junrushao1994 commented on a change in pull request #5962: URL: https://github.com/apache/incubator-tvm/pull/5962#discussion_r450413013
########## File path: python/tvm/ansor/loop_state.py ########## @@ -0,0 +1,223 @@ +# 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=unused-import + +""" +The definition of the "state" in search. + +Each LoopState corresponds to a schedule for its ComputeDAG. +A LoopState consists of: 1. a current loop structure; 2. a list of transformation steps used to +construct the loop structure. +The loop structure keeps a preview of how the schedule will finally look like after lowering the +current state (e.g. number of iterators, the extent of each iterator, the compute_at locations ...). +During the schedule search process, the loop structure can provide search policy with necessary +information on how to manipulate the current state. +The transform history is a sequence of `TransformStep` which will finally be mapped to TVM schedule +primitives. The steps can also be used for the serialization of a state. + +The LoopState can be seen as a lightweight loop structure IR specifically for schedule search. +We don't use the existing TVM IR but to extend a new structure on it is because: +1. We want fast incremental change to the loop structures. The search policy needs to get the +immediate loop structures update rather than after TVM lowering; +2. We want serializable transform history for replay, backtracking, and mutation; +3. We may create some macro schedule primitives that represent the combination of several +TVM schedule primitives. + +When the search is complete, we will lower the state to TVM IR with TVM's schedule primitives. +Since we share a lot of common objects during search, the transformation is implemented in +copy on write style. All objects are immutable, which is similar to TVM IR. +""" + +import tvm._ffi +from tvm.te.tensor import Operation, Tensor +from tvm.runtime import Object +from . import _ffi_api + + +@tvm._ffi.register_object("ansor.Iterator") +class Iterator(Object): + """ A loop iterator structure. """ + + +@tvm._ffi.register_object("ansor.Stage") +class Stage(Object): + """ A stage in the compute declaration. Similar to tvm.te.schedule.Stage. """ + + +@tvm._ffi.register_object("ansor.State") +class StateObject(Object): + """ The internal State object """ + def __eq__(self, other): + return _ffi_api.StateEqual(self, other) + + +class State: + """ + A state in the search process. It consists of the current loop structure + and a list of transformation steps used to construct it. + + Each State corresponds to a specific schedule for its ComputeDAG. + + Parameters + ---------- + state_object : StateObject + The StateObject corresponding to C++ internal State object. + dag : ComputeDAG + The original ComputeDAG of this State. + + Notes + ----- + This is a wrapper class of StateObject to deal with copy-on-write property + """ + def __init__(self, state_object, dag): + self.state_object = state_object + self.compute_dag = dag + + self.stages_cache = None # A list to cache all stages Review comment: Hey I think I got confused too with this cache. Would love to hear elaboration :-) What is this cache used for? If this is not performance critical, IMHO in python we don't need a cache; if it is perf critical, we should deal with it in CXX if possible. ---------------------------------------------------------------- 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: [email protected]
