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[mlir][linalg] Update vectorization logic for linalg.unpack#149156

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banach-space merged 1 commit intollvm:mainfrombanach-space:andrzej/linalg/unpack_no_empty

Jul 17, 2025

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[mlir][linalg] Update vectorization logic for linalg.unpack#149156

banach-space merged 1 commit intollvm:mainfrombanach-space:andrzej/linalg/unpack_no_empty

Jul 17, 2025

+31 −25

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banach-space commentedJul 16, 2025•
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This PR makes sure that we don't generate unnecessarytensor.empty
when vectorizinglinalg.unpack.

To better visualize the changes implemented here, consider this IR:

func.func@example(%source:tensor<8x4x16x16xf32>,%dest:tensor<64x127xf32>) ->tensor<64x127xf32> {%res =linalg.unpack%source      outer_dims_perm = [1,0]      inner_dims_pos = [0,1]      inner_tiles = [16,16]    into%dest :tensor<8x4x16x16xf32> ->tensor<64x127xf32>    return%res :tensor<64x127xf32> }

Below is the output after vectorization, BEFORE and AFTER this PR.

BEFORE (notetensor.empty and the fact that%arg1 is not used):

  func.func@example(%arg0:tensor<8x4x16x16xf32>,%arg1:tensor<64x127xf32>) ->tensor<64x127xf32> {%cst =arith.constant 0.000000e+00 :f32%c0 =arith.constant0 :index%0 =vector.transfer_read%arg0[%c0,%c0,%c0,%c0],%cst {in_bounds= [true,true,true,true]} :tensor<8x4x16x16xf32>,vector<8x4x16x16xf32>%1 =vector.transpose%0, [1,2,0,3] :vector<8x4x16x16xf32>tovector<4x16x8x16xf32>%2 =vector.shape_cast%1 :vector<4x16x8x16xf32>tovector<64x128xf32>%3 =tensor.empty() :tensor<64x127xf32>%c0_0 =arith.constant0 :index%4 =vector.transfer_write%2,%3[%c0_0,%c0_0] {in_bounds= [true,false]} :vector<64x128xf32>,tensor<64x127xf32>    return%4 :tensor<64x127xf32>  }

AFTER (note that%arg1 is correctly used):

  func.func@example(%arg0:tensor<8x4x16x16xf32>,%arg1:tensor<64x127xf32>) ->tensor<64x127xf32> {%cst =arith.constant 0.000000e+00 :f32%c0 =arith.constant0 :index%0 =vector.transfer_read%arg0[%c0,%c0,%c0,%c0],%cst {in_bounds= [true,true,true,true]} :tensor<8x4x16x16xf32>,vector<8x4x16x16xf32>%1 =vector.transpose%0, [1,2,0,3] :vector<8x4x16x16xf32>tovector<4x16x8x16xf32>%2 =vector.shape_cast%1 :vector<4x16x8x16xf32>tovector<64x128xf32>%c0_0 =arith.constant0 :index%3 =vector.transfer_write%2,%arg1[%c0_0,%c0_0] {in_bounds= [true,false]} :vector<64x128xf32>,tensor<64x127xf32>    return%3 :tensor<64x127xf32>  }

