y)
|
|
|
|
self.inputs = {'X': x, 'Y': y}
|
|
self.outputs = {'Out': out}
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestElementwiseBitwiseXorOp_Stride(OpTest):
|
|
no_need_check_grad = True
|
|
|
|
def setUp(self):
|
|
self.op_type = "bitwise_xor"
|
|
self.python_api = paddle.tensor.bitwise_xor
|
|
self.public_python_api = paddle.tensor.bitwise_xor
|
|
self.transpose_api = paddle.transpose
|
|
self.as_stride_api = paddle.as_strided
|
|
self.init_dtype()
|
|
self.init_bound()
|
|
self.init_input_output()
|
|
|
|
self.inputs_stride = {
|
|
'X': self.x,
|
|
'Y': self.y_trans,
|
|
}
|
|
|
|
self.inputs = {
|
|
'X': self.x,
|
|
'Y': self.y,
|
|
}
|
|
|
|
self.outputs = {'Out': self.out}
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.int32
|
|
|
|
def test_check_output(self):
|
|
place = get_device_place()
|
|
self.check_strided_forward = True
|
|
self.check_output_with_place(
|
|
place,
|
|
)
|
|
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(
|
|
self.low, self.high, [13, 17], dtype=self.dtype
|
|
)
|
|
self.y = np.random.randint(
|
|
self.low, self.high, [13, 17], dtype=self.dtype
|
|
)
|
|
self.out = np.bitwise_xor(self.x, self.y)
|
|
self.perm = [1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
def init_bound(self):
|
|
self.low = -100
|
|
self.high = 100
|
|
|
|
def test_check_grad(self):
|
|
pass
|
|
|
|
|
|
class TestElementwiseBitwiseXorOp_Stride1(TestElementwiseBitwiseXorOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(
|
|
self.low, self.high, [20, 2, 13, 17], dtype=self.dtype
|
|
)
|
|
self.y = np.random.randint(
|
|
self.low, self.high, [20, 2, 13, 17], dtype=self.dtype
|
|
)
|
|
self.out = np.bitwise_xor(self.x, self.y)
|
|
self.perm = [0, 1, 3, 2]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseBitwiseXorOp_Stride2(TestElementwiseBitwiseXorOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(
|
|
self.low, self.high, [20, 2, 13, 17], dtype=self.dtype
|
|
)
|
|
self.y = np.random.randint(
|
|
self.low, self.high, [20, 2, 13, 17], dtype=self.dtype
|
|
)
|
|
self.out = np.bitwise_xor(self.x, self.y)
|
|
self.perm = [0, 2, 1, 3]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseBitwiseXorOp_Stride3(TestElementwiseBitwiseXorOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(
|
|
self.low, self.high, [20, 2, 13, 17], dtype=self.dtype
|
|
)
|
|
self.y = np.random.randint(
|
|
self.low, self.high, [20, 2, 13, 1], dtype=self.dtype
|
|
)
|
|
self.out = np.bitwise_xor(self.x, self.y)
|
|
self.perm = [0, 1, 3, 2]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseBitwiseXorOp_Stride4(TestElementwiseBitwiseXorOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(
|
|
self.low, self.high, [1, 2, 13, 17], dtype=self.dtype
|
|
)
|
|
self.y = np.random.randint(
|
|
self.low, self.high, [20, 2, 13, 1], dtype=self.dtype
|
|
)
|
|
self.out = np.bitwise_xor(self.x, self.y)
|
|
self.perm = [1, 0, 2, 3]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseBitwiseXorOp_Stride5(TestElementwiseBitwiseXorOp_Stride):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "as_stride"
|
|
self.x = np.random.randint(
|
|
self.low, self.high, [23, 10, 1, 17], dtype=self.dtype
|
|
)
|
|
self.y = np.random.randint(
|
|
self.low, self.high, [23, 2, 13, 20], dtype=self.dtype
|
|
)
|
|
self.y_trans = self.y
|
|
self.y = self.y[:, 0:1, :, 0:1]
|
|
self.out = np.bitwise_xor(self.x, self.y)
|
|
self.shape_param = [23, 1, 13, 1]
|
|
self.stride_param = [520, 260, 20, 1]
|
|
|
|
|
|
class TestElementwiseBitwiseXorOp_Stride_ZeroDim1(
|
|
