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Fix examples of fluid.layers.sums and fluid.layers.DynamicRNN (#17308)

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* Fix examples of fluid.layers.sums.
test=document_preview

* Correct the example of DynamicRNN and its functions.
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* Add 'import paddle.fluid as fluid' to examples.
test=develop

* Update API.spec.
test=develop

* Add space lines.
test=develop

* Update the API.spec.
test=develop
fix_ema
Yiqun Liu 6 years ago committed by GitHub
parent 2281ebf0f3
commit cf5d271c5a
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3 changed files with 104 additions and 70 deletions
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8
paddle/fluid/API.spec
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@ -257,7 +257,7 @@ paddle.fluid.layers.create_global_var (ArgSpec(args=['shape', 'value', 'dtype',
paddle.fluid.layers.cast (ArgSpec(args=['x', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '992eb42590fc1c380841a6db72ce78b3'))
paddle.fluid.layers.tensor_array_to_tensor (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'b12717d3d4567e6119589f7f655b0cbb'))
paddle.fluid.layers.concat (ArgSpec(args=['input', 'axis', 'name'], varargs=None, keywords=None, defaults=(0, None)), ('document', 'f9e905b48123914c78055a45fe23106a'))
paddle.fluid.layers.sums (ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,)), ('document', '42912092418620b4be07f36af31e7816'))
paddle.fluid.layers.sums (ArgSpec(args=['input', 'out'], varargs=None, keywords=None, defaults=(None,)), ('document', '5df743d578638cd2bbb9369499b44af4'))
paddle.fluid.layers.assign (ArgSpec(args=['input', 'output'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b690184f3537df5501e4d9d8f31152a5'))
paddle.fluid.layers.fill_constant_batch_size_like (ArgSpec(args=['input', 'shape', 'dtype', 'value', 'input_dim_idx', 'output_dim_idx'], varargs=None, keywords=None, defaults=(0, 0)), ('document', 'd4059a2f5763036b07018d76429f9acb'))
paddle.fluid.layers.fill_constant (ArgSpec(args=['shape', 'dtype', 'value', 'force_cpu', 'out'], varargs=None, keywords=None, defaults=(False, None)), ('document', '1d8b14729639fa38509c79b9784740fa'))
@ -297,10 +297,10 @@ paddle.fluid.layers.IfElse.output (ArgSpec(args=['self'], varargs='outs', keywor
paddle.fluid.layers.IfElse.true_block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.DynamicRNN.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.DynamicRNN.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6d3e0a5d9aa519a9773a36e1620ea9b7'))
paddle.fluid.layers.DynamicRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'value', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, False, 'float32')), ('document', 'b9174d4e91505b0c8ecc193eb51e248d'))
paddle.fluid.layers.DynamicRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'value', 'need_reorder', 'dtype'], varargs=None, keywords=None, defaults=(None, None, 0.0, False, 'float32')), ('document', '57cdd0a63747f4c670cdb9d250ceb7e1'))
paddle.fluid.layers.DynamicRNN.output (ArgSpec(args=['self'], varargs='outputs', keywords=None, defaults=None), ('document', 'b439a176a3328de8a75bdc5c08eece4a'))
paddle.fluid.layers.DynamicRNN.static_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', 'f29ad2478b6b2ad4f413d2936a331ea0'))
paddle.fluid.layers.DynamicRNN.step_input (ArgSpec(args=['self', 'x', 'level'], varargs=None, keywords=None, defaults=(0,)), ('document', '7568c5ac7622a10288d3307a94134655'))
paddle.fluid.layers.DynamicRNN.static_input (ArgSpec(args=['self', 'x'], varargs=None, keywords=None, defaults=None), ('document', '55ab9c562edd7dabec0bd6fd6c1a28cc'))
paddle.fluid.layers.DynamicRNN.step_input (ArgSpec(args=['self', 'x', 'level'], varargs=None, keywords=None, defaults=(0,)), ('document', '4b300851b5201891d0e11c406e4c7d07'))
paddle.fluid.layers.DynamicRNN.update_memory (ArgSpec(args=['self', 'ex_mem', 'new_mem'], varargs=None, keywords=None, defaults=None), ('document', '5d83987da13b98363d6a807a52d8024f'))
paddle.fluid.layers.StaticRNN.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.StaticRNN.memory (ArgSpec(args=['self', 'init', 'shape', 'batch_ref', 'init_value', 'init_batch_dim_idx', 'ref_batch_dim_idx'], varargs=None, keywords=None, defaults=(None, None, None, 0.0, 0, 1)), ('document', 'f1b60dc4194d0bb714d6c6f5921b227f'))

