[mlir][linalg] Enable pack consumer fusion for all perfect tiling cases. #150672
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It was disabled because there may be artificial padding. After [refining the pack op semantics](llvm@773e158), we can assume that there are no artificial padding. Thus, the check can be removed, and we can unconditionally enable the consumer fusion if it is a perfect tiling case. Signed-off-by: hanhanW <[email protected]>
hanhanW
requested review from
dcaballe,
nicolasvasilache and
rengolin
as code owners
Member
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@llvm/pr-subscribers-mlir Author: Han-Chung Wang (hanhanW) ChangesIt was disabled because there may be artificial padding. After refining the pack op semantics, we can assume that there is no artificial padding. Thus, the check can be removed, and we can unconditionally enable the consumer fusion if it is a perfect tiling case. Full diff: https://github.com/llvm/llvm-project/pull/150672.diff 2 Files Affected:
diff --git a/mlir/lib/Dialect/Linalg/Transforms/TilingInterfaceImpl.cpp b/mlir/lib/Dialect/Linalg/Transforms/TilingInterfaceImpl.cpp
index dad352643abe3..57b610b31e964 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/TilingInterfaceImpl.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/TilingInterfaceImpl.cpp
@@ -932,20 +932,6 @@ struct PackOpTiling
continue;
}
- // If the dimension needs padding, it is not supported because there are
- // iterations that only write padding values to the whole tile. The
- // consumer fusion is driven by the source, so it is not possible to map
- // an empty slice to the tile.
- bool needExtraPadding =
- ShapedType::isDynamic(destDimSize) || !cstInnerSize ||
- destDimSize * cstInnerSize.value() != srcDimSize;
- // Prioritize the case that the op already says that it does not need
- // padding.
- if (!packOp.getPaddingValue())
- needExtraPadding = false;
- if (needExtraPadding)
- return failure();
-
// Currently fusing `packOp` as consumer only expects perfect tiling
// scenario because even if without padding semantic, the `packOp` may
// also yield incomplete tiles. E.g. tensor<30xf32> -> tensor<5x6xf32>,
diff --git a/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir b/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir
index e48e5c6c308be..78884625ce7dc 100644
--- a/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir
+++ b/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir
@@ -595,16 +595,17 @@ module attributes {transform.with_named_sequence} {
// -----
-// It is valid to fuse the pack op with padding semantics if the tiled
-// dimensions do not need padding.
+// It is valid to fuse the pack op with padding semantics if it is a perfect
+// tiling case.
func.func @fuse_pack_consumer_with_padding_semantics(%arg0: tensor<64x32xf32>, %arg1: tensor<64x32xf32>) -> tensor<22x2x3x16xf32> {
- %0 = scf.forall (%arg2) = (0) to (32) step (16) shared_outs(%arg3 = %arg1) -> (tensor<64x32xf32>) {
- %src = tensor.extract_slice %arg0[0, %arg2] [64, 16] [1, 1] : tensor<64x32xf32> to tensor<64x16xf32>
- %dest = tensor.extract_slice %arg3[0, %arg2] [64, 16] [1, 1] : tensor<64x32xf32> to tensor<64x16xf32>
- %2 = linalg.exp ins(%src : tensor<64x16xf32>) outs(%dest : tensor<64x16xf32>) -> tensor<64x16xf32>
+ %0 = scf.forall (%arg2, %arg3) = (0, 0) to (64, 32) step (15, 16) shared_outs(%arg4 = %arg1) -> (tensor<64x32xf32>) {
+ %size = affine.min affine_map<(d0) -> (-d0 + 64, 15)>(%arg2)
+ %src = tensor.extract_slice %arg0[%arg2, %arg3] [%size, 16] [1, 1] : tensor<64x32xf32> to tensor<?x16xf32>
+ %dest = tensor.extract_slice %arg4[%arg2, %arg3] [%size, 16] [1, 1] : tensor<64x32xf32> to tensor<?x16xf32>
+ %2 = linalg.exp ins(%src : tensor<?x16xf32>) outs(%dest : tensor<?x16xf32>) -> tensor<?x16xf32>
scf.forall.in_parallel {
- tensor.parallel_insert_slice %2 into %arg3[0, %arg2] [64, 16] [1, 1] : tensor<64x16xf32> into tensor<64x32xf32>
+ tensor.parallel_insert_slice %2 into %arg4[%arg2, %arg3] [%size, 16] [1, 1] : tensor<?x16xf32> into tensor<64x32xf32>
}
}
%1 = tensor.empty() : tensor<22x2x3x16xf32>
