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[CINN] Add ReductionFactoring rule (PaddlePaddle#57569)
Add ReductionFactoring rule
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paddle/cinn/auto_schedule/search_space/auto_gen_rule/reduction_factoring.cc
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| // Copyright (c) 2023 CINN Authors. All Rights Reserved. | ||
| // | ||
| // Licensed 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. | ||
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| #include "paddle/cinn/auto_schedule/search_space/auto_gen_rule/reduction_factoring.h" | ||
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| #include <glog/logging.h> | ||
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| #include "paddle/cinn/auto_schedule/analysis/analyze_ir.h" | ||
| #include "paddle/cinn/ir/ir_printer.h" | ||
| #include "paddle/cinn/ir/schedule/ir_schedule.h" | ||
| #include "paddle/cinn/ir/schedule/ir_schedule_util.h" | ||
| #include "paddle/cinn/ir/tensor.h" | ||
| #include "paddle/cinn/ir/utils/ir_copy.h" | ||
| #include "paddle/cinn/ir/utils/ir_nodes_collector.h" | ||
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| namespace cinn { | ||
| namespace auto_schedule { | ||
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| bool ReductionFactoring::CanApply(const std::string& block_name, | ||
| ir::IRSchedule* ir_schedule) const { | ||
| ir::Expr block_expr = ir_schedule->GetBlock(block_name); | ||
| ir::ScheduleBlockRealize* block_realize = | ||
| block_expr.As<ir::ScheduleBlockRealize>(); | ||
| CHECK_NOTNULL(block_realize); | ||
| ir::ScheduleBlock* sch_block = | ||
| block_realize->schedule_block.As<ir::ScheduleBlock>(); | ||
| CHECK_NOTNULL(sch_block); | ||
| AnalyzeScheduleBlockReadWriteBuffer(sch_block); | ||
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| // 1. The block must have write buffer | ||
| if (sch_block->write_buffers.empty()) { | ||
| return false; | ||
| } | ||
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| // 2. The block must have at least one reduce axis | ||
| const std::vector<ir::Var>& iter_vars = sch_block->iter_vars; | ||
| bool find_reduce_axis = false; | ||
| for (int i = 0; i < iter_vars.size(); ++i) { | ||
| if (iter_vars[i]->is_reduce_axis) { | ||
| find_reduce_axis = true; | ||
| break; | ||
| } | ||
| } | ||
| if (!find_reduce_axis) { | ||
| return false; | ||
| } | ||
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| // 3. Each loop's body only contains one sub loop or block, except reduce_init | ||
| // block | ||
| std::vector<ir::Expr> loops = ir_schedule->GetLoops(block_name); | ||
| for (const ir::Expr& loop : loops) { | ||
| const ir::Expr& body = loop.As<ir::For>()->body; | ||
| if (body.As<ir::Block>()) { | ||
| if (body.As<ir::Block>()->stmts.size() == 1) { | ||
| if (body.As<ir::Block>()->stmts[0].As<ir::For>() == nullptr && | ||
| body.As<ir::Block>()->stmts[0].As<ir::ScheduleBlockRealize>() == | ||
| nullptr) { | ||
| return false; | ||
| } | ||
| } else if (body.As<ir::Block>()->stmts.size() == 2) { | ||
| if (body.As<ir::Block>()->stmts[0].As<ir::ScheduleBlockRealize>() == | ||
| nullptr || | ||
| !ir::IsReduceInitTensorName( | ||
| GetBlockName(body.As<ir::Block>()->stmts[0]))) { | ||
| return false; | ||
| } | ||
| if (body.As<ir::Block>()->stmts[1].As<ir::For>() == nullptr && | ||
| body.As<ir::Block>()->stmts[1].As<ir::ScheduleBlockRealize>() == | ||
| nullptr) { | ||
| return false; | ||
| } | ||
| } else { | ||
| return false; | ||
| } | ||
| } else if (body.As<ir::For>() || body.As<ir::ScheduleBlockRealize>()) { | ||
| continue; | ||
| } else { | ||
| return false; | ||
| } | ||
| } | ||
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| return true; | ||
| } | ||
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| RuleApplyType ReductionFactoring::AnalyseApplyType( | ||
| SearchState state, const std::string& block_name) const { | ||