141
python/paddle/fluid/layers/control_flow.py
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@ -1645,23 +1645,7 @@ class DynamicRNN(object):
sample sequence can be different. This API automatically process them in
batch.
The input lod must be set. Please reference `lod_tensor`
>>> import paddle.fluid as fluid
>>> data = fluid.layers.data(name='sentence', dtype='int64', lod_level=1)
>>> embedding = fluid.layers.embedding(input=data, size=[65535, 32],
>>> is_sparse=True)
>>>
>>> drnn = fluid.layers.DynamicRNN()
>>> with drnn.block():
>>> word = drnn.step_input(embedding)
>>> prev = drnn.memory(shape=[200])
>>> hidden = fluid.layers.fc(input=[word, prev], size=200, act='relu')
>>> drnn.update_memory(prev, hidden) # set prev to hidden
>>> drnn.output(hidden)
>>>
>>> # last is the last time step of rnn. It is the encoding result.
>>> last = fluid.layers.sequence_last_step(drnn())
The input lod must be set. Please reference to `lod_tensor`.
The dynamic RNN will unfold sequence into timesteps. Users need to define
how to process each time step during the :code:`with` block.
@ -1671,10 +1655,30 @@ class DynamicRNN(object):
The dynamic RNN can mark multiple variables as its output. Use `drnn()` to
get the output sequence.
NOTES:
Currently it is not supported that setting is_sparse to True of any
layers within DynamicRNN.
Examples:
.. code-block:: python
import paddle.fluid as fluid
sentence = fluid.layers.data(name='sentence', shape=[1], dtype='int64', lod_level=1)
embedding = fluid.layers.embedding(input=sentence, size=[65536, 32], is_sparse=True)
drnn = fluid.layers.DynamicRNN()
with drnn.block():
word = drnn.step_input(embedding)
prev = drnn.memory(shape=[200])
hidden = fluid.layers.fc(input=[word, prev], size=200, act='relu')
drnn.update_memory(prev, hidden) # set prev to hidden
drnn.output(hidden)
# Get the last time step of rnn. It is the encoding result.
rnn_output = drnn()
last = fluid.layers.sequence_last_step(rnn_output)
"""
BEFORE_RNN = 0
IN_RNN = 1
@ -1701,8 +1705,8 @@ class DynamicRNN(object):
Mark a sequence as a dynamic RNN input.
Args:
x(Variable): The input sequence.
level(int): The level of lod used to split steps. Default: 0.
x (Variable): The input sequence which should have lod information.
level (int): The level of lod used to split steps. Default: 0.
Returns:
The current timestep in the input sequence.
@ -1753,13 +1757,37 @@ class DynamicRNN(object):
def static_input(self, x):
"""
Mark a variable as a RNN input. The input will not be scattered into
time steps.
time steps. It is optional.
Args:
x(Variable): The input variable.
x (Variable): The input variable.
Returns:
The input variable that can access in RNN.
Examples:
.. code-block:: python
import paddle.fluid as fluid
sentence = fluid.layers.data(name='sentence', dtype='float32', shape=[32], lod_level=1)
encoder_proj = fluid.layers.data(name='encoder_proj', dtype='float32', shape=[32], lod_level=1)
decoder_boot = fluid.layers.data(name='boot', dtype='float32', shape=[10], lod_level=1)
drnn = fluid.layers.DynamicRNN()
with drnn.block():
current_word = drnn.step_input(sentence)
encoder_word = drnn.static_input(encoder_proj)
hidden_mem = drnn.memory(init=decoder_boot, need_reorder=True)
fc_1 = fluid.layers.fc(input=encoder_word, size=30, bias_attr=False)
fc_2 = fluid.layers.fc(input=current_word, size=30, bias_attr=False)
decoder_inputs = fc_1 + fc_2
h, _, _ = fluid.layers.gru_unit(input=decoder_inputs, hidden=hidden_mem, size=30)
drnn.update_memory(hidden_mem, h)
out = fluid.layers.fc(input=h, size=10, bias_attr=True, act='softmax')
drnn.output(out)
rnn_output = drnn()
"""
self._assert_in_rnn_block_("static_input")
if not isinstance(x, Variable):
@ -1836,54 +1864,51 @@ class DynamicRNN(object):
the input variable. It should be set to true when the initialized memory
depends on the input sample.
For example,
>>> import paddle.fluid as fluid
>>> sentence = fluid.layers.data(
>>> name='sentence', dtype='float32', shape=[32])
>>> boot_memory = fluid.layers.data(
>>> name='boot', dtype='float32', shape=[10])
>>>
>>> drnn = fluid.layers.DynamicRNN()
>>> with drnn.block():
>>> word = drnn.step_input(sentence)
>>> memory = drnn.memory(init=boot_memory, need_reorder=True)
>>> hidden = fluid.layers.fc(
>>> input=[word, memory], size=10, act='tanh')
>>> drnn.update_memory(ex_mem=memory, new_mem=hidden)
>>> drnn.output(hidden)
>>> rnn_output = drnn()
Examples:
.. code-block:: python
import paddle.fluid as fluid
sentence = fluid.layers.data(name='sentence', shape=[32], dtype='float32', lod_level=1)
boot_memory = fluid.layers.data(name='boot', shape=[10], dtype='float32', lod_level=1)
drnn = fluid.layers.DynamicRNN()
with drnn.block():
word = drnn.step_input(sentence)
memory = drnn.memory(init=boot_memory, need_reorder=True)
hidden = fluid.layers.fc(input=[word, memory], size=10, act='tanh')
drnn.update_memory(ex_mem=memory, new_mem=hidden)
drnn.output(hidden)
rnn_output = drnn()
Otherwise, if :code:`shape`, :code:`value`, :code:`dtype` are set, the
:code:`memory` will be initialized by this :code:`value`.
For example,
Examples:
.. code-block:: python
>>> import paddle.fluid as fluid
>>> sentence = fluid.layers.data(
>>> name='sentence', dtype='float32', shape=[32])
>>>
>>> drnn = fluid.layers.DynamicRNN()
>>> with drnn.block():
>>> word = drnn.step_input(sentence)
>>> memory = drnn.memory(shape=[10], dtype='float32', value=0)
>>> hidden = fluid.layers.fc(
>>> input=[word, memory], size=10, act='tanh')
>>> drnn.update_memory(ex_mem=memory, new_mem=hidden)
>>> drnn.output(hidden)
>>> rnn_output = drnn()
import paddle.fluid as fluid
sentence = fluid.layers.data(name='sentence', dtype='float32', shape=[32], lod_level=1)
drnn = fluid.layers.DynamicRNN()
with drnn.block():
word = drnn.step_input(sentence)
memory = drnn.memory(shape=[10], dtype='float32', value=0)
hidden = fluid.layers.fc(input=[word, memory], size=10, act='tanh')
drnn.update_memory(ex_mem=memory, new_mem=hidden)
drnn.output(hidden)
Args:
init(Variable|None): The initialized variable.
rnn_output = drnn()
shape(list|tuple): The memory shape. NOTE the shape does not contain batch_size.
Args:
init(Variable|None): The initialized variable.
shape(list|tuple): The memory shape. The shape does not contain batch_size.
value(float): the initalized value.
need_reorder(bool): True if the initialized memory depends on the input sample.
dtype(str|numpy.dtype): The data type of the initialized memory.
Returns:

