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291 lines
10 KiB
291 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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import inspect
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import numpy as np
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import paddle
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from .. import framework
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from .. import core
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from ..framework import Variable, Parameter, ParamBase
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from .base import switch_to_static_graph
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from .math_op_patch import monkey_patch_math_varbase
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from .parallel import scale_loss
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def monkey_patch_varbase():
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@switch_to_static_graph
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def _to_static_var(self, to_parameter=False, **kwargs):
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"""
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**Notes**:
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**This API is ONLY available in Dygraph mode**
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Transform a VarBase into static Variable with same attributes. It's a low level interface used
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in dy2static and shall not be called directly.
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Args:
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to_parameter (bool): It takes effect only if the input a VarBase. If set True,
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the VarBase will be converted into framework.Parameters. Otherwise, it will
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be converted into framework.Variable. Default False.
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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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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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var_base = to_variable(data)
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static_var = var_base._to_static_var()
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"""
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# Note: getattr(self, attr, None) will call x.grad=x.gradient(), but gradient() only available in dygraph.
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# It will fail. So, for propery in dygraph only, should not let it getattr(self, attr, None).
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attr_not_need_keys = ['grad']
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if isinstance(self, ParamBase):
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attr_kwargs = self.__dict__.copy()
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else:
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attr_names = []
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for name in dir(self):
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if name not in attr_not_need_keys and not (
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inspect.ismethod(getattr(self, name)) or
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name.startswith('_')):
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attr_names.append(name)
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attr_kwargs = {name: getattr(self, name) for name in attr_names}
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attr_keys = ['block', 'shape', 'dtype', 'type', 'name', 'persistable']
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for attr in attr_keys:
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attr_kwargs[attr] = getattr(self, attr, None)
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attr_kwargs.update(kwargs)
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if to_parameter or isinstance(self, ParamBase):
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del attr_kwargs['persistable']
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static_var = Parameter(**attr_kwargs)
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else:
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static_var = Variable(**attr_kwargs)
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return static_var
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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 available 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, retain_graph=False):
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"""
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Run backward of current Graph which starts from current Tensor.
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The new gradient will accumulat on previous gradient.
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You can clear gradient by ``Tensor.clear_grad()`` .
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Args:
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retain_graph(bool, optional): If False, the graph used to compute grads will be freed. If you would
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like to add more ops to the built graph after calling this method( :code:`backward` ), set the parameter
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:code:`retain_graph` to True, then the grads will be retained. Thus, seting it to False is much more memory-efficient.
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Defaults to False.
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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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x = paddle.to_tensor(5., stop_gradient=False)
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for i in range(5):
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y = paddle.pow(x, 4.0)
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y.backward()
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print("{}: {}".format(i, x.grad))
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# 0: [500.]
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# 1: [1000.]
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# 2: [1500.]
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# 3: [2000.]
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# 4: [2500.]
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x.clear_grad()
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print("{}".format(x.grad))
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# 0.
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"""
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if framework.in_dygraph_mode():
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if paddle.distributed.get_world_size() > 1:
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scaled_loss = scale_loss(self)
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scaled_loss._run_backward(framework._dygraph_tracer(),
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retain_graph)
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else:
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self._run_backward(framework._dygraph_tracer(), retain_graph)
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else:
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raise ValueError(
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"Variable.backward() is only available 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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Get the Gradient of Current Tensor.
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Returns:
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ndarray: Numpy value of the gradient of current Tensor
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Examples:
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.. code-block:: python
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import paddle
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x = paddle.to_tensor(5., stop_gradient=False)
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y = paddle.pow(x, 4.0)
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y.backward()
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print("grad of x: {}".format(x.grad))
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# [500.]
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"""
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if self._grad_ivar() is None:
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return None
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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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@property
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def grad(self):
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"""
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The alias of gradient().
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"""
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return self.gradient()
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def clear_grad(self):
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"""
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The alias of clear_gradient().
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"""
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self.clear_gradient()
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@property
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def inplace_version(self):
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"""
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The inplace version of current Tensor.
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The version number is incremented whenever the current Tensor is modified through an inplace operation.
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**Notes: This is a read-only property**
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Examples:
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.. code-block:: python
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import paddle
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var = paddle.ones(shape=[4, 2, 3], dtype="float32")
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print(var.inplace_version) # 0
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var[1] = 2.2
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print(var.inplace_version) # 1
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"""
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return self._inplace_version()
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def __str__(self):
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"""
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Convert a VarBase object to a readable string.
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Returns(str): A readable string.
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Examples:
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.. code-block:: python
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import paddle
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x = paddle.rand([2, 5])
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print(x)
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# Tensor(shape=[2, 5], dtype=float32, place=CPUPlace,
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# [[0.30574632, 0.55739117, 0.30902600, 0.39413780, 0.44830436],
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# [0.79010487, 0.53972793, 0.09495186, 0.44267157, 0.72112119]])
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"""
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from paddle.tensor.to_string import to_string
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return to_string(self)
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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 __nonzero__(self):
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numel = np.prod(self.shape)
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assert numel == 1, "When Variable is used as the condition of if/while , Variable can only contain one element."
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tensor = self.value().get_tensor()
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assert tensor._is_initialized(), "tensor not initialized"
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return bool(np.all(tensor.__array__() > 0))
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def __bool__(self):
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return self.__nonzero__()
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for method_name, method in (
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("__bool__", __bool__), ("__nonzero__", __nonzero__),
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("_to_static_var", _to_static_var), ("set_value", set_value),
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("block", block), ("backward", backward), ("clear_grad", clear_grad),
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("inplace_version", inplace_version), ("grad", grad),
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("gradient", gradient), ("__str__", __str__), ("__repr__", __str__),
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("__module__", "paddle"), ("__name__", "Tensor")):
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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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