[mlir][TilingInterface] NFC code changes separated out from introduction of scf::tileUsingSCFForallop.

This patch contains NFC changes that are precursor to the introduction
of `scf::tileUsingSCFForallOp` method.

Author: MaheshRavishankar

mlir/include/mlir/Dialect/SCF/Transforms/TileUsingInterface.h

@@ -60,7 +60,7 @@ struct SCFTilingResult {
   /// of the last op.
   SmallVector<Operation *> tiledOps;
   /// The `scf.for` operations that iterate over the tiles.
-  SmallVector<scf::ForOp> loops;
+  SmallVector<Operation *> loops;
   /// Values to use as replacements for the untiled op. Is the same size as the
   /// number of results of the untiled op.
   SmallVector<Value> replacements;
@@ -160,7 +160,7 @@ struct SCFTileAndFuseResult {
   /// generated operation.
   llvm::SetVector<Operation *> tiledAndFusedOps;
   /// The `scf.for` operations that iterate over the tiles.
-  SmallVector<scf::ForOp> loops;
+  SmallVector<Operation *> loops;
   /// The replacement values to use for the tiled and fused operations.
   llvm::DenseMap<Value, Value> replacements;
 };

mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp

@@ -434,16 +434,12 @@ static LogicalResult applyTilingToAll(
     SmallVector<Operation *> opsToReplace{target};
     llvm::append_range(opsToReplace, tiledResults->fusedProducers);
     for (Operation *toReplace : opsToReplace) {
-      SmallVector<Value> replacements;
-      replacements.reserve(toReplace->getNumResults());
-      for (OpResult res : toReplace->getResults()) {
-        auto it = tiledResults->replacements.find(res);
-        if (it == tiledResults->replacements.end())
-          replacements.push_back(res);
-        else
-          replacements.push_back(it->getSecond());
+      for (OpResult res : toReplace->getResults())
+        if (auto replacement = tiledResults->replacements.lookup(res))
+          rewriter.replaceAllUsesWith(res, replacement);
+      if (toReplace->use_empty()) {
+        rewriter.eraseOp(toReplace);
       }
-      rewriter.replaceOp(toReplace, replacements);
     }
 
     // Report back the relevant handles to the transform op.

mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp

@@ -55,6 +55,30 @@ fillInterchangeVector(ArrayRef<int64_t> interchangeVector,
   return filledVector;
 }
 
+/// Convert a list of ops of type `SrcOpTy` to list of `Operation *`.
+template <typename SrcOpTy>
+static SmallVector<Operation *> getAsOperations(ArrayRef<SrcOpTy> ops) {
+  return llvm::to_vector(
+      llvm::map_range(ops, [](auto op) -> Operation * { return op; }));
+}
+template <typename SrcOpTy>
+static SmallVector<Operation *>
+getAsOperations(const SmallVector<SrcOpTy> &ops) {
+  return getAsOperations(ArrayRef<SrcOpTy>(ops));
+}
+
+/// Convert a list of `Operation *` to a list of `DstOpTy.
+template <typename DstOpTy>
+static SmallVector<DstOpTy> castToTypedOperations(ArrayRef<Operation *> ops) {
+  return llvm::to_vector(
+      llvm::map_range(ops, [](Operation *op) { return cast<DstOpTy>(op); }));
+}
+template <typename DstOpTy>
+static SmallVector<DstOpTy>
+castToTypedOperations(const SmallVector<Operation *> &ops) {
+  return castToTypedOperations<DstOpTy>(ArrayRef<Operation *>(ops));
+}
+
 //===----------------------------------------------------------------------===//
 // tileUsingSCFForOp implementation.
 //===----------------------------------------------------------------------===//
@@ -77,10 +101,9 @@ static bool tileDividesIterationDomain(Range loopRange) {
 /// `tileSize`, i.e., `min(tileSize, range.end() - iv)`.
 static OpFoldResult getBoundedTileSize(OpBuilder &b, Location loc,
                                        Range loopRange, Value iv,
-                                       Value tileSize) {
-  std::optional<int64_t> ts = getConstantIntValue(tileSize);
-  if (ts && ts.value() == 1)
-    return getAsOpFoldResult(tileSize);
+                                       OpFoldResult tileSize) {
+  if (isConstantIntValue(tileSize, 1))
+    return tileSize;
 
   if (tileDividesIterationDomain(
           Range{loopRange.offset, loopRange.size, tileSize}))
@@ -295,8 +318,8 @@ mlir::scf::tileUsingSCFForOp(RewriterBase &rewriter, TilingInterface op,
     tileSizeVector.append(numLoops - tileSizeVector.size(), zero);
   }
 
