wehub-resource-sync
819 lines
30 KiB
Python
819 lines
30 KiB
Python
# Copyright (c) 2023 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 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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_SIGNED_TO_UNSIGNED_TABLE = {
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"int8": "uint8",
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"int16": "uint16",
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"int32": "uint32",
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"int64": "uint64",
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}
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_UNSIGNED_TO_SIGNED_TABLE = {
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"uint8": "int8",
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"uint16": "int16",
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"uint32": "int32",
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"uint64": "int64",
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}
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_UNSIGNED_LIST = ['uint8', 'uint16', 'uint32', 'uint64']
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def ref_left_shift_arithmetic(x, y):
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out = np.left_shift(x, y)
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return out
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def ref_left_shift_logical(x, y):
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out = np.left_shift(x, y)
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return out
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def ref_right_shift_arithmetic(x, y):
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return np.right_shift(x, y)
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def ref_right_shift_logical(x, y):
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if str(x.dtype) in _UNSIGNED_LIST:
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return np.right_shift(x, y)
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else:
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orig_dtype = x.dtype
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unsigned_dtype = _SIGNED_TO_UNSIGNED_TABLE[str(orig_dtype)]
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x = x.astype(unsigned_dtype)
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y = y.astype(unsigned_dtype)
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res = np.right_shift(x, y)
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return res.astype(orig_dtype)
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class TestBitwiseLeftShiftAPI(unittest.TestCase):
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def setUp(self):
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self.init_input()
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self.place = get_device_place()
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def init_input(self):
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self.x = np.random.randint(0, 256, [200, 300]).astype('uint8')
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self.y = np.random.randint(0, 256, [200, 300]).astype('uint8')
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def test_static_api_arithmetic(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('x', self.x.shape, dtype=self.x.dtype)
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y = paddle.static.data('y', self.y.shape, dtype=self.y.dtype)
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out = paddle.bitwise_left_shift(
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x,
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y,
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)
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out_ = x << y
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exe = paddle.static.Executor(self.place)
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res = exe.run(feed={'x': self.x, 'y': self.y}, fetch_list=[out])
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res_ = exe.run(feed={'x': self.x, 'y': self.y}, fetch_list=[out_])
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out_ref = ref_left_shift_arithmetic(self.x, self.y)
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np.testing.assert_allclose(out_ref, res[0])
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np.testing.assert_allclose(out_ref, res_[0])
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def test_dygraph_api_arithmetic(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.x)
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y = paddle.to_tensor(self.y)
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out = paddle.bitwise_left_shift(
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x,
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y,
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)
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out_ = x << y
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out_ref = ref_left_shift_arithmetic(self.x, self.y)
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np.testing.assert_allclose(out_ref, out.numpy())
