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未验证 提交 3d96601b 编写于 作者: L lilong12 提交者： GitHub
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modify pipeline optimizer to only support the mode of sync pipeline training (#25065) 

* modify pipeline optimizer, test=develop
上级 b555378e
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Showing with 931 addition and 813 deletion
paddle/fluid/API.spec 0 → 100644
浏览文件 @ 3d96601b
paddle.fluid.optimizer.PipelineOptimizer (paddle.fluid.optimizer.PipelineOptimizer, ('document', '2e55a29dbeb874934f7a1a1af3a22b8c'))
paddle.fluid.optimizer.PipelineOptimizer.__init__ (ArgSpec(args=['self', 'optimizer', 'num_microbatches', 'start_cpu_core_id'], varargs=None, keywords=None, defaults=(1, 0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.PipelineOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
python/paddle/fluid/optimizer.py
浏览文件 @ 3d96601b
此差异已折叠。
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ 3d96601b
......@@ -49,7 +49,6 @@ endif()
LIST(REMOVE_ITEM TEST_OPS test_conv2d_transpose_op)
if(WIN32)
LIST(REMOVE_ITEM TEST_OPS test_boxps)
LIST(REMOVE_ITEM TEST_OPS test_paddlebox_datafeed)
LIST(REMOVE_ITEM TEST_OPS test_trainer_desc)
LIST(REMOVE_ITEM TEST_OPS test_multiprocess_reader_exception)
LIST(REMOVE_ITEM TEST_OPS test_avoid_twice_initialization)
......@@ -89,7 +88,6 @@ endif()
if(NOT WITH_GPU OR WIN32)
LIST(REMOVE_ITEM TEST_OPS test_pipeline)
LIST(REMOVE_ITEM TEST_OPS test_boxps)
LIST(REMOVE_ITEM TEST_OPS test_paddlebox_datafeed)
endif()
list(REMOVE_ITEM TEST_OPS test_seq_concat_op) # FIXME(helin): https://github.com/PaddlePaddle/Paddle/issues/8290
list(REMOVE_ITEM TEST_OPS test_lstm_unit_op) # # FIXME(qijun) https://github.com/PaddlePaddle/Paddle/issues/5185
......
python/paddle/fluid/tests/unittests/test_boxps.py
浏览文件 @ 3d96601b
......@@ -87,117 +87,5 @@ class TestRunCmd(unittest.TestCase):
self.assertTrue(ret2 == 0)
class TestBoxPSPreload(unittest.TestCase):
""" TestCases for BoxPS Preload """
def test_boxps_cpu(self):
self.run_boxps_preload(True, True)
self.run_boxps_preload(True, False)
def test_boxps_gpu(self):
self.run_boxps_preload(False, True)
self.run_boxps_preload(False, False)
def run_boxps_preload(self, is_cpu=True, random_with_lineid=False):
program = fluid.Program()
with fluid.program_guard(program):
x = fluid.layers.data(
name='x', shape=[1], dtype='int64', lod_level=0)
y = fluid.layers.data(
name='y', shape=[1], dtype='int64', lod_level=0)
z = layers.data(name='z', shape=[1], dtype='int64')
emb_x, emb_y = _pull_box_sparse([x, y], size=2)
emb_xp = _pull_box_sparse(x, size=2)
concat = layers.concat([emb_x, emb_y], axis=1)
fc = layers.fc(input=concat,
name="fc",
size=1,
num_flatten_dims=1,
bias_attr=False)
loss = layers.reduce_mean(fc)
place = fluid.CPUPlace(
) if is_cpu or not core.is_compiled_with_cuda(
) else fluid.CUDAPlace(0)
exe = fluid.Executor(place)
batch_size = 100
def binary_print(slot, fout):
fout.write(str(len(slot)) + " ")
for e in slot:
fout.write(str(e) + " ")
batch1 = np.ones(
(batch_size, 2, 1)).astype("int64").reshape(batch_size, 2, 1)
filelist = []
place_str = "cpu" if is_cpu else "gpu"
for i in range(2):
filelist.append("test_hdfs_" + place_str + "_" + str(i))
