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未验证 提交 31536016 编写于 作者: J Jiabin Yang 提交者： GitHub
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test=develop, test=document_preview, fix 13 api doc and code (#17293) 

* test=develop, test=document_preview, fix all 13 api doc and code

* test=develop, fix rst

* test=develop, refresh API.spec
上级 bc833945
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Showing with 237 addition and 90 deletion
paddle/fluid/API.spec
浏览文件 @ 31536016
paddle.fluid.Program.__init__ (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.Program.block (ArgSpec(args=['self', 'index'], varargs=None, keywords=None, defaults=None), ('document', 'af5346376065ff4cf6832a8ac0ae0945'))
paddle.fluid.Program.clone (ArgSpec(args=['self', 'for_test'], varargs=None, keywords=None, defaults=(False,)), ('document', 'ebb7765b2962bd2be041d19720e49d0f'))
paddle.fluid.Program.current_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '5e162d3bf8dd625703463d9e4be36adb'))
paddle.fluid.Program.global_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'cfb7e05a002b2e64650778cabde7301c'))
paddle.fluid.Program.list_vars (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '1c8647b14fe57c7824b1c9562394dd3c'))
paddle.fluid.Program.block (ArgSpec(args=['self', 'index'], varargs=None, keywords=None, defaults=None), ('document', '86cd9499e226be661a3d686260ee1150'))
paddle.fluid.Program.clone (ArgSpec(args=['self', 'for_test'], varargs=None, keywords=None, defaults=(False,)), ('document', 'fe23e081126d2d4422b0780c2fea1a6a'))
paddle.fluid.Program.current_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'd601c7719e425e3d9cf862ea4ad194ca'))
paddle.fluid.Program.global_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'd64ea1dc96e9f674499ea3006d470aa4'))
paddle.fluid.Program.list_vars (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '32c14b0f12baae4b352200fa09b5e789'))
paddle.fluid.Program.parse_from_string (ArgSpec(args=['binary_str'], varargs=None, keywords=None, defaults=None), ('document', 'b6a7ffb239a30bf2ce58cfaca8d8b8d5'))
paddle.fluid.Program.to_string (ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,)), ('document', 'faec17e5a04af28e3776160e34504d15'))
paddle.fluid.default_startup_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '99e5d53d92d82797093332719c9e3ccd'))
paddle.fluid.Program.to_string (ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,)), ('document', '89acca639baf00f3ad08b9d827e81706'))
paddle.fluid.default_startup_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'ba609cb02e4e55e8d626723567ef1778'))
paddle.fluid.default_main_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '5430f54ab4895f9f47db6bebbaf71659'))
paddle.fluid.program_guard (ArgSpec(args=['main_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ae5f806f082cfaeaa5194cacc253a5e4'))
paddle.fluid.name_scope (ArgSpec(args=['prefix'], varargs=None, keywords=None, defaults=(None,)), ('document', '61660461e1f44e0480ca22fa8a482c41'))
......
python/paddle/fluid/framework.py
浏览文件 @ 31536016
......@@ -992,12 +992,12 @@ class Operator(object):
if op_maker.kOpRoleAttrName() not in op_attrs:
op_attrs[op_maker.kOpRoleAttrName(
)] = self.block.program.op_role
)] = self.block.program._op_role
role_var_name = op_maker.kOpRoleVarAttrName()
if len(self.block.program.
op_role_var) != 0 and role_var_name not in op_attrs:
op_attrs[role_var_name] = self.block.program.op_role_var
_op_role_var) != 0 and role_var_name not in op_attrs:
op_attrs[role_var_name] = self.block.program._op_role_var
if role_var_name in op_attrs and len(op_attrs[role_var_name]) == 0:
del op_attrs[role_var_name]
......@@ -1380,7 +1380,9 @@ class Block(object):
Examples:
.. code-block:: python
cur_program = Program()
import paddle.fluid as fluid
cur_program = fluid.Program()
cur_block = cur_program.current_block()
var = cur_block.create_var(name="X",
shape=[-1, 23, 48],
......@@ -2706,12 +2708,19 @@ class Program(object):
A empty program.
