[API compatibility] add paddle.ravel

python/paddle/__init__.py

@@ -328,6 +328,7 @@
  masked_scatter_,
  moveaxis,
  put_along_axis,
+ ravel,
  repeat_interleave,
  reshape,
  reshape_,
@@ -1092,6 +1093,7 @@
  'std',
  'flatten',
  'flatten_',
+ 'ravel',
  'asin',
  'multiply',
  'multiply_',

python/paddle/tensor/__init__.py

@@ -193,6 +193,7 @@
  moveaxis,
  put_along_axis,
  put_along_axis_,
+ ravel,
  repeat_interleave,
  reshape,
  reshape_,

python/paddle/tensor/manipulation.py

@@ -1992,6 +1992,46 @@ def flatten(
  return out
 
 
+def ravel(input: Tensor) -> Tensor:
+ """
+ Return a contiguous flattened tensor. A copy is made only if needed.
+ Note:
+ The output Tensor will share data with origin Tensor and doesn't have a Tensor copy in ``dygraph`` mode.
+ If you want to use the Tensor copy version, please use `Tensor.clone` like ``ravel_clone_x = x.ravel().clone()``.
+ For example:
+
+ .. code-block:: text
+ Case 1:
+ Given
+ X.shape = (3, 100, 100, 4)
+
+ We get:
+ Out.shape = (3 * 100 * 100 * 4)
+ Args:
+ x (Tensor): A tensor of number of dimensions >= axis. A tensor with data type float16, float32,
+ float64, int8, int32, int64, uint8.
+
+ Returns:
+ Tensor: A tensor with the contents of the input tensor, whose input axes are flattened by indicated :attr:`start_axis` and :attr:`stop_axis`, and data type is the same as input :attr:`x`.
+
+ Examples:
+
+ .. code-block:: python
+
+ >>> import paddle
+
+ >>> image_shape=(2, 3, 4, 4)
+
+ >>> x = paddle.arange(end=image_shape[0] * image_shape[1] * image_shape[2] * image_shape[3])
+ >>> img = paddle.reshape(x, image_shape)
+
+ >>> out = paddle.ravel(img)
+ >>> print(out.shape)
+ [96]
+ """
+ return flatten(input)
+
+
 @inplace_apis_in_dygraph_only
 def flatten_(
  x: Tensor, start_axis: int = 0, stop_axis: int = -1, name: str | None = None

test/legacy_test/test_ravel.py

@@ -0,0 +1,132 @@
+# Copyright (c) 2025 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
+
+
+class TestPaddleRavel(unittest.TestCase):
+ def setUp(self):
+ self.input_np = np.array([[1, 2, 3], [4, 5, 6]], dtype="float32")
+ self.input_shape = self.input_np.shape
+ self.input_dtype = "float32"
+ self.op_static = lambda x: paddle.ravel(x)
+ self.op_dygraph = lambda x: paddle.ravel(x)
+ self.expected = lambda x: x.flatten()
+ self.places = [None, paddle.CPUPlace()]
+
+ def check_static_result(self, place):
+ paddle.enable_static()
+ main_prog = paddle.static.Program()
+ startup_prog = paddle.static.Program()
+ with paddle.static.program_guard(main_prog, startup_prog):
+ input_name = 'input'
+ input_var = paddle.static.data(
+ name=input_name, shape=self.input_shape, dtype=self.input_dtype
+ )
+ res = self.op_static(input_var)
+ exe = base.Executor(place)
+ fetches = exe.run(
+ main_prog,
+ feed={input_name: self.input_np},
+ fetch_list=[res],
+ )
+ expect = (
+ self.expected(self.input_np)
+ if callable(self.expected)
+ else self.expected
+ )
+ np.testing.assert_allclose(fetches[0], expect, rtol=1e-05)
+
+ def test_static(self):
+ for place in self.places:
+ self.check_static_result(place=place)
+
+ def check_dygraph_result(self, place):
+ with base.dygraph.guard(place):
+ input = paddle.to_tensor(self.input_np, stop_gradient=False)
+ result = self.op_dygraph(input)
+ expect = (
+ self.expected(self.input_np)
+ if callable(self.expected)
+ else self.expected
+ )
+ # check forward
+ np.testing.assert_allclose(result.numpy(), expect, rtol=1e-05)
+
+ # check backward
+ paddle.autograd.backward([result])
+ np.testing.assert_allclose(
+ input.grad.numpy(), np.ones_like(self.input_np), rtol=1e-05
+ )
+
+ def test_dygraph(self):
+ for place in self.places:
+ self.check_dygraph_result(place=place)
+
+
+class TestPaddleRavel_case1(TestPaddleRavel):
+ def setUp(self):
+ # check Ravel 1d
+ self.input_np = np.array([7, 8, 9], dtype="float32")
+ self.input_shape = self.input_np.shape
+ self.input_dtype = "float32"
+ self.op_static = lambda x: paddle.ravel(x)
+ self.op_dygraph = lambda x: paddle.ravel(x)
+ self.expected = lambda x: x.flatten()
+ self.places = [None, paddle.CPUPlace()]
+
+
+class TestPaddleRavel_case2(TestPaddleRavel):
+ def setUp(self):
+ # check Ravel 3d
+ self.input_np = np.arange(24, dtype="float32").reshape(2, 3, 4)
+ self.input_shape = self.input_np.shape
+ self.input_dtype = "float32"
+ self.op_static = lambda x: paddle.ravel(x)
+ self.op_dygraph = lambda x: paddle.ravel(x)
+ self.expected = lambda x: x.flatten()
+ self.places = [None, paddle.CPUPlace()]
+
+
+class TestPaddleRavel_case3(TestPaddleRavel):
+ def setUp(self):
+ # check Ravel 0d (scalar)
+ self.input_np = np.array(5.0, dtype="float32")
+ self.input_shape = self.input_np.shape
+ self.input_dtype = "float32"
+ self.op_static = lambda x: paddle.ravel(x)
+ self.op_dygraph = lambda x: paddle.ravel(x)
+ self.expected = lambda x: x.flatten()
+ self.places = [None, paddle.CPUPlace()]
+
+
+class TestPaddleRavel_case4(TestPaddleRavel):
+ def setUp(self):
+ # check Ravel empty array
+ self.input_np = np.array([], dtype="float32").reshape(0, 3)
+ self.input_shape = self.input_np.shape
+ self.input_dtype = "float32"
+ self.op_static = lambda x: paddle.ravel(x)
+ self.op_dygraph = lambda x: paddle.ravel(x)
+ self.expected = lambda x: x.flatten()
+ self.places = [None, paddle.CPUPlace()]
+
+
+if __name__ == "__main__":
+ unittest.main()