[mlir] Add support for fusion into TiledLoopOp.

[gysit]

mlir/lib/Dialect/Linalg/Transforms/Fusion.cpp

149–150

Could an assert(resultInputOperand XOR resultOutputOperand) make sense? Or is that obvious from the context?

160

I believe this can go away?

[gysit]

mlir/include/mlir/Dialect/Linalg/IR/LinalgOps.td

728

Nit: A newline is missing.

Plus I think you could use llvm::find instead of llvm::find_if and avoid the lambda?

[pifon2a]

Address the comments.

[pifon2a]

Remove empty line

[pifon2a]

mlir/lib/Dialect/Linalg/Transforms/Fusion.cpp

160

ahaha, of course.

[pifon2a]

Rebase

mlir/include/mlir/Dialect/Linalg/IR/LinalgOps.td

OpBuilder<(ins "ValueRange":$lowerBounds, "ValueRange":$upperBounds,
       "ValueRange":$steps, "ValueRange":$inputs, "ValueRange":$outputs,
       "ArrayAttr":$iteratorTypes,
       CArg<"function_ref<void (OpBuilder &, Location, /*ivs=*/ValueRange,"
         "/*inputs=*/ValueRange, /*outputs=*/ValueRange)>",
         "nullptr">:$bodyBuilderFn)>,
   ];
 
   let extraClassDeclaration = [{
+    /// Number of loops
+    unsigned getNumLoops() { return step().size(); }
+
+    /// Number of input operands
+    unsigned getNumInputs() { return inputs().size(); }
+
+    /// Number of output operands
+    unsigned getNumOutputs() { return outputs().size(); }
+
     /// Number of operands controlling the loop: lbs, ubs, steps
     unsigned getNumControlOperands() { return 3 * getNumLoops(); }
 
     ValueRange getInductionVars() {
       return getBody()->getArguments().take_front(getNumLoops());
     }
     ValueRange getRegionInputArgs() {
       return getBody()->getArguments().slice(getNumLoops(), inputs().size());
     }
     ValueRange getRegionOutputArgs() {
       return getBody()->getArguments().take_back(outputs().size());
     }
 
-
     void setLowerBounds(ValueRange lowerBounds) {
       unsigned numLoops = getNumLoops();
       assert(lowerBounds.size() == numLoops &&
              "expected lower bounds for every loop dimension");
       for (unsigned i = 0; i < numLoops; ++i)
         setOperand(i, lowerBounds[i]);
     }
 
     void setUpperBounds(ValueRange upperBounds) {
       unsigned numLoops = getNumLoops();
       assert(upperBounds.size() == numLoops &&
              "expected upper bounds for every loop dimension");
       for (unsigned i = 0, pos = numLoops; i < numLoops; ++i, ++pos)
         setOperand(pos, upperBounds[i]);
     }
 
     void setSteps(ValueRange steps) {
       unsigned numLoops = getNumLoops();
       assert(steps.size() == numLoops &&
              "expected upper bounds for every loop dimension");
       for (unsigned i = 0, pos = 2 * numLoops; i < numLoops; ++i, ++pos)
         setOperand(pos, steps[i]);
     }
 
+    /// Block argument that corresponds to the `input` or `output` operand.
+    BlockArgument getTiedBlockArgument(OpOperand& operand) {
+      auto operandIndex = operand.getOperandNumber();
+      assert(
+          operandIndex >= getNumControlOperands() &&
+          operandIndex < getNumOperands() &&
+          "tied block arg is defined only for `input` and `output` arguments");
+      return getBody()->getArgument(operandIndex - 2 * getNumLoops());
+    }
+
    /// Result that corresponds to the `outputs` argument of tensor type.
    OpResult getTiedOpResult(OpOperand& opOperand) {
       // No result can correspond to a memref argument.
       if (opOperand.get().getType().isa<MemRefType>()) return OpResult();
 
       // Check whether the operand index is in bounds of `outputs()` arg.
       int operandIndex = opOperand.getOperandNumber();
       int outputIndexStart =
           getNumControlOperands() + inputs().size();
       int outputIndexEnd = outputIndexStart + outputs().size();
       if (operandIndex < outputIndexStart || operandIndex >= outputIndexEnd)
         return OpResult();
 
       // Count tensor arguments in `outputs` to compute the result index.
       int tensorId = -1;
       for (int i = outputIndexStart; i <= operandIndex; ++i)
         tensorId += getOperand(i).getType().isa<RankedTensorType>();
       return getOperation()->getResult(tensorId);
     }
 