25
python/paddle/fluid/layers/tensor.py
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@ -275,14 +275,23 @@ def sums(input, out=None):
Examples:
.. code-block:: python
tmp = fluid.layers.zeros(shape=[10], dtype='int32')
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
a0 = layers.array_read(array=tmp, i=i)
i = layers.increment(x=i)
a1 = layers.array_read(array=tmp, i=i)
mean_a0 = layers.mean(a0)
mean_a1 = layers.mean(a1)
a_sum = layers.sums(input=[mean_a0, mean_a1])
import paddle.fluid as fluid
# sum of several tensors
a0 = fluid.layers.fill_constant(shape=[1], dtype='int64', value=1)
a1 = fluid.layers.fill_constant(shape=[1], dtype='int64', value=2)
a2 = fluid.layers.fill_constant(shape=[1], dtype='int64', value=3)
sums = fluid.layers.sums(input=[a0, a1, a2])
# sum of a tensor array
array = fluid.layers.create_array('int64')
i = fluid.layers.zeros(shape=[1], dtype='int64', force_cpu=True)
fluid.layers.array_write(a0, array=array, i=i)
i = fluid.layers.increment(x=i)
fluid.layers.array_write(a1, array=array, i=i)
i = fluid.layers.increment(x=i)
fluid.layers.array_write(a2, array=array, i=i)
sums = fluid.layers.sums(input=array)
"""
helper = LayerHelper('sum', **locals())
if out is None:

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