wehub-resource-sync
388 lines
15 KiB
Python
388 lines
15 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 (
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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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import paddle
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from paddle import base
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from paddle.base import core
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def np_sinc(x: np.ndarray):
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tmp = np.sinc(x)
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return np.where(~np.isnan(tmp), tmp, np.full_like(x, 1.0))
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def np_sinc_gradient(x: np.ndarray):
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x = np.pi * np.where(x == 0, 1.0e-20, x)
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s = np.sin(x)
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c = np.cos(x)
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tmp = np.pi * (x * c - s) / x**2
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return np.where(~np.isnan(tmp), tmp, np.full_like(x, 0.0))
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class TestSincAPI(unittest.TestCase):
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def setUp(self):
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self.support_dtypes = [
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'float32',
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'float64',
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]
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self.place = get_places()
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self.shapes = [[6], [16, 64]]
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def test_dtype(self):
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def run_dygraph(place):
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paddle.disable_static(place)
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for dtype in self.support_dtypes:
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for shape in self.shapes:
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x_data = np.random.rand(*shape).astype(dtype)
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x = paddle.to_tensor(x_data)
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x.stop_gradient = False
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out = paddle.sinc(x)
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out.backward()
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x_grad = x.grad
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out_expected = np_sinc(x_data)
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np_grad_expected = np_sinc_gradient(x_data)
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np.testing.assert_allclose(
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out.numpy(), out_expected, rtol=1e-6, atol=1e-6
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)
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np.testing.assert_allclose(
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x_grad.numpy(), np_grad_expected, rtol=1e-6, atol=0.02
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)
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def run_static(place):
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paddle.enable_static()
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for dtype in self.support_dtypes:
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for shape in self.shapes:
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x_data = np.random.rand(*shape).astype(dtype)
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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exe = base.Executor(place)
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with paddle.static.program_guard(
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main_program, startup_program
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):
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x = paddle.static.data(
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name='x', shape=shape, dtype=dtype
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)
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x.stop_gradient = False
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res = paddle.sinc(x)
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x_grad = paddle.static.gradients(res, x)
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[static_result, static_grad_result] = exe.run(
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feed={'x': x_data}, fetch_list=[res, x_grad]
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)
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out_expected = np_sinc(x_data)
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np_grad_expected = np_sinc_gradient(x_data)
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np.testing.assert_allclose(
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static_result, out_expected, rtol=1e-6, atol=1e-6
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)
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np.testing.assert_allclose(
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static_grad_result,
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np_grad_expected,
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rtol=1e-6,
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atol=0.02,
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)
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for place in self.place:
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run_dygraph(place)
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run_static(place)
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def test_zero(self):
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def run_dygraph(place):
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paddle.disable_static(place)
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for dtype in self.support_dtypes:
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for shape in self.shapes:
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x_data = np.random.rand(*shape).astype(dtype)
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mask = (
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(np.random.rand(*shape) > 0.5)
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.astype('int')
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.astype(dtype)
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)
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x_data = x_data * mask
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x = paddle.to_tensor(x_data)
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x.stop_gradient = False
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out = paddle.sinc(x)
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out.backward()
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x_grad = x.grad
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out_expected = np_sinc(x_data)
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np_grad_expected = np_sinc_gradient(x_data)
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np.testing.assert_allclose(
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out.numpy(), out_expected, rtol=1e-6, atol=1e-6
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)
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np.testing.assert_allclose(
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x_grad.numpy(), np_grad_expected, rtol=1e-6, atol=0.02
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)
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for place in self.place:
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run_dygraph(place)
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def test_input_type_error(self):
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with self.assertRaises(TypeError):
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x = np.random.rand(6).astype('float32')
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x = paddle.sinc(x)
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def test_input_dype_error(self):
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paddle.enable_static()
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place = paddle.CPUPlace()
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with self.assertRaises(TypeError):
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x_data = np.random.rand(6).astype('int32')
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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exe = base.Executor(place)
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(name='x', shape=[6], dtype='int32')
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res = paddle.sinc(x)
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static_result = exe.run(feed={'x': x_data}, fetch_list=[res])[0]
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with self.assertRaises(TypeError):
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x_data = np.random.rand(6).astype('int64')
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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exe = base.Executor(place)
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(name='x', shape=[6], dtype='int64')
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res = paddle.sinc(x)
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static_result = exe.run(feed={'x': x_data}, fetch_list=[res])[0]
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class TestSincInplaceAPI(unittest.TestCase):
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def setUp(self):
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self.support_dtypes = [
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'float32',
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'float64',
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]
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self.place = get_places()
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self.shapes = [[6], [16, 64]]
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def test_inplace(self):
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def run_dygraph(place):
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paddle.disable_static(place)
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for dtype in self.support_dtypes:
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for shape in self.shapes:
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x_data = np.random.rand(*shape).astype(dtype)
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x = paddle.to_tensor(x_data)
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paddle.sinc_(x)
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out_expected = np_sinc(x_data)
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np.testing.assert_allclose(
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x.numpy(), out_expected, rtol=1e-6, atol=1e-6
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)
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for place in self.place:
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run_dygraph(place)
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def test_inplace_input_type_error(self):
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with self.assertRaises(TypeError):
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x = np.random.rand(6).astype('float32')
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paddle.sinc_(x)
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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_float16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the float16",
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)
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class TestSincAPIFP16(unittest.TestCase):
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def setUp(self):
