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