wehub-resource-sync
128 lines
3.7 KiB
Python
128 lines
3.7 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 math
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import unittest
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import numpy as np
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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from scipy import special
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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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class TestLgammaOp(OpTest):
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def setUp(self):
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self.op_type = 'lgamma'
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self.python_api = paddle.lgamma
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self.init_dtype_type()
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self.init_shape()
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shape = self.shape
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data = np.random.random(shape).astype(self.dtype) + 1
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self.inputs = {'X': data}
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result = np.ones(shape).astype(self.dtype)
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for i in range(shape[0]):
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for j in range(shape[1]):
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result[i][j] = math.lgamma(data[i][j])
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self.outputs = {'Out': result}
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def init_dtype_type(self):
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self.dtype = np.float64
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def init_shape(self):
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self.shape = (5, 20)
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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def test_check_grad_normal(self):
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self.check_grad(['X'], 'Out', numeric_grad_delta=1e-7, check_pir=True)
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class TestLgammaOpFp32(TestLgammaOp):
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def init_dtype_type(self):
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self.dtype = np.float32
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def test_check_grad_normal(self):
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self.check_grad(['X'], 'Out', numeric_grad_delta=0.005, check_pir=True)
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class TestLgammaFP16Op(TestLgammaOp):
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def init_dtype_type(self):
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self.dtype = np.float16
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def test_check_grad_normal(self):
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self.check_grad(['X'], 'Out', check_pir=True)
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class TestLgammaOp_ZeroSize(TestLgammaOp):
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def init_shape(self):
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self.shape = (5, 0)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestLgammaBF16Op(OpTest):
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def setUp(self):
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self.op_type = 'lgamma'
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self.python_api = paddle.lgamma
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self.dtype = np.uint16
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shape = (5, 20)
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data = np.random.random(shape).astype("float32") + 1
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self.inputs = {'X': convert_float_to_uint16(data)}
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result = np.ones(shape).astype("float32")
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for i in range(shape[0]):
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for j in range(shape[1]):
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result[i][j] = math.lgamma(data[i][j])
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self.outputs = {'Out': convert_float_to_uint16(result)}
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def test_check_output(self):
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# After testing, bfloat16 needs to set the parameter place
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self.check_output_with_place(
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get_device_place(), check_pir=True, check_symbol_infer=False
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)
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def test_check_grad_normal(self):
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self.check_grad_with_place(
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get_device_place(), ['X'], 'Out', check_pir=True
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)
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class TestLgammaOpApi(unittest.TestCase):
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def test_lgamma(self):
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paddle.disable_static()
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self.dtype = "float32"
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shape = (1, 4)
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data = np.random.random(shape).astype(self.dtype) + 1
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data_ = paddle.to_tensor(data)
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out = paddle.lgamma(data_)
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result = special.gammaln(data)
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np.testing.assert_allclose(result, out.numpy(), rtol=1e-05)
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paddle.enable_static()
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if __name__ == "__main__":
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unittest.main()
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