extension.py

#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# TODO: define the extention functions

import numpy as np
from ...fluid.data_feeder import check_dtype
from ...fluid.layer_helper import LayerHelper
from ...static import Variable
from ...tensor.creation import assign
from ...fluid import dygraph_utils
from ...tensor.layer_function_generator import templatedoc
from paddle import in_dynamic_mode
from paddle import _C_ops, _legacy_C_ops
from ...fluid.framework import _non_static_mode, _in_legacy_dygraph, in_dygraph_mode
from ...fluid.data_feeder import check_variable_and_dtype, check_type
from ...framework import core
from ...common_ops_import import convert_np_dtype_to_dtype_

__all__ = []


def diag_embed(input, offset=0, dim1=-2, dim2=-1):
    """
    This OP creates a tensor whose diagonals of certain 2D planes (specified by dim1 and dim2) 
    are filled by ``input``. By default, a 2D plane formed by the last two dimensions 
    of the returned tensor will be selected.

    The argument ``offset`` determines which diagonal is generated:

    - If offset = 0, it is the main diagonal.
    - If offset > 0, it is above the main diagonal.
    - If offset < 0, it is below the main diagonal.

    Args:
        input(Tensor|numpy.ndarray): The input tensor. Must be at least 1-dimensional. The input data type should be float32, float64, int32, int64.
        offset(int, optional): Which diagonal to consider. Default: 0 (main diagonal).
        dim1(int, optional): The first dimension with respect to which to take diagonal. Default: -2.
        dim2(int, optional): The second dimension with respect to which to take diagonal. Default: -1.
    
    Returns:
        Tensor, the output data type is the same as input data type.
    
    Examples:
        .. code-block:: python

            import paddle.nn.functional as F
            import numpy as np
            
            diag_embed = np.random.randn(2, 3).astype('float32')
            # [[ 0.7545889 , -0.25074545,  0.5929117 ],
            #  [-0.6097662 , -0.01753256,  0.619769  ]]

            data1 = F.diag_embed(diag_embed)
            data1.numpy()
            # [[[ 0.7545889 ,  0.        ,  0.        ],
            #  [ 0.        , -0.25074545,  0.        ],
            #   [ 0.        ,  0.        ,  0.5929117 ]],

            # [[-0.6097662 ,  0.        ,  0.        ],
            #  [ 0.        , -0.01753256,  0.        ],
            #  [ 0.        ,  0.        ,  0.619769  ]]]

            data2 = F.diag_embed(diag_embed, offset=-1, dim1=0, dim2=2)
            data2.numpy()
            # [[[ 0.        ,  0.        ,  0.        ,  0.        ],
            #   [ 0.7545889 ,  0.        ,  0.        ,  0.        ],
            #   [ 0.        , -0.25074545,  0.        ,  0.        ],
            #   [ 0.        ,  0.        ,  0.5929117 ,  0.        ]],
            #
            #  [[ 0.        ,  0.        ,  0.        ,  0.        ],
            #   [-0.6097662 ,  0.        ,  0.        ,  0.        ],
            #   [ 0.        , -0.01753256,  0.        ,  0.        ],
            #   [ 0.        ,  0.        ,  0.619769  ,  0.        ]]]

            data3 = F.diag_embed(diag_embed, offset=1, dim1=0, dim2=2)
            data3.numpy()
            # [[[ 0.        ,  0.7545889 ,  0.        ,  0.        ],
            #   [ 0.        , -0.6097662 ,  0.        ,  0.        ]],
            #
            #  [[ 0.        ,  0.        , -0.25074545,  0.        ],
            #   [ 0.        ,  0.        , -0.01753256,  0.        ]],
            #
            #  [[ 0.        ,  0.        ,  0.        ,  0.5929117 ],
            #   [ 0.        ,  0.        ,  0.        ,  0.619769  ]],
            #
            #  [[ 0.        ,  0.        ,  0.        ,  0.        ],
            #   [ 0.        ,  0.        ,  0.        ,  0.        ]]]
    """
    if not isinstance(input, Variable):
        input = assign(input)

    if in_dygraph_mode():
        return _C_ops.diag_embed(input, offset, dim1, dim2)
    elif in_dynamic_mode():
        return _legacy_C_ops.diag_embed(input, "offset", offset, "dim1", dim1,
                                        "dim2", dim2)

    inputs = {'Input': [input]}
    attrs = {'offset': offset, 'dim1': dim1, 'dim2': dim2}

    def __check_input(input, offset, dim1, dim2):
        check_dtype(input.dtype, 'Input',
                    ['int32', 'int64', 'float16', 'float32', 'float64'],
                    'diag_embed')

