# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from op_test import get_device_place, is_custom_device import paddle from paddle.base import core _SIGNED_TO_UNSIGNED_TABLE = { "int8": "uint8", "int16": "uint16", "int32": "uint32", "int64": "uint64", } _UNSIGNED_TO_SIGNED_TABLE = { "uint8": "int8", "uint16": "int16", "uint32": "int32", "uint64": "int64", } _UNSIGNED_LIST = ['uint8', 'uint16', 'uint32', 'uint64'] def ref_left_shift_arithmetic(x, y): out = np.left_shift(x, y) return out def ref_left_shift_logical(x, y): out = np.left_shift(x, y) return out def ref_right_shift_arithmetic(x, y): return np.right_shift(x, y) def ref_right_shift_logical(x, y): if str(x.dtype) in _UNSIGNED_LIST: return np.right_shift(x, y) else: orig_dtype = x.dtype unsigned_dtype = _SIGNED_TO_UNSIGNED_TABLE[str(orig_dtype)] x = x.astype(unsigned_dtype) y = y.astype(unsigned_dtype) res = np.right_shift(x, y) return res.astype(orig_dtype) class TestBitwiseLeftShiftAPI(unittest.TestCase): def setUp(self): self.init_input() self.place = get_device_place() def init_input(self): self.x = np.random.randint(0, 256, [200, 300]).astype('uint8') self.y = np.random.randint(0, 256, [200, 300]).astype('uint8') def test_static_api_arithmetic(self): paddle.enable_static() with paddle.static.program_guard(paddle.static.Program()): x = paddle.static.data('x', self.x.shape, dtype=self.x.dtype) y = paddle.static.data('y', self.y.shape, dtype=self.y.dtype) out = paddle.bitwise_left_shift( x, y, ) out_ = x << y exe = paddle.static.Executor(self.place) res = exe.run(feed={'x': self.x, 'y': self.y}, fetch_list=[out]) res_ = exe.run(feed={'x': self.x, 'y': self.y}, fetch_list=[out_]) out_ref = ref_left_shift_arithmetic(self.x, self.y) np.testing.assert_allclose(out_ref, res[0]) np.testing.assert_allclose(out_ref, res_[0]) def test_dygraph_api_arithmetic(self): paddle.disable_static() x = paddle.to_tensor(self.x) y = paddle.to_tensor(self.y) out = paddle.bitwise_left_shift( x, y, ) out_ = x << y 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_static_api_logical(self): paddle.enable_static() with paddle.static.program_guard(paddle.static.Program()): x = paddle.static.data('x', self.x.shape, dtype=self.x.dtype) y = paddle.static.data('y', self.y.shape, dtype=self.y.dtype) out = paddle.bitwise_left_shift(x, y, False) out_ = x.__lshift__(y, False) exe = paddle.static.Executor(self.place) res = exe.run(feed={'x': self.x, 'y': self.y}, fetch_list=[out]) res_ = exe.run(feed={'x': self.x, 'y': self.y}, fetch_list=[out_]) out_ref = ref_left_shift_logical(self.x, self.y) np.testing.assert_allclose(out_ref, res[0]) np.testing.assert_allclose(out_ref, res_[0]) def test_dygraph_api_logical(self): paddle.disable_static() x = paddle.to_tensor(self.x) y = paddle.to_tensor(self.y) out = paddle.bitwise_left_shift(x, y, False) out_ = x.__lshift__(y, 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 TestBitwiseLeftShiftAPI_UINT8(TestBitwiseLeftShiftAPI): def init_input(self): self.x = np.random.randint(0, 256, [200, 300]).astype('uint8') self.y = np.random.randint(0, 256, [200, 300]).astype('uint8') class TestBitwiseLeftShiftAPI_UINT8_broadcast1(TestBitwiseLeftShiftAPI): def init_input(self): self.x = np.random.randint(0, 256, [200, 300]).astype('uint8') self.y = np.random.randint(0, 256, [300]).astype('uint8') class TestBitwiseLeftShiftAPI_UINT8_broadcast2(TestBitwiseLeftShiftAPI): def init_input(self): self.x = np.random.randint(0, 256, [300]).astype('uint8') self.y = np.random.randint(0, 256, [200, 300]).astype('uint8') class TestBitwiseLeftShiftAPI_INT8(TestBitwiseLeftShiftAPI): def init_input(self): self.x = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8') self.y = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8') class TestBitwiseLeftShiftAPI_INT8_broadcast1(TestBitwiseLeftShiftAPI): def init_input(self): self.x = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8') self.y = np.random.randint(-(2**7), 2**7, [300]).astype('int8') class TestBitwiseLeftShiftAPI_INT8_broadcast2(TestBitwiseLeftShiftAPI): def init_input(self): self.x = np.random.randint(-(2**7), 2**7, [300]).astype('int8') self.y = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8') class TestBitwiseLeftShiftAPI_INT16(TestBitwiseLeftShiftAPI): def init_input(self): self.x = np.random.randint(-(2**15), 2**15, [200, 300]).astype('int16') self.y = np.random.randint(-(2**15), 2**15, [200, 300]).astype('int16') class TestBitwiseLeftShiftAPI_INT16_broadcast1(TestBitwiseLeftShiftAPI): def init_input(self): self.x = np.random.randint(-(2**15), 2**15, [200, 300]).astype('int16') self.y = np.random.randint(-(2**15), 2**15, [300]).astype('int16') class TestBitwiseLeftShiftAPI_INT16_broadcast2(TestBitwiseLeftShiftAPI): def init_input(self): 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 TestBitwiseLeftShiftAPI_INT32(TestBitwiseLeftShiftAPI): 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 TestBitwiseLeftShiftAPI_INT32_broadcast1(TestBitwiseLeftShiftAPI): 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 TestBitwiseLeftShiftAPI_INT32_broadcast2(TestBitwiseLeftShiftAPI): 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 TestBitwiseLeftShiftAPI_INT64(TestBitwiseLeftShiftAPI): 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 TestBitwiseLeftShiftAPI_INT64_broadcast1(TestBitwiseLeftShiftAPI): 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 TestBitwiseLeftShiftAPI_INT64_broadcast2(TestBitwiseLeftShiftAPI): 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 TestBitwiseLeftShiftAPI_special_case1(TestBitwiseLeftShiftAPI): def init_input(self): self.x = np.array([0b11111111], dtype='int16') self.y = np.array([1], dtype='int16') class TestBitwiseLeftShiftAPI_special_case2(TestBitwiseLeftShiftAPI): def init_input(self): self.x = np.array([0b11111111], dtype='int16') self.y = np.array([10], dtype='int16') class TestBitwiseLeftShiftAPI_special_case3(TestBitwiseLeftShiftAPI): def init_input(self): self.x = np.array([0b11111111], dtype='uint8') self.y = np.array([1], dtype='uint8') class TestBitwiseLeftShiftAPI_special_case4(TestBitwiseLeftShiftAPI): def init_input(self): self.x = np.array([0b11111111], dtype='uint8') self.y = np.array([10], dtype='uint8') class TestTensorRlshiftAPI(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_rlshift(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_rlshift(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_left_shift_arithmetic(self.x, self.y) np.testing.assert_allclose(out_ref, res[0]) class TestTensorRlshiftAPI_UINT8(TestTensorRlshiftAPI): def init_input(self): self.x = np.random.randint(0, 64) self.y = np.random.randint(0, 64, [200, 300]).astype('uint8') class TestTensorRlshiftAPI_INT8(TestTensorRlshiftAPI): def init_input(self): self.x = np.random.randint(-64, 64) self.y = np.random.randint(0, 64, [200, 300]).astype('int8') class TestTensorRlshiftAPI_INT16(TestTensorRlshiftAPI): def init_input(self): self.x = np.random.randint(-256, 256) self.y = np.random.randint(0, 256, [200, 300]).astype('int16') class TestTensorRlshiftAPI_INT64(TestTensorRlshiftAPI): def init_input(self): self.x = np.random.randint(-255, 256) self.y = np.random.randint(0, 256, [200, 300]).astype('int64') class TestBitwiseRightShiftAPI(unittest.TestCase): def setUp(self): self.init_input() self.place = get_device_place() def