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[mlir][linalg] Update vectorization logic for linalg.pack (#149156) #158926

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Merged
merged 2 commits into from
Sep 18, 2025
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[mlir][linalg] Update vectorization logic for linalg.pack (#149156) #158926

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0b9fed9
[mlir][linalg] Update vectorization logic for linalg.pack (#149156)
banach-space Sep 16, 2025
058b49c
Fix test failures that I missed before
banach-space Sep 17, 2025
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13 changes: 2 additions & 11 deletions mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
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Expand Up @@ -1776,12 +1776,6 @@ vectorizeAsTensorPackOp(RewriterBase &rewriter, linalg::PackOp packOp,
rewriter, loc,
rewriter.getZeroAttr(packOp.getSourceType().getElementType()));
}
Comment on lines 1776 to 1778
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ig we cant fix it in this patch but this is wrong and should be using posion

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Thanks for pointing this out! #159536

ReifiedRankedShapedTypeDims reifiedReturnShapes;
LogicalResult status =
cast<ReifyRankedShapedTypeOpInterface>(packOp.getOperation())
.reifyResultShapes(rewriter, reifiedReturnShapes);
(void)status; // prevent unused variable warning on non-assert builds.
assert(succeeded(status) && "failed to reify result shapes");

// If the input vector sizes are not provided, then the vector sizes are
// determined by the result tensor shape. In case the vector sizes aren't
Expand Down Expand Up @@ -1823,11 +1817,8 @@ vectorizeAsTensorPackOp(RewriterBase &rewriter, linalg::PackOp packOp,
rewriter, loc, shapeCastOp.getResult(), destPermutation);

// Create TransferWriteOp.
Value dest = tensor::EmptyOp::create(
rewriter, loc, reifiedReturnShapes[0],
transposeOp.getResult().getType().getElementType());
Operation *write =
createWriteOrMaskedWrite(rewriter, loc, transposeOp.getResult(), dest);
Operation *write = createWriteOrMaskedWrite(
rewriter, loc, transposeOp.getResult(), packOp.getDest());
newResults.push_back(write->getResult(0));
return success();
}
Expand Down
6 changes: 2 additions & 4 deletions mlir/test/Dialect/Linalg/vectorization/linalg-ops-with-patterns.mlir
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Expand Up @@ -344,8 +344,7 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[VAL_4:.*]] = vector.transfer_read %[[VAL_0]]{{\[}}%[[VAL_3]], %[[VAL_3]], %[[VAL_3]]], %[[VAL_2]] {in_bounds = [true, true, true]} : tensor<32x8x16xf32>, vector<32x8x16xf32>
// CHECK: %[[VAL_5:.*]] = vector.shape_cast %[[VAL_4]] : vector<32x8x16xf32> to vector<32x4x2x1x16xf32>
// CHECK: %[[VAL_6:.*]] = vector.transpose %[[VAL_5]], [1, 3, 0, 4, 2] : vector<32x4x2x1x16xf32> to vector<4x1x32x16x2xf32>
// CHECK: %[[VAL_7:.*]] = tensor.empty() : tensor<4x1x32x16x2xf32>
// CHECK: %[[VAL_8:.*]] = vector.transfer_write %[[VAL_6]], %[[VAL_7]]{{\[}}%[[VAL_3]], %[[VAL_3]], %[[VAL_3]], %[[VAL_3]], %[[VAL_3]]] {in_bounds = [true, true, true, true, true]} : vector<4x1x32x16x2xf32>, tensor<4x1x32x16x2xf32>
// CHECK: %[[VAL_8:.*]] = vector.transfer_write %[[VAL_6]], %[[VAL_1]]{{\[}}%[[VAL_3]], %[[VAL_3]], %[[VAL_3]], %[[VAL_3]], %[[VAL_3]]] {in_bounds = [true, true, true, true, true]} : vector<4x1x32x16x2xf32>, tensor<4x1x32x16x2xf32>
// CHECK: return %[[VAL_8]] : tensor<4x1x32x16x2xf32>