-  scf::SCFTilingResult tilingResult;
   SmallVector<OpFoldResult> offsets, sizes;
+  SmallVector<scf::ForOp> forLoops;
   {
     // If there is an interchange specified, permute the iteration domain and
     // the tile sizes.
@@ -319,8 +342,8 @@ mlir::scf::tileUsingSCFForOp(RewriterBase &rewriter, TilingInterface op,
     // 3. Materialize an empty loop nest that iterates over the tiles. These
     // loops for now do not return any values even if the original operation has
     // results.
-    tilingResult.loops = generateTileLoopNest(
-        rewriter, op.getLoc(), iterationDomain, tileSizeVector, offsets, sizes);
+    forLoops = generateTileLoopNest(rewriter, op.getLoc(), iterationDomain,
+                                    tileSizeVector, offsets, sizes);
 
     if (!interchangeVector.empty()) {
       auto inversePermutation = invertPermutationVector(interchangeVector);
@@ -330,30 +353,30 @@ mlir::scf::tileUsingSCFForOp(RewriterBase &rewriter, TilingInterface op,
   }
 
   LLVM_DEBUG({
-    if (!tilingResult.loops.empty()) {
+    if (!forLoops.empty()) {
       llvm::dbgs() << "LoopNest shell :\n";
-      tilingResult.loops.front().dump();
+      forLoops.front().dump();
       llvm::dbgs() << "\n";
     }
   });
 
   // 4. Generate the tiled implementation within the inner most loop.
-  if (!tilingResult.loops.empty())
-    rewriter.setInsertionPoint(
-        tilingResult.loops.back().getBody()->getTerminator());
+  if (!forLoops.empty())
+    rewriter.setInsertionPoint(forLoops.back().getBody()->getTerminator());
   FailureOr<TilingResult> tiledImplementation =
       op.getTiledImplementation(rewriter, offsets, sizes);
-  tilingResult.tiledOps.append(tiledImplementation->tiledOps);
+
   if (op->getNumResults() == 0) {
-    // nothing more to do.
-    return tilingResult;
+    return scf::SCFTilingResult{
+        tiledImplementation->tiledOps, getAsOperations(forLoops), {}};
   }
 
   // If loops are empty, the tiled op is used as the replacement for the untiled
   // op.
-  if (tilingResult.loops.empty()) {
-    tilingResult.replacements = tiledImplementation->tiledValues;
-    return tilingResult;
+  if (forLoops.empty()) {
+    return scf::SCFTilingResult{tiledImplementation->tiledOps,
+                                getAsOperations(forLoops),
+                                tiledImplementation->tiledValues};
   }
 
   // 5. Yield all the results of the tiled operation. The surrounding loop
@@ -377,18 +400,18 @@ mlir::scf::tileUsingSCFForOp(RewriterBase &rewriter, TilingInterface op,
                                              destinationTensors)))
     return rewriter.notifyMatchFailure(op, "failed to get destinations");
 
-  tilingResult.replacements = yieldTiledValues(
+  SmallVector<Value> replacements = yieldTiledValues(
       rewriter, destinationTensors, tiledImplementation.value(),
-      resultOffsetsList, resultSizesList, tilingResult.loops);
-
+      resultOffsetsList, resultSizesList, forLoops);
   LLVM_DEBUG({
-    if (!tilingResult.loops.empty()) {
+    if (!forLoops.empty()) {
       llvm::dbgs() << "After tiled implementation :\n";
-      tilingResult.loops.front().dump();
+      forLoops.front().dump();
       llvm::dbgs() << "\n";
     }
   });
-  return tilingResult;
+  return scf::SCFTilingResult{tiledImplementation->tiledOps,
+                              getAsOperations(forLoops), replacements};
 }
 
