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- # Copyright (c) 2018 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 get_test_cover_info import (
- XPUOpTestWrapper,
- create_test_class,
- get_xpu_op_support_types,
- )
- from op_test import convert_float_to_uint16, convert_uint16_to_float
- from op_test_xpu import XPUOpTest
-
- import paddle
- from paddle import base
- from paddle.base import Program, core, program_guard
-
- typeid_dict = {
- 'int32': int(core.VarDesc.VarType.INT32),
- 'int64': int(core.VarDesc.VarType.INT64),
- 'float32': int(core.VarDesc.VarType.FP32),
- 'float16': int(core.VarDesc.VarType.FP16),
- 'bfloat16': int(core.VarDesc.VarType.BF16),
- 'bool': int(core.VarDesc.VarType.BOOL),
- 'int8': int(core.VarDesc.VarType.INT8),
- 'uint8': int(core.VarDesc.VarType.UINT8),
- 'float64': int(core.VarDesc.VarType.FP64),
- }
-
-
- class XPUTestCastOp(XPUOpTestWrapper):
- def __init__(self):
- self.op_name = 'cast'
- self.use_dynamic_create_class = True
-
- def dynamic_create_class(self):
- base_class = self.TestCastOp
- classes = []
- for out_type in {
- 'float16',
- 'bfloat16',
- 'float32',
- 'int32',
- 'int64',
- 'int8',
- 'uint8',
- 'bool',
- 'float64',
- }:
- class_name = 'XPUTestCastOp_outtype_' + out_type
- attr_dict = {'out_typename': out_type}
- classes.append([class_name, attr_dict])
- return base_class, classes
-
- class TestCastOp(XPUOpTest):
- def setUp(self):
- ipt = np.random.random(size=[10, 10])
- in_typename = self.in_type_str
- out_typename = (
- 'float32'
- if not hasattr(self, 'out_typename')
- else self.out_typename
- )
-
- if in_typename == "bfloat16":
- ipt_x = convert_float_to_uint16(ipt)
- else:
- ipt_x = ipt.astype(in_typename)
-
- if out_typename == "bfloat16":
- opt = convert_uint16_to_float(convert_float_to_uint16(ipt_x))
- else:
- opt = ipt_x.astype(out_typename)
-
- self.inputs = {'X': ipt_x}
- self.outputs = {'Out': opt}
- self.attrs = {
- 'in_dtype': typeid_dict[in_typename],
- 'out_dtype': typeid_dict[out_typename],
- }
- self.op_type = 'cast'
- self.__class__.no_need_check_grad = True
-
- def test_check_output(self):
- self.check_output()
-
-
- support_types = get_xpu_op_support_types('cast')
- for stype in support_types:
- create_test_class(globals(), XPUTestCastOp, stype)
-
-
- class TestCastOpError(unittest.TestCase):
- def test_errors(self):
- with program_guard(Program(), Program()):
- # The input type of cast_op must be Variable.
- x1 = base.create_lod_tensor(
- np.array([[-1]]), [[1]], base.XPUPlace(0)
- )
- self.assertRaises(TypeError, paddle.cast, x1, 'int32')
-
-
- class TestCastOpEmpty(unittest.TestCase):
- def test_cast_op_empty(self):
- if paddle.is_compiled_with_xpu():
- paddle.set_device('xpu')
- paddle.disable_static()
- data = paddle.ones([0, 10], dtype='float32')
- out = paddle.cast(data, 'int32')
- self.assertEqual(out.shape, data.shape)
- self.assertEqual(out.dtype, paddle.int32)
- paddle.enable_static()
-
-
- if __name__ == '__main__':
- paddle.enable_static()
- unittest.main()
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