Examples:
>>> main_program = fluid.Program()
>>> startup_program = fluid.Program()
>>> with fluid.program_guard(main_program=main_program, startup_program=startup_program):
>>> fluid.layers.data(name="x", shape=[-1, 784], dtype='float32')
>>> fluid.layers.data(name="y", shape=[-1, 1], dtype='int32')
>>> fluid.layers.fc(name="fc", shape=[10], dtype='float32', act="relu")
.. code-block:: python
import paddle.fluid as fluid
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program=main_program, startup_program=startup_program):
x = fluid.layers.data(name="x", shape=[-1, 784], dtype='float32')
y = fluid.layers.data(name="y", shape=[-1, 1], dtype='int32')
z = fluid.layers.fc(name="fc", input=x, size=10, act="relu")
print("main program is: {}".format(main_program))
print("start up program is: {}".format(startup_program))
"""
......@@ -2721,7 +2730,7 @@ class Program(object):
self.current_block_idx = 0
self._seed = 0
self._current_role = core.op_proto_and_checker_maker.OpRole.Forward
self._op_role_var = []
self.__op_role_var = []
# for distribute training
# _is_distributed = True if under distributed training
......@@ -2762,7 +2771,7 @@ class Program(object):
self.__is_mem_optimized = target
@property
def op_role(self):
def _op_role(self):
"""
The operator role. In a enum {Forward, Backward, Optimize}.
......@@ -2771,31 +2780,27 @@ class Program(object):
For example, the forward operator should be executed on every device.
The backward operator should be executed on every device and the
parameter gradient of backward (use :code:`op_role_var` to get this
parameter gradient of backward (use :code:`_op_role_var` to get this
variable) operator should be merged to one device. The optimization
operators should be executed on only one device and broadcast the
optimization result, i.e., the new parameter, to every other device.
"""
return self._current_role
@op_role.setter
def op_role(self, role):
@_op_role.setter
def _op_role(self, role):
self._current_role = role
@property
def op_role_var(self):
def _op_role_var(self):
"""
The auxiliary variables for :code:`op_role` property.
The auxiliary variables for :code:`_op_role` property.
See Also: :code:`Program.op_role`'s documentation for details.
See Also: :code:`Program._op_role`'s documentation for details.
Notes: This is a very low-level API. Users should not use it directly.
"""
return self._op_role_var
@op_role_var.setter
def set_op_role_var(self, var_name):
self._op_role_var = [var_name]
return self.__op_role_var
@contextlib.contextmanager
def _backward_role_guard(self):
......@@ -2824,16 +2829,16 @@ class Program(object):
>>> p = p - 0.001 * g
"""
tmp_role = self._current_role
tmp_var = self._op_role_var
tmp_var = self.__op_role_var
OpRole = core.op_proto_and_checker_maker.OpRole
self._current_role = OpRole.Optimize
self._op_role_var = [
self.__op_role_var = [
var.name if isinstance(var, Variable) else var
for var in param_and_grads
]
yield
self._op_role_var = tmp_var
self.__op_role_var = tmp_var
self._current_role = tmp_role
@signature_safe_contextmanager
......@@ -2858,16 +2863,16 @@ class Program(object):
"""
tmp_role = self._current_role
tmp_var = self._op_role_var
tmp_var = self.__op_role_var
OpRole = core.op_proto_and_checker_maker.OpRole
self._current_role = OpRole.LRSched
if is_with_opt:
self._current_role = int(OpRole.LRSched) | int(OpRole.Optimize)
# TODO(typhoonzero): how to set target learning rate var
self._op_role_var = []
self.__op_role_var = []
yield
self._op_role_var = tmp_var
self.__op_role_var = tmp_var
self._current_role = tmp_role
def __str__(self):
......@@ -2901,6 +2906,15 @@ class Program(object):
ValueError: If any of required fields is not set and throw_on_error is
True.
Examples:
.. code-block:: python
import paddle.fluid as fluid
prog = fluid.default_main_program()
prog_string = prog.to_string(throw_on_error=True, with_details=False)
print(prog_string)
"""
assert isinstance(throw_on_error, bool) and isinstance(with_details,
bool)
......@@ -2939,14 +2953,18 @@ class Program(object):
attribute of them to :code:`True` when :code:`for_test=True`.
* Set for_test to False when we want to clone the program for training.
* Set for_test to True when we want to clone the program for testing.
* Set for_test to True when we want to clone the program for testing. We will not do any prune
on program here, So if you just want an forward program for testing, please use :code:`clone`
before using :code:`Opimizer.minimize`
Notes: This API DOES NOT prune any operator. Use
:code:`clone(for_test=True)` before backward and optimization please. e.g.