 FailureOr<scf::SCFReductionTilingResult>
@@ -466,6 +489,7 @@ mlir::scf::tileReductionUsingScf(RewriterBase &b,
   results.mergeOp = mergeOp;
   return results;
 }
+
 //===----------------------------------------------------------------------===//
 // tileConsumerAndFuseProducerGreedilyUsingSCFForOp implementation.
 //===----------------------------------------------------------------------===//
@@ -636,28 +660,31 @@ mlir::scf::tileConsumerAndFuseProducerGreedilyUsingSCFForOp(
   }
 
   // 1. First tile the consumer.
-  scf::SCFTileAndFuseResult tileAndFuseResult;
+  SmallVector<scf::ForOp> forLoops;
+  SetVector<Operation *> fusedProducers, tiledAndFusedOps;
+  DenseMap<Value, Value> replacements;
   llvm::SmallDenseMap<Value, int64_t> yieldedValueToResultNumber;
   {
     FailureOr<scf::SCFTilingResult> tilingResult =
         tileUsingSCFForOp(rewriter, consumer, options.tilingOptions);
     if (failed(tilingResult))
       return rewriter.notifyMatchFailure(consumer, "failed to tile consumer");
     for (auto *tiledOp : tilingResult->tiledOps)
-      tileAndFuseResult.tiledAndFusedOps.insert(tiledOp);
-    tileAndFuseResult.loops = std::move(tilingResult->loops);
-    for (const auto &result : llvm::enumerate(
-             llvm::zip(consumer->getResults(), tilingResult->replacements))) {
-      tileAndFuseResult.replacements[std::get<0>(result.value())] =
-          std::get<1>(result.value());
+      tiledAndFusedOps.insert(tiledOp);
+    forLoops = castToTypedOperations<scf::ForOp>(tilingResult->loops);
+    for (auto [index, origValue, replacement] :
+         llvm::enumerate(consumer->getResults(), tilingResult->replacements)) {
+      replacements[origValue] = replacement;
       yieldedValueToResultNumber[tilingResult->tiledOps.back()->getResult(
-          result.index())] = result.index();
+          index)] = index;
     }
   }
 
   // If there are no loops generated, fusion is immaterial.
-  if (tileAndFuseResult.loops.empty())
-    return tileAndFuseResult;
+  if (forLoops.empty()) {
+    return scf::SCFTileAndFuseResult{fusedProducers, tiledAndFusedOps,
+                                     getAsOperations(forLoops), replacements};
+  }
 
   // 2. Typically, the operands of the tiled operation are slices of the
   //    operands of the untiled operation. These are expressed in IR using
@@ -674,7 +701,7 @@ mlir::scf::tileConsumerAndFuseProducerGreedilyUsingSCFForOp(
   };
 
   std::deque<tensor::ExtractSliceOp> candidates;
-  addCandidateSlices(tileAndFuseResult.tiledAndFusedOps.back(), candidates);
+  addCandidateSlices(tiledAndFusedOps.back(), candidates);
   OpBuilder::InsertionGuard g(rewriter);
   while (!candidates.empty()) {
     // Traverse the slices in BFS fashion.
@@ -684,19 +711,20 @@ mlir::scf::tileConsumerAndFuseProducerGreedilyUsingSCFForOp(
     // The operands of the fused producer might themselved be slices of
     // values produced by operations that implement the `TilingInterface`.
     // Add these operations to the worklist.
-    std::optional<scf::SCFFuseProducerOfSliceResult> fusedProducer =
-        tileAndFuseProducerOfSlice(rewriter, candidateSliceOp,
-                                   tileAndFuseResult.loops);
-    if (!fusedProducer)
+    std::optional<scf::SCFFuseProducerOfSliceResult> fusedResult =
+        tileAndFuseProducerOfSlice(rewriter, candidateSliceOp, forLoops);
+    if (!fusedResult)
       continue;
 
