[API compatibility] add paddle.Tensor.ravel (#74454)

* add ravel api

* use paddle.Tensor.ravel for testcase

* replace ravel param x with input

* change copyright time

* add only Tensor.ravel

Author: Starrysea996

python/paddle/tensor/__init__.py

@@ -680,6 +680,7 @@
     'expand',
     'broadcast_to',
     'expand_as',
+    'ravel',
     'flatten',
     'flatten_',
     'gather',

test/legacy_test/test_ravel_op.py

@@ -0,0 +1,237 @@
+#   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
+from op_test import OpTest, convert_float_to_uint16
+
+import paddle
+from paddle.base import core
+
+
+class TestRavelOp(OpTest):
+    def setUp(self):
+        self.python_api = paddle.Tensor.ravel
+        self.public_python_api = paddle.Tensor.ravel
+        self.python_out_sig = ["Out"]
+        self.op_type = "flatten_contiguous_range"
+        self.prim_op_type = "comp"
+        self.start_axis = 0
+        self.stop_axis = -1
+        self.if_enable_cinn()
+        self.init_test_case()
+        self.init_test_dtype()
+        self.init_input_data()
+        self.init_attrs()
+        self.outputs = {
+            "Out": self.inputs["X"].reshape(self.new_shape),
+            "XShape": np.random.random(self.in_shape).astype("float32"),
+        }
+
+    def if_enable_cinn(self):
+        pass
+
+    def test_check_output(self):
+        if str(self.dtype) in {"float16", "uint16"}:
+            self.check_output_with_place(
+                core.CUDAPlace(0),
+                no_check_set=["XShape"],
+                check_prim=True,
+                check_pir=True,
+                check_prim_pir=True,
+            )
+        else:
+            self.check_output(
+                no_check_set=["XShape"],
+                check_prim=True,
+                check_pir=True,
+                check_prim_pir=True,
+            )
+
+    def test_check_grad(self):
+        if str(self.dtype) in {"float16", "uint16"}:
+            self.check_grad_with_place(
+                core.CUDAPlace(0),
+                ["X"],
+                "Out",
+                check_prim=True,
+                check_pir=True,
+            )
+        else:
+            self.check_grad(["X"], "Out", check_prim=True, check_pir=True)
+
+    def init_test_case(self):
+        self.in_shape = (3, 2, 5, 4)
+        self.start_axis = 0
+        self.stop_axis = -1
+        self.new_shape = 120
+
+    def init_attrs(self):
+        self.attrs = {
+            "start_axis": self.start_axis,
+            "stop_axis": self.stop_axis,
+        }
+
+    def init_test_dtype(self):
+        self.dtype = "float64"
+
+    def init_input_data(self):
+        if str(self.dtype) != "uint16":
+            x = np.random.random(self.in_shape).astype(self.dtype)
+        else:
+            x = np.random.random(self.in_shape).astype("float32")
+            x = convert_float_to_uint16(x)
+
+        self.inputs = {"X": x}
+
+
+class TestRavelFP32Op(TestRavelOp):
+    def init_test_dtype(self):
+        self.dtype = "float32"
+
+
+@unittest.skipIf(
+    not core.is_compiled_with_cuda(),
+    "core is not compiled with CUDA",
+)
+class TestRavelFP16Op(TestRavelOp):
+    def init_test_dtype(self):
+        self.dtype = "float16"
+
+
+@unittest.skipIf(
+    not core.is_compiled_with_cuda()
+    or not core.is_bfloat16_supported(core.CUDAPlace(0)),
+    "core is not compiled with CUDA and not support the bfloat16",
+)
+class TestRavelBF16Op(TestRavelOp):
+    def if_enable_cinn(self):
+        pass
+
+    def init_test_dtype(self):
+        self.dtype = "uint16"
+
+
+class TestRavelOp_ZeroDim(TestRavelOp):
+    def init_test_case(self):
+        self.in_shape = ()
+        self.start_axis = 0
+        self.stop_axis = -1
+        self.new_shape = (1,)
+
+    def if_enable_cinn(self):
+        self.enable_cinn = False
+
+    def init_attrs(self):
+        self.attrs = {
+            "start_axis": self.start_axis,
+            "stop_axis": self.stop_axis,
+        }
+
+
+class TestRavelFP32Op_ZeroDim(TestRavelOp_ZeroDim):
+    def init_test_dtype(self):
+        self.dtype = "float32"
+
+
+@unittest.skipIf(
+    not core.is_compiled_with_cuda(),
+    "core is not compiled with CUDA",
+)
+class TestRavelFP16Op_ZeroDim(TestRavelOp_ZeroDim):
+    def init_test_dtype(self):
+        self.dtype = "float16"
+
+
+class TestRavelOpError(unittest.TestCase):
+    def test_errors(self):
+        image_shape = (2, 3, 4, 4)
+        x = (
+            np.arange(
+                image_shape[0]
+                * image_shape[1]
+                * image_shape[2]
+                * image_shape[3]
+            ).reshape(image_shape)
+            / 100.0
+        )
+        x = x.astype('float32')
+
+        def test_InputError():
+            out = paddle.Tensor.ravel(x)
+
+        self.assertRaises(ValueError, test_InputError)
+
+
+class TestStaticRavelPythonAPI(unittest.TestCase):
+    def execute_api(self, x):
+        return paddle.Tensor.ravel(x)
+
+    def test_static_api(self):
+        paddle.enable_static()
+        np_x = np.random.rand(2, 3, 4, 4).astype('float32')
+
+        main_prog = paddle.static.Program()
+        with paddle.static.program_guard(main_prog, paddle.static.Program()):
+            x = paddle.static.data(
+                name="x", shape=[2, 3, 4, 4], dtype='float32'
+            )
+            out = self.execute_api(x)
+
+        exe = paddle.static.Executor(place=paddle.CPUPlace())
+        fetch_out = exe.run(main_prog, feed={"x": np_x}, fetch_list=[out])
+        self.assertTrue((96,) == fetch_out[0].shape)
+
+
+class TestStaticRavelInferShapePythonAPI(unittest.TestCase):
+    def execute_api(self, x):
+        return paddle.Tensor.ravel(x)
+
+    def test_static_api(self):
+        paddle.enable_static()
+        main_prog = paddle.static.Program()
+        with paddle.static.program_guard(main_prog, paddle.static.Program()):
+            x = paddle.static.data(
+                name="x", shape=[-1, 3, -1, -1], dtype='float32'
+            )
+            out = self.execute_api(x)
+        self.assertTrue((-1,) == tuple(out.shape))
+
+
+class TestRavelZeroSizedTensorAPI(unittest.TestCase):
+    def test_dygraph(self):
+        paddle.disable_static()
+        data = np.random.randn(2, 3, 0)
+        x = paddle.to_tensor(data)
+        out = paddle.Tensor.ravel(x)
+        out_np = data.flatten()
+        np.testing.assert_equal(out.numpy(), out_np)
+
+    def test_static(self):
+        paddle.enable_static()
+        data = np.random.randn(2, 3, 0)
+        main_prog = paddle.static.Program()
+        with paddle.static.program_guard(main_prog, paddle.static.Program()):
+            x = paddle.static.data(name="x", shape=[2, 3, 0], dtype='float64')
+            out = paddle.Tensor.ravel(x)
+
+        exe = paddle.static.Executor(place=paddle.CPUPlace())
+        fetch_out = exe.run(main_prog, feed={"x": data}, fetch_list=[out])[0]
+        out_np = data.flatten()
+        np.testing.assert_equal(fetch_out, out_np)
+
+
+if __name__ == "__main__":
+    unittest.main()