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# Copyright 2015 The TensorFlow 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.
# ==============================================================================
"""Benchmark for Matmul operator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import time
import numpy as np
from tensorflow.python.client import session as session_lib
from tensorflow.python.framework import ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
def build_graph(device, n, m, k, transpose_a, transpose_b, dtype):
"""Build a graph containing a sequence of matmul operations.
Args:
device: String, the device to run on.
n: tensor A's first dimension size.
m: tensor A's second dimension size.
k: tensor B's second dimension size.
transpose_a: boolean value to show if tensor A is transposed.
transpose_b: boolean value to show if tensor B is transposed.
dtype: numpy data type of the input tensor.
Returns:
A matmul operation to run()
"""
with ops.device('%s' % device):
if not transpose_a:
x = variables.Variable(random_ops.random_uniform([n, m], dtype=dtype))
else:
x = variables.Variable(random_ops.random_uniform([m, n], dtype=dtype))
if not transpose_b:
y = variables.Variable(random_ops.random_uniform([m, k], dtype=dtype))
else:
y = variables.Variable(random_ops.random_uniform([k, m], dtype=dtype))
z = math_ops.matmul(x, y, transpose_a=transpose_a, transpose_b=transpose_b)
return control_flow_ops.group(z)
class MatmulBenchmark(test.Benchmark):
"""Benchmark matmul!"""
def run_graph(self, device, n, m, k, transpose_a, transpose_b, num_iters,
dtype):
"""Run the graph and print its execution time.
Args:
device: String, the device to run on.
n: tensor A's first dimension size.
m: tensor A's second dimension size.
k: tensor B's second dimension size.
transpose_a: boolean value to show if tensor A is transposed.
transpose_b: boolean value to show if tensor B is transposed.
num_iters: number of iterations to run the benchmark.
dtype: numpy data type of the input tensor.
Returns:
The duration of the run in seconds.
"""
graph = ops.Graph()
with graph.as_default():
output = build_graph(device, n, m, k, transpose_a, transpose_b, dtype)
with session_lib.Session(graph=graph) as session:
variables.global_variables_initializer().run()
for _ in range(500):
session.run(output)
start_time = time.time()
for _ in range(num_iters):
session.run(output)
duration = (time.time() - start_time)
num_items = n * m * k * 2
throughput = num_items * num_iters / duration / 1e9
print('%s %s input_info:%s %d %.4fsec, %.4fGitems/s.' %
(device, str(dtype), str(n) + 'x' + str(m) + 'x' + str(k) + ',ta:'
+ str(transpose_a) + '.tb:' + str(transpose_b), num_iters,
duration, throughput))
name_template = ('matmul_{device}_{dtype}_input_info_{inputinfo}')
self.report_benchmark(
name=name_template.format(
device=device,
dtype=str(dtype).replace(' ', ''),
inputinfo=str(n) + 'x' + str(m) + 'x' + str(k) + ',ta:' +
str(transpose_a) + ',tb:' + str(transpose_b)).replace(' ', ''),
iters=num_iters,
wall_time=duration)
return duration
def run_test_gpu(self, n, m, k, transpose_a, transpose_b, dtype, num_iters):
self.run_graph(test.gpu_device_name(), n, m, k, transpose_a, transpose_b, num_iters, dtype)
def test_round(self, num_iters):
dtypes = [np.float32, np.float64]
for dtype in dtypes:
for n, m, (transpose_a, transpose_b) in itertools.product(
[512, 1024], [1, 8, 16, 128], [(False, False), (True, False),
(False, True)]):
k = n
self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters)
for n, m, k, (transpose_a, transpose_b) in itertools.product(
[200], [1, 8, 20], [10000], [(False, False), (True, False), (False,
True)]):
self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters)
for (n, m, k), (transpose_a, transpose_b) in itertools.product(
[(200, 20, 20000), (1, 10000, 200)], [(False, False), (True, False),
(False, True)]):
self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters)
def benchmark_matmul(self):
self.test_round(num_iters=200)
if __name__ == '__main__':
test.main()
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