[Mlir-commits] [mlir] [MLIR][TilingInterface] Extend consumer fusion for multi-use of producer (PR #110105)
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llvmlistbot at llvm.org
Thu Sep 26 03:58:41 PDT 2024
llvmbot wrote:
<!--LLVM PR SUMMARY COMMENT-->
@llvm/pr-subscribers-mlir
Author: Abhishek Varma (Abhishek-Varma)
<details>
<summary>Changes</summary>
-- 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 <abhvarma@<!-- -->amd.com>
---
Full diff: https://github.com/llvm/llvm-project/pull/110105.diff
2 Files Affected:
- (modified) mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp (+27-15)
- (modified) mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir (+71)
``````````diff
diff --git a/mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp b/mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp
index 7cfd772a72b175..cbf468b201653f 100644
--- a/mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp
+++ b/mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp
@@ -1481,21 +1481,33 @@ 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.
+ Operation *visitedConsumerOp = nullptr;
+ for (OpOperand &opOperand : val.getUses()) {
+ Operation *consumerOp = opOperand.getOwner();
+ if (isa<scf::YieldOp, tensor::ParallelInsertSliceOp>(consumerOp))
+ continue;
+ if (visitedConsumerOp && visitedConsumerOp != consumerOp)
+ 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();
+ visitedConsumerOp = consumerOp;
+ }
+
+ for (OpOperand &opOperand : val.getUses()) {
+ Operation *consumerOp = opOperand.getOwner();
+ if (isa<scf::YieldOp, tensor::ParallelInsertSliceOp>(consumerOp))
+ continue;
+ return &opOperand;
+ }
+ 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 :
``````````
</details>
https://github.com/llvm/llvm-project/pull/110105
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