【Hackathon No.8】add api hypot & hypot_

python/paddle/__init__.py

@@ -407,6 +407,8 @@
     i1e,
     polygamma,
     polygamma_,
+    hypot,
+    hypot_,
 )
 
 from .tensor.random import (
@@ -904,4 +906,6 @@
     'i1e',
     'polygamma',
     'polygamma_',
+    'hypot',
+    'hypot_',
 ]

python/paddle/tensor/__init__.py

@@ -329,6 +329,8 @@
 from .math import polygamma_  # noqa: F401
 from .math import renorm  # noqa: F401
 from .math import renorm_  # noqa: F401
+from .math import hypot  # noqa: F401
+from .math import hypot_  # noqa: F401
 
 from .random import multinomial  # noqa: F401
 from .random import standard_normal  # noqa: F401
@@ -464,6 +466,8 @@
     'sum',
     'nan_to_num',
     'nan_to_num_',
+    'hypot',
+    'hypot_',
     'nansum',
     'nanmean',
     'count_nonzero',

python/paddle/tensor/math.py

@@ -6932,3 +6932,56 @@ def ldexp_(x, y, name=None):
     y = paddle.cast(y, dtype=out_dtype)
     two = paddle.to_tensor(2, dtype=out_dtype)
     return paddle.multiply_(x, paddle.pow(two, y))
+
+
+def hypot(x, y, name=None):
+    """
+    Calculate the length of the hypotenuse of a right-angle triangle. The equation is:
+
+    .. math::
+        out = {\\sqrt{x^2 + y^2}}
+
+    Args:
+        x (Tensor): The input Tensor, the data type is float32, float64, int32 or int64.
+        y (Tensor): The input Tensor, the data type is float32, float64, int32 or int64.
+        name (str, optional): Name for the operation (optional, default is None).For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        out (Tensor): An N-D Tensor. If x, y have different shapes and are "broadcastable", the resulting tensor shape is the shape of x and y after broadcasting. If x, y have the same shape, its shape is the same as x and y. And the data type is float32 or float64.
+
+    Examples:
+
+        .. code-block:: python
+
+            >>> import paddle
+
+            >>> x = paddle.to_tensor([3], dtype='float32')
+            >>> y = paddle.to_tensor([4], dtype='float32')
+            >>> res = paddle.hypot(x, y)
+            >>> print(res)
+            Tensor(shape=[1], dtype=float32, place=Place(cpu), stop_gradient=True,
+            [5.])
+
+    """
+    if not isinstance(x, (paddle.Tensor, Variable)):
+        raise TypeError(f"x must be tensor type, but got {type(x)}")
+    if not isinstance(y, (paddle.Tensor, Variable)):
+        raise TypeError(f"y must be tensor type, but got {type(y)}")
+
+    out = (paddle.pow(x, 2) + paddle.pow(y, 2)).sqrt()
+    return out
+
+
+@inplace_apis_in_dygraph_only
+def hypot_(x, y, name=None):
+    r"""
+    Inplace version of ``hypot`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_hypot`.
+    """
+    if not isinstance(x, (paddle.Tensor, Variable)):
+        raise TypeError(f"x must be tensor type, but got {type(x)}")
+    if not isinstance(y, (paddle.Tensor, Variable)):
+        raise TypeError(f"y must be tensor type, but got {type(y)}")
+
+    out = x.pow_(2).add_(y.pow(2)).sqrt_()
+    return out

