[Mlir-commits] [mlir] [mlir][linalg] Restrict linalg.pack to not have extra padding sizes. (PR #149624)

Han-Chung Wang llvmlistbot at llvm.org
Fri Jul 18 17:49:01 PDT 2025


https://github.com/hanhanW created https://github.com/llvm/llvm-project/pull/149624

None

>From 59aa0793dc0a7b93a2a5e6cab207143a43d0e930 Mon Sep 17 00:00:00 2001
From: hanhanW <hanhan0912 at gmail.com>
Date: Fri, 18 Jul 2025 17:46:08 -0700
Subject: [PATCH] [mlir][linalg] Restrict linalg.pack to not have extra padding
 sizes.

Signed-off-by: hanhanW <hanhan0912 at gmail.com>
---
 mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp      | 17 ++--
 .../Transforms/PackAndUnpackPatterns.cpp      | 29 +++++++
 mlir/test/Dialect/Linalg/canonicalize.mlir    | 25 +++---
 .../Linalg/data-layout-propagation.mlir       | 22 ++---
 mlir/test/Dialect/Linalg/invalid.mlir         | 15 ++--
 .../Dialect/Linalg/transform-lower-pack.mlir  | 16 ++--
 .../Tensor/fold-into-pack-and-unpack.mlir     | 30 +++++--
 .../tile-and-fuse-consumer.mlir               | 81 -------------------
 8 files changed, 102 insertions(+), 133 deletions(-)

diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
index 3aa6ac3ea0918..1d34d64b2198e 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -4601,8 +4601,8 @@ static bool isInvalidPackingPosSpecification(ArrayRef<int64_t> dimsPos,
 
 /// Returns true if the dimension of `sourceShape` is smaller than the dimension
 /// of the `limitShape`.
-static bool areAllInBound(ArrayRef<int64_t> sourceShape,
-                          ArrayRef<int64_t> limitShape) {
+static bool isCompatibleShape(ArrayRef<int64_t> sourceShape,
+                              ArrayRef<int64_t> limitShape) {
   assert(
       sourceShape.size() == limitShape.size() &&
       "expected source shape rank, and limit of the shape to have same rank");
@@ -4611,7 +4611,7 @@ static bool areAllInBound(ArrayRef<int64_t> sourceShape,
         int64_t sourceExtent = std::get<0>(it);
         int64_t limit = std::get<1>(it);
         return ShapedType::isDynamic(sourceExtent) ||
-               ShapedType::isDynamic(limit) || sourceExtent <= limit;
+               ShapedType::isDynamic(limit) || sourceExtent == limit;
       });
 }
 
@@ -4673,11 +4673,6 @@ static LogicalResult commonVerifierPackAndUnPackOp(OpTy packOrUnPack) {
   // represents full tiles.
   RankedTensorType expectedPackedType = PackOp::inferPackedType(
       unpackedType, packOrUnPack.getStaticTiles(), innerDimsPos, outerDimPerm);
-  if (!areAllInBound(expectedPackedType.getShape(), packedType.getShape())) {
-    return op->emitError("the shape of output is not large enough to hold the "
-                         "packed data. Expected at least ")
-           << expectedPackedType << ", got " << packedType;
-  }
   if (!llvm::all_of(
           llvm::zip(packedType.getShape().take_back(mixedTiles.size()),
                     mixedTiles),
@@ -4694,6 +4689,12 @@ static LogicalResult commonVerifierPackAndUnPackOp(OpTy packOrUnPack) {
     return op->emitError("mismatch in inner tile sizes specified and shaped of "
                          "tiled dimension in the packed type");
   }
+  if (!isCompatibleShape(expectedPackedType.getShape(),
+                          packedType.getShape())) {
+    return op->emitError("the shape of output is not large enough to hold the "
+                         "packed data. Expected at least ")
+           << expectedPackedType << ", got " << packedType;
+  }
   return success();
 }
 
diff --git a/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp b/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp
index 2afa2f9b71c2a..02fdd01ed548b 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/PackAndUnpackPatterns.cpp
@@ -10,6 +10,7 @@
 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Dialect/Utils/IndexingUtils.h"
+#include "mlir/Dialect/Utils/StaticValueUtils.h"
 #include "mlir/IR/PatternMatch.h"
 
 namespace mlir {
@@ -220,6 +221,34 @@ struct FoldPadWithPackOp : public OpRewritePattern<PackOp> {
       if (!isEqualConstantIntOrValue(paddingValue, constantPaddingValue))
         return failure();
 
