This repository has been archived by the owner on Nov 17, 2023. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 6.8k
RowSparse pull/push #7015
Closed
Closed
RowSparse pull/push #7015
Changes from all commits
Commits
Show all changes
3 commits
Select commit
Hold shift + click to select a range
File filter
Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,15 @@ | ||
# pylint: skip-file | ||
import os, gzip | ||
import pickle as pickle | ||
import sys | ||
|
||
def get_libsvm_data(data_dir, data_name, url, data_origin_name): | ||
if not os.path.isdir(data_dir): | ||
os.system("mkdir " + data_dir) | ||
os.chdir(data_dir) | ||
if (not os.path.exists(data_name)): | ||
import urllib | ||
zippath = os.path.join(data_dir, data_origin_name) | ||
urllib.urlretrieve(url, zippath) | ||
os.system("bzip2 -d %r" % data_origin_name) | ||
os.chdir("..") | ||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,178 @@ | ||
import mxnet as mx | ||
from mxnet.test_utils import * | ||
from get_data import get_libsvm_data | ||
import time | ||
import argparse | ||
import os | ||
|
||
parser = argparse.ArgumentParser(description="Run sparse linear regression " \ | ||
"with distributed kvstore", | ||
formatter_class=argparse.ArgumentDefaultsHelpFormatter) | ||
parser.add_argument('--profiler', type=int, default=0, | ||
help='whether to use profiler') | ||
parser.add_argument('--num-epoch', type=int, default=1, | ||
help='number of epochs to train') | ||
parser.add_argument('--batch-size', type=int, default=512, | ||
help='number of examples per batch') | ||
parser.add_argument('--num-batch', type=int, default=99999999, | ||
help='number of batches per epoch') | ||
parser.add_argument('--dummy-iter', type=int, default=0, | ||
help='whether to use dummy iterator to exclude io cost') | ||
parser.add_argument('--kvstore', type=str, default='dist_sync', | ||
help='what kvstore to use [local, dist_sync, etc]') | ||
parser.add_argument('--log-level', type=str, default='debug', | ||
help='logging level [debug, info, error]') | ||
parser.add_argument('--dataset', type=str, default='avazu', | ||
help='what test dataset to use') | ||
|
||
class DummyIter(mx.io.DataIter): | ||
"A dummy iterator that always return the same batch, used for speed testing" | ||
def __init__(self, real_iter): | ||
super(DummyIter, self).__init__() | ||
self.real_iter = real_iter | ||
self.provide_data = real_iter.provide_data | ||
self.provide_label = real_iter.provide_label | ||
self.batch_size = real_iter.batch_size | ||
|
||
for batch in real_iter: | ||
self.the_batch = batch | ||
break | ||
|
||
def __iter__(self): | ||
return self | ||
|
||
def next(self): | ||
return self.the_batch | ||
|
||
# testing dataset sources | ||
avazu = { | ||
'data_name': 'avazu-app.t', | ||
'data_origin_name': 'avazu-app.t.bz2', | ||
'url': "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/avazu-app.t.bz2", | ||
'feature_dim': 1000000, | ||
} | ||
|
||
kdda = { | ||
'data_name': 'kdda.t', | ||
'data_origin_name': 'kdda.t.bz2', | ||
'url': "https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/kdda.t.bz2", | ||
'feature_dim': 20216830, | ||
} | ||
|
||
datasets = { 'kdda' : kdda, 'avazu' : avazu } | ||
|
||
def regression_model(feature_dim): | ||
initializer = mx.initializer.Normal() | ||
x = mx.symbol.Variable("data", stype='csr') | ||
norm_init = mx.initializer.Normal(sigma=0.01) | ||
v = mx.symbol.Variable("v", shape=(feature_dim, 1), init=norm_init, stype='row_sparse') | ||
embed = mx.symbol.dot(x, v) | ||
y = mx.symbol.Variable("softmax_label") | ||
model = mx.symbol.LinearRegressionOutput(data=embed, label=y, name="out") | ||