@@ -621,28 +622,39 @@ module attributes {transform.with_named_sequence} {
transform.yield
}
}
-// CHECK: #[[PACK_RESULT_MAP:.*]] = affine_map<(d0) -> (d0 floordiv 16)>
+// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0) -> (-d0 + 64, 15)>
+// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0) -> (d0 floordiv 3)>
+// CHECK-DAG: #[[MAP2:.*]] = affine_map<(d0) -> (d0 ceildiv 3)>
+// CHECK-DAG: #[[MAP3:.*]] = affine_map<(d0) -> (d0 floordiv 16)>
// CHECK: func.func @fuse_pack_consumer_with_padding_semantics(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]
// CHECK-DAG: %[[OUT_INIT:.*]] = tensor.empty() : tensor<22x2x3x16xf32>
// CHECK-DAG: %[[PAD_VAL:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %{{.*}}:2 = scf.forall (%[[IV:.*]]) = (0) to (32) step (16)
-// CHECK-SAME: shared_outs(%[[FIRST_OUT_ARG:.*]] = %[[ARG1]], %[[PACK_OUT_ARG:.*]] = %[[OUT_INIT]])
-// CHECK: %[[ELEM_SRC:.*]] = tensor.extract_slice %[[ARG0]][0, %[[IV]]] [64, 16] [1, 1]
-// CHECK: %[[ELEM_DEST:.*]] = tensor.extract_slice %[[FIRST_OUT_ARG]][0, %[[IV]]] [64, 16] [1, 1]
+// CHECK: %{{.*}}:2 = scf.forall (%[[I:.*]], %[[J:.*]]) = (0, 0) to (64, 32) step (15, 16)
+// CHECK-SAME: shared_outs(%[[ELEM_OUT:.*]] = %[[ARG1]], %[[PACK_OUT:.*]] = %[[OUT_INIT]])
+// CHECK: %[[SIZE:.+]] = affine.min #[[MAP0]](%[[I]])
+// CHECK: %[[ELEM_SRC:.*]] = tensor.extract_slice %[[ARG0]]
+// CHECK-SAME: [%[[I]], %[[J]]] [%[[SIZE]], 16] [1, 1]
+// CHECK: %[[ELEM_DEST:.*]] = tensor.extract_slice %[[ELEM_OUT]]
+// CHECK-SAME: [%[[I]], %[[J]]] [%[[SIZE]], 16] [1, 1]
// CHECK: %[[ELEM:.*]] = linalg.exp
// CHECK-SAME: ins(%[[ELEM_SRC]]
// CHECK-SAME: outs(%[[ELEM_DEST]]
-// CHECK-DAG: %[[PACK_RESULT_OFFSET:.*]] = affine.apply #[[PACK_RESULT_MAP]](%[[IV]])
-// CHECK-DAG: %[[TILED_PACK_DEST:.*]] = tensor.extract_slice %[[PACK_OUT_ARG]][0, %[[PACK_RESULT_OFFSET]], 0, 0] [22, 1, 3, 16] [1, 1, 1, 1]
-// CHECK: %[[TILED_PACK_OUT:.*]] = linalg.pack %[[ELEM]]
+// CHECK-DAG: %[[D0_OFFSET:.*]] = affine.apply #[[MAP1]](%[[I]])
+// CHECK-DAG: %[[D0_SIZE:.*]] = affine.apply #[[MAP2]](%[[SIZE]])
+// CHECK-DAG: %[[D1_OFFSET:.*]] = affine.apply #[[MAP3]](%[[J]])
+// CHECK-DAG: %[[PACK_INIT:.*]] = tensor.extract_slice %[[PACK_OUT]]
+// CHECK-SAME: [%[[D0_OFFSET]], %[[D1_OFFSET]], 0, 0] [%[[D0_SIZE]], 1, 3, 16] [1, 1, 1, 1]
+// CHECK: %[[PACK:.*]] = linalg.pack %[[ELEM]]
// CHECK-SAME: padding_value(%[[PAD_VAL]] : f32)
// CHECK-SAME: inner_dims_pos = [0, 1] inner_tiles = [3, 16]
// CHECK-SAME: into %[[TILED_PACK_DEST]]
// CHECK: scf.forall.in_parallel {
-// CHECK: tensor.parallel_insert_slice %[[GENERIC_OUT]] into %[[FIRST_OUT_ARG]][0, %[[IV]]] [64, 16] [1, 1]
-// CHECK: tensor.parallel_insert_slice %[[TILED_PACK_OUT]] into %[[PACK_OUT_ARG]][0, %[[PACK_RESULT_OFFSET]], 0, 0] [22, 1, 3, 16] [1, 1, 1, 1]
+// CHECK: tensor.parallel_insert_slice %[[ELEM]] into %[[ELEM_OUT]]
+// CHECK-SAME: [%[[I]], %[[J]]] [%[[SIZE]], 16] [1, 1]
+// CHECK: tensor.parallel_insert_slice %[[PACK]] into %[[PACK_OUT]]
+// CHECK-SAME: [%[[D0_OFFSET]], %[[D1_OFFSET]], 0, 0] [%[[D0_SIZE]], 1, 3, 16] [1, 1, 1, 1]
// -----
|
Member
|
@llvm/pr-subscribers-mlir-linalg Author: Han-Chung Wang (hanhanW) ChangesIt was disabled because there may be artificial padding. After refining the pack op semantics, we can assume that there is no artificial padding. Thus, the check can be removed, and we can unconditionally enable the consumer fusion if it is a perfect tiling case. Full diff: https://github.com/llvm/llvm-project/pull/150672.diff 2 Files Affected:
diff --git a/mlir/lib/Dialect/Linalg/Transforms/TilingInterfaceImpl.cpp b/mlir/lib/Dialect/Linalg/Transforms/TilingInterfaceImpl.cpp
index dad352643abe3..57b610b31e964 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/TilingInterfaceImpl.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/TilingInterfaceImpl.cpp
@@ -932,20 +932,6 @@ struct PackOpTiling
continue;
}
- // If the dimension needs padding, it is not supported because there are
- // iterations that only write padding values to the whole tile. The
- // consumer fusion is driven by the source, so it is not possible to map
- // an empty slice to the tile.
