math_op_patch.py

#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import warnings
import inspect

from .. import core
from ..framework import Variable, unique_name, static_only
from .layer_function_generator import OpProtoHolder
from paddle.fluid.dygraph.base import in_declarative_mode

_supported_int_dtype_ = [
    core.VarDesc.VarType.BOOL,
    core.VarDesc.VarType.UINT8,
    core.VarDesc.VarType.INT8,
    core.VarDesc.VarType.INT16,
    core.VarDesc.VarType.INT32,
    core.VarDesc.VarType.INT64,
]

compare_ops = ['__eq__', '__ne__', '__lt__', '__le__', '__gt__', '__ge__']

EXPRESSION_MAP = {
    "__add__": "A + B",
    "__radd__": "A += B",
    "__sub__": "A - B",
    "__rsub__": "A -= B",
    "__mul__": "A * B",
    "__rmul__": "A *= B",
    "__div__": "A / B",
    "__truediv__": "A / B",
    "__rdiv__": "A /= B",
    "__rtruediv__": "A /= B",
    "__pow__": "A ** B",
    "__rpow__": "A **= B",
    "__floordiv__": "A //B",
    "__mod__": "A % B",
    "__matmul__": "A @ B",
    "__eq__": "A == B",
    "__ne__": "A != B",
    "__lt__": "A < B",
    "__le__": "A <= B",
    "__gt__": "A > B",
    "__ge__": "A >= B",
}

_already_patch_variable = False


def monkey_patch_variable():
    def unique_tmp_name():
        return unique_name.generate("tmp")

    def safe_get_dtype(var):
        try:
            dtype = var.dtype
        except:
            raise ValueError("Cannot get data type from %s", var.name)
        return dtype

    def current_block(var):
        return var.block.program.current_block()

    def create_new_tmp_var(block, dtype):
        tmp_name = unique_tmp_name()
        return block.create_var(name=tmp_name, dtype=dtype)

    def create_new_tmp_sparse_var(block, dtype, type):
        tmp_name = unique_tmp_name()
        return block.create_var(name=tmp_name, dtype=dtype, type=type)

    def create_tensor(block, value, dtype, shape):
        value = float(value)
        var = create_new_tmp_var(block, dtype)
        block.append_op(
            type="fill_constant",
            outputs={'Out': [var]},
            attrs={
                'dtype': var.dtype,
                'shape': shape,
                'value': value,
                'force_cpu': False,
            },
            stop_gradient=True,
        )
        var.stop_gradient = True
        return var

    def create_scalar(block, value, dtype):
        # TODO(zhouwei): will change to [] which is 0-D Tensor
        return create_tensor(block, value, dtype, shape=[1])

    def create_tensor_with_batchsize(ref_var, value, dtype):
        assert isinstance(ref_var, Variable)
        value = float(value)
        block = current_block(ref_var)
        var = create_new_tmp_var(block, dtype)
        batch_dim = -1
        out_shape = []
        for i, d in enumerate(ref_var.shape):
            if d < 0:
                if batch_dim < 0:
                    batch_dim = i
                    out_shape.append(d)
                else:
                    out_shape.append(1)
            else:
                out_shape.append(d)
        assert batch_dim != -1
        block.append_op(
            type='fill_constant_batch_size_like',
            outputs={'Out': [var]},
            inputs={'Input': [ref_var]},
            attrs={
                'shape': out_shape,
                'value': value,
                'input_dim_idx': batch_dim,
                'output_dim_idx': batch_dim,
            },
            stop_gradient=True,
        )

        var.stop_gradient = True
        return var

    @static_only
    def cpu(self):
        """
        Variable should not have cpu() and cuda() interface.
        But this interface can greatly facilitate dy2static.
        We do nothing here.
        """
        return self

    @static_only
    def cuda(self):
        """
        Variable should not have cpu() and cuda() interface.
        But this interface can greatly facilitate dy2static.
        We do nothing here.
        """
        return self

    @static_only
    def place(self):
        """
        Variable don't have 'place' interface in static graph mode
        But this interface can greatly facilitate dy2static.
        So we give a warnning here and return None.
        """
        warnings.warn(
            "Variable do not have 'place' interface for static graph mode, try not to use it. None will be returned."
        )
        return None

    def astype(self, dtype):
        """
        **Notes**:
            **The variable must be a** :ref:`api_fluid_Tensor`

        Cast a variable to a specified data type.

