| /* 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 "llvm/ADT/ArrayRef.h" |
| #include "llvm/ADT/STLExtras.h" |
| #include "llvm/ADT/SmallSet.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/BlockAndValueMapping.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/TypeUtilities.h" // from @llvm-project |
| #include "mlir/Pass/Pass.h" // from @llvm-project |
| #include "mlir/Support/LogicalResult.h" // from @llvm-project |
| #include "mlir/Transforms/GreedyPatternRewriteDriver.h" // from @llvm-project |
| #include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h" |
| |
| namespace mlir { |
| namespace TFL { |
| namespace { |
| |
| // Module pass to optimize TensorFlow functional ops. |
| struct OptimizeFunctionalOpsPass |
| : public PassWrapper<OptimizeFunctionalOpsPass, OperationPass<ModuleOp>> { |
| MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(OptimizeFunctionalOpsPass) |
| |
| 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-optimize-functional-ops"; |
| } |
| StringRef getDescription() const final { |
| // This is a brief description of the pass. |
| return "Optimize TensorFlow functional ops"; |
| } |
| }; |
| |
| // Updates function return type of the given functions to match the terminator |
| // op operands' types. |
| // |
| // Requires the function has exactly one block. |
| void UpdateFuncType(func::FuncOp func) { |
| Operation* terminator = func.front().getTerminator(); |
| auto return_types = llvm::to_vector<4>(terminator->getOperandTypes()); |
| |
| FunctionType func_type = func.getFunctionType(); |
| if (llvm::makeArrayRef(return_types) == func_type.getResults()) return; |
| |
| auto updated_type = |
| FunctionType::get(func.getContext(), func_type.getInputs(), return_types); |
| func.setType(updated_type); |
| } |
| |
| // TODO(jpienaar): Remove when recursive side-effect modeling is added. |
| bool IsSideEffectFree(func::FuncOp func) { |
| return !func.getBody() |
| .walk([&](Operation* op) { |
| if (!MemoryEffectOpInterface::hasNoEffect(op) && |
| !op->hasTrait<OpTrait::IsTerminator>()) |
| return WalkResult::interrupt(); |
| return WalkResult::advance(); |
| }) |
| .wasInterrupted(); |
| } |
| |
| // Folds TensorFlow If op with constant conditional operand by inlining the |
| // function body based on the conditional value. |
| class FoldIfOp : public OpRewritePattern<TF::IfOp> { |
| public: |
| explicit FoldIfOp(MLIRContext* context) |
| : OpRewritePattern<TF::IfOp>(context) {} |
| |
| LogicalResult matchAndRewrite(TF::IfOp op, |
| PatternRewriter& rewriter) const override { |
| // This pattern is restricted to if ops in functions with exactly one block |
| // and therefore one terminator op. So, that function return type can be |
| // updated if operands' shapes change after inlining. Without this |
| // restriction, it would require tensor cast ops. |
| func::FuncOp parent_op = op->getParentOfType<func::FuncOp>(); |
| if (!llvm::hasSingleElement(parent_op)) return failure(); |
| |
| // Find the then and else branch functions. |
| func::FuncOp then_func = op.then_function(); |
| func::FuncOp else_func = op.else_function(); |
| |
| // If the If has no uses and its functions are side-effect free, then |
| // remove. |
| // TODO(jpienaar): Remove once recusive side-effects are supported. |
| if (op.use_empty() && |
| (op.is_stateless() || |
| (IsSideEffectFree(then_func) && IsSideEffectFree(else_func)))) { |
| rewriter.eraseOp(op.getOperation()); |
| return success(); |
| } |
| |
| // Extract the constant cond value. |
| DenseElementsAttr cond; |
| if (!matchPattern(op.cond(), m_Constant(&cond))) return failure(); |
| |
| // TODO(hinsu): Handle constants that are not scalar booleans. |
| auto cond_type = cond.getType().dyn_cast<RankedTensorType>(); |
| if (!cond_type || !cond_type.getShape().equals({}) || |
| !cond_type.getElementType().isInteger(/*width=*/1)) |
| return failure(); |
| |
| // Identify the branch to inline. |
| bool cond_value = (*cond.value_begin<APInt>()).getSExtValue(); |
| func::FuncOp func = cond_value ? then_func : else_func; |
| |
| // Make sure that the function has exactly one block to simplify inlining. |
| // TFLite doesn't use control flow with blocks so functions with more than |
| // one blocks are not encountered in practice. |
| if (!llvm::hasSingleElement(func)) return failure(); |
| |
| BlockAndValueMapping mapper; |
| for (int i = 0, e = func.getNumArguments(); i != e; ++i) |
| mapper.map(func.getArgument(i), op.getOperand(i + 1)); |
| |
| llvm::SmallVector<Value, 4> updated_results; |
| for (auto& op_to_inline : func.front()) { |
| // If this is a terminator, identify the values to use to replace the |
| // original If op. |
| if (op_to_inline.hasTrait<OpTrait::IsTerminator>()) { |
| updated_results.reserve(op_to_inline.getNumOperands()); |
| for (Value operand : op_to_inline.getOperands()) |
| updated_results.push_back(mapper.lookup(operand)); |
| break; |
| } |
| |
| // Otherwise, clone the op here. |
| rewriter.clone(op_to_inline, mapper); |
| } |
| rewriter.replaceOp(op, updated_results); |
| |
| // Here, shapes of the updated_results may not match the original values. If |
| // any of the values are operands of the terminator op, then the function |
| // return type should be updated. |
| UpdateFuncType(parent_op); |
| |
| return success(); |
| } |
| }; |
| |
| void OptimizeFunctionalOpsPass::runOnOperation() { |
| RewritePatternSet patterns(&getContext()); |
| |
| patterns.add<FoldIfOp>(&getContext()); |
| |
| ModuleOp module = getOperation(); |
| (void)applyPatternsAndFoldGreedily(module, std::move(patterns)); |
| } |
| |
| PassRegistration<OptimizeFunctionalOpsPass> pass; |
| } // namespace |
| |
| std::unique_ptr<OperationPass<ModuleOp>> CreateOptimizeFunctionalOpsPass() { |
| return std::make_unique<OptimizeFunctionalOpsPass>(); |
| } |
| |
| } // namespace TFL |
| } // namespace mlir |