creation.py 81.6 KB
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#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
# 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.

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# TODO: define functions to get create a tensor

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import math
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import re
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import warnings

import numpy as np

import paddle
from paddle import _C_ops, _legacy_C_ops
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from paddle.common_ops_import import fill_constant
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from ..fluid.data_feeder import (
    check_dtype,
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    check_type,
    check_variable_and_dtype,
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    convert_dtype,
)
from ..fluid.framework import (
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    Variable,
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    _in_eager_without_dygraph_check,
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    _in_legacy_dygraph,
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    device_guard,
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)
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from ..fluid.initializer import Initializer
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from ..fluid.layers import utils
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from ..fluid.param_attr import ParamAttr
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from ..framework import (
    LayerHelper,
    _current_expected_place,
    _get_paddle_place,
    _non_static_mode,
    convert_np_dtype_to_dtype_,
    core,
    in_dygraph_mode,
)
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__all__ = []

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def _complex_to_real_dtype(dtype):
    if dtype == core.VarDesc.VarType.COMPLEX64:
        return core.VarDesc.VarType.FP32
    elif dtype == core.VarDesc.VarType.COMPLEX128:
        return core.VarDesc.VarType.FP64
    else:
        return dtype


def _real_to_complex_dtype(dtype):
    if dtype == core.VarDesc.VarType.FP32:
        return core.VarDesc.VarType.COMPLEX64
    elif dtype == core.VarDesc.VarType.FP64:
        return core.VarDesc.VarType.COMPLEX128
    else:
        return dtype


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def create_parameter(
    shape, dtype, name=None, attr=None, is_bias=False, default_initializer=None
):
    """
    This function creates a parameter. The parameter is a learnable variable, which can have
    gradient, and can be optimized.

    Note:
        This is a very low-level API. This API is useful when you create operator by your self, instead of using layers.

    Args:
        shape (list of int): Shape of the parameter
        dtype (str): Data type of the parameter
        name (str, optional): For detailed information, please refer to
           :ref:`api_guide_Name` . Usually name is no need to set and None by default.
        attr (ParamAttr, optional): Attributes of the parameter
        is_bias (bool, optional): This can affect which default initializer is chosen
                       when default_initializer is None. If is_bias,
                       initializer.Constant(0.0) will be used. Otherwise,
                       Xavier() will be used.
        default_initializer (Initializer, optional): Initializer for the parameter

    Returns:
        The created parameter.

    Examples:
        .. code-block:: python

            import paddle
            paddle.enable_static()
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            W = paddle.create_parameter(shape=[784, 200], dtype='float32')
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    """
    check_type(shape, 'shape', (list, tuple, np.ndarray), 'create_parameter')
    for item in shape:
        check_type(
            item,
            'item of shape',
            (
                int,
                np.uint8,
                np.int8,
                np.int16,
                np.int32,
                np.int64,
            ),
            'create_parameter',
        )

    check_dtype(
        dtype,
        'dtype',
        [
            'bool',
            'float16',
            'float32',
            'float64',
            'int8',
            'int16',
            'int32',
            'int64',
            'uint8',
        ],
        'create_parameter',
    )
    check_type(attr, 'attr', (type(None), ParamAttr), 'create_parameter')
    check_type(
        default_initializer,
        'default_initializer',
        (type(None), Initializer),
        'create_parameter',
    )

    helper = LayerHelper("create_parameter", **locals())
    if attr is None:
        attr = ParamAttr(name=name)
    return helper.create_parameter(
        attr, shape, convert_dtype(dtype), is_bias, default_initializer
    )


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def create_tensor(dtype, name=None, persistable=False):
    """
    Create a variable, which will hold a Tensor with data type dtype.

    Args:
        dtype(string|numpy.dtype): the data type of Tensor to be created, the
            data type is bool, float16, float32, float64, int8, int16, int32 and int64.
        name(string, 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`
        persistable(bool): Set the persistable flag of the create tensor.
            default value is False.

    Returns:
        Variable: The tensor to be created according to dtype.

    Examples:
        .. code-block:: python

          import paddle
          tensor = paddle.tensor.create_tensor(dtype='float32')
    """
    check_dtype(
        dtype,
        'dtype',
        [
            'bool',
            'float16',
            'float32',
            'float64',
            'int8',
            'int32',
            'int32',
            'int64',
        ],
        'create_tensor',
    )
    helper = LayerHelper("create_tensor", **locals())
    return helper.create_variable(
        name=helper.name, dtype=dtype, persistable=persistable
    )


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def linspace(start, stop, num, dtype=None, name=None):
    r"""
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    Return fixed number of evenly spaced values within a given interval.
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    Args:
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        start(int|float|Tensor): The input :attr:`start` is start of range. It is a int, float, \
            or a 0-D Tensor with data type int32, int64, float32 or float64.
        stop(int|float|Tensor): The input :attr:`stop` is start variable of range. It is a int, float, \
            or a 0-D Tensor with data type int32, int64, float32 or float64.
        num(int|Tensor): The input :attr:`num` is given num of the sequence. It is an int, \
            or a 0-D Tensor with data type int32.
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        dtype(np.dtype|str, optional): The data type of output tensor, it could be
            int32, int64, float32 and float64. Default: if None, the data type is float32.
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        name(str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
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    Returns:
        Tensor: the output data type will be float32, float64. The 1-D tensor with fixed number of evenly spaced values, \
        the data shape of this tensor is :math:`[num]` . If the :attr:`num` is set 1, the output tensor just has \
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        the value with input :attr:`start`.
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    Examples:
        .. code-block:: python

             import paddle
             data = paddle.linspace(0, 10, 5, 'float32') # [0.0,  2.5,  5.0,  7.5, 10.0]
             data = paddle.linspace(0, 10, 1, 'float32') # [0.0]

    """
    if dtype is None:
        dtype = 'float32'
    tensor_num = num
    tensor_start = start
    tensor_stop = stop
    if not isinstance(num, Variable):
        check_type(num, 'num', (int), 'linspace')
    if not isinstance(dtype, core.VarDesc.VarType):
        dtype = convert_np_dtype_to_dtype_(dtype)
    if not isinstance(start, Variable):
        with device_guard("cpu"):
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            tensor_start = fill_constant([1], dtype, start, force_cpu=True)
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    if not isinstance(stop, Variable):
        with device_guard("cpu"):
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            tensor_stop = fill_constant([1], dtype, stop, force_cpu=True)
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    if not isinstance(num, Variable):
        with device_guard("cpu"):
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            tensor_num = fill_constant([1], 'int32', num, force_cpu=True)
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    if in_dygraph_mode():
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        return _C_ops.linspace(
            tensor_start,
            tensor_stop,
            tensor_num,
            dtype,
            _current_expected_place(),
        )
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    if _in_legacy_dygraph():
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        return _legacy_C_ops.linspace(
            tensor_start, tensor_stop, tensor_num, 'dtype', dtype
        )
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    helper = LayerHelper("linspace", **locals())

    start_dtype = convert_dtype(tensor_start.dtype)
    stop_dtype = convert_dtype(tensor_stop.dtype)
    out_dtype = convert_dtype(dtype)
    if isinstance(start, Variable):
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        check_dtype(
            start.dtype,
            'start',
            ['float32', 'float64', 'int32', 'int64'],
            'linspace',
        )
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    else:
        check_type(start, 'start', (int, float), 'linspace')

    if isinstance(stop, Variable):
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        check_dtype(
            stop.dtype,
            'stop',
            ['float32', 'float64', 'int32', 'int64'],
            'linspace',
        )
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    else:
        check_type(stop, 'stop', (int, float), 'linspace')
    if isinstance(num, Variable):
        check_dtype(num.dtype, 'num', ['int32'], 'linspace')
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    check_dtype(
        dtype, 'dtype', ['int32', 'int64', 'float32', 'float64'], 'linspace'
    )
    if (
        (stop_dtype == "float64" or start_dtype == "float64")
        and out_dtype in ["float32", "int32"]
    ) or (
        (stop_dtype == "int64" or start_dtype == "int64")
        and out_dtype == "int32"
    ):
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        raise ValueError(
            "The dtype of start/stop is {}/{} but the attr(dtype) of linspace is {}, "
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            "which may cause data type overflows. Please reset attr(dtype) of linspace.".format(
                start_dtype, stop_dtype, dtype
            )
        )
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    out = helper.create_variable_for_type_inference(dtype=dtype)

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    helper.append_op(
        type='linspace',
        inputs={'Start': tensor_start, 'Stop': tensor_stop, 'Num': tensor_num},
        attrs={'dtype': dtype},
        outputs={'Out': [out]},
    )
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    if isinstance(num, int):
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        out.desc.set_shape((num,))
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    return out