- bool needExtraPadding =
- ShapedType::isDynamic(destDimSize) || !cstInnerSize ||
- destDimSize * cstInnerSize.value() != srcDimSize;
- // Prioritize the case that the op already says that it does not need
- // padding.
- if (!packOp.getPaddingValue())
- needExtraPadding = false;
- if (needExtraPadding)
- return failure();
-
// Currently fusing `packOp` as consumer only expects perfect tiling
// scenario because even if without padding semantic, the `packOp` may
// also yield incomplete tiles. E.g. tensor<30xf32> -> tensor<5x6xf32>,
diff --git a/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir b/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir
index e48e5c6c308be..78884625ce7dc 100644
--- a/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir
+++ b/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir
@@ -595,16 +595,17 @@ module attributes {transform.with_named_sequence} {
// -----
-// It is valid to fuse the pack op with padding semantics if the tiled
-// dimensions do not need padding.
+// It is valid to fuse the pack op with padding semantics if it is a perfect
+// tiling case.
func.func @fuse_pack_consumer_with_padding_semantics(%arg0: tensor<64x32xf32>, %arg1: tensor<64x32xf32>) -> tensor<22x2x3x16xf32> {
- %0 = scf.forall (%arg2) = (0) to (32) step (16) shared_outs(%arg3 = %arg1) -> (tensor<64x32xf32>) {
- %src = tensor.extract_slice %arg0[0, %arg2] [64, 16] [1, 1] : tensor<64x32xf32> to tensor<64x16xf32>
- %dest = tensor.extract_slice %arg3[0, %arg2] [64, 16] [1, 1] : tensor<64x32xf32> to tensor<64x16xf32>
- %2 = linalg.exp ins(%src : tensor<64x16xf32>) outs(%dest : tensor<64x16xf32>) -> tensor<64x16xf32>
+ %0 = scf.forall (%arg2, %arg3) = (0, 0) to (64, 32) step (15, 16) shared_outs(%arg4 = %arg1) -> (tensor<64x32xf32>) {
+ %size = affine.min affine_map<(d0) -> (-d0 + 64, 15)>(%arg2)
+ %src = tensor.extract_slice %arg0[%arg2, %arg3] [%size, 16] [1, 1] : tensor<64x32xf32> to tensor<?x16xf32>
+ %dest = tensor.extract_slice %arg4[%arg2, %arg3] [%size, 16] [1, 1] : tensor<64x32xf32> to tensor<?x16xf32>
+ %2 = linalg.exp ins(%src : tensor<?x16xf32>) outs(%dest : tensor<?x16xf32>) -> tensor<?x16xf32>
scf.forall.in_parallel {
- tensor.parallel_insert_slice %2 into %arg3[0, %arg2] [64, 16] [1, 1] : tensor<64x16xf32> into tensor<64x32xf32>
+ tensor.parallel_insert_slice %2 into %arg4[%arg2, %arg3] [%size, 16] [1, 1] : tensor<?x16xf32> into tensor<64x32xf32>
}
}
%1 = tensor.empty() : tensor<22x2x3x16xf32>
@@ -621,28 +622,39 @@ module attributes {transform.with_named_sequence} {
transform.yield
}
}
-// CHECK: #[[PACK_RESULT_MAP:.*]] = affine_map<(d0) -> (d0 floordiv 16)>
+// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0) -> (-d0 + 64, 15)>
+// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0) -> (d0 floordiv 3)>
+// CHECK-DAG: #[[MAP2:.*]] = affine_map<(d0) -> (d0 ceildiv 3)>
+// CHECK-DAG: #[[MAP3:.*]] = affine_map<(d0) -> (d0 floordiv 16)>
