remove range from fluid (#48086)

* remove range

python/paddle/fluid/dygraph/dygraph_to_static/convert_operators.py

@@ -20,7 +20,6 @@ from paddle.fluid.dygraph.dygraph_to_static.variable_trans_func import (
 )
 from paddle.fluid.framework import core, Variable
 from paddle.fluid.layers import Assert, Print
-from paddle.fluid.layers import range as paddle_range
 from paddle.fluid.layers import (
     array_length,
     array_read,
@@ -570,7 +569,7 @@ class VariableTuple:
         self.var = var
         self.len = convert_len(var)
         if isinstance(self.len, Variable):
-            self.rag = paddle_range(start, start + self.len, 1, paddle.int64)
+            self.rag = paddle.arange(start, start + self.len, 1, paddle.int64)
         else:
             self.rag = range(start, start + self.len)
 
@@ -592,11 +591,11 @@ def convert_range(*args):
     has_variable = any(map(lambda x: isinstance(x, Variable), args))
     if has_variable:
         if len(args) == 1:
-            return paddle_range(0, args[0], 1, paddle.int64)
+            return paddle.arange(0, args[0], 1, paddle.int64)
         if len(args) == 2:
-            return paddle_range(args[0], args[1], 1, paddle.int64)
+            return paddle.arange(args[0], args[1], 1, paddle.int64)
         if len(args) == 3:
-            return paddle_range(args[0], args[1], args[2], paddle.int64)
+            return paddle.arange(args[0], args[1], args[2], paddle.int64)
     return range(*args)
 
 

python/paddle/fluid/layers/rnn.py

@@ -1163,7 +1163,7 @@ class BeamSearchDecoder(Decoder):
         batch_size.stop_gradient = True  # TODO: remove this
         batch_pos = paddle.tile(
             nn.unsqueeze(
-                tensor.range(0, batch_size, 1, dtype=indices.dtype), [1]
+                paddle.arange(0, batch_size, 1, dtype=indices.dtype), [1]
             ),
             [1, self.beam_size],
         )

python/paddle/fluid/layers/tensor.py

@@ -66,7 +66,6 @@ __all__ = [
     'has_inf',
     'has_nan',
     'isfinite',
-    'range',
     'linspace',
     'zeros_like',
     'ones_like',
@@ -1635,108 +1634,6 @@ def isfinite(x):
     return out
 