for f in filelist:
with open(f, "w") as fout:
for ins in batch1:
for slot in ins:
binary_print(slot, fout)
fout.write("\n")
def create_dataset():
dataset = fluid.DatasetFactory().create_dataset("BoxPSDataset")
dataset.set_date("20190930")
dataset.set_use_var([x, y])
dataset.set_batch_size(2)
dataset.set_thread(1)
dataset.set_filelist(filelist)
return dataset
datasets = []
datasets.append(create_dataset())
datasets.append(create_dataset())
optimizer = fluid.optimizer.SGD(learning_rate=0.5)
optimizer = fluid.optimizer.PipelineOptimizer(
optimizer,
cut_list=[],
place_list=[place],
concurrency_list=[1],
queue_size=1,
sync_steps=-1)
optimizer.minimize(loss)
program._pipeline_opt["dump_fields"] = [
"fc.tmp_0", "fc.tmp_0@GRAD", "fake_var", "z",
"reduce_mean_3.tmp_0"
]
# fake_var: not in scope
# z: in scope, but no initialized
# reduce_mean_0.tmp_0, dimension is not right
program._pipeline_opt["dump_fields_path"] = "./dump_log/"
program._pipeline_opt["dump_param"] = ["fc.w_0"]
program._pipeline_opt["enable_random_dump"] = True
program._pipeline_opt["dump_interval"] = 10
program._pipeline_opt["random_with_lineid"] = random_with_lineid
exe.run(fluid.default_startup_program())
datasets[0].load_into_memory()
datasets[0].begin_pass()
datasets[0].slots_shuffle([])
datasets[1].preload_into_memory()
exe.train_from_dataset(
program=fluid.default_main_program(),
dataset=datasets[0],
print_period=1)
datasets[0].end_pass(True)
datasets[1].wait_preload_done()
datasets[1].begin_pass()
exe.train_from_dataset(
program=fluid.default_main_program(),
dataset=datasets[1],
print_period=1,
debug=True)
datasets[1].end_pass(False)
for f in filelist:
os.remove(f)
if os.path.isdir("dump_log"):
shutil.rmtree("dump_log")
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_data_norm_op.py
浏览文件 @ 3d96601b
......@@ -437,129 +437,6 @@ class TestDataNormOpWithSlotDim(OpTest):
self.check_grad(['X'], 'Y', no_grad_set=set([]))
class TestDataNormOpWithSyncStats(unittest.TestCase):
"""
test class for data norm op
test forward and backward
"""
def test_sync_stats(self):
if not core.is_compiled_with_cuda():
return
if os.name == 'nt':
print(
'Skip TestDataNormOpWithSyncStats because nccl is not supported on windows'
)
return
x = fluid.layers.data(name='x', shape=[1], dtype='int64', lod_level=0)
emb = layers.embedding(
input=x,
param_attr=fluid.ParamAttr(name="embx"),
size=[10, 2],
is_sparse=False)
dn = layers.data_norm(
input=emb,
name="hehe",
epsilon=1e-4,
param_attr={
"batch_size": 1e4,
"batch_sum": 1e5,
"batch_square": 1e4
},
summary_decay_rate=1,
sync_stats=True) #[-1,3]
loss = layers.mean(dn)
optimizer = fluid.optimizer.SGD(learning_rate=0.5)
optimizer = fluid.optimizer.PipelineOptimizer(
optimizer,
cut_list=[[emb], [loss]],
place_list=[
fluid.CUDAPlace(0), fluid.CUDAPlace(0), fluid.CPUPlace()
],
concurrency_list=[1, 1, 1],
queue_size=1,
sync_steps=10000000, )
all_p = fluid.default_main_program().global_block().all_parameters()
parameter_without_datanorm = []
for e in all_p:
if e.name.find("batch_size") != -1 or e.name.find(
"batch_sq") != -1 or e.name.find("batch_sum") != -1:
continue
parameter_without_datanorm.append(e.name)
optimizer.minimize(loss, parameter_list=parameter_without_datanorm)
