| /* Copyright 2019 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 <vector> |
| |
| #include "llvm/ADT/DenseSet.h" |
| #include "llvm/ADT/StringMap.h" |
| #include "llvm/Support/Casting.h" |
| #include "mlir/Dialect/Func/IR/FuncOps.h" // from @llvm-project |
| #include "mlir/IR/Attributes.h" // from @llvm-project |
| #include "mlir/IR/Block.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/IR/Matchers.h" // from @llvm-project |
| #include "mlir/IR/Operation.h" // from @llvm-project |
| #include "mlir/IR/OperationSupport.h" // from @llvm-project |
| #include "mlir/IR/SymbolTable.h" // from @llvm-project |
| #include "mlir/IR/Types.h" // from @llvm-project |
| #include "mlir/IR/Value.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 "mlir/Support/LogicalResult.h" // from @llvm-project |
| #include "tensorflow/compiler/mlir/lite/ir/tfl_ops.h" |
| #include "tensorflow/compiler/mlir/lite/utils/stateful_ops_utils.h" |
| |
| // Background info: |
| // Currently the model taken to MLIRConverter is frozen (all the variables have |
| // been converted to constants, all the assign ops are gone, etc.). However, |
| // TFLite has these variable tensors semantics. So the variable mapping from TF |
| // to TFLite is actually broken here, we sort of hard-code the variable tensors |
| // based on the actual ops using them, such as unidirectional_sequence_lstm. |
| // |
| // MLIRConverter also benefits from lots of typical compiler optimization like |
| // merging same input values if they're identical. These optimizations are |
| // desirable but not for those TFLite ops which have variable tensors as inputs. |
| // Yes, they have identical input values, but those identical values are |
| // "stateful", their values can change during invocations. |
| // |
| // A typical example is unidirectional_sequence_lstm have two variable tensor |
| // inputs: activation state & cell state. They may have same initial values |
| // (typical zero-initialized), but their values will be changed. So we cannot |
| // just merge those values. |
| // |
| // This pass is more like short-term workaround since we don't have a good |
| // variable representation right now. |
| // |
| // This pass will duplicate input values for those variable tensor inputs. |
| |
| namespace mlir { |
| namespace TFL { |
| namespace { |
| |
| struct SplitMergedOperandsPass |
| : public PassWrapper<SplitMergedOperandsPass, OperationPass<func::FuncOp>> { |
| MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(SplitMergedOperandsPass) |
| |
| void runOnOperation() override; |
| |
| StringRef getArgument() const final { |
| // This is the argument used to refer to the pass in |
| // the textual format (on the commandline for example). |
| return "tfl-split-merged-operands"; |
| } |
| StringRef getDescription() const final { |
| // This is a brief description of the pass. |
| return "Split merged stateful operands for tfl operations."; |
| } |
| }; |
| |
| LogicalResult DuplicateValueIfNeeded(Operation* op, |
| llvm::DenseSet<Value>* values, |
| OpBuilder* builder) { |
| std::vector<int> stateful_operands_index; |
| if (!IsStatefulOp(op, &stateful_operands_index)) return success(); |
| |
| for (int index : stateful_operands_index) { |
| Value operand = op->getOperand(index); |
| auto inserted_value = values->insert(operand).second; |
| if (inserted_value) continue; |
| // We can only clone the constant op at this point. |
| // Since all ops have been legalized to tflite ops, so we only care about |
| // ConstOp or QConstOp or mlir constant op/ |
| Operation* input_op = operand.getDefiningOp(); |
| if (input_op == nullptr) return failure(); |
| |
| Attribute attr; |
| if (!matchPattern(input_op, m_Constant(&attr))) { |
| op->emitError() |
| << "We cannot duplicate the value since it's not constant.\n"; |
| return failure(); |
| } |
| builder->setInsertionPoint(op); |
| Operation* duplicated_input_op = builder->clone(*input_op); |
| |
| // Rewire the inputs. |
| op->setOperand(index, duplicated_input_op->getResult(0)); |
| } |
| return success(); |
| } |
| |
| void SplitMergedOperandsPass::runOnOperation() { |
| llvm::DenseSet<Value> stateful_values; |
| auto func = getOperation(); |
| OpBuilder builder(func); |
| for (auto& bb : func.getBody()) { |
| for (auto& op : bb) { |
| if (failed(DuplicateValueIfNeeded(&op, &stateful_values, &builder))) { |
| func.emitError() << "Failed to duplicate values for the stateful op\n"; |
| return signalPassFailure(); |
| } |
| } |
| } |
| } |
| |
| } // namespace |
| |
| /// Creates an instance of the TensorFlow Lite dialect SplitMergedOperands |
| /// pass. |
| std::unique_ptr<OperationPass<func::FuncOp>> CreateSplitMergedOperandsPass() { |
| return std::make_unique<SplitMergedOperandsPass>(); |
| } |
| |
| static PassRegistration<SplitMergedOperandsPass> pass; |
| |
| } // namespace TFL |
| } // namespace mlir |