+    RankedTensorType srcType = packOp.getSourceType();
+    RankedTensorType destType = packOp.getDestType();
+    SmallVector<int64_t> outerShapeWithoutTranspose(
+        destType.getShape().take_front(srcType.getRank()));
+    if (!packOp.getOuterDimsPerm().empty()) {
+      applyPermutationToVector(
+          outerShapeWithoutTranspose,
+          invertPermutationVector(packOp.getOuterDimsPerm()));
+    }
+    for (auto [pos, tileSize, high] :
+         llvm::zip_equal(packOp.getInnerDimsPos(), packOp.getStaticInnerTiles(),
+                         padOp.getMixedHighPad())) {
+      if (srcType.isDynamicDim(pos))
+        return failure();
+      if (ShapedType::isDynamic(outerShapeWithoutTranspose[pos]))
+        return failure();
+      if (ShapedType::isDynamic(tileSize))
+        return failure();
+      std::optional<int64_t> cstHigh = getConstantIntValue(high);
+      if (!cstHigh)
+        return failure();
+      int64_t paddingSize =
+          outerShapeWithoutTranspose[pos] * tileSize - srcType.getDimSize(pos);
+      // Do not fold the ops if it requires extra padding sizes.
+      if (paddingSize + cstHigh.value() >= tileSize)
+        return failure();
+    }
+
     rewriter.replaceOpWithNewOp<PackOp>(
         packOp, padOp.getSource(), packOp.getDest(), packOp.getInnerDimsPos(),
         packOp.getMixedTiles(), constantPaddingValue,
diff --git a/mlir/test/Dialect/Linalg/canonicalize.mlir b/mlir/test/Dialect/Linalg/canonicalize.mlir
index 7284ae7dbd673..dfe3bfd4a967a 100644
--- a/mlir/test/Dialect/Linalg/canonicalize.mlir
+++ b/mlir/test/Dialect/Linalg/canonicalize.mlir
@@ -1387,42 +1387,43 @@ func.func @recursive_effect(%arg : tensor<1xf32>) {
 // CHECK-LABEL: @recursive_effect
 //       CHECK: linalg.map
 
+// -----
+
 //===----------------------------------------------------------------------===//
 // linalg.pack
 //===----------------------------------------------------------------------===//
 
 // CHECK-LABEL: func @fold_pack_constant_splat
 //   CHECK-NOT: linalg.pack
-//       CHECK: arith.constant dense<1.000000e-01> : tensor<8x16x8x32xf32>
-func.func @fold_pack_constant_splat(%dest : tensor<8x16x8x32xf32>) -> tensor<8x16x8x32xf32> {
+//       CHECK: arith.constant dense<1.000000e-01> : tensor<4x8x8x32xf32>
+func.func @fold_pack_constant_splat(%dest : tensor<4x8x8x32xf32>) -> tensor<4x8x8x32xf32> {
   %cst = arith.constant dense<1.000000e-01> : tensor<64x128xf32>
   %0 = linalg.pack %cst outer_dims_perm = [1, 0] inner_dims_pos = [0, 1]
-    inner_tiles = [8, 32] into %dest : tensor<64x128xf32> -> tensor<8x16x8x32xf32>
-  return %0 : tensor<8x16x8x32xf32>
+    inner_tiles = [8, 32] into %dest : tensor<64x128xf32> -> tensor<4x8x8x32xf32>
+  return %0 : tensor<4x8x8x32xf32>
 }
 