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np.testing.assert_allclose(out_ref, out_.numpy())
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paddle.enable_static()
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def test_static_api_logical(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('x', self.x.shape, dtype=self.x.dtype)
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y = paddle.static.data('y', self.y.shape, dtype=self.y.dtype)
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out = paddle.bitwise_left_shift(x, y, False)
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out_ = x.__lshift__(y, False)
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exe = paddle.static.Executor(self.place)
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res = exe.run(feed={'x': self.x, 'y': self.y}, fetch_list=[out])
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res_ = exe.run(feed={'x': self.x, 'y': self.y}, fetch_list=[out_])
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out_ref = ref_left_shift_logical(self.x, self.y)
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np.testing.assert_allclose(out_ref, res[0])
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np.testing.assert_allclose(out_ref, res_[0])
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def test_dygraph_api_logical(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.x)
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y = paddle.to_tensor(self.y)
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out = paddle.bitwise_left_shift(x, y, False)
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out_ = x.__lshift__(y, False)
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out_ref = ref_left_shift_logical(self.x, self.y)
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np.testing.assert_allclose(out_ref, out.numpy())
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np.testing.assert_allclose(out_ref, out_.numpy())
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paddle.enable_static()
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class TestBitwiseLeftShiftAPI_UINT8(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.random.randint(0, 256, [200, 300]).astype('uint8')
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self.y = np.random.randint(0, 256, [200, 300]).astype('uint8')
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class TestBitwiseLeftShiftAPI_UINT8_broadcast1(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.random.randint(0, 256, [200, 300]).astype('uint8')
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self.y = np.random.randint(0, 256, [300]).astype('uint8')
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class TestBitwiseLeftShiftAPI_UINT8_broadcast2(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.random.randint(0, 256, [300]).astype('uint8')
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self.y = np.random.randint(0, 256, [200, 300]).astype('uint8')
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class TestBitwiseLeftShiftAPI_INT8(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8')
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self.y = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8')
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class TestBitwiseLeftShiftAPI_INT8_broadcast1(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8')
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self.y = np.random.randint(-(2**7), 2**7, [300]).astype('int8')
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class TestBitwiseLeftShiftAPI_INT8_broadcast2(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**7), 2**7, [300]).astype('int8')
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self.y = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8')
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class TestBitwiseLeftShiftAPI_INT16(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**15), 2**15, [200, 300]).astype('int16')
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self.y = np.random.randint(-(2**15), 2**15, [200, 300]).astype('int16')
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class TestBitwiseLeftShiftAPI_INT16_broadcast1(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**15), 2**15, [200, 300]).astype('int16')
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self.y = np.random.randint(-(2**15), 2**15, [300]).astype('int16')
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class TestBitwiseLeftShiftAPI_INT16_broadcast2(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**15), 2**15, [300]).astype('int16')
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self.y = np.random.randint(-(2**15), 2**15, [200, 300]).astype('int16')
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class TestBitwiseLeftShiftAPI_INT32(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**31), 2**31, [200, 300]).astype('int32')
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self.y = np.random.randint(-(2**31), 2**31, [200, 300]).astype('int32')
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class TestBitwiseLeftShiftAPI_INT32_broadcast1(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**31), 2**31, [200, 300]).astype('int32')
