varbase_patch_methods.py

# Copyright (c) 2019 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 inspect
from .. import framework
from .. import core
from . import BackwardStrategy
from ..framework import Variable, Parameter, ParamBase
from .base import switch_to_static_graph
import numpy as np
from .math_op_patch import monkey_patch_math_varbase


def monkey_patch_varbase():
    @switch_to_static_graph
    def _to_static_var(self, to_parameter=False, **kwargs):
        """
        **Notes**:
            **This API is ONLY available in Dygraph mode**

        Transform a VarBase into static Variable with same attributes. It's a low level interface used
        in dy2static and shall not be called directly.

        Args:
            to_parameter (bool): It takes effect only if the input a VarBase. If set True,
                                 the VarBase will be converted into framework.Parameters. Otherwise, it will
                                 be converted into framework.Variable. Default False.

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                from paddle.fluid.dygraph.base import to_variable
                import numpy as np

                data = np.ones([3, 1024], dtype='float32')
                with fluid.dygraph.guard():
                    var_base = to_variable(data)
                    static_var = var_base._to_static_var()

        """

        # Note: getattr(self, attr, None) will call x.grad=x.gradient(), but gradient() only available in dygraph. 
        # It will fail. So, for propery in dygraph only, should not let it getattr(self, attr, None).
        attr_not_need_keys = ['grad']
        if isinstance(self, ParamBase):
            attr_kwargs = self.__dict__.copy()
        else:
            attr_names = []
            for name in dir(self):
                if name not in attr_not_need_keys and not (
                        inspect.ismethod(getattr(self, name)) or
                        name.startswith('_')):
                    attr_names.append(name)
            attr_kwargs = {name: getattr(self, name) for name in attr_names}

        attr_keys = ['block', 'shape', 'dtype', 'type', 'name', 'persistable']
        for attr in attr_keys:
            attr_kwargs[attr] = getattr(self, attr, None)

        attr_kwargs.update(kwargs)

        if to_parameter or isinstance(self, ParamBase):
            del attr_kwargs['persistable']
            static_var = Parameter(**attr_kwargs)
        else:
            static_var = Variable(**attr_kwargs)
        return static_var

    # TODO(jiabin): move this to cplusplus end if we find some performance issue on it
    @framework.dygraph_only
    def set_value(self, value):
        """
        **Notes**:
            **This API is ONLY available in Dygraph mode**

        Set a new value for this Variable.

        Args:
            value (Variable|np.ndarray): the new value.

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                from paddle.fluid.dygraph.base import to_variable
                from paddle.fluid.dygraph import Linear
                import numpy as np

                data = np.ones([3, 1024], dtype='float32')
                with fluid.dygraph.guard():
                    linear = fluid.dygraph.Linear(1024, 4)
                    t = to_variable(data)
                    linear(t)  # call with default weight
                    custom_weight = np.random.randn(1024, 4).astype("float32")
                    linear.weight.set_value(custom_weight)  # change existing weight
                    out = linear(t)  # call with different weight

        """
        assert isinstance(value, (np.ndarray, core.VarBase)), \
            "Variable set_value function, arguments type only support Variable, numpy, VarBase"

        value_np = value
        if isinstance(value, core.VarBase):
            value_np = value.numpy()

        self_tensor_np = self.numpy()

        assert self_tensor_np.shape == value_np.shape, \
            "Variable Shape not match, Variable [ {} ] need tensor with shape {} but load set tensor with shape {}".format(
                self.name, self_tensor_np.shape, value_np.shape)

        assert self_tensor_np.dtype == value_np.dtype, \
            "Variable dtype not match, Variable [ {} ] need tensor with dtype {}  but load tensor with dtype {}".format(
                self.name, self_tensor_np.dtype, value_np.dtype)

        self.value().get_tensor().set(value_np,
                                      framework._current_expected_place())

    @framework.dygraph_only
    def backward(self, backward_strategy=None, retain_graph=False):
        """
        **Notes**:
            **This API is ONLY available in Dygraph mode**

        Run backward of current Graph which starts from current Variable

        Args:
            backward_strategy( :ref:`api_fluid_dygraph_BackwardStrategy` ): The Backward Strategy to run backward
            retain_graph(bool, optional): If False, the graph used to compute grads will be freed. If you would
            like to add more ops to the built graph after calling this method(`backward`), set the parameter
            `retain_graph` to True, then the grads will be retained. Thus, seting it to False is much more memory-efficient.
            Defaults to False.