[mlir][linalg] Update vectorization logic for linalg.unpack

feb7e99

This PR makes sure that we don't generate unnecessary `tensor.empty`when vectorizing `linalg.unpack`.To better visualize the changes implemented here, consider this IR:```mlirfunc.func@example(  %source: tensor<8x4x16x16xf32>,  %dest: tensor<64x127xf32>) -> tensor<64x127xf32> {    %res = linalg.unpack %source      outer_dims_perm = [1, 0]      inner_dims_pos = [0, 1]      inner_tiles = [16, 16]    into %dest : tensor<8x4x16x16xf32> -> tensor<64x127xf32>    return %res : tensor<64x127xf32> }```BEFORE (note `tensor.empty` and the fact that `%arg1` is not used):```mlir  func.func@example(%arg0: tensor<8x4x16x16xf32>, %arg1: tensor<64x127xf32>) -> tensor<64x127xf32> {    %cst = arith.constant 0.000000e+00 : f32    %c0 = arith.constant 0 : index    %0 = vector.transfer_read %arg0[%c0, %c0, %c0, %c0], %cst {in_bounds = [true, true, true, true]} : tensor<8x4x16x16xf32>, vector<8x4x16x16xf32>    %1 = vector.transpose %0, [1, 2, 0, 3] : vector<8x4x16x16xf32> to vector<4x16x8x16xf32>    %2 = vector.shape_cast %1 : vector<4x16x8x16xf32> to vector<64x128xf32>    %3 = tensor.empty() : tensor<64x127xf32>    %c0_0 = arith.constant 0 : index    %4 = vector.transfer_write %2, %3[%c0_0, %c0_0] {in_bounds = [true, false]} : vector<64x128xf32>, tensor<64x127xf32>    return %4 : tensor<64x127xf32>  }```AFTER (note that `%arg1` is correctly used):```mlir  func.func@example(%arg0: tensor<8x4x16x16xf32>, %arg1: tensor<64x127xf32>) -> tensor<64x127xf32> {    %cst = arith.constant 0.000000e+00 : f32    %c0 = arith.constant 0 : index    %0 = vector.transfer_read %arg0[%c0, %c0, %c0, %c0], %cst {in_bounds = [true, true, true, true]} : tensor<8x4x16x16xf32>, vector<8x4x16x16xf32>    %1 = vector.transpose %0, [1, 2, 0, 3] : vector<8x4x16x16xf32> to vector<4x16x8x16xf32>    %2 = vector.shape_cast %1 : vector<4x16x8x16xf32> to vector<64x128xf32>    %c0_0 = arith.constant 0 : index    %3 = vector.transfer_write %2, %arg1[%c0_0, %c0_0] {in_bounds = [true, false]} : vector<64x128xf32>, tensor<64x127xf32>    return %3 : tensor<64x127xf32>  }```

banach-space requested review fromdcaballe,hanhanW,nicolasvasilache andGroverkss ascode owners

July 16, 2025 18:06

llvmbot added mlir:linalg mlir labels

Jul 16, 2025

banach-space requested a review fromegebeysel

July 16, 2025 18:06

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llvmbot commentedJul 16, 2025•
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@llvm/pr-subscribers-mlir-linalg

@llvm/pr-subscribers-mlir

Author: Andrzej Warzyński (banach-space)

Changes

This PR makes sure that we don't generate unnecessarytensor.empty
when vectorizinglinalg.unpack.

To better visualize the changes implemented here, consider this IR:

func.func@<!-- -->example(%source:tensor&lt;8x4x16x16xf32&gt;,%dest:tensor&lt;64x127xf32&gt;) -&gt;tensor&lt;64x127xf32&gt; {%res =linalg.unpack%source      outer_dims_perm = [1,0]      inner_dims_pos = [0,1]      inner_tiles = [16,16]    into%dest :tensor&lt;8x4x16x16xf32&gt; -&gt;tensor&lt;64x127xf32&gt;    return%res :tensor&lt;64x127xf32&gt; }

BEFORE (notetensor.empty and the fact that%arg1 is not used):

  func.func@<!-- -->example(%arg0:tensor&lt;8x4x16x16xf32&gt;,%arg1:tensor&lt;64x127xf32&gt;) -&gt;tensor&lt;64x127xf32&gt; {%cst =arith.constant 0.000000e+00 :f32%c0 =arith.constant0 :index%0 =vector.transfer_read%arg0[%c0,%c0,%c0,%c0],%cst {in_bounds= [true,true,true,true]} :tensor&lt;8x4x16x16xf32&gt;,vector&lt;8x4x16x16xf32&gt;%1 =vector.transpose%0, [1,2,0,3] :vector&lt;8x4x16x16xf32&gt;tovector&lt;4x16x8x16xf32&gt;%2 =vector.shape_cast%1 :vector&lt;4x16x8x16xf32&gt;tovector&lt;64x128xf32&gt;%3 =tensor.empty() :tensor&lt;64x127xf32&gt;%c0_0 =arith.constant0 :index%4 =vector.transfer_write%2,%3[%c0_0,%c0_0] {in_bounds= [true,false]} :vector&lt;64x128xf32&gt;,tensor&lt;64x127xf32&gt;    return%4 :tensor&lt;64x127xf32&gt;  }

