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[API 2.0]Migrate api example for gradients/append_backward/program_guard (#27570)

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* modify sample code

* variable -> tensor

* migrate program_guard sample code

* refine error message

* migrate program_guard

* refine comment style

* fix indent
revert-27356-init_low_level_gloo
Aurelius84 4 years ago committed by GitHub
parent 4e8f18ab25
commit 7c5162400f
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2 changed files with 86 additions and 79 deletions
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126
python/paddle/fluid/backward.py
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@ -1291,17 +1291,17 @@ def append_backward(loss,
It will be automatically invoked by the optimizer's `minimize` function.
Parameters:
loss( :ref:`api_guide_Variable_en` ): The loss variable of the network.
parameter_list(list[Variable|str], optional): List of Parameters or Parameter.names
loss(Tensor): The loss Tensor of the network.
parameter_list(list[Tensor|str], optional): List of Parameters or Parameter.names
that need to be updated by optimizers.
If it is None, all parameters
will be updated.
Default: None.
no_grad_set(set[Variable|str], optional): Set of Variables or Variable.names in the :ref:`api_guide_Block_en` 0 whose gradients
should be ignored. All variables with
no_grad_set(set[Tensor|str], optional): Set of Tensors or Tensor.names in the :ref:`api_guide_Block_en` 0 whose gradients
should be ignored. All Tensors with
`stop_gradient=True` from all blocks will
be automatically added into this set.
If this parameter is not None, the Variables or Variable.names in this set will be added to the default set.
If this parameter is not None, the Tensors or Tensor.names in this set will be added to the default set.
Default: None.
callbacks(list[callable object], optional): List of callback functions.
The callbacks are used for
@ -1312,70 +1312,73 @@ def append_backward(loss,
new gradient operator is added
into the program. The callable
object must have two input
parameters: 'block' and 'context'.
The 'block' is the :ref:`api_guide_Block_en` which
parameters: ``block`` and ``context`` .
The ``block`` is the :ref:`api_guide_Block_en` which
the new gradient operator will
be added to. The 'context' is a
be added to. The ``context`` is a
map, whose keys are gradient
variable names and values are
corresponding original :ref:`api_guide_Variable_en` .
In addition to this, the 'context'
Tensor names and values are
corresponding original :ref:`api_guide_tensor_en` .
In addition to this, the ``context``
has another special key-value pair:
the key is string '__current_op_desc__'
the key is string ``__current_op_desc__``
and the value is the op_desc of the
gradient operator who has just
triggered the callable object.
Default: None.
Returns:
list of tuple ( :ref:`api_guide_Variable_en` , :ref:`api_guide_Variable_en` ): Pairs of parameter and its corresponding gradients.
The key is the parameter and the value is gradient variable.
list of tuple ( :ref:`api_guide_tensor_en` , :ref:`api_guide_tensor_en` ): Pairs of parameter and its corresponding gradients.
The key is the parameter and the value is gradient Tensor.
Raises:
AssertionError: If `loss` is not an instance of Variable.
AssertionError: If ``loss`` is not an instance of Tensor.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
import paddle.nn.functional as F
x = fluid.data(name='x', shape=[None, 13], dtype='int64')
y = fluid.data(name='y', shape=[None, 1], dtype='float32')
x_emb = fluid.embedding(x, size=[100, 256])
y_predict = fluid.layers.fc(input=x_emb, size=1, act=None, name='my_fc')
loss = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_loss = fluid.layers.mean(loss)
paddle.enable_static()
x = paddle.static.data(name='x', shape=[None, 13], dtype='int64')
y = paddle.static.data(name='y', shape=[None, 1], dtype='float32')
x_emb = paddle.static.nn.embedding(x, size=[100, 256])
y_predict = paddle.static.nn.fc(input=x_emb, size=1, act=None, name='my_fc')
loss = F.square_error_cost(input=y_predict, label=y)
avg_loss = paddle.mean(loss)
# Get all weights in main_program, not include bias.
all_weights = [param for param in fluid.default_main_program().block(0).all_parameters() if 'w_' in param.name]
all_weights = [param for param in paddle.static.default_main_program().block(0).all_parameters() if 'w_' in param.name]
all_weights_name = [w.name for w in all_weights]
# return all param_grads needed to be updated if parameter_list set default None.
p_g_list1 = fluid.backward.append_backward(loss=avg_loss)
p_g_list1 = paddle.static.append_backward(loss=avg_loss)
# output: [(embedding_0.w_0, embedding_0.w_0@GRAD), (my_fc.w_0, my_fc.w_0@GRAD), (my_fc.b_0, my_fc.b_0@GRAD)]
# return the param_grads corresponding to parameter_list that can be list of param (Variable).
p_g_list2 = fluid.backward.append_backward(loss=avg_loss, parameter_list=all_weights)
# return the param_grads corresponding to parameter_list that can be list of param (Tensor).
p_g_list2 = paddle.static.append_backward(loss=avg_loss, parameter_list=all_weights)
# output: [(embedding_0.w_0, embedding_0.w_0@GRAD), (my_fc.w_0, my_fc.w_0@GRAD)]
# parameter_list can be list of param.name (str).
p_g_list3 = fluid.backward.append_backward(loss=avg_loss, parameter_list=all_weights_name)
p_g_list3 = paddle.static.append_backward(loss=avg_loss, parameter_list=all_weights_name)
# output: [(embedding_0.w_0, embedding_0.w_0@GRAD), (my_fc.w_0, my_fc.w_0@GRAD)]
# no_grad_set can be set of Variables that means grad will be cut off from these Variables.
p_g_list4 = fluid.backward.append_backward(loss=avg_loss, no_grad_set=set([x_emb]))
