ScalarizeFunctionResult.cpp

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//===- ScalarizeFunctionResult.cpp - Scalarize tensor returns ---------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
 
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/Dialect/Tensor/Transforms/Passes.h"
#include "mlir/IR/BuiltinOps.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/SymbolTable.h"
#include "llvm/ADT/DenseMap.h"
 
namespace mlir {
namespace tensor {
#define GEN_PASS_DEF_SCALARIZESINGLEELEMENTTENSORRETURNPASS
#include "mlir/Dialect/Tensor/Transforms/Passes.h.inc"
} // namespace tensor
} // namespace mlir
 
using namespace mlir;
 
namespace {
 
// Analysis state used by the memoized DFS below. It classifies whether a
// candidate private function can be scalarized. The rewrite only consumes the
// functions classified as `rewritable`.
enum class ScalarizationState {
  // No DFS classification has been computed for this function yet.
  unknown,
  // The function is currently on the recursive DFS stack. Re-entering this
  // state means a cycle was found, which this pass conservatively blocks.
  visiting,
  // A symbol user or a candidate caller blocks the rewrite.
  blocked,
  // The function is locally eligible and all transitive users considered by
  // this pass can be updated consistently.
  rewritable
};
 
// Info analyzed to decide scalarizing a locally eligible function: a private
// definition with exactly one statically-shaped ranked tensor result containing
// one element. Functions not meeting these criteria are not represented here,
// although they may still appear in the broader module analysis as callers or
// blockers.
struct ScalarizableFunctionInfo {
  RankedTensorType tensorType;
  SmallVector<func::ReturnOp> returnOps;
};
 
// Returns per-function scalarization info when this function is locally
// eligible, i.e. it is a private definition with one statically-shaped ranked
// tensor result containing exactly one element.
static FailureOr<ScalarizableFunctionInfo>
getScalarizableFunctionInfoIfEligible(func::FuncOp func) {
  if (func.isDeclaration() || !func.isPrivate())
    return failure();
 
  FunctionType functionType = func.getFunctionType();
  if (functionType.getNumResults() != 1)
    return failure();
 
  auto tensorType = dyn_cast<RankedTensorType>(functionType.getResult(0));
  if (!tensorType || !tensorType.hasStaticShape() ||
      tensorType.getNumElements() != 1)
    return failure();
 
  ScalarizableFunctionInfo sfi{tensorType, {}};
  for (Block &block : func.getBody()) {
    auto returnOp = dyn_cast<func::ReturnOp>(block.getTerminator());
    if (returnOp)
      sfi.returnOps.push_back(returnOp);
  }
 
  // While FuncOp is guaranteed to contain terminator ops, there is no guarantee
  // that it will contain ReturnOp(s). Hence the check.
  if (sfi.returnOps.empty())
    return failure();
  return sfi;
}
 
struct ScalarizationAnalysis {
  explicit ScalarizationAnalysis(SymbolUserMap &userMap) : userMap(userMap) {}
 
  SymbolUserMap &userMap;
  DenseMap<func::FuncOp, ScalarizableFunctionInfo> candidateInfos;
  DenseMap<func::FuncOp, SmallVector<func::CallOp>> callUsers;
  DenseMap<func::FuncOp, ScalarizationState> states;
  // DFS completion order for rewritable functions. The rewrite phase walks
  // this list in reverse so callees are rewritten before candidate callers.
  SmallVector<func::FuncOp> rewriteOrder;
};
 
// Classifies a locally eligible candidate using memoized DFS through candidate
// callers. Local eligibility must be cached for the entire module first.
static ScalarizationState
computeScalarizationState(func::FuncOp func, ScalarizationAnalysis &analysis) {
  auto [it, inserted] =
      analysis.states.try_emplace(func, ScalarizationState::unknown);
  if (!inserted) {
    // Conservatively reject recursive cycles instead of reasoning about SCCs.
    if (it->second == ScalarizationState::visiting)
      return ScalarizationState::blocked;
    return it->second;
  }
 