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def logspace(start, stop, num, base=10.0, dtype=None, name=None):
    r"""
    Return fixed number of logarithmical-evenly spaced values within the interval \
    :math:`[base^{start}, base^{stop}]`.
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    Notes:
        This API does not compute the gradient.
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    Args:
        start(int|float|Tensor): The input :attr:`start` is exponent of first entry in \
            the sequence. It is a scalar, or a Tensor of shape [1] with input data \
            type int32, int64, float32 or float64.
        stop(int|float|Tensor): The input :attr:`stop` is exponent of last entry in the \
            sequence. It is a scalar, or a Tensor of shape [1] with input data \
            type int32, int64, float32 or float64.
        num(int|Tensor): The input :attr:`num` is given number of items in the sequence. \
            It is an int scalar, or a Tensor of shape [1] with data type int32.
        base(int|float|Tensor): The input :attr:`base` is base of the logarithm function. \
            It is a scalar, or a Tensor of shape [1] with input data type int32, int64, \
            float32 or float64.
        dtype(np.dtype|str, optional): The data type of output tensor, it could be \
            int32, int64, float32 or float64. Default: if None, the data type is float32. \
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        name(str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
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    Returns:
        Tensor: The output data type will be float32, float64. The 1-D tensor with \
        fixed number of logarithmical-evenly spaced values, the data shape of this \
        tensor is :math:`[num]`. If the :attr:`num` is set 1, the output tensor \
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        just has the value with exponential of :attr:`start` with base :attr:`base`.
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    Examples:
        .. code-block:: python

            import paddle
            data = paddle.logspace(0, 10, 5, 2, 'float32')
            # [1.          , 5.65685415  , 32.         , 181.01933289, 1024.       ]
            data = paddle.logspace(0, 10, 1, 2, 'float32')
            # [1.]
    """
    if dtype is None:
        dtype = 'float32'
    tensor_num = num
    tensor_start = start
    tensor_stop = stop
    tensor_base = base
    if not isinstance(num, Variable):
        check_type(num, 'num', (int), 'logspace')
    if not isinstance(dtype, core.VarDesc.VarType):
        dtype = convert_np_dtype_to_dtype_(dtype)
    if not isinstance(start, Variable):
        with device_guard("cpu"):
            tensor_start = fill_constant([1], dtype, start)
    if not isinstance(stop, Variable):
        with device_guard("cpu"):
            tensor_stop = fill_constant([1], dtype, stop)
    if not isinstance(num, Variable):
        with device_guard("cpu"):
            tensor_num = fill_constant([1], 'int32', num)
    if not isinstance(base, Variable):
        with device_guard("cpu"):
            tensor_base = fill_constant([1], dtype, base)
    if _non_static_mode():
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        return _legacy_C_ops.logspace(
            tensor_start, tensor_stop, tensor_num, tensor_base, 'dtype', dtype
        )
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    helper = LayerHelper("logspace", **locals())

    start_dtype = convert_dtype(tensor_start.dtype)
    stop_dtype = convert_dtype(tensor_stop.dtype)
    base_dtype = convert_dtype(tensor_base.dtype)
    out_dtype = convert_dtype(dtype)
    if isinstance(start, Variable):
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        check_dtype(
            start.dtype,
            'start',
            ['float32', 'float64', 'int32', 'int64'],
            'logspace',
        )
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    else:
        check_type(start, 'start', (int, float), 'logspace')

    if isinstance(stop, Variable):
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        check_dtype(
            stop.dtype,
            'stop',
            ['float32', 'float64', 'int32', 'int64'],
            'logspace',
        )
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    else:
        check_type(stop, 'stop', (int, float), 'logspace')

    if isinstance(num, Variable):
        check_dtype(num.dtype, 'num', ['int32'], 'logspace')

    if isinstance(base, Variable):
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        check_dtype(
            base.dtype,
            'base',
            ['float32', 'float64', 'int32', 'int64'],
            'logspace',
        )
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    else:
        check_type(base, 'base', (int, float), 'logspace')

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    check_dtype(
        dtype, 'dtype', ['int32', 'int64', 'float32', 'float64'], 'logspace'
    )
    if (
        (
            stop_dtype == "float64"
            or start_dtype == "float64"
            or base_dtype == "float64"
        )
        and out_dtype in ["float32", "int32"]
    ) or (
        (
            stop_dtype == "int64"
            or start_dtype == "int64"
            or base_dtype == "int64"
        )
        and out_dtype == "int32"
    ):
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        raise ValueError(
            "The dtype of start/stop/base is {}/{}/{} but the attr(dtype) of logspace is {}, "
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            "which may cause data type overflows. Please reset attr(dtype) of logspace.".format(
                start_dtype, stop_dtype, base_dtype, dtype
            )
        )
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    out = helper.create_variable_for_type_inference(dtype=dtype)

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    helper.append_op(
        type='logspace',
        inputs={
            'Start': tensor_start,
            'Stop': tensor_stop,
            'Num': tensor_num,
            'Base': tensor_base,
        },
        attrs={'dtype': dtype},
        outputs={'Out': [out]},
    )
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    if isinstance(num, int):
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        out.desc.set_shape((num,))
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    return out


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def _to_tensor_non_static(data, dtype=None, place=None, stop_gradient=True):
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    if not isinstance(data, np.ndarray):
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        def _handle_dtype(data, dtype):
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            if dtype:
                if convert_dtype(dtype) != convert_dtype(data.dtype):
                    return data.astype(convert_dtype(dtype))
            return data

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        if np.isscalar(data) and not isinstance(data, str):
            data = np.array([data])
        elif isinstance(data, (list, tuple)):
            data = np.array(data)
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            if data.dtype == np.object_:
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                raise ValueError(
                    "\n\tFaild to convert input data to a regular ndarray :\n\t - Usually "
                    "this means the input data contains nested lists with different lengths. "
                )
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        elif isinstance(data, paddle.Tensor) and not in_dygraph_mode():
            data = data._copy_to(place, False)
            data = _handle_dtype(data, dtype)
            data.stop_gradient = stop_gradient
            return data
        elif isinstance(data, core.eager.Tensor) and in_dygraph_mode():
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            data = data._copy_to(place, False)
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            data = _handle_dtype(data, dtype)
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            data.stop_gradient = stop_gradient
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            return data
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        elif isinstance(data, (core.LoDTensor, core.Tensor)):
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            # should't expose it to users, just for internal use.
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            # convert core.Tensor/core.LoDTensor to VarBase first
            # Currenly, there is no copy when places are same
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            if in_dygraph_mode():
                data = core.eager.Tensor(data)
            else:
                data = paddle.Tensor(data)
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            if not data.place._equals(place):
                data = data._copy_to(place, False)
            data = _handle_dtype(data, dtype)
            data.stop_gradient = stop_gradient
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            return data
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        else:
            raise TypeError(
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                "Can't constructs a 'paddle.Tensor' with data type {}, data type must be scalar|list|tuple|np.ndarray|paddle.Tensor".format(
                    type(data)
                )
            )
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        if not dtype:
            if data.dtype in [
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                'float16',
                'float32',
                'float64',
                'complex64',
                'complex128',
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            ]:
                default_type = paddle.get_default_dtype()
                if np.iscomplexobj(data):
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                    default_type = (
                        'complex64'
                        if default_type in ['float16', 'float32']
                        else 'complex128'
                    )
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                data = data.astype(default_type)
            # Windows default type is 'int32', while Linux/Mac is 'int64'. Unify they.
            if data.dtype in ['int32']:
                default_type = "int64"
                data = data.astype(default_type)
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    if dtype and convert_dtype(dtype) != data.dtype:
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        data = data.astype(convert_dtype(dtype))
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    if _in_eager_without_dygraph_check() and isinstance(data, np.ndarray):
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        return core.eager.Tensor(
            value=data,
            place=place,
            persistable=False,
            zero_copy=False,
            name=None,
            stop_gradient=stop_gradient,
        )
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    else:
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        return paddle.Tensor(
            value=data,
            place=place,
            persistable=False,
            zero_copy=False,
            stop_gradient=stop_gradient,
        )
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def _to_tensor_static(data, dtype=None, stop_gradient=None):

    if isinstance(data, Variable) and (dtype is None or dtype == data.dtype):
        output = data
    else:
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        if not isinstance(data, np.ndarray):
            if np.isscalar(data) and not isinstance(data, str):
                data = np.array([data])
            elif isinstance(data, (list, tuple)):
                data = np.array(data)

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            if (
                isinstance(data, np.ndarray)
                and not dtype
                and data.dtype != 'object'
            ):
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                if data.dtype in ['float16', 'float32', 'float64']:
                    data = data.astype(paddle.get_default_dtype())
                elif data.dtype in ['int32']:
                    data = data.astype('int64')

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        if dtype:
            target_dtype = dtype
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        elif hasattr(data, 'dtype') and data.dtype != 'object':
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            target_dtype = data.dtype
        else:
            target_dtype = paddle.get_default_dtype()

        target_dtype = convert_dtype(target_dtype)

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        if (
            isinstance(data, np.ndarray)
            and len(data.shape) > 0
            and any(isinstance(x, Variable) for x in data)
        ):
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            if not all(
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                [x.shape == (1,) for x in data if isinstance(x, Variable)]
            ):
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                raise TypeError(
                    "Unsupport paddle.to_tensor([Variable, Variable...]) with non-scalar variable."
                )
            to_stack_list = [None] * data.shape[0]
            for idx, d in enumerate(data):
                to_stack_list[idx] = _to_tensor_static(d, dtype, stop_gradient)
            data = paddle.stack(to_stack_list)
            data = paddle.squeeze(data, -1)

        if not isinstance(data, Variable):
            output = assign(data)
        else:
            output = data
        if convert_dtype(output.dtype) != target_dtype:
            output = paddle.cast(output, target_dtype)

    output.stop_gradient = stop_gradient

    return output


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def to_tensor(data, dtype=None, place=None, stop_gradient=True):
    r"""
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    Constructs a ``paddle.Tensor`` from ``data`` ,
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    which can be scalar, tuple, list, numpy\.ndarray, paddle\.Tensor.

    If the ``data`` is already a Tensor, copy will be performed and return a new tensor.
    If you only want to change stop_gradient property, please call ``Tensor.stop_gradient = stop_gradient`` directly.

    Args:
        data(scalar|tuple|list|ndarray|Tensor): Initial data for the tensor.
            Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor.
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        dtype(str|np.dtype, optional): The desired data type of returned tensor. Can be 'bool' , 'float16' ,
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            'float32' , 'float64' , 'int8' , 'int16' , 'int32' , 'int64' , 'uint8',
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            'complex64' , 'complex128'. Default: None, infers dtype from ``data``
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            except for python float number which gets dtype from ``get_default_type`` .
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        place(CPUPlace|CUDAPinnedPlace|CUDAPlace|str, optional): The place to allocate Tensor. Can be
            CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place. If ``place`` is
            string, It can be ``cpu``, ``gpu:x`` and ``gpu_pinned``, where ``x`` is the index of the GPUs.
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        stop_gradient(bool, optional): Whether to block the gradient propagation of Autograd. Default: True.