// -----
Expand All @@ -364,8 +363,7 @@ func.func @test_vectorize_padded_pack(%arg0: tensor<32x7x15xf32>, %arg1: tensor<
// CHECK: %[[VAL_4:.*]] = vector.transfer_read %[[VAL_0]]{{\[}}%[[VAL_3]], %[[VAL_3]], %[[VAL_3]]], %[[VAL_2]] {in_bounds = [true, false, false]} : tensor<32x7x15xf32>, vector<32x8x16xf32>
// CHECK: %[[VAL_5:.*]] = vector.shape_cast %[[VAL_4]] : vector<32x8x16xf32> to vector<32x4x2x1x16xf32>
// CHECK: %[[VAL_6:.*]] = vector.transpose %[[VAL_5]], [0, 1, 3, 4, 2] : vector<32x4x2x1x16xf32> to vector<32x4x1x16x2xf32>
// CHECK: %[[VAL_7:.*]] = tensor.empty() : tensor<32x4x1x16x2xf32>
// CHECK: %[[VAL_8:.*]] = vector.transfer_write %[[VAL_6]], %[[VAL_7]]{{\[}}%[[VAL_3]], %[[VAL_3]], %[[VAL_3]], %[[VAL_3]], %[[VAL_3]]] {in_bounds = [true, true, true, true, true]} : vector<32x4x1x16x2xf32>, tensor<32x4x1x16x2xf32>
// CHECK: %[[VAL_8:.*]] = vector.transfer_write %[[VAL_6]], %[[VAL_1]]{{\[}}%[[VAL_3]], %[[VAL_3]], %[[VAL_3]], %[[VAL_3]], %[[VAL_3]]] {in_bounds = [true, true, true, true, true]} : vector<32x4x1x16x2xf32>, tensor<32x4x1x16x2xf32>
// CHECK: return %[[VAL_8]] : tensor<32x4x1x16x2xf32>

module attributes {transform.with_named_sequence} {
Expand Down
159 changes: 82 additions & 77 deletions mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir
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Expand Up @@ -1301,25 +1301,27 @@ func.func @test_vectorize_unpack_no_vector_sizes_permute(%source: tensor<4x7x4xf
// different - vector sizes are inferred (rather than user-specified) and hence
// masking was used.

func.func @test_vectorize_pack(%arg0: tensor<32x8x16xf32>, %arg1: tensor<4x1x32x16x2xf32>) -> tensor<4x1x32x16x2xf32> {
%pack = linalg.pack %arg0 outer_dims_perm = [1, 2, 0] inner_dims_pos = [2, 1] inner_tiles = [16, 2] into %arg1 : tensor<32x8x16xf32> -> tensor<4x1x32x16x2xf32>
// CHECK-LABEL: func @test_vectorize_pack
// CHECK-SAME: %[[SRC:.*]]: tensor<32x8x16xf32>,
// CHECK-SAME: %[[DEST:.*]]: tensor<4x1x32x16x2xf32>
func.func @test_vectorize_pack(%src: tensor<32x8x16xf32>, %dest: tensor<4x1x32x16x2xf32>) -> tensor<4x1x32x16x2xf32> {
%pack = linalg.pack %src outer_dims_perm = [1, 2, 0] inner_dims_pos = [2, 1] inner_tiles = [16, 2] into %dest : tensor<32x8x16xf32> -> tensor<4x1x32x16x2xf32>
return %pack : tensor<4x1x32x16x2xf32>
}
// CHECK-DAG: %[[cst:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
// CHECK: %[[read:.*]] = vector.transfer_read %{{.*}}[%[[c0]], %[[c0]], %[[c0]]], %[[cst]]
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[READ:.*]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]], %[[C0]]], %[[CST]]
// CHECK-SAME: {in_bounds = [true, true, true]} : tensor<32x8x16xf32>, vector<32x8x16xf32>
// CHECK: %[[shape_cast:.*]] = vector.shape_cast %[[read]] : vector<32x8x16xf32> to vector<32x4x2x1x16xf32>
// CHECK: %[[transpose:.*]] = vector.transpose %[[shape_cast]], [1, 3, 0, 4, 2] : vector<32x4x2x1x16xf32> to vector<4x1x32x16x2xf32>
// CHECK-DAG: %[[c0_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[empty:.*]] = tensor.empty() : tensor<4x1x32x16x2xf32>
// CHECK: %[[write:.*]] = vector.transfer_write %[[transpose]], %[[empty]][%[[c0_1]], %[[c0_1]], %[[c0_1]], %[[c0_1]], %[[c0_1]]]
// CHECK: %[[SC:.*]] = vector.shape_cast %[[READ]] : vector<32x8x16xf32> to vector<32x4x2x1x16xf32>
// CHECK: %[[TR:.*]] = vector.transpose %[[SC]], [1, 3, 0, 4, 2] : vector<32x4x2x1x16xf32> to vector<4x1x32x16x2xf32>
// CHECK-DAG: %[[C0_1:.*]] = arith.constant 0 : index
// CHECK: %[[write:.*]] = vector.transfer_write %[[TR]], %[[DEST]][%[[C0_1]], %[[C0_1]], %[[C0_1]], %[[C0_1]], %[[C0_1]]]
// CHECK-SAME: {in_bounds = [true, true, true, true, true]} : vector<4x1x32x16x2xf32>, tensor<4x1x32x16x2xf32>
// CHECK: return %[[write]] : tensor<4x1x32x16x2xf32>