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self.y = np.random.randint(-(2**31), 2**31, [300]).astype('int32')
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class TestBitwiseLeftShiftAPI_INT32_broadcast2(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**31), 2**31, [300]).astype('int32')
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self.y = np.random.randint(-(2**31), 2**31, [200, 300]).astype('int32')
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class TestBitwiseLeftShiftAPI_INT64(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**63), 2**63, [200, 300], dtype=np.int64)
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self.y = np.random.randint(-(2**63), 2**63, [200, 300], dtype=np.int64)
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class TestBitwiseLeftShiftAPI_INT64_broadcast1(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**63), 2**63, [200, 300], dtype=np.int64)
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self.y = np.random.randint(-(2**63), 2**63, [300], dtype=np.int64)
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class TestBitwiseLeftShiftAPI_INT64_broadcast2(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**63), 2**63, [300], dtype=np.int64)
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self.y = np.random.randint(-(2**63), 2**63, [200, 300], dtype=np.int64)
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class TestBitwiseLeftShiftAPI_special_case1(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.array([0b11111111], dtype='int16')
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self.y = np.array([1], dtype='int16')
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class TestBitwiseLeftShiftAPI_special_case2(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.array([0b11111111], dtype='int16')
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self.y = np.array([10], dtype='int16')
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class TestBitwiseLeftShiftAPI_special_case3(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.array([0b11111111], dtype='uint8')
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self.y = np.array([1], dtype='uint8')
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class TestBitwiseLeftShiftAPI_special_case4(TestBitwiseLeftShiftAPI):
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def init_input(self):
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self.x = np.array([0b11111111], dtype='uint8')
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self.y = np.array([10], dtype='uint8')
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class TestTensorRlshiftAPI(unittest.TestCase):
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def setUp(self):
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self.init_input()
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self.place = get_device_place()
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def init_input(self):
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self.x = np.random.randint(-255, 256)
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self.y = np.random.randint(0, 256, [200, 300]).astype('int32')
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def test_dygraph_tensor_rlshift(self):
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paddle.disable_static()
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x = self.x
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y = paddle.to_tensor(self.y, dtype=self.y.dtype)
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out = x << y
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expected_out = x << y.numpy()
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np.testing.assert_allclose(out.numpy(), expected_out)
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paddle.enable_static()
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def test_static_rlshift(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = self.x
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y = paddle.static.data('y', self.y.shape, dtype=self.y.dtype)
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out = x << y
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exe = paddle.static.Executor(self.place)
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res = exe.run(
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feed={'x': self.x, 'y': self.y},
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fetch_list=[out],
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)
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out_ref = ref_left_shift_arithmetic(self.x, self.y)
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np.testing.assert_allclose(out_ref, res[0])
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class TestTensorRlshiftAPI_UINT8(TestTensorRlshiftAPI):
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def init_input(self):
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self.x = np.random.randint(0, 64)
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self.y = np.random.randint(0, 64, [200, 300]).astype('uint8')
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class TestTensorRlshiftAPI_INT8(TestTensorRlshiftAPI):
