add sinc

Author: NKNaN

python/paddle/__init__.py

@@ -487,6 +487,8 @@
  signbit,
  sin,
  sin_,
+ sinc,
+ sinc_,
  sinh,
  sinh_,
  sqrt,

python/paddle/tensor/__init__.py

@@ -374,6 +374,8 @@
  signbit,
  sin,
  sin_,
+ sinc,
+ sinc_,
  sinh,
  sinh_,
  sqrt,
@@ -519,6 +521,7 @@
  'scale_',
  'sign',
  'sin',
+ 'sinc',
  'sinh',
  'sqrt',
  'sqrt_',
@@ -792,7 +795,7 @@
  'cos_',
  'cosh_',
  'sin_',
- 'sinh_',
+ 'sinc_' 'sinh_',
  'acosh_',
  'asinh_',
  'diag',

python/paddle/tensor/math.py

@@ -7652,3 +7652,47 @@ def signbit(x, name=None):
  x = paddle.sign(neg_zero_x)
  out = paddle.cast(x < 0, dtype='bool')
  return out
+
+
+def sinc(x, name=None):
+ r"""
+ Calculate the normalized sinc of ``x`` elementwise.
+
+ .. math::
+
+ out_i =
+ \left\{
+ \begin{aligned}
+ &1 & \text{ if $x_i = 0$} \\
+ &\frac{\sin(\pi x_i)}{\pi x_i} & \text{ otherwise}
+ \end{aligned}
+ \right.
+
+ Args:
+ x (Tensor): The input Tensor. Must be one of the following types: float16, float32, float64.
+ name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
+
+ Returns:
+ out (Tensor): The Tensor of elementwise computed normalized sinc result.
+ """
+ if not isinstance(x, (paddle.Tensor, Variable, paddle.pir.Value)):
+ raise TypeError(f"x must be tensor type, but got {type(x)}")
+
+ tmp = math.pi * paddle.where(x == 0, 1.0e-20, x)
+ return paddle.divide(tmp.sin(), tmp)
+
+
+@inplace_apis_in_dygraph_only
+def sinc_(x, name=None):
+ r"""
+ Inplace version of ``sinc`` API, the output Tensor will be inplaced with input ``x``.
+ Please refer to :ref:`api_paddle_sinc`.
+ """
+ if not isinstance(x, (paddle.Tensor, Variable)):
+ raise TypeError(f"x must be tensor type, but got {type(x)}")
+
+ paddle.where_(x != 0, x, paddle.full_like(x, 1.0e-20))
+ paddle.multiply_(x, paddle.to_tensor(math.pi, dtype=x.dtype))
+ paddle.sin_(x)
+ tmp = paddle.asin(x)
+ return paddle.divide_(x, tmp)