     if (Operation *tiledAndFusedOp =
-            fusedProducer->tiledAndFusedProducer.getDefiningOp()) {
-      tileAndFuseResult.tiledAndFusedOps.insert(tiledAndFusedOp);
+            fusedResult->tiledAndFusedProducer.getDefiningOp()) {
+      fusedProducers.insert(fusedResult->origProducer.getDefiningOp());
+      tiledAndFusedOps.insert(tiledAndFusedOp);
       addCandidateSlices(tiledAndFusedOp, candidates);
     }
   }
-  return tileAndFuseResult;
+  return scf::SCFTileAndFuseResult{fusedProducers, tiledAndFusedOps,
+                                   getAsOperations(forLoops), replacements};
 }
 
 //===----------------------------------------------------------------------===//

mlir/test/Interfaces/TilingInterface/tile-and-fuse-using-interface.mlir

@@ -8,7 +8,7 @@ func.func @gemm_fill_fusion(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>) ->
   %d1 = tensor.dim %arg1, %c1 : tensor<?x?xf32>
   %init = tensor.empty(%d0, %d1) : tensor<?x?xf32>
   %fill = linalg.fill ins(%cst : f32) outs(%init : tensor<?x?xf32>) -> tensor<?x?xf32>
-  %gemm = linalg.matmul {__internal_linalg_transform__ = "fusion"}
+  %gemm = linalg.matmul {__internal_transform__ = "fusion"}
       ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
       outs(%fill : tensor<?x?xf32>) -> tensor<?x?xf32>
   return %gemm : tensor<?x?xf32>
@@ -47,7 +47,7 @@ func.func @gemm_generic_fusion(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32>,
       ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
       outs(%fill : tensor<?x?xf32>) -> tensor<?x?xf32>
   %generic = linalg.generic {
-      __internal_linalg_transform__ = "fusion",
+      __internal_transform__ = "fusion",
       indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1)>, affine_map<(d0, d1) -> (d0, d1)>],
       iterator_types = ["parallel", "parallel"]}
       ins(%gemm, %arg2 : tensor<?x?xf32>, tensor<?xf32>) outs(%init : tensor<?x?xf32>) {
@@ -97,7 +97,7 @@ func.func @gemm_gemm_fusion(%lhs0 : tensor<?x?xf32>, %rhs0 : tensor<?x?xf32>, %r
   %d2 = tensor.dim %rhs1, %c1 : tensor<?x?xf32>
   %init1 = tensor.empty(%d0, %d2) : tensor<?x?xf32>
   %fill1 = linalg.fill ins(%cst : f32) outs(%init1 : tensor<?x?xf32>) -> tensor<?x?xf32>
-  %gemm1 = linalg.matmul  {__internal_linalg_transform__ = "gemm_fusion"}
+  %gemm1 = linalg.matmul  {__internal_transform__ = "gemm_fusion"}
       ins(%gemm0, %rhs1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%fill1 : tensor<?x?xf32>) -> tensor<?x?xf32>
   return %gemm1 : tensor<?x?xf32>
 }
@@ -147,7 +147,7 @@ func.func @gemm_transpose_fusion(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?xf32
       outs(%fill : tensor<?x?xf32>) -> tensor<?x?xf32>
   %init1 = tensor.empty(%d1, %d0) : tensor<?x?xf32>
   %transpose = linalg.generic {
-      __internal_linalg_transform__ = "fusion",
+      __internal_transform__ = "fusion",
       indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d1, d0)>],
       iterator_types = ["parallel", "parallel"]}
       ins(%gemm : tensor<?x?xf32>) outs(%init1 : tensor<?x?xf32>) {
@@ -198,7 +198,7 @@ func.func @interchange_matmul_fusion(%arg0 : tensor<?x?xf32>, %arg1 : tensor<?x?
       ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
       outs(%1 : tensor<?x?xf32>) -> tensor<?x?xf32>