    Returns:
        Tensor: A Tensor constructed from ``data`` .

    Examples:

    .. code-block:: python

        import paddle
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        type(paddle.to_tensor(1))
        # <class 'paddle.Tensor'>

        paddle.to_tensor(1)
        # Tensor(shape=[1], dtype=int64, place=CPUPlace, stop_gradient=True,
        #        [1])

        x = paddle.to_tensor(1, stop_gradient=False)
        print(x)
        # Tensor(shape=[1], dtype=int64, place=CPUPlace, stop_gradient=False,
        #        [1])

        paddle.to_tensor(x)  # A new tensor will be created with default stop_gradient=True
        # Tensor(shape=[1], dtype=int64, place=CPUPlace, stop_gradient=True,
652
        #        [1])
653 654 655 656 657 658 659 660 661 662 663 664 665 666

        paddle.to_tensor([[0.1, 0.2], [0.3, 0.4]], place=paddle.CPUPlace(), stop_gradient=False)
        # Tensor(shape=[2, 2], dtype=float32, place=CPUPlace, stop_gradient=False,
        #        [[0.10000000, 0.20000000],
        #         [0.30000001, 0.40000001]])

        type(paddle.to_tensor([[1+1j, 2], [3+2j, 4]], dtype='complex64'))
        # <class 'paddle.Tensor'>

        paddle.to_tensor([[1+1j, 2], [3+2j, 4]], dtype='complex64')
        # Tensor(shape=[2, 2], dtype=complex64, place=CPUPlace, stop_gradient=True,
        #        [[(1+1j), (2+0j)],
        #         [(3+2j), (4+0j)]])
    """
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    place = _get_paddle_place(place)
    if place is None:
        place = _current_expected_place()

671 672 673 674 675
    if _non_static_mode():
        return _to_tensor_non_static(data, dtype, place, stop_gradient)

    # call assign for static graph
    else:
676
        re_exp = re.compile(r'[(](.+?)[)]', re.S)
677 678 679
        place_str = re.findall(re_exp, str(place))[0]

        with paddle.static.device_guard(place_str):
680
            return _to_tensor_static(data, dtype, stop_gradient)
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683
def full_like(x, fill_value, dtype=None, name=None):
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    """
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    This function creates a tensor filled with ``fill_value`` which has identical shape of ``x`` and ``dtype``.
    If the ``dtype`` is None, the data type of Tensor is same with ``x``.
688

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    Args:
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        x(Tensor): The input tensor which specifies shape and data type. The data type can be bool, float16, float32, float64, int32, int64.
        fill_value(bool|float|int): The value to fill the tensor with. Note: this value shouldn't exceed the range of the output data type.
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        dtype(np.dtype|str, optional): The data type of output. The data type can be one
693
            of bool, float16, float32, float64, int32, int64. The default value is None, which means the output
694
            data type is the same as input.
695
        name(str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
696

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    Returns:
698
        Tensor: Tensor which is created according to ``x``, ``fill_value`` and ``dtype``.
699

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    Examples:
        .. code-block:: python
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          import paddle
704

705
          input = paddle.full(shape=[2, 3], fill_value=0.0, dtype='float32', name='input')
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          output = paddle.full_like(input, 2.0)
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          # [[2. 2. 2.]
          #  [2. 2. 2.]]
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    """

    if dtype is None:
712
        dtype = x.dtype
713
    else:
714 715 716
        if not isinstance(dtype, core.VarDesc.VarType):
            dtype = convert_np_dtype_to_dtype_(dtype)

717
    if in_dygraph_mode():
718
        return _C_ops.full_like(x, fill_value, dtype, x.place)
719 720

    if _in_legacy_dygraph():
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        return _legacy_C_ops.fill_any_like(
            x, 'value', fill_value, 'dtype', dtype
        )
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    helper = LayerHelper("full_like", **locals())
726
    check_variable_and_dtype(
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        x,
        'x',
729
        ['bool', 'float16', 'float32', 'float64', 'int16', 'int32', 'int64'],
730 731
        'full_like',
    )
732
    check_dtype(
733 734
        dtype,
        'dtype',
735
        ['bool', 'float16', 'float32', 'float64', 'int16', 'int32', 'int64'],
736 737
        'full_like/zeros_like/ones_like',
    )
738
    out = helper.create_variable_for_type_inference(dtype=dtype)
739

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    helper.append_op(
        type='fill_any_like',
        inputs={'X': [x]},
        attrs={'value': fill_value, "dtype": dtype},
        outputs={'Out': [out]},
    )
746
    out.stop_gradient = True
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    return out


750
def ones(shape, dtype=None, name=None):
751
    """
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    Create a Tensor of specified :attr:`shape` and :attr:`dtype` and fill it with 1.
753 754

    Args:
755 756 757
        shape (tuple|list|Tensor): Shape of the Tensor to be created. The data type is ``int32`` or ``int64`` .
            If ``shape`` is a list or tuple, the elements of it should be integers or 0-D Tensor with shape [].
            If ``shape`` is an Tensor, it should be an 1-D Tensor which represents a list.
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        dtype (np.dtype|str, optional): Data type of output Tensor, it should be one of
            bool, float16, float32, float64, int32 and int64. If it is set to None, the data type will be float32.
        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
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    Returns:
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        Tensor: A Tensor of data type :attr:`dtype` with shape :attr:`shape` and all elements are 1.
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    Examples:
        .. code-block:: python

768
            import paddle
769

770
            # shape is a list/tuple
771
            data1 = paddle.ones(shape=[3, 2])
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            # [[1. 1.]
            #  [1. 1.]
            #  [1. 1.]]

            # shape is a Tensor
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            shape = paddle.to_tensor([3, 2])
            data2 = paddle.ones(shape=shape)
            # [[1. 1.]
            #  [1. 1.]
            #  [1. 1.]]

            # shape is a Tensor List
            shape = [paddle.to_tensor(3), paddle.to_tensor(2)]
            data3 = paddle.ones(shape=shape)
            # [[1. 1.]
            #  [1. 1.]
            #  [1. 1.]]
789
    """
790 791 792
    if dtype is None:
        dtype = 'float32'
    return fill_constant(value=1.0, shape=shape, dtype=dtype, name=name)
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795
def ones_like(x, dtype=None, name=None):
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    """
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    Returns a Tensor filled with the value 1, with the same shape and
798
    data type (use ``dtype`` if ``dtype`` is not None) as ``x``.
799 800

    Args:
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        x(Tensor): The input tensor which specifies shape and dtype. The
            dtype of ``x`` can be bool, float16, float32, float64, int32, int64.
803
        dtype(str|np.dtype, optional): The data type of the
804 805 806
            output tensor. Supported data types: bool, float16, float32, float64,
            int32, int64. If ``dtype`` is None, the data type is the same as ``x``.
            Default is None.
807
        name(str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
808

809
    Returns:
810 811 812
        Tensor: A Tensor filled with the value 1, with the same shape and
        data type (use ``dtype`` if ``dtype`` is not None) as ``x``.

813 814 815
    Examples:
        .. code-block:: python

816
            import paddle
817

818
            x = paddle.to_tensor([1,2,3])
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            out1 = paddle.ones_like(x) # [1., 1., 1.]
            out2 = paddle.ones_like(x, dtype='int32') # [1, 1, 1]
821

822 823
    """
    return full_like(x=x, fill_value=1, dtype=dtype, name=name)
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826
def zeros(shape, dtype=None, name=None):
827
    """
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    Creates a tensor of specified :attr:`shape` and :attr:`dtype`, and fills it with 0.
829 830

    Args:
831 832 833
        shape (tuple|list|Tensor): Shape of the Tensor to be created. The data type is ``int32`` or ``int64`` .
            If ``shape`` is a list or tuple, each element of it should be integer or 0-D Tensor with shape [].
            If ``shape`` is an Tensor, it should be an 1-D Tensor which represents a list.
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        dtype(np.dtype|str, optional): Data type of output Tensor, it supports
835 836 837
            bool, float16, float32, float64, int32 and int64. Default: if None, the date type is float32.
        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`.
838 839

    Returns:
840
        Tensor: A tensor of data type :attr:`dtype` with shape :attr:`shape` and all elements set to 0.
841 842 843 844

    Examples:
        .. code-block:: python

845
            import paddle
846

847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865
            # shape is a list/tuple
            data1 = paddle.zeros(shape=[3, 2])
            # [[0. 0.]
            #  [0. 0.]
            #  [0. 0.]]

            # shape is a Tensor
            shape = paddle.to_tensor([3, 2])
            data2 = paddle.zeros(shape=shape)
            # [[0. 0.]
            #  [0. 0.]
            #  [0. 0.]]

