jcf94 commented on a change in pull request #5962: URL: https://github.com/apache/incubator-tvm/pull/5962#discussion_r452006736
########## File path: python/tvm/ansor/auto_schedule.py ########## @@ -0,0 +1,200 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +""" +User interface for Ansor auto-scheduler. + +The basic schedule search process for Ansor is designed to be: +`Program sampling` -> `Performance Tuning`. + +In `Program sampling`, we use some predefined precise or heuristic rules to generate several +initial schedules. Based on these initial starting points, we perform `Performance Tuning` which +uses cost model based evolutionary search to select schedules with the best performance. + +Candidate schedules are measured against the specific hardware target. +""" + +import tvm._ffi +from tvm.runtime import Object +from .compute_dag import ComputeDAG +from .measure import LocalBuilder, LocalRunner +from . import _ffi_api + + +@tvm._ffi.register_object("ansor.HardwareParams") +class HardwareParams(Object): + """ The parameters of target hardware used to guide the search process of SearchPolicy. + + TODO(jcf94): This is considering to merge with the new Target: + https://discuss.tvm.ai/t/rfc-tvm-target-specification/6844 + + Parameters + ---------- + num_cores : int + The number of device cores. + vector_unit_bytes : int + The width of vector units in bytes. + cache_line_bytes : int + The size of cache line in bytes. + """ + def __init__(self, num_cores, vector_unit_bytes, cache_line_bytes): + self.__init_handle_by_constructor__(_ffi_api.HardwareParams, num_cores, + vector_unit_bytes, cache_line_bytes) + + +@tvm._ffi.register_object("ansor.SearchTask") +class SearchTask(Object): + """ The computation information and hardware parameters for a specific schedule search task. + + Parameters + ---------- + dag : ComputeDAG + The ComputeDAG for the corresponding compute declaration. + workload_key : str + The workload key for the corresponding compute declaration. + target : tvm.target.Target + The target device of this search task. + target_host : Optional[tvm.target.Target] + The target host device of this search task. + hardware_params : Optional[HardwareParams] + Hardware parameters used in this search task. + """ + def __init__(self, dag, workload_key, target, target_host=None, + hardware_params=None): + self.__init_handle_by_constructor__(_ffi_api.SearchTask, dag, + workload_key, target, target_host, + hardware_params) + + +@tvm._ffi.register_object("ansor.SearchPolicy") +class SearchPolicy(Object): + """ The base class of search policies. """ + + +@tvm._ffi.register_object("ansor.EmptyPolicy") +class EmptyPolicy(SearchPolicy): + """ This is an example empty search policy which will always generate + the init state of ComputeDAG. + """ + def __init__(self): + self.__init_handle_by_constructor__(_ffi_api.EmptyPolicy) + + +@tvm._ffi.register_object("ansor.TuningOptions") +class TuningOptions(Object): + """ This controls the options of performance tuning. + + Parameters + ---------- + num_measure_trials: int = 0 + The number of measurement trials. + The search policy measures `num_measure_trials` schedules in total and returns the best one + among them. + With `num_measure_trials` == 0, the policy will do the schedule search but won't involve + measurement. + This can be used to get a runnable schedule quickly without auto-tuning. + early_stopping: int = -1 + Stop the tuning early if getting no improvement after n measurements. + num_measures_per_round: int = 64 + The number of schedules to be measured at each search round. + The whole schedule search process will try a total number of `num_measure_trials` in several + rounds. + verbose: int = 1 + Verbosity level. 0 for silent, 1 to output information during schedule search. + builder: Union[ProgramBuilder, str] = 'local' + ProgramBuilder which builds the program. + runner: Union[ProgramRunner, str] = 'local' + ProgramRunner which runs the program and measures time costs. + measure_callbacks: Optional[List[MeasureCallback]] + Callback functions called after each measurement. + Candidates: + - ansor.LogToFile + pre_search_callbacks: Optional[List[SearchCallback]] + Callback functions called before the search process. + Candidates: + - ansor.PreloadMeasuredStates + - ansor.PreloadCustomSketchRule + TODO(jcf94): Add these implementation in later PRs. + """ + def __init__(self, num_measure_trials=0, early_stopping=-1, num_measures_per_round=64, + verbose=1, builder='local', runner='local', measure_callbacks=None, + pre_search_callbacks=None): + if isinstance(builder, str): + if builder == 'local': + builder = LocalBuilder() + else: + raise ValueError("Invalid builder: " + builder) + + if isinstance(runner, str): + if runner == 'local': + runner = LocalRunner() + else: + raise ValueError("Invalid runner: " + runner) + + measure_callbacks = [] if measure_callbacks is None else measure_callbacks Review comment: Update the C++ constructor to accept None as input, now the code is more clear here. ########## File path: python/tvm/ansor/auto_schedule.py ########## @@ -0,0 +1,200 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you 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. + +""" +User interface for Ansor auto-scheduler. + +The basic schedule search process for Ansor is designed to be: +`Program sampling` -> `Performance Tuning`. + +In `Program sampling`, we use some predefined precise or heuristic rules to generate several +initial schedules. Based on these initial starting points, we perform `Performance Tuning` which +uses cost model based evolutionary search to select schedules with the best performance. + +Candidate schedules are measured against the specific hardware target. +""" + +import tvm._ffi +from tvm.runtime import Object +from .compute_dag import ComputeDAG +from .measure import LocalBuilder, LocalRunner +from . import _ffi_api + + +@tvm._ffi.register_object("ansor.HardwareParams") +class HardwareParams(Object): + """ The parameters of target hardware used to guide the search process of SearchPolicy. + + TODO(jcf94): This is considering to merge with the new Target: + https://discuss.tvm.ai/t/rfc-tvm-target-specification/6844 + + Parameters + ---------- + num_cores : int + The number of device cores. + vector_unit_bytes : int + The width of vector units in bytes. + cache_line_bytes : int + The size of cache line in bytes. + """ + def __init__(self, num_cores, vector_unit_bytes, cache_line_bytes): + self.__init_handle_by_constructor__(_ffi_api.HardwareParams, num_cores, + vector_unit_bytes, cache_line_bytes) + + +@tvm._ffi.register_object("ansor.SearchTask") +class SearchTask(Object): + """ The computation information and hardware parameters for a specific schedule search task. + + Parameters + ---------- + dag : ComputeDAG + The ComputeDAG for the corresponding compute declaration. + workload_key : str + The workload key for the corresponding compute declaration. + target : tvm.target.Target + The target device of this search task. + target_host : Optional[tvm.target.Target] + The target host device of this search task. + hardware_params : Optional[HardwareParams] + Hardware parameters used in this search task. + """ + def __init__(self, dag, workload_key, target, target_host=None, + hardware_params=None): + self.__init_handle_by_constructor__(_ffi_api.SearchTask, dag, + workload_key, target, target_host, + hardware_params) + + +@tvm._ffi.register_object("ansor.SearchPolicy") +class SearchPolicy(Object): + """ The base class of search policies. """ + + +@tvm._ffi.register_object("ansor.EmptyPolicy") +class EmptyPolicy(SearchPolicy): + """ This is an example empty search policy which will always generate + the init state of ComputeDAG. + """ + def __init__(self): + self.__init_handle_by_constructor__(_ffi_api.EmptyPolicy) + + +@tvm._ffi.register_object("ansor.TuningOptions") +class TuningOptions(Object): + """ This controls the options of performance tuning. + + Parameters + ---------- + num_measure_trials: int = 0 + The number of measurement trials. + The search policy measures `num_measure_trials` schedules in total and returns the best one + among them. + With `num_measure_trials` == 0, the policy will do the schedule search but won't involve + measurement. + This can be used to get a runnable schedule quickly without auto-tuning. + early_stopping: int = -1 + Stop the tuning early if getting no improvement after n measurements. + num_measures_per_round: int = 64 + The number of schedules to be measured at each search round. + The whole schedule search process will try a total number of `num_measure_trials` in several + rounds. + verbose: int = 1 + Verbosity level. 0 for silent, 1 to output information during schedule search. + builder: Union[ProgramBuilder, str] = 'local' + ProgramBuilder which builds the program. + runner: Union[ProgramRunner, str] = 'local' + ProgramRunner which runs the program and measures time costs. + measure_callbacks: Optional[List[MeasureCallback]] + Callback functions called after each measurement. + Candidates: + - ansor.LogToFile + pre_search_callbacks: Optional[List[SearchCallback]] + Callback functions called before the search process. + Candidates: + - ansor.PreloadMeasuredStates + - ansor.PreloadCustomSketchRule + TODO(jcf94): Add these implementation in later PRs. + """ + def __init__(self, num_measure_trials=0, early_stopping=-1, num_measures_per_round=64, + verbose=1, builder='local', runner='local', measure_callbacks=None, + pre_search_callbacks=None): + if isinstance(builder, str): + if builder == 'local': + builder = LocalBuilder() + else: + raise ValueError("Invalid builder: " + builder) + + if isinstance(runner, str): + if runner == 'local': + runner = LocalRunner() + else: + raise ValueError("Invalid runner: " + runner) + + measure_callbacks = [] if measure_callbacks is None else measure_callbacks Review comment: Updated the C++ constructor to accept None as input, now the code is more clear here. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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