masahi commented on code in PR #11088: URL: https://github.com/apache/tvm/pull/11088#discussion_r856858011
########## src/meta_schedule/postproc/rewrite_tensorize.cc: ########## @@ -0,0 +1,104 @@ +/* + * 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. + */ +#include <tvm/runtime/container/base.h> + +#include <algorithm> + +#include "../utils.h" + +namespace tvm { +namespace meta_schedule { + +using tir::BlockRV; +using tir::LoopRV; + +void ApplyTensorization(const tir::Schedule& sch, const String& func_name, + const tir::PrimFuncNode* func, bool vectorize_init_loop) { + std::vector<std::pair<std::string, std::function<void(tir::BlockRV)>>> jobs; + + tir::PostOrderVisit(func->body, [=, &jobs](const ObjectRef& obj) { + if (const auto* block = obj.as<tir::BlockNode>()) { + tir::StmtSRef block_sref = sch->GetSRef(block); + if (Optional<String> intrin_name = + tir::GetAnn<String>(block_sref, tir::attr::meta_schedule_auto_tensorize)) { + std::string block_name = block_sref->StmtAs<tir::BlockNode>()->name_hint; + if (block_name.find("init") == std::string::npos) { + jobs.emplace_back(block_name, [sch, intrin_name](tir::BlockRV block) { + try { + sch->Tensorize(block, intrin_name.value()); + } catch (const std::exception& e) { + LOG(WARNING) << "Tensorize failed with error " << e.what(); + } + }); + } else if (vectorize_init_loop) { + jobs.emplace_back(block_name, [sch](tir::BlockRV block) { + Array<BlockRV> child_blocks = sch->GetChildBlocks(block); + ICHECK(child_blocks.size() == 1); + Array<LoopRV> init_loops = sch->GetLoops(child_blocks[0]); + ICHECK(init_loops.size() == 1); + sch->Vectorize(init_loops[0]); Review Comment: > Quite interesting.. So here the case is, on one hand we don’t want the block being annotated by rule ParallelVectorizeUnroll, but on the other hand we do want its init block to be vectorized after the decomposition. Am I right? Exactly. > how does the vectorization in RewriteTensorize bypass the compact dataflow issue BTW? That's a great question! Until recently, vectorization of the init loop after `DecomposeReduction` was rejected by the compact dataflow check. I brought this topic to @Hzfengsy and the team came up with a relaxation of the constraint that allows vectorizing init loop. This is the PR https://github.com/apache/tvm/pull/10705 Yeah, the ideally all outer loop parallelizations and inner loop vectorization can be done by one pass of `ParallelVectorizeUnroll`, meaning we run it after `DecomposeReduction`. Currently outer loop parallelization after `DecomposeReduction would be rejected by the compact dataflow check, but I think this is too restrictive. -- 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. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
