comaniac commented on a change in pull request #4870: [AutoTVM] Support range in index based tuners URL: https://github.com/apache/incubator-tvm/pull/4870#discussion_r379544397
########## File path: python/tvm/autotvm/tuner/index_based_tuner.py ########## @@ -0,0 +1,121 @@ +# 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. +# pylint: disable=abstract-method +"""Grid search tuner and random tuner""" + +import numpy as np + +from .tuner import Tuner + +class IndexBaseTuner(Tuner): + """Base class for index based tuner + This type of tuner determine the next batch of configs based on config indices. + + Parameters + ---------- + task: autotvm.task.Task + The tuning task + range_idx: Optional[Tuple[int, int]] + A tuple of index range that this tuner can select from + """ + def __init__(self, task, range_idx=None): + super(IndexBaseTuner, self).__init__(task) + assert range_idx is None or isinstance(range_idx, tuple), \ + "range_idx must be None or (int, int)" + + self.range_length = len(self.task.config_space) + self.index_offset = 0 + if range_idx is not None: + assert range_idx[1] > range_idx[0], "Index range must be positive" + assert range_idx[0] >= 0, "Start index must be positive" + self.range_length = range_idx[1] - range_idx[0] + 1 + self.index_offset = range_idx[0] + self.counter = 0 + + def has_next(self): + return self.counter < self.range_length + + def load_history(self, data_set): + pass + + +class GridSearchTuner(IndexBaseTuner): + """Enumerate the search space in a grid search order""" + + def next_batch(self, batch_size): + ret = [] + for _ in range(batch_size): + if self.counter >= self.range_length: + continue + index = self.counter + self.index_offset + ret.append(self.task.config_space.get(index)) + self.counter = self.counter + 1 + return ret + + def __getstate__(self): + return {"counter": self.counter} + + def __setstate__(self, state): + self.counter = state['counter'] + + +class RandomTuner(IndexBaseTuner): + """Enumerate the search space in a random order + + Parameters + ---------- + task: autotvm.task.Task + Tuning Task + + range_idx: Optional[Tuple[int, int]] + A tuple of index range to random + """ + def __init__(self, task, range_idx=None): + super(RandomTuner, self).__init__(task, range_idx) + + # Use a dict to mimic a range(n) list without storing rand_state[i] = i entries so that + # we can generate non-repetitive random indices. + self.rand_state = {} + self.rand_max = self.range_length + self.visited = [] + + def next_batch(self, batch_size): + ret = [] + for _ in range(batch_size): + if self.rand_max == 0: + break + + # Random an indirect index. + index_ = np.random.randint(self.rand_max) + self.rand_max -= 1 + + # Use the indirect index to get a direct index. + index = self.rand_state.get(index_, index_) + self.index_offset + ret.append(self.task.config_space.get(index)) + self.visited.append(index) + + # Update the direct index map. + self.rand_state[index_] = self.rand_state.get(self.rand_max, self.rand_max) + self.rand_state.pop(self.rand_max, None) + self.counter += 1 + return ret + + def __getstate__(self): + return {"visited": self.counter} Review comment: Should this function return a list of visited config indices (i.e., `self.visited`)? Then should we change `__setstate__` accordingly to make it consistent? ---------------------------------------------------------------- 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 With regards, Apache Git Services
