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

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Merged
hanhanW merged 2 commits into from
Nov 6, 2023
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[mlir][linalg] Add support for vectorizing dynamic elementwise named ops #71454

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[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
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3 changes: 2 additions & 1 deletion mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
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Expand Up @@ -1466,7 +1466,8 @@ static LogicalResult reductionPreconditions(LinalgOp op) {

static LogicalResult vectorizeDynamicLinalgOpPrecondition(linalg::LinalgOp op) {
// TODO: Masking only supports dynamic generic ops for now.
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@dcaballe dcaballe Nov 6, 2023

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We could probably remove this TODO and add a comment on that these are the ops for which dynamic shapes vectorization is enabled.

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@hanhanW hanhanW Nov 6, 2023

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Good point, thanks! I also remove linalg.fill op from the list because it is an element-wise op.

if (!isa<linalg::GenericOp, linalg::FillOp, linalg::CopyOp,
if (!isElementwise(op) &&
!isa<linalg::GenericOp, linalg::FillOp, linalg::CopyOp,
linalg::ContractionOpInterface>(op.getOperation()))
return failure();

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28 changes: 28 additions & 0 deletions mlir/test/Dialect/Linalg/vectorization.mlir
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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
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