wehub-resource-sync
241 lines
7.2 KiB
Python
241 lines
7.2 KiB
Python
# Copyright (c) 2023 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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from op_test import OpTest, get_places
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from scipy import special
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import paddle
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np.random.seed(42)
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paddle.seed(42)
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def reference_i1e(x):
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return special.i1e(x)
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def reference_i1e_grad(x, dout):
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eps = np.finfo(x.dtype).eps
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not_tiny = abs(x) > eps
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safe_x = np.where(not_tiny, x, eps)
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gradx = special.i0e(safe_x) - special.i1e(x) * (np.sign(x) + 1 / safe_x)
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gradx = np.where(not_tiny, gradx, 0.5)
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return dout * gradx
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class TestI1e_API(unittest.TestCase):
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DTYPE = "float64"
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DATA = [0, 1, 2, 3, 4, 5]
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def setUp(self):
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self.x = np.array(self.DATA).astype(self.DTYPE)
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self.place = get_places()
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def test_api_static(self):
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def run(place):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data(
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name="x", shape=self.x.shape, dtype=self.DTYPE
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)
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y = paddle.i1e(x)
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exe = paddle.static.Executor(place)
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res = exe.run(
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paddle.static.default_main_program(),
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feed={"x": self.x},
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fetch_list=[y],
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)
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out_ref = reference_i1e(self.x)
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np.testing.assert_allclose(out_ref, res[0], rtol=1e-5)
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paddle.disable_static()
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for place in self.place:
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run(place)
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def test_api_dygraph(self):
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def run(place):
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paddle.disable_static(place)
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x = paddle.to_tensor(self.x)
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out = paddle.i1e(x)
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out_ref = reference_i1e(self.x)
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np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-5)
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paddle.enable_static()
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for place in self.place:
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run(place)
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def test_empty_input_error(self):
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for place in self.place:
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paddle.disable_static(place)
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x = None
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self.assertRaises(ValueError, paddle.i1e, x)
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paddle.enable_static()
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class Testi1eFloat32Zero2EightCase(TestI1e_API):
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DTYPE = "float32"
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DATA = [0, 1, 2, 3, 4, 5, 6, 7, 8]
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class Testi1eFloat32OverEightCase(TestI1e_API):
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DTYPE = "float32"
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DATA = [9, 10, 11, 12, 13, 14, 15, 16, 17]
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class Testi1eFloat64Zero2EightCase(TestI1e_API):
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DTYPE = "float64"
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DATA = [0, 1, 2, 3, 4, 5, 6, 7, 8]
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class Testi1eFloat64OverEightCase(TestI1e_API):
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DTYPE = "float64"
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DATA = [9, 10, 11, 12, 13, 14, 15, 16, 17]
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class TestI1eOp(OpTest):
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# 配置 op 信息以及输入输出等参数
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def setUp(self):
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self.op_type = "i1e"
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self.python_api = paddle.i1e
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self.init_config()
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self.outputs = {'out': self.target}
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# 测试前向输出结果
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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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# 测试反向梯度输出
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def test_check_grad(self):
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self.check_grad(
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['x'],
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'out',
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user_defined_grads=[
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reference_i1e_grad(
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self.case,
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1 / self.case.size,
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)
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],
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check_pir=True,
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)
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# 生成随机的输入数据并计算对应输出
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def init_config(self):
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zero_case = np.zeros(1).astype('float64')
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rand_case = np.random.randn(250).astype('float64')
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over_eight_case = np.random.uniform(low=8, high=9, size=250).astype(
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'float64'
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)
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self.case = np.concatenate([zero_case, rand_case, over_eight_case])
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self.inputs = {'x': self.case}
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self.target = reference_i1e(self.inputs['x'])
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class TestI1eOp_ZeroSize(OpTest):
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def setUp(self) -> None:
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self.__class__.op_type = "i1e"
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self.op_type = "i1e"
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self.python_api = paddle.i1e
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self.init_config()
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x = np.random.randn(3, 4, 0)
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self.inputs = {'x': x.astype(self.dtype)}
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self.attrs = {}
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self.outputs = {'out': special.i1e(x)}
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def init_config(self):
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self.dtype = np.float32
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['x'], 'out')
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class TestI1EAPI_Compatibility(unittest.TestCase):
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DTYPE = "float64"
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DATA = [0, 1, 2, 3, 4, 5]
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def setUp(self):
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self.x = np.array(self.DATA).astype(self.DTYPE)
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self.place = get_places()
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def test_dygraph_Compatibility(self):
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def run(place):
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paddle.disable_static(place)
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x = paddle.to_tensor(self.x)
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paddle_dygraph_out = []
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# Position args (args)
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out1 = paddle.i1e(x)
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paddle_dygraph_out.append(out1)
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# Key words args (kwargs) for paddle
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out2 = paddle.i1e(x=x)
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paddle_dygraph_out.append(out2)
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# Key words args for torch
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out3 = paddle.i1e(input=x)
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paddle_dygraph_out.append(out3)
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# Tensor method kwargs
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out4 = x.i1e()
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paddle_dygraph_out.append(out4)
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# Test out
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out5 = paddle.empty([])
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paddle.i1e(x, out=out5)
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paddle_dygraph_out.append(out5)
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# scipy reference out
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ref_out = reference_i1e(self.x)
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# Check
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for out in paddle_dygraph_out:
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np.testing.assert_allclose(out.numpy(), ref_out, rtol=1e-5)
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paddle.enable_static()
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for place in self.place:
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run(place)
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def test_static_Compatibility(self):
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def run(place):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data(
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name="x", shape=self.x.shape, dtype=self.DTYPE
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)
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# Position args (args)
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out1 = paddle.i1e(x)
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# Key words args (kwargs) for paddle
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out2 = paddle.i1e(x=x)
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# Key words args for torch
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out3 = paddle.i1e(input=x)
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# Tensor method args
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out4 = x.i1e()
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exe = paddle.static.Executor(place)
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fetches = exe.run(
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paddle.static.default_main_program(),
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feed={"x": self.x},
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fetch_list=[out1, out2, out3, out4],
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)
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ref_out = reference_i1e(self.x)
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for out in fetches:
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np.testing.assert_allclose(out, ref_out, rtol=1e-5)
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paddle.disable_static()
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for place in self.place:
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run(place)
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if __name__ == "__main__":
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unittest.main()
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