jcf94 commented on a change in pull request #5962: URL: https://github.com/apache/incubator-tvm/pull/5962#discussion_r448134777
########## File path: python/tvm/ansor/auto_schedule.py ########## @@ -0,0 +1,186 @@ +# 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 auto-scheduler""" + +import tvm._ffi +from tvm.runtime import Object +from .measure import LocalBuilder, LocalRunner +from . import _ffi_api + + +@tvm._ffi.register_object("ansor.HardwareParams") +class HardwareParams(Object): + """ The parameters of target hardware, this is used to guide the search process of + SearchPolicy. + + 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. + max_unroll_vec : int + The max length of an axis to be unrolled or vectorized. + max_innermost_split_factor : int + The max split factor for the innermost tile. + """ + def __init__(self, num_cores, vector_unit_bytes, cache_line_bytes, + max_unroll_vec, max_innermost_split_factor): + self.__init_handle_by_constructor__(_ffi_api.HardwareParams, num_cores, + vector_unit_bytes, cache_line_bytes, + max_unroll_vec, max_innermost_split_factor) + + +@tvm._ffi.register_object("ansor.SearchTask") +class SearchTask(Object): + """ The meta-information of a search task. + + Parameters + ---------- + dag : ComputeDAG + The ComputeDAG for target compute declaration. + workload_key : str + The workload key for target compute declaration. + target : tvm.target.Target + The target device of this search task. + target_host : tvm.target.Target + The target host device of this search task. + hardware_params : 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 for search policy """ + + +@tvm._ffi.register_object("ansor.EmptyPolicy") +class EmptyPolicy(SearchPolicy): + """ This is an example empty search policy which will always generate + the init state of target ComputeDAG. + """ + def __init__(self): + self.__init_handle_by_constructor__(_ffi_api.EmptyPolicy) + + +@tvm._ffi.register_object("ansor.TuneOption") +class TuneOption(Object): + """ The options for tuning + + Parameters + ---------- + n_trials: int + Number of total measurement trials + early_stopping: int + Stops early the tuning if no improvement after n measurements + num_measure_per_round: int + The number of programs to be measured at each iteration + verbose: int + Verbosity level. 0 means silent. + builder: Builder + Builder which builds the program + runner: Runner + Runner which runs the program and measure time costs + measure_callbacks: List[MeasureCallback] + Callback functions called after each measure + Candidates: + - ansor.LogToFile + pre_search_callbacks: List[SearchCallback] + Callback functions called before the search process + Candidates: + - ansor.PreloadMeasuredStates(will be added later) + - ansor.PreloadCustomSketchRule(will be added later) + """ + def __init__(self, n_trials=0, early_stopping=-1, num_measure_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 builder: " + runner) + + if measure_callbacks is None: + measure_callbacks = [] + + if pre_search_callbacks is None: + pre_search_callbacks = [] + + self.__init_handle_by_constructor__( + _ffi_api.TuneOption, n_trials, early_stopping, num_measure_per_round, + verbose, builder, runner, measure_callbacks, pre_search_callbacks) + + +def auto_schedule(workload, target=None, + target_host=None, search_policy='default', + hardware_params=None, tune_option=None): + """ Do auto scheduling for a computation declaration. + + The workload parameter can be a `string` as workload_key, or directly + passing a `SearchTask` as input. + + Parameters + ---------- + workload : Union[SearchTask, str] + The target search task or workload key. + target : Target + The target device of this schedule search. + target_host : Target = None + The target host device of this schedule search. + search_policy : Union[SearchPolicy, str] + The search policy to be used for schedule search. + hardware_params : HardwareParams + The hardware parameters of this schedule search. + tune_option : TuneOption + Tuning and measurement options. + + Returns + ------- + A `te.schedule` and the target `te.Tensor`s to be used in `tvm.lower` or `tvm.build` + """ + if isinstance(search_policy, str): + if search_policy == 'default': + search_policy = EmptyPolicy() + else: + raise ValueError("Invalid search policy: " + search_policy) + + if tune_option is None: + tune_option = TuneOption(n_trials=0) Review comment: How about we setting the default value to 1, since 0&1 are the some in search policy code. I'll add more explanation in the fuction doc. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