| return this->CanApply(block_name, &(state->ir_schedule)) | ||
| ? RuleApplyType::kApply | ||
| : RuleApplyType::kCannotApply; | ||
| } | ||
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| std::vector<SearchState> ReductionFactoring::ApplyOnBlock( | ||
| SearchState state, const std::string& block_name) { | ||
| SearchState new_state = state.Copy(); | ||
| Apply(block_name, &(new_state->ir_schedule)); | ||
| return {new_state}; | ||
| } | ||
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| void ReductionFactoring::Apply(const std::string& block_name, | ||
| ir::IRSchedule* ir_schedule) { | ||
| ir::Expr block = ir_schedule->GetBlock(block_name); | ||
| std::vector<ir::Expr> all_loops = ir_schedule->GetLoops(block_name); | ||
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| std::vector<ir::Expr> new_loop_order; | ||
| size_t num_spatial_loops = 0; | ||
| size_t num_reduction_loops = 0; | ||
| // 1. Add all spatial loops | ||
| std::unordered_set<std::string> reduce_loop_var_names = | ||
| GetReduceLoopVarNames(block); | ||
| for (const ir::Expr& expr : all_loops) { | ||
| if (reduce_loop_var_names.count(expr.As<ir::For>()->loop_var->name) == 0) { | ||
| new_loop_order.push_back(expr); | ||
| ++num_spatial_loops; | ||
| } | ||
| } | ||
| // 2. Add all reduction loops | ||
| for (const ir::Expr& expr : all_loops) { | ||
| if (reduce_loop_var_names.count(expr.As<ir::For>()->loop_var->name) > 0) { | ||
| new_loop_order.push_back(expr); | ||
| ++num_reduction_loops; | ||
| } | ||
| } | ||
| if (num_reduction_loops == 0) { | ||
| return; | ||
| } | ||
| // 3. Reorder if new_loop_order differs from the original order | ||
| CHECK_EQ(all_loops.size(), new_loop_order.size()); | ||
| for (int i = 0; i < all_loops.size(); ++i) { | ||
| if (all_loops[i].As<ir::For>()->loop_var->name != | ||
| new_loop_order[i].As<ir::For>()->loop_var->name) { | ||
| ir_schedule->Reorder(new_loop_order); | ||
| break; | ||
| } | ||
| } | ||
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| // TODO(BiynXu): After implementing the factorize_reduction schedule | ||
| // primitive, restore the following annotations. The factorize_reduction | ||
| // schedule primitive needs to support complex subscripts to support pre | ||
| // schedule transformations. | ||
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| // // 4. Fuse all reduction loops | ||
| // ir::Expr fused_reduce_loop; | ||
| // if (num_reduction_loops > 1) { | ||
| // std::vector<int> reduction_loop_indices; | ||
| // for (int i = num_spatial_loops - 1; i < all_loops.size(); ++i) { | ||
| // reduction_loop_indices.push_back(i); | ||
| // } | ||
| // CHECK_EQ(reduction_loop_indices.size(), num_reduction_loops); | ||
| // fused_reduce_loop = ir_schedule->Fuse(block_name, | ||
| // reduction_loop_indices); | ||
| // } else { | ||
| // all_loops = ir_schedule->GetLoops(block_name); | ||
| // fused_reduce_loop = all_loops.back(); | ||
| // } | ||
| // // 5. Split the reduction loop into 2 part | ||
| // int factor = 1; | ||
| // int extent = ir::GetLoopExtent(fused_reduce_loop); | ||
| // for (int i = ceil(sqrt(extent)); i >= 1; --i) { | ||
| // if (extent % i == 0) { | ||
| // factor = i; | ||
| // break; | ||
| // } | ||
| // } | ||
| // std::vector<cinn::ir::Expr> splited_reduction_loops = | ||
| // ir_schedule->Split(fused_reduce_loop, {-1, factor}); | ||
| // // Apply FactorizeReduction | ||
| // LOG(INFO) << "before FactorizeReduction: " << | ||
| // ir_schedule->GetModule().GetExprs()[0]; | ||
| // ir_schedule->FactorizeReduction(splited_reduction_loops[0], | ||
| // num_spatial_loops); | ||
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| // Apply rfactor | ||
| all_loops = ir_schedule->GetLoops(block_name); | ||
| ir_schedule->Rfactor(all_loops[num_spatial_loops], num_spatial_loops); | ||
| } | ||
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| } // namespace auto_schedule | ||
| } // namespace cinn |
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