-    unsigned getNumLoops() { return step().size(); }
+    /// Append `operand` to the `input` arguments.
+    OpOperand& appendInputOperand(OpBuilder& builder, Value operand) {
+      int numLoops = getNumLoops();
+      int numInputs = getNumInputs();
+      int numOutputs = getNumOutputs();
+
+      getOperation()->insertOperands(getNumControlOperands() + numInputs,
+                                     operand);
+      getBody()->insertArgument(numLoops + numInputs, operand.getType());
+      getOperation()->setAttr(
+          TiledLoopOp::getOperandSegmentSizeAttr(),
+          builder.getI32VectorAttr(
+              {numLoops, numLoops, numLoops, numInputs + 1, numOutputs}));
+      return getOperation()->getOpOperand(getNumControlOperands() + numInputs);
+    }
+
+    /// Append `operand` to the `output` arguments.
+    OpOperand& appendOutputOperand(OpBuilder& builder, Value operand) {
+      int numLoops = getNumLoops();
+      int numInputs = getNumInputs();
+      int numOutputs = getNumOutputs();
+
+      getOperation()->insertOperands(
+          getNumControlOperands() + numInputs + numOutputs, operand);
+      getBody()->insertArgument(numLoops + numInputs + numOutputs,
+                                operand.getType());
+      getOperation()->setAttr(
+          TiledLoopOp::getOperandSegmentSizeAttr(),
+          builder.getI32VectorAttr(
+              {numLoops, numLoops, numLoops, numInputs, numOutputs + 1}));
+      return getOperation()->getOpOperand(getNumControlOperands() + numInputs +
+                                          numOutputs);
+    }
+
+    /// Erase `operand` from the `input` or `output` arguments.
+    void eraseOperand(OpBuilder& builder, OpOperand& operand) {
+      int numInputs = getNumInputs();
+      int numLoops = getNumLoops();
+      int numOutputs = getNumOutputs();
+      int numControlOperands = getNumControlOperands();
+
+      auto operandIndex = operand.getOperandNumber();
+      assert(operandIndex >= numControlOperands &&
+             operandIndex < getNumOperands() &&
+             "Can erase only `input` or `output` operand");
+
+      if (operandIndex >= numControlOperands + numInputs)
+        --numOutputs;
+      else
+        --numInputs;
+
+      getOperation()->eraseOperand(operandIndex);
+      getBody()->eraseArgument(operandIndex - 2 * numLoops);
+      getOperation()->setAttr(
+          TiledLoopOp::getOperandSegmentSizeAttr(),
+          builder.getI32VectorAttr(
+              {numLoops, numLoops, numLoops, numInputs, numOutputs}));
+    }
+
+    OpOperand* findInputOperand(Value value) {
+      OperandRange::iterator it = llvm::find(inputs(), value);
+      if (it == inputs().end()) return nullptr;
+      return it.getBase();
+    }
+
+    OpOperand* findOutputOperand(Value value) {
+      OperandRange::iterator it = llvm::find(outputs(), value);
+      if (it == outputs().end()) return nullptr;
+      return it.getBase();
+    }
   }];
 
   let hasCanonicalizer = 1;
   let hasFolder = 1;
 }
 
 def Linalg_IndexOp : Linalg_Op<"index", [NoSideEffect]>,
     Arguments<(ins Confined<I64Attr, [IntMinValue<0>]>:$dim)>,

mlir/lib/Dialect/Linalg/Transforms/Fusion.cpp

for (auto en2 : llvm::enumerate(map.getResults())) {
                    << "getShapeDefiningLoopRange shape: " << shape << "\n");
         return ShapeDimension{shape, static_cast<unsigned>(en2.index())};
       }
     }
   }
   llvm_unreachable("Expect to be able to extract a shape defining loop range");
 }
 
