merrymercy 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_r379586297
 
 

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 File path: python/tvm/autotvm/tuner/index_based_tuner.py
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 @@ -0,0 +1,122 @@
+# 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:
+                break
+            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.visited}
+
+    def __setstate__(self, state):
+        self.visited = state['visited']
+        self.counter = len(self.visited)
 
 Review comment:
   The `__getstate__`, `__setstate__` are for serialization (pickle). Although 
it is never used in the current code base.
   Your current implementation is still wrong. Because `x != 
x.setstate(x.getstate())`. (after this round trip, you will visit already 
visited states).
   You should store all members of `RandomTuner`

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