return model | ||
|
||
if __name__ == '__main__': | ||
|
||
# arg parser | ||
args = parser.parse_args() | ||
num_epoch = args.num_epoch | ||
num_batch = args.num_batch | ||
kvstore = args.kvstore | ||
profiler = args.profiler > 0 | ||
batch_size = args.batch_size | ||
dummy_iter = args.dummy_iter | ||
dataset = args.dataset | ||
log_level = args.log_level | ||
|
||
# create kvstore | ||
kv = mx.kvstore.create(kvstore) | ||
rank = kv.rank | ||
num_worker = kv.num_workers | ||
|
||
# only print log for rank 0 worker | ||
import logging | ||
if rank != 0: | ||
log_level = logging.ERROR | ||
elif log_level == 'DEBUG': | ||
log_level = logging.DEBUG | ||
else: | ||
log_level = logging.INFO | ||
head = '%(asctime)-15s %(message)s' | ||
logging.basicConfig(level=log_level, format=head) | ||
|
||
# dataset | ||
assert(dataset in datasets), "unknown dataset " + dataset | ||
metadata = datasets[dataset] | ||
feature_dim = metadata['feature_dim'] | ||
if logging: | ||
logging.debug('preparing data ... ') | ||
data_dir = os.path.join(os.getcwd(), 'data') | ||
path = os.path.join(data_dir, metadata['data_name']) | ||
if not os.path.exists(path): | ||
get_libsvm_data(data_dir, metadata['data_name'], metadata['url'], | ||
metadata['data_origin_name']) | ||
assert os.path.exists(path) | ||
|
||
# data iterator | ||
train_data = mx.io.LibSVMIter(data_libsvm=path, data_shape=(feature_dim,), | ||
batch_size=batch_size, num_parts=num_worker, | ||
part_index=rank) | ||
if dummy_iter: | ||
train_data = DummyIter(train_data) | ||
|
||
# model | ||
model = regression_model(feature_dim) | ||
|
||
# module | ||
mod = mx.mod.Module(symbol=model, data_names=['data'], label_names=['softmax_label']) | ||
mod.bind(data_shapes=train_data.provide_data, label_shapes=train_data.provide_label) | ||
mod.init_params(initializer=mx.init.Uniform(scale=.1)) | ||
sgd = mx.optimizer.SGD(momentum=0.0, clip_gradient=5.0, | ||
learning_rate=0.1, rescale_grad=1.0/batch_size/num_worker) | ||
mod.init_optimizer(optimizer=sgd, kvstore=kv) | ||
# use accuracy as the metric | ||
metric = mx.metric.create('MSE') | ||
|
||
# start profiler | ||
if profiler: | ||
import random | ||
name = 'profile_output_' + str(num_worker) + '.json' | ||
mx.profiler.profiler_set_config(mode='all', filename=name) | ||
mx.profiler.profiler_set_state('run') | ||
|
||
logging.debug('start training ...') | ||
start = time.time() | ||
data_iter = iter(train_data) | ||
for epoch in range(num_epoch): | ||
nbatch = 0 | ||
end_of_batch = False | ||
data_iter.reset() | ||
metric.reset() | ||
next_batch = next(data_iter) | ||
while not end_of_batch: | ||
nbatch += 1 | ||
batch = next_batch | ||
# TODO(haibin) remove extra copy after Jun's change | ||
row_ids = batch.data[0].indices.copyto(mx.cpu()) | ||
# pull sparse weight | ||
index = mod._exec_group.param_names.index('v') | ||
kv.row_sparse_pull('v', mod._exec_group.param_arrays[index], | ||
priority=-index, row_ids=[row_ids]) | ||
mod.forward_backward(batch) | ||
# update parameters | ||
mod.update() | ||
try: | ||
# pre fetch next batch | ||
next_batch = next(data_iter) | ||
if nbatch == num_batch: | ||
raise StopIteration | ||
except StopIteration: | ||
end_of_batch = True | ||
# accumulate prediction accuracy | ||
mod.update_metric(metric, batch.label) | ||
logging.info('epoch %d, %s' % (epoch, metric.get())) | ||
if profiler: | ||
mx.profiler.profiler_set_state('stop') | ||
end = time.time() | ||
time_cost = end - start | ||
logging.info('num_worker = ' + str(num_worker) + ', time cost = ' + str(time_cost)) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Consider using test_utils.download