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.pack"]} in %arg0 : (!transform.any_op) -> !transform.any_op
transform.named_sequence @__transform_main(%src: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.pack"]} in %src : (!transform.any_op) -> !transform.any_op
transform.structured.vectorize %0 vector_sizes [4, 1, 32] : !transform.any_op
transform.yield
}
Expand All @@ -1331,26 +1333,28 @@ module attributes {transform.with_named_sequence} {
// different - vector sizes are inferred (rather than user-specified) and hence
// masking was used.

func.func @test_vectorize_padded_pack(%arg0: tensor<32x7x15xf32>, %arg1: tensor<32x4x1x16x2xf32>) -> tensor<32x4x1x16x2xf32> {
// CHECK-LABEL: func @test_vectorize_padded_pack
// CHECK-SAME: %[[SRC:.*]]: tensor<32x7x15xf32>,
// CHECK-SAME: %[[DEST:.*]]: tensor<32x4x1x16x2xf32>
func.func @test_vectorize_padded_pack(%src: tensor<32x7x15xf32>, %dest: tensor<32x4x1x16x2xf32>) -> tensor<32x4x1x16x2xf32> {
%pad = arith.constant 0.000000e+00 : f32
%pack = linalg.pack %arg0 padding_value(%pad : f32) inner_dims_pos = [2, 1] inner_tiles = [16, 2] into %arg1 : tensor<32x7x15xf32> -> tensor<32x4x1x16x2xf32>
%pack = linalg.pack %src padding_value(%pad : f32) inner_dims_pos = [2, 1] inner_tiles = [16, 2] into %dest : tensor<32x7x15xf32> -> tensor<32x4x1x16x2xf32>
return %pack : tensor<32x4x1x16x2xf32>
}
// CHECK-DAG: %[[cst:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[c32:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[c7:.*]] = arith.constant 7 : index
// CHECK-DAG: %[[c15:.*]] = arith.constant 15 : index
// CHECK: %[[mask:.*]] = vector.create_mask %[[c32]], %[[c7]], %[[c15]] : vector<32x8x16xi1>
// CHECK: %[[masked_read:.*]] = vector.mask %[[mask]] {
// CHECK-SAME: vector.transfer_read %{{.*}}[%[[c0]], %[[c0]], %[[c0]]], %[[cst]]
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C32:.*]] = arith.constant 32 : index
// CHECK-DAG: %[[C7:.*]] = arith.constant 7 : index
// CHECK-DAG: %[[C15:.*]] = arith.constant 15 : index
// CHECK: %[[MASK:.*]] = vector.create_mask %[[C32]], %[[C7]], %[[C15]] : vector<32x8x16xi1>
// CHECK: %[[READ:.*]] = vector.mask %[[MASK]] {
// CHECK-SAME: vector.transfer_read %{{.*}}[%[[C0]], %[[C0]], %[[C0]]], %[[CST]]
// CHECK-SAME: {in_bounds = [true, true, true]} : tensor<32x7x15xf32>, vector<32x8x16xf32>
// CHECK-SAME: } : vector<32x8x16xi1> -> vector<32x8x16xf32>
// CHECK: %[[shape_cast:.*]] = vector.shape_cast %[[masked_read]] : vector<32x8x16xf32> to vector<32x4x2x1x16xf32>
// CHECK: %[[transpose:.*]] = vector.transpose %[[shape_cast]], [0, 1, 3, 4, 2] : vector<32x4x2x1x16xf32> to vector<32x4x1x16x2xf32>
// CHECK-DAG: %[[c0_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[empty:.*]] = tensor.empty() : tensor<32x4x1x16x2xf32>
// CHECK: %[[write:.*]] = vector.transfer_write %[[transpose]], %[[empty]][%[[c0_1]], %[[c0_1]], %[[c0_1]], %[[c0_1]], %[[c0_1]]]
// CHECK: %[[SC:.*]] = vector.shape_cast %[[READ]] : vector<32x8x16xf32> to vector<32x4x2x1x16xf32>
// CHECK: %[[TR:.*]] = vector.transpose %[[SC]], [0, 1, 3, 4, 2] : vector<32x4x2x1x16xf32> to vector<32x4x1x16x2xf32>
// CHECK-DAG: %[[C0_1:.*]] = arith.constant 0 : index
// CHECK: %[[write:.*]] = vector.transfer_write %[[TR]], %[[DEST]][%[[C0_1]], %[[C0_1]], %[[C0_1]], %[[C0_1]], %[[C0_1]]]
// CHECK-SAME: {in_bounds = [true, true, true, true, true]} : vector<32x4x1x16x2xf32>, tensor<32x4x1x16x2xf32>
// CHECK: return %[[write]] : tensor<32x4x1x16x2xf32>