# no_grad_set can be set of Tensors that means grad will be cut off from these Tensors.
p_g_list4 = paddle.static.append_backward(loss=avg_loss, no_grad_set=set([x_emb]))
# output: [(my_fc.w_0, my_fc.w_0@GRAD), (my_fc.b_0, my_fc.b_0@GRAD)]
# no_grad_set can be set of Variable.name when the Variable is created inside layers and can't be specified explicitly.
p_g_list5 = fluid.backward.append_backward(loss=avg_loss, no_grad_set=set(['my_fc.b_0']))
# no_grad_set can be set of Tensor.name when the Tensor is created inside layers and can't be specified explicitly.
p_g_list5 = paddle.static.append_backward(loss=avg_loss, no_grad_set=set(['my_fc.b_0']))
# output: [(embedding_0.w_0, embedding_0.w_0@GRAD), (my_fc.w_0, my_fc.w_0@GRAD)]
# return [] because all param_grads are filtered by no_grad_set.
p_g_list6 = fluid.backward.append_backward(loss=avg_loss, parameter_list=all_weights, no_grad_set=set(all_weights))
p_g_list6 = paddle.static.append_backward(loss=avg_loss, parameter_list=all_weights, no_grad_set=set(all_weights))
"""
check_type(loss, 'loss', framework.Variable,
'fluid.backward.append_backward')
'paddle.static.append_backward')
if loss.op is None:
# the loss is from a cloned program. Find loss op manually.
@ -1387,7 +1390,7 @@ def append_backward(loss,
if callbacks is not None:
check_type(callbacks, 'callbacks', list,
'fluid.backward.append_backward')
'paddle.static.append_backward')
program = loss.block.program
root_block = program.block(0)
@ -1727,21 +1730,21 @@ def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None):
Backpropagate the gradients of targets to inputs.
Args:
targets(Variable|list[Variable]): The target variables
inputs(Variable|list[Variable]): The input variables
target_gradients (Variable|list[Variable], optional): The gradient variables
targets(Tensor|list[Tensor]): The target Tensors
inputs(Tensor|list[Tensor]): The input Tensors
target_gradients (Tensor|list[Tensor], optional): The gradient Tensors
of targets which has the same shape with targets, If None, ones will
be created for them.
no_grad_set(set[Variable|str], optional): Set of Variables or Variable.names in the :ref:`api_guide_Block_en` 0 whose gradients
should be ignored. All variables with
no_grad_set(set[Tensor|str], optional): Set of Tensors or Tensor.names in the :ref:`api_guide_Block_en` 0 whose gradients
should be ignored. All Tensors with
`stop_gradient=True` from all blocks will
be automatically added into this set.
If this parameter is not None, the Variables or Variable.names in this set will be added to the default set.
If this parameter is not None, the Tensors or Tensor.names in this set will be added to the default set.
Default: None.
Return:
(list[Variable]): A list of gradients for inputs
If an input does not affect targets, the corresponding gradient variable
(list[Tensor]): A list of gradients for inputs
If an input does not affect targets, the corresponding gradient Tensor
will be None
"""
targets = _as_list(targets)
@ -1865,41 +1868,42 @@ def gradients(targets, inputs, target_gradients=None, no_grad_set=None):
Backpropagate the gradients of targets to inputs.
Args:
targets (Variable|list[Variable]): The target variables.
inputs (Variable|list[Variable]): The input variables.
target_gradients (Variable|list[Variable], optional): The gradient variables
targets (Tensor|list[Tensor]): The target Tensors.
inputs (Tensor|list[Tensor]): The input Tensors.
target_gradients (Tensor|list[Tensor], optional): The gradient Tensor
of targets which has the same shape with targets, If None, ones will
be created for them.
no_grad_set (set[Variable|str], optional): Set of Variables or Variable.names in the :ref:`api_guide_Block_en` 0 whose gradients
should be ignored. All variables with `stop_gradient=True` from all blocks will
be automatically added into this set. If this parameter is not None, the Variables or Variable.names
no_grad_set (set[Tensor|str], optional): Set of Tensors or Tensor.names in the :ref:`api_guide_Block_en` 0 whose gradients
should be ignored. All Tensors with ``stop_gradient=True`` from all blocks will
be automatically added into this set. If this parameter is not None, the Tensors or Tensor.names
in this set will be added to the default set. Default: None.
Return:
(list[Variable]): A list of gradients for inputs
If an input does not affect targets, the corresponding gradient variable
(list[Tensor]): A list of gradients for inputs
If an input does not affect targets, the corresponding gradient Tensor
will be None.
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
import paddle.nn.functional as F
paddle.enable_static()
x = fluid.data(name='x', shape=[None,2,8,8], dtype='float32')
x = paddle.static.data(name='x', shape=[None, 2, 8, 8], dtype='float32')
x.stop_gradient=False
y = fluid.layers.conv2d(x, 4, 1, bias_attr=False)
y = fluid.layers.relu(y)
y = fluid.layers.conv2d(y, 4, 1, bias_attr=False)
y = fluid.layers.relu(y)
z = fluid.gradients([y], x)
print(z)
y = paddle.static.nn.conv2d(x, 4, 1, bias_attr=False)
y = F.relu(y)
z = paddle.static.gradients([y], x)
print(z) # [var x@GRAD : fluid.VarType.LOD_TENSOR.shape(-1L, 2L, 8L, 8L).astype(VarType.FP32)]
"""
check_type(targets, 'targets', (framework.Variable, list),
'fluid.backward.gradients')
'paddle.static.gradients')
check_type(inputs, 'inputs', (framework.Variable, list),
'fluid.backward.gradients')
'paddle.static.gradients')
check_type(target_gradients, 'target_gradients', (
framework.Variable, list, type(None)), 'fluid.backward.gradients')
framework.Variable, list, type(None)), 'paddle.static.gradients')
outs = calc_gradient(targets, inputs, target_gradients, no_grad_set)
return _as_list(outs)