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def init_input(self):
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self.x = np.random.randint(-64, 64)
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self.y = np.random.randint(0, 64, [200, 300]).astype('int8')
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class TestTensorRlshiftAPI_INT16(TestTensorRlshiftAPI):
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def init_input(self):
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self.x = np.random.randint(-256, 256)
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self.y = np.random.randint(0, 256, [200, 300]).astype('int16')
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class TestTensorRlshiftAPI_INT64(TestTensorRlshiftAPI):
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def init_input(self):
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self.x = np.random.randint(-255, 256)
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self.y = np.random.randint(0, 256, [200, 300]).astype('int64')
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class TestBitwiseRightShiftAPI(unittest.TestCase):
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def setUp(self):
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self.init_input()
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self.place = get_device_place()
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def init_input(self):
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self.x = np.random.randint(0, 256, [200, 300]).astype('uint8')
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self.y = np.random.randint(0, 256, [200, 300]).astype('uint8')
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def test_static_api_arithmetic(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('x', self.x.shape, dtype=self.x.dtype)
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y = paddle.static.data('y', self.y.shape, dtype=self.y.dtype)
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out = paddle.bitwise_right_shift(
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x,
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y,
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)
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out_ = x >> y
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exe = paddle.static.Executor(self.place)
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res = exe.run(feed={'x': self.x, 'y': self.y}, fetch_list=[out])
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res_ = exe.run(feed={'x': self.x, 'y': self.y}, fetch_list=[out_])
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out_ref = ref_right_shift_arithmetic(self.x, self.y)
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np.testing.assert_allclose(out_ref, res[0])
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np.testing.assert_allclose(out_ref, res_[0])
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def test_dygraph_api_arithmetic(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.x)
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y = paddle.to_tensor(self.y)
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out = paddle.bitwise_right_shift(
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x,
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y,
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)
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out_ = x >> y
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out_ref = ref_right_shift_arithmetic(self.x, self.y)
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np.testing.assert_allclose(out_ref, out.numpy())
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np.testing.assert_allclose(out_ref, out_.numpy())
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paddle.enable_static()
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def test_static_api_logical(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('x', self.x.shape, dtype=self.x.dtype)
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y = paddle.static.data('y', self.y.shape, dtype=self.y.dtype)
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out = paddle.bitwise_right_shift(x, y, False)
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out_ = x.__rshift__(y, False)
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exe = paddle.static.Executor(self.place)
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res = exe.run(feed={'x': self.x, 'y': self.y}, fetch_list=[out])
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res_ = exe.run(feed={'x': self.x, 'y': self.y}, fetch_list=[out_])
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out_ref = ref_right_shift_logical(self.x, self.y)
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np.testing.assert_allclose(out_ref, res[0])
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np.testing.assert_allclose(out_ref, res_[0])
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def test_dygraph_api_logical(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.x)
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y = paddle.to_tensor(self.y)
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out = paddle.bitwise_right_shift(x, y, False)