+// Return tiled operands for the fused producer op. When fusing into
+// `linalg.tiled_loop` one has to update `input` and `output` arguments of the
+// loop correspondingly.
+// Each input tensor of the producer op has to be added to `inputs` of the
+// `tiled_loop` if it is not present there already. Each output tensor has to
+// be added either to `inputs` or to `outputs` of `linalg.tiled_loop` depending
+// on whether the correponding result is an input or an output to the loop.
+//
+// NOTE: This way of updating the arguments of the `tiled_loop` assumes that the
+// intermediate result is not used by any other operation but the consumer. A
+// more generic way is to append all missing output tensors of the producer to
+// the tiled loop outputs and hence modify the number of the results, since we
+// would need to add the intermediate results to `linalg.yield`. After that a
+// canonicalization pass would move the unused output args of the `tiled_loop`
+// to the `input` section.
+static SmallVector<Value, 4> getTiledOperands(OpBuilder &b, LinalgOp producer) {
+  auto tiledLoop = dyn_cast<TiledLoopOp>(b.getBlock()->getParentOp());
+  if (!tiledLoop)
+    return llvm::to_vector<4>(producer.getShapedOperands());
+
+  SmallVector<Value, 4> tiledOperands;
+  assert(producer.hasTensorSemantics() &&
+         "only fusion on tensors is currently supported for TiledLinalgOp");
+
+  for (auto producerInput : producer.getInputTensors()) {
+    OpOperand *addedInput = tiledLoop.findInputOperand(producerInput);
+    if (addedInput == nullptr)
+      addedInput = &tiledLoop.appendInputOperand(b, producerInput);
+    BlockArgument addedBlockArg = tiledLoop.getTiedBlockArgument(*addedInput);
+    tiledOperands.push_back(addedBlockArg);
+  }
+  for (auto &en : llvm::enumerate(producer.getOutputTensors())) {
+    Value producerOutput = en.value();
+
+    Value result = producer->getResult(en.index());
+    OpOperand *resultInputOperand = tiledLoop.findInputOperand(result);
+    OpOperand *resultOutputOperand = tiledLoop.findOutputOperand(result);
+    assert((resultInputOperand != nullptr) ^ (resultOutputOperand != nullptr) &&
+           "The result should be present in `input` or `output` args of "
+           "`tiled_loop");
+
+    bool isInput = resultInputOperand;
+    int opNumber = isInput ? resultInputOperand->getOperandNumber()
+                           : resultOutputOperand->getOperandNumber();
+
+    OpOperand *addedOutput = tiledLoop.findOutputOperand(producerOutput);
+    if (addedOutput == nullptr)
+      addedOutput = isInput ? &tiledLoop.appendInputOperand(b, producerOutput)
+                            : &tiledLoop.appendOutputOperand(b, producerOutput);
+
+    OpOperand &resultOperand = tiledLoop->getOpOperand(opNumber);
+    auto addedBlockArg = tiledLoop.getTiedBlockArgument(*addedOutput);
+    auto resultOperandBlockArg = tiledLoop.getTiedBlockArgument(resultOperand);
+    resultOperandBlockArg.replaceAllUsesWith(addedBlockArg);
+    tiledLoop.eraseOperand(b, resultOperand);
+    tiledOperands.push_back(addedBlockArg);
+  }
+  return tiledOperands;
+}
+
 /// Fuses the producer by cloning the `producer`. The `fusedLoopsAndRanges`
 /// provides the loop range information for the fused loops. The rest are
 /// obtained from the producer itself, since they are not tiled + fused.
 static LinalgOp fuse(OpBuilder &b, LinalgOp producer,
                      const DenseMap<unsigned, Range> &fusedLoopsAndRanges) {
   SmallVector<Value, 8> ivs, tileSizes, sizeBounds;
   SmallVector<Range, 8> loopRanges;
   Location loc = producer.getLoc();
if (it != fusedLoopsAndRanges.end()) {
                               << loopRanges.back() << "\n");
     }
   }
 
   SmallVector<Value, 8> clonedShapes;
   clonedShapes.reserve(producer.getNumShapedOperands());
 
   // Compute subranges for all tensor input/output operands.
-  auto tiledOperands = llvm::to_vector<4>(producer.getShapedOperands());
-  clonedShapes.append(makeTiledShapes(b, loc, producer, tiledOperands, ivs,
+  clonedShapes.append(makeTiledShapes(b, loc, producer,
+                                      getTiledOperands(b, producer), ivs,
                                       tileSizes, sizeBounds));
 
   // Append the other operands.
   auto operands = producer.getAssumedNonShapedOperands();
   clonedShapes.append(operands.begin(), operands.end());
 
   // Iterate over the results in order.
   // Extract the subtensor type from the linearized range.
fuseOperations(OpBuilder &b, LinalgOp rootOp, TiledLinalgOp tiledLinalgOp,
   DenseMap<Operation *, LinalgOp> origOpToFusedOp;
   origOpToFusedOp[rootOp.getOperation()] = tiledOp;
   for (auto candidate : enumerate(llvm::reverse(fusionCandidates))) {
     LinalgOp origOp = candidate.value();
     LinalgOp fusedOp = fuse(b, origOp, fusedLoopsAndRanges);
     origOpToFusedOp[origOp.getOperation()] = fusedOp;
     fusedOps[fusionCandidates.size() - candidate.index() - 1] = fusedOp;
 
-    // Prepare the b for the next insertion point.
+    // Prepare the builder for the next insertion point.
     auto guard = llvm::make_scope_exit([&]() { b.setInsertionPoint(fusedOp); });
     if (!origOp.hasTensorSemantics())
       continue;
 