39
python/paddle/fluid/framework.py
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@ -5396,13 +5396,13 @@ def program_guard(main_program, startup_program=None):
"""
:api_attr: Static Graph
Change the global main program and startup program with `"with"` statement.
Layer functions in the Python `"with"` block will append operators and
variables to the new main programs.
Change the global main program and startup program with ``with`` statement.
Layer functions in the Python ``with`` block will append operators and
Tensors to the new main programs.
Args:
main_program(Program): New main program inside `"with"` statement.
startup_program(Program, optional): New startup program inside `"with"`
main_program(Program): New main program inside ``with`` statement.
startup_program(Program, optional): New startup program inside ``with``
statement. :code:`None` means not changing startup program,
default_startup_program is still used.
Default: None.
@ -5410,13 +5410,14 @@ def program_guard(main_program, startup_program=None):
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
data = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10, act='relu')
paddle.enable_static()
main_program = paddle.static.Program()
startup_program = paddle.static.Program()
with paddle.static.program_guard(main_program, startup_program):
data = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32')
hidden = paddle.static.nn.fc(input=data, size=10, act='relu')
Notes: The temporary :code:`Program` can be used if the user does not need
to construct either of startup program or main program.
@ -5424,20 +5425,22 @@ def program_guard(main_program, startup_program=None):
Examples:
.. code-block:: python
import paddle.fluid as fluid
import paddle
main_program = fluid.Program()
# does not care about startup program. Just pass a temporary value.
with fluid.program_guard(main_program, fluid.Program()):
data = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
paddle.enable_static()
main_program = paddle.static.Program()
# does not care about startup program. Just pass a temporary value.
with paddle.static.program_guard(main_program, paddle.static.Program()):
data = paddle.static.data(name='image', shape=[None, 784, 784], dtype='float32')
"""
from .data_feeder import check_type
check_type(main_program, 'main_program', Program, 'fluid.program_guard')
check_type(main_program, 'main_program', Program,
'paddle.static.program_guard')
main_program = switch_main_program(main_program)
if startup_program is not None:
check_type(startup_program, 'startup_program', Program,
'fluid.program_guard')
'paddle.static.program_guard')
startup_program = switch_startup_program(startup_program)
try:
yield

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