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[mlir][arith] Add neutral element support to arith.maxnumf/arith.minnumf #93278

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Merged
merged 1 commit into from
May 29, 2024
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[mlir][arith] Add neutral element support to arith.maxnumf/arith.minnumf #93278

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14 changes: 14 additions & 0 deletions mlir/lib/Dialect/Arith/IR/ArithOps.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -2467,6 +2467,12 @@ TypedAttr mlir::arith::getIdentityValueAttr(AtomicRMWKind kind, Type resultType,
: APFloat::getInf(semantic, /*Negative=*/true);
return builder.getFloatAttr(resultType, identity);
}
case AtomicRMWKind::maxnumf: {
const llvm::fltSemantics &semantic =
llvm::cast<FloatType>(resultType).getFloatSemantics();
APFloat identity = APFloat::getNaN(semantic, /*Negative=*/true);
return builder.getFloatAttr(resultType, identity);
}
case AtomicRMWKind::addf:
case AtomicRMWKind::addi:
case AtomicRMWKind::maxu:
Expand All @@ -2489,6 +2495,12 @@ TypedAttr mlir::arith::getIdentityValueAttr(AtomicRMWKind kind, Type resultType,

return builder.getFloatAttr(resultType, identity);
}
case AtomicRMWKind::minnumf: {
const llvm::fltSemantics &semantic =
llvm::cast<FloatType>(resultType).getFloatSemantics();
APFloat identity = APFloat::getNaN(semantic, /*Negative=*/false);
return builder.getFloatAttr(resultType, identity);
}
case AtomicRMWKind::mins:
return builder.getIntegerAttr(
resultType, APInt::getSignedMaxValue(
Expand Down Expand Up @@ -2518,6 +2530,8 @@ std::optional<TypedAttr> mlir::arith::getNeutralElement(Operation *op) {
.Case([](arith::MulFOp op) { return AtomicRMWKind::mulf; })
.Case([](arith::MaximumFOp op) { return AtomicRMWKind::maximumf; })
.Case([](arith::MinimumFOp op) { return AtomicRMWKind::minimumf; })
.Case([](arith::MaxNumFOp op) { return AtomicRMWKind::maxnumf; })
.Case([](arith::MinNumFOp op) { return AtomicRMWKind::minnumf; })
// Integer operations.
.Case([](arith::AddIOp op) { return AtomicRMWKind::addi; })
.Case([](arith::OrIOp op) { return AtomicRMWKind::ori; })
Expand Down
92 changes: 92 additions & 0 deletions mlir/test/Dialect/Linalg/transform-op-split-reduction.mlir
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Expand Up @@ -407,3 +407,95 @@ module attributes {transform.with_named_sequence} {
transform.yield
}
}

// -----
// Checks we use nan as the neutral element for maxnumf op.
func.func @generic_split_maxnumf(%in: tensor<32xf32>, %out: tensor<f32>) -> tensor<f32> {
%r = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>,
affine_map<(d0) -> ()>],
iterator_types = ["reduction"]}
ins(%in : tensor<32xf32>)
outs(%out : tensor<f32>) {
^bb0(%arg1: f32, %arg2: f32):
%y = arith.maxnumf %arg1, %arg2 : f32
linalg.yield %y : f32
} -> tensor<f32>
return %r : tensor<f32>
}

// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d1)>
// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0) -> (d0)>
// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0) -> ()>
// CHECK-LABEL: func @generic_split_maxnumf
// The float value 0xFFC00000 that is filled into the init tensor represents negative NaN.
// CHECK-DAG: %[[ID:.*]] = arith.constant 0xFFC00000 : f32
// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1]] output_shape [8, 4] : tensor<32xf32> into tensor<8x4xf32>
// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<4xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<4xf32>) -> tensor<4xf32>
// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["reduction", "parallel"]}
// CHECK-SAME: ins(%[[I1]] : tensor<8x4xf32>) outs(%[[F]] : tensor<4xf32>) {
// CHECK: arith.maxnumf
// CHECK: linalg.yield
// CHECK: } -> tensor<4xf32>
// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP2]], #[[$MAP3]]], iterator_types = ["reduction"]}
// CHECK-SAME: ins(%[[G]] : tensor<4xf32>) outs(%{{.*}} : tensor<f32>) {
// CHECK: arith.maxnumf {{.*}}
// CHECK: linalg.yield
// CHECK: } -> tensor<f32>
// CHECK: return %[[R]] : tensor<f32>

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 0, inner_parallel}
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}

// -----
// Checks we use nan as the neutral element for minnumf op.
func.func @generic_split_minnumf(%in: tensor<32xf32>, %out: tensor<f32>) -> tensor<f32> {
%r = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>,
affine_map<(d0) -> ()>],
iterator_types = ["reduction"]}
ins(%in : tensor<32xf32>)
outs(%out : tensor<f32>) {
^bb0(%arg1: f32, %arg2: f32):
%y = arith.minnumf %arg1, %arg2 : f32
linalg.yield %y : f32
} -> tensor<f32>
return %r : tensor<f32>
}

// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d1)>
// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0) -> (d0)>
// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0) -> ()>
// CHECK-LABEL: func @generic_split_minnumf
// The float value 0x7FC00000 that is filled into the init tensor represents positive NaN.
// CHECK-DAG: %[[ID:.*]] = arith.constant 0x7FC00000 : f32
// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1]] output_shape [8, 4] : tensor<32xf32> into tensor<8x4xf32>
// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<4xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<4xf32>) -> tensor<4xf32>
// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]], iterator_types = ["reduction", "parallel"]}
// CHECK-SAME: ins(%[[I1]] : tensor<8x4xf32>) outs(%[[F]] : tensor<4xf32>) {
// CHECK: arith.minnumf
// CHECK: linalg.yield
// CHECK: } -> tensor<4xf32>
// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP2]], #[[$MAP3]]], iterator_types = ["reduction"]}
// CHECK-SAME: ins(%[[G]] : tensor<4xf32>) outs(%{{.*}} : tensor<f32>) {
// CHECK: arith.minnumf {{.*}}
// CHECK: linalg.yield
// CHECK: } -> tensor<f32>
// CHECK: return %[[R]] : tensor<f32>

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 0, inner_parallel}
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
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