        Args:

            self(Variable): The source variable

            dtype: The target data type

        Returns:
            Variable: Variable with new dtype

        Examples:
            In Static Graph Mode:

            .. code-block:: python

                import paddle.fluid as fluid

                startup_prog = fluid.Program()
                main_prog = fluid.Program()
                with fluid.program_guard(startup_prog, main_prog):
                    original_variable = fluid.data(name = "new_variable", shape=[2,2], dtype='float32')
                    new_variable = original_variable.astype('int64')
                    print("new var's dtype is: {}".format(new_variable.dtype))

            In Dygraph Mode:

            .. code-block:: python

                import paddle.fluid as fluid
                import numpy as np

                x = np.ones([2, 2], np.float32)
                with fluid.dygraph.guard():
                    original_variable = fluid.dygraph.to_variable(x)
                    print("original var's dtype is: {}, numpy dtype is {}".format(original_variable.dtype, original_variable.numpy().dtype))
                    new_variable = original_variable.astype('int64')
                    print("new var's dtype is: {}, numpy dtype is {}".format(new_variable.dtype, new_variable.numpy().dtype))

        """
        block = current_block(self)
        out = create_new_tmp_var(block, dtype)
        block.append_op(
            type="cast",
            inputs={"X": [self]},
            outputs={"Out": [out]},
            attrs={"in_dtype": self.dtype, "out_dtype": out.dtype},
        )
        out.stop_gradient = self.stop_gradient
        return out

    @static_only
    def append(self, var):
        """
        **Notes**:
           **The type variable must be LoD Tensor Array.

        """
        if not isinstance(var, Variable):
            if in_declarative_mode():
                """in dy2static mode, x may be tensorable values such as int, float, np.array"""
                from paddle.tensor.creation import to_tensor

                var = to_tensor(var)
            else:
                raise TypeError(
                    "Required input var should be Variable, but received {}".format(
                        type(var)
                    )
                )
        if self.type != core.VarDesc.VarType.LOD_TENSOR_ARRAY:
            raise TypeError(
                "Only Variable with VarType.LOD_TENSOR_ARRAY support `append` method, but received type: {}".format(
                    self.type
                )
            )
        from paddle.tensor.array import array_length, array_write

        array_write(x=var, i=array_length(self), array=self)

    @static_only
    def _item(self):
        """
        In order to be compatible with the item interface introduced by the dynamic graph, it does nothing but returns self.
        It will check that the shape must be a 1-D tensor
        """
        if len(self.shape) > 1:
            raise TypeError(
                "Required input var should be 1-D Variable, but received {}".format(
                    self.shape
                )
            )
        return self

    @static_only
    def pop(self, *args):
        """
        The type variable must be LoD Tensor Array.
        When self is LoDTensorArray, calling pop is similar to Python's pop on list.
        This interface is used to simplify dygraph to static graph operations.

        Args:
            self(Variable): The source variable, which must be LOD_TENSOR_ARRAY
            *args: optional, a int means index.
        Returns:
            Variable: self[index]
        """
        from paddle.jit.dy2static.convert_operators import (
            _run_paddle_pop,
        )

        if self.type != core.VarDesc.VarType.LOD_TENSOR_ARRAY:
            raise TypeError(
                "Only Variable with VarType.LOD_TENSOR_ARRAY support `append` method, but received type: {}".format(
                    self.type
                )
            )
        return _run_paddle_pop(self, *args)

    def _scalar_op_(var, scale, bias):
        block = current_block(var)
        out = create_new_tmp_var(block, var.dtype)
        block.append_op(
            type="scale",
            inputs={"X": [var]},
            outputs={"Out": [out]},
            attrs={"scale": scale, "bias": bias},
        )
        return out

    def _neg_(var):
        return _scalar_op_(var, -1.0, 0.0)

    @property
    def _ndim_(self):
        """
        Returns the dimension of current Variable

        Returns:
            the dimension

        Examples:
            .. code-block:: python

                import paddle

                paddle.enable_static()