AFTER (note that%arg1 is correctly used):

  func.func@<!-- -->example(%arg0:tensor&lt;8x4x16x16xf32&gt;,%arg1:tensor&lt;64x127xf32&gt;) -&gt;tensor&lt;64x127xf32&gt; {%cst =arith.constant 0.000000e+00 :f32%c0 =arith.constant0 :index%0 =vector.transfer_read%arg0[%c0,%c0,%c0,%c0],%cst {in_bounds= [true,true,true,true]} :tensor&lt;8x4x16x16xf32&gt;,vector&lt;8x4x16x16xf32&gt;%1 =vector.transpose%0, [1,2,0,3] :vector&lt;8x4x16x16xf32&gt;tovector&lt;4x16x8x16xf32&gt;%2 =vector.shape_cast%1 :vector&lt;4x16x8x16xf32&gt;tovector&lt;64x128xf32&gt;%c0_0 =arith.constant0 :index%3 =vector.transfer_write%2,%arg1[%c0_0,%c0_0] {in_bounds= [true,false]} :vector&lt;64x128xf32&gt;,tensor&lt;64x127xf32&gt;    return%3 :tensor&lt;64x127xf32&gt;  }

Full diff:https://github.com/llvm/llvm-project/pull/149156.diff

2 Files Affected:

(modified) mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp (+1-4)
(modified) mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir (+30-21)

diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cppindex b467114c72f7d..363a7c1a1a557 100644--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp@@ -1935,11 +1935,8 @@ vectorizeAsTensorUnpackOp(RewriterBase &rewriter, linalg::UnPackOp unpackOp,       unpackOp.getDestType().hasStaticShape()           ? vectorSizes           : shapeCastOp.getResultVectorType().getShape());-  Value dest = rewriter.create<tensor::EmptyOp>(-      loc, reifiedRetShapes[0],-      shapeCastOp.getResult().getType().getElementType());   Operation *write = createWriteOrMaskedWrite(-      rewriter, loc, shapeCastOp.getResult(), dest,+      rewriter, loc, shapeCastOp.getResult(), unpackOp.getDest(),       /*writeIndices=*/{}, useInBoundsInsteadOfMasking);   newResults.push_back(write->getResult(0));   return success();diff --git a/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir b/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlirindex 6722de817f6bf..11c86f1c31406 100644--- a/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir+++ b/mlir/test/Dialect/Linalg/vectorization/linalg-ops.mlir@@ -1158,6 +1158,7 @@ module attributes {transform.with_named_sequence} { // -----  // CHECK-LABEL: func @test_vectorize_dynamic_shapes_unpack+// CHECK-SAME:      %[[ARG_0:.*]]: tensor<?x?xf32>, func.func @test_vectorize_dynamic_shapes_unpack(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?x16x2xf32>) -> tensor<?x?xf32> { // CHECK: %[[C0:.*]] = arith.constant 0 // CHECK: %[[DIM:.*]] = tensor.dim %arg0, %[[C0]] : tensor<?x?xf32>@@ -1175,9 +1176,8 @@ func.func @test_vectorize_dynamic_shapes_unpack(%arg0: tensor<?x?xf32>, %arg1: t // CHECK: %[[read0:.*]] = vector.mask %[[readMsk0]] {{.*}} vector.transfer_read %{{.*}} : tensor<?x?x16x2xf32>, vector<2x1x16x2xf32> } : vector<2x1x16x2xi1> -> vector<2x1x16x2xf32> // CHECK: %[[trans0:.*]] = vector.transpose %[[read0]], [0, 3, 1, 2] : vector<2x1x16x2xf32> to vector<2x2x1x16xf32> // CHECK: %[[sc0:.*]] = vector.shape_cast %[[trans0]] : vector<2x2x1x16xf32> to vector<4x16xf32>-// CHECK: %[[empt0:.*]] = tensor.empty // CHECK: %[[writeMsk0:.*]] = vector.create_mask {{.*}} : vector<4x16xi1>-// CHECK: %[[write0:.*]] = vector.mask %[[writeMsk0:.*]] {{.*}} vector.transfer_write %[[sc0]], %[[empt0]]+// CHECK: %[[write0:.*]] = vector.mask %[[writeMsk0:.*]] {{.*}} vector.transfer_write %[[sc0]], %[[ARG_0]] // CHECK: return %[[write0]]  %ret = linalg.unpack %arg1 inner_dims_pos = [1, 0] inner_tiles = [16, 2] into %arg0 : tensor<?x?x16x2xf32> -> tensor<?x?xf32>  return %ret : tensor<?x?xf32>@@ -1193,6 +1193,8 @@ module attributes {transform.with_named_sequence} { // -----  // CHECK-LABEL: func @test_vectorize_unpack+// CHECK-SAME:      %[[SRC:.*]]: tensor<8x8x32x16xf32>+// CHECK-SAME:      %[[DEST:.*]]: tensor<256x128xf32> func.func @test_vectorize_unpack(%source: tensor<8x8x32x16xf32>, %dest: tensor<256x128xf32>) -> tensor<256x128xf32> {     // CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32     // CHECK: %[[C0:.*]]= arith.constant 0 : index@@ -1201,15 +1203,14 @@ func.func @test_vectorize_unpack(%source: tensor<8x8x32x16xf32>, %dest: tensor<2     // CHECK: %[[C32:.*]] = arith.constant 32 : index     // CHECK: %[[C16:.*]] = arith.constant 16 : index     // CHECK: %[[MSK0:.*]] = vector.create_mask %[[C8]], %[[C80]], %[[C32]], %[[C16]] : vector<16x8x32x16xi1>-    // CHECK: %[[READ0:.*]] = vector.mask %[[MSK0]] {{.*}} : vector<16x8x32x16xi1> -> vector<16x8x32x16xf32>+    // CHECK: %[[READ0:.*]] = vector.mask %[[MSK0]] { vector.transfer_read %[[SRC]]{{.*}}} : vector<16x8x32x16xi1> -> vector<16x8x32x16xf32>     // CHECK: %[[TRANSP0:.*]] = vector.transpose %[[READ0]], [0, 2, 1, 3] : vector<16x8x32x16xf32> to vector<16x32x8x16xf32>     // CHECK: %[[SHAPC:.*]] = vector.shape_cast %[[TRANSP0]] : vector<16x32x8x16xf32> to vector<512x128xf32>-    // CHECK: %[[EMPT:.*]] = tensor.empty() : tensor<256x128xf32>     // CHECK: %[[C01:.*]] = arith.constant 0 : index     // CHECK: %[[C256:.*]] = arith.constant 256 : index     // CHECK: %[[C128:.*]] = arith.constant 128 : index     // CHECK: %[[WRITEMSK:.*]] = vector.create_mask %[[C256]], %[[C128]] : vector<512x128xi1>-    // CHECK: %[[WRIT:.