        Returns:
            NoneType: None

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                import numpy as np

                x = np.ones([2, 2], np.float32)
                with fluid.dygraph.guard():
                    inputs2 = []
                    for _ in range(10):
                        tmp = fluid.dygraph.base.to_variable(x)
                        # if we don't set tmp's stop_gradient as False then, all path to loss will has no gradient since
                        # there is no one need gradient on it.
                        tmp.stop_gradient=False
                        inputs2.append(tmp)
                    ret2 = fluid.layers.sums(inputs2)
                    loss2 = fluid.layers.reduce_sum(ret2)
                    backward_strategy = fluid.dygraph.BackwardStrategy()
                    backward_strategy.sort_sum_gradient = True
                    loss2.backward(backward_strategy)

        """
        if framework.in_dygraph_mode():
            if backward_strategy is None:
                backward_strategy = BackwardStrategy()
                backward_strategy.sort_sum_gradient = False

            self._run_backward(backward_strategy,
                               framework._dygraph_tracer(), retain_graph)
        else:
            raise ValueError(
                "Variable.backward() is only available in DyGraph mode")

    @framework.dygraph_only
    def gradient(self):
        """
        **Notes**:
            **This API is ONLY available in Dygraph mode**

        Get the Gradient of Current Variable

        Returns:
            ndarray: Numpy value of the gradient of current Variable

        Examples:
            .. code-block:: python

                import paddle.fluid as fluid
                import numpy as np

                x = np.ones([2, 2], np.float32)
                with fluid.dygraph.guard():
                    inputs2 = []
                    for _ in range(10):
                        tmp = fluid.dygraph.base.to_variable(x)
                        tmp.stop_gradient=False
                        inputs2.append(tmp)
                    ret2 = fluid.layers.sums(inputs2)
                    loss2 = fluid.layers.reduce_sum(ret2)
                    backward_strategy = fluid.dygraph.BackwardStrategy()
                    backward_strategy.sort_sum_gradient = True
                    loss2.backward(backward_strategy)
                    print(loss2.gradient())

        """
        if self._grad_ivar() is None:
            return None

        new_ivar = self._grad_ivar()._copy_to(core.CPUPlace(), True)
        if self._grad_ivar().type == core.VarDesc.VarType.SELECTED_ROWS:
            return (np.array(new_ivar.value().get_selected_rows().get_tensor()),
                    np.array(new_ivar.value().get_selected_rows().rows()))
        else:
            return np.array(new_ivar.value().get_tensor())

    @property
    def grad(self):
        """
        The alias of gradient().
        """

        return self.gradient()

    def __str__(self):
        """
        Convert a VarBase object to a readable string.

        Returns(str): A readable string.

        Examples:
            .. code-block:: python

                import paddle
                paddle.disable_static()
                x = paddle.rand([1, 5])
                print(x)
                # Variable: eager_tmp_0
                #   - place: CUDAPlace(0)
                #   - shape: [1, 5]
                #   - layout: NCHW
                #   - dtype: float
                #   - data: [0.645307 0.597973 0.732793 0.646921 0.540328]
                paddle.enable_static()
        """
        tensor = self.value().get_tensor()
        if tensor._is_initialized():
            return 'Tensor: %s\n%s' % (self.name, str(tensor))
        else:
            return 'Tensor: %s, not initialized' % (self.name)

    @property
    def block(self):
        return framework.default_main_program().global_block()

    def __nonzero__(self):
        numel = np.prod(self.shape)
        assert numel == 1, "When Variable is used as the condition of if/while , Variable can only contain one element."
        tensor = self.value().get_tensor()
        assert tensor._is_initialized(), "tensor not initialized"
        return bool(np.all(tensor.__array__() > 0))

    def __bool__(self):
        return self.__nonzero__()

    for method_name, method in (
        ("__bool__", __bool__), ("__nonzero__", __nonzero__),
        ("_to_static_var", _to_static_var), ("set_value", set_value),
        ("block", block), ("backward", backward), ("grad", grad),
        ("gradient", gradient), ("__str__", __str__), ("__repr__", __str__),
        ("__module__", "paddle"), ("__name__", "Tensor")):
        setattr(core.VarBase, method_name, method)

    # patch math methods for varbase
    monkey_patch_math_varbase()