[Mlir-commits] [mlir] [mlir][linalg] Retain Op Type of linalg ops in fuseWithReshapeByExpansion pattern (PR #129128)
Nirvedh Meshram
llvmlistbot at llvm.org
Thu Feb 27 13:44:28 PST 2025
https://github.com/nirvedhmeshram created https://github.com/llvm/llvm-project/pull/129128
This PR preserve linalg Op types instead of fusion always resulting in a generic Op.
>From 620f2a80aecf8013dd242a2f7868510e3b8ed3da Mon Sep 17 00:00:00 2001
From: Nirvedh Meshram <nirvedh at nod-labs.com>
Date: Thu, 27 Feb 2025 12:55:44 -0600
Subject: [PATCH] [mlir][linalg] Retain named ops in fuseWithReshapeByExpansion
pattern
Signed-off-by: Nirvedh Meshram <nirvedh at gmail.com>
---
.../Linalg/Transforms/ElementwiseOpFusion.cpp | 48 +++++++++++++-----
mlir/test/Dialect/Linalg/reshape_fusion.mlir | 49 ++++++++++++++-----
2 files changed, 75 insertions(+), 22 deletions(-)
diff --git a/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp b/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
index f4b6955823085..f64151db8e5a0 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
@@ -927,17 +927,43 @@ fuseWithReshapeByExpansion(LinalgOp linalgOp, Operation *reshapeOp,
iteratorTypes[j] = type;
TypeRange resultTypes = ValueRange(outputs).getTypes();
- auto fusedOp =
- rewriter.create<GenericOp>(linalgOp.getLoc(), resultTypes,
- /*inputs=*/expandedOpOperands, outputs,
- expandedOpIndexingMaps, iteratorTypes);
- Region &fusedRegion = fusedOp->getRegion(0);
- Region &originalRegion = linalgOp->getRegion(0);
- rewriter.cloneRegionBefore(originalRegion, fusedRegion, fusedRegion.begin());
-
- // Update the index accesses after the expansion.
- updateExpandedGenericOpRegion(rewriter, loc, fusedRegion, expansionInfo);
-
+ Operation *fusedOp;
+
+ TypeSwitch<Operation *>(linalgOp.getOperation())
+ .Case<GenericOp>([&](GenericOp op) {
+ fusedOp = rewriter.create<GenericOp>(
+ linalgOp.getLoc(), resultTypes, expandedOpOperands, outputs,
+ expandedOpIndexingMaps, iteratorTypes);
+ Region &fusedRegion = fusedOp->getRegion(0);
+ Region &originalRegion = linalgOp->getRegion(0);
+ rewriter.cloneRegionBefore(originalRegion, fusedRegion,
+ fusedRegion.begin());
+
+ // Update the index accesses after the expansion.
+ updateExpandedGenericOpRegion(rewriter, loc, fusedRegion,
+ expansionInfo);
+ })
+ .Case<TransposeOp>([&](TransposeOp op) {
+ SmallVector<ReassociationIndices> reassociation =
+ isExpanding ? expandingReshapeOp.getReassociationIndices()
+ : collapsingReshapeOp.getReassociationIndices();
+ applyPermutationToVector(reassociation, op.getPermutation());
+ SmallVector<int64_t> newPerm;
+ for (auto reassoc : reassociation) {
+ for (auto dim : reassoc) {
+ newPerm.push_back(dim);
+ }
+ }
+ fusedOp = rewriter.create<TransposeOp>(
+ linalgOp.getLoc(), expandedOpOperands[0], outputs[0], newPerm);
+ })
+ // All other expandable linalg ops that are not generic or transpose can
+ // be cloned with the expanded input and output operands.
+ .Default([&](Operation *op) {
+ fusedOp = clone(
+ rewriter, linalgOp, resultTypes,
+ llvm::to_vector(llvm::concat<Value>(expandedOpOperands, outputs)));
+ });
// Reshape the result values to their original shape if this is a collapsing
// reshape folded into its consumer.