                # create a static Variable
                x = paddle.static.data(name='x', shape=[3, 2, 1])
                # print the dimension of the Variable
                print(x.ndim)
        """
        return len(self.shape)

    def _scalar_add_(var, value):
        return _scalar_op_(var, 1.0, value)

    def _scalar_sub_(var, value):
        return _scalar_op_(var, 1.0, -value)

    def _scalar_rsub_(var, value):
        return _scalar_op_(var, -1.0, value)

    def _scalar_mul_(var, value):
        return _scalar_op_(var, value, 0.0)

    def _scalar_div_(var, value):
        return _scalar_op_(var, 1.0 / value, 0.0)

    def _binary_creator_(
        method_name, op_type, reverse=False, scalar_method=None
    ):
        def __impl__(self, other_var):
            # 1. scalar exists cases
            # we need combine the tensor.dtype and scalar.dtype, cast correct object
            if isinstance(other_var, float):
                # in all cases(+, -, *, /, **, //, %), we need cast tensor.dtype to float
                if self.dtype in _supported_int_dtype_:
                    self = astype(self, 'float32')
                # here use `scale` replace `elementwise` to get better performance
                # but only +, -, *, / can use this method
                if scalar_method is not None:
                    return scalar_method(self, other_var)
            elif isinstance(other_var, int):
                # in all cases(+, -, *, /, **, //, %), we can cast it to float
                # because the output tensor.dtype depend on the type of input tensor
                other_var = float(other_var)
                # division is a special case
                # NOTE(chenweihang): because we cast tensor to float32 instead float64,
                # the division result can only guarantee the numerical accuracy of 6 digits
                # after the decimal point. The result of numpy calculation is of float64 type,
                # so the calculation result here and the calculation result of numpy are
                # different after 6 decimal point. If necessary, we can also use float64 here.
                # torch's behavior here is consistent with ours
                if (
                    op_type == 'elementwise_div'
                    and self.dtype in _supported_int_dtype_
                ):
                    self = astype(self, 'float32')
                # here use `scale` replace `elementwise` to get better performance
                # but only +, -, *, / can use this method
                if scalar_method is not None:
                    return scalar_method(self, other_var)
            else:
                # do nothing
                pass

            # 2. create variable for scalar
            lhs_dtype = safe_get_dtype(self)
            if not isinstance(other_var, Variable):
                if reverse:
                    has_batch_size = False
                    for elem in self.shape:
                        if elem < 0:
                            has_batch_size = True
                            break
                    if not has_batch_size:
                        other_var = create_tensor(
                            current_block(self),
                            other_var,
                            dtype=lhs_dtype,
                            shape=self.shape,
                        )
                    else:
                        other_var = create_tensor_with_batchsize(
                            self, other_var, lhs_dtype
                        )
                else:
                    # add fill_op to current_block
                    other_var = create_scalar(
                        current_block(self), value=other_var, dtype=lhs_dtype
                    )

            # 3. unify right var type to left var
            rhs_dtype = safe_get_dtype(other_var)
            if lhs_dtype != rhs_dtype:
                other_var = astype(other_var, lhs_dtype)
            if reverse:
                tmp = self
                self = other_var
                other_var = tmp

            # NOTE(zhiqiu): the output of compare operator should be bool.
            if method_name in compare_ops:
                out = create_new_tmp_var(current_block(self), dtype="bool")
            else:
                out = create_new_tmp_var(current_block(self), dtype=lhs_dtype)

            axis = -1
            if other_var.ndim > 0 and other_var.shape[0] == -1:
                stack = inspect.stack()[1]
                file_name = stack[1]
                line_num = stack[2]
                warnings.warn(
                    "%s:%s\nThe behavior of expression %s has been unified with %s(X, Y, axis=-1) from Paddle 2.0. "
                    "If your code works well in the older versions but crashes in this version, try to use "
                    "%s(X, Y, axis=0) instead of %s. This transitional warning will be dropped in the future."
                    % (
                        file_name,
                        line_num,
                        EXPRESSION_MAP[method_name],
                        op_type,
                        op_type,
                        EXPRESSION_MAP[method_name],
                    ),
                    category=DeprecationWarning,
                )
            current_block(self).append_op(
                type=op_type,
                inputs={'X': [self], 'Y': [other_var]},
                outputs={'Out': out},
                attrs={'axis': axis},
            )
            return out

        comment = OpProtoHolder.instance().get_op_proto(op_type).comment

        __impl__.__doc__ = """
        {0}
        Args:
            self(Variable): left hand variable
            other_var(Variable|float|int): right hand variable