 // -----
 
 // CHECK-LABEL: func @fold_padding_value_pack_constant_splat
 //   CHECK-NOT: linalg.pack
-//       CHECK: arith.constant dense<1.000000e-01> : tensor<8x16x8x32xf32>
-func.func @fold_padding_value_pack_constant_splat(%dest : tensor<8x16x8x32xf32>) -> tensor<8x16x8x32xf32> {
+//       CHECK: arith.constant dense<1.000000e-01> : tensor<4x8x8x32xf32>
+func.func @fold_padding_value_pack_constant_splat(%dest : tensor<4x8x8x32xf32>) -> tensor<4x8x8x32xf32> {
   %pad = arith.constant 1.000000e-01 : f32
   %cst = arith.constant dense<1.000000e-01> : tensor<63x127xf32>
   %0 = linalg.pack %cst
     padding_value(%pad : f32)
     outer_dims_perm = [1, 0] inner_dims_pos = [0, 1]
-    inner_tiles = [8, 32] into %dest : tensor<63x127xf32> -> tensor<8x16x8x32xf32>
-  return %0 : tensor<8x16x8x32xf32>
+    inner_tiles = [8, 32] into %dest : tensor<63x127xf32> -> tensor<4x8x8x32xf32>
+  return %0 : tensor<4x8x8x32xf32>
 }
 
-
 // -----
 
 // CHECK-LABEL: func @nofold_padding_value_pack_constant_splat
 //       CHECK: arith.constant dense<1.000000e-01> : tensor<63x127xf32>
 //       CHECK: linalg.pack
-func.func @nofold_padding_value_pack_constant_splat(%dest : tensor<8x16x8x32xf32>) -> tensor<8x16x8x32xf32> {
+func.func @nofold_padding_value_pack_constant_splat(%dest : tensor<4x8x8x32xf32>) -> tensor<4x8x8x32xf32> {
   %pad = arith.constant 0.0 : f32
   %cst = arith.constant dense<1.000000e-01> : tensor<63x127xf32>
   %0 = linalg.pack %cst
@@ -1430,8 +1431,8 @@ func.func @nofold_padding_value_pack_constant_splat(%dest : tensor<8x16x8x32xf32
     outer_dims_perm = [1, 0]
     inner_dims_pos = [0, 1]
     inner_tiles = [8, 32]
-    into %dest : tensor<63x127xf32> -> tensor<8x16x8x32xf32>
-  return %0 : tensor<8x16x8x32xf32>
+    into %dest : tensor<63x127xf32> -> tensor<4x8x8x32xf32>
+  return %0 : tensor<4x8x8x32xf32>
 }
 
 // -----
diff --git a/mlir/test/Dialect/Linalg/data-layout-propagation.mlir b/mlir/test/Dialect/Linalg/data-layout-propagation.mlir
index 6fc8d9f152f4e..ae87fffd1af02 100644
--- a/mlir/test/Dialect/Linalg/data-layout-propagation.mlir
+++ b/mlir/test/Dialect/Linalg/data-layout-propagation.mlir
@@ -1295,21 +1295,21 @@ func.func @no_bubble_up_pack_expanded_padding_through_expand_cannot_reassociate(
 
