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
import numpy as np
import warnings
import weakref

import paddle
from .. import framework
from .. import core
from .. import unique_name
from ..framework import Variable, Parameter, ParamBase, _getitem_impl_
from .base import switch_to_static_graph
from .math_op_patch import monkey_patch_math_varbase
from .parallel import scale_loss
from paddle.fluid.data_feeder import convert_dtype, _PADDLE_DTYPE_2_NUMPY_DTYPE


class TensorHookRemoveHelper(object):
    """
    A helper class that for removing Tensor gradient's hook.
    """

    def __init__(self, tensor, hook_id):
        self._tensor_ref = weakref.ref(tensor)
        self._hook_id = hook_id

    def remove(self):
        """
        Remove reference Tensor's hook.

        Returns:
            bool: Return True if removed successfully
        """
        tensor = self._tensor_ref()
        if tensor is not None:
            res = tensor._remove_grad_hook(self._hook_id)
            if res is True:
                return True
            else:
                warnings.warn(
                    "The backward hook (ID: %d) of Tensor `%s` you want to remove does not exist or has been removed."
                    % (self._hook_id, tensor.name), RuntimeWarning)
        return False


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, grad_tensor=None, retain_graph=False):
        """
        Run backward of current Graph which starts from current Tensor.

        The new gradient will accumulat on previous gradient.

        You can clear gradient by ``Tensor.clear_grad()`` .

        Args:
            grad_tensor(Tensor, optional): initial gradient values of the current Tensor. If `grad_tensor` is None, 
            the initial gradient values of the current Tensor would be Tensor filled with 1.0; 
            if `grad_tensor` is not None, it must have the same length as the current Tensor.
            Teh default value is None.

            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( :code:`backward` ), set the parameter
                :code:`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
                x = paddle.to_tensor(5., stop_gradient=False)
                for i in range(5):
                    y = paddle.pow(x, 4.0)
                    y.backward()
                    print("{}: {}".format(i, x.grad))
                # 0: [500.]
                # 1: [1000.]
                # 2: [1500.]
                # 3: [2000.]
                # 4: [2500.]

                x.clear_grad()
                print("{}".format(x.grad))
                # 0.

                grad_tensor=paddle.to_tensor(2.)
                for i in range(5):
                    y = paddle.pow(x, 4.0)
                    y.backward(grad_tensor)
                    print("{}: {}".format(i, x.grad))
                # 0: [1000.]
                # 1: [2000.]
                # 2: [3000.]
                # 3: [4000.]
                # 4: [5000.]

        """
        if framework.in_dygraph_mode():
            if grad_tensor is not None:
                assert isinstance(
                    grad_tensor, paddle.
                    Tensor), "The type of grad_tensot must be paddle.Tensor"
                assert grad_tensor.shape == self.shape, \
                    "Tensor shape not match, Tensor of grad_tensor [ {} ] with shape {} mismatch Tensor [ {} ] with shape {}".format(
                    grad_tensor.name, grad_tensor.shape, self.name, self.shape)

            if paddle.is_compiled_with_xpu():
                # TODO(liuyuhui): Currently only for xpu. Will be removed in the future.
                scaled_loss = scale_loss(self)
                core.dygraph_run_backward([scaled_loss], [grad_tensor],
                                          retain_graph,
                                          framework._dygraph_tracer())
            else:
                core.dygraph_run_backward([self], [grad_tensor], retain_graph,
                                          framework._dygraph_tracer())
        else:
            raise ValueError(
                "Variable.backward() is only available in DyGraph mode")

    @framework.dygraph_only
    def gradient(self):
        """
        Get the Gradient of Current Tensor.

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

        Examples:
            .. code-block:: python

                import paddle

                x = paddle.to_tensor(5., stop_gradient=False)
                y = paddle.pow(x, 4.0)
                y.backward()
                print("grad of x: {}".format(x.grad))
                # [500.]

        """
        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())

    @framework.dygraph_only
    def register_hook(self, hook):
        """
        Registers a backward hook for current Tensor.

        The hook will be called every time the gradient Tensor of current Tensor is computed.

        The hook should not modify the input gradient Tensor, but it can optionally return
        a new gradient Tensor which will be used in place of current Tensor's gradient.

        The hook should have the following signature:

            hook(grad) -> Tensor or None

        Args:
            hook(function): A backward hook to be registered for Tensor.grad

        Returns:
            TensorHookRemoveHelper: A helper object that can be used to remove the registered hook by calling `remove()` method.

        Examples:
            .. code-block:: python

                import paddle

                # hook function return None
                def print_hook_fn(grad):
                    print(grad)

                # hook function return Tensor
                def double_hook_fn(grad):
                    grad = grad * 2
                    return grad

                x = paddle.to_tensor([0., 1., 2., 3.], stop_gradient=False)
                y = paddle.to_tensor([4., 5., 6., 7.], stop_gradient=False)
                z = paddle.to_tensor([1., 2., 3., 4.])