            # shape is a Tensor List
            shape = [paddle.to_tensor(3), paddle.to_tensor(2)]
            data3 = paddle.zeros(shape=shape)
            # [[0. 0.]
            #  [0. 0.]
            #  [0. 0.]]
866
    """
867 868 869
    if dtype is None:
        dtype = 'float32'
    return fill_constant(value=0.0, shape=shape, dtype=dtype, name=name)
870 871


872
def zeros_like(x, dtype=None, name=None):
873
    """
874
    Returns a Tensor filled with the value 0, with the same shape and
875
    data type (use ``dtype`` if ``dtype`` is not None) as ``x``.
876 877

    Args:
878 879
        x(Tensor): The input tensor which specifies shape and dtype. The
            dtype of ``x`` can be bool, float16, float32, float64, int32, int64.
880
        dtype(str|np.dtype, optional): The data type of the
881 882 883
            output tensor. Supported data types: bool, float16, float32, float64,
            int32, int64. If ``dtype`` is None, the data type is the same as ``x``.
            Default is None.
884
        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
885 886

    Returns:
887 888
        Tensor: A Tensor filled with the value 0, with the same shape and
        data type (use ``dtype`` if ``dtype`` is not None) as ``x``.
889

890

891 892 893
    Examples:
        .. code-block:: python

894
            import paddle
895

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            x = paddle.to_tensor([1, 2, 3])
897 898
            out1 = paddle.zeros_like(x) # [0., 0., 0.]
            out2 = paddle.zeros_like(x, dtype='int32') # [0, 0, 0]
899

900 901
    """
    return full_like(x=x, fill_value=0, dtype=dtype, name=name)
902 903


904
def eye(num_rows, num_columns=None, dtype=None, name=None):
905
    """
906

907
    This function constructs 2-D Tensor with ones on the diagonal and zeros elsewhere.
908

909
    Args:
910 911
        num_rows(int): the number of rows in each batch Tensor.
        num_columns(int, optional): the number of columns in each batch Tensor.
912
            If None, default: num_rows.
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        dtype(np.dtype|str, optional): The data type of the returned Tensor.
914 915
            It should be int32, int64, float16, float32, float64. Default: if None, the data type
            is float32.
916
        name(str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
917

918
    Returns:
919
        Tensor: An identity Tensor or LoDTensor of shape [num_rows, num_columns].
920

921 922
    Examples:
        .. code-block:: python
923

924
          import paddle
925

926
          data = paddle.eye(3, dtype='int32')
927 928 929
          # [[1 0 0]
          #  [0 1 0]
          #  [0 0 1]]
930
          data = paddle.eye(2, 3, dtype='int32')
931 932
          # [[1 0 0]
          #  [0 1 0]]
933 934
    """

935 936 937 938 939 940 941 942
    def _check_attr(attr, message):
        if isinstance(attr, ((Variable, core.VarBase, core.eager.Tensor))):
            assert len(attr.shape) == 1 and attr.shape[0] in [1, -1]
        elif not isinstance(attr, int) or attr < 0:
            raise TypeError("{} should be a non-negative int.".format(message))

    _check_attr(num_rows, "num_rows")

943 944
    if dtype is None:
        dtype = 'float32'
945 946 947
    if not isinstance(dtype, core.VarDesc.VarType):
        dtype = convert_np_dtype_to_dtype_(dtype)
    if num_columns is not None:
948
        _check_attr(num_columns, "num_columns")
949 950 951 952
    else:
        num_columns = num_rows

    if _non_static_mode():
953
        if in_dygraph_mode():
954 955 956
            out = _C_ops.eye(
                num_rows, num_columns, dtype, _current_expected_place()
            )
957
        elif _in_legacy_dygraph():
958 959 960
            out = _legacy_C_ops.eye(
                'dtype', dtype, 'num_rows', num_rows, 'num_columns', num_columns
            )
961 962 963

    else:
        helper = LayerHelper("eye", **locals())
964 965 966 967 968 969
        check_dtype(
            dtype,
            'dtype',
            ['float16', 'float32', 'float64', 'int32', 'int64'],
            'eye',
        )
970
        out = helper.create_variable_for_type_inference(dtype=dtype)
971 972 973 974 975 976 977 978 979 980 981
        helper.append_op(
            type='eye',
            inputs={},
            outputs={'Out': [out]},
            attrs={
                'num_rows': num_rows,
                'num_columns': num_columns,
                'dtype': dtype,
            },
            stop_gradient=True,
        )
982 983 984

    out.stop_gradient = True
    return out
985 986


987
def full(shape, fill_value, dtype=None, name=None):
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    """
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990
    Return a Tensor with the ``fill_value`` which size is same as ``shape``.
991

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    Args:
993 994 995 996 997
        shape (tuple|list|Tensor): Shape of the Tensor to be created. The data type is ``int32`` or ``int64`` .
            If ``shape`` is a list or tuple, each element of it should be integer or 0-D Tensor with shape [].
            If ``shape`` is an Tensor, it should be an 1-D Tensor which represents a list.
        fill_value(bool|float|int|Tensor): The constant value used to initialize the Tensor to be created.
            If ``fill_value`` is an Tensor, it shoule be an 0-D Tensor which represents a scalar.
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        dtype(np.dtype|str, optional): Data type of the output Tensor
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            which can be float16, float32, float64, int32, int64, if dytpe is `None`, the data
1000 1001
            type of created Tensor is `float32`.
        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
1002

1003
    Returns:
1004
        Tensor: Tensor which is created according to ``shape``, ``fill_value`` and ``dtype``.
1005

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    Examples:
        .. code-block:: python

1009
            import paddle
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1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036
            # shape is a list/tuple
            data1 = paddle.full(shape=[3, 2], fill_value=1.)
            # [[1. 1.]
            #  [1. 1.]
            #  [1. 1.]]

            # shape is a Tensor
            shape = paddle.to_tensor([3, 2])
            data2 = paddle.full(shape=shape, fill_value=2.)
            # [[2. 2.]
            #  [2. 2.]
            #  [2. 2.]]

            # shape is a Tensor List
            shape = [paddle.to_tensor(3), paddle.to_tensor(2)]
            data3 = paddle.full(shape=shape, fill_value=3.)
            # [[3. 3.]
            #  [3. 3.]
            #  [3. 3.]]

            # fill_value is a Tensor.
            val = paddle.full([], 2.0, "float32")
            data5 = paddle.full(shape=[3, 2], fill_value=val)
            # [[2. 2.]
            #  [2. 2.]
            #  [2. 2.]]
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    """

    if dtype is None:
        dtype = 'float32'

1042
    return fill_constant(shape=shape, dtype=dtype, value=fill_value, name=name)
1043 1044


1045
def arange(start=0, end=None, step=1, dtype=None, name=None):
1046
    """
1047
    Returns a 1-D Tensor with spaced values within a given interval.
1048

1049 1050
    Values are generated into the half-open interval [``start``, ``end``) with
    the ``step``. (the interval including ``start`` but excluding ``end``).
1051

1052 1053
    If ``dtype`` is float32 or float64, we advise adding a small epsilon to
    ``end`` to avoid floating point rounding errors when comparing against ``end``.
1054 1055

    Parameters:
1056 1057
        start(float|int|Tensor): Start of interval. The interval includes this
            value. If ``end`` is None, the half-open interval is [0, ``start``).
1058 1059
            If ``start`` is a Tensor, it is a 0-D Tensor which represents a scalar
            and data type is int32, int64, float32, float64. Default is 0.
1060
        end(float|int|Tensor, optional): End of interval. The interval does not
1061 1062 1063 1064
            include this value. If ``end`` is a Tensor, it is a 0-D Tensor which
            represents a scalar and data type is int32, int64, float32, float64.
            If ``end`` is None, the half-open interval is [0, ``start``).
            Default is None.
1065 1066
        step(float|int|Tensor, optional): Spacing between values. For any out,
            it is the istance between two adjacent values, out[i+1] - out[i].
1067 1068
            If ``step`` is a Tensor, it is a 0-D Tensor which represents a scalar
            and data type is int32, int64, float32, float64. . Default is 1.
1069
        dtype(str|np.dtype, optional): The data type of the
1070 1071
            output tensor. Supported data types: int32, int64, float32, float64.
            If ``dytpe`` is None, the data type is float32. Default is None.
1072
        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
1073

1074
    Returns:
1075
        Tensor: A 1-D Tensor with values from the interval [``start``, ``end``)
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        taken with common difference ``step`` beginning from ``start``. Its
        data type is set by ``dtype``.
1078

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    Examples:
1080 1081
        .. code-block:: python

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1082
            import paddle
1083

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1084 1085
            out1 = paddle.arange(5)
            # [0, 1, 2, 3, 4]
1086

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1087 1088
            out2 = paddle.arange(3, 9, 2.0)
            # [3, 5, 7]
1089

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1090 1091 1092
            # use 4.999 instead of 5.0 to avoid floating point rounding errors
            out3 = paddle.arange(4.999, dtype='float32')
            # [0., 1., 2., 3., 4.]
1093

1094
            start_var = paddle.to_tensor(3)
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            out4 = paddle.arange(start_var, 7)
            # [3, 4, 5, 6]
1097

1098 1099 1100 1101 1102 1103
    """
    if dtype is None:
        dtype = 'int64'
    if end is None:
        end = start
        start = 0
1104

1105
    out_shape = None
1106 1107 1108 1109 1110
    if (
        not isinstance(start, Variable)
        and not isinstance(end, Variable)
        and not isinstance(step, Variable)
    ):
1111 1112
        out_shape = [int(math.ceil((end - start) / step))]

1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134
    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 = paddle.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 = paddle.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 = paddle.cast(step, dtype)

    if in_dygraph_mode():
1135
        return _C_ops.arange(start, end, step, dtype, _current_expected_place())
1136 1137

    if _in_legacy_dygraph():
1138
        out = _legacy_C_ops.range(start, end, step)
1139 1140 1141
        out.stop_gradient = True
        return out

1142 1143 1144
    check_dtype(
        dtype, 'dtype', ['float32', 'float64', 'int32', 'int64'], 'range/arange'
    )
1145 1146
    helper = LayerHelper('range', **locals())
    out = helper.create_variable_for_type_inference(dtype, shape=out_shape)
1147 1148 1149 1150 1151
    helper.append_op(
        type='range',
        inputs={'Start': start, 'End': end, 'Step': step},
        outputs={'Out': out},
    )
1152
    out.stop_gradient = True
1153 1154
    if out_shape is not None:
        out.desc.set_shape(out_shape)
1155
    return out
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def _tril_triu_op(helper):
1159
    """Base op of tril_op and triu_op"""
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    op_type = helper.layer_type
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    x = helper.kwargs.get('x', None)
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    assert x is not None, 'x cannot be None in {}'.format(op_type)
1164
    check_variable_and_dtype(
1165 1166 1167 1168 1169
        x,
        'x',
        ['float16', 'float32', 'float64', 'int32', 'int64', 'bool'],
        op_type,
    )
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    if len(x.shape) < 2:
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        raise ValueError("x shape in {} must be at least 2-D".format(op_type))
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    diagonal = helper.kwargs.get('diagonal', 0)
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    if not isinstance(diagonal, (int,)):
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        raise TypeError("diagonal in {} must be a python Int".format(op_type))
    name = helper.kwargs.get('name', None)

    if name is None:
        out = helper.create_variable_for_type_inference(dtype=x.dtype)
    else:
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        out = helper.create_variable(
            name=name, dtype=x.dtype, persistable=False
        )
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    helper.append_op(
        type="tril_triu",
        inputs={"X": x},
        attrs={
            "diagonal": diagonal,
            "lower": True if op_type == 'tril' else False,
        },
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        outputs={"Out": out},
    )
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    return out


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def tril(x, diagonal=0, name=None):
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    r"""
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    Returns the lower triangular part of a matrix (2-D tensor) or batch
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    of matrices :attr:`x`, the other elements of the result tensor are set
    to 0. The lower triangular part of the matrix is defined as the elements
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    on and below the diagonal.