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out_ = x.__rshift__(y, False)
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out_ref = ref_right_shift_logical(self.x, self.y)
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np.testing.assert_allclose(out_ref, out.numpy())
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np.testing.assert_allclose(out_ref, out_.numpy())
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paddle.enable_static()
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class TestBitwiseRightShiftAPI_UINT8(TestBitwiseRightShiftAPI):
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def init_input(self):
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self.x = np.random.randint(0, 256, [200, 300]).astype('uint8')
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self.y = np.random.randint(0, 256, [200, 300]).astype('uint8')
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class TestBitwiseRightShiftAPI_UINT8_broadcast1(TestBitwiseRightShiftAPI):
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def init_input(self):
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self.x = np.random.randint(0, 256, [200, 300]).astype('uint8')
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self.y = np.random.randint(0, 256, [300]).astype('uint8')
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class TestBitwiseRightShiftAPI_UINT8_broadcast2(TestBitwiseRightShiftAPI):
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def init_input(self):
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self.x = np.random.randint(0, 256, [300]).astype('uint8')
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self.y = np.random.randint(0, 256, [200, 300]).astype('uint8')
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class TestBitwiseRightShiftAPI_INT8(TestBitwiseRightShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8')
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self.y = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8')
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class TestBitwiseRightShiftAPI_INT8_broadcast1(TestBitwiseRightShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8')
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self.y = np.random.randint(-(2**7), 2**7, [300]).astype('int8')
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class TestBitwiseRightShiftAPI_INT8_broadcast2(TestBitwiseRightShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**7), 2**7, [300]).astype('int8')
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self.y = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8')
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class TestBitwiseRightShiftAPI_INT16(TestBitwiseRightShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**15), 2**15, [200, 300]).astype('int16')
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self.y = np.random.randint(-(2**15), 2**15, [200, 300]).astype('int16')
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class TestBitwiseRightShiftAPI_INT16_broadcast1(TestBitwiseRightShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**15), 2**15, [200, 300]).astype('int16')
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self.y = np.random.randint(-(2**15), 2**15, [300]).astype('int16')
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class TestBitwiseRightShiftAPI_INT16_broadcast2(TestBitwiseRightShiftAPI):
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def init_input(self):
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self.x = np.random.randint(-(2**15), 2**15, [300]).astype('int16')
|
|
self.y = np.random.randint(-(2**15), 2**15, [200, 300]).astype('int16')
|
|
|
|
|
|
class TestBitwiseRightShiftAPI_INT32(TestBitwiseRightShiftAPI):
|
|
def init_input(self):
|
|
self.x = np.random.randint(-(2**31), 2**31, [200, 300]).astype('int32')
|
|
self.y = np.random.randint(-(2**31), 2**31, [200, 300]).astype('int32')
|
|
|
|
|
|
class TestBitwiseRightShiftAPI_INT32_broadcast1(TestBitwiseRightShiftAPI):
|
|
def init_input(self):
|
|
self.x = np.random.randint(-(2**31), 2**31, [200, 300]).astype('int32')
|
|
self.y = np.random.randint(-(2**31), 2**31, [300]).astype('int32')
|
|
|
|
|
|
class TestBitwiseRightShiftAPI_INT32_broadcast2(TestBitwiseRightShiftAPI):
|
|
def init_input(self):
|
|
self.x = np.random.randint(-(2**31), 2**31, [300]).astype('int32')
|
|
self.y = np.random.randint(-(2**31), 2**31, [200, 300]).astype('int32')
|
|
|
|
|
|
class TestBitwiseRightShiftAPI_INT64(TestBitwiseRightShiftAPI):
|
|
def init_input(self):
|
|
self.x = np.random.randint(-(2**63), 2**63, [200, 300], dtype=np.int64)
|
|
self.y = np.random.randint(-(2**63), 2**63, [200, 300], dtype=np.int64)
|
|
|
|
|
|
class TestBitwiseRightShiftAPI_INT64_broadcast1(TestBitwiseRightShiftAPI):
|
|
def init_input(self):
|
|
self.x = np.random.randint(-(2**63), 2**63, [200, 300], dtype=np.int64)
|
|
self.y = np.random.randint(-(2**63), 2**63, [300], dtype=np.int64)
|
|
|
|
|
|
class TestBitwiseRightShiftAPI_INT64_broadcast2(TestBitwiseRightShiftAPI):
|
|
def init_input(self):
|
|
self.x = np.random.randint(-(2**63), 2**63, [300], dtype=np.int64)
|
|
self.y = np.random.randint(-(2**63), 2**63, [200, 300], dtype=np.int64)
