221 lines
8.8 KiB
Python
221 lines
8.8 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests and benchmarks for interacting with the `tf.compat.v1.Session`."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import time
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import numpy as np
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from six.moves import xrange # pylint: disable=redefined-builtin
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from tensorflow.python.client import session
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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from tensorflow.python.training import server_lib
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class SessionBenchmark(test.Benchmark):
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"""Tests and benchmarks for interacting with the `tf.compat.v1.Session`."""
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def _benchmarkFeed(self, name, target, size, iters):
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"""Runs a microbenchmark to measure the cost of feeding a tensor.
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Reports the median cost of feeding a tensor of `size` * `sizeof(float)`
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bytes.
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Args:
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name: A human-readable name for logging the output.
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target: The session target to use for the benchmark.
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size: The number of floating-point numbers to be feed.
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iters: The number of iterations to perform.
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"""
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feed_val = np.random.rand(size).astype(np.float32)
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times = []
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with ops.Graph().as_default():
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p = array_ops.placeholder(dtypes.float32, shape=[size])
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# Fetch the operation rather than the tensor, to avoid measuring the time
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# to fetch back the value.
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no_op = array_ops.identity(p).op
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with session.Session(target) as sess:
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sess.run(no_op, feed_dict={p: feed_val}) # Warm-up run.
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for _ in xrange(iters):
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start_time = time.time()
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sess.run(no_op, feed_dict={p: feed_val})
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end_time = time.time()
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times.append(end_time - start_time)
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print("%s %d %f" % (name, size, np.median(times)))
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self.report_benchmark(iters=1, wall_time=np.median(times), name=name)
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def _benchmarkFetch(self, name, target, size, iters):
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"""Runs a microbenchmark to measure the cost of fetching a tensor.
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Reports the median cost of fetching a tensor of `size` * `sizeof(float)`
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bytes.
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Args:
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name: A human-readable name for logging the output.
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target: The session target to use for the benchmark.
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size: The number of floating-point numbers to be fetched.
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iters: The number of iterations to perform.
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"""
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times = []
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with ops.Graph().as_default():
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# Define the tensor to be fetched as a variable, to avoid
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# constant-folding.
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v = variables.Variable(random_ops.random_normal([size]))
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with session.Session(target) as sess:
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sess.run(v.initializer)
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sess.run(v) # Warm-up run.
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for _ in xrange(iters):
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start_time = time.time()
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sess.run(v)
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end_time = time.time()
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times.append(end_time - start_time)
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print("%s %d %f" % (name, size, np.median(times)))
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self.report_benchmark(iters=1, wall_time=np.median(times), name=name)
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def _benchmarkFetchPrebuilt(self, name, target, size, iters):
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"""Runs a microbenchmark to measure the cost of fetching a tensor.
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Reports the median cost of fetching a tensor of `size` * `sizeof(float)`
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bytes.
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Args:
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name: A human-readable name for logging the output.
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target: The session target to use for the benchmark.
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size: The number of floating-point numbers to be fetched.
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iters: The number of iterations to perform.
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"""
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times = []
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with ops.Graph().as_default():
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# Define the tensor to be fetched as a variable, to avoid
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# constant-folding.
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v = variables.Variable(random_ops.random_normal([size]))
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with session.Session(target) as sess:
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sess.run(v.initializer)
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runner = sess.make_callable(v)
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runner() # Warm-up run.
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for _ in xrange(iters):
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start_time = time.time()
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runner()
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end_time = time.time()
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times.append(end_time - start_time)
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print("%s %d %f" % (name, size, np.median(times)))
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self.report_benchmark(iters=1, wall_time=np.median(times), name=name)
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def _benchmarkRunOp(self, name, target, iters):
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"""Runs a microbenchmark to measure the cost of running an op.
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Reports the median cost of running a trivial (Variable) op.
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Args:
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name: A human-readable name for logging the output.
