wehub-resource-sync
155 lines
5.8 KiB
Python
155 lines
5.8 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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"""Benchmark for Transpose op."""
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import time
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import numpy as np
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from tensorflow.python.client import session as session_lib
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from tensorflow.python.framework import constant_op
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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 control_flow_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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def build_graph(device, input_shape, perm, datatype, num_iters):
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"""builds a graph containing a sequence of conv2d operations.
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Args:
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device: String, the device to run on.
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input_shape: Shape of the input tensor.
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perm: A list of ints with the same length as input tensor's dimension.
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datatype: numpy data type of the input tensor.
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num_iters: number of iterations to run transpose.
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Returns:
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An array of tensors to run()
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"""
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with ops.device("/%s:0" % device):
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total_size = np.prod(input_shape)
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inp = np.arange(1, total_size + 1, dtype=datatype).reshape(input_shape)
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t = constant_op.constant(inp, shape=input_shape)
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outputs = []
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transpose_op = array_ops.transpose(t, perm)
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outputs.append(transpose_op)
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for _ in range(1, num_iters):
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with ops.control_dependencies([transpose_op]):
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transpose_op = array_ops.transpose(t, perm)
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outputs.append(transpose_op)
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return control_flow_ops.group(*outputs)
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class TransposeBenchmark(test.Benchmark):
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"""Benchmark transpose!"""
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def _run_graph(self, device, input_shape, perm, num_iters, datatype):
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"""runs the graph and print its execution time.
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Args:
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device: String, the device to run on.
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input_shape: Shape of the input tensor.
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perm: A list of ints with the same length as input tensor's dimension.
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num_iters: Number of iterations to run the benchmark.
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datatype: numpy data type of the input tensor.
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Returns:
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The duration of the run in seconds.
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"""
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graph = ops.Graph()
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with graph.as_default():
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outputs = build_graph(device, input_shape, perm, datatype, num_iters)
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with session_lib.Session(graph=graph) as session:
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variables.global_variables_initializer().run()
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# warmup runs
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session.run(outputs)
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start_time = time.time()
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session.run(outputs)
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duration = (time.time() - start_time) / num_iters
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throughput = np.prod(
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np.array(input_shape)) * datatype().itemsize * 2 / duration / 1e9
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print("%s %s inputshape:%s perm:%s %d %.6fsec, %.4fGB/s." %
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(device, str(datatype), str(input_shape).replace(" ", ""),
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str(perm).replace(" ", ""), num_iters, duration, throughput))
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name_template = (
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"transpose_{device}_{dtype}_input_shape_{inputshape}_perm_{perm}")
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self.report_benchmark(
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name=name_template.format(
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device=device,
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dtype=str(datatype).replace(" ", ""),
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inputshape=str(input_shape).replace(" ", ""),
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perm=str(perm).replace(" ", "")).replace(" ", ""),
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iters=num_iters,
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wall_time=duration)
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return duration
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def benchmark_transpose(self):
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print("transpose benchmark:")
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datatypes = [np.complex128, np.float64, np.float32, np.float16, np.int8]
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small_shapes = [[2, 20, 20, 20, 16], [2, 16, 20, 20, 20]] * 2
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small_shapes += [[2, 100, 100, 16], [2, 16, 100, 100]] * 2
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small_shapes += [[2, 5000, 16], [2, 16, 5000]] * 2
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small_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2
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small_perms += [[0, 3, 1, 2], [0, 2, 3, 1]] + [[3, 1, 2, 0]] * 2
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small_perms += [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
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large_shapes = [[2, 40, 40, 40, 32], [2, 40, 40, 40, 64]] * 2 + [[
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2, 300, 300, 32
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], [2, 300, 300, 64]] * 2 + [[2, 100000, 32], [2, 100000, 64]] * 2
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large_perms = [[0, 4, 1, 2, 3], [0, 2, 3, 4, 1]] + [[4, 1, 2, 3, 0]] * 2 + [
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[0, 3, 1, 2], [0, 2, 3, 1]
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] + [[3, 1, 2, 0]] * 2 + [[0, 2, 1]] * 2 + [[2, 1, 0]] * 2
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num_iters = 40
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for datatype in datatypes:
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for ishape, perm in zip(small_shapes, small_perms):
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self._run_graph("gpu", ishape, perm, num_iters, datatype)
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if datatype is not np.complex128:
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if datatype is not np.float16:
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for ishape, perm in zip(large_shapes, large_perms):
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self._run_graph("gpu", ishape, perm, num_iters, datatype)
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small_dim_large_shapes = [[2, 10000, 3], [2, 3, 10000], [2, 10000, 8],
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[2, 8, 10000]]
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small_dim_small_shapes = [[2, 5000, 3], [2, 3, 5000], [2, 5000, 8],
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[2, 8, 5000]]
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small_dim_perms = [[0, 2, 1]] * 4
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num_iters = 320
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small_dim_large_shape_datatypes = [np.float64, np.float32, np.int8]
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for datatype in small_dim_large_shape_datatypes:
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for ishape, perm in zip(small_dim_large_shapes, small_dim_perms):
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self._run_graph("gpu", ishape, perm, num_iters, datatype)
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small_dim_small_shape_datatypes = [np.complex128, np.float16]
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for datatype in small_dim_small_shape_datatypes:
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for ishape, perm in zip(small_dim_small_shapes, small_dim_perms):
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self._run_graph("gpu", ishape, perm, num_iters, datatype)
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if __name__ == "__main__":
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test.main()
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