*]] = vector.mask %[[WRITEMSK]] {{.*}} : vector<512x128xi1> -> tensor<256x128xf32>+    // CHECK: %[[WRIT:.*]] = vector.mask %[[WRITEMSK]] { vector.transfer_write %[[SHAPC]], %[[DEST]]{{.*}}} : vector<512x128xi1> -> tensor<256x128xf32>     // CHECK: return %[[WRIT]] : tensor<256x128xf32>    %0 = linalg.unpack %source inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %dest : tensor<8x8x32x16xf32> -> tensor<256x128xf32>    return %0 : tensor<256x128xf32>@@ -1225,15 +1226,16 @@ func.func @test_vectorize_unpack(%source: tensor<8x8x32x16xf32>, %dest: tensor<2 // -----  // CHECK-LABEL: func @test_vectorize_unpack_no_masks+// CHECK-SAME:      %[[SRC:.*]]: tensor<8x8x32x16xf32>+// CHECK-SAME:      %[[DEST:.*]]: tensor<256x128xf32> func.func @test_vectorize_unpack_no_masks(%source: tensor<8x8x32x16xf32>, %dest: tensor<256x128xf32>) -> tensor<256x128xf32> {   // CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32   // CHECK: %[[C0:.*]] = arith.constant 0 : index-  // CHECK: %[[READ:.*]] = vector.transfer_read {{.*}} : tensor<8x8x32x16xf32>, vector<8x8x32x16xf32>+  // CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{.*}}} : tensor<8x8x32x16xf32>, vector<8x8x32x16xf32>   // CHECK: %[[TRANSP:.*]] = vector.transpose %[[READ]], [0, 2, 1, 3] : vector<8x8x32x16xf32> to vector<8x32x8x16xf32>   // CHECK: %[[SHAPC:.*]] = vector.shape_cast %[[TRANSP]] : vector<8x32x8x16xf32> to vector<256x128xf32>-  // CHECK: %[[EMPT:.*]] = tensor.empty() : tensor<256x128xf32>   // CHECK: %[[C00:.*]] = arith.constant 0 : index-  // CHECK: %[[WRIT:.*]] = vector.transfer_write %[[SHAPC]], {{.*}} : vector<256x128xf32>, tensor<256x128xf32>+  // CHECK: %[[WRIT:.*]] = vector.transfer_write %[[SHAPC]], %[[DEST]]{{.*}}} : vector<256x128xf32>, tensor<256x128xf32>   // CHECK: return %[[WRIT]] : tensor<256x128xf32>    %0 = linalg.unpack %source inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %dest : tensor<8x8x32x16xf32> -> tensor<256x128xf32>    return %0 : tensor<256x128xf32>@@ -1248,16 +1250,17 @@ func.func @test_vectorize_unpack_no_masks(%source: tensor<8x8x32x16xf32>, %dest:    // ------  // CHECK-LABEL: test_vectorize_unpack_with_outer_perm+// CHECK-LABEL: test_vectorize_unpack_with_outer_perm+// CHECK-SAME:      %[[SRC:.*]]: tensor<8x8x32x16xf32>+// CHECK-SAME:      %[[DEST:.*]]: tensor<256x128xf32>   func.func @test_vectorize_unpack_with_outer_perm(%source: tensor<8x8x32x16xf32>, %dest: tensor<256x128xf32>) -> tensor<256x128xf32> {   // CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32   // CHECK: %[[C0:.*]] = arith.constant 0 : index-  // CHECK: %[[READ:.*]] = vector.transfer_read {{.*}} : tensor<8x8x32x16xf32>, vector<8x8x32x16xf32>+  // CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{.*}}} : tensor<8x8x32x16xf32>, vector<8x8x32x16xf32>   // CHECK: %[[TRANSP:.*]] = vector.transpose %[[READ]], [1, 2, 0, 3] : vector<8x8x32x16xf32> to vector<8x32x8x16xf32>   // CHECK: %[[SHAPC:.*]] = vector.shape_cast %[[TRANSP]] : vector<8x32x8x16xf32> to vector<256x128xf32>-  // CHECK: %[[EMPT:.*]] = tensor.empty() : tensor<256x128xf32>   // CHECK: %[[C00:.*]] = arith.constant 0 : index-  // CHECK: %[[WRIT:.*]] = vector.transfer_write %[[SHAPC]], {{.*}} : vector<256x128xf32>, tensor<256x128xf32>+  // CHECK: %[[WRIT:.*]] = vector.transfer_write %[[SHAPC]], %[[DEST]]{{.*}}} : vector<256x128xf32>, tensor<256x128xf32>   // CHECK: return %[[WRIT]] : tensor<256x128xf32>    %0 = linalg.unpack %source outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %dest : tensor<8x8x32x16xf32> -> tensor<256x128xf32>    return %0 : tensor<256x128xf32>@@ -1327,15 +1330,17 @@ module attributes {transform.with_named_sequence} {    // -----+// CHECK-LABEL: test_vectorize_unpack_no_vector_sizes+// CHECK-SAME:      %[[SRC:.*]]: tensor<8x8x32x16xf32>+// CHECK-SAME:      %[[DEST:.*]]: tensor<256x128xf32> func.func @test_vectorize_unpack_no_vector_sizes(%source: tensor<8x8x32x16xf32>, %dest: tensor<256x128xf32>) -> tensor<256x128xf32> {   // CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32   // CHECK: %[[C0:.*]] = arith.constant 0 : index-  // CHECK: %[[READ:.*]] = vector.transfer_read {{.