|
|
|
|
|
|
class TestBitwiseRightShiftAPI_special_case1(TestBitwiseRightShiftAPI):
|
|
def init_input(self):
|
|
self.x = np.array([0b11111111]).astype('int8')
|
|
self.y = np.array([1]).astype('int8')
|
|
|
|
|
|
class TestBitwiseRightShiftAPI_special_case2(TestBitwiseRightShiftAPI):
|
|
def init_input(self):
|
|
self.x = np.array([0b11111111]).astype('int8')
|
|
self.y = np.array([10]).astype('int8')
|
|
|
|
|
|
class TestBitwiseRightShiftAPI_special_case3(TestBitwiseRightShiftAPI):
|
|
def init_input(self):
|
|
self.x = np.array([0b11111111], dtype='uint8')
|
|
self.y = np.array([1], dtype='uint8')
|
|
|
|
|
|
class TestBitwiseRightShiftAPI_special_case4(TestBitwiseRightShiftAPI):
|
|
def init_input(self):
|
|
self.x = np.array([0b11111111], dtype='uint8')
|
|
self.y = np.array([10], dtype='uint8')
|
|
|
|
|
|
class TestTensorRrshiftAPI(unittest.TestCase):
|
|
def setUp(self):
|
|
self.init_input()
|
|
self.place = get_device_place()
|
|
|
|
def init_input(self):
|
|
self.x = np.random.randint(-255, 256)
|
|
self.y = np.random.randint(0, 256, [200, 300]).astype('int32')
|
|
|
|
def test_dygraph_tensor_rrshift(self):
|
|
paddle.disable_static()
|
|
x = self.x
|
|
y = paddle.to_tensor(self.y, dtype=self.y.dtype)
|
|
out = x >> y
|
|
expected_out = x >> y.numpy()
|
|
np.testing.assert_allclose(out.numpy(), expected_out)
|
|
paddle.enable_static()
|
|
|
|
def test_static_rrshift(self):
|
|
paddle.enable_static()
|
|
with paddle.static.program_guard(paddle.static.Program()):
|
|
x = self.x
|
|
y = paddle.static.data('y', self.y.shape, dtype=self.y.dtype)
|
|
out = x >> y
|
|
exe = paddle.static.Executor(self.place)
|
|
res = exe.run(
|
|
feed={'x': self.x, 'y': self.y},
|
|
fetch_list=[out],
|
|
)
|
|
out_ref = ref_right_shift_arithmetic(self.x, self.y)
|
|
np.testing.assert_allclose(out_ref, res[0])
|
|
|
|
|
|
class TestTensorRrshiftAPI_UINT8(TestTensorRrshiftAPI):
|
|
def init_input(self):
|
|
self.x = np.random.randint(0, 64)
|
|
self.y = np.random.randint(0, 64, [200, 300]).astype('uint8')
|
|
|
|
|
|
class TestTensorRrshiftAPI_INT8(TestTensorRrshiftAPI):
|
|
def init_input(self):
|
|
self.x = np.random.randint(-64, 64)
|
|
self.y = np.random.randint(0, 64, [200, 300]).astype('int8')
|
|
|
|
|
|
class TestTensorRrshiftAPI_INT16(TestTensorRrshiftAPI):
|
|
def init_input(self):
|
|
self.x = np.random.randint(-256, 256)
|
|
self.y = np.random.randint(0, 256, [200, 300]).astype('int16')
|
|
|
|
|
|
class TestTensorRrshiftAPI_INT64(TestTensorRrshiftAPI):
|
|
def init_input(self):
|
|
self.x = np.random.randint(-255, 256)
|
|
self.y = np.random.randint(0, 256, [200, 300]).astype('int64')
|
|
|
|
|
|
class TestTensorShiftAPI_FLOAT(unittest.TestCase):
|
|
def setup(self):
|
|
paddle.disable_static()
|
|
self.place = get_device_place()
|
|
|
|
def test_lshift_float(self):
|
|
x = paddle.to_tensor(np.random.randint(-255, 256, [200, 300]))
|
|
y = np.random.uniform(0, 256)
|
|
with self.assertRaises(TypeError):
|
|
x.__lshift__(y)
|
|
|
|
def test_rshift_float(self):
|
|
x = paddle.to_tensor(np.random.randint(-255, 256, [200, 300]))
|
|
y = np.random.uniform(0, 256)
|
|
with self.assertRaises(TypeError):
|
|
x.__rshift__(y)
|
|
|
|
def test_rlshift_float(self):
|
|
x = np.random.uniform(0, 256)
|
|
y = paddle.to_tensor(np.random.randint(-255, 256, [200, 300]))
|
|
with self.assertRaises(TypeError):
|
|
y.__rlshift__(x)
|
|
|
|
def test_rrshift_float(self):
|
|
x = np.random.uniform(0, 256)
|
|
y = paddle.to_tensor(np.random.randint(-255, 256, [200, 300]))
|
|
with self.assertRaises(TypeError):
|
|
y.__rrshift__(x)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestBitwiseRightShiftOp_Stride(unittest.TestCase):
|
|
def setUp(self):
|
|
self.init_input()
|
|
self.place = get_device_place()
|
|
|
|
def init_input(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(0, 256, [200, 300]).astype('uint8')
|
|
self.y = np.random.randint(0, 256, [200, 300]).astype('uint8')
|
|
self.perm = [1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
def test_dygraph_api_arithmetic(self):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(self.x)
|
|
y_trans = paddle.to_tensor(self.y_trans)
|
|
if self.strided_input_type == "transpose":
|
|
y_non_conti = paddle.transpose(y_trans, self.perm)
|
|
elif self.strided_input_type == "as_stride":
|
|
y_non_conti = paddle.as_strided(
|
|
y_trans, self.shape_param, self.stride_param
|
|
)
|
|
else:
|
|
raise TypeError(f"Unsupported test type {self.strided_input_type}.")
|
|
out = paddle.bitwise_right_shift(
|
|
x,
|
|
y_non_conti,
|
|
)
|
|
out_ = x >> y_non_conti
|
|
out_ref = ref_right_shift_arithmetic(self.x, self.y)
|
|
np.testing.assert_allclose(out_ref, out.numpy())
|
|
np.testing.assert_allclose(out_ref, out_.numpy())
|
|
paddle.enable_static()
|
|
|
|
def test_dygraph_api_logical(self):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(self.x)
|
|
y_trans = paddle.to_tensor(self.y_trans)
|
|
if self.strided_input_type == "transpose":
|
|
y_non_conti = paddle.transpose(y_trans, self.perm)
|
|
elif self.strided_input_type == "as_stride":
|
|
y_non_conti = paddle.as_strided(
|
|
y_trans, self.shape_param, self.stride_param
|
|
)
|
|
else:
|
|
raise TypeError(f"Unsupported test type {self.strided_input_type}.")
