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[mlir][linalg] Extend FuseElementwiseOps pattern to work with named ops #144922

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[mlir][linalg] Extend FuseElementwiseOps pattern to work with named ops #144922

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c76a8cc
Make fusion work on any LinalgOp
srcarroll Jun 19, 2025
fa60fa5
Merge branch 'main' into generalize-fusion