                # one Tensor can register multiple hooks
                h = x.register_hook(print_hook_fn)
                x.register_hook(double_hook_fn)

                w = x + y
                # register hook by lambda function
                w.register_hook(lambda grad: grad * 2)

                o = z.matmul(w)
                o.backward()
                # print_hook_fn print content in backward
                # Tensor(shape=[4], dtype=float32, place=CUDAPlace(0), stop_gradient=False,
                #        [2., 4., 6., 8.])

                print("w.grad:", w.grad) # w.grad: [1. 2. 3. 4.]
                print("x.grad:", x.grad) # x.grad: [ 4.  8. 12. 16.]
                print("y.grad:", y.grad) # y.grad: [2. 4. 6. 8.]

                # remove hook
                h.remove()
        """
        if self.stop_gradient is True:
            raise RuntimeError(
                "Cannot register hook on a tensor that stop gradient.")

        hook_id = self._register_grad_hook(hook)
        helper = TensorHookRemoveHelper(self, hook_id)
        return helper

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

        return self.gradient()

    def clear_grad(self):
        """
        The alias of clear_gradient().
        """
        self.clear_gradient()

    @property
    def inplace_version(self):
        """
        The inplace version of current Tensor.
        The version number is incremented whenever the current Tensor is modified through an inplace operation.

        **Notes: This is a read-only property**

        Examples:
          .. code-block:: python

            import paddle
            var = paddle.ones(shape=[4, 2, 3], dtype="float32")
            print(var.inplace_version)  # 0

            var[1] = 2.2
            print(var.inplace_version)  # 1

        """
        return self._inplace_version()

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

        Returns(str): A readable string.

        Examples:
            .. code-block:: python

                import paddle
                x = paddle.rand([2, 5])
                print(x)
                
                # Tensor(shape=[2, 5], dtype=float32, place=CPUPlace,
                #        [[0.30574632, 0.55739117, 0.30902600, 0.39413780, 0.44830436],
                #         [0.79010487, 0.53972793, 0.09495186, 0.44267157, 0.72112119]])
        """
        from paddle.tensor.to_string import to_string
        return to_string(self)

    def __deepcopy__(self, memo):
        """
        Deep copy Tensor, it will always performs Tensor copy.

        Examples:
            .. code-block:: python

                import paddle
                import copy
                x = paddle.to_tensor(2.)
                y = copy.deepcopy(x)
                
                print(x)
                # Tensor(shape=[1], dtype=float32, place=CPUPlace, stop_gradient=True,
                #        [2.])

                print(y)
                # Tensor(shape=[1], dtype=float32, place=CPUPlace, stop_gradient=True,
                #        [2.])

        """
        if not self.is_leaf:
            raise RuntimeError(
                "Only Leaf Tensor support the deepcopy at the moment, non-Leaf Tensors contains graph information that does't support deepcopy"
            )
        new_varbase = core.VarBase()
        new_varbase.name = self.name + unique_name.generate("_deepcopy")
        memo[id(self)] = new_varbase
        new_varbase.copy_(self, True)
        return new_varbase

    @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__()

    def __array__(self, dtype=None):
        return self.numpy().astype(dtype)

    def __getitem__(self, item):
        def contain_tensor(item):
            if not isinstance(item, tuple):
                item = [item]

            for slice_item in item:
                if isinstance(slice_item, slice):
                    if isinstance(slice_item.start, Variable)  \
                        or isinstance(slice_item.stop, Variable) \
                           or isinstance(slice_item.step, Variable):
                        return True
                else:
                    if isinstance(slice_item, Variable):
                        return True
            return False

        if contain_tensor(item):
            # 1. Call _getitem_impl_ when item contains tensor.
            # Why not call a c++ function ? Because item can't be parsed when it contains tensor.
            return _getitem_impl_(self, item)

        else:
            # 2. Call c++ func getitem_index_not_tensor to speedup.
            return self._getitem_index_not_tensor(item)

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

    # NOTE(zhiqiu): pybind11 will set a default __str__ method of enum class.
    # So, we need to overwrite it to a more readable one.
    # See details in https://github.com/pybind/pybind11/issues/2537.
    origin = getattr(core.VarDesc.VarType, "__repr__")

    def dtype_str(dtype):
        if dtype in _PADDLE_DTYPE_2_NUMPY_DTYPE:
            prefix = 'paddle.'
            return prefix + _PADDLE_DTYPE_2_NUMPY_DTYPE[dtype]
        else:
            # for example, paddle.fluid.core.VarDesc.VarType.LOD_TENSOR
            return origin(dtype)

    setattr(core.VarDesc.VarType, "__repr__", dtype_str)

    # patch math methods for varbase
    monkey_patch_math_varbase()