    Args:
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        x (Tensor): The input x which is a Tensor.
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            Support data types: ``bool``, ``float64``, ``float32``, ``int32``, ``int64``.
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        diagonal (int, optional): The diagonal to consider, default value is 0.
            If :attr:`diagonal` = 0, all elements on and below the main diagonal are
            retained. A positive value includes just as many diagonals above the main
            diagonal, and similarly a negative value excludes just as many diagonals below
            the main diagonal. The main diagonal are the set of indices
            :math:`\{(i, i)\}` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where
            :math:`d_{1}, d_{2}` are the dimensions of the matrix.
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        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
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    Returns:
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        Tensor: Results of lower triangular operation by the specified diagonal of input tensor x,
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        it's data type is the same as x's Tensor.
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    Examples:
        .. code-block:: python

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            import paddle
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            data = paddle.arange(1, 13, dtype="int64").reshape([3,-1])
            # Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[1 , 2 , 3 , 4 ],
            #         [5 , 6 , 7 , 8 ],
            #         [9 , 10, 11, 12]])
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            tril1 = paddle.tril(data)
            # Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[1 , 0 , 0 , 0 ],
            #         [5 , 6 , 0 , 0 ],
            #         [9 , 10, 11, 0 ]])
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            # example 2, positive diagonal value
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            tril2 = paddle.tril(data, diagonal=2)
            # Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[1 , 2 , 3 , 0 ],
            #         [5 , 6 , 7 , 8 ],
            #         [9 , 10, 11, 12]])
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            # example 3, negative diagonal value
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            tril3 = paddle.tril(data, diagonal=-1)
            # Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[0 , 0 , 0 , 0 ],
            #         [5 , 0 , 0 , 0 ],
            #         [9 , 10, 0 , 0 ]])
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    """
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    if in_dygraph_mode():
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        return _C_ops.tril(x, diagonal, True)
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    if _in_legacy_dygraph():
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        op = getattr(_legacy_C_ops, 'tril_triu')
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        return op(x, 'diagonal', diagonal, "lower", True)
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    return _tril_triu_op(LayerHelper('tril', **locals()))


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def triu(x, diagonal=0, name=None):
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    r"""
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    Return the upper triangular part of a matrix (2-D tensor) or batch of matrices
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    :attr:`x`, the other elements of the result tensor are set to 0.
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    The upper triangular part of the matrix is defined as the elements on and
    above the diagonal.

    Args:
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        x (Tensor): The input x which is a Tensor.
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            Support data types: ``float64``, ``float32``, ``int32``, ``int64``.
        diagonal (int, optional): The diagonal to consider, default value is 0.
            If :attr:`diagonal` = 0, all elements on and above the main diagonal are
            retained. A positive value excludes just as many diagonals above the main
            diagonal, and similarly a negative value includes just as many diagonals below
            the main diagonal. The main diagonal are the set of indices
            :math:`\{(i, i)\}` for :math:`i \in [0, \min\{d_{1}, d_{2}\} - 1]` where
            :math:`d_{1}, d_{2}` are the dimensions of the matrix.
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        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
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    Returns:
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        Tensor: Results of upper triangular operation by the specified diagonal of input tensor x,
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        it's data type is the same as x's Tensor.
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    Examples:
        .. code-block:: python

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            import paddle
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            x = paddle.arange(1, 13, dtype="int64").reshape([3,-1])
            # Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[1 , 2 , 3 , 4 ],
            #         [5 , 6 , 7 , 8 ],
            #         [9 , 10, 11, 12]])
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            # example 1, default diagonal
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            triu1 = paddle.tensor.triu(x)
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            # Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[1 , 2 , 3 , 4 ],
            #         [0 , 6 , 7 , 8 ],
            #         [0 , 0 , 11, 12]])
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            # example 2, positive diagonal value
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            triu2 = paddle.tensor.triu(x, diagonal=2)
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            # Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[0, 0, 3, 4],
            #         [0, 0, 0, 8],
            #         [0, 0, 0, 0]])
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            # example 3, negative diagonal value
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            triu3 = paddle.tensor.triu(x, diagonal=-1)
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            # Tensor(shape=[3, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[1 , 2 , 3 , 4 ],
            #         [5 , 6 , 7 , 8 ],
            #         [0 , 10, 11, 12]])
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    """
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    if in_dygraph_mode():
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        return _C_ops.triu(x, diagonal, False)
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    if _in_legacy_dygraph():
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        op = getattr(_legacy_C_ops, 'tril_triu')
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        return op(x, 'diagonal', diagonal, "lower", False)
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    return _tril_triu_op(LayerHelper('triu', **locals()))
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def meshgrid(*args, **kwargs):
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    """
1329

1330
    Takes a list of N tensors as input :attr:`*args`, each of which is 1-dimensional vector, and creates N-dimensional grids.
1331

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    Args:
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        *args(Tensor|list of Tensor) : tensors (tuple(list) of tensor): the shapes of input k tensors are (N1,),
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            (N2,),..., (Nk,). Support data types: ``float64``, ``float32``, ``int32``, ``int64``.
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        **kwargs (optional): Currently, only accept name in **kwargs
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            The default value is None. Normally there is no need for
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            user to set this property. For more information, please refer to :ref:`api_guide_Name`.
1338

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    Returns:
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         Tensor: k tensors. The shape of each tensor is (N1, N2, ..., Nk)
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    Examples:
      .. code-block:: python

          import paddle

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          x = paddle.randint(low=0, high=100, shape=[100])
          y = paddle.randint(low=0, high=100, shape=[200])

          grid_x, grid_y = paddle.meshgrid(x, y)
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          print(grid_x.shape)
          print(grid_y.shape)
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          #the shape of res_1 is (100, 200)
          #the shape of res_2 is (100, 200)

    """

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    if len(args) == 1 and isinstance(args[0], (list, tuple)):
        args = args[0]
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    if _in_legacy_dygraph():
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        num = len(args)
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        out = _legacy_C_ops.meshgrid(list(args), num)
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        return out
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    if in_dygraph_mode():
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        return _C_ops.meshgrid(list(args))
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    name = kwargs.get("name", None)
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    helper = LayerHelper('meshgrid', **locals())

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    if not isinstance(args, (list, tuple)):
        raise TypeError("The type of input args in meshgrid should be list.")
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1375
    for id, input_ in enumerate(args):
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        check_dtype(
            input_.dtype,
            'create data type',
            ['float16', 'float32', 'float64', 'int32', 'int64'],
            'meshgrid',
        )
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1383
    num = len(args)
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    out = [
1385
        helper.create_variable_for_type_inference(dtype=args[i].dtype)
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        for i in range(num)
    ]
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    helper.append_op(
        type='meshgrid', inputs={'X': list(args)}, outputs={'Out': out}
    )
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    return out
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def diagflat(x, offset=0, name=None):
    """
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    If ``x`` is a vector (1-D tensor), a 2-D square tensor with the elements of ``x`` as the diagonal is returned.
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    If ``x`` is a tensor (more than 1-D), a 2-D square tensor with the elements of flattened ``x`` as the diagonal is returned.

    The argument ``offset`` controls the diagonal offset.


    If ``offset`` = 0, it is the main diagonal.

    If ``offset`` > 0, it is superdiagonal.

    If ``offset`` < 0, it is subdiagonal.

    Args:
        x (Tensor): The input tensor. It can be any shape. Its data type should be float32, float64, int32, int64.
        offset (int, optional): The diagonal offset. A positive value represents superdiagonal, 0 represents the main diagonal, and a negative value represents subdiagonal. Default: 0 (main diagonal).
1413
        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
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    Returns:
        Tensor, a square matrix. The output data type is the same as input data type.