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
#prepare data
batch_size = 1
def binary_print(slot, fout):
num = np.int16(len(slot) + 1)
num.tofile(fout)
a = np.int64(batch_size)
a.tofile(fout)
slot.tofile(fout)
#batch1 = np.array([[0,1], [1,2], [2,3]]).astype("int64").reshape(batch_size,2,1)
#batch2 = np.array([[1,2], [2,3], [3,4]]).astype("int64").reshape(batch_size,2,1)
batch1 = np.ones(
(batch_size, 1)).astype("int64").reshape(batch_size, 1, 1)
batch2 = np.ones(
(batch_size, 1)).astype("int64").reshape(batch_size, 1, 1)
data = [batch1, batch2]
data = [batch1]
filelist = []
for i in range(2):
filelist.append("test_pipeline_input_" + str(i))
for f in filelist:
with open(f, "wb") as fout:
for batch_data in data:
for ins in batch_data:
for slot in ins:
binary_print(slot, fout)
dataset = fluid.DatasetFactory().create_dataset("FileInstantDataset")
dataset.set_use_var([x])
dataset.set_batch_size(batch_size)
dataset.set_filelist(filelist)
block = fluid.default_startup_program().global_block()
block.append_op(
type='c_comm_init_all', attrs={'ring_id': 0,
'devices': [0, 1]})
with open("main_program", "w") as fout:
fout.write(str(fluid.default_main_program()))
with open("startup_program", "w") as fout:
fout.write(str(fluid.default_startup_program()))
exe.run(fluid.default_startup_program())
emb_t = fluid.global_scope().find_var("embx").get_tensor()
para = np.ones((10, 2)).astype("float32")
emb_t.set(para, place)
for epoch in range(1):
exe.train_from_dataset(
fluid.default_main_program(),
dataset,
thread=2,
debug=False,
fetch_list=[],
fetch_info=[],
print_period=1)
batch_size = np.array(fluid.global_scope().find_var("hehe.batch_size")
.get_tensor())
self.assertEqual(batch_size[0], 10002)
b = np.array(fluid.global_scope().find_var("hehe.batch_sum").get_tensor(
))
self.assertEqual(b[0], 100002)
c = np.array(fluid.global_scope().find_var("hehe.batch_square_sum")
.get_tensor())
self.assertEqual(c[0], 10162)
for f in filelist:
os.remove(f)
class TestDataNormOpErrorr(unittest.TestCase):
def test_errors(self):
with program_guard(Program(), Program()):
......
python/paddle/fluid/tests/unittests/test_paddlebox_datafeed.py 已删除 100644 → 0
浏览文件 @ b555378e
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import paddle.fluid as fluid
import paddle.fluid.core as core
import os
import unittest
import paddle.fluid.layers as layers
class TestDataFeed(unittest.TestCase):
""" TestBaseCase(Merge PV) """
def setUp(self):
self.batch_size = 10
self.pv_batch_size = 10
self.enable_pv_merge = True
self.merge_by_sid = True
def set_data_config(self):
self.dataset = fluid.DatasetFactory().create_dataset("BoxPSDataset")
self.dataset.set_feed_type("PaddleBoxDataFeed")
self.dataset.set_parse_logkey(True)
self.dataset.set_thread(1)
self.dataset.set_enable_pv_merge(self.enable_pv_merge)
self.dataset.set_batch_size(self.batch_size)
if self.enable_pv_merge:
self.dataset.set_merge_by_sid(self.merge_by_sid)
self.dataset.set_rank_offset("rank_offset")
self.dataset.set_pv_batch_size(self.pv_batch_size)
def test_pboxdatafeed(self):
self.run_dataset(False)
def test_pboxdatafeed(self):
self.run_dataset(True)
def run_dataset(self, is_cpu):
x = fluid.layers.data(name='x', shape=[1], dtype='int64', lod_level=0)
