# 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()