test/legacy_test/test_hypot.py

@@ -0,0 +1,104 @@
+#   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
+
+paddle.enable_static()
+
+
+class TestHypotAPI(unittest.TestCase):
+    def setUp(self):
+        self.x_shape = [10, 10]
+        self.y_shape = [10, 1]
+        self.x_np = np.random.uniform(-10, 10, self.x_shape).astype(np.float32)
+        self.y_np = np.random.uniform(-10, 10, self.y_shape).astype(np.float32)
+
+    def test_static_graph(self):
+        paddle.enable_static()
+        startup_program = base.Program()
+        train_program = base.Program()
+        with base.program_guard(startup_program, train_program):
+            x = paddle.static.data(
+                name='input1', dtype='float32', shape=self.x_shape
+            )
+            y = paddle.static.data(
+                name='input2', dtype='float32', shape=self.y_shape
+            )
+            out = paddle.hypot(x, y)
+
+            place = (
+                base.CUDAPlace(0)
+                if core.is_compiled_with_cuda()
+                else base.CPUPlace()
+            )
+            exe = base.Executor(place)
+            res = exe.run(
+                base.default_main_program(),
+                feed={'input1': self.x_np, 'input2': self.y_np},
+                fetch_list=[out],
+            )
+            np_out = np.hypot(self.x_np, self.y_np)
+            np.testing.assert_allclose(res[0], np_out, atol=1e-5, rtol=1e-5)
+            paddle.disable_static()
+
+    def test_dygraph(self):
+        paddle.disable_static()
+        x = paddle.to_tensor(self.x_np)
+        y = paddle.to_tensor(self.y_np)
+        result = paddle.hypot(x, y)
+        np.testing.assert_allclose(
+            np.hypot(self.x_np, self.y_np), result.numpy(), rtol=1e-05
+        )
+
+        paddle.enable_static()
+
+    def test_error(self):
+        x = paddle.to_tensor(self.x_np)
+        y = 3.8
+        self.assertRaises(TypeError, paddle.hypot, x, y)
+        self.assertRaises(TypeError, paddle.hypot, y, x)
+
+
+class TestHypotAPIBroadCast(TestHypotAPI):
+    def setUp(self):
+        self.x_np = np.arange(6).astype(np.float32)
+        self.y_np = np.array([20]).astype(np.float32)
+        self.x_shape = [6]
+        self.y_shape = [1]
+
+
+class TestHypotAPI3(TestHypotAPI):
+    def setUp(self):
+        self.x_shape = []
+        self.y_shape = []
+        self.x_np = np.random.uniform(-10, 10, self.x_shape).astype(np.float32)
+        self.y_np = np.random.uniform(-10, 10, self.y_shape).astype(np.float32)
+
+
+class TestHypotAPI4(TestHypotAPI):
+    def setUp(self):
+        self.x_shape = [1]
+        self.y_shape = [1]
+        self.x_np = np.random.uniform(-10, 10, self.x_shape).astype(np.float32)
+        self.y_np = np.random.uniform(-10, 10, self.y_shape).astype(np.float32)
+
+
+if __name__ == "__main__":
+    unittest.main()

test/legacy_test/test_inplace.py

@@ -834,6 +834,59 @@ def test_error(self):
         self.assertRaises(ValueError, paddle.gcd_, x, y)
 
 
+class TestDygraphInplaceHypot(TestDygraphInplace):
+    def init_data(self):
+        self.input_var_numpy = np.random.randint(2, size=200)
+        self.input_var_numpy = self.input_var_numpy.reshape([10, 20])
+        self.dtype = "float32"
+        self.y = paddle.randn(shape=[10, 20], dtype="float32")
+
+    def inplace_api_processing(self, var):
+        return paddle.hypot_(var, self.y)
+
+    def non_inplace_api_processing(self, var):
+        return paddle.hypot(var, self.y)
+
+    def test_errors(self):
+        x = 3.0
+        self.assertRaises(TypeError, paddle.hypot_, x, self.y)
+        self.assertRaises(TypeError, paddle.hypot_, self.y, x)
+
+    def test_forward_version(self):
+        with paddle.base.dygraph.guard():
+            var = paddle.to_tensor(self.input_var_numpy).astype(self.dtype)
+            self.assertEqual(var.inplace_version, 0)
+
+            inplace_var = self.inplace_api_processing(var)
+            self.assertEqual(var.inplace_version, 3)
+
+            inplace_var[0] = 2.0
+            self.assertEqual(var.inplace_version, 4)
+
+            inplace_var = self.inplace_api_processing(inplace_var)
+            self.assertEqual(var.inplace_version, 7)
+
+    def test_backward_error(self):
+        # It raises an error because the inplace operator will result
+        # in incorrect gradient computation.
+        with paddle.base.dygraph.guard():
+            var_a = paddle.to_tensor(self.input_var_numpy).astype(self.dtype)
+            var_a.stop_gradient = False
+
+            var_b = var_a**2
+            # Here, the gradient computation will use the value of var_b
+            var_c = var_b**2
+            self.inplace_api_processing(var_b)
+            var_c = paddle.cast(var_c, "float32")
+
+            loss = paddle.nn.functional.relu(var_c)
+            with self.assertRaisesRegex(
+                RuntimeError,
+                f"received tensor_version:{3} != wrapper_version_snapshot:{0}",
+            ):
+                loss.backward()
+
+
 class TestDygraphInplaceNanToNum(TestDygraphInplace):
     def init_data(self):
         self.input_var_numpy = np.array(