// CHECK: func.func @fuse_pack_consumer_with_padding_semantics(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]
// CHECK-DAG: %[[OUT_INIT:.*]] = tensor.empty() : tensor<22x2x3x16xf32>
// CHECK-DAG: %[[PAD_VAL:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %{{.*}}:2 = scf.forall (%[[IV:.*]]) = (0) to (32) step (16)
-// CHECK-SAME: shared_outs(%[[FIRST_OUT_ARG:.*]] = %[[ARG1]], %[[PACK_OUT_ARG:.*]] = %[[OUT_INIT]])
-// CHECK: %[[ELEM_SRC:.*]] = tensor.extract_slice %[[ARG0]][0, %[[IV]]] [64, 16] [1, 1]
-// CHECK: %[[ELEM_DEST:.*]] = tensor.extract_slice %[[FIRST_OUT_ARG]][0, %[[IV]]] [64, 16] [1, 1]
+// CHECK: %{{.*}}:2 = scf.forall (%[[I:.*]], %[[J:.*]]) = (0, 0) to (64, 32) step (15, 16)
+// CHECK-SAME: shared_outs(%[[ELEM_OUT:.*]] = %[[ARG1]], %[[PACK_OUT:.*]] = %[[OUT_INIT]])
+// CHECK: %[[SIZE:.+]] = affine.min #[[MAP0]](%[[I]])
+// CHECK: %[[ELEM_SRC:.*]] = tensor.extract_slice %[[ARG0]]
+// CHECK-SAME: [%[[I]], %[[J]]] [%[[SIZE]], 16] [1, 1]
+// CHECK: %[[ELEM_DEST:.*]] = tensor.extract_slice %[[ELEM_OUT]]
+// CHECK-SAME: [%[[I]], %[[J]]] [%[[SIZE]], 16] [1, 1]
// CHECK: %[[ELEM:.*]] = linalg.exp
// CHECK-SAME: ins(%[[ELEM_SRC]]
// CHECK-SAME: outs(%[[ELEM_DEST]]
-// CHECK-DAG: %[[PACK_RESULT_OFFSET:.*]] = affine.apply #[[PACK_RESULT_MAP]](%[[IV]])
-// CHECK-DAG: %[[TILED_PACK_DEST:.*]] = tensor.extract_slice %[[PACK_OUT_ARG]][0, %[[PACK_RESULT_OFFSET]], 0, 0] [22, 1, 3, 16] [1, 1, 1, 1]
-// CHECK: %[[TILED_PACK_OUT:.*]] = linalg.pack %[[ELEM]]
+// CHECK-DAG: %[[D0_OFFSET:.*]] = affine.apply #[[MAP1]](%[[I]])
+// CHECK-DAG: %[[D0_SIZE:.*]] = affine.apply #[[MAP2]](%[[SIZE]])
+// CHECK-DAG: %[[D1_OFFSET:.*]] = affine.apply #[[MAP3]](%[[J]])
+// CHECK-DAG: %[[PACK_INIT:.*]] = tensor.extract_slice %[[PACK_OUT]]
+// CHECK-SAME: [%[[D0_OFFSET]], %[[D1_OFFSET]], 0, 0] [%[[D0_SIZE]], 1, 3, 16] [1, 1, 1, 1]
+// CHECK: %[[PACK:.*]] = linalg.pack %[[ELEM]]
// CHECK-SAME: padding_value(%[[PAD_VAL]] : f32)
// CHECK-SAME: inner_dims_pos = [0, 1] inner_tiles = [3, 16]
// CHECK-SAME: into %[[TILED_PACK_DEST]]
// CHECK: scf.forall.in_parallel {
-// CHECK: tensor.parallel_insert_slice %[[GENERIC_OUT]] into %[[FIRST_OUT_ARG]][0, %[[IV]]] [64, 16] [1, 1]
-// CHECK: tensor.parallel_insert_slice %[[TILED_PACK_OUT]] into %[[PACK_OUT_ARG]][0, %[[PACK_RESULT_OFFSET]], 0, 0] [22, 1, 3, 16] [1, 1, 1, 1]
+// CHECK: tensor.parallel_insert_slice %[[ELEM]] into %[[ELEM_OUT]]
+// CHECK-SAME: [%[[I]], %[[J]]] [%[[SIZE]], 16] [1, 1]
+// CHECK: tensor.parallel_insert_slice %[[PACK]] into %[[PACK_OUT]]
+// CHECK-SAME: [%[[D0_OFFSET]], %[[D1_OFFSET]], 0, 0] [%[[D0_SIZE]], 1, 3, 16] [1, 1, 1, 1]
// -----
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It was disabled because there may be artificial padding. After refining the pack op semantics, we can assume that there is no artificial padding. Thus, the check can be removed, and we can unconditionally enable the consumer fusion if it is a perfect tiling case.