y = fluid.layers.data(name='y', shape=[1], dtype='int64', lod_level=0)
rank_offset = fluid.layers.data(
name="rank_offset",
shape=[-1, 7],
dtype="int32",
lod_level=0,
append_batch_size=False)
emb_x, emb_y = fluid.contrib.layers._pull_box_extended_sparse(
[x, y], size=2, extend_size=128)
concat = layers.concat([emb_x[0], emb_x[1], emb_y[0], emb_y[1]], axis=1)
fc = layers.fc(input=concat,
name="fc",
size=1,
num_flatten_dims=1,
bias_attr=False)
loss = layers.reduce_mean(fc)
place = fluid.CPUPlace() if is_cpu or not core.is_compiled_with_cuda(
) else fluid.CUDAPlace(0)
exe = fluid.Executor(place)
with open("test_run_with_dump_a.txt", "w") as f:
data = "1 1702f830eee19501ad7429505f714c1d 1 1 1 9\n"
data += "1 1702f830eee19502ad7429505f714c1d 1 2 1 8\n"
data += "1 1702f830eee19503ad7429505f714c1d 1 3 1 7\n"
data += "1 1702f830eee0de01ad7429505f714c2d 1 4 1 6\n"
data += "1 1702f830eee0df01ad7429505f714c3d 1 5 1 5\n"
data += "1 1702f830eee0df02ad7429505f714c3d 1 6 1 4\n"
f.write(data)
with open("test_run_with_dump_b.txt", "w") as f:
data = "1 1702f830fff22201ad7429505f715c1d 1 1 1 1\n"
data += "1 1702f830fff22202ad7429505f715c1d 1 2 1 2\n"
data += "1 1702f830fff22203ad7429505f715c1d 1 3 1 3\n"
data += "1 1702f830fff22101ad7429505f714ccd 1 4 1 4\n"
data += "1 1702f830fff22102ad7429505f714ccd 1 5 1 5\n"
data += "1 1702f830fff22103ad7429505f714ccd 1 6 1 6\n"
data += "1 1702f830fff22104ad7429505f714ccd 1 6 1 7\n"
f.write(data)
self.set_data_config()
self.dataset.set_use_var([x, y])
self.dataset.set_filelist(
["test_run_with_dump_a.txt", "test_run_with_dump_b.txt"])
optimizer = fluid.optimizer.SGD(learning_rate=0.5)
optimizer = fluid.optimizer.PipelineOptimizer(
optimizer,
cut_list=[],
place_list=[place],
concurrency_list=[1],
queue_size=1,
sync_steps=-1)
optimizer.minimize(loss)
exe.run(fluid.default_startup_program())
self.dataset.set_current_phase(1)
self.dataset.load_into_memory()
self.dataset.preprocess_instance()
self.dataset.begin_pass()
pv_num = self.dataset.get_pv_data_size()
exe.train_from_dataset(
program=fluid.default_main_program(),
dataset=self.dataset,
print_period=1)
self.dataset.set_current_phase(0)
self.dataset.postprocess_instance()
exe.train_from_dataset(
program=fluid.default_main_program(),
dataset=self.dataset,
print_period=1)
self.dataset.end_pass(True)
os.remove("test_run_with_dump_a.txt")
os.remove("test_run_with_dump_b.txt")
class TestDataFeed2(TestDataFeed):
""" TestBaseCase(Merge PV not merge by sid) """
def setUp(self):
self.batch_size = 10
self.pv_batch_size = 10
self.enable_pv_merge = True
self.merge_by_sid = False
class TestDataFeed3(TestDataFeed):
""" TestBaseCase(Not Merge PV) """
def setUp(self):
self.batch_size = 10
self.pv_batch_size = 10
self.enable_pv_merge = False
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/tests/unittests/test_pipeline.py
浏览文件 @ 3d96601b
......@@ -19,137 +19,162 @@ import numpy as np
import os
import shutil
import unittest
class TestPipelineConfig(unittest.TestCase):
""" TestCases for Config in Pipeline Training. """
def config(self, filelist_length, pipeline_num, reader_concurrency):
filelist = []
for i in range(filelist_length):
filelist.append("file" + str(i))
self.dataset.set_filelist(filelist)