  // Starting from one locally-eligible private function, walk its symbol users
  // upward through private callers. Memoization avoids re-traversing the same
  // subgraph from the outer linear scan, and early blocking truncates the DFS
  // when an unsupported symbol user or a blocked candidate caller is found.
  // Non-candidate callers, including public functions, stop recursion without
  // blocking callsite adaptation.
  auto setBlocked = [&] {
    analysis.states[func] = ScalarizationState::blocked;
    return ScalarizationState::blocked;
  };
  auto setRewritable = [&] {
    analysis.states[func] = ScalarizationState::rewritable;
    analysis.rewriteOrder.push_back(func);
    return ScalarizationState::rewritable;
  };
 
  analysis.states[func] = ScalarizationState::visiting;
 
  SmallVector<func::CallOp> directCallUsers;
  for (Operation *user : analysis.userMap.getUsers(func.getOperation())) {
    auto directCall = dyn_cast<func::CallOp>(user);
    // Non-call symbol uses, such as func.constant, prevent updating all users
    // consistently, so the current function stays blocked.
    if (!directCall)
      return setBlocked();
    directCallUsers.push_back(directCall);
 
    func::FuncOp caller = directCall->getParentOfType<func::FuncOp>();
    // Cache every direct call, but recurse only into candidate callers. Other
    // callers, including public functions, keep their signatures and existing
    // tensor uses through reboxing. Calls outside any func.func stop here too.
    if (!caller || !analysis.candidateInfos.contains(caller))
      continue;
 
    if (computeScalarizationState(caller, analysis) !=
        ScalarizationState::rewritable)
      return setBlocked();
  }
 
  analysis.callUsers.try_emplace(func, std::move(directCallUsers));
  return setRewritable();
}
 
// Builds the module-level analysis state used by the rewrite phase.
static void computeScalarizationAnalysis(ModuleOp module,
                                         ScalarizationAnalysis &analysis) {
  // Cache local eligibility for all direct functions before starting DFS.
  for (func::FuncOp func : module.getOps<func::FuncOp>()) {
    FailureOr<ScalarizableFunctionInfo> sfi =
        getScalarizableFunctionInfoIfEligible(func);
    if (succeeded(sfi))
      analysis.candidateInfos.try_emplace(func, std::move(*sfi));
  }
  // Then run the memoized DFS for candidate roots.
  for (func::FuncOp func : module.getOps<func::FuncOp>())
    if (analysis.candidateInfos.contains(func))
      (void)computeScalarizationState(func, analysis);
}
 
// Rewrites one function that has already been proven rewritable and updates
// the direct call users cached before any IR mutation started.
static void rewriteScalarizableFunction(func::FuncOp func,
                                        const ScalarizableFunctionInfo &sfi,
                                        ArrayRef<func::CallOp> directCalls,
                                        RewriterBase &rewriter) {
  OpBuilder::InsertionGuard guard(rewriter);
  // Scalarize the unique element before each return.
  RankedTensorType tensorType = sfi.tensorType;
  SmallVector<Value> zeroIndices;
  if (tensorType.getRank() != 0) {
    rewriter.setInsertionPointToStart(&func.getBody().front());
    Value zero = arith::ConstantIndexOp::create(rewriter, func.getLoc(), 0);
    zeroIndices.assign(tensorType.getRank(), zero);
  }
 
  Type scalarType = tensorType.getElementType();
  for (func::ReturnOp funcReturn : sfi.returnOps) {
    assert(funcReturn.getNumOperands() == 1 &&
           "func.return must have exactly one operand");
    assert(funcReturn.getOperand(0).getType() == tensorType &&
           "func.return operand type must match the function result type");
    rewriter.setInsertionPoint(funcReturn);
    Value scalar = rewriter.createOrFold<tensor::ExtractOp>(
        funcReturn.getLoc(), funcReturn.getOperand(0), zeroIndices);
    rewriter.replaceOpWithNewOp<func::ReturnOp>(funcReturn, scalar);
  }
 