|
|
out = paddle.bitwise_right_shift(x, y_non_conti, False)
|
|
out_ = x.__rshift__(y_non_conti, False)
|
|
out_ref = ref_right_shift_logical(self.x, self.y)
|
|
np.testing.assert_allclose(out_ref, out.numpy())
|
|
np.testing.assert_allclose(out_ref, out_.numpy())
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestBitwiseRightShiftOp_Stride1(TestBitwiseRightShiftOp_Stride):
|
|
def init_input(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(0, 256, [20, 2, 13, 17]).astype('uint8')
|
|
self.y = np.random.randint(0, 256, [20, 2, 13, 17]).astype('uint8')
|
|
self.perm = [0, 1, 3, 2]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestBitwiseRightShiftOp_Stride2(TestBitwiseRightShiftOp_Stride):
|
|
def init_input(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(0, 256, [20, 2, 13, 17]).astype('uint8')
|
|
self.y = np.random.randint(0, 256, [20, 2, 13, 17]).astype('uint8')
|
|
self.perm = [0, 2, 1, 3]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestBitwiseRightShiftOp_Stride3(TestBitwiseRightShiftOp_Stride):
|
|
def init_input(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(0, 256, [20, 2, 13, 17]).astype('uint8')
|
|
self.y = np.random.randint(0, 256, [20, 2, 13, 1]).astype('uint8')
|
|
self.perm = [0, 1, 3, 2]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestBitwiseRightShiftOp_Stride4(TestBitwiseRightShiftOp_Stride):
|
|
def init_input(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(0, 256, [1, 2, 13, 17]).astype('uint8')
|
|
self.y = np.random.randint(0, 256, [20, 2, 13, 1]).astype('uint8')
|
|
self.perm = [1, 0, 2, 3]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestBitwiseRightShiftOp_Stride5(TestBitwiseRightShiftOp_Stride):
|
|
def init_input(self):
|
|
self.strided_input_type = "as_stride"
|
|
self.x = np.random.randint(0, 256, [23, 10, 1, 17]).astype('uint8')
|
|
self.y = np.random.randint(0, 256, [23, 2, 13, 20]).astype('uint8')
|
|
self.y_trans = self.y
|
|
self.y = self.y[:, 0:1, :, 0:1]
|
|
self.shape_param = [23, 1, 13, 1]
|
|
self.stride_param = [520, 260, 20, 1]
|
|
|
|
|
|
class TestBitwiseRightShiftOp_Stride_ZeroDim1(TestBitwiseRightShiftOp_Stride):
|
|
def init_input(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(0, 256, []).astype('uint8')
|
|
self.y = np.random.randint(0, 256, [13, 17]).astype('uint8')
|
|
self.perm = [1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestBitwiseRightShiftOp_Stride_ZeroSize1(TestBitwiseRightShiftOp_Stride):
|
|
def init_data(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.rand(1, 0, 2).astype('uint8')
|
|
self.y = np.random.rand(3, 0, 1).astype('uint8')
|
|
self.perm = [2, 1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
@unittest.skipIf(
|
|
not (core.is_compiled_with_cuda() or is_custom_device()),
|
|
"core is not compiled with CUDA",
|
|
)
|
|
class TestBitwiseLeftShiftOp_Stride(unittest.TestCase):
|
|
def setUp(self):
|
|
self.init_input()
|
|
self.place = get_device_place()
|
|
|
|
def init_input(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(0, 256, [200, 300]).astype('uint8')
|
|
self.y = np.random.randint(0, 256, [200, 300]).astype('uint8')
|
|
self.perm = [1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
def test_dygraph_api_arithmetic(self):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(self.x)
|
|
y_trans = paddle.to_tensor(self.y_trans)
|
|
if self.strided_input_type == "transpose":
|
|
y_non_conti = paddle.transpose(y_trans, self.perm)
|
|
elif self.strided_input_type == "as_stride":
|
|
y_non_conti = paddle.as_strided(
|
|
y_trans, self.shape_param, self.stride_param
|
|
)
|
|
else:
|
|
raise TypeError(f"Unsupported test type {self.strided_input_type}.")
