wehub-resource-sync
143 lines
4.6 KiB
Python
143 lines
4.6 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 unittest
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import numpy as np
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import scipy.stats
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from op_test import OpTest, convert_float_to_uint16, convert_uint16_to_float
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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 TestDirichletOp(OpTest):
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# Because dirichlet random sample have not gradient, we skip gradient check.
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no_need_check_grad = True
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def setUp(self):
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self.op_type = "dirichlet"
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self.alpha = np.array((1.0, 2.0))
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self.sample_shape = (100000, 2)
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self.inputs = {'Alpha': np.broadcast_to(self.alpha, self.sample_shape)}
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self.attrs = {}
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self.outputs = {'Out': np.zeros(self.sample_shape)}
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def test_check_output(self):
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self.check_output_customized(self._hypothesis_testing)
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def _hypothesis_testing(self, outs):
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self.assertEqual(outs[0].shape, self.sample_shape)
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self.assertTrue(np.all(outs[0] > 0.0))
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sample = outs[0][:, 0].astype(np.float64)
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self.assertLess(
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scipy.stats.kstest(
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sample,
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# scipy dirichlet have not cdf, use beta to replace it.
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scipy.stats.beta(a=self.alpha[0], b=self.alpha[1]).cdf,
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)[0],
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0.01,
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)
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class TestDirichletFP16Op(OpTest):
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# Because dirichlet random sample have not gradient, we skip gradient check.
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no_need_check_grad = True
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def setUp(self):
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self.op_type = "dirichlet"
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self.alpha = np.array((1.0, 2.0))
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self.sample_shape = (100000, 2)
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self.dtype = np.float16
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self.inputs = {
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'Alpha': np.broadcast_to(self.alpha, self.sample_shape).astype(
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self.dtype
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)
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}
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self.attrs = {}
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self.outputs = {'Out': np.zeros(self.sample_shape).astype(self.dtype)}
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def test_check_output(self):
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self.check_output_customized(self._hypothesis_testing)
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def _hypothesis_testing(self, outs):
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self.assertEqual(outs[0].shape, self.sample_shape)
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self.assertTrue(np.all(outs[0] > 0.0))
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sample = outs[0][:, 0].astype(np.float64)
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self.assertLess(
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scipy.stats.kstest(
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sample,
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# scipy dirichlet have not cdf, use beta to replace it.
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scipy.stats.beta(a=self.alpha[0], b=self.alpha[1]).cdf,
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)[0],
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0.01,
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)
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@unittest.skipIf(
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not core.is_compiled_with_cuda()
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or not core.is_bfloat16_supported(core.CUDAPlace(0)),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestDirichletBF16Op(OpTest):
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# Because dirichlet random sample have not gradient, we skip gradient check.
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no_need_check_grad = True
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def setUp(self):
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self.op_type = "dirichlet"
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self.alpha = np.array((1.0, 2.0))
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self.sample_shape = (10000, 2)
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self.dtype = np.uint16
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self.np_dtype = np.float32
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self.inputs = {
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'Alpha': np.broadcast_to(self.alpha, self.sample_shape).astype(
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self.np_dtype
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)
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}
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self.attrs = {}
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self.outputs = {
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'Out': np.zeros(self.sample_shape).astype(self.np_dtype)
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}
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self.inputs['Alpha'] = convert_float_to_uint16(self.inputs['Alpha'])
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self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
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self.place = core.CUDAPlace(0)
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def test_check_output(self):
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self.check_output_with_place_customized(
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self._hypothesis_testing, place=core.CUDAPlace(0)
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)
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def _hypothesis_testing(self, outs):
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outs = convert_uint16_to_float(outs)
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self.assertEqual(outs[0].shape, self.sample_shape)
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self.assertTrue(np.all(outs[0] > 0.0))
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self.assertLess(
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scipy.stats.kstest(
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outs[0][:, 0],
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# scipy dirichlet have not cdf, use beta to replace it.
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scipy.stats.beta(a=self.alpha[0], b=self.alpha[1]).cdf,
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)[0],
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0.3, # The bfloat16 test difference is below 0.3
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)
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if __name__ == '__main__':
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unittest.main()
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