TestElementwiseBitwiseXorOp_Stride
|
|
):
|
|
def init_input_output(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(self.low, self.high, [], dtype=self.dtype)
|
|
self.y = np.random.randint(
|
|
self.low, self.high, [13, 17], dtype=self.dtype
|
|
)
|
|
self.out = np.bitwise_xor(self.x, self.y)
|
|
self.perm = [1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestElementwiseBitwiseXorOp_Stride_ZeroSize1(
|
|
TestElementwiseBitwiseXorOp_Stride
|
|
):
|
|
def init_data(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.rand(1, 0, 2).astype('float32')
|
|
self.y = np.random.rand(3, 0, 1).astype('float32')
|
|
self.out = np.bitwise_xor(self.x, self.y)
|
|
self.perm = [2, 1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
# --------------- TEST OP: BitwiseNot ----------------- #
|
|
class TestBitwiseNot(OpTest):
|
|
def setUp(self):
|
|
self.op_type = "bitwise_not"
|
|
self.python_api = paddle.tensor.bitwise_not
|
|
|
|
self.init_dtype()
|
|
self.init_shape()
|
|
self.init_bound()
|
|
|
|
x = np.random.randint(
|
|
self.low, self.high, self.x_shape, dtype=self.dtype
|
|
)
|
|
out = np.bitwise_not(x)
|
|
|
|
self.inputs = {'X': x}
|
|
self.outputs = {'Out': out}
|
|
|
|
def test_check_output(self):
|
|
self.check_output(
|
|
check_cinn=True, check_pir=True, check_symbol_infer=False
|
|
)
|
|
|
|
def test_check_grad(self):
|
|
pass
|
|
|
|
def init_dtype(self):
|
|
self.dtype = np.int32
|
|
|
|
def init_shape(self):
|
|
self.x_shape = [2, 3, 4, 5]
|
|
|
|
def init_bound(self):
|
|
self.low = -100
|
|
self.high = 100
|
|
|
|
|
|
class TestBitwiseNot_ZeroDim(TestBitwiseNot):
|
|
def init_shape(self):
|
|
self.x_shape = []
|
|
|
|
|
|
class TestBitwiseNot_ZeroSize(TestBitwiseNot):
|
|
def init_shape(self):
|
|
self.x_shape = [0, 3, 4, 5]
|
|
|
|
|
|
class TestBitwiseNotUInt8(TestBitwiseNot):
|
|
def init_dtype(self):
|
|
self.dtype = np.uint8
|
|
|
|
def init_bound(self):
|
|
self.low = 0
|
|
self.high = 100
|
|
|
|
|
|
class TestBitwiseNotInt8(TestBitwiseNot):
|
|
def init_dtype(self):
|
|
self.dtype = np.int8
|
|
|
|
def init_shape(self):
|
|
self.x_shape = [4, 5]
|
|
|
|
|
|
class TestBitwiseNotInt16(TestBitwiseNot):
|
|
def init_dtype(self):
|
|
self.dtype = np.int16
|
|
|
|
def init_shape(self):
|
|
self.x_shape = [2, 3, 4, 5]
|
|
|
|
|
|
class TestBitwiseNotInt64(TestBitwiseNot):
|
|
def init_dtype(self):
|
|
self.dtype = np.int64
|
|
|
|
def init_shape(self):
|
|
self.x_shape = [1, 4, 1]
|
|
|
|
|
|
class TestBitwiseNotBool(TestBitwiseNot):
|
|
def setUp(self):
|
|
self.op_type = "bitwise_not"
|
|
self.python_api = paddle.tensor.bitwise_not
|
|
self.init_shape()
|
|
|
|
x = np.random.choice([True, False], self.x_shape)
|
|
out = np.bitwise_not(x)
|
|
|
|
self.inputs = {'X': x}
|
|
self.outputs = {'Out': out}
|
|
|
|
|
|
class TestBitwiseInvertApi(unittest.TestCase):
|
|
def setUp(self):
|
|
paddle.disable_static()
|
|
|
|
self.dtype = np.int32
|
|
self.shape = [2, 3, 4, 5]
|
|
self.low = -100
|
|
self.high = 100
|
|
x = np.random.randint(self.low, self.high, self.shape, dtype=self.dtype)
|
|
self.x = paddle.to_tensor(x)
|
|
self.expected_out = np.bitwise_not(x)
|
|
|
|
def test_bitwise_invert_out_of_place(self):
|
|
result = paddle.bitwise_invert(self.x)
|
|
np.testing.assert_array_equal(result.numpy(), self.expected_out)
|
|
|
|
def test_bitwise_invert_in_place(self):
|
|
x_copy = self.x.clone()
|
|
x_copy.bitwise_invert_()
|
|
np.testing.assert_array_equal(x_copy.numpy(), self.expected_out)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|