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# Copyright 2017 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.
# ==============================================================================
"""Tests for matmul_benchmark.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import numpy as np
from tensorflow.core.framework import graph_pb2
from tensorflow.core.framework import node_def_pb2
from tensorflow.python.framework import ops
from tensorflow.python.ops import matmul_benchmark
from tensorflow.python.platform import test as googletest
from tensorflow.python.platform import tf_logging
def BuildGraphTest(n, m, k, transpose_a, transpose_b, dtype):
def Test(self):
if not googletest.is_gpu_available():
tf_logging.info("Skipping BuildGraphTest %s", (n, m, k, transpose_a,
transpose_b))
return
tf_logging.info("Testing BuildGraphTest %s", (n, m, k, transpose_a,
transpose_b))
self._VerifyBuildGraph(n, m, k, transpose_a, transpose_b, dtype)
return Test
def RunGraphTest(n, m, k, transpose_a, transpose_b, dtype):
def Test(self):
if not googletest.is_gpu_available():
tf_logging.info("Skipping RunGraphTest %s", (n, m, k, transpose_a,
transpose_b))
return
tf_logging.info("Testing RunGraphTest %s", (n, m, k, transpose_a,
transpose_b))
self._VerifyRunGraph(n, m, k, transpose_a, transpose_b, dtype)
return Test
class MatmulBenchmarkTest(googletest.TestCase):
def _StripNode(self, nd):
snode = node_def_pb2.NodeDef(name=nd.name, op=nd.op, input=nd.input)
if nd.device:
snode.device = nd.device
return snode
def _StripGraph(self, gd):
return graph_pb2.GraphDef(node=[self._StripNode(nd) for nd in gd.node])
def _VerifyBuildGraph(self, n, m, k, transpose_a, transpose_b, dtype):
graph = ops.Graph()
with graph.as_default():
matmul_benchmark.build_graph(googletest.gpu_device_name(), n, m, k, transpose_a, transpose_b,
dtype)
gd = graph.as_graph_def()
dev=googletest.gpu_device_name()
proto_expected = """
node { name: "random_uniform/shape" op: "Const" device: \""""+ dev +"""\" }
node { name: "random_uniform/min" op: "Const" device: \""""+ dev +"""\" }
node { name: "random_uniform/max" op: "Const" device: \""""+ dev +"""\" }
node { name: "random_uniform/RandomUniform" op: "RandomUniform" input: "random_uniform/shape" device: \""""+ dev +"""\" }
node { name: "random_uniform/sub" op: "Sub" input: "random_uniform/max" input: "random_uniform/min" device: \""""+ dev +"""\" }
node { name: "random_uniform/mul" op: "Mul" input: "random_uniform/RandomUniform" input: "random_uniform/sub" device: \""""+ dev +"""\" }
node { name: "random_uniform" op: "Add" input: "random_uniform/mul" input: "random_uniform/min" device: \""""+ dev +"""\" }
node { name: "Variable" op: "VariableV2" device: \""""+ dev +"""\" }
node { name: "Variable/Assign" op: "Assign" input: "Variable" input: "random_uniform" device: \""""+ dev +"""\" }
node { name: "Variable/read" op: "Identity" input: "Variable" device: \""""+ dev +"""\" }
node { name: "random_uniform_1/shape" op: "Const" device: \""""+ dev +"""\" }
node { name: "random_uniform_1/min" op: "Const" device: \""""+ dev +"""\" }
node { name: "random_uniform_1/max" op: "Const" device: \""""+ dev +"""\" }
node { name: "random_uniform_1/RandomUniform" op: "RandomUniform" input: "random_uniform_1/shape" device: \""""+ dev +"""\" }
node { name: "random_uniform_1/sub" op: "Sub" input: "random_uniform_1/max" input: "random_uniform_1/min" device: \""""+ dev +"""\" }
node { name: "random_uniform_1/mul" op: "Mul" input: "random_uniform_1/RandomUniform" input: "random_uniform_1/sub" device: \""""+ dev +"""\" }
node { name: "random_uniform_1" op: "Add" input: "random_uniform_1/mul" input: "random_uniform_1/min" device: \""""+ dev +"""\" }
node { name: "Variable_1" op: "VariableV2" device: \""""+ dev +"""\" }
node { name: "Variable_1/Assign" op: "Assign" input: "Variable_1" input: "random_uniform_1" device: \""""+ dev +"""\" }
node { name: "Variable_1/read" op: "Identity" input: "Variable_1" device: \""""+ dev +"""\" }
node { name: "MatMul" op: "MatMul" input: "Variable/read" input: "Variable_1/read" device: \""""+ dev +"""\" }
node { name: "group_deps" op: "NoOp" input: "^MatMul" device: \""""+ dev +"""\" }
"""
self.assertProtoEquals(str(proto_expected), self._StripGraph(gd))
def _VerifyRunGraph(self, n, m, k, transpose_a, transpose_b, dtype):
benchmark_instance = matmul_benchmark.MatmulBenchmark()
duration = benchmark_instance.run_graph(googletest.gpu_device_name(), n, m, k, transpose_a,
transpose_b, 1, dtype)
self.assertTrue(duration > 1e-6)
if __name__ == "__main__":
dtypes = [np.float32, np.float64]
index = 0
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
setattr(MatmulBenchmarkTest, "testBuildGraph_" + str(index),
BuildGraphTest(_n, _m, _k, _transpose_a, _transpose_b, _dtype))
setattr(MatmulBenchmarkTest, "testRunGraph_" + str(index),
RunGraphTest(_n, _m, _k, _transpose_a, _transpose_b, _dtype))
index += 1
googletest.main()
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