    Examples:
        .. code-block:: python
1420
            :name: code-example-1
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            import paddle

            x = paddle.to_tensor([1, 2, 3])
            y = paddle.diagflat(x)
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            print(y)
            # Tensor(shape=[3, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[1, 0, 0],
            #         [0, 2, 0],
            #         [0, 0, 3]])
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            y = paddle.diagflat(x, offset=1)
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            print(y)
            # Tensor(shape=[4, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[0, 1, 0, 0],
            #         [0, 0, 2, 0],
            #         [0, 0, 0, 3],
            #         [0, 0, 0, 0]])
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            y = paddle.diagflat(x, offset=-1)
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            print(y)
            # Tensor(shape=[4, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[0, 0, 0, 0],
            #         [1, 0, 0, 0],
            #         [0, 2, 0, 0],
            #         [0, 0, 3, 0]])
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        .. code-block:: python
1449
            :name: code-example-2
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1451
            import paddle
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1453 1454
            x = paddle.to_tensor([[1, 2], [3, 4]])
            y = paddle.diagflat(x)
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            print(y)
            # Tensor(shape=[4, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[1, 0, 0, 0],
            #         [0, 2, 0, 0],
            #         [0, 0, 3, 0],
            #         [0, 0, 0, 4]])
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            y = paddle.diagflat(x, offset=1)
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            print(y)
            # Tensor(shape=[5, 5], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[0, 1, 0, 0, 0],
            #         [0, 0, 2, 0, 0],
            #         [0, 0, 0, 3, 0],
            #         [0, 0, 0, 0, 4],
            #         [0, 0, 0, 0, 0]])
1470 1471

            y = paddle.diagflat(x, offset=-1)
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            print(y)
            # Tensor(shape=[5, 5], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[0, 0, 0, 0, 0],
            #         [1, 0, 0, 0, 0],
            #         [0, 2, 0, 0, 0],
            #         [0, 0, 3, 0, 0],
            #         [0, 0, 0, 4, 0]])
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    """
    padding_value = 0
1481 1482
    if in_dygraph_mode():
        if len(x.shape) == 1:
1483
            return _C_ops.diag(x, offset, padding_value)
1484
        else:
1485 1486
            y = _C_ops.flatten(x, 0, -1)
            return _C_ops.diag(y, offset, padding_value)
1487 1488

    if _in_legacy_dygraph():
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        if len(x.shape) == 1:
1490 1491 1492
            return _legacy_C_ops.diag_v2(
                x, "offset", offset, "padding_value", padding_value
            )
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        else:
1494
            y, _ = _legacy_C_ops.flatten_contiguous_range(
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                x, "start_axis", 0, "stop_axis", -1
            )
            return _legacy_C_ops.diag_v2(
                y, "offset", offset, "padding_value", padding_value
            )
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    check_type(x, 'x', (Variable), 'diagflat')
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    check_dtype(
        x.dtype, 'x', ['float32', 'float64', 'int32', 'int64'], 'diagflat'
    )
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    check_type(offset, 'offset', (int), 'diagflat')

    helper = LayerHelper("diagflat", **locals())
    out1 = helper.create_variable_for_type_inference(dtype=x.dtype)
    out1_shape = helper.create_variable_for_type_inference(x.dtype)
    out2 = helper.create_variable_for_type_inference(dtype=x.dtype)

    if len(x.shape) == 1:
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        helper.append_op(
            type='diag_v2',
            inputs={'X': x},
            outputs={'Out': out2},
            attrs={'offset': offset, 'padding_value': padding_value},
        )
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    else:
1520 1521 1522 1523 1524 1525
        helper.append_op(
            type='flatten_contiguous_range',
            inputs={'X': x},
            outputs={'Out': out1, 'XShape': out1_shape},
            attrs={'start_axis': 0, 'stop_axis': -1},
        )
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        out1.stop_gradient = True

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        helper.append_op(
            type='diag_v2',
            inputs={'X': out1},
            outputs={'Out': out2},
            attrs={'offset': offset, 'padding_value': padding_value},
        )
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    out2.stop_gradient = True
    return out2


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def diag(x, offset=0, padding_value=0, name=None):
    """
1540
    If ``x`` is a vector (1-D tensor), a 2-D square tensor with the elements of ``x`` as the diagonal is returned.
1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555

    If ``x`` is a matrix (2-D tensor), a 1-D tensor with the diagonal elements of ``x`` is returned.

    The argument ``offset`` controls the diagonal offset:

    If ``offset`` = 0, it is the main diagonal.

    If ``offset`` > 0, it is superdiagonal.

    If ``offset`` < 0, it is subdiagonal.

    Args:
        x (Tensor): The input tensor. Its shape is either 1-D or 2-D. Its data type should be float32, float64, int32, int64.
        offset (int, optional): The diagonal offset. A positive value represents superdiagonal, 0 represents the main diagonal, and a negative value represents subdiagonal.
        padding_value (int|float, optional): Use this value to fill the area outside the specified diagonal band. Only takes effect when the input is a 1-D Tensor. The default value is 0.
1556
        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
1557

1558 1559 1560 1561 1562
    Returns:
        Tensor, a square matrix or a vector. The output data type is the same as input data type.

    Examples:
        .. code-block:: python
1563
            :name: code-example-1
1564

1565
            import paddle
1566

1567 1568 1569
            paddle.disable_static()
            x = paddle.to_tensor([1, 2, 3])
            y = paddle.diag(x)
1570 1571 1572 1573 1574
            print(y)
            # Tensor(shape=[3, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[1, 0, 0],
            #         [0, 2, 0],
            #         [0, 0, 3]])
1575 1576

            y = paddle.diag(x, offset=1)
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            print(y)
            # Tensor(shape=[4, 4], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[0, 1, 0, 0],
            #         [0, 0, 2, 0],
            #         [0, 0, 0, 3],
            #         [0, 0, 0, 0]])
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            y = paddle.diag(x, padding_value=6)
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            print(y)
            # Tensor(shape=[3, 3], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [[1, 6, 6],
            #         [6, 2, 6],
            #         [6, 6, 3]])
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        .. code-block:: python
1592
            :name: code-example-2
1593

1594
            import paddle
1595

1596 1597 1598
            paddle.disable_static()
            x = paddle.to_tensor([[1, 2, 3], [4, 5, 6]])
            y = paddle.diag(x)
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            print(y)
            # Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [1, 5])
1602

1603
            y = paddle.diag(x, offset=1)
1604 1605 1606
            print(y)
            # Tensor(shape=[2], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [2, 6])
1607

1608
            y = paddle.diag(x, offset=-1)
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            print(y)
            # Tensor(shape=[1], dtype=int64, place=Place(cpu), stop_gradient=True,
            #        [4])
1612
    """
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    if in_dygraph_mode():
1614
        return _C_ops.diag(x, offset, padding_value)
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    else:
        if _in_legacy_dygraph():
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            return _legacy_C_ops.diag_v2(
                x, "offset", offset, "padding_value", padding_value
            )
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        else:
            check_type(x, 'x', (Variable), 'diag_v2')
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            check_dtype(
                x.dtype,
                'x',
                ['float32', 'float64', 'int32', 'int64'],
                'diag_v2',
            )
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            check_type(offset, 'offset', (int), 'diag_v2')
            check_type(padding_value, 'padding_value', (int, float), 'diag_v2')
            if len(x.shape) != 1 and len(x.shape) != 2:
                raise ValueError(
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                    "The dimension of input x must be either 1 or 2, but received {}".format(
                        len(x.shape)
                    )
                )
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            helper = LayerHelper("diag_v2", **locals())
1638

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            out = helper.create_variable_for_type_inference(dtype=x.dtype)
1640

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            helper.append_op(
                type='diag_v2',
                inputs={'X': x},
                outputs={'Out': out},
                attrs={'offset': offset, 'padding_value': padding_value},
            )
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            out.stop_gradient = True
            return out
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def empty(shape, dtype=None, name=None):
    """
1654
    Returns a Tensor with uninitialized data which size is same as ``shape``.
1655

1656
    Args:
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        shape (tuple|list|Tensor): Shape of the Tensor to be created. The data type is ``int32`` or ``int64`` .
            If ``shape`` is a list or tuple, each element of it should be integer or 0-D Tensor with shape [].
            If ``shape`` is an Tensor, it should be an 1-D Tensor which represents a list.
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        dtype(np.dtype|str, optional): Data type of the output Tensor
            which can be bool, float16, float32, float64, int32, int64, if dytpe is `None`, the data
            type of created Tensor use global default dtype (see ``get_default_dtype``
            for details).
1664
        name(str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
1665

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    Returns:
        Tensor: Tensor which is created according to ``shape`` and ``dtype``, and is uninitialized.

    Examples:
        .. code-block:: python

1672
            import paddle
1673

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            # shape is a list/tuple
            data1 = paddle.empty(shape=[3, 2])
            # [[1. 1.]
            #  [1. 1.]
            #  [1. 1.]]

            # shape is a Tensor
            shape = paddle.to_tensor([3, 2])
            data2 = paddle.empty(shape=shape)
            # [[1. 1.]
            #  [1. 1.]
            #  [1. 1.]]

            # shape is a Tensor List
            shape = [paddle.to_tensor(3), paddle.to_tensor(2)]
            data3 = paddle.empty(shape=shape)
            # [[1. 1.]
            #  [1. 1.]
            #  [1. 1.]]
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    """

    if dtype is None:
        dtype = paddle.get_default_dtype()

    dtype = convert_dtype(dtype)

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    if in_dygraph_mode():
        shape = utils.convert_shape_to_list(shape)
1702 1703 1704
        out = _C_ops.empty(
            shape, convert_np_dtype_to_dtype_(dtype), _current_expected_place()
        )
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        out.stop_gradient = True
        return out

    if _in_legacy_dygraph():
1709
        shape = utils.convert_shape_to_list(shape)
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        out = _legacy_C_ops.empty(
            'shape', shape, 'dtype', convert_np_dtype_to_dtype_(dtype)
        )
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        out.stop_gradient = True
        return out

    helper = LayerHelper("empty", **locals())
    inputs = {}

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    check_dtype(
        dtype,
        'dtype',
        ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
        'empty',
    )
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    check_type(shape, 'shape', (Variable, list, tuple), 'empty')

    if isinstance(shape, Variable):
        check_dtype(shape.dtype, 'shape', ['int32', 'int64'], 'empty')

    attrs = {}
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    utils.get_shape_tensor_inputs(
        inputs=inputs, attrs=attrs, shape=shape, op_type='empty'
    )
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    out = helper.create_variable_for_type_inference(dtype=dtype)
    attrs['dtype'] = convert_np_dtype_to_dtype_(dtype)
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    helper.append_op(
        type='empty',
        inputs=inputs,
        outputs={'Out': [out]},
        attrs=attrs,
        stop_gradient=True,
    )
1744 1745
    out.stop_gradient = True
    return out
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def empty_like(x, dtype=None, name=None):
    """
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    Returns a Tensor with uninitialized data which has identical shape of ``x`` and ``dtype``.
1751
    If the ``dtype`` is None, the data type of Tensor is same with ``x``.
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    Args:
        x(Tensor): The input tensor which specifies shape and data type. The data type can be bool, float16, float32, float64, int32, int64.
        dtype(np.dtype|str, optional): The data type of output. The data type can be one
1756
            of bool, float16, float32, float64, int32, int64. The default value is None, which means the output
1757
            data type is the same as input.
1758
        name(str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
1759

1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779
    Returns:
        Tensor: Tensor which is created according to ``x`` and ``dtype``, and is uninitialized.