Expand All @@ -1364,38 +1368,37 @@ module attributes {transform.with_named_sequence} {

// -----

func.func @test_vectorize_dynamic_pack(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?x16x2xf32>) -> tensor<?x?x16x2xf32> {
%pack = linalg.pack %arg0 inner_dims_pos = [1, 0] inner_tiles = [16, 2] into %arg1 : tensor<?x?xf32> -> tensor<?x?x16x2xf32>
// CHECK-LABEL: func @test_vectorize_dynamic_pack
// CHECK-SAME: %[[SRC:.*]]: tensor<?x?xf32>,
// CHECK-SAME: %[[DEST:.*]]: tensor<?x?x16x2xf32>
func.func @test_vectorize_dynamic_pack(%src: tensor<?x?xf32>, %dest: tensor<?x?x16x2xf32>) -> tensor<?x?x16x2xf32> {
%pack = linalg.pack %src inner_dims_pos = [1, 0] inner_tiles = [16, 2] into %dest : tensor<?x?xf32> -> tensor<?x?x16x2xf32>
return %pack : tensor<?x?x16x2xf32>
}
// CHECK-DAG: %[[cst:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[c1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[d0:.*]] = tensor.dim {{.*}} %[[c0]] : tensor<?x?x16x2xf32>
// CHECK-DAG: %[[d1:.*]] = tensor.dim {{.*}} %[[c1]] : tensor<?x?x16x2xf32>
// CHECK-DAG: %[[c0_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[c0_0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[c1_0:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[d0_0:.*]] = tensor.dim {{.*}} %[[c0_0]] : tensor<?x?xf32>
// CHECK-DAG: %[[d1_0:.*]] = tensor.dim {{.*}} %[[c1_0]] : tensor<?x?xf32>
// CHECK: %[[mask:.*]] = vector.create_mask %[[d0_0]], %[[d1_0]] : vector<8x16xi1>
// CHECK: %[[masked_read:.*]] = vector.mask %[[mask]] {
// CHECK-SAME: vector.transfer_read %{{.*}}[%[[c0_1]], %[[c0_1]]], %[[cst]]

// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[C0_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C0_0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C1_0:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[D0_0:.*]] = tensor.dim {{.*}} %[[C0_0]] : tensor<?x?xf32>
// CHECK-DAG: %[[D1_0:.*]] = tensor.dim {{.*}} %[[C1_0]] : tensor<?x?xf32>
// CHECK: %[[MASK:.*]] = vector.create_mask %[[D0_0]], %[[D1_0]] : vector<8x16xi1>
// CHECK: %[[READ:.*]] = vector.mask %[[MASK]] {
// CHECK-SAME: vector.transfer_read %{{.*}}[%[[C0_1]], %[[C0_1]]], %[[CST]]
// CHECK-SAME: {in_bounds = [true, true]} : tensor<?x?xf32>, vector<8x16xf32>
// CHECK-SAME: } : vector<8x16xi1> -> vector<8x16xf32>
// CHECK: %[[shape_cast:.*]] = vector.shape_cast %[[masked_read]] : vector<8x16xf32> to vector<4x2x1x16xf32>
// CHECK: %[[transpose:.*]] = vector.transpose %[[shape_cast]], [0, 2, 3, 1] : vector<4x2x1x16xf32> to vector<4x1x16x2xf32>
// CHECK-DAG: %[[c0_2:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[c16:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[c2:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[empty:.*]] = tensor.empty(%[[d0]], %[[d1]]) : tensor<?x?x16x2xf32>
// CHECK-DAG: %[[d2:.*]] = tensor.dim %[[empty]], {{.*}} : tensor<?x?x16x2xf32>
// CHECK-DAG: %[[d3:.*]] = tensor.dim %[[empty]], {{.*}} : tensor<?x?x16x2xf32>
// CHECK: %[[mask_0:.*]] = vector.create_mask %[[d2]], %[[d3]], %[[c16]], %[[c2]] : vector<4x1x16x2xi1>
// CHECK: %[[masked_write:.*]] = vector.mask %[[mask_0]] {
// CHECK-SAME: vector.transfer_write %[[transpose]], %[[empty]][%[[c0_2]], %[[c0_2]], %[[c0_2]], %[[c0_2]]]
// CHECK: %[[SC:.*]] = vector.shape_cast %[[READ]] : vector<8x16xf32> to vector<4x2x1x16xf32>
// CHECK: %[[TR:.*]] = vector.transpose %[[SC]], [0, 2, 3, 1] : vector<4x2x1x16xf32> to vector<4x1x16x2xf32>
// CHECK-DAG: %[[C0_2:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C16:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[D2:.*]] = tensor.dim %[[DEST]], {{.*}} : tensor<?x?x16x2xf32>
// CHECK-DAG: %[[D3:.*]] = tensor.dim %[[DEST]], {{.*}} : tensor<?x?x16x2xf32>
// CHECK: %[[MASK_0:.*]] = vector.create_mask %[[D2]], %[[D3]], %[[C16]], %[[C2]] : vector<4x1x16x2xi1>
// CHECK: %[[WRITE:.*]] = vector.mask %[[MASK_0]] {
// CHECK-SAME: vector.transfer_write %[[TR]], %[[DEST]][%[[C0_2]], %[[C0_2]], %[[C0_2]], %[[C0_2]]]
// CHECK-SAME: {in_bounds = [true, true, true, true]} : vector<4x1x16x2xf32>, tensor<?x?x16x2xf32>
// CHECK: return %[[masked_write]] : tensor<?x?x16x2xf32>
// CHECK: return %[[WRITE]] : tensor<?x?x16x2xf32>

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
Expand All @@ -1407,22 +1410,23 @@ module attributes {transform.with_named_sequence} {

// -----

// CHECK-LABEL: test_vectorize_pack_no_vector_sizes
func.func @test_vectorize_pack_no_vector_sizes(%arg0: tensor<64x4xf32>, %arg1: tensor<2x4x16x2xf32>) -> tensor<2x4x16x2xf32> {
%pack = linalg.pack %arg0 outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [16, 2] into %arg1 : tensor<64x4xf32> -> tensor<2x4x16x2xf32>
// CHECK-LABEL: func @test_vectorize_pack_no_vector_sizes
// CHECK-SAME: %[[SRC:.*]]: tensor<64x4xf32>,
// CHECK-SAME: %[[DEST:.*]]: tensor<2x4x16x2xf32>
func.func @test_vectorize_pack_no_vector_sizes(%src: tensor<64x4xf32>, %dest: tensor<2x4x16x2xf32>) -> tensor<2x4x16x2xf32> {
%pack = linalg.pack %src outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [16, 2] into %dest : tensor<64x4xf32> -> tensor<2x4x16x2xf32>
return %pack : tensor<2x4x16x2xf32>
}
// CHECK-DAG: %[[cst:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
// CHECK: %[[read:.*]] = vector.transfer_read %{{.*}}[%[[c0]], %[[c0]]], %[[cst]]
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[READ:.*]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]]], %[[CST]]
// CHECK-SAME: {in_bounds = [true, true]} : tensor<64x4xf32>, vector<64x4xf32>
// CHECK: %[[shape_cast:.*]] = vector.shape_cast %[[read]] : vector<64x4xf32> to vector<4x16x2x2xf32>
// CHECK: %[[transpose:.*]] = vector.transpose %[[shape_cast]], [2, 0, 1, 3] : vector<4x16x2x2xf32> to vector<2x4x16x2xf32>
// CHECK-DAG: %[[c0_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[empty:.*]] = tensor.empty() : tensor<2x4x16x2xf32>
// CHECK: %[[write:.*]] = vector.transfer_write %[[transpose]], %[[empty]][%[[c0_1]], %[[c0_1]], %[[c0_1]], %[[c0_1]]]
// CHECK: %[[SC:.*]] = vector.shape_cast %[[READ]] : vector<64x4xf32> to vector<4x16x2x2xf32>
// CHECK: %[[TR:.*]] = vector.transpose %[[SC]], [2, 0, 1, 3] : vector<4x16x2x2xf32> to vector<2x4x16x2xf32>
// CHECK-DAG: %[[C0_1:.*]] = arith.constant 0 : index
// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[TR]], %[[DEST]][%[[C0_1]], %[[C0_1]], %[[C0_1]], %[[C0_1]]]
// CHECK-SAME: {in_bounds = [true, true, true, true]} : vector<2x4x16x2xf32>, tensor<2x4x16x2xf32>
// CHECK: return %[[write]] : tensor<2x4x16x2xf32>
// CHECK: return %[[WRITE]] : tensor<2x4x16x2xf32>

