# Copyright (c) 2019 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 eager_op_test import OpTest, convert_float_to_uint16 import paddle import paddle.framework.dtype as dtypes from paddle.fluid import core def fill_any_like_wrapper(x, value, out_dtype=None, name=None): if isinstance(out_dtype, int): tmp_dtype = dtypes.dtype(out_dtype) else: tmp_dtype = out_dtype return paddle.full_like(x, value, tmp_dtype, name) class TestFillAnyLikeOp(OpTest): def setUp(self): self.op_type = "fill_any_like" self.prim_op_type = "comp" self.python_api = fill_any_like_wrapper self.public_python_api = fill_any_like_wrapper self.dtype = np.int32 self.value = 0.0 self.init() self.inputs = {'X': np.random.random((219, 232)).astype(self.dtype)} self.attrs = {'value': self.value} self.outputs = {'Out': self.value * np.ones_like(self.inputs["X"])} self.if_enable_cinn() def init(self): pass def test_check_output(self): self.check_output(check_prim=True) def if_enable_cinn(self): pass class TestFillAnyLikeOpFloat32(TestFillAnyLikeOp): def init(self): self.dtype = np.float32 self.value = 0.0 def if_enable_cinn(self): pass @unittest.skipIf( not core.is_compiled_with_cuda(), "core is not compiled with CUDA" ) class TestFillAnyLikeOpBfloat16(OpTest): def setUp(self): self.op_type = "fill_any_like" self.prim_op_type = "comp" self.python_api = fill_any_like_wrapper self.public_python_api = fill_any_like_wrapper self.dtype = np.uint16 self.value = 0.0 self.inputs = {'X': np.random.random((219, 232)).astype(np.float32)} self.attrs = {'value': self.value, 'dtype': core.VarDesc.VarType.BF16} self.outputs = { 'Out': convert_float_to_uint16( self.value * np.ones_like(self.inputs["X"]) ) } self.if_enable_cinn() def test_check_output(self): place = core.CUDAPlace(0) self.check_output_with_place(place, check_prim=True) def if_enable_cinn(self): pass class TestFillAnyLikeOpValue1(TestFillAnyLikeOp): def init(self): self.value = 1.0 def if_enable_cinn(self): pass class TestFillAnyLikeOpValue2(TestFillAnyLikeOp): def init(self): self.value = 1e-10 def if_enable_cinn(self): pass class TestFillAnyLikeOpValue3(TestFillAnyLikeOp): def init(self): self.value = 1e-100 def if_enable_cinn(self): pass class TestFillAnyLikeOpType(TestFillAnyLikeOp): def setUp(self): self.op_type = "fill_any_like" self.prim_op_type = "comp" self.python_api = fill_any_like_wrapper self.public_python_api = fill_any_like_wrapper self.dtype = np.int32 self.value = 0.0 self.init() self.inputs = {'X': np.random.random((219, 232)).astype(self.dtype)} self.attrs = { 'value': self.value, 'dtype': int(core.VarDesc.VarType.FP32), } self.outputs = { 'Out': self.value * np.ones_like(self.inputs["X"]).astype(np.float32) } self.if_enable_cinn() def if_enable_cinn(self): pass class TestFillAnyLikeOpFloat16(TestFillAnyLikeOp): def init(self): self.dtype = np.float16 def if_enable_cinn(self): pass if __name__ == "__main__": paddle.enable_static() unittest.main()