    Examples:
        .. code-block:: python

          import paddle

          paddle.set_device("cpu")  # and use cpu device

          x = paddle.randn([2, 3], 'float32')
          output = paddle.empty_like(x)
          #[[1.8491974e+20 1.8037303e+28 1.7443726e+28]     # uninitialized
          # [4.9640171e+28 3.0186127e+32 5.6715899e-11]]    # uninitialized
    """

    if dtype is None:
        dtype = x.dtype
    dtype = convert_dtype(dtype)

1780
    if in_dygraph_mode():
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        out = _C_ops.empty(
            x.shape,
            convert_np_dtype_to_dtype_(dtype),
            _current_expected_place(),
        )
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        out.stop_gradient = True
        return out

    if _in_legacy_dygraph():
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        out = _legacy_C_ops.empty(
            'shape', x.shape, 'dtype', convert_np_dtype_to_dtype_(dtype)
        )
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        out.stop_gradient = True
        return out

    helper = LayerHelper("empty_like", **locals())
    check_variable_and_dtype(
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        x,
        'x',
        ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
        'empty_like',
    )
    check_dtype(
        dtype,
        'dtype',
        ['bool', 'float16', 'float32', 'float64', 'int32', 'int64'],
        'empty_like',
    )
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    out = helper.create_variable_for_type_inference(dtype=dtype)

    inputs = {}
    attrs = {}
    attrs['dtype'] = convert_np_dtype_to_dtype_(dtype)
    shape = paddle.shape(x)
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    utils.get_shape_tensor_inputs(
        inputs=inputs, attrs=attrs, shape=shape, op_type='empty_like'
    )

    helper.append_op(
        type='empty',
        inputs=inputs,
        outputs={'Out': [out]},
        attrs=attrs,
        stop_gradient=True,
    )
1826 1827
    out.stop_gradient = True
    return out
1828 1829 1830 1831


def assign(x, output=None):
    """
1832

1833
    Copy value of the :attr:`x` to the :attr:`output`.
1834

1835
    Parameters:
1836 1837
        x (Tensor|np.ndarray|list|tuple|scalar): A Tensor, numpy ndarray, tuple/list of scalar,
            or scalar. Its data type can be float16, float32, float64, int32, int64 or bool. Note: the float64 data will be converted to float32 because of current platform protobuf
1838
            data limitation.
1839
        output (Tensor, optional): A Tensor. If :attr:`output` is None, a new Tensor will be created as :attr:`output`. Default: None.
1840

1841
    Returns:
1842
        Tensor: A Tensor with the same shape, data type and value as :attr:`x`.
1843

1844 1845
    Examples:
        .. code-block:: python
1846

1847 1848 1849 1850 1851 1852 1853 1854 1855 1856
            import paddle
            import numpy as np
            data = paddle.full(shape=[3, 2], fill_value=2.5, dtype='float64') # [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
            array = np.array([[1, 1],
                                [3, 4],
                                [1, 3]]).astype(np.int64)
            result1 = paddle.zeros(shape=[3, 3], dtype='float32')
            paddle.assign(array, result1) # result1 = [[1, 1], [3 4], [1, 3]]
            result2 = paddle.assign(data)  # result2 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
            result3 = paddle.assign(np.array([[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]], dtype='float32')) # result3 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
1857
    """
1858 1859
    input = x
    helper = LayerHelper('assign', **locals())
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    check_type(
        input,
        'input',
        (Variable, np.ndarray, list, tuple, float, int, bool),
        'assign',
    )
1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876
    is_inplace = True if output is not None else False

    if np.isscalar(input) and not isinstance(input, str):
        input = np.array([input])
    elif isinstance(input, (list, tuple)):
        input = np.array(input)
    # NOTE(Aurelius84): Why we judge core.VarBase?
    # In case of @to_static, a VarBase can be as input of `assign`,
    # but _non_static_mode()==False under @to_static, which means
    # isinstance(VarBase, Variable) == False. It will cause return None
    # after this api.
1877
    if isinstance(input, (Variable, core.VarBase, core.eager.Tensor)):
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        if in_dygraph_mode():
1879
            if output is None:
1880
                output = _C_ops.assign(input)
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            else:
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                _C_ops.assign_out_(input, output)
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        elif _in_legacy_dygraph():
            if output is None:
                output = core.VarBase()
1886
            _legacy_C_ops.assign(input, output)
1887
        else:
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            check_dtype(
                input.dtype,
                'input',
                [
                    'float16',
                    'uint16',
                    'float32',
                    'float64',
                    'int32',
                    'int64',
                    'uint8',
                    'bool',
                ],
                'assign',
                '(When the type of input in assign is Variable.)',
            )
1904 1905
            if output is None:
                output = helper.create_variable_for_type_inference(
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                    dtype=input.dtype
                )
            helper.append_op(
                type='assign', inputs={'X': [input]}, outputs={'Out': [output]}
            )
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    elif isinstance(input, np.ndarray):
1912
        # We now support the form of [var, VAR...] if the Var.shape=[1,]
1913
        if len(input.shape) > 0 and any(isinstance(x, Variable) for x in input):
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            # We only deal with the case where the list is nested one level, convert all scalars into variables, and then use stack to process. It is necessary to ensure the consistency of types.
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            if not all(
                [
                    x.shape == (1,)
                    for x in input
1919
                    if isinstance(x, (Variable, core.eager.Tensor))
1920 1921
                ]
            ):
1922 1923 1924 1925 1926
                raise TypeError(
                    "Unsupport paddle.assign([Variable, Variable...]) with non-scalar variable."
                )

            def convert_scalar(x):
1927
                if not isinstance(x, (Variable, core.eager.Tensor)):
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                    return assign(x)
                return x

            to_stack_list = list(map(convert_scalar, input))
            ret = paddle.stack(to_stack_list)
            ret = paddle.squeeze(ret, -1)
            return ret

        if input.dtype == 'object':
1937
            """may be this form [[Var], [Var], [3], [4]], we reject them."""
1938
            raise TypeError(
1939
                "The type of received input == `object`, it is not supported to convert to tensor, such as [[Var], [Var], [3], [4]]"
1940
            )
1941

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        dtype = convert_np_dtype_to_dtype_(input.dtype)
        if dtype == core.VarDesc.VarType.FP64:
            # Setting FP64 numpy data is not supported in Paddle, so we
            # use FP32 here
            warnings.warn(
                "paddle.assign doesn't support float64 input now due "
                "to current platform protobuf data limitation, we convert "
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                "it to float32"
            )
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            dtype = core.VarDesc.VarType.FP32
        if dtype == core.VarDesc.VarType.BOOL:
            value_name = "bool_values"
            values = [int(v) for v in input.flat]
        elif dtype == core.VarDesc.VarType.FP32:
            value_name = "fp32_values"
            values = [float(v) for v in input.flat]
        elif dtype == core.VarDesc.VarType.INT32:
            value_name = "int32_values"
            values = [int(v) for v in input.flat]
        elif dtype == core.VarDesc.VarType.INT64:
            value_name = "int64_values"
            values = [int(v) for v in input.flat]
        else:
            raise TypeError(
                "When the type of 'input' in assign is numpy.ndarray, "
                "the data type of 'input' must be bool, float32, int32 or int64, but "
1968 1969
                "received %s." % convert_dtype(dtype)
            )
1970
        if input.size > 1024 * 1024:
1971 1972 1973 1974
            raise ValueError(
                "The size of input is too big. Please consider "
                "saving it to file and 'load_op' to load it"
            )
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        if in_dygraph_mode():
            if output is None:
                output = zeros(list(input.shape), dtype)
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            _C_ops.assign_value_(
                output,
                list(input.shape),
                dtype,
                values,
                _current_expected_place(),
            )
1985 1986 1987
        elif _in_legacy_dygraph():
            if output is None:
                output = core.VarBase()
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            _legacy_C_ops.assign_value(
                output,
                'shape',
                list(input.shape),
                'dtype',
                dtype,
                value_name,
                values,
            )
1997
        else:
1998 1999
            if output is None:
                output = helper.create_variable_for_type_inference(
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                    dtype=input.dtype
                )
            helper.append_op(
                type='assign_value',
                outputs={'Out': [output]},
                attrs={
                    'dtype': dtype,
                    'shape': list(input.shape),
                    value_name: values,
                },
            )
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    if is_inplace and _in_legacy_dygraph():
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        output._bump_inplace_version()

    return output
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2018 2019
def clone(x, name=None):
    """
2020 2021
    Returns a copy of input Tensor. It will always have a Tensor copy.

2022 2023 2024 2025
    In addition, This function is derivable, so gradients will flow back from the output to input.

    Parameters:
        x (Tensor): The input Tensor.
2026
        name(str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
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2028
    Returns:
2029
        Tensor, A Tensor copied from ``input``.
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    Examples:
        .. code-block:: python

            import paddle

            x = paddle.ones([2])
            x.stop_gradient = False
            clone_x = paddle.clone(x)

            y = clone_x**3
            y.backward()
            print(clone_x.grad)          # [3]
            print(x.grad)                # [3]
    """
    return x.clone()


2048
# NOTE(zhiqiu): not public
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def _memcpy(input, place=None, output=None):
    """

    The OP copies the :attr:`input` to the :attr:`output`.
    NOTE: currently, only support CUDAPlace <-> CUDAPinnedPlace or NPUPlace <-> CPUPlace.