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
Expand All @@ -1435,22 +1439,23 @@ module attributes {transform.with_named_sequence} {
// -----

// CHECK-LABEL: test_vectorize_padded_pack_no_vector_sizes
func.func @test_vectorize_padded_pack_no_vector_sizes(%arg0: tensor<32x7x15xf32>, %arg1: tensor<32x4x1x16x2xf32>) -> tensor<32x4x1x16x2xf32> {
// CHECK-SAME: %[[SRC:.*]]: tensor<32x7x15xf32>,
// CHECK-SAME: %[[DEST:.*]]: tensor<32x4x1x16x2xf32>
func.func @test_vectorize_padded_pack_no_vector_sizes(%src: tensor<32x7x15xf32>, %dest: tensor<32x4x1x16x2xf32>) -> tensor<32x4x1x16x2xf32> {
%pad = arith.constant 0.000000e+00 : f32
%pack = linalg.pack %arg0 padding_value(%pad : f32) inner_dims_pos = [2, 1] inner_tiles = [16, 2] into %arg1 : tensor<32x7x15xf32> -> tensor<32x4x1x16x2xf32>
%pack = linalg.pack %src padding_value(%pad : f32) inner_dims_pos = [2, 1] inner_tiles = [16, 2] into %dest : tensor<32x7x15xf32> -> tensor<32x4x1x16x2xf32>
return %pack : tensor<32x4x1x16x2xf32>
}
// CHECK-DAG: %[[cst:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
// CHECK: %[[transfer_read:.*]] = vector.transfer_read %{{.*}}[%[[c0]], %[[c0]], %[[c0]]], %[[cst]]
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[READ:.*]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]], %[[C0]]], %[[CST]]
// CHECK-SAME: {in_bounds = [true, false, false]} : tensor<32x7x15xf32>, vector<32x8x16xf32>
// CHECK: %[[shape_cast:.*]] = vector.shape_cast %[[transfer_read]] : vector<32x8x16xf32> to vector<32x4x2x1x16xf32>
// CHECK: %[[transpose:.*]] = vector.transpose %[[shape_cast]], [0, 1, 3, 4, 2] : vector<32x4x2x1x16xf32> to vector<32x4x1x16x2xf32>
// CHECK-DAG: %[[c0_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[empty:.*]] = tensor.empty() : tensor<32x4x1x16x2xf32>
// CHECK: %[[write:.*]] = vector.transfer_write %[[transpose]], %[[empty]][%[[c0_1]], %[[c0_1]], %[[c0_1]], %[[c0_1]], %[[c0_1]]]
// CHECK: %[[SC:.*]] = vector.shape_cast %[[READ]] : vector<32x8x16xf32> to vector<32x4x2x1x16xf32>
// CHECK: %[[TR:.*]] = vector.transpose %[[SC]], [0, 1, 3, 4, 2] : vector<32x4x2x1x16xf32> to vector<32x4x1x16x2xf32>
// CHECK-DAG: %[[C0_1:.*]] = arith.constant 0 : index
// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[TR]], %[[DEST]][%[[C0_1]], %[[C0_1]], %[[C0_1]], %[[C0_1]], %[[C0_1]]]
// CHECK-SAME: {in_bounds = [true, true, true, true, true]} : vector<32x4x1x16x2xf32>, tensor<32x4x1x16x2xf32>
// CHECK: return %[[write]] : tensor<32x4x1x16x2xf32>
// CHECK: return %[[WRITE]] : tensor<32x4x1x16x2xf32>

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
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