|
|
out = paddle.bitwise_left_shift(
|
|
x,
|
|
y_non_conti,
|
|
)
|
|
out_ = x << y_non_conti
|
|
out_ref = ref_left_shift_arithmetic(self.x, self.y)
|
|
np.testing.assert_allclose(out_ref, out.numpy())
|
|
np.testing.assert_allclose(out_ref, out_.numpy())
|
|
paddle.enable_static()
|
|
|
|
def test_dygraph_api_logical(self):
|
|
paddle.disable_static()
|
|
x = paddle.to_tensor(self.x)
|
|
y_trans = paddle.to_tensor(self.y_trans)
|
|
if self.strided_input_type == "transpose":
|
|
y_non_conti = paddle.transpose(y_trans, self.perm)
|
|
elif self.strided_input_type == "as_stride":
|
|
y_non_conti = paddle.as_strided(
|
|
y_trans, self.shape_param, self.stride_param
|
|
)
|
|
else:
|
|
raise TypeError(f"Unsupported test type {self.strided_input_type}.")
|
|
out = paddle.bitwise_left_shift(x, y_non_conti, False)
|
|
out_ = x.__lshift__(y_non_conti, False)
|
|
out_ref = ref_left_shift_logical(self.x, self.y)
|
|
np.testing.assert_allclose(out_ref, out.numpy())
|
|
np.testing.assert_allclose(out_ref, out_.numpy())
|
|
paddle.enable_static()
|
|
|
|
|
|
class TestBitwiseLeftShiftOp_Stride1(TestBitwiseLeftShiftOp_Stride):
|
|
def init_input(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(0, 256, [20, 2, 13, 17]).astype('uint8')
|
|
self.y = np.random.randint(0, 256, [20, 2, 13, 17]).astype('uint8')
|
|
self.perm = [0, 1, 3, 2]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestBitwiseLeftShiftOp_Stride2(TestBitwiseLeftShiftOp_Stride):
|
|
def init_input(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(0, 256, [20, 2, 13, 17]).astype('uint8')
|
|
self.y = np.random.randint(0, 256, [20, 2, 13, 17]).astype('uint8')
|
|
self.perm = [0, 2, 1, 3]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestBitwiseLeftShiftOp_Stride3(TestBitwiseLeftShiftOp_Stride):
|
|
def init_input(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(0, 256, [20, 2, 13, 17]).astype('uint8')
|
|
self.y = np.random.randint(0, 256, [20, 2, 13, 1]).astype('uint8')
|
|
self.perm = [0, 1, 3, 2]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestBitwiseLeftShiftOp_Stride4(TestBitwiseLeftShiftOp_Stride):
|
|
def init_input(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(0, 256, [1, 2, 13, 17]).astype('uint8')
|
|
self.y = np.random.randint(0, 256, [20, 2, 13, 1]).astype('uint8')
|
|
self.perm = [1, 0, 2, 3]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestBitwiseLeftShiftOp_Stride5(TestBitwiseLeftShiftOp_Stride):
|
|
def init_input(self):
|
|
self.strided_input_type = "as_stride"
|
|
self.x = np.random.randint(0, 256, [23, 10, 1, 17]).astype('uint8')
|
|
self.y = np.random.randint(0, 256, [23, 2, 13, 20]).astype('uint8')
|
|
self.y_trans = self.y
|
|
self.y = self.y[:, 0:1, :, 0:1]
|
|
self.shape_param = [23, 1, 13, 1]
|
|
self.stride_param = [520, 260, 20, 1]
|
|
|
|
|
|
class TestBitwiseLeftShiftOp_Stride_ZeroDim1(TestBitwiseLeftShiftOp_Stride):
|
|
def init_input(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.randint(0, 256, []).astype('uint8')
|
|
self.y = np.random.randint(0, 256, [13, 17]).astype('uint8')
|
|
self.perm = [1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
class TestBitwiseLeftShiftOp_Stride_ZeroSize1(TestBitwiseLeftShiftOp_Stride):
|
|
def init_data(self):
|
|
self.strided_input_type = "transpose"
|
|
self.x = np.random.rand(1, 0, 2).astype('uint8')
|
|
self.y = np.random.rand(3, 0, 1).astype('uint8')
|
|
self.perm = [2, 1, 0]
|
|
self.y_trans = np.transpose(self.y, self.perm)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
paddle.enable_static()
|
|
unittest.main()
|