self.pipeline_opt["concurrency_list"][0] = reader_concurrency
self.pipeline_num = pipeline_num
def helper(self, in_filelist_length, in_pipeline_num, in_reader_concurrency,
out_pipeline_num, out_reader_concurrency, out_dataset_thread):
self.config(in_filelist_length, in_pipeline_num, in_reader_concurrency)
res = self.exe._adjust_pipeline_resource(
self.pipeline_opt, self.dataset, self.pipeline_num)
self.assertEqual(self.pipeline_opt["concurrency_list"][0],
out_reader_concurrency)
self.assertEqual(res, out_pipeline_num)
self.assertEqual(self.dataset.thread_num, out_dataset_thread)
def test_adjust_pipeline_resource(self):
self.exe = fluid.Executor(fluid.CPUPlace())
self.dataset = fluid.DatasetFactory().create_dataset(
"FileInstantDataset")
self.pipeline_opt = {"concurrency_list": [0, 1, 2]}
self.pipeline_num = 0
self.helper(7, 2, 2, 2, 2, 4)
self.helper(7, 2, 3, 2, 3, 6)
self.helper(7, 2, 4, 2, 3, 6)
self.helper(8, 2, 3, 2, 3, 6)
self.helper(8, 2, 4, 2, 4, 8)
self.helper(8, 2, 5, 2, 4, 8)
self.helper(3, 4, 1, 3, 1, 3)
self.helper(3, 4, 2, 3, 1, 3)
import math
def conv_bn_layer(input, num_filters, filter_size, stride=1, groups=1,
act=None):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) // 2,
groups=groups,
act=None,
bias_attr=False)
return fluid.layers.batch_norm(
input=conv,
act=act, )
def shortcut(input, ch_out, stride, is_first):
ch_in = input.shape[1]
if ch_in != ch_out or stride != 1 or is_first == True:
return conv_bn_layer(input, ch_out, 1, stride)
else:
return input
def bottleneck_block(input, num_filters, stride):
conv0 = conv_bn_layer(
input=input, num_filters=num_filters, filter_size=1, act='relu')
conv1 = conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
stride=stride,
act='relu')
conv2 = conv_bn_layer(
input=conv1, num_filters=num_filters * 4, filter_size=1, act=None)
short = shortcut(input, num_filters * 4, stride, is_first=False)
return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
def basic_block(input, num_filters, stride, is_first):
conv0 = conv_bn_layer(
input=input,
num_filters=num_filters,
filter_size=3,
act='relu',
stride=stride)
conv1 = conv_bn_layer(
input=conv0, num_filters=num_filters, filter_size=3, act=None)
short = shortcut(input, num_filters, stride, is_first)
return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
def build_network(input, layers=50, class_dim=1000):
supported_layers = [18, 34, 50, 101, 152]
assert layers in supported_layers
depth = None
if layers == 18:
depth = [2, 2, 2, 2]
elif layers == 34 or layers == 50:
depth = [3, 4, 6, 3]
elif layers == 101:
depth = [3, 4, 23, 3]
elif layers == 152:
depth = [3, 8, 36, 3]
num_filters = [64, 128, 256, 512]
with fluid.device_guard("cpu"):
conv = conv_bn_layer(
input=input, num_filters=64, filter_size=7, stride=2, act='relu')
conv = fluid.layers.pool2d(
input=conv,
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
if layers >= 50:
for block in range(len(depth)):
with fluid.device_guard("cpu"):
for i in range(depth[block]):
conv = bottleneck_block(
input=conv,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1)
with fluid.device_guard("gpu:0"):
pool = fluid.layers.pool2d(
input=conv, pool_size=7, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(
input=pool,