 // -----
 
-func.func @no_bubble_up_pack_extending_dimension_through_expand_cannot_reassociate(%arg0: tensor<32x64xf32>) -> tensor<8x4x16x8xf32> {
-  %empty = tensor.empty() : tensor<8x4x16x8xf32>
+func.func @bubble_up_pack_extending_dimension_through_expand_can_reassociate(%arg0: tensor<32x64xf32>) -> tensor<4x4x16x8xf32> {
+  %empty = tensor.empty() : tensor<4x4x16x8xf32>
   %expanded = tensor.expand_shape %arg0 [[0], [1, 2]] output_shape [32, 4, 16] : tensor<32x64xf32> into tensor<32x4x16xf32>
-  %pack = linalg.pack %expanded inner_dims_pos = [0] inner_tiles = [8] into %empty : tensor<32x4x16xf32> -> tensor<8x4x16x8xf32>
-  return %pack : tensor<8x4x16x8xf32>
+  %pack = linalg.pack %expanded inner_dims_pos = [0] inner_tiles = [8] into %empty : tensor<32x4x16xf32> -> tensor<4x4x16x8xf32>
+  return %pack : tensor<4x4x16x8xf32>
 }
-// CHECK-LABEL: func.func @no_bubble_up_pack_extending_dimension_through_expand_cannot_reassociate(
+// CHECK-LABEL: func.func @bubble_up_pack_extending_dimension_through_expand_can_reassociate(
 // CHECK-SAME:      %[[ARG0:[a-zA-Z0-9]+]]
-// CHECK:         %[[EMPTY:.+]] = tensor.empty() : tensor<8x4x16x8xf32>
-// CHECK:         %[[EXPANDED:.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0], [1, 2]]
-// CHECK-SAME:      output_shape [32, 4, 16] : tensor<32x64xf32> into tensor<32x4x16xf32>
-// CHECK:         %[[PACK:.+]] = linalg.pack %[[EXPANDED]]
+// CHECK:         %[[EMPTY:.+]] = tensor.empty() : tensor<4x64x8xf32>
+// CHECK:         %[[PACK:.+]] = linalg.pack %[[ARG0]]
 // CHECK-SAME:      inner_dims_pos = [0] inner_tiles = [8] into %[[EMPTY]]
-// CHECK-SAME:      : tensor<32x4x16xf32> -> tensor<8x4x16x8xf32>
-// CHECK:         return %[[PACK]] : tensor<8x4x16x8xf32>
+// CHECK-SAME:      : tensor<32x64xf32> -> tensor<4x64x8xf32>
+// CHECK:         %[[EXPANDED:.+]] = tensor.expand_shape %[[PACK]] {{\[}}[0], [1, 2], [3]]
+// CHECK-SAME:      output_shape [4, 4, 16, 8] : tensor<4x64x8xf32> into tensor<4x4x16x8xf32>
+// CHECK:         return %[[EXPANDED]] : tensor<4x4x16x8xf32>
 
 // -----
 
diff --git a/mlir/test/Dialect/Linalg/invalid.mlir b/mlir/test/Dialect/Linalg/invalid.mlir
index da1dfc7b6a624..83611a217f652 100644
--- a/mlir/test/Dialect/Linalg/invalid.mlir
+++ b/mlir/test/Dialect/Linalg/invalid.mlir
@@ -1760,6 +1760,7 @@ func.func @pack_invalid(%input: tensor<256x128xf32>, %output: tensor<8x8x32x16xf
 }
 
 // -----
+
 func.func @pack_mismatch_inner_tile_size_and_output_shape(
   %input : tensor<?x?xf32>, %output : tensor<?x?x8x8xf32>) -> tensor<?x?x8x8xf32> {
   // expected-error at +1 {{mismatch in inner tile sizes specified and shaped of tiled dimension in the packed type}}
@@ -1834,17 +1835,17 @@ func.func @pack_invalid_result_shape(%input: tensor<256x128xf32>, %output: tenso
 
 // -----
 
-func.func @pack_invalid(%input: tensor<256x128xf32>, %output: tensor<8x8x32x16xf32>) -> tensor<8x8x32x16xf32> {
-  // expected-error at +1 {{the shape of output is not large enough to hold the packed data. Expected at least 'tensor<8x8x16x32xf32>', got 'tensor<8x8x32x16xf32>'}}
-  %0 = linalg.pack %input inner_dims_pos = [1, 0] inner_tiles = [16, 32] into %output : tensor<256x128xf32> -> tensor<8x8x32x16xf32>
-  return %0 : tensor<8x8x32x16xf32>
+func.func @pack_invalid(%input: tensor<256x128xf32>, %output: tensor<8x7x16x32xf32>) -> tensor<8x7x16x32xf32> {
+  // expected-error at +1 {{the shape of output is not large enough to hold the packed data. Expected at least 'tensor<8x8x16x32xf32>', got 'tensor<8x7x16x32xf32>'}}
+  %0 = linalg.pack %input inner_dims_pos = [1, 0] inner_tiles = [16, 32] into %output : tensor<256x128xf32> -> tensor<8x7x16x32xf32>
+  return %0 : tensor<8x7x16x32xf32>
 }
 