     // If the producer consumer operations are linalg operations on tensors, the
     // dependence is due to value produced (as a return tensor) by the producer
     // and used in the consumer. The returned value of the fused op needs to be
     // made the operand of the tiled/fused consumer operation. By construction
for (auto candidate : enumerate(llvm::reverse(fusionCandidates))) {
     // been replaced. However, there may still be "output tensor"-like uses
     // coming from WAW dependencies.
     // All these uses are iter_args of the outermost loop (TODO: add a check).
     // Such iter_args uses serve 2 purposes:
     //  1. give a shape to the output
     //  2. encode destructive updates that may be inplaceable by bufferization.
     // To keep the second type of information while letting the unfused op die
     // unused, we need to forward the producer output operand.
-    for (auto &operand :
-         cast<scf::ForOp>(tiledLinalgOp.loops.front()).getIterOpOperands())
+    if (auto forOp = dyn_cast<scf::ForOp>(tiledLinalgOp.loops.front())) {
+      for (auto &operand : forOp.getIterOpOperands())
         if (auto opResult = operand.get().dyn_cast<OpResult>())
           if (opResult.getOwner() == origOp)
             operand.set(origOp.getOutputTensors()[opResult.getResultNumber()]);
     }
+  }
   return fusedOps;
 }
 
-template <typename LoopType>
 static Optional<TiledAndFusedLinalgOps>
 tileAndFuseLinalgOpsImpl(OpBuilder &b, ArrayRef<LinalgOp> ops,
                          const LinalgDependenceGraph &dependenceGraph,
                          const LinalgTilingOptions &tilingOptions) {
   if (ops.size() < 2)
     return llvm::None;
   LinalgOp rootOp = ops.back();
   if (!llvm::all_of(

 }
 
 Optional<TiledAndFusedLinalgOps>
 mlir::linalg::tileAndFuseLinalgOps(OpBuilder &b, ArrayRef<LinalgOp> ops,
                                    const LinalgDependenceGraph &dependenceGraph,
                                    const LinalgTilingOptions &tilingOptions) {
   switch (tilingOptions.loopType) {
   case LinalgTilingLoopType::Loops:
-    return tileAndFuseLinalgOpsImpl<scf::ForOp>(b, ops, dependenceGraph,
-                                                tilingOptions);
   case LinalgTilingLoopType::ParallelLoops:
-    return tileAndFuseLinalgOpsImpl<scf::ParallelOp>(b, ops, dependenceGraph,
-                                                     tilingOptions);
+  case LinalgTilingLoopType::TiledLoops:
+    return tileAndFuseLinalgOpsImpl(b, ops, dependenceGraph, tilingOptions);
   default:;
   }
   return llvm::None;
 }

mlir/test/Dialect/Linalg/fusion-tensor-pattern.mlir

-// RUN: mlir-opt %s -test-linalg-tensor-fusion-transform-patterns -canonicalize -cse -split-input-file -verify-diagnostics | FileCheck %s
+// RUN: mlir-opt %s -test-linalg-tensor-fusion-transform-patterns -canonicalize -cse --split-input-file | FileCheck %s
+// RUN: mlir-opt %s -test-linalg-tiled-loop-fusion-transform-patterns -canonicalize -cse  --split-input-file | FileCheck %s --check-prefix=TLOOP
 
 module {
-  func @matmul_fusion(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>,
-                      %arg2: tensor<?x?xf32>, %arg3: tensor<?x?xf32>,
-                      %arg4: tensor<?x?xf32>) -> tensor<?x?xf32> {
-    %0 = linalg.matmul ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
-      outs(%arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>   // <MxN1> <N1xN2>
-    %1 = linalg.matmul {__internal_linalg_transform__ = "lhs_fusion"}
-      ins(%0, %arg3 : tensor<?x?xf32>, tensor<?x?xf32>)
-      outs(%arg4 : tensor<?x?xf32>) -> tensor<?x?xf32>   // <MxN2> <N2xN3>
-    return %1 : tensor<?x?xf32>
+  func @matmul_fusion(%A: tensor<?x?xf32>, %B: tensor<?x?xf32>,
+                      %AB_init: tensor<?x?xf32>, %C: tensor<?x?xf32>,
+                      %ABC_init: tensor<?x?xf32>) -> tensor<?x?xf32> {
+    %AB = linalg.matmul ins(%A, %B : tensor<?x?xf32>, tensor<?x?xf32>)
+      outs(%AB_init : tensor<?x?xf32>) -> tensor<?x?xf32>   // <MxN1> <N1xN2>
+    %ABC = linalg.matmul {__internal_linalg_transform__ = "lhs_fusion"}
+      ins(%AB, %C : tensor<?x?xf32>, tensor<?x?xf32>)
+      outs(%ABC_init : tensor<?x?xf32>) -> tensor<?x?xf32>   // <MxN2> <N2xN3>
+    return %ABC : tensor<?x?xf32>
   }
 }
 //  CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1) -> (32, d0 - d1)>
 //  CHECK-DAG: #[[MAP2:.+]] = affine_map<(d0)[s0] -> (16, -d0 + s0)>
 //  CHECK-DAG: #[[MAP3:.+]] = affine_map<(d0)[s0] -> (64, -d0 + s0)>
 //  CHECK-DAG: #[[MAP4:.+]] = affine_map<(d0, d1) -> (64, d0 - d1)>
 //  CHECK-DAG: #[[MAP5:.+]] = affine_map<(d0)[s0, s1] -> (-d0 + s0, 32, -d0 + s1)>
 