        input_shape = list(input.shape)
        assert len(input_shape) >= 1,                     \
                "Input must be at least 1-dimensional, "   \
                "But received Input's dimensional: %s.\n" %  \
                len(input_shape)

        assert np.abs(dim1) <= len(input_shape),    \
            "Dim1 is out of range (expected to be in range of [%d, %d], but got %d).\n"  \
            % (-(len(input_shape) + 1), len(input_shape), dim1)

        assert np.abs(dim2) <= len(input_shape),      \
            "Dim2 is out of range (expected to be in range of [%d, %d], but got %d).\n"  \
            % (-(len(input_shape) + 1), len(input_shape), dim2)

        dim1_ = dim1 if dim1 >= 0 else len(input_shape) + dim1 + 1
        dim2_ = dim2 if dim2 >= 0 else len(input_shape) + dim2 + 1
        assert dim1_ != dim2_,       \
               "dim1 and dim2 cannot be the same dimension." \
                "But received dim1 = %d, dim2 = %d\n"%(dim1, dim2)

    __check_input(input, offset, dim1, dim2)
    helper = LayerHelper("diag_embed", **locals())

    out = helper.create_variable_for_type_inference(dtype=input.dtype)

    helper.append_op(type='diag_embed',
                     inputs={'Input': [input]},
                     attrs={
                         'offset': offset,
                         'dim1': dim1,
                         'dim2': dim2
                     },
                     outputs={'Out': [out]})
    out.stop_gradient = True
    return out


def sequence_mask(x, maxlen=None, dtype='int64', name=None):
    r"""
    **SequenceMask Layer**

    This layer outputs a mask according to the input :code:`x` and
    :code:`maxlen` with data type of :code:`dtype`.

    Supposing :code:`x` is a Tensor with shape [d_1, d_2, ..., d_n], the
    :code:`y` is a mask with shape [d_1, d_2, ..., d_n, maxlen], where:

    .. math::

        y(i_1, i_2,..., i_n, j) = (j < x(i_1, i_2,..., i_n))

    .. code-block:: text

        Case:

        Consider input:
            x = [3, 1, 1, 0]    max_len = 4

        then we get out:
            mask = [[1, 1, 1, 0],
                    [1, 0, 0, 0],
                    [1, 0, 0, 0],
                    [0, 0, 0, 0]]

    Args:
        x (Variable): Input tensor of sequence_mask layer, \
            whose elements are integers less than :code:`maxlen`. \
            Tensor or LodTensor with shape [d_1, d_2, ..., d_n].
        maxlen (int, optional): Maximum length of the sequence. If :code:`maxlen` \
                           is None, it would be replace with :math:`max(x)`.
        dtype (np.dtype|paddle.dtype|str, optional): Data type of the output, \
             ``int64`` by default.
        name(str, optional): For detailed information, please refer \
            to :ref:`api_guide_Name`. Usually name is no need to set and \
            None by default.

    Returns: 
            Tensor, The output sequence mask. Tensor with shape [d_1, d_2, ..., d_n, maxlen] \
            and data type of :code:`dtype`. The data type should be bool, float32, float64, int8, \
            int32 or int64.

    Examples:
        .. code-block:: python

            import paddle

            lengths = paddle.to_tensor([10, 9, 8])
            mask = paddle.nn.functional.sequence_mask(lengths)

            print(mask.numpy())
            # [[1 1 1 1 1 1 1 1 1 1]
            #  [1 1 1 1 1 1 1 1 1 0]
            #  [1 1 1 1 1 1 1 1 0 0]]

    """

    if in_dygraph_mode():
        if not isinstance(dtype, core.VarDesc.VarType):
            dtype = convert_np_dtype_to_dtype_(dtype)
        if maxlen is not None:
            if isinstance(maxlen, core.eager.Tensor):
                attrs = ('out_dtype', dtype)
                out = _legacy_C_ops.sequence_mask(x, maxlen, *attrs)
            else:
                attrs = ('out_dtype', dtype, 'maxlen', maxlen)
                out = _legacy_C_ops.sequence_mask(x, None, *attrs)
            out.stop_gradient = True
            return out

    helper = LayerHelper('sequence_mask', **locals())
    out = helper.create_variable_for_type_inference(dtype=dtype)

    inputs = {'X': [x]}
    attrs = {'out_dtype': out.dtype}
    if maxlen is not None:
        if isinstance(maxlen, Variable):
            inputs['MaxLenTensor'] = maxlen
        else:
            attrs['maxlen'] = maxlen

    helper.append_op(type='sequence_mask',
                     inputs=inputs,
                     outputs={'Y': out},
                     attrs=attrs)

    out.stop_gradient = True
    return out


def gather_tree(ids, parents):
    r"""
    To be used after beam search. After beam search, we get selected ids at
    each time step and the corresponding parents in the search tree. Both ids
    and parents have the layout :attr:`[max_time, batch_size, beam_size]`. Then
    :attr:`gather_tree` is used to backtrace from the last time step and
    generate the full sequences by collecting selected ids.

