| /* Copyright 2021 The TensorFlow 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. |
| ==============================================================================*/ |
| |
| #include <memory> |
| #include <string> |
| |
| #include "absl/strings/str_cat.h" |
| #include "llvm/ADT/ArrayRef.h" |
| #include "llvm/ADT/DenseMap.h" |
| #include "llvm/ADT/DenseSet.h" |
| #include "llvm/ADT/SmallVector.h" |
| #include "llvm/ADT/StringRef.h" |
| #include "llvm/Support/Casting.h" |
| #include "mlir/Dialect/Func/IR/FuncOps.h" // from @llvm-project |
| #include "mlir/IR/Builders.h" // from @llvm-project |
| #include "mlir/IR/BuiltinOps.h" // from @llvm-project |
| #include "mlir/IR/BuiltinTypes.h" // from @llvm-project |
| #include "mlir/IR/MLIRContext.h" // from @llvm-project |
| #include "mlir/Pass/Pass.h" // from @llvm-project |
| #include "mlir/Pass/PassRegistry.h" // from @llvm-project |
| #include "mlir/Support/LLVM.h" // from @llvm-project |
| #include "tensorflow/compiler/mlir/lite/experimental/tac/common/subgraph.h" |
| #include "tensorflow/compiler/mlir/lite/experimental/tac/transforms/passes.h" |
| #include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h" |
| |
| namespace mlir { |
| namespace TFL { |
| namespace tac { |
| namespace { |
| |
| // This pass is used to fold tfl.const ops to each subgraph (func::FuncOp): |
| // See the example below: |
| // |
| // In main: |
| // %0 = tfl.const... |
| // %1 = tfl.const... |
| // %2 = call func_1(..., %0,...) |
| // %3 = call func_2(..., %0, ..., %1...) |
| // ... |
| // |
| // Then those consts will be copied into each function and replace their usage. |
| // func_1: |
| // %0 = tfl.const... |
| // func_2: |
| // %0 = tfl.const... |
| // %1 = tfl.const... |
| class FoldConstantsToSubgraphPass |
| : public mlir::PassWrapper<FoldConstantsToSubgraphPass, |
| mlir::OperationPass<ModuleOp>> { |
| public: |
| MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(FoldConstantsToSubgraphPass) |
| |
| llvm::StringRef getArgument() const final { |
| return "tfl-fold-constants-to-subgraph"; |
| } |
| llvm::StringRef getDescription() const final { |
| return "Fold constants into each subgraph."; |
| } |
| FoldConstantsToSubgraphPass() = default; |
| FoldConstantsToSubgraphPass(const FoldConstantsToSubgraphPass& other) { |
| this->fold_all_constants_flag_ = other.fold_all_constants_flag_; |
| } |
| explicit FoldConstantsToSubgraphPass(bool fold_all_constants) { |
| fold_all_constants_flag_ = fold_all_constants; |
| } |
| |
| private: |
| void runOnOperation() override; |
| |
| Option<bool> fold_all_constants_flag_{ |
| *this, "fold-all-constants", |
| llvm::cl::desc("Whether to fold all constants or just i32."), |
| llvm::cl::init(false)}; |
| }; |
| |
| void CopyConstantIntoFunc(int argument_index, Operation* const_op, |
| func::FuncOp func) { |
| assert((llvm::isa<TFL::ConstOp, TFL::QConstOp>(const_op)) && |
| "Expect QConst or Const op."); |
| OpBuilder builder(func.getBody()); |
| auto cloned_const_op = const_op->clone(); |
| cloned_const_op->setLoc(func.getBody().getLoc()); |
| builder.insert(cloned_const_op); |
| // Rewire the usage. |
| func.getArgument(argument_index) |
| .replaceAllUsesWith(cloned_const_op->getResult(0)); |
| } |
| |
| bool IsConstOrQConstInt(Operation* op) { |
| if (!llvm::isa<TFL::ConstOp, TFL::QConstOp>(op)) return false; |
| |
| if (auto const_op = dyn_cast_or_null<TFL::ConstOp>(op)) { |
| // ConstOp path. |
| auto type = const_op.getType() |
| .dyn_cast_or_null<RankedTensorType>() |
| .getElementType(); |
| if (!type.isInteger(32) && !type.isInteger(64)) return false; |
| } else { |
| // QConstOp path. |
| auto qconst_op = dyn_cast<TFL::QConstOp>(op); |
| auto type = |
| quant::QuantizedType::getQuantizedElementType(qconst_op.getType()); |
| if (type.getStorageTypeIntegralWidth() != 32) { |
| return false; |
| } |
| } |
| return true; |
| } |
| |
| void FoldConstantsToSubgraphPass::runOnOperation() { |
| auto module = getOperation(); |
| |
| for (auto fn : module.getOps<func::FuncOp>()) { |
| fn.walk([&](Operation* op) { |
| if (!llvm::isa<TFL::ConstOp, TFL::QConstOp>(op)) return; |
| |
| // We only fold int32/int64 for Const and i32 for QConst if not specify |
| // all constants flag. (Since they're more like "configs" or i32 biases.) |
| // We will fold every const ops (and q_const ops) if we speicfy the |
| // fold_all_constants_flag. |
| if (!fold_all_constants_flag_) { |
| if (!IsConstOrQConstInt(op)) return; |
| } |
| |
| for (auto consumer : op->getResult(0).getUsers()) { |
| auto consumer_call = llvm::dyn_cast_or_null<func::CallOp>(consumer); |
| |
| if (!consumer_call) continue; |
| |
| auto function_name = consumer_call.getCallee(); |
| |
| // Locate the argument position of the use. |
| int argument_index = -1; |
| for (int i = 0; i < consumer_call.getNumOperands(); ++i) { |
| if (consumer_call.getOperand(i) == op->getResult(0)) { |
| argument_index = i; |
| break; |
| } |
| } |
| |
| // Copy the const into the consumer func and replace their usages. |
| func::FuncOp func = module.lookupSymbol<func::FuncOp>(function_name); |
| |
| CopyConstantIntoFunc(argument_index, op, func); |
| } |
| }); |
| } |
| } |
| |
| } // namespace |
| |
| std::unique_ptr<OperationPass<ModuleOp>> CreateFoldConstantsToSubgraphPass( |
| bool fold_all_constants) { |
| return std::make_unique<FoldConstantsToSubgraphPass>(fold_all_constants); |
| } |
| |
| static PassRegistration<FoldConstantsToSubgraphPass> pass; |
| |
| } // namespace tac |
| } // namespace TFL |
| } // namespace mlir |