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target: The session target to use for the benchmark.
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iters: The number of iterations to perform.
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"""
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times = []
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with ops.Graph().as_default():
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# Define the op to be run as a variable, to avoid
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# constant-folding.
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v = variables.Variable(random_ops.random_normal([]))
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with session.Session(target) as sess:
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sess.run(v.initializer)
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sess.run(v.op) # Warm-up run.
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for _ in xrange(iters):
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start_time = time.time()
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sess.run(v.op)
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end_time = time.time()
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times.append(end_time - start_time)
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print("%s %f" % (name, np.median(times)))
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self.report_benchmark(iters=1, wall_time=np.median(times), name=name)
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def _benchmarkRunOpPrebuilt(self, name, target, iters):
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"""Runs a microbenchmark to measure the cost of running an op.
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Reports the median cost of running a trivial (Variable) op.
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Args:
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name: A human-readable name for logging the output.
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target: The session target to use for the benchmark.
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iters: The number of iterations to perform.
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"""
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times = []
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with ops.Graph().as_default():
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# Define the op to be run as a variable, to avoid
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# constant-folding.
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v = variables.Variable(random_ops.random_normal([]))
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with session.Session(target) as sess:
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sess.run(v.initializer)
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runner = sess.make_callable(v.op)
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runner() # Warm-up run.
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for _ in xrange(iters):
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start_time = time.time()
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runner()
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end_time = time.time()
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times.append(end_time - start_time)
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print("%s %f" % (name, np.median(times)))
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self.report_benchmark(iters=1, wall_time=np.median(times), name=name)
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def benchmarkGrpcSession(self):
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server = server_lib.Server.create_local_server()
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self._benchmarkFeed("benchmark_session_feed_grpc_4B", server.target, 1,
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30000)
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session.Session.reset(server.target)
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self._benchmarkFeed("benchmark_session_feed_grpc_4MB", server.target,
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1 << 20, 25000)
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session.Session.reset(server.target)
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self._benchmarkFetch("benchmark_session_fetch_grpc_4B", server.target, 1,
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40000)
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session.Session.reset(server.target)
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self._benchmarkFetch("benchmark_session_fetch_grpc_4MB", server.target,
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1 << 20, 20000)
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session.Session.reset(server.target)
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self._benchmarkFetchPrebuilt("benchmark_session_fetchprebuilt_grpc_4B",
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server.target, 1, 50000)
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session.Session.reset(server.target)
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self._benchmarkFetchPrebuilt("benchmark_session_fetchprebuilt_grpc_4MB",
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server.target, 1 << 20, 50000)
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session.Session.reset(server.target)
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self._benchmarkRunOp("benchmark_session_runop_grpc", server.target, 50000)
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session.Session.reset(server.target)
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self._benchmarkRunOpPrebuilt("benchmark_session_runopprebuilt_grpc",
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server.target, 100000)
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session.Session.reset(server.target)
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def benchmarkDirectSession(self):
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self._benchmarkFeed("benchmark_session_feed_direct_4B", "", 1, 80000)
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self._benchmarkFeed("benchmark_session_feed_direct_4MB", "", 1 << 20, 20000)
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self._benchmarkFetch("benchmark_session_fetch_direct_4B", "", 1, 100000)
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self._benchmarkFetch("benchmark_session_fetch_direct_4MB", "", 1 << 20,
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20000)
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self._benchmarkFetchPrebuilt("benchmark_session_fetchprebuilt_direct_4B",
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"", 1, 200000)
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self._benchmarkFetchPrebuilt("benchmark_session_fetchprebuilt_direct_4MB",
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"", 1 << 20, 200000)
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self._benchmarkRunOp("benchmark_session_runop_direct", "", 200000)
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self._benchmarkRunOpPrebuilt("benchmark_session_runopprebuilt_direct", "",
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200000)
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if __name__ == "__main__":
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test.main()
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