*}} : tensor<8x8x32x16xf32>, vector<8x8x32x16xf32>+  // CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{.*}}} : tensor<8x8x32x16xf32>, vector<8x8x32x16xf32>   // CHECK: %[[TRANSP:.*]] = vector.transpose %[[READ]], [0, 2, 1, 3] : vector<8x8x32x16xf32> to vector<8x32x8x16xf32>   // CHECK: %[[SHAPC:.*]] = vector.shape_cast %[[TRANSP]] : vector<8x32x8x16xf32> to vector<256x128xf32>-  // CHECK: %[[EMPT:.*]] = tensor.empty() : tensor<256x128xf32>   // CHECK: %[[C00:.*]] = arith.constant 0 : index-  // CHECK: %[[WRIT:.*]] = vector.transfer_write %[[SHAPC]], {{.*}} : vector<256x128xf32>, tensor<256x128xf32>+  // CHECK: %[[WRIT:.*]] = vector.transfer_write %[[SHAPC]], %[[DEST]]{{.*}}} : vector<256x128xf32>, tensor<256x128xf32>   // CHECK: return %[[WRIT]] : tensor<256x128xf32>    %0 = linalg.unpack %source inner_dims_pos = [0, 1] inner_tiles = [32, 16] into %dest : tensor<8x8x32x16xf32> -> tensor<256x128xf32>    return %0 : tensor<256x128xf32>@@ -1350,15 +1355,17 @@ func.func @test_vectorize_unpack_no_vector_sizes(%source: tensor<8x8x32x16xf32>,    // -----+// CHECK-LABEL: test_vectorize_unpack_no_vector_sizes_slice_output+// CHECK-SAME:      %[[SRC:.*]]: tensor<8x4x16x16xf32>+// CHECK-SAME:      %[[DEST:.*]]: tensor<64x127xf32> func.func @test_vectorize_unpack_no_vector_sizes_slice_output(%source: tensor<8x4x16x16xf32>, %dest: tensor<64x127xf32>) -> tensor<64x127xf32> {   //      CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32   //      CHECK: %[[C0:.*]] = arith.constant 0 : index-  //      CHECK: %[[READ:.*]] = vector.transfer_read {{.*}} : tensor<8x4x16x16xf32>, vector<8x4x16x16xf32>+  //      CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{.*}}} : tensor<8x4x16x16xf32>, vector<8x4x16x16xf32>   //      CHECK: %[[TRANSP:.*]] = vector.transpose %[[READ]], [1, 2, 0, 3] : vector<8x4x16x16xf32> to vector<4x16x8x16xf32>   //      CHECK: %[[SHAPC:.*]] = vector.shape_cast %[[TRANSP]] : vector<4x16x8x16xf32> to vector<64x128xf32>-  //      CHECK: %[[EMPT:.*]] = tensor.empty() : tensor<64x127xf32>   //      CHECK: %[[C00:.*]] = arith.constant 0 : index-  //      CHECK: %[[WRIT:.*]] = vector.transfer_write %[[SHAPC]], %[[EMPT]]{{\[}}%[[C00]], %[[C00]]]+  //      CHECK: %[[WRIT:.*]] = vector.transfer_write %[[SHAPC]], %[[DEST]]   // CHECK-SAME:  {in_bounds = [true, false]} : vector<64x128xf32>, tensor<64x127xf32>   //      CHECK: return %[[WRIT]] : tensor<64x127xf32>    %0 = linalg.unpack %source outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [16, 16] into %dest : tensor<8x4x16x16xf32> -> tensor<64x127xf32>@@ -1374,18 +1381,20 @@ func.func @test_vectorize_unpack_no_vector_sizes_slice_output(%source: tensor<8x  // -----+// CHECK-LABEL: test_vectorize_unpack_no_vector_sizes_permute+// CHECK-SAME:      %[[SRC:.*]]:  tensor<4x7x4xf32>+// CHECK-SAME:      %[[DEST:.*]]:  tensor<7x16xf32> func.func @test_vectorize_unpack_no_vector_sizes_permute(%source: tensor<4x7x4xf32>, %dest: tensor<7x16xf32>) -> tensor<7x16xf32> {    %0 = linalg.unpack %source outer_dims_perm=[1, 0] inner_dims_pos = [1] inner_tiles = [4] into %dest : tensor<4x7x4xf32> -> tensor<7x16xf32>    return %0 : tensor<7x16xf32>  }   // CHECK: %[[CST:.*]] = arith.constant 0.000000e+00 : f32   // CHECK: %[[C0:.*]] = arith.constant 0 : index-  // CHECK: %[[READ:.*]] = vector.transfer_read {{.*}} : tensor<4x7x4xf32>, vector<4x7x4xf32>+  // CHECK: %[[READ:.*]] = vector.transfer_read %[[SRC]]{{.*}}} : tensor<4x7x4xf32>, vector<4x7x4xf32>   // CHECK: %[[TRANSP:.*]] = vector.transpose %[[READ]], [1, 0, 2] : vector<4x7x4xf32> to vector<7x4x4xf32>   // CHECK: %[[SHAPC:.*]] = vector.shape_cast %[[TRANSP]] : vector<7x4x4xf32> to vector<7x16xf32>-  // CHECK: %[[EMPT:.*]] = tensor.empty() : tensor<7x16xf32>   // CHECK: %[[C00:.*]] = arith.constant 0 : index-  // CHECK: %[[WRIT:.*]] = vector.transfer_write %[[SHAPC]], {{.*}} : vector<7x16xf32>, tensor<7x16xf32>+  // CHECK: %[[WRIT:.*]] = vector.transfer_write %[[SHAPC]], %[[DEST]]{{.*}}} : vector<7x16xf32>, tensor<7x16xf32>   // CHECK: return %[[WRIT]] : tensor<7x16xf32>  module attributes {transform.with_named_sequence} {   transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {

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