        Returns:
            Variable
        """.format(
            comment
        )
        __impl__.__name__ = method_name
        return __impl__

    def values(var):
        block = current_block(var)
        out = create_new_tmp_var(block, var.dtype)
        block.append_op(
            type="sparse_values",
            inputs={"x": [var]},
            outputs={"out": [out]},
            attrs={},
        )
        return out

    def indices(var):
        block = current_block(var)
        out = create_new_tmp_var(block, var.dtype)
        block.append_op(
            type="sparse_indices",
            inputs={"x": [var]},
            outputs={"out": [out]},
            attrs={},
        )
        return out

    def to_dense(var):
        block = current_block(var)
        out = create_new_tmp_var(block, var.dtype)
        block.append_op(
            type="sparse_to_dense",
            inputs={"x": [var]},
            outputs={"out": [out]},
            attrs={},
        )
        return out

    variable_methods = [
        #   b=-a
        ('__neg__', _neg_),
        ('astype', astype),
        ('cpu', cpu),
        ('cuda', cuda),
        ('place', place),
        ('append', append),
        ('item', _item),
        ('pop', pop),
        ('dim', lambda x: len(x.shape)),
        ('ndimension', lambda x: len(x.shape)),
        ('ndim', _ndim_),
        (
            '__add__',
            _binary_creator_('__add__', 'elementwise_add', False, _scalar_add_),
        ),
        #  a+b == b+a. Do not need to reverse explicitly
        (
            '__radd__',
            _binary_creator_(
                '__radd__', 'elementwise_add', False, _scalar_add_
            ),
        ),
        (
            '__sub__',
            _binary_creator_('__sub__', 'elementwise_sub', False, _scalar_sub_),
        ),
        (
            '__rsub__',
            _binary_creator_(
                '__rsub__', 'elementwise_sub', True, _scalar_rsub_
            ),
        ),
        (
            '__mul__',
            _binary_creator_('__mul__', 'elementwise_mul', False, _scalar_mul_),
        ),
        #  a*b == b*a. Do not need to reverse explicitly
        (
            '__rmul__',
            _binary_creator_(
                '__rmul__', 'elementwise_mul', False, _scalar_mul_
            ),
        ),
        (
            '__div__',
            _binary_creator_('__div__', 'elementwise_div', False, _scalar_div_),
        ),
        (
            '__truediv__',
            _binary_creator_(
                '__truediv__', 'elementwise_div', False, _scalar_div_
            ),
        ),
        (
            '__rdiv__',
            _binary_creator_('__rdiv__', 'elementwise_div', True, None),
        ),
        (
            '__rtruediv__',
            _binary_creator_('__rtruediv__', 'elementwise_div', True, None),
        ),
        (
            '__pow__',
            _binary_creator_('__pow__', 'elementwise_pow', False, None),
        ),
        (
            '__rpow__',
            _binary_creator_('__rpow__', 'elementwise_pow', True, None),
        ),
        (
            '__floordiv__',
            _binary_creator_(
                '__floordiv__', 'elementwise_floordiv', False, None
            ),
        ),
        (
            '__mod__',
            _binary_creator_('__mod__', 'elementwise_mod', False, None),
        ),
        (
            '__matmul__',
            _binary_creator_('__matmul__', "matmul_v2", False, None),
        ),
        #  for logical compare
        ('__eq__', _binary_creator_('__eq__', 'equal', False, None)),
        ('__ne__', _binary_creator_('__ne__', 'not_equal', False, None)),
        ('__lt__', _binary_creator_('__lt__', 'less_than', False, None)),
        ('__le__', _binary_creator_('__le__', 'less_equal', False, None)),
        ('__gt__', _binary_creator_('__gt__', 'greater_than', False, None)),
        ('__ge__', _binary_creator_('__ge__', 'greater_equal', False, None)),
        ('values', values),
        ('indices', indices),
        ('to_dense', to_dense),
    ]

    global _already_patch_variable
    if not _already_patch_variable:
        for method in variable_methods:
            method_name = method[0]
            method_impl = method[1]
            setattr(Variable, method_name, method_impl)
    else:
        import paddle.tensor

        for method_name in paddle.tensor.tensor_method_func:
            if hasattr(Variable, method_name):
                continue
            method_impl = getattr(paddle.tensor, method_name, None)
            if method_impl:
                setattr(Variable, method_name, method_impl)

        for magic_method, origin_method in paddle.tensor.magic_method_func:
            impl = getattr(paddle.tensor, origin_method, None)
            if impl:
                setattr(Variable, magic_method, impl)

    _already_patch_variable = True