    Here is an example:

    .. code-block:: text

            Given:
                ids = [[[2 2]
                        [6 1]]
                       [[3 9]
                        [6 1]]
                       [[0 1]
                        [9 0]]]
                parents = [[[0 0]
                            [1 1]]
                           [[1 0]
                            [1 0]]
                           [[0 0]
                            [0 1]]]

            Then:
                gather_tree(ids, parents)
                         = [[[2 2]
                             [1 6]]
                            [[3 3]
                             [6 1]]
                            [[0 1]
                             [9 0]]]

    Args:
        ids(Tensor): A Tensor with shape :attr:`[length, batch_size, beam_size]`
            and data type :attr:`int32` or :attr:`int64`. It contains the selected
            ids of all time steps.
        parents(Tensor): A Tensor with the same shape and data type as :attr:`ids`,
            It contains the parents corresponding to selected ids when searching
            among beams.

    Returns:
            A Tensor with the same shape and data type as :attr:`ids`. \
            It contains the full sequences. The sequences are collected from \
            :attr:`ids` by backtracing according to :attr:`parents`.

    Examples:
        .. code-block:: python

            import paddle

            ids = paddle.to_tensor([[[2, 2], [6, 1]], [[3, 9], [6, 1]], [[0, 1], [9, 0]]])

            parents = paddle.to_tensor([[[0, 0], [1, 1]], [[1, 0], [1, 0]], [[0, 0], [0, 1]]])

            final_sequences = paddle.nn.functional.gather_tree(ids, parents)
            # [[[2, 2], [1, 6]], [[3, 3], [6, 1]], [[0, 1], [9, 0]]]

    """
    if in_dygraph_mode():
        return _C_ops.gather_tree(ids, parents)
    else:
        if _in_legacy_dygraph():
            return _legacy_C_ops.gather_tree(ids, parents)
        else:
            helper = LayerHelper('gather_tree', **locals())
            check_variable_and_dtype(ids, 'ids', ['int32', 'int64'],
                                     'gather_tree')
            check_variable_and_dtype(parents, 'parents', ['int32', 'int64'],
                                     'gather_tree')
            out = helper.create_variable_for_type_inference(dtype=ids.dtype)

            helper.append_op(type="gather_tree",
                             inputs={
                                 "Ids": ids,
                                 "Parents": parents
                             },
                             outputs={"Out": out})

            return out


@templatedoc()
def temporal_shift(x, seg_num, shift_ratio=0.25, name=None, data_format="NCHW"):
    """

    **Temporal Shift Operator**

    ${comment}

    Args:
        x(Tensor): ${x_comment}
        seg_num(int): ${seg_num_comment}
        shift_ratio(float): ${shift_ratio_comment}
        name(str, optional): For detailed information, please refer
                             to :ref:`api_guide_Name`. Usually name is no need to set and
                             None by default.
        data_format(str, optional): Data format that specifies the layout of input.
            It can be "NCHW" or "NHWC". Default: "NCHW".

    Returns:
        out(Tensor): The temporal shifting result is a tensor with the
        same shape and same data type as the input.

    Raises:
        TypeError: seg_num must be int type.

    Examples:
        .. code-block:: python

            import paddle
            import paddle.nn.functional as F

            input = paddle.randn([6, 4, 2, 2])
            out = F.temporal_shift(x=input, seg_num=2, shift_ratio=0.2)
    """
    if data_format not in ["NCHW", "NHWC"]:
        raise ValueError("Attr(data_format) should be 'NCHW' or 'NHWC'. "
                         "Received Attr(data_format): {}.".format(data_format))
    if in_dygraph_mode():
        return _C_ops.temporal_shift(x, seg_num, shift_ratio, data_format)
    if _non_static_mode():
        return _legacy_C_ops.temporal_shift(x, 'seg_num', seg_num,
                                            'shift_ratio', shift_ratio,
                                            'data_format', data_format)

    helper = LayerHelper("temporal_shift", **locals())
    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'temporal_shift')
    check_type(seg_num, 'seg_num', int, 'temporal_shift')
    check_type(shift_ratio, 'shift_ratio', float, 'temporal_shift')

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

    if not isinstance(seg_num, int):
        raise TypeError("seg_num must be int type.")

    helper.append_op(type="temporal_shift",
                     inputs={"X": x},
                     outputs={"Out": out},
                     attrs={
                         "seg_num": seg_num,
                         "shift_ratio": shift_ratio,
                         "data_format": data_format
                     })
    return out