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self.shapes = [[6], [16, 64]]
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self.dtype = 'float16'
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self.place = get_device_place()
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def test_dtype(self):
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def run_static(place):
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paddle.enable_static()
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for shape in self.shapes:
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x_data = np.random.rand(*shape).astype(self.dtype)
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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exe = base.Executor(place)
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(
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name='x', shape=shape, dtype=self.dtype
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)
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x.stop_gradient = False
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res = paddle.sinc(x)
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x_grad = paddle.static.gradients(res, x)
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[static_result, static_grad_result] = exe.run(
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feed={'x': x_data}, fetch_list=[res, x_grad]
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)
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out_expected = np_sinc(x_data)
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np_grad_expected = np_sinc_gradient(x_data)
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np.testing.assert_allclose(
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static_result, out_expected, rtol=1e-6, atol=1e-6
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)
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np.testing.assert_allclose(
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static_grad_result, np_grad_expected, rtol=0.1, atol=0.1
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)
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run_static(self.place)
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def test_zero(self):
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def run_static(place):
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paddle.enable_static()
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for shape in self.shapes:
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x_data = np.random.rand(*shape).astype(self.dtype)
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mask = (
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(np.random.rand(*shape) > 0.5)
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.astype('int')
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.astype(self.dtype)
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)
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x_data = x_data * mask
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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exe = base.Executor(place)
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(
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name='x', shape=shape, dtype=self.dtype
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)
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x.stop_gradient = False
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res = paddle.sinc(x)
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x_grad = paddle.static.gradients(res, x)
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[static_result, static_grad_result] = exe.run(
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feed={'x': x_data}, fetch_list=[res, x_grad]
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)
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out_expected = np_sinc(x_data)
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np_grad_expected = np_sinc_gradient(x_data)
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np.testing.assert_allclose(
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static_result, out_expected, rtol=1e-6, atol=1e-6
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)
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np.testing.assert_allclose(
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static_grad_result, np_grad_expected, rtol=0.1, atol=0.1
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)
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run_static(self.place)
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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 TestSincAPIBF16(unittest.TestCase):
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def setUp(self):
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self.shapes = [[6], [16, 64]]
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self.dtype = 'uint16'
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self.place = get_device_place()
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def test_dtype(self):
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def run(place):
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paddle.enable_static()
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for shape in self.shapes:
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x_data_np = np.random.rand(*shape).astype('float32')
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x_data = convert_float_to_uint16(x_data_np)
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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exe = base.Executor(place)
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(
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name='x', shape=shape, dtype=self.dtype
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)
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x.stop_gradient = False
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res = paddle.sinc(x)
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x_grad = paddle.static.gradients(res, x)
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[static_result, static_grad_result] = exe.run(
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feed={'x': x_data}, fetch_list=[res, x_grad]
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)
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out_expected = np_sinc(x_data_np)
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np_grad_expected = np_sinc_gradient(x_data_np)
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result = convert_uint16_to_float(static_result)
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grad_result = convert_uint16_to_float(static_grad_result)
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np.testing.assert_allclose(
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result, out_expected, rtol=1e-3, atol=1e-2
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)
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np.testing.assert_allclose(
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grad_result, np_grad_expected, atol=0.2
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)
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run(self.place)
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def test_zero(self):
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def run(place):
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paddle.enable_static()
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for shape in self.shapes:
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x_data_np = np.random.rand(*shape).astype('float32')
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mask = (
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(np.random.rand(*shape) > 0.5)
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.astype('int')
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.astype('float32')
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)
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x_data_np = x_data_np * mask
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x_data = convert_float_to_uint16(x_data_np)
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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exe = base.Executor(place)
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(
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name='x', shape=shape, dtype=self.dtype
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)
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x.stop_gradient = False
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res = paddle.sinc(x)
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x_grad = paddle.static.gradients(res, x)
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[static_result, static_grad_result] = exe.run(
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feed={'x': x_data}, fetch_list=[res, x_grad]
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)
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out_expected = np_sinc(x_data_np)
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np_grad_expected = np_sinc_gradient(x_data_np)
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result = convert_uint16_to_float(static_result)
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grad_result = convert_uint16_to_float(static_grad_result)
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np.testing.assert_allclose(
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result, out_expected, rtol=1e-3, atol=1e-2
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)
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np.testing.assert_allclose(
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grad_result, np_grad_expected, atol=0.2
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)
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run(self.place)
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class TestSincAPI_ZeroSize(unittest.TestCase):
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def setUp(self):
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self.support_dtypes = [
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'float32',
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'float64',
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]
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self.place = get_places()
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self.shapes = [[0], [16, 0]]
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def test_dygraph(self):
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def run_dygraph(place):
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paddle.disable_static(place)
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for dtype in self.support_dtypes:
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for shape in self.shapes:
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x_data = np.random.rand(*shape).astype(dtype)
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x = paddle.to_tensor(x_data)
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x.stop_gradient = False
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out = paddle.sinc(x)
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out_expected = np_sinc(x_data)
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np.testing.assert_allclose(
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out.numpy(), out_expected, rtol=1e-6, atol=1e-6
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)
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loss = paddle.sum(out)
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loss.backward()
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np.testing.assert_allclose(x.grad.shape, x.shape)
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for place in self.place:
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run_dygraph(place)
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if __name__ == "__main__":
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unittest.main()
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