   %3 = linalg.generic {
-      __internal_linalg_transform__ = "gemm_interchange_fusion",
+      __internal_transform__ = "gemm_interchange_fusion",
       indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0, d1)>],
       iterator_types = ["parallel", "parallel"]}
       ins(%2 : tensor<?x?xf32>) outs(%0 : tensor<?x?xf32>) {
@@ -249,7 +249,7 @@ func.func @matmul_plus_matmul(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>,
                       affine_map<(d0, d1) -> (d0, d1)>,
                       affine_map<(d0, d1) -> (d0, d1)>],
      iterator_types = ["parallel", "parallel"],
-     __internal_linalg_transform__ = "gemm_plus_gemm_fusion"}
+     __internal_transform__ = "gemm_plus_gemm_fusion"}
     ins(%2, %2 : tensor<?x?xf32>, tensor<?x?xf32>)
     outs(%5 : tensor<?x?xf32>) {
     ^bb0(%arg3 : f32, %arg4 : f32, %arg5 : f32) :
@@ -302,7 +302,7 @@ func.func @matmul_plus_transpose_matmul(%arg0: tensor<?x?xf32>, %arg1: tensor<?x
                       affine_map<(d0, d1) -> (d1, d0)>,
                       affine_map<(d0, d1) -> (d0, d1)>],
      iterator_types = ["parallel", "parallel"],
-     __internal_linalg_transform__ = "gemm_plus_gemm_fusion"}
+     __internal_transform__ = "gemm_plus_gemm_fusion"}
     ins(%2, %2 : tensor<?x?xf32>, tensor<?x?xf32>)
     outs(%5 : tensor<?x?xf32>) {
     ^bb0(%arg3 : f32, %arg4 : f32, %arg5 : f32) :
@@ -352,7 +352,7 @@ func.func @matmul_sequence_fusion(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>
   %1 = linalg.matmul ins(%0, %arg3 : tensor<?x?xf32>, tensor<?x?xf32>)
     outs(%arg4 : tensor<?x?xf32>) -> tensor<?x?xf32> // [M, N1] * [N1, N2]
   %2 = linalg.matmul
-    {__internal_linalg_transform__ = "gemm_sequence_fusion"}
+    {__internal_transform__ = "gemm_sequence_fusion"}
     ins(%1, %arg5 : tensor<?x?xf32>, tensor<?x?xf32>)
     outs(%arg6 : tensor<?x?xf32>) -> tensor<?x?xf32> // [M, N2] * [N2, N3]
   return %2 : tensor<?x?xf32>
@@ -425,7 +425,7 @@ func.func @reduction_sequence(%arg0: tensor<30x3xf32>) -> tensor<30x3xf32> {
       linalg.yield %10, %9 : f32, f32
     } -> (tensor<30xf32>, tensor<30x3xf32>)
   %6 = linalg.generic {
-      __internal_linalg_transform__ = "reduction_sequence_fusion",
+      __internal_transform__ = "reduction_sequence_fusion",
       indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>, affine_map<(d0, d1) -> (d0)>,
                        affine_map<(d0, d1) -> (d0, d1)>],
       iterator_types = ["parallel", "parallel"]}

mlir/test/Interfaces/TilingInterface/tile-fuse-and-yield-using-interface.mlir

@@ -13,7 +13,7 @@ func.func @gemm_gemm_fusion_yield_both(%lhs0 : tensor<?x?xf32>, %rhs0 : tensor<?
       ins(%lhs0, %rhs0 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%fill0 : tensor<?x?xf32>) -> tensor<?x?xf32>
   %d2 = tensor.dim %rhs1, %c1 : tensor<?x?xf32>
   %fill1 = linalg.fill ins(%cst : f32) outs(%init1 : tensor<?x?xf32>) -> tensor<?x?xf32>
-  %gemm1 = linalg.matmul  {__internal_linalg_transform__ = "gemm_sequence_fusion_and_yield"}
+  %gemm1 = linalg.matmul  {__internal_transform__ = "gemm_sequence_fusion_and_yield"}
       ins(%gemm0, %rhs1 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%fill1 : tensor<?x?xf32>) -> tensor<?x?xf32>
   return %gemm0, %gemm1 : tensor<?x?xf32>, tensor<?x?xf32>
 }