  FunctionType functionType = func.getFunctionType();
  // Update the function type:
  // This is a 1-result to 1-result type replacement, so the existing result
  // attribute dictionary remains attached to result #0 without reordering,
  // hence the rewrite is done directly without function_interface methods.
  func.setType(FunctionType::get(func.getContext(), functionType.getInputs(),
                                 TypeRange{scalarType}));
  // Fix direct call users that were recorded during analysis.
  for (func::CallOp directCall : directCalls) {
    rewriter.setInsertionPoint(directCall);
    func::CallOp newDirectCall = func::CallOp::create(
        rewriter, directCall.getLoc(), func, directCall.getOperands());
    newDirectCall->setDiscardableAttrs(
        directCall->getDiscardableAttrDictionary());
 
    if (!directCall.getResult(0).use_empty()) {
      Value wrappedResult = tensor::FromElementsOp::create(
          rewriter, directCall.getLoc(), tensorType,
          ValueRange{newDirectCall.getResult(0)});
      rewriter.replaceOp(directCall, wrappedResult);
    } else {
      rewriter.eraseOp(directCall);
    }
  }
}
 
/// Drives the complete module-level transform: analyze the original module,
/// determine which private functions can be scalarized safely, then
/// rewrite only that precomputed set.
///
/// BEFORE (both callee and private caller rewritten)
///   private callee(x : tensor<1xT>) -> tensor<1xT> { return x }
///   private caller(x : tensor<1xT>) -> tensor<1xT> {
///     y = call callee(x)
///     return y
///   }
///
/// AFTER
///   private callee(x : tensor<1xT>) -> T {
///     return tensor.extract x[0]
///   }
///   private caller(x : tensor<1xT>) -> T {
///     y = call callee(x)
///     return y
///   }
///
/// BEFORE (callee rewritten, unchanged private caller reboxes)
///   private callee(x : tensor<1xT>) -> tensor<1xT> { return x }
///   private caller(x : tensor<1xT>, z : tensor<1xT>) -> tensor<1xT> {
///     y = call callee(x)
///     r = tensor_op(y, z)
///     return r
///   }
///
/// AFTER
///   private callee(x : tensor<1xT>) -> T {
///     return tensor.extract x[0]
///   }
///   private caller(x : tensor<1xT>, z : tensor<1xT>) -> tensor<1xT> {
///     y = call callee(x)
///     y_boxed = tensor.from_elements y
///     r = tensor_op(y_boxed, z)
///     return r
///   }
static LogicalResult
ScalarizeSingleElementTensorReturns(ModuleOp module, RewriterBase &rewriter) {
  // The transform depends on module-scoped `SymbolUserMap` state and on a
  // precomputed DFS result, so it runs as a direct module analysis + rewrite
  // instead of exposing a pattern-testing / transform-dialect pattern path.
  SymbolTableCollection symbolTable;
  // Take a snapshot of symbol users for the original module. This is safe
  // because it is consulted only during `computeScalarizationAnalysis`, before
  // rewriting starts, and this pass invocation has exclusive access to the
  // current module.
  SymbolUserMap userMap(symbolTable, module);
  ScalarizationAnalysis analysis(userMap);
  computeScalarizationAnalysis(module, analysis);
 
  for (func::FuncOp func : llvm::reverse(analysis.rewriteOrder)) {
    const ScalarizableFunctionInfo &sfi =
        analysis.candidateInfos.find(func)->second;
    ArrayRef<func::CallOp> directCalls = analysis.callUsers.find(func)->second;
    rewriteScalarizableFunction(func, sfi, directCalls, rewriter);
  }
 
  return success();
}
 
struct ScalarizeSingleElementTensorReturnPass
    : public tensor::impl::ScalarizeSingleElementTensorReturnPassBase<
          ScalarizeSingleElementTensorReturnPass> {
  using Base::Base;
 
  void runOnOperation() override {
    IRRewriter rewriter(&getContext());
    if (failed(ScalarizeSingleElementTensorReturns(getOperation(), rewriter)))
      signalPassFailure();
  }
};
 
} // namespace