# 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. from .. import framework from .. import core from . import BackwardStrategy from ..framework import Variable, _getitem_impl_ from .. import unique_name import numpy as np from .math_op_patch import monkey_patch_math_varbase def monkey_patch_varbase(): # 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): """ **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 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()) 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()) def __str__(self): return self.to_string(True) @property def block(self): return framework.default_main_program().global_block() def to_string(self, throw_on_error, with_details=False): """ Get debug string. Args: throw_on_error (bool): True if raise an exception when self is not initialized. with_details (bool): more details about variables and parameters (e.g. trainable, optimize_attr, ...) will be printed when with_details is True. Default value is False; Returns: str: The debug string. Examples: .. code-block:: python import paddle.fluid as fluid cur_program = fluid.Program() cur_block = cur_program.current_block() new_variable = cur_block.create_var(name="X", shape=[-1, 23, 48], dtype='float32') print(new_variable.to_string(True)) print("=============with detail===============") print(new_variable.to_string(True, True)) """ if framework.in_dygraph_mode(): # TODO(panyx0718): add more dygraph debug info. tensor = self.value().get_tensor() if tensor._is_initialized(): return 'name %s, dtype: %s shape: %s %s' % ( self.name, self.dtype, self.shape, str(tensor)) else: return 'name %s, shape: %s, not inited' % (self.name, self.shape) 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__), ("set_value", set_value), ("block", block), ("backward", backward), ("gradient", gradient), ("__str__", __str__), ("to_string", to_string)): setattr(core.VarBase, method_name, method) # patch math methods for varbase monkey_patch_math_varbase()