    Parameters:
        input (Tensor): A tensor. Its data type supports float16, float32, float64, int32, int64, and bool.
        device (Place): Target place for the output.
        output (Tensor, optional): A tensor. If :attr:`output` is None, a new tensor will
            be created as :attr:`output`. Default: None.

    Returns:
2062
        Tensor, A tensor with the same shape, data type and value as :attr:`input`.
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    Examples:
        .. code-block:: python

          import paddle
2068

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          data = paddle.full(shape=[3, 2], fill_value=2.5, dtype='float64') # [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
          result = paddle._memcpy(data, place=paddle.CPUPlace())  # result2 = [[2.5, 2.5], [2.5, 2.5], [2.5, 2.5]]
    """
    helper = LayerHelper('memcpy', **locals())
    check_type(input, 'input', (Variable), 'memcpy')

    if isinstance(input, (Variable, core.VarBase)):
2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091
        check_dtype(
            input.dtype,
            'input',
            [
                'float16',
                'uint16',
                'float32',
                'float64',
                'int32',
                'int64',
                'uint8',
                'bool',
            ],
            'memcpy',
            '(When the type of input in memcpy is Variable.)',
        )
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    if output is None:
        output = helper.create_variable_for_type_inference(dtype=input.dtype)

    dst_place_type = -1
    if place is None:
        dst_place_type = -1
    else:
        p = core.Place()
        p.set_place(place)
        if p.is_cpu_place():
            dst_place_type = 0
        elif p.is_gpu_place():
            dst_place_type = 1
        elif p.is_cuda_pinned_place():
            dst_place_type = 2
        elif p.is_xpu_place():
            dst_place_type = 3
        elif p.is_npu_place():
            dst_place_type = 4

    attrs = {'dst_place_type': dst_place_type}
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    helper.append_op(
        type='memcpy',
        inputs={'X': [input]},
        outputs={'Out': [output]},
        attrs=attrs,
    )
2119
    return output
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def complex(real, imag, name=None):
    """Return a compelx tensor given the real and image component.

    Args:
        real (Tensor): The real component. The data type should be 'float32' or 'float64'.
        imag (Tensor): The image component. The data type should be the same as ``real``.
2128
        name (str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None.
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    Returns:
        Tensor: The output tensor. The data type is 'complex64' or 'complex128', with the same precision as ``real`` and ``imag``.

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    Note:
        ``paddle.complex`` supports broadcasting. If you want know more about broadcasting, please refer to `Introduction to Tensor`_ .

        .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor
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    Examples:
        .. code-block:: python

            import paddle
            x = paddle.arange(2, dtype=paddle.float32).unsqueeze(-1)
            y = paddle.arange(3, dtype=paddle.float32)
            z = paddle.complex(x, y)
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            print(z)
            # Tensor(shape=[2, 3], dtype=complex64, place=Place(cpu), stop_gradient=True,
            #        [[0j    , 1j    , 2j    ],
            #         [(1+0j), (1+1j), (1+2j)]])
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    """
2150
    if in_dygraph_mode():
2151
        return _C_ops.complex(real, imag)
2152

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    if paddle.in_dynamic_mode():
2154
        return paddle._legacy_C_ops.complex(real, imag)
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    check_variable_and_dtype(real, 'real', ['float32', 'float64'], 'complex')
    check_variable_and_dtype(imag, 'imag', ['float32', 'float64'], 'complex')

    op_type = "complex"
    helper = LayerHelper(op_type, **locals())
    inputs = {"X": real, "Y": imag}
    out = helper.create_variable_for_type_inference(
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        dtype=_real_to_complex_dtype(real.dtype)
    )
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    outputs = {"Out": out}
    attrs = {}
    helper.append_op(type=op_type, inputs=inputs, attrs=attrs, outputs=outputs)
    return out
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def tril_indices(row, col, offset=0, dtype='int64'):
    """
2173 2174
    Return the indices of the lower triangular part of the 2-D matrix
    whose row and col is knowed.Indices are ordered based on row and then columns.
2175 2176
    The lower triangular part of the matrix is defined as the elements on
    and below the diagonal.
2177

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    Args:
        row (int): The input x which is a int number describe the number of row of the matrix.
        col (int): The input x which is a int number describe the number of col of the matrix.
        offset (int, optional): The offset to consider, default value is 0.

2183 2184 2185 2186
            - If offset = 0, all elements on and below the main diagonal are retained.
            - If offset > 0, include just as many diagonals above the main diagonal.
            - If offset < 0, excludes just as many diagonals below the main diagonal.

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        dtype (int, optional): the data type of the output tensor, can be int32, int64.

    Returns:
        Tensor: Results of the indices of lower triangular part of a row * col matrix,
        where the first row contains row coordinates of and the second row contains column coordinates.

    Examples:
        .. code-block:: python

            import paddle
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2198 2199 2200
            # example 1, default offset value
            data1 = paddle.tril_indices(4,4,0)
            print(data1)
2201
            # [[0, 1, 1, 2, 2, 2, 3, 3, 3, 3],
2202 2203 2204 2205 2206
            #  [0, 0, 1, 0, 1, 2, 0, 1, 2, 3]]

            # example 2, positive offset value
            data2 = paddle.tril_indices(4,4,2)
            print(data2)
2207
            # [[0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3],
2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231
            #  [0, 1, 2, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]]

            # example 3, negative offset value
            data3 = paddle.tril_indices(4,4,-1)
            print(data3)
            # [[ 1, 2, 2, 3, 3, 3],
            #  [ 0, 0, 1, 0, 1, 2]]
    """
    if not isinstance(row, int) or row < 0:
        raise TypeError("row should be a non-negative int")

    if col is not None:
        if not isinstance(col, int) or col < 0:
            raise TypeError("col should be a non-negative int")
    else:
        col = row

    if not isinstance(offset, int):
        raise TypeError("offset should be a  int")

    if not isinstance(dtype, core.VarDesc.VarType):
        dtype = convert_np_dtype_to_dtype_(dtype)

    if in_dygraph_mode():
2232 2233 2234
        out = _C_ops.tril_indices(
            row, col, offset, dtype, _current_expected_place()
        )
2235 2236 2237
        return out

    if _in_legacy_dygraph():
2238 2239 2240
        out = _legacy_C_ops.tril_indices(
            'rows', row, 'cols', col, 'offset', offset, "dtype", dtype
        )
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        return out

    else:
        helper = LayerHelper("tril_indices", **locals())

        out = helper.create_variable_for_type_inference(dtype=dtype)

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        helper.append_op(
            type='tril_indices',
            inputs={},
            outputs={'out': [out]},
            attrs={'rows': row, 'cols': col, 'offset': offset, 'dtype': dtype},
        )
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    return out
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def triu_indices(row, col=None, offset=0, dtype='int64'):
    """
    Return the indices of the upper triangular part of the 2-D matrix
    whose row and col is known. Indices are ordered based on row and then columns.
    The upper triangular part of the matrix is defined as the elements on
    and above the diagonal.

    Args:
        row (int): The input x which is a int number describe the number of row of the matrix.
        col (int, optional): The input x which is a int number describe the number of col of the matrix.
            default value for col is None, then it will be set equal to row, indicting a square matix.
        offset (int, optional): The offset to consider, default value is 0.

            - If offset = 0, all elements on and above the main diagonal are retained.
            - If offset > 0, include just as few diagonals above the main diagonal.
            - If offset < 0, excludes just as few diagonals below the main diagonal.

        dtype (str|np.dtype|paddle.dtype, optional): the data type of the output tensor,
            can be int32, int64, default value is int64.
    Returns:
        Tensor: Results of the indices of upper triangular part of a row * col matrix,
        where the first row contains row coordinates of and the second row contains column coordinates.

    Examples:
        .. code-block:: python

            import paddle
            # example 1, default offset value
            data1 = paddle.triu_indices(4,4,0)
            print(data1)
            # [[0, 0, 0, 0, 1, 1, 1, 2, 2, 3],
            #  [0, 1, 2, 3, 1, 2, 3, 2, 3, 3]]
            # example 2, positive offset value
            data2 = paddle.triu_indices(4,4,2)
            print(data2)
            # [[0, 0, 1],
            #  [2, 3, 3]]
            # example 3, negative offset value
            data3 = paddle.triu_indices(4,4,-1)
            print(data3)
            # [[0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 3, 3],
            #  [0, 1, 2, 3, 0, 1, 2, 3, 1, 2, 3, 2, 3]]
    """
    if not isinstance(row, int) or row < 0:
        raise TypeError("row should be a non-negative int")

    if col is not None:
        if not isinstance(col, int) or col < 0:
            raise TypeError("col should be a non-negative int")
    else:
        col = row

    if not isinstance(offset, int):
        raise TypeError("offset should be a int")

    if not isinstance(dtype, core.VarDesc.VarType):
        dtype = convert_np_dtype_to_dtype_(dtype)

    if in_dygraph_mode():
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        out = _C_ops.triu_indices(
            row, col, offset, dtype, _current_expected_place()
        )
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        return out

    if _in_legacy_dygraph():
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        out = _legacy_C_ops.triu_indices(
            'row', row, 'col', col, 'offset', offset, "dtype", dtype
        )
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        return out

    else:
        helper = LayerHelper("triu_indices", **locals())

        out = helper.create_variable_for_type_inference(dtype=dtype)

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        helper.append_op(
            type='triu_indices',
            inputs={},
            outputs={'out': [out]},
            attrs={'row': row, 'col': col, 'offset': offset, 'dtype': dtype},
        )
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    return out