 // -----
 
-func.func @unpack_invalid(%output: tensor<256x128xf32>, %input: tensor<8x8x32x16xf32>) -> tensor<256x128xf32> {
-  // expected-error at +1 {{the shape of output is not large enough to hold the packed data. Expected at least 'tensor<8x32x4x32xf32>', got 'tensor<8x8x32x16xf32>'}}
-  %0 = linalg.unpack %input inner_dims_pos = [1, 0] inner_tiles = [4, 32] into %output : tensor<8x8x32x16xf32> -> tensor<256x128xf32>
+func.func @unpack_invalid(%output: tensor<256x128xf32>, %input: tensor<8x8x4x32xf32>) -> tensor<256x128xf32> {
+  // expected-error at +1 {{the shape of output is not large enough to hold the packed data. Expected at least 'tensor<8x32x4x32xf32>', got 'tensor<8x8x4x32xf32>'}}
+  %0 = linalg.unpack %input inner_dims_pos = [1, 0] inner_tiles = [4, 32] into %output : tensor<8x8x4x32xf32> -> tensor<256x128xf32>
   return %0 : tensor<256x128xf32>
 }
 
diff --git a/mlir/test/Dialect/Linalg/transform-lower-pack.mlir b/mlir/test/Dialect/Linalg/transform-lower-pack.mlir
index 81fd7a8a947d7..9e7681d1a1b7d 100644
--- a/mlir/test/Dialect/Linalg/transform-lower-pack.mlir
+++ b/mlir/test/Dialect/Linalg/transform-lower-pack.mlir
@@ -326,23 +326,23 @@ module attributes {transform.with_named_sequence} {
 // -----
 
 // CHECK-LABEL: func.func @pack_with_pad(
-func.func @pack_with_pad(%src: tensor<4225x12xf32>, %dest: tensor<265x16x16x1xf32>)
-    -> tensor<265x16x16x1xf32> {
+func.func @pack_with_pad(%src: tensor<4225x12xf32>, %dest: tensor<265x12x16x1xf32>)
+    -> tensor<265x12x16x1xf32> {
   //      CHECK: tensor.pad {{.*}} low[0, 0]
-  //      CHECK:   : tensor<4225x12xf32> to tensor<4240x16xf32>
+  //      CHECK:   : tensor<4225x12xf32> to tensor<4240x12xf32>
   //      CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1], [2, 3]]
-  // CHECK-SAME:   : tensor<4240x16xf32> into tensor<265x16x16x1xf32>
+  // CHECK-SAME:   : tensor<4240x12xf32> into tensor<265x16x12x1xf32>
   //      CHECK: linalg.transpose
-  // CHECK-SAME:   ins(%{{[a-zA-Z0-9]*}} : tensor<265x16x16x1xf32>)
-  // CHECK-SAME:   outs(%{{[a-zA-Z0-9]*}} : tensor<265x16x16x1xf32>)
+  // CHECK-SAME:   ins(%{{[a-zA-Z0-9]*}} : tensor<265x16x12x1xf32>)
+  // CHECK-SAME:   outs(%{{[a-zA-Z0-9]*}} : tensor<265x12x16x1xf32>)
   // CHECK-SAME:   permutation = [0, 2, 1, 3]
   %cst = arith.constant 0.000000e+00 : f32
   %0 = linalg.pack %src
     padding_value(%cst : f32)
     inner_dims_pos = [0, 1]
     inner_tiles = [16, 1] into %dest
-    : tensor<4225x12xf32> -> tensor<265x16x16x1xf32>
-  return %0 : tensor<265x16x16x1xf32>
+    : tensor<4225x12xf32> -> tensor<265x12x16x1xf32>
+  return %0 : tensor<265x12x16x1xf32>
 }
 
 module attributes {transform.with_named_sequence} {
diff --git a/mlir/test/Dialect/Tensor/fold-into-pack-and-unpack.mlir b/mlir/test/Dialect/Tensor/fold-into-pack-and-unpack.mlir
index 16efa73f87a2a..eb62de13ebc94 100644
--- a/mlir/test/Dialect/Tensor/fold-into-pack-and-unpack.mlir
+++ b/mlir/test/Dialect/Tensor/fold-into-pack-and-unpack.mlir
@@ -59,13 +59,13 @@ func.func @nofold_unpack_slice_rank_reduced(%arg0 : tensor<?x?x8x4xf32>, %arg1 :
 
