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281 lines
10 KiB
281 lines
10 KiB
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from .. import framework
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from .. import core
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from . import BackwardStrategy
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from ..framework import Variable, _getitem_impl_
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from .. import unique_name
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import numpy as np
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from .math_op_patch import monkey_patch_math_varbase
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def monkey_patch_varbase():
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# TODO(jiabin): move this to cplusplus end if we find some performance issue on it
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@framework.dygraph_only
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def set_value(self, value):
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"""
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**Notes**:
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**This API is ONLY avaliable in Dygraph mode**
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Set a new value for this Variable.
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Args:
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value (Variable|np.ndarray): the new value.
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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from paddle.fluid.dygraph.base import to_variable
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from paddle.fluid.dygraph import Linear
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import numpy as np
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data = np.ones([3, 1024], dtype='float32')
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with fluid.dygraph.guard():
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linear = fluid.dygraph.Linear(1024, 4)
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t = to_variable(data)
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linear(t) # call with default weight
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custom_weight = np.random.randn(1024, 4).astype("float32")
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linear.weight.set_value(custom_weight) # change existing weight
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out = linear(t) # call with different weight
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"""
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assert isinstance(value, (np.ndarray, core.VarBase)), \
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"Variable set_value function, arguments type only support Variable, numpy, VarBase"
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value_np = value
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if isinstance(value, core.VarBase):
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value_np = value.numpy()
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self_tensor_np = self.numpy()
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assert self_tensor_np.shape == value_np.shape, \
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"Variable Shape not match, Variable [ {} ] need tensor with shape {} but load set tensor with shape {}".format(
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self.name, self_tensor_np.shape, value_np.shape)
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assert self_tensor_np.dtype == value_np.dtype, \
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"Variable dtype not match, Variable [ {} ] need tensor with dtype {} but load tensor with dtype {}".format(
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self.name, self_tensor_np.dtype, value_np.dtype)
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self.value().get_tensor().set(value_np,
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framework._current_expected_place())
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@framework.dygraph_only
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def backward(self, backward_strategy=None):
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"""
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**Notes**:
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**This API is ONLY avaliable in Dygraph mode**
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Run backward of current Graph which starts from current Variable
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Args:
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backward_strategy( :ref:`api_fluid_dygraph_BackwardStrategy` ): The Backward Strategy to run backward
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Returns:
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NoneType: None
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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import numpy as np
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x = np.ones([2, 2], np.float32)
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with fluid.dygraph.guard():
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inputs2 = []
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for _ in range(10):
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tmp = fluid.dygraph.base.to_variable(x)
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# if we don't set tmp's stop_gradient as False then, all path to loss will has no gradient since
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# there is no one need gradient on it.
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tmp.stop_gradient=False
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inputs2.append(tmp)
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ret2 = fluid.layers.sums(inputs2)
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loss2 = fluid.layers.reduce_sum(ret2)
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backward_strategy = fluid.dygraph.BackwardStrategy()
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backward_strategy.sort_sum_gradient = True
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loss2.backward(backward_strategy)
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"""
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if framework.in_dygraph_mode():
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if backward_strategy is None:
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backward_strategy = BackwardStrategy()
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backward_strategy.sort_sum_gradient = False
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self._run_backward(backward_strategy, framework._dygraph_tracer())
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else:
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raise ValueError(
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"Variable.backward() is only avaliable in DyGraph mode")
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@framework.dygraph_only
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def gradient(self):
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"""
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**Notes**:
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**This API is ONLY avaliable in Dygraph mode**
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Get the Gradient of Current Variable
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Returns:
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ndarray: Numpy value of the gradient of current Variable
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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import numpy as np
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x = np.ones([2, 2], np.float32)
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with fluid.dygraph.guard():
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inputs2 = []
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for _ in range(10):
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tmp = fluid.dygraph.base.to_variable(x)
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tmp.stop_gradient=False
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inputs2.append(tmp)
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ret2 = fluid.layers.sums(inputs2)
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loss2 = fluid.layers.reduce_sum(ret2)
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backward_strategy = fluid.dygraph.BackwardStrategy()
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backward_strategy.sort_sum_gradient = True
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loss2.backward(backward_strategy)
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print(loss2.gradient())
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"""
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if self._grad_ivar() is None:
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raise ValueError(
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"%s has no grad, Please set Variable.stop_gradient=False, or "
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"check if this is the first and only variable need grad, if so, please set its pre-Variable's "
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"stop_gradient=False, to make sure it has gradient " %
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self.name)
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new_ivar = self._grad_ivar()._copy_to(core.CPUPlace(), True)
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if self._grad_ivar().type == core.VarDesc.VarType.SELECTED_ROWS:
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return (np.array(new_ivar.value().get_selected_rows().get_tensor()),
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np.array(new_ivar.value().get_selected_rows().rows()))
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else:
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return np.array(new_ivar.value().get_tensor())
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def __str__(self):
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return self.to_string(True)
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@property
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def block(self):
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return framework.default_main_program().global_block()
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def to_string(self, throw_on_error, with_details=False):
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"""
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Get debug string.
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Args:
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throw_on_error (bool): True if raise an exception when self is not initialized.
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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;
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Returns:
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str: The debug string.
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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cur_program = fluid.Program()
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cur_block = cur_program.current_block()
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new_variable = cur_block.create_var(name="X",
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shape=[-1, 23, 48],
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dtype='float32')
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print(new_variable.to_string(True))
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print("=============with detail===============")
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print(new_variable.to_string(True, True))
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"""
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if framework.in_dygraph_mode():
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# TODO(panyx0718): add more dygraph debug info.
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tensor = self.value().get_tensor()
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if tensor._is_initialized():
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return 'name %s, dtype: %s shape: %s %s' % (
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self.name, self.dtype, self.shape, str(tensor))
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else:
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return 'name %s, shape: %s, not inited' % (self.name,
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self.shape)
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def __getitem__(self, item):
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if not isinstance(item, tuple):
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item = [item]
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decrease_axis = []
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slice_axis = []
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slice_start = []
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slice_end = []
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reverse_axis = []
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for dim, slice_item in enumerate(item):
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if isinstance(slice_item, slice):
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start = slice_item.start
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end = slice_item.stop
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step = slice_item.step if slice_item.step else 1
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assert (step == 1 or step == -1)
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if step == -1:
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reverse_axis.append(dim)
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assert (start is None and end is None)
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if start is None and end is None:
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continue
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if start is None:
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start = 0
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if end is None:
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end = 10000000
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slice_axis.append(dim)
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slice_start.append(start)
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slice_end.append(end)
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else:
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# int
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decrease_axis.append(dim)
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slice_axis.append(dim)
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slice_start.append(slice_item)
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slice_end.append(slice_item + 1
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if slice_item != -1 else 10000000)
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out = self
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if len(slice_axis) > 0:
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# append slice_op here
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inputs = {'Input': [out]}
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attrs = {
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'axes': slice_axis,
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'starts': slice_start,
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'ends': slice_end,
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'decrease_axis': decrease_axis
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}
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outs = core.ops.slice(inputs, attrs)
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out = outs['Out'][0]
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if len(reverse_axis) > 0:
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inputs = {'X': [out]}
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attrs = {'axis': reverse_axis}
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outs = core.ops.reverse(inputs, attrs)
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out = outs['Out'][0]
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return out
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for method_name, method in (("set_value", set_value), ("block", block),
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("backward", backward), ("gradient", gradient),
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("__str__", __str__), ("to_string", to_string),
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("__getitem__", __getitem__)):
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setattr(core.VarBase, method_name, method)
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# patch math methods for varbase
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monkey_patch_math_varbase()
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