>>> test_program = fluid.default_main_program().clone(for_test=True)
>>> optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
>>> optimizer.minimize()
.. code-block:: python
test_program = fluid.default_main_program().clone(for_test=True)
optimizer = fluid.optimizer.Momentum(learning_rate=0.01, momentum=0.9)
optimizer.minimize()
Args:
for_test(bool): True if change the :code:`is_test` attribute of
......@@ -2957,55 +2975,102 @@ class Program(object):
Examples:
1. To clone a test program, the sample code is:
>>> import paddle.fluid as fluid
>>> train_program = fluid.Program()
>>> startup_program = fluid.Program()
>>> with fluid.program_guard(train_program, startup_program):
>>> img = fluid.layers.data(name='image', shape=[784])
>>> hidden = fluid.layers.fc(input=img, size=200, act='relu')
>>> hidden = fluid.layers.dropout(hidden, dropout_prob=0.5)
>>> loss = fluid.layers.cross_entropy(
>>> input=fluid.layers.fc(hidden, size=10, act='softmax'),
>>> label=fluid.layers.data(name='label', shape=[1], dtype='int64'))
>>>
>>> test_program = train_program.clone(for_test=True)
>>>
>>> sgd = fluid.optimizer.SGD(learning_rate=1e-3)
>>> with fluid.program_guard(train_program, startup_program):
>>> sgd.minimize(loss)
2. The :code:`clone` method can be avoid if you create program for
training and program for testing individually.
>>> import paddle.fluid as fluid
>>>
>>> def network(is_test):
>>> img = fluid.layers.data(name='image', shape=[784])
>>> hidden = fluid.layers.fc(input=img, size=200, act='relu')
>>> hidden = fluid.layers.dropout(hidden, dropout_prob=0.5, is_test=is_test)
>>> loss = fluid.layers.cross_entropy(
>>> input=fluid.layers.fc(hidden, size=10, act='softmax'),
>>> label=fluid.layers.data(name='label', shape=[1], dtype='int64'))
>>> return loss
>>>
>>> train_program = fluid.Program()
>>> startup_program = fluid.Program()
>>> test_program = fluid.Program()
>>>
>>> with fluid.program_guard(train_program, startup_program):
>>> with fluid.unique_name.guard():
>>> loss = network(is_test=False)
>>> sgd = fluid.optimizer.SGD(learning_rate=1e-3)
>>> sgd.minimize(loss)
>>>
>>> # the test startup program is not used.
>>> with fluid.program_guard(test_program, fluid.Program()):
>>> with fluid.unique_name.guard():
>>> loss = network(is_test=True)
The two code snippets above will generate same programs.
Notes: The Program Descs' order maybe different after :code:`clone` and this will not affect your training or testing progress. In the following example we give you an simple method :code:`print_prog(program)` to print Program Descs inorder to make sure you have same print result after :code:`clone`:
.. code-block:: python
import paddle.fluid as fluid
import six
def print_prog(prog):
for name, value in sorted(six.iteritems(prog.block(0).vars)):
print(value)
for op in prog.block(0).ops:
print("op type is {}".format(op.type))
print("op inputs are {}".format(op.input_arg_names))
print("op outputs are {}".format(op.output_arg_names))
for key, value in sorted(six.iteritems(op.all_attrs())):
if key not in ['op_callstack', 'op_role_var']:
print(" [ attrs: {}: {} ]".format(key, value))
1. To clone a test program, the sample code is:
.. code-block:: python
import paddle.fluid as fluid
import six
def print_prog(prog):
for name, value in sorted(six.iteritems(prog.block(0).vars)):
print(value)
for op in prog.block(0).ops:
print("op type is {}".format(op.type))
print("op inputs are {}".format(op.input_arg_names))
print("op outputs are {}".format(op.output_arg_names))
for key, value in sorted(six.iteritems(op.all_attrs())):
if key not in ['op_callstack', 'op_role_var']:
print(" [ attrs: {}: {} ]".format(key, value))
train_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
with fluid.unique_name.guard():
img = fluid.layers.data(name='image', shape=[784])
hidden = fluid.layers.fc(input=img, size=200, act='relu')
hidden = fluid.layers.dropout(hidden, dropout_prob=0.5)
loss = fluid.layers.cross_entropy(
input=fluid.layers.fc(hidden, size=10, act='softmax'),
label=fluid.layers.data(name='label', shape=[1], dtype='int64'))
avg_loss = fluid.layers.mean(loss)
test_program = train_program.clone(for_test=False)
print_prog(test_program)
with fluid.program_guard(train_program, startup_program):
with fluid.unique_name.guard():
sgd = fluid.optimizer.SGD(learning_rate=1e-3)
sgd.minimize(avg_loss)
