[Clean Fluid API]Remove lod_reset (#48516)

* solve conflicts of remove_lod_reset and latest develop version

* add lod_reset to the no_grad_set_white_list.py

python/paddle/fluid/layers/nn.py

@@ -70,7 +70,6 @@ __all__ = [
     'spectral_norm',
     'one_hot',
     'autoincreased_step_counter',
-    'lod_reset',
     'clip',
     'clip_by_norm',
     'merge_selected_rows',
@@ -1385,110 +1384,6 @@ def unsqueeze(input, axes, name=None):
     return out
 
 
-def lod_reset(x, y=None, target_lod=None):
-    """
-    Set LoD of :attr:`x` to a new one specified by :attr:`y` or
-    :attr:`target_lod`. When :attr:`y` provided, :attr:`y.lod` would be
-    considered as target LoD first, otherwise :attr:`y.data` would be
-    considered as target LoD. If :attr:`y` is not provided, target LoD should
-    be specified by :attr:`target_lod`. If target LoD is specified by
-    :attr:`y.data` or :attr:`target_lod`, only one level LoD is supported.
-
-    .. code-block:: text
-
-        * Example 1:
-
-            Given a 1-level LoDTensor x:
-                x.lod =  [[ 2,           3,                   1 ]]
-                x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
-                x.dims = [6, 1]
-
-            target_lod: [4, 2]
-
-            then we get a 1-level LoDTensor:
-                out.lod =  [[4,                          2]]
-                out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
-                out.dims = [6, 1]
-
-        * Example 2:
-
-            Given a 1-level LoDTensor x:
-                x.lod =  [[2,            3,                   1]]
-                x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
-                x.dims = [6, 1]
-
-            y is a Tensor:
-                y.data = [[2, 4]]
-                y.dims = [1, 3]
-
-            then we get a 1-level LoDTensor:
-                out.lod =  [[2,            4]]
-                out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
-                out.dims = [6, 1]
-
-        * Example 3:
-
-            Given a 1-level LoDTensor x:
-                x.lod =  [[2,            3,                   1]]
-                x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
-                x.dims = [6, 1]
-
-            y is a 2-level LoDTensor:
-                y.lod =  [[2, 2], [2, 2, 1, 1]]
-                y.data = [[1.1], [2.1], [3.1], [4.1], [5.1], [6.1]]
-                y.dims = [6, 1]
-
-            then we get a 2-level LoDTensor:
-                out.lod =  [[2, 2], [2, 2, 1, 1]]
-                out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
-                out.dims = [6, 1]
-
-    Args:
-        x (Variable): Input variable which could be a Tensor or LoDTensor.
-                      The data type should be int32, int64, float32 or float64.
-        y (Variable, optional): If provided, output's LoD would be derived from :attr:`y`.
-                                If y's lod level>0, the data type can be any type.
-                                If y's lod level=0, the data type should be int32.
-        target_lod (list|tuple, optional): One level LoD which should be considered
-                                      as target LoD when :attr:`y` not provided.
-
-    Returns:
-        Variable: Output variable with LoD specified by this layer.
-
-    Raises:
-        ValueError: If :attr:`y` and :attr:`target_lod` are both None.
-
-    Examples:
-        .. code-block:: python
-
-            import paddle.fluid as fluid
-            x = fluid.layers.data(name='x', shape=[10])
-            y = fluid.layers.data(name='y', shape=[10, 20], lod_level=2)
-            out = fluid.layers.lod_reset(x=x, y=y)
-    """
-    check_variable_and_dtype(
-        x, 'x', ['float32', 'float64', 'int32', 'int64'], 'lod_reset'
-    )
-    helper = LayerHelper("lod_reset", **locals())
-    out = helper.create_variable_for_type_inference(dtype=x.dtype)
-    if y is not None:
-        check_type(y, 'y', (Variable), 'lod_reset')
-        # TODO: check y.lod_level = 0 dtype
-        helper.append_op(
-            type="lod_reset", inputs={'X': x, 'Y': y}, outputs={'Out': out}
-        )
-    elif target_lod is not None:
-        helper.append_op(
-            type="lod_reset",
-            inputs={'X': x},
-            attrs={'target_lod': target_lod},
-            outputs={'Out': out},
-        )
-    else:
-        raise ValueError("y and target_lod should not be both none.")
-    return out
-
-
 def _logical_op(op_name, x, y, out=None, name=None, binary_op=True):
     if _non_static_mode():
         op = getattr(_legacy_C_ops, op_name)

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -500,7 +500,6 @@ set(TEST_OPS_WITH_GC
     test_gather_op
     test_gather_nd_op
     test_linear_chain_crf_op
-    test_lod_reset_op
     test_lookup_table_op
     test_mean_op
     test_pad2d_op

python/paddle/fluid/tests/unittests/test_layers.py

@@ -2593,25 +2593,6 @@ class TestBook(LayerTest):
             out = paddle.nn.functional.square_error_cost(input=x, label=y)
             return out
 
-    def test_lod_reset(self):
-        # TODO(minqiyang): dygraph do not support lod now
-        with self.static_graph():
-            # case 1
-            x = layers.data(name='x', shape=[10], dtype='float32')
-            y = layers.data(
-                name='y', shape=[10, 20], dtype='float32', lod_level=2
-            )
-            z = layers.lod_reset(x=x, y=y)
-            self.assertTrue(z.lod_level == 2)
-            # case 2
-            lod_tensor_in = layers.data(name='lod_in', shape=[1], dtype='int32')
-            z = layers.lod_reset(x=x, y=lod_tensor_in)
-            self.assertTrue(z.lod_level == 1)
-            # case 3
-            z = layers.lod_reset(x=x, target_lod=[1, 2, 3])
-            self.assertTrue(z.lod_level == 1)
-            return z
-
     def test_affine_grid(self):
         with self.static_graph():
             data = layers.data(name='data', shape=[2, 3, 3], dtype="float32")

python/paddle/fluid/tests/unittests/test_lod_reset_op.py

@@ -17,9 +17,6 @@ import unittest
 import numpy as np
 from op_test import OpTest
 
-import paddle.fluid as fluid
-from paddle.fluid import Program, program_guard
-
 
 class TestLodResetOpByAttr(OpTest):
     def setUp(self):
@@ -134,24 +131,5 @@ class TestLodAppendOpByAttr(OpTest):
         self.check_grad(["X"], "Out", check_dygraph=False)
 
 
-class TestLodResetOpError(unittest.TestCase):
-    def test_errors(self):
-        with program_guard(Program(), Program()):
-            # The input must be Variable.
-            x1 = np.array([0.9383, 0.1983, 3.2, 1.2]).astype("float64")
-            target_lod = [2, 2]
-            self.assertRaises(TypeError, fluid.layers.lod_reset, x1, target_lod)
-
-            # Input(x) dtype must be float32 or float64 or int32 or int64
-            for dtype in ["bool", "float16"]:
-                x2 = fluid.layers.data(
-                    name='x2' + dtype, shape=[4], dtype=dtype
-                )
-                y2 = fluid.layers.data(
-                    name='y2' + dtype, shape=[4], dtype='int32', lod_level=2
-                )
-                self.assertRaises(TypeError, fluid.layers.lod_reset, x2, y2)
-
-
 if __name__ == '__main__':
     unittest.main()