test/legacy_test/test_sinc.py

@@ -0,0 +1,173 @@
+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+
+import paddle
+from paddle import base
+from paddle.base import core
+
+
+def np_sinc(x: np.ndarray):
+ return np.sinc(x)
+
+
+class TestSincAPI(unittest.TestCase):
+ def setUp(self) -> None:
+ self.cpu_support_dtypes = [
+ 'float16',
+ 'float32',
+ 'float64',
+ ]
+ self.cuda_support_dtypes = [
+ 'float16',
+ 'float32',
+ 'float64',
+ ]
+ self.place = [paddle.CPUPlace()]
+ if core.is_compiled_with_cuda():
+ self.place.append(paddle.CUDAPlace(0))
+ self.shapes = [[6], [16, 64], [128, 512, 1024]]
+
+ def test_dtype(self):
+ def run_dygraph(place):
+ paddle.disable_static(place)
+ if core.is_compiled_with_cuda():
+ support_dtypes = self.cuda_support_dtypes
+ else:
+ support_dtypes = self.cpu_support_dtypes
+
+ for dtype in support_dtypes:
+ for shape in self.shapes:
+ x_data = np.random.rand(*shape).astype(dtype)
+ x = paddle.to_tensor(x_data)
+ out = paddle.sinc(x)
+ out_expected = np_sinc(x_data)
+ np.testing.assert_allclose(out.numpy(), out_expected)
+
+ def run_static(place):
+ paddle.enable_static()
+ if core.is_compiled_with_cuda():
+ support_dtypes = self.cuda_support_dtypes
+ else:
+ support_dtypes = self.cpu_support_dtypes
+ for dtype in support_dtypes:
+ for shape in self.shapes:
+ x_data = np.random.rand(*shape).astype(dtype)
+ startup_program = paddle.static.Program()
+ main_program = paddle.static.Program()
+ exe = base.Executor(place)
+ with paddle.static.program_guard(
+ main_program, startup_program
+ ):
+ x = paddle.static.data(
+ name='x', shape=shape, dtype=dtype
+ )
+ res = paddle.sinc(x)
+ static_result = exe.run(
+ feed={'x': x_data}, fetch_list=[res]
+ )
+ out_expected = np_sinc(x_data)
+ np.testing.assert_allclose(static_result, out_expected)
+
+ for place in self.place:
+ run_dygraph(place)
+ run_static(place)
+
+ def test_zero(self):
+ def run_dygraph(place):
+ paddle.disable_static(place)
+ if core.is_compiled_with_cuda():
+ support_dtypes = self.cuda_support_dtypes
+ else:
+ support_dtypes = self.cpu_support_dtypes
+
+ for dtype in support_dtypes:
+ for shape in self.shapes:
+ x_data = np.random.rand(*shape).astype(dtype)
+ mask = (
+ (np.random.rand(*shape) > 0.5)
+ .astype('int')
+ .astype(dtype)
+ )
+ x_data = x_data * mask
+ x = paddle.to_tensor(x_data)
+ out = paddle.sinc(x)
+ out_expected = np_sinc(x_data)
+ np.testing.assert_allclose(out.numpy(), out_expected)
+
+ def run_static(place):
+ paddle.enable_static()
+ if core.is_compiled_with_cuda():
+ support_dtypes = self.cuda_support_dtypes
+ else:
+ support_dtypes = self.cpu_support_dtypes
+ for dtype in support_dtypes:
+ for shape in self.shapes:
+ x_data = np.random.rand(*shape).astype(dtype)
+ mask = (
+ (np.random.rand(*shape) > 0.5)
+ .astype('int')
+ .astype(dtype)
+ )
+ x_data = x_data * mask
+ startup_program = paddle.static.Program()
+ main_program = paddle.static.Program()
+ exe = base.Executor(place)
+ with paddle.static.program_guard(
+ main_program, startup_program
+ ):
+ x = paddle.static.data(
+ name='x', shape=shape, dtype=dtype
+ )
+ res = paddle.sinc(x)
+ static_result = exe.run(
+ feed={'x': x_data}, fetch_list=[res]
+ )
+ out_expected = np_sinc(x_data)
+ np.testing.assert_allclose(static_result, out_expected)
+
+ for place in self.place:
+ run_dygraph(place)
+ run_static(place)
+
+ def test_input_type_error(self):
+ with self.assertRaises(TypeError):
+ x = np.random.rand(6).astype('float32')
+ x = paddle.sinc(x)
+
+ def test_inplace(self):
+ def run_dygraph(place):
+ paddle.disable_static(place)
+ if core.is_compiled_with_cuda():
+ support_dtypes = self.cuda_support_dtypes
+ else:
+ support_dtypes = self.cpu_support_dtypes
+
+ for dtype in support_dtypes:
+ for shape in self.shapes:
+ x_data = np.random.rand(*shape).astype(dtype)
+ x = paddle.to_tensor(x_data)
+ paddle.sinc_(x)
+ out_expected = np_sinc(x_data)
+ np.testing.assert_allclose(x.numpy(), out_expected)
+
+ for place in self.place:
+ run_dygraph(place)
+
+
+if __name__ == "__main__":
+ unittest.main()