Skip to content

[mlir][linalg] Add support for vectorizing dynamic elementwise named ops #71454

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Nov 6, 2023
Merged

[mlir][linalg] Add support for vectorizing dynamic elementwise named ops #71454

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
2eb707a
[mlir][linalg] Add support for vectorizing dynamic elementwise named ops
hanhanW Nov 6, 2023
6bed846
update todo and remove fill from list.
hanhanW Nov 6, 2023
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 5 additions & 3 deletions mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1465,9 +1465,11 @@ static LogicalResult reductionPreconditions(LinalgOp op) {
}

static LogicalResult vectorizeDynamicLinalgOpPrecondition(linalg::LinalgOp op) {
// TODO: Masking only supports dynamic generic ops for now.
if (!isa<linalg::GenericOp, linalg::FillOp, linalg::CopyOp,
linalg::ContractionOpInterface>(op.getOperation()))
// TODO: Masking only supports dynamic element-wise ops, linalg.generic ops,
// linalg.copy ops and ops that implement ContractionOpInterface for now.
if (!isElementwise(op) &&
!isa<linalg::GenericOp, linalg::CopyOp, linalg::ContractionOpInterface>(
op.getOperation()))
return failure();

LDBG("Dynamically-shaped op meets vectorization pre-conditions\n");
Expand Down
28 changes: 28 additions & 0 deletions mlir/test/Dialect/Linalg/vectorization.mlir
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -368,6 +368,34 @@ module attributes {transform.with_named_sequence} {

// -----

// CHECK: #[[MAP:.*]] = affine_map<(d0, d1) -> (d1, d0)>
// CHECK: func @test_masked_vectorize_linalg_transpose
func.func @test_masked_vectorize_linalg_transpose(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[D0:.*]] = tensor.dim %arg0, %[[C0]]
// CHECK: %[[C1:.*]] = arith.constant 1 : index
// CHECK: %[[D1:.*]] = tensor.dim %arg0, %[[C1]]
// CHECK: %[[MASK0:.*]] = vector.create_mask %[[D0]], %[[D1]]
// CHECK: %[[LOAD:.*]] = vector.mask %[[MASK0]] { vector.transfer_read %arg0{{.+}} }
// CHECK-SAME: vector<2x4xi1> -> vector<2x4xf32>
// CHECK: %[[MASK1:.*]] = vector.create_mask %[[D1]], %[[D0]]
// CHECK: %[[WRITE:.*]] = vector.mask %[[MASK1]] { vector.transfer_write %[[LOAD]], %arg1{{.+}} permutation_map = #[[MAP]]{{.+}} }
// CHECK-SAME: vector<4x2xi1> -> tensor<?x?xf32>
// CHECK: return %[[WRITE]]
%0 = linalg.transpose ins(%arg0 : tensor<?x?xf32>) outs(%arg1 : tensor<?x?xf32>) permutation = [1, 0]
return %0 : tensor<?x?xf32>
}

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.transpose"]} in %arg1 : (!transform.any_op) -> !transform.any_op
transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op
transform.yield
}
}

// -----

// CHECK-LABEL: func @test_masked_vectorize_linalg_copy
func.func @test_masked_vectorize_linalg_copy(%A : memref<?x?xf32>, %B : memref<?x?xf32>) {
// CHECK: %[[c0:.*]] = arith.constant 0 : index
Expand Down