2. The clone method can be avoid if you create program for training and program for testing individually.
.. code-block:: python
import paddle.fluid as fluid
import six
def print_prog(prog):
for name, value in sorted(six.iteritems(prog.block(0).vars)):
print(value)
for op in prog.block(0).ops:
print("op type is {}".format(op.type))
print("op inputs are {}".format(op.input_arg_names))
print("op outputs are {}".format(op.output_arg_names))
for key, value in sorted(six.iteritems(op.all_attrs())):
if key not in ['op_callstack', 'op_role_var']:
print(" [ attrs: {}: {} ]".format(key, value))
def network(is_test):
img = fluid.layers.data(name='image', shape=[784])
hidden = fluid.layers.fc(input=img, size=200, act='relu')
hidden = fluid.layers.dropout(hidden, dropout_prob=0.5)
loss = fluid.layers.cross_entropy(
input=fluid.layers.fc(hidden, size=10, act='softmax'),
label=fluid.layers.data(name='label', shape=[1], dtype='int64'))
avg_loss = fluid.layers.mean(loss)
return avg_loss
train_program_2 = fluid.Program()
startup_program_2 = fluid.Program()
test_program_2 = fluid.Program()
with fluid.program_guard(train_program_2, startup_program_2):
with fluid.unique_name.guard():
sgd = fluid.optimizer.SGD(learning_rate=1e-3)
sgd.minimize(avg_loss)
# the test startup program is not used.
with fluid.program_guard(test_program_2, fluid.Program()):
with fluid.unique_name.guard():
loss = network(is_test=True)
print(test_program_2)
The two code snippets above will generate and print same programs.
"""
if for_test:
p = self._inference_optimize(prune_read_op=False)
......@@ -3019,7 +3084,7 @@ class Program(object):
]
p._current_role = self._current_role
p._op_role_var = self._op_role_var
p.__op_role_var = self.__op_role_var
p._sync_with_cpp()
......@@ -3175,6 +3240,17 @@ class Program(object):
the random seed from random device.
Notes: It must be set before the operators have been added.
Examples:
.. code-block:: python
import paddle.fluid as fluid
prog = fluid.default_main_program()
random_seed = prog.random_seed
print(random_seed)
prog.random_seed = 1
print(prog.random_seed)
"""
return self._seed
......@@ -3182,6 +3258,15 @@ class Program(object):
def num_blocks(self):
"""
The number of blocks in this program.
Examples:
.. code-block:: python
import paddle.fluid as fluid
prog = fluid.default_main_program()
num_blocks = prog.num_blocks
print(num_blocks)
"""
return self.desc.num_blocks()
......@@ -3197,6 +3282,15 @@ class Program(object):
def global_block(self):
"""
Get the first block of this program.
Examples:
.. code-block:: python
import paddle.fluid as fluid
prog = fluid.default_main_program()
gb_block = prog.global_block()
print(gb_block)
"""
return self.blocks[0]
......@@ -3208,6 +3302,15 @@ class Program(object):
Returns:
Block: The :code:`index` block
Examples:
.. code-block:: python
import paddle.fluid as fluid
prog = fluid.default_main_program()
block_0 = prog.block(0)
print(block_0)
"""
return self.blocks[index]
......@@ -3215,6 +3318,15 @@ class Program(object):
"""
Get the current block. The :code:`current` block is the block to append
operators.
Examples:
.. code-block:: python
import paddle.fluid as fluid
prog = fluid.default_main_program()
current_blk = prog.current_block()
print(current_blk)
"""
return self.blocks[self.current_block_idx]
......@@ -3333,6 +3445,17 @@ class Program(object):
Returns:
iterable: The generator will yield every variable in this program.
Examples:
.. code-block:: python
import paddle.fluid as fluid
prog = fluid.default_main_program()
img = fluid.layers.data(name='img', shape=[1,28,28], dtype='float32')
label = fluid.layers.data(name='label', shape=[128,1], dtype='int64')
for var in prog.list_vars():
print(var)
"""
for each_block in self.blocks:
for each_var in list(each_block.vars.values()):
......@@ -3401,6 +3524,15 @@ class Parameter(Variable):
Returns(str): The debug string.
Examples:
.. code-block:: python
import paddle.fluid as fluid
prog = fluid.default_main_program()
rlt = fluid.layers.data("fake_data", shape=[1,1], dtype='float32')
debug_str = prog.to_string(throw_on_error=True, with_details=False)
print(debug_str)
"""
assert isinstance(throw_on_error, bool) and isinstance(with_details,
bool)
......@@ -3437,6 +3569,21 @@ def default_startup_program():
Returns:
Program: startup program
Examples:
.. code-block:: python
import paddle.fluid as fluid
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program=main_program, startup_program=startup_program):
x = fluid.layers.data(name="x", shape=[-1, 784], dtype='float32')
y = fluid.layers.data(name="y", shape=[-1, 1], dtype='int32')
z = fluid.layers.fc(name="fc", input=x, size=10, act="relu")
print("main program is: {}".format(fluid.default_main_program()))
print("start up program is: {}".format(fluid.default_startup_program()))
"""
return _startup_program_
......
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