init_input(self): self.x = np.random.randint(0, 256, [200, 300]).astype('uint8') self.y = np.random.randint(0, 256, [200, 300]).astype('uint8') def test_static_api_arithmetic(self): paddle.enable_static() with paddle.static.program_guard(paddle.static.Program()): x = paddle.static.data('x', self.x.shape, dtype=self.x.dtype) y = paddle.static.data('y', self.y.shape, dtype=self.y.dtype) out = paddle.bitwise_right_shift( x, y, ) out_ = x >> y exe = paddle.static.Executor(self.place) res = exe.run(feed={'x': self.x, 'y': self.y}, fetch_list=[out]) 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]) np.testing.assert_allclose(out_ref, res_[0]) def test_dygraph_api_arithmetic(self): paddle.disable_static() x = paddle.to_tensor(self.x) y = paddle.to_tensor(self.y) out = paddle.bitwise_right_shift( x, y, ) out_ = x >> y 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_static_api_logical(self): paddle.enable_static() with paddle.static.program_guard(paddle.static.Program()): x = paddle.static.data('x', self.x.shape, dtype=self.x.dtype) y = paddle.static.data('y', self.y.shape, dtype=self.y.dtype) out = paddle.bitwise_right_shift(x, y, False) out_ = x.__rshift__(y, False) exe = paddle.static.Executor(self.place) res = exe.run(feed={'x': self.x, 'y': self.y}, fetch_list=[out]) res_ = exe.run(feed={'x': self.x, 'y': self.y}, fetch_list=[out_]) out_ref = ref_right_shift_logical(self.x, self.y) np.testing.assert_allclose(out_ref, res[0]) np.testing.assert_allclose(out_ref, res_[0]) def test_dygraph_api_logical(self): paddle.disable_static() x = paddle.to_tensor(self.x) y = paddle.to_tensor(self.y) out = paddle.bitwise_right_shift(x, y, False) out_ = x.__rshift__(y, 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 TestBitwiseRightShiftAPI_UINT8(TestBitwiseRightShiftAPI): def init_input(self): self.x = np.random.randint(0, 256, [200, 300]).astype('uint8') self.y = np.random.randint(0, 256, [200, 300]).astype('uint8') class TestBitwiseRightShiftAPI_UINT8_broadcast1(TestBitwiseRightShiftAPI): def init_input(self): self.x = np.random.randint(0, 256, [200, 300]).astype('uint8') self.y = np.random.randint(0, 256, [300]).astype('uint8') class TestBitwiseRightShiftAPI_UINT8_broadcast2(TestBitwiseRightShiftAPI): def init_input(self): self.x = np.random.randint(0, 256, [300]).astype('uint8') self.y = np.random.randint(0, 256, [200, 300]).astype('uint8') class TestBitwiseRightShiftAPI_INT8(TestBitwiseRightShiftAPI): def init_input(self): self.x = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8') self.y = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8') class TestBitwiseRightShiftAPI_INT8_broadcast1(TestBitwiseRightShiftAPI): def init_input(self): self.x = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8') self.y = np.random.randint(-(2**7), 2**7, [300]).astype('int8') class TestBitwiseRightShiftAPI_INT8_broadcast2(TestBitwiseRightShiftAPI): def init_input(self): self.x = np.random.randint(-(2**7), 2**7, [300]).astype('int8') self.y = np.random.randint(-(2**7), 2**7, [200, 300]).astype('int8') class TestBitwiseRightShiftAPI_INT16(TestBitwiseRightShiftAPI): def init_input(self): self.x = np.random.randint(-(2**15), 2**15, [200, 300]).astype('int16') self.y = np.random.randint(-(2**15), 2**15, [200, 300]).astype('int16') class TestBitwiseRightShiftAPI_INT16_broadcast1(TestBitwiseRightShiftAPI): def init_input(self): self.x = np.random.randint(-(2**15), 2**15, [200, 300]).astype('int16') self.y = np.random.randint(-(2**15), 2**15, [300]).astype('int16') class TestBitwiseRightShiftAPI_INT16_broadcast2(TestBitwiseRightShiftAPI): def init_input(self): 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()