 // -----
 
-func.func @pad_pack(%src: tensor<16641x16xf32>) -> tensor<2082x1x8x32xf32> {
+func.func @pad_pack(%src: tensor<16649x16xf32>) -> tensor<2082x1x8x32xf32> {
   %c0 = arith.constant 0 : index
   %cst = arith.constant 0.000000e+00 : f32
-  %padded = tensor.pad %src low[0, 0] high[15, 0] {
+  %padded = tensor.pad %src low[0, 0] high[7, 0] {
   ^bb0(%arg0: index, %arg1: index):
     tensor.yield %cst : f32
-  } : tensor<16641x16xf32> to tensor<16656x16xf32>
+  } : tensor<16649x16xf32> to tensor<16656x16xf32>
   %empty = tensor.empty() : tensor<2082x1x8x32xf32>
   %pack = linalg.pack %padded padding_value(%cst : f32) inner_dims_pos = [0, 1] inner_tiles = [8, 32] into %empty
       : tensor<16656x16xf32> -> tensor<2082x1x8x32xf32>
@@ -81,10 +81,10 @@ func.func @pad_pack(%src: tensor<16641x16xf32>) -> tensor<2082x1x8x32xf32> {
 
 // -----
 
-func.func @nofold_pad_pack(%src: tensor<16641x16xf32>) -> tensor<2082x1x8x32xf32> {
+func.func @nofold_pad_pack_extra_padding(%src: tensor<16641x16xf32>) -> tensor<2082x1x8x32xf32> {
   %c0 = arith.constant 0 : index
   %cst = arith.constant 0.000000e+00 : f32
-  %padded = tensor.pad %src nofold low[0, 0] high[15, 0] {
+  %padded = tensor.pad %src low[0, 0] high[15, 0] {
   ^bb0(%arg0: index, %arg1: index):
     tensor.yield %cst : f32
   } : tensor<16641x16xf32> to tensor<16656x16xf32>
@@ -93,7 +93,25 @@ func.func @nofold_pad_pack(%src: tensor<16641x16xf32>) -> tensor<2082x1x8x32xf32
       : tensor<16656x16xf32> -> tensor<2082x1x8x32xf32>
   return %pack : tensor<2082x1x8x32xf32>
 }
-// CHECK-LABEL: func.func @nofold_pad_pack
+// CHECK-LABLE: func.func @nofold_pad_pack_extra_padding(
+// CHECK:         tensor.pad
+// CHECK:         linalg.pack
+
+// -----
+
+func.func @nofold_pad_pack(%src: tensor<16649x16xf32>) -> tensor<2082x1x8x32xf32> {
+  %c0 = arith.constant 0 : index
+  %cst = arith.constant 0.000000e+00 : f32
+  %padded = tensor.pad %src nofold low[0, 0] high[7, 0] {
+  ^bb0(%arg0: index, %arg1: index):
+    tensor.yield %cst : f32
+  } : tensor<16649x16xf32> to tensor<16656x16xf32>
+  %empty = tensor.empty() : tensor<2082x1x8x32xf32>
+  %pack = linalg.pack %padded padding_value(%cst : f32) inner_dims_pos = [0, 1] inner_tiles = [8, 32] into %empty
+      : tensor<16656x16xf32> -> tensor<2082x1x8x32xf32>
+  return %pack : tensor<2082x1x8x32xf32>
+}
+// CHECK-LABEL: func.func @nofold_pad_pack(
 // CHECK:         tensor.pad
 // CHECK:         linalg.pack
 
diff --git a/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir b/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir
index cdbca7228ded3..e48e5c6c308be 100644
--- a/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir
+++ b/mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir
@@ -646,87 +646,6 @@ module attributes {transform.with_named_sequence} {
 