size=class_dim,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv)))
else:
for block in range(len(depth)):
with fluid.device_guard("cpu"):
for i in range(depth[block]):
conv = basic_block(
input=conv,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
is_first=block == i == 0)
with fluid.device_guard("gpu:0"):
pool = fluid.layers.pool2d(
input=conv, pool_size=7, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(
input=pool,
size=class_dim,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv)))
return out
class TestPipeline(unittest.TestCase):
""" TestCases for Pipeline Training. """
def test_pipeline(self):
x = fluid.layers.data(name='x', shape=[1], dtype='int64', lod_level=0)
y = fluid.layers.data(name='y', shape=[1], dtype='int64', lod_level=0)
emb_x = layers.embedding(
input=x,
param_attr=fluid.ParamAttr(name="embx"),
size=[10, 2],
is_sparse=False)
emb_y = layers.embedding(
input=y,
param_attr=fluid.ParamAttr(
name="emby", learning_rate=0.9),
size=[10, 2],
is_sparse=False)
concat = layers.concat([emb_x, emb_y], axis=1)
fc = layers.fc(input=concat,
name="fc",
size=1,
num_flatten_dims=1,
bias_attr=False)
loss = layers.reduce_mean(fc)
optimizer = fluid.optimizer.SGD(learning_rate=0.5)
with fluid.device_guard("cpu"):
image = fluid.layers.data(
name="image", shape=[3, 224, 224], dtype="float32")
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
data_loader = fluid.io.DataLoader.from_generator(
feed_list=[image, label],
capacity=64,
use_double_buffer=True,
iterable=False)
fc = build_network(image, layers=50)
with fluid.device_guard("gpu:0"):
out, prob = fluid.layers.softmax_with_cross_entropy(
logits=fc, label=label, return_softmax=True)
loss = fluid.layers.mean(out)
acc_top1 = fluid.layers.accuracy(input=prob, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=prob, label=label, k=5)
base_lr = 0.1
passes = [30, 60, 80, 90]
total_images = 1281167
steps_per_pass = total_images // 128
bd = [steps_per_pass * p for p in passes]
lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
lr_val = fluid.layers.piecewise_decay(boundaries=bd, values=lr)
optimizer = fluid.optimizer.Momentum(
lr_val,
momentum=0.9,
regularization=fluid.regularizer.L2Decay(1e-4))
optimizer = fluid.optimizer.PipelineOptimizer(
optimizer,
cut_list=[[emb_x, emb_y], [loss]],
place_list=[
fluid.CPUPlace(), fluid.CUDAPlace(0), fluid.CPUPlace()
],
concurrency_list=[1, 1, 1],
queue_size=1,
sync_steps=10000000, )
optimizer, num_microbatches=2)
optimizer.minimize(loss)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
#prepare data
batch_size = 100
def binary_print(slot, fout):
num = np.int16(len(slot) + 1)
num.tofile(fout)
a = np.int64(batch_size)
a.tofile(fout)
slot.tofile(fout)
#batch1 = np.array([[0,1], [1,2], [2,3]]).astype("int64").reshape(batch_size,2,1)
#batch2 = np.array([[1,2], [2,3], [3,4]]).astype("int64").reshape(batch_size,2,1)
batch1 = np.ones(
(batch_size, 2, 1)).astype("int64").reshape(batch_size, 2, 1)
batch2 = np.ones(
(batch_size, 2, 1)).astype("int64").reshape(batch_size, 2, 1)
data = [batch1, batch2]
filelist = []
for i in range(2):
filelist.append("test_pipeline_input_" + str(i))
for f in filelist:
with open(f, "wb") as fout:
for batch_data in data:
for ins in batch_data:
for slot in ins:
binary_print(slot, fout)
dataset = fluid.DatasetFactory().create_dataset("FileInstantDataset")
dataset.set_use_var([x, y])
dataset.set_batch_size(batch_size)
dataset.set_filelist(filelist)
for epoch in range(1):
exe.train_from_dataset(
fluid.default_main_program(),
dataset,
thread=1,
debug=False,
fetch_list=[],
fetch_info=[],
print_period=1)
for f in filelist:
os.remove(f)
def single_section(self, random_dump):
program = fluid.Program()
with fluid.program_guard(program):
def test_pipeline_noneoptimizer(self):
with fluid.device_guard("gpu:0"):
x = fluid.layers.data(
name='x', shape=[1], dtype='int64', lod_level=0)
y = fluid.layers.data(
......@@ -159,94 +184,18 @@ class TestPipeline(unittest.TestCase):
param_attr=fluid.ParamAttr(name="embx"),
size=[10, 2],
is_sparse=False)
emb_y = layers.embedding(
input=y,
param_attr=fluid.ParamAttr(
name="emby", learning_rate=0.9),
size=[10, 2],
is_sparse=False)
concat = layers.concat([emb_x, emb_y], axis=1)
fc = layers.fc(input=concat,
fc = layers.fc(input=emb_x,
name="fc",
size=1,
num_flatten_dims=1,
bias_attr=False)
loss = layers.reduce_mean(fc)
optimizer = fluid.optimizer.SGD(learning_rate=0.5)
optimizer = fluid.optimizer.SGD(learning_rate=0.5)
with self.assertRaises(ValueError):
optimizer = fluid.optimizer.PipelineOptimizer(
optimizer,
cut_list=[],
#place_list=[fluid.CPUPlace()],
place_list=[fluid.CUDAPlace(0)],
concurrency_list=[1],
queue_size=1,
sync_steps=-1)
optimizer.minimize(loss)
program._pipeline_opt["dump_fields"] = ["fc.tmp_0", "fc.tmp_0@GRAD"]
program._pipeline_opt["dump_fields_path"] = "./dump_log/"
program._pipeline_opt["dump_param"] = ["embx"]
program._pipeline_opt["enable_random_dump"] = random_dump
program._pipeline_opt["dump_interval"] = 10
program._pipeline_opt["random_with_lineid"] = False
#print(program._pipeline_opt)
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
#prepare data
batch_size = 100
def binary_print(slot, fout):
num = np.int16(len(slot) + 1)
num.tofile(fout)
a = np.int64(batch_size)
a.tofile(fout)
slot.tofile(fout)
#batch1 = np.array([[0,1], [1,2], [2,3]]).astype("int64").reshape(batch_size,2,1)
#batch2 = np.array([[1,2], [2,3], [3,4]]).astype("int64").reshape(batch_size,2,1)
batch1 = np.ones(
(batch_size, 2, 1)).astype("int64").reshape(batch_size, 2, 1)
batch2 = np.ones(
(batch_size, 2, 1)).astype("int64").reshape(batch_size, 2, 1)
data = [batch1, batch2]
filelist = []
for i in range(2):
filelist.append("test_pipeline_input_" + str(i))
for f in filelist:
with open(f, "wb") as fout:
for batch_data in data:
for ins in batch_data:
for slot in ins:
binary_print(slot, fout)
dataset = fluid.DatasetFactory().create_dataset(
"FileInstantDataset")
dataset.set_use_var([x, y])
dataset.set_batch_size(batch_size)
dataset.set_filelist(filelist)
for epoch in range(1):
exe.train_from_dataset(
fluid.default_main_program(),
dataset,
thread=1,
debug=True,
fetch_list=[],
fetch_info=[],
print_period=1)
for f in filelist:
os.remove(f)
if os.path.isdir("dump_log"):
shutil.rmtree("dump_log")
def test_pipeline(self):
self.single_section(True)
self.single_section(False)
dict(), num_microbatches=2)
if __name__ == '__main__':
......
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