diff --git a/mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp b/mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp index 7cfd772a72b175..50cfd29e6bf907 100644 --- a/mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp +++ b/mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp @@ -1481,21 +1481,29 @@ checkAssumptionForFusingConsumer(tensor::InsertSliceOp candidateSliceOp) { /// failure otherwise. static FailureOr getConsumerFromUses(Value val, Block *containingOpBlock) { - // Step 1. Check that the value has exactly one use. - if (!llvm::hasSingleElement(val.getUses())) - return failure(); - // Step 2. Get uses. - OpOperand &operand = (*val.getUses().begin()); - Operation *consumerOp = operand.getOwner(); - // TODO: We have to init result of consumer before scf.for, use - // DestinationStyleOpInterface to get result shape from init for now. - // Add support for other op such as op has InferTypeOpInterface. - if (!isa(consumerOp) || - !isa(consumerOp)) - return failure(); - if (containingOpBlock != consumerOp->getBlock()) - return failure(); - return &operand; + // Check that the value has exactly one use which isn't a scf.yield or a + // tensor.parallel_insert_slice op. + OpOperand *operand = nullptr; + for (OpOperand &opOperand : val.getUses()) { + Operation *consumerOp = opOperand.getOwner(); + if (isa(consumerOp)) + continue; + if (operand) + return failure(); + // TODO: We have to init result of consumer before scf.for, use + // DestinationStyleOpInterface to get result shape from init for now. + // Add support for other op such as op has InferTypeOpInterface. + if (!isa(consumerOp) || + !isa(consumerOp)) + return failure(); + if (containingOpBlock != consumerOp->getBlock()) + return failure(); + operand = &opOperand; + } + + if (operand) + return operand; + return failure(); } /// Find the perfectly nested loops outside of given loop(included) sorted from diff --git a/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir b/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir index fdefdcc453ae7a..f5f703d95e2d5b 100644 --- a/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir +++ b/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir @@ -437,3 +437,74 @@ module attributes {transform.with_named_sequence} { // CHECK: scf.yield %[[LOOP_RESULT2]]#0, %[[LOOP_RESULT2]]#1 : // CHECK: } // CHECK: return %[[LOOP_RESULT1]]#1 : + +// ----- + +// This test case checks fusion of consumer even if the producer has multiple uses. +// The multiple uses of the producer essentially means that besides the consumer +// op in concern, the only other uses of the producer are allowed in :- +// 1. scf.yield +// 2. tensor.parallel_insert_slice + +module { + module { + func.func @fuse_consumer_for_multi_use_producer(%arg0: tensor<256x512xf32>, %arg1: tensor<512x256xf32>, %arg2: tensor<256x256xf32>) -> (tensor<256x256xf32>, tensor<256x256xf32>) { + %c0 = arith.constant 0 : index + %c64 = arith.constant 64 : index + %c256 = arith.constant 256 : index + %cst = arith.constant 0.000000e+00 : f32 + %0 = tensor.empty() : tensor<256x256xf32> + %1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32> + %2:2 = scf.for %arg3 = %c0 to %c256 step %c64 iter_args(%arg4 = %1, %arg5 = %arg2) -> (tensor<256x256xf32>, tensor<256x256xf32>) { + %3 = scf.for %arg6 = %c0 to %c256 step %c64 iter_args(%arg7 = %arg4) -> (tensor<256x256xf32>) { + %extracted_slice = tensor.extract_slice %arg7[%arg3, %arg6] [64, 64] [1, 1] : tensor<256x256xf32> to tensor<64x64xf32> + %extracted_slice_0 = tensor.extract_slice %arg0[%arg3, 0] [64, 512] [1, 1] : tensor<256x512xf32> to tensor<64x512xf32> + %extracted_slice_1 = tensor.extract_slice %arg1[0, %arg6] [512, 64] [1, 1] : tensor<512x256xf32> to tensor<512x64xf32> + %5 = linalg.matmul ins(%extracted_slice_0, %extracted_slice_1 : tensor<64x512xf32>, tensor<512x64xf32>) outs(%extracted_slice : tensor<64x64xf32>) -> tensor<64x64xf32> + %inserted_slice = tensor.insert_slice %5 into %arg7[%arg3, %arg6] [64, 64] [1, 1] : tensor<64x64xf32> into tensor<256x256xf32> + scf.yield %inserted_slice : tensor<256x256xf32> + } + %4 = linalg.add ins(%3, %arg5 : tensor<256x256xf32>, tensor<256x256xf32>) outs(%0 : tensor<256x256xf32>) -> tensor<256x256xf32> + scf.yield %3, %4 : tensor<256x256xf32>, tensor<256x256xf32> + } + return %2#0, %2#1 : tensor<256x256xf32>, tensor<256x256xf32> + } + } + module attributes {transform.with_named_sequence} { + transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) { + %0 = transform.structured.match ops{["tensor.insert_slice"]} in %arg0 : (!transform.any_op) -> !transform.any_op + %consumer, %fused_consumer = transform.test.fuse_consumer %0 : (!transform.any_op) -> (!transform.any_op, !transform.any_op) + transform.yield + } + } +} +// CHECK: func.func @fuse_consumer_for_multi_use_producer( +// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<256x512xf32> +// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<512x256xf32> +// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<256x256xf32> +// CHECK: %[[dest0:.*]] = tensor.empty() : tensor<256x256xf32> +// CHECK: %[[dest1:.*]] = linalg.fill +// CHECK-SAME: outs(%[[dest0]] : +// CHECK: %[[LOOP_RESULT1:.*]]:2 = scf.for %[[IV1:.*]] = %[[C0]] +// CHECK-SAME: iter_args(%[[FIRST_OUT_ARG1:.*]] = %[[dest1]], %[[SECOND_OUT_ARG1:.*]] = %[[ARG2]]) +// CHECK-SAME: { +// CHECK: %[[LOOP_RESULT2:.*]]:2 = scf.for %[[IV2:.*]] = %[[C0]] +// CHECK-SAME: iter_args(%[[FIRST_OUT_ARG2:.*]] = %[[FIRST_OUT_ARG1]], %[[SECOND_OUT_ARG2:.*]] = %[[dest0]]) +// CHECK-SAME: { +// CHECK: %[[MAT_OUT_SLICE:.*]] = tensor.extract_slice %[[FIRST_OUT_ARG2]][%[[IV1]], %[[IV2]]] [64, 64] [1, 1] +// CHECK: %[[INPUT_SLICE:.*]] = tensor.extract_slice %[[ARG0]][%[[IV1]], 0] [64, 512] [1, 1] +// CHECK: %[[WEIGHT_SLICE:.*]] = tensor.extract_slice %[[ARG1]][0, %[[IV2]]] [512, 64] [1, 1] +// CHECK: %[[TILED_MAT_OUT:.*]] = linalg.matmul +// CHECK-SAME: outs(%[[MAT_OUT_SLICE]] : +// CHECK: %[[INSERT_MAT:.*]] = tensor.insert_slice %[[TILED_MAT_OUT]] into %[[FIRST_OUT_ARG2]][%[[IV1]], %[[IV2]]] [64, 64] [1, 1] +// CHECK: %[[ADD_OPERAND2_SLICE:.*]] = tensor.extract_slice %[[SECOND_OUT_ARG1]][%[[IV1]], %[[IV2]]] [64, 64] [1, 1] +// CHECK: %[[ADD_OUT_SLICE:.*]] = tensor.extract_slice %[[SECOND_OUT_ARG2]][%[[IV1]], %[[IV2]]] [64, 64] [1, 1] +// CHECK: %[[TILED_ADD_OUT:.*]] = linalg.add +// CHECK-SAME: ins(%[[TILED_MAT_OUT]], %[[ADD_OPERAND2_SLICE]] : +// CHECK-SAME: outs(%[[ADD_OUT_SLICE]] : +// CHECK: %[[INSERT_ADD:.*]] = tensor.insert_slice %[[TILED_ADD_OUT]] into %[[SECOND_OUT_ARG2]][%[[IV1]], %[[IV2]]] [64, 64] [1, 1] +// CHECK: scf.yield %[[INSERT_MAT]], %[[INSERT_ADD]] : +// CHECK: } +// CHECK: scf.yield %[[LOOP_RESULT2]]#0, %[[LOOP_RESULT2]]#1 : +// CHECK: } +// CHECK: return %[[LOOP_RESULT1]]#0, %[[LOOP_RESULT1]]#1 :