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127 lines
5.2 KiB
127 lines
5.2 KiB
# Copyright (c) 2018 PaddlePaddle 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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import core
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from contextlib import contextmanager
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import os
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__all__ = ['cuda_profiler', 'reset_profiler', 'profiler']
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NVPROF_CONFIG = [
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"gpustarttimestamp",
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"gpuendtimestamp",
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"gridsize3d",
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"threadblocksize",
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"streamid",
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"enableonstart 0",
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"conckerneltrace",
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]
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@contextmanager
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def cuda_profiler(output_file, output_mode=None, config=None):
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"""The CUDA profiler.
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This fuctions is used to profile CUDA program by CUDA runtime application
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programming interface. The profiling result will be written into
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`output_file` with Key-Value pair format or Comma separated values format.
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The user can set the output mode by `output_mode` argument and set the
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counters/options for profiling by `config` argument. The default config
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is ['gpustarttimestamp', 'gpustarttimestamp', 'gridsize3d',
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'threadblocksize', 'streamid', 'enableonstart 0', 'conckerneltrace'].
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Args:
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output_file (string) : The output file name, the result will be
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written into this file.
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output_mode (string) : The output mode has Key-Value pair format and
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Comma separated values format. It should be 'kvp' or 'csv'.
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config (list of string) : The profiler options and counters can refer
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to "Compute Command Line Profiler User Guide".
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"""
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if output_mode is None:
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output_mode = 'csv'
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if output_mode not in ['kvp', 'csv']:
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raise ValueError("The output mode must be 'kvp' or 'csv'.")
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config = NVPROF_CONFIG if config is None else config
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config_file = 'nvprof_config_file'
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with open(config_file, 'wb') as fp:
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fp.writelines(["%s\n" % item for item in config])
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core.nvprof_init(output_file, output_mode, config_file)
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# Enables profiler collection by the active CUDA profiling tool.
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core.nvprof_start()
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yield
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# Disables profiler collection.
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core.nvprof_stop()
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os.remove(config_file)
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def reset_profiler():
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"""The profiler clear interface.
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reset_profiler will clear the previous time record.
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"""
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core.reset_profiler()
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@contextmanager
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def profiler(state, sorted_key=None, profile_path='/tmp/profile'):
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"""The profiler interface.
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Different from cuda_profiler, this profiler can be used to profile both CPU
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and GPU program. By defalut, it records the CPU and GPU operator kernels,
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if you want to profile other program, you can refer the profiling tutorial
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to add more records.
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Args:
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state (string) : The profiling state, which should be 'CPU' or 'GPU',
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telling the profiler to use CPU timer or GPU timer for profiling.
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Although users may have already specified the execution place
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(CPUPlace/CUDAPlace) in the begining, for flexibility the profiler
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would not inherit this place.
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sorted_key (string) : If None, the profiling results will be printed
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in the order of first end time of events. Otherwise, the profiling
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results will be sorted by the this flag. This flag should be one
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of 'calls', 'total', 'max', 'min' or 'ave'.
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The `calls` means sorting by the number of calls.
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The `total` means sorting by the total execution time.
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The `max` means sorting by the maximum execution time.
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The `min` means sorting by the minimum execution time.
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The `ave` means sorting by the average execution time.
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profile_path (string) : If state == 'All', it will write a profile
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proto output file.
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"""
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if state not in ['CPU', 'GPU', "All"]:
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raise ValueError("The state must be 'CPU' or 'GPU' or 'All'.")
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if state == "GPU":
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prof_state = core.ProfilerState.kCUDA
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elif state == "CPU":
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prof_state = core.ProfilerState.kCPU
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else:
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prof_state = core.ProfilerState.kAll
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core.enable_profiler(prof_state)
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yield
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sorted_key = 'default' if sorted_key is None else sorted_key
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if sorted_key not in ['default', 'calls', 'total', 'max', 'min', 'ave']:
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raise ValueError("The sorted_key must be None or in 'calls', 'total', "
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"'max', 'min' and 'ave'")
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key_map = {
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'default': core.EventSortingKey.kDefault,
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'calls': core.EventSortingKey.kCalls,
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'total': core.EventSortingKey.kTotal,
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'max': core.EventSortingKey.kMax,
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'min': core.EventSortingKey.kMin,
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'ave': core.EventSortingKey.kAve,
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}
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# TODO(qingqing) : redirect C++ ostream to Python stream.
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# with core.ostream_redirect(stdout=True, stderr=True):
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core.disable_profiler(key_map[sorted_key], profile_path)
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