SmallVector<Value> resultVals;
diff --git a/mlir/test/Dialect/Linalg/reshape_fusion.mlir b/mlir/test/Dialect/Linalg/reshape_fusion.mlir
index ef853e4d662a7..80cebab590f6f 100644
--- a/mlir/test/Dialect/Linalg/reshape_fusion.mlir
+++ b/mlir/test/Dialect/Linalg/reshape_fusion.mlir
@@ -753,7 +753,6 @@ func.func @linalg_add_reshape_consumer_fusion(%arg0 : tensor<?x?xf32>,
return %1 : tensor<?x?x4x5xf32>
}
-// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
// CHECK: func @linalg_add_reshape_consumer_fusion
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
@@ -774,18 +773,13 @@ func.func @linalg_add_reshape_consumer_fusion(%arg0 : tensor<?x?xf32>,
// CHECK: %[[DIM_5:.+]] = tensor.dim %[[ARG2]], %[[C1]] : tensor<?x?xf32>
// CHECK: %[[VAL_2:.+]] = arith.divsi %[[DIM_5]], %[[C20]] : index
// CHECK: %[[T3:.+]] = tensor.expand_shape %[[ARG2]] {{\[\[}}0], [1, 2, 3]] output_shape [%[[DIM_4]], %[[VAL_2]], 4, 5] : tensor<?x?xf32> into tensor<?x?x4x5xf32>
-// CHECK: %[[T4:.+]] = linalg.generic
-// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]], #[[MAP]]]
-// CHECK-SAME: ["parallel", "parallel", "parallel", "parallel"]
+// CHECK: %[[T4:.+]] = linalg.add
// CHECK-SAME: ins(%[[T1]], %[[T2]] : tensor<?x?x4x5xf32>, tensor<?x?x4x5xf32>)
// CHECK-SAME: outs(%[[T3]] : tensor<?x?x4x5xf32>)
// CHECK: return %[[T4]] : tensor<?x?x4x5xf32>
// -----
-#map0 = affine_map<(d0, d1, d2) -> (d2, d0)>
-#map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
-#map2 = affine_map<(d0, d1, d2) -> (d0, d2)>
func.func @linalg_add_reshape_producer_fusion(%arg0 : tensor<?x7x?x8xf32>,
%arg1 : tensor<?x?xf32>,
%arg2 : tensor<?x?xf32>) ->
@@ -798,7 +792,6 @@ func.func @linalg_add_reshape_producer_fusion(%arg0 : tensor<?x7x?x8xf32>,
return %1 : tensor<?x?xf32>
}
-// CHECK-DAG: #[[$MAP:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
// CHECK: func @linalg_add_reshape_producer_fusion
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x7x?x8xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
@@ -817,9 +810,7 @@ func.func @linalg_add_reshape_producer_fusion(%arg0 : tensor<?x7x?x8xf32>,
// CHECK: %[[VAL_2:.+]] = arith.divsi %[[DIM_1]], %[[C7]] : index
// CHECK: %[[VAL_3:.+]] = arith.divsi %[[DIM_2]], %[[C8]] : index
// CHECK: %[[T2:.+]] = tensor.expand_shape %[[ARG2]] {{\[\[}}0, 1], [2, 3]] output_shape [%[[VAL_2]], 7, %[[VAL_3]], 8] : tensor<?x?xf32> into tensor<?x7x?x8xf32>
-// CHECK: %[[T3:.+]] = linalg.generic
-// CHECK-SAME: indexing_maps = [#[[$MAP]], #[[$MAP]], #[[$MAP]]]
-// CHECK-SAME: ["parallel", "parallel", "parallel", "parallel"]
+// CHECK: %[[T3:.+]] = linalg.add
// CHECK-SAME: ins(%[[ARG0]], %[[T1]] : tensor<?x7x?x8xf32>, tensor<?x7x?x8xf32>)
// CHECK-SAME: outs(%[[T2]] : tensor<?x7x?x8xf32>)
// CHECK: %[[T4:.+]] = tensor.collapse_shape %[[T3]]
@@ -827,6 +818,42 @@ func.func @linalg_add_reshape_producer_fusion(%arg0 : tensor<?x7x?x8xf32>,
// CHECK-SAME: tensor<?x7x?x8xf32> into tensor<?x?xf32>
// CHECK: return %[[T4]]
+// -----
+
+func.func @linalg_transpose_reshape_producer_fusion(%arg0 : tensor<?x7x?x8xf32>,
+ %arg1 : tensor<?x?xf32>) ->
+ tensor<?x?xf32>
+{
+ %0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] :
+ tensor<?x7x?x8xf32> into tensor<?x?xf32>
+ %1 = linalg.transpose ins(%0 : tensor<?x?xf32>)
+ outs(%arg1 : tensor<?x?xf32>) permutation = [1, 0]
+ return %1 : tensor<?x?xf32>
+}
+
+// CHECK: func @linalg_transpose_reshape_producer_fusion
+// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<?x7x?x8xf32>
+// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<?x?xf32>
+// CHECK-DAG: %[[C8:.+]] = arith.constant 8 : index
+// CHECK-DAG: %[[C7:.+]] = arith.constant 7 : index
+// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
+// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
+// CHECK-DAG: %[[DIM:.+]] = tensor.dim %[[ARG1]], %[[C0]] : tensor<?x?xf32>
+// CHECK-DAG: %[[DIM_0:.+]] = tensor.dim %[[ARG1]], %[[C1]] : tensor<?x?xf32>
+// CHECK-DAG: %[[VAL_0:.+]] = arith.divsi %[[DIM_0]], %[[C7]] : index
+// CHECK-DAG: %[[VAL_1:.+]] = arith.divsi %[[DIM]], %[[C8]] : index
+// CHECK: %[[T1:.+]] = tensor.expand_shape %[[ARG1]] {{\[\[}}0, 1], [2, 3]] output_shape [%[[VAL_1]], 8, %[[VAL_0]], 7] : tensor<?x?xf32> into tensor<?x8x?x7xf32>
+// CHECK: %[[T2:.+]] = linalg.transpose
+// CHECK-SAME: ins(%[[ARG0]] : tensor<?x7x?x8xf32>)
+// CHECK-SAME: outs(%[[T1]] : tensor<?x8x?x7xf32>)
+// CHECK-SAME: permutation = [2, 3, 0, 1]
+// CHECK: %[[T3:.+]] = tensor.collapse_shape %[[T2]]
+// CHECK-SAME: [0, 1], [2, 3]
+// CHECK-SAME: tensor<?x8x?x7xf32> into tensor<?x?xf32>
+// CHECK: return %[[T3]]
+
+
+
// -----
func.func @fuse_by_expanding_pad(%arg0 : tensor<2x3x4x5x6x7x8x9xi32>) -> tensor<8x12x17x336x14xi32> {
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