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[xdoctest] reformat example code with google style in No.371~374 #57825

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Oct 17, 2023
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[xdoctest] reformat example code with google style in No.371~374 #57825

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3ccf152
0928
yuchen202 Sep 27, 2023
fea8d4b
Revert "0928"
yuchen202 Sep 27, 2023
d666564
Revert "Revert "0928""
yuchen202 Oct 10, 2023
377dc13
1011修改
yuchen202 Oct 10, 2023
40ec4f7
Update quantized_linear.py
yuchen202 Oct 11, 2023
f72564b
Update quantized_linear.py
yuchen202 Oct 11, 2023
6c6140b
Update quantized_linear.py
yuchen202 Oct 11, 2023
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SigureMo Oct 12, 2023
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82 changes: 41 additions & 41 deletions python/paddle/incubate/optimizer/pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -48,47 +48,47 @@ class PipelineOptimizer:
Examples:
.. code-block:: python

import paddle
import paddle.base as base
import paddle.base.layers as layers
import numpy as np

paddle.enable_static()
with base.device_guard("gpu:0"):
x = paddle.static.data(name='x', shape=[-1, 1], dtype='int64', lod_level=0)
y = paddle.static.data(name='y', shape=[-1, 1], dtype='int64', lod_level=0)
data_loader = base.io.DataLoader.from_generator(
feed_list=[x, y],
capacity=64,
use_double_buffer=True,
iterable=False)

emb_x = layers.embedding(input=x, param_attr=base.ParamAttr(name="embx"), size=[10,2], is_sparse=False)
emb_y = layers.embedding(input=y, param_attr=base.ParamAttr(name="emby",learning_rate=0.9), size=[10,2], is_sparse=False)

with base.device_guard("gpu:1"):
concat = layers.concat([emb_x, emb_y], axis=1)
fc = paddle.static.nn.fc(x=concat, name="fc", size=1, num_flatten_dims=1, bias_attr=False)
loss = paddle.mean(fc)
optimizer = paddle.optimizer.SGD(learning_rate=0.5)
optimizer = paddle.incubate.optimizer.PipelineOptimizer(optimizer)
optimizer.minimize(loss)

def train_reader():
for _ in range(4):
x = np.random.random(size=[1]).astype('int64')
y = np.random.random(size=[1]).astype('int64')
yield x, y
data_loader.set_sample_generator(train_reader, batch_size=1)

place = base.CUDAPlace(0)
exe = base.Executor(place)
exe.run(base.default_startup_program())
batch_size = 1
data_loader.start()
exe.train_from_dataset(
base.default_main_program())
data_loader.reset()
>>> import paddle
>>> import paddle.base as base
>>> import paddle.base.layers as layers
>>> import numpy as np

>>> paddle.enable_static()
>>> with base.device_guard("gpu:0"):
... x = paddle.static.data(name='x', shape=[-1, 1], dtype='int64', lod_level=0)
... y = paddle.static.data(name='y', shape=[-1, 1], dtype='int64', lod_level=0)
... data_loader = base.io.DataLoader.from_generator(
... feed_list=[x, y],
... capacity=64,
... use_double_buffer=True,
... iterable=False)

... emb_x = layers.embedding(input=x, param_attr=base.ParamAttr(name="embx"), size=[10,2], is_sparse=False)
... emb_y = layers.embedding(input=y, param_attr=base.ParamAttr(name="emby",learning_rate=0.9), size=[10,2], is_sparse=False)

>>> with base.device_guard("gpu:1"):
... concat = layers.concat([emb_x, emb_y], axis=1)
... fc = paddle.static.nn.fc(x=concat, name="fc", size=1, num_flatten_dims=1, bias_attr=False)
... loss = paddle.mean(fc)
>>> optimizer = paddle.optimizer.SGD(learning_rate=0.5)
>>> optimizer = paddle.incubate.optimizer.PipelineOptimizer(optimizer)
>>> optimizer.minimize(loss)

>>> def train_reader():
... for _ in range(4):
... x = np.random.random(size=[1]).astype('int64')
... y = np.random.random(size=[1]).astype('int64')
... yield x, y
>>> data_loader.set_sample_generator(train_reader, batch_size=1)

>>> place = paddle.CUDAPlace(0)
>>> exe = paddle.static.Executor(place)
>>> exe.run(paddle.static.default_startup_program())
>>> batch_size = 1
>>> data_loader.start()
>>> exe.train_from_dataset(
... paddle.static.default_main_program())
>>> data_loader.reset()
"""

def __init__(self, optimizer, num_microbatches=1, start_cpu_core_id=0):
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