 // -----
 
-// It is valid to fuse the pack if the dimension is not tiled even when it needs
-// extra padding.
-
-func.func @fuse_pack_consumer_with_untiled_extra_padding(%arg0: tensor<64x32xf32>, %arg1: tensor<64x32xf32>) -> tensor<33x2x3x16xf32> {
-  %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>
-    scf.forall.in_parallel {
-      tensor.parallel_insert_slice %2 into %arg3[0, %arg2] [64, 16] [1, 1] : tensor<64x16xf32> into tensor<64x32xf32>
-    }
-  }
-  %1 = tensor.empty() : tensor<33x2x3x16xf32>
-  %cst = arith.constant 0.000000e+00 : f32
-  %pack = linalg.pack %0 padding_value(%cst : f32) inner_dims_pos = [0, 1] inner_tiles = [3, 16] into %1 : tensor<64x32xf32> -> tensor<33x2x3x16xf32>
-  return %pack : tensor<33x2x3x16xf32>
-}
-
-module attributes {transform.with_named_sequence} {
-  transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
-    %0 = transform.structured.match ops{["tensor.parallel_insert_slice"]} in %arg0 : (!transform.any_op) -> !transform.any_op
-    %1 = transform.structured.match ops{["scf.forall"]} in %arg0 : (!transform.any_op) -> !transform.any_op
-    %consumer, %fused_consumer = transform.test.fuse_consumer %0 in(%1) : (!transform.any_op, !transform.any_op) -> (!transform.any_op, !transform.any_op)
-    transform.yield
-  }
-}
-//      CHECK: #[[PACK_RESULT_MAP:.*]] = affine_map<(d0) -> (d0 floordiv 16)>
-//      CHECK: func.func @fuse_pack_consumer_with_untiled_extra_padding(
-// CHECK-SAME:     %[[ARG0:[a-zA-Z0-9]+]]
-// CHECK-SAME:     %[[ARG1:[a-zA-Z0-9]+]]
-//  CHECK-DAG:   %[[OUT_INIT:.*]] = tensor.empty() : tensor<33x2x3x16xf32>
-//  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:      %[[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] [33, 1, 3, 16] [1, 1, 1, 1]
-//      CHECK:      %[[TILED_PACK_OUT:.*]] = 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] [33, 1, 3, 16] [1, 1, 1, 1]
-
-// -----
-
-// If the dimension is tiled and it needs extra padding, do not fuse the pack
-// op.
-
-func.func @nofuse_pack_consumer_with_extra_padding(%arg0: tensor<64x32xf32>, %arg1: tensor<64x32xf32>) -> tensor<23x32x3x16xf32> {
-  %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>
-    scf.forall.in_parallel {
-      // expected-error @below {{failed to fuse consumer of slice}}
-      tensor.parallel_insert_slice %2 into %arg3[0, %arg2] [64, 16] [1, 1] : tensor<64x16xf32> into tensor<64x32xf32>
-    }
-  }
-  %1 = tensor.empty() : tensor<23x32x3x16xf32>
-  %cst = arith.constant 0.000000e+00 : f32
-  %pack = linalg.pack %0 padding_value(%cst : f32) inner_dims_pos = [0, 1] inner_tiles = [3, 16] into %1 : tensor<64x32xf32> -> tensor<23x32x3x16xf32>
-  return %pack : tensor<23x32x3x16xf32>
-}
-
-module attributes {transform.with_named_sequence} {
-  transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
-    %0 = transform.structured.match ops{["tensor.parallel_insert_slice"]} in %arg0 : (!transform.any_op) -> !transform.any_op
-    %1 = transform.structured.match ops{["scf.forall"]} in %arg0 : (!transform.any_op) -> !transform.any_op
-    %consumer, %fused_consumer = transform.test.fuse_consumer %0 in(%1) : (!transform.any_op, !transform.any_op) -> (!transform.any_op, !transform.any_op)
-    transform.yield
-  }
-}
-
-// -----
-
 // Imperfect tiling is not supported in pack op consumer fusion.
 
 #map = affine_map<(d0) -> (d0 * 5)>



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