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[MLIR][TilingInterface] Extend consumer fusion for multi-use of produ…
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…cer shared by terminator ops (#110105)

-- This commit extends consumer fusion to take place 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

Signed-off-by: Abhishek Varma <[email protected]>
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Abhishek-Varma authored Sep 30, 2024
1 parent 8e0daab commit b8c974f
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Showing 2 changed files with 94 additions and 15 deletions.
38 changes: 23 additions & 15 deletions mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1481,21 +1481,29 @@ checkAssumptionForFusingConsumer(tensor::InsertSliceOp candidateSliceOp) {
/// failure otherwise.
static FailureOr<OpOperand *> 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<TilingInterface>(consumerOp) ||
!isa<DestinationStyleOpInterface>(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<scf::YieldOp, tensor::ParallelInsertSliceOp>(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<TilingInterface>(consumerOp) ||
!isa<DestinationStyleOpInterface>(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
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71 changes: 71 additions & 0 deletions mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -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 :

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