 
-def range(start, end, step, dtype, name=None):
-    """
-    This OP returns a 1-D Tensor with spaced values within a given interval.
-
-    Values are generated into the half-open interval [``start``, ``end``) with
-    the ``step``. (the interval including ``start`` but excluding ``end``).
-
-    If ``dtype`` is float32 or float64, we advise adding a small epsilon to
-    ``end`` to avoid floating point rounding errors when comparing against ``end``.
-
-    Parameters:
-        start(float|int|Tensor): Start of interval. The interval includes this
-            value. If ``start`` is a Tensor, it is a 1-D Tensor with shape [1],
-            with data type int32, int64, float32, float64.
-        end(float|int|Tensor): End of interval. The interval does not include
-            this value. If ``end`` is a Tensor, it is a 1-D Tensor with shape
-            [1], with data type int32, int64, float32, float64.
-        step(float|int|Tensor): Spacing between values. For any out, it is
-            the istance between two adjacent values, out[i+1] - out[i]. If
-            ``step`` is a Tensor, it is a 1-D Tensor with shape [1], with data
-            type int32, int64, float32, float64.
-        dtype(str|np.dtype|core.VarDesc.VarType, optional): The data type of the
-            output tensor. Supported data types: int32, int64, float32, float64.
-        name(str, optional): The default value is None. Normally there is no
-            need for user to set this property. For more information, please
-            refer to :ref:`api_guide_Name`.
-
-    Returns:
-        Tensor: A 1-D Tensor with values from the interval [``start``, ``end``)
-            taken with common difference ``step`` beginning from ``start``. Its
-            data type is set by ``dtype``.
-
-    Raises:
-        TypeError: If ``dtype`` is not int32, int64, float32, float64.
-
-    examples:
-
-        .. code-block:: python
-
-            import paddle.fluid as fluid
-
-            out1 = fluid.layers.range(0, 10, 2, 'int32')
-            # [0, 2, 4, 6, 8]
-
-            start_var = fluid.layers.fill_constant([1], 'int64', 3)
-            out2 = fluid.layers.range(start_var, 7, 1, 'int64')
-            # [3, 4, 5, 6]
-
-    """
-    out_shape = None
-    if (
-        not isinstance(start, Variable)
-        and not isinstance(end, Variable)
-        and not isinstance(step, Variable)
-    ):
-        out_shape = [int(math.ceil((end - start) / step))]
-
-    if not isinstance(dtype, core.VarDesc.VarType):
-        dtype = convert_np_dtype_to_dtype_(dtype)
-
-    if not isinstance(start, Variable):
-        with device_guard("cpu"):
-            start = fill_constant([1], dtype, start, force_cpu=True)
-    elif start.dtype != dtype:
-        start = cast(start, dtype)
-
-    if not isinstance(end, Variable):
-        with device_guard("cpu"):
-            end = fill_constant([1], dtype, end, force_cpu=True)
-    elif end.dtype != dtype:
-        end = cast(end, dtype)
-
-    if not isinstance(step, Variable):
-        with device_guard("cpu"):
-            step = fill_constant([1], dtype, step, force_cpu=True)
-    elif step.dtype != dtype:
-        step = cast(step, dtype)
-
-    if in_dygraph_mode():
-        return _C_ops.arange(start, end, step, dtype, _current_expected_place())
-
-    if _in_legacy_dygraph():
-        out = _legacy_C_ops.range(start, end, step)
-        out.stop_gradient = True
-        return out
-
-    check_dtype(
-        dtype, 'dtype', ['float32', 'float64', 'int32', 'int64'], 'range/arange'
-    )
-    helper = LayerHelper('range', **locals())
-    out = helper.create_variable_for_type_inference(dtype, shape=out_shape)
-    helper.append_op(
-        type='range',
-        inputs={'Start': start, 'End': end, 'Step': step},
-        outputs={'Out': out},
-    )
-    out.stop_gradient = True
-    if out_shape is not None:
-        out.desc.set_shape(out_shape)
-    return out
-
-
 def linspace(start, stop, num, dtype=None, name=None):
     r"""
     This OP return fixed number of evenly spaced values within a given interval.

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

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import numpy as np
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 import unittest
@@ -43,7 +44,7 @@ def build_and_run_program(place, batch_size, beam_size, stop_gradient=False):
             bs = layers.cast(bs, 'int64')
         bs.stop_gradient = stop_gradient
         batch_pos = layers.expand(
-            layers.unsqueeze(layers.range(0, bs, 1, dtype=bs.dtype), [1]),
+            layers.unsqueeze(paddle.arange(0, bs, 1, dtype=bs.dtype), [1]),
             [1, beam_size],
         )
         topk_coordinates = paddle.stack([batch_pos, indices], axis=2)

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

@@ -4023,13 +4023,13 @@ class TestBook(LayerTest):
         with program_guard(
             fluid.default_main_program(), fluid.default_startup_program()
         ):
-            layers.range(0, 10, 2, 'int32')
-            layers.range(0.1, 10.0, 0.2, 'float32')
-            layers.range(0.1, 10.0, 0.2, 'float64')
+            paddle.arange(0, 10, 2, 'int32')
+            paddle.arange(0.1, 10.0, 0.2, 'float32')
+            paddle.arange(0.1, 10.0, 0.2, 'float64')
             start = layers.fill_constant(shape=[1], value=0.1, dtype="float32")
             end = layers.fill_constant(shape=[1], value=10.0, dtype="float32")
             step = layers.fill_constant(shape=[1], value=0.2, dtype="float32")
-            y = layers.range(start, end, step, 'float64')
+            y = paddle.arange(start, end, step, 'float64')
             return y
 
     def make_spectral_norm(self):