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#!/usr/bin/python
from hypothesis import given, settings
import hypothesis.strategies as st
from multiprocessing import Process
import numpy as np
import tempfile
import shutil
import caffe2.python.hypothesis_test_util as hu
op_engine = 'GLOO'
class TemporaryDirectory:
def __enter__(self):
self.tmpdir = tempfile.mkdtemp()
return self.tmpdir
def __exit__(self, type, value, traceback):
shutil.rmtree(self.tmpdir)
def allcompare_process(filestore_dir, process_id, data, num_procs):
from caffe2.python import core, data_parallel_model, workspace, dyndep
from caffe2.python.model_helper import ModelHelper
from caffe2.proto import caffe2_pb2
dyndep.InitOpsLibrary("@/caffe2/caffe2/distributed:file_store_handler_ops")
workspace.RunOperatorOnce(
core.CreateOperator(
"FileStoreHandlerCreate", [], ["store_handler"], path=filestore_dir
)
)
rendezvous = dict(
kv_handler="store_handler",
shard_id=process_id,
num_shards=num_procs,
engine=op_engine,
exit_nets=None
)
model = ModelHelper()
model._rendezvous = rendezvous
workspace.FeedBlob("test_data", data)
data_parallel_model._RunComparison(
model, "test_data", core.DeviceOption(caffe2_pb2.CPU, 0)
)
class TestAllCompare(hu.HypothesisTestCase):
@given(
d=st.integers(1, 5), n=st.integers(2, 11), num_procs=st.integers(1, 8)
)
@settings(deadline=10000)
def test_allcompare(self, d, n, num_procs):
dims = []
for _ in range(d):
dims.append(np.random.randint(1, high=n))
test_data = np.random.ranf(size=tuple(dims)).astype(np.float32)
with TemporaryDirectory() as tempdir:
processes = []
for idx in range(num_procs):
process = Process(
target=allcompare_process,
args=(tempdir, idx, test_data, num_procs)
)
processes.append(process)
process.start()
while len(processes) > 0:
process = processes.pop()
process.join()
if __name__ == "__main__":
import unittest
unittest.main()
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