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123 lines
6.0 KiB
Python
123 lines
6.0 KiB
Python
# Copyright 2017 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 for matmul_benchmark.py."""
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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 itertools
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import numpy as np
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from tensorflow.core.framework import graph_pb2
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from tensorflow.core.framework import node_def_pb2
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import matmul_benchmark
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from tensorflow.python.platform import test as googletest
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from tensorflow.python.platform import tf_logging
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def BuildGraphTest(n, m, k, transpose_a, transpose_b, dtype):
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def Test(self):
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if not googletest.is_gpu_available():
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tf_logging.info("Skipping BuildGraphTest %s", (n, m, k, transpose_a,
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transpose_b))
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return
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tf_logging.info("Testing BuildGraphTest %s", (n, m, k, transpose_a,
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transpose_b))
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self._VerifyBuildGraph(n, m, k, transpose_a, transpose_b, dtype)
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return Test
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def RunGraphTest(n, m, k, transpose_a, transpose_b, dtype):
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def Test(self):
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if not googletest.is_gpu_available():
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tf_logging.info("Skipping RunGraphTest %s", (n, m, k, transpose_a,
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transpose_b))
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return
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tf_logging.info("Testing RunGraphTest %s", (n, m, k, transpose_a,
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transpose_b))
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self._VerifyRunGraph(n, m, k, transpose_a, transpose_b, dtype)
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return Test
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class MatmulBenchmarkTest(googletest.TestCase):
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def _StripNode(self, nd):
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snode = node_def_pb2.NodeDef(name=nd.name, op=nd.op, input=nd.input)
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if nd.device:
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snode.device = nd.device
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return snode
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def _StripGraph(self, gd):
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return graph_pb2.GraphDef(node=[self._StripNode(nd) for nd in gd.node])
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def _VerifyBuildGraph(self, n, m, k, transpose_a, transpose_b, dtype):
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graph = ops.Graph()
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with graph.as_default():
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matmul_benchmark.build_graph("gpu", n, m, k, transpose_a, transpose_b,
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dtype)
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gd = graph.as_graph_def()
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self.assertProtoEquals("""
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node { name: "random_uniform/shape" op: "Const" device: "/device:GPU:0" }
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node { name: "random_uniform/min" op: "Const" device: "/device:GPU:0" }
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node { name: "random_uniform/max" op: "Const" device: "/device:GPU:0" }
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node { name: "random_uniform/RandomUniform" op: "RandomUniform" input: "random_uniform/shape" device: "/device:GPU:0" }
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node { name: "random_uniform/sub" op: "Sub" input: "random_uniform/max" input: "random_uniform/min" device: "/device:GPU:0" }
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node { name: "random_uniform/mul" op: "Mul" input: "random_uniform/RandomUniform" input: "random_uniform/sub" device: "/device:GPU:0" }
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node { name: "random_uniform" op: "Add" input: "random_uniform/mul" input: "random_uniform/min" device: "/device:GPU:0" }
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node { name: "Variable" op: "VariableV2" device: "/device:GPU:0" }
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node { name: "Variable/Assign" op: "Assign" input: "Variable" input: "random_uniform" device: "/device:GPU:0" }
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node { name: "Variable/read" op: "Identity" input: "Variable" device: "/device:GPU:0" }
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node { name: "random_uniform_1/shape" op: "Const" device: "/device:GPU:0" }
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node { name: "random_uniform_1/min" op: "Const" device: "/device:GPU:0" }
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node { name: "random_uniform_1/max" op: "Const" device: "/device:GPU:0" }
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node { name: "random_uniform_1/RandomUniform" op: "RandomUniform" input: "random_uniform_1/shape" device: "/device:GPU:0" }
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node { name: "random_uniform_1/sub" op: "Sub" input: "random_uniform_1/max" input: "random_uniform_1/min" device: "/device:GPU:0" }
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node { name: "random_uniform_1/mul" op: "Mul" input: "random_uniform_1/RandomUniform" input: "random_uniform_1/sub" device: "/device:GPU:0" }
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node { name: "random_uniform_1" op: "Add" input: "random_uniform_1/mul" input: "random_uniform_1/min" device: "/device:GPU:0" }
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node { name: "Variable_1" op: "VariableV2" device: "/device:GPU:0" }
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node { name: "Variable_1/Assign" op: "Assign" input: "Variable_1" input: "random_uniform_1" device: "/device:GPU:0" }
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node { name: "Variable_1/read" op: "Identity" input: "Variable_1" device: "/device:GPU:0" }
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node { name: "MatMul" op: "MatMul" input: "Variable/read" input: "Variable_1/read" device: "/device:GPU:0" }
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node { name: "group_deps" op: "NoOp" input: "^MatMul" device: "/device:GPU:0" }
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""", self._StripGraph(gd))
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def _VerifyRunGraph(self, n, m, k, transpose_a, transpose_b, dtype):
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benchmark_instance = matmul_benchmark.MatmulBenchmark()
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duration = benchmark_instance.run_graph("gpu", n, m, k, transpose_a,
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transpose_b, 1, dtype)
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self.assertTrue(duration > 1e-6)
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if __name__ == "__main__":
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dtypes = [np.float32, np.float64]
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index = 0
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for _dtype in dtypes:
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for _n, _m, (_transpose_a, _transpose_b) in itertools.product(
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[512, 1024], [1, 8, 16, 128], [(False, False), (True, False), (False,
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True)]):
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_k = _n
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setattr(MatmulBenchmarkTest, "testBuildGraph_" + str(index),
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BuildGraphTest(_n, _m, _k, _transpose_a, _transpose_b, _dtype))
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setattr(MatmulBenchmarkTest, "testRunGraph_" + str(index),
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RunGraphTest(_n, _m, _k, _transpose_a, _transpose_b, _dtype))
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index += 1
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googletest.main()
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