wehub-resource-sync
221 lines
6.8 KiB
Python
221 lines
6.8 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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from op_test import (
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OpTest,
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convert_float_to_uint16,
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convert_uint16_to_float,
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get_device_place,
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get_places,
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is_custom_device,
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)
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from scipy.special import erfinv
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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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np.random.seed(0)
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class TestErfinvOp(OpTest):
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def setUp(self):
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self.op_type = "erfinv"
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self.python_api = paddle.erfinv
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self.init_dtype()
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self.init_shape()
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self.x = np.random.uniform(-1, 1, size=self.shape).astype(self.dtype)
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self.res_ref = erfinv(self.x).astype(self.dtype)
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self.grad_out = np.ones(self.shape, self.dtype)
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self.gradient = (
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np.sqrt(np.pi) / 2 * np.exp(np.square(self.res_ref)) * self.grad_out
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)
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self.inputs = {'X': self.x}
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self.outputs = {'Out': self.res_ref}
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def init_shape(self):
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self.shape = [11, 17]
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def init_dtype(self):
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self.dtype = np.float64
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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(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=[self.gradient],
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user_defined_grad_outputs=self.grad_out,
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check_pir=True,
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)
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class TestErfinvFP64Op(TestErfinvOp):
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def init_dtype(self):
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self.dtype = np.float64
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class TestErfinvOp_ZeroSize(TestErfinvOp):
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def init_shape(self):
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self.shape = [0, 17]
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class TestErfinvAPIOp(unittest.TestCase):
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def init_dtype(self):
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self.dtype = 'float32'
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def setUp(self):
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self.init_dtype()
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self.x = np.random.rand(5).astype(self.dtype)
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self.res_ref = erfinv(self.x)
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self.place = get_places()
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def test_static_api(self):
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paddle.enable_static()
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def run(place):
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('x', [1, 5], dtype=self.dtype)
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out = paddle.erfinv(x)
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exe = paddle.static.Executor(place)
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res = exe.run(feed={'x': self.x.reshape([1, 5])})
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for r in res:
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np.testing.assert_allclose(self.res_ref, r, rtol=1e-05)
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for place in self.place:
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run(place)
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def test_dygraph_api(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.erfinv(x)
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np.testing.assert_allclose(self.res_ref, out.numpy(), rtol=1e-05)
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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_inplace_api(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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x.erfinv_()
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np.testing.assert_allclose(self.res_ref, x.numpy(), rtol=1e-05)
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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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class TestErfinvFP16Op(TestErfinvOp):
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def init_dtype(self):
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self.dtype = np.float16
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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 and not support the bfloat16",
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)
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class TestErfinvBF16Op(OpTest):
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def setUp(self):
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self.op_type = "erfinv"
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self.public_python_api = paddle.erfinv
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self.python_api = paddle.erfinv
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self.dtype = np.uint16
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self.shape = [11, 17]
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self.datatype = np.float32
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self.input_data = np.random.uniform(-1, 1, size=self.shape).astype(
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self.datatype
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)
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self.inputs = {'X': convert_float_to_uint16(self.input_data)}
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self.inputs_data = convert_uint16_to_float(self.inputs['X'])
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out_ref = erfinv(self.input_data)
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self.grad_out = np.ones(self.shape, self.datatype)
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self.gradient = (
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np.sqrt(np.pi) / 2 * np.exp(np.square(out_ref)) * self.grad_out
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)
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self.outputs = {'Out': convert_float_to_uint16(out_ref)}
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(
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place, check_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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place = get_device_place()
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self.check_grad_with_place(place, ['X'], 'Out', check_pir=True)
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class TestErfinvOutOfDomainNaN(unittest.TestCase):
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"""|x| > 1 must return NaN to match PyTorch / scipy; +/-1 stays +/-inf.
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Covers issues #78106 / #78107 / #78121.
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"""
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def _run(self, place, dtype):
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paddle.disable_static(place)
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try:
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values = [-1.5, -1.0, -0.5, 0.0, 0.5, 1.0, 1.5, np.nan]
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if dtype == 'bfloat16':
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# NumPy does not support bfloat16; build with float32 first.
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x_tensor = paddle.to_tensor(
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np.asarray(values, dtype='float32')
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).astype('bfloat16')
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out = paddle.erfinv(x_tensor).astype('float32').numpy()
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else:
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x_np = np.asarray(values, dtype=dtype)
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out = paddle.erfinv(paddle.to_tensor(x_np)).numpy()
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# |x| > 1 -> NaN
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self.assertTrue(np.isnan(out[0])) # -1.5
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self.assertTrue(np.isnan(out[6])) # +1.5
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# +/-1 -> +/-inf (not NaN)
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self.assertTrue(np.isneginf(out[1]))
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self.assertTrue(np.isposinf(out[5]))
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# In-domain values stay finite
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for idx in (2, 3, 4):
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self.assertTrue(np.isfinite(out[idx]))
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# NaN input propagates as NaN
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self.assertTrue(np.isnan(out[7]))
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finally:
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paddle.enable_static()
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def test_dygraph_cpu(self):
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for dtype in ('float32', 'float64'):
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self._run(paddle.CPUPlace(), dtype)
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@unittest.skipIf(
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not core.is_compiled_with_cuda(),
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'core is not compiled with CUDA',
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)
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def test_dygraph_gpu(self):
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place = paddle.CUDAPlace(0)
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for dtype in ('float32', 'float64', 'float16'):
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self._run(place, dtype)
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if core.is_bfloat16_supported(place):
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self._run(place, 'bfloat16')
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if __name__ == "__main__":
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unittest.main()
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