 //      CHECK:       scf.yield %[[YIELD1]]
 //      CHECK:     }
 //      CHECK:     %[[UPDATE0:.+]] = subtensor_insert %[[YIELD0]] into
 // CHECK-SAME:       %[[ARG6]][%[[IV0]], 0] [%[[TILE_M_2]], %[[N3]]]
 //      CHECK:     scf.yield %[[UPDATE0]]
 //      CHECK:   }
 //      CHECK:   return %[[RESULT]]
 
+// TLOOP-LABEL:  func @matmul_fusion(
+// TLOOP-SAME: %[[A:[a-zA-Z0-9_]+]]: tensor<?x?xf32>,
+// TLOOP-SAME: %[[B:[a-zA-Z0-9_]+]]: tensor<?x?xf32>,
+// TLOOP-SAME: %[[AB_INIT:[a-zA-Z0-9_]+]]: tensor<?x?xf32>,
+// TLOOP-SAME: %[[C:[a-zA-Z0-9_]+]]: tensor<?x?xf32>,
+// TLOOP-SAME: %[[ABC_INIT:[a-zA-Z0-9_]+]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
+
+// TLOOP:  %[[C32:.*]] = constant 32 : index
+// TLOOP:  %[[C64:.*]] = constant 64 : index
+// TLOOP:  %[[C16:.*]] = constant 16 : index
+// TLOOP:  %[[C0:.*]] = constant 0 : index
+// TLOOP:  %[[C1:.*]] = constant 1 : index
+
+// TLOOP:  %[[DIM_A0:.*]] = memref.dim %[[A]], %[[C0]] : [[TY:.*]]
+
+// TLOOP:  %[[ABC:.*]] = linalg.tiled_loop (%[[IV0:.*]]) = (%[[C0]]) 
+// TLOOP-SAME: to (%[[DIM_A0]]) step (%[[C32]]) 
+// TLOOP-SAME: ins (%[[C_:.*]] = %[[C]]: tensor<?x?xf32>,
+// TLOOP-SAME:      %[[A_:.*]] = %[[A]]: tensor<?x?xf32>,
+// TLOOP-SAME:      %[[B_:.*]] = %[[B]]: tensor<?x?xf32>,
+// TLOOP-SAME:      %[[AB_INIT_:.*]] = %[[AB_INIT]]: tensor<?x?xf32>)
+// TLOOP-SAME: outs (%[[ABC_INIT_:.*]] = %[[ABC_INIT]]: tensor<?x?xf32>) {
+
+// TLOOP:    %[[ABC_INIT_SUB:.*]] = subtensor %[[ABC_INIT_]][%[[IV0]], 0]
+// TLOOP:    %[[A_SUB:.*]] = subtensor %[[A_]][%[[IV0]], 0]
+// TLOOP:    %[[AB_INIT_SUB:.*]] = subtensor %[[AB_INIT_]][%[[IV0]], 0]
+
+// TLOOP:    %[[AB_SUB:.*]] = linalg.matmul
+// TLOOP-SAME:  ins(%[[A_SUB]], %[[B_]] : {{.*}}) outs(%[[AB_INIT_SUB]]
+
+// TLOOP:    %[[DIM_B_1:.*]] = memref.dim %[[B_]], %[[C1]] : [[TY]]
+// TLOOP:    %[[DIM_C_1:.*]] = memref.dim %[[C_]], %[[C1]] : [[TY]]
+
+// TLOOP:    %[[ABC_SUB_:.*]] = linalg.tiled_loop (%[[IV1:.*]], %[[IV2:.*]]) = 
+// TLOOP-SAME: (%[[C0]], %[[C0]]) to (%[[DIM_C_1]], %[[DIM_B_1]])
+// TLOOP-SAME: step (%[[C64]], %[[C16]]) 
+// TLOOP-SAME: ins (%[[AB_SUB_:.*]] = %[[AB_SUB]]: [[TY]],
+// TLOOP-SAME:      %[[C__:.*]] = %[[C_]]: [[TY]])
+// TLOOP-SAME: outs (%[[ABC_INIT_SUB_:.*]] = %[[ABC_INIT_SUB]]: [[TY]])
+// TLOOP-SAME: iterators["parallel", "reduction"] {
+
+// TLOOP:      %[[AB_SUB_SUB:.*]] = subtensor %[[AB_SUB_]][0, %[[IV2]]]
+// TLOOP:      %[[C__SUB:.*]] = subtensor %[[C__]][%[[IV2]], %[[IV1]]]
+// TLOOP:      %[[ABS_INIT_SUB_SUB:.*]] = subtensor %[[ABC_INIT_SUB_]][0, %[[IV1]]]
+
+// TLOOP:      %[[ABC_SUB_SUB:.*]] = linalg.matmul
+// TLOOP-SAME:  ins(%[[AB_SUB_SUB]], %[[C__SUB]] : [[TY]], [[TY]])
+// TLOOP-SAME:  outs(%[[ABS_INIT_SUB_SUB]] : [[TY]]) -> [[TY]]
+
+// TLOOP:      %[[RES0:.*]] = subtensor_insert %[[ABC_SUB_SUB]]
+// TLOOP-SAME:   into %[[ABC_INIT_SUB_]][0, %[[IV1]]]
+// TLOOP:      linalg.yield %[[RES0]] : [[TY]]
+// TLOOP:    }
+// TLOOP:    %[[RES1:.*]] = subtensor_insert %[[ABC_SUB_]] into %[[ABC_INIT_]][%[[IV0]], 0]
+// TLOOP:    linalg.yield %[[RES1]] : [[TY]]
+// TLOOP:  }
+// TLOOP:  return %[[ABC]] : [[TY]]
+
 // -----
 
 module {
   func @matmul_plus_matmul(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>,
                            %arg2: tensor<?x?xf32>) -> tensor<?x?xf32>{
     %c0 = constant 0 : index
     %c1 = constant 1 : index
     %0 = memref.dim %arg2, %c0 : tensor<?x?xf32>

 //  CHECK-SAME:         ins(%[[LHS]] : tensor<?x?xf32>)
 //  CHECK-SAME:         outs(%[[ST_ARG6]] : tensor<?x?xf32>)
 //       CHECK:       %[[UPDATE:.+]] = subtensor_insert %[[ST_RESULT]]
 //  CHECK-SAME:         into %[[ARG6]][%[[IV0]], %[[IV1]]]
 //       CHECK:       scf.yield %[[UPDATE]]
 //       CHECK:     scf.yield %[[YIELD]]
 //       CHECK:   return %[[RESULT]]
 
+// TLOOP-LABEL: func @matmul_plus_matmul
+// TLOOP-SAME:    %[[A:[a-zA-Z0-9_]+]]: tensor<?x?xf32>,
+// TLOOP-SAME:    %[[B:[a-zA-Z0-9_]+]]: tensor<?x?xf32>,
+// TLOOP-SAME:    %[[AB:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
+
+// TLOOP:  %[[C32:.*]] = constant 32 : index
+// TLOOP:  %[[C64:.*]] = constant 64 : index
+// TLOOP:  %[[C0:.*]] = constant 0 : index
+// TLOOP:  %[[C1:.*]] = constant 1 : index
+
+// TLOOP:  %[[DIM_A_0:.*]] = memref.dim %[[A]], %[[C0]] : [[TY:.*]]
+// TLOOP:  %[[DIM_B_1:.*]] = memref.dim %[[B]], %[[C1]] : [[TY]]
+
+// TLOOP:  %[[INIT:.*]] = linalg.init_tensor [%[[DIM_A_0]], %[[DIM_B_1]]]
+
+// TLOOP:  %[[RESULT:.*]] = linalg.tiled_loop (%[[IV0:.*]], %[[IV1:.*]]) =
+// TLOOP-SAME: (%[[C0]], %[[C0]]) to (%[[DIM_A_0]], %[[DIM_B_1]])
+// TLOOP-SAME: step (%[[C32]], %[[C64]])
+// TLOOP-SAME: ins (%[[A_:.*]] = %[[A]]: [[TY]],
+// TLOOP-SAME:      %[[B_:.*]] = %[[B]]: [[TY]],
+// TLOOP-SAME:      %[[AB_:.*]] = %[[AB]]: [[TY]])
+// TLOOP-SAME: outs (%[[INIT_:.*]] = %[[INIT]]: [[TY]]) {
+
+// TLOOP:    %[[INIT_SUB:.*]] = subtensor %[[INIT_]][%[[IV0]], %[[IV1]]]
+// TLOOP:    %[[A_SUB:.*]] = subtensor %[[A_]][%[[IV0]], 0]
+// TLOOP:    %[[B_SUB:.*]] = subtensor %[[B_]][0, %[[IV1]]]
+// TLOOP:    %[[AB_SUB_INIT:.*]] = subtensor %[[AB_]][%[[IV0]], %[[IV1]]]
+
+// TLOOP:    %[[AB_SUB:.*]] = linalg.matmul
+// TLOOP-SAME:  ins(%[[A_SUB]], %[[B_SUB]] : [[TY]], [[TY]])
+// TLOOP-SAME:  outs(%[[AB_SUB_INIT]] : [[TY]])
+
+// TLOOP:    %[[DOUBLE_AB:.*]] = linalg.generic
+// TLOOP-SAME:  ins(%[[AB_SUB]] : [[TY]]) outs(%[[INIT_SUB]] : [[TY]])
+
+// TLOOP:    %[[RESULT_SUB:.*]] = subtensor_insert
+// TLOOP-SAME:  %[[DOUBLE_AB:.*]] into %[[INIT_]][%[[IV0]], %[[IV1]]]
+
+// TLOOP:    linalg.yield %[[RESULT_SUB]] : [[TY]]
+// TLOOP:  }
+// TLOOP:  return %[[RESULT]] : [[TY]]
+
 // -----
 
 module {
   func @matmul_out_fusion(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>,
                       %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
     %c0 = constant 0.0 : f32
     %0 = linalg.fill(%arg0, %c0) : tensor<?x?xf32>, f32 -> tensor<?x?xf32>
     %1 = linalg.matmul {__internal_linalg_transform__ = "out_fusion"}

 //       CHECK:     %[[ST:.*]] = subtensor %[[ARG0]]
 //       CHECK:     %[[ST_FILL:.*]] = linalg.fill(%[[ST]], %[[C0]]) {__internal_linalg_transform__ = "after_out_fusion_producer"} : tensor<?x?xf32>, f32 -> tensor<?x?xf32>
 //       CHECK:     %[[ST_MM_RES:.*]] = scf.for %[[K:.*]]{{.*}}iter_args(%[[BB:.*]] = %[[ST_FILL]]) -> (tensor<?x?xf32>) {
 //   CHECK-NOT:       fill
 //       CHECK:       %[[ST_MM:.*]] = linalg.matmul {__internal_linalg_transform__ = "after_out_fusion"} ins(%{{.*}}, %{{.*}} : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[BB]] : tensor<?x?xf32>) -> tensor<?x?xf32>
 //       CHECK:       scf.yield %[[ST_MM]] : tensor<?x?xf32>
 //       CHECK:     %[[MM:.*]] = subtensor_insert %[[ST_MM_RES]] into {{.*}}
 //       CHECK:     scf.yield %[[MM]] : tensor<?x?xf32>
+
+
+// TLOOP-LABEL: func @matmul_out_fusion(
+// TLOOP-SAME:    %[[OUT:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
+// TLOOP-SAME:    %[[A:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
+// TLOOP-SAME:    %[[B:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
+
+// TLOOP-DAG:  %[[C0_F32:.*]] = constant 0.0
+// TLOOP-DAG:  %[[C32:.*]] = constant 32 : index
+// TLOOP-DAG:  %[[C64:.*]] = constant 64 : index
+// TLOOP-DAG:  %[[C16:.*]] = constant 16 : index
+// TLOOP-DAG:  %[[C0:.*]] = constant 0 : index
+// TLOOP-DAG:  %[[C1:.*]] = constant 1 : index
+
+// TLOOP:  %[[DIM_A_0:.*]] = memref.dim %[[A]], %[[C0]] : [[TY:.*]]
+// TLOOP:  %[[DIM_B_1:.*]] = memref.dim %[[B]], %[[C1]] : [[TY]]
+
+// TLOOP:  %[[AB:.*]] = linalg.tiled_loop (%[[I:.*]], %[[J:.*]]) = 
+// TLOOP-SAME: (%[[C0]], %[[C0]]) to (%[[DIM_A_0]], %[[DIM_B_1]])
+// TLOOP-SAME: step (%[[C32]], %[[C64]])
+// TLOOP-SAME: ins (%[[A_:.*]] = %[[A]]: [[TY]],
+// TLOOP-SAME:      %[[B_:.*]] = %[[B]]: [[TY]])
+// TLOOP-SAME: outs (%[[OUT_:.*]] = %[[OUT]]: [[TY]]) {
+
+// TLOOP:    %[[DIM_A__1:.*]] = memref.dim %[[A_]], %[[C1]] : [[TY]]
+// TLOOP:    %[[A_SUB:.*]] = subtensor %[[A_]][%[[I]], 0]
+// TLOOP:    %[[B_SUB:.*]] = subtensor %[[B_]][0, %[[J]]]
+// TLOOP:    %[[OUT_SUB:.*]] = subtensor %[[OUT_]][%[[I]], %[[J]]]
+// TLOOP:    %[[INIT_SUB:.*]] = linalg.fill(%[[OUT_SUB]], %[[C0_F32]])
+
+// TLOOP:    %[[AB_SUB:.*]] = linalg.tiled_loop (%[[K:.*]]) = (%[[C0]]) 
+// TLOOP-SAME: to (%[[DIM_A__1]]) step (%[[C16]])
+// TLOOP-SAME: ins (%[[A_SUB_:.*]] = %[[A_SUB]]: [[TY]],
+// TLOOP-SAME:      %[[B_SUB_:.*]] = %[[B_SUB]]: [[TY]])
+// TLOOP-SAME: outs (%[[INIT_SUB_:.*]] = %[[INIT_SUB]]: [[TY]])
+// TLOOP-SAME: iterators["reduction"] {
+
+// TLOOP:      %[[A_SUB_SUB:.*]] = subtensor %[[A_SUB_]][0, %[[K]]]
+// TLOOP:      %[[B_SUB_SUB:.*]] = subtensor %[[B_SUB_]][%[[K]], 0]
+
+// TLOOP:      %[[AB_SUB_SUB:.*]] = linalg.matmul
+// TLOOP-SAME:   ins(%[[A_SUB_SUB]], %[[B_SUB_SUB]] : [[TY]], [[TY]])
+// TLOOP-SAME:   outs(%[[INIT_SUB_]] : [[TY]]) -> [[TY]]
+// TLOOP:      linalg.yield %[[AB_SUB_SUB]] : [[TY]]
+// TLOOP:    }
+// TLOOP:    %[[SUB_RESULT:.*]] = subtensor_insert %[[AB_SUB]]
+// TLOOP-SAME:  into %[[OUT_]][%[[I]], %[[J]]]
+// TLOOP:    linalg.yield %[[SUB_RESULT]] : [[TY]]
+// TLOOP:  }
+// TLOOP:  return %[[AB]] : [[TY]]

mlir/test/lib/Dialect/Linalg/TestLinalgFusionTransforms.cpp

 }
 void registerTestLinalgTensorFusionTransforms() {
   PassRegistration<TestLinalgFusionTransforms<LinalgTilingLoopType::Loops>>
       testTensorFusionTransformsPass(
           "test-linalg-tensor-fusion-transform-patterns",
           "Test Linalg on tensor fusion transformation "
           "patterns by applying them greedily.");
 }
+void registerTestLinalgTiledLoopFusionTransforms() {
+  PassRegistration<TestLinalgFusionTransforms<LinalgTilingLoopType::TiledLoops>>
+      testTiledLoopFusionTransformsPass(
+          "test-linalg-tiled-loop-fusion-transform-patterns",
+          "Test Linalg on tensor fusion transformation "
+          "patterns by applying them greedily.");
+}
 void registerTestLinalgGreedyFusion() {
   PassRegistration<TestLinalgGreedyFusion> testFusionTransformsPass(
       "test-linalg-greedy-fusion",
       "Test Linalg fusion by applying a greedy test transformation.");
 }
 void registerTestLinalgTileAndFuseSequencePass() {
   PassRegistration<TestLinalgTileAndFuseSequencePass>
       testTileAndFuseSequencePass(
           "test-linalg-tile-and-fuse",
           "Test Linalg tiling and fusion of a sequence of Linalg operations.");
 }
 
 } // namespace test
 } // namespace mlir

mlir/tools/mlir-opt/mlir-opt.cpp

 void registerTestGpuParallelLoopMappingPass();
 void registerTestIRVisitorsPass();
 void registerTestInterfaces();
 void registerTestLinalgCodegenStrategy();
 void registerTestLinalgElementwiseFusion();
 void registerTestPushExpandingReshape();
 void registerTestLinalgFusionTransforms();
 void registerTestLinalgTensorFusionTransforms();
+void registerTestLinalgTiledLoopFusionTransforms();
 void registerTestLinalgGreedyFusion();
 void registerTestLinalgHoisting();
 void registerTestLinalgTileAndFuseSequencePass();
 void registerTestLinalgTransforms();
 void registerTestLivenessPass();
 void registerTestLoopFusion();
 void registerTestLoopMappingPass();
 void registerTestLoopUnrollingPass();
#endif
   test::registerTestGpuParallelLoopMappingPass();
   test::registerTestIRVisitorsPass();
   test::registerTestInterfaces();
   test::registerTestLinalgCodegenStrategy();
   test::registerTestLinalgElementwiseFusion();
   test::registerTestPushExpandingReshape();
   test::registerTestLinalgFusionTransforms();
   test::registerTestLinalgTensorFusionTransforms();
+  test::registerTestLinalgTiledLoopFusionTransforms();
   test::registerTestLinalgGreedyFusion();
   test::registerTestLinalgHoisting();
   test::registerTestLinalgTileAndFuseSequencePass();
   test::registerTestLinalgTransforms();
   test::registerTestLivenessPass();
   test::registerTestLoopFusion();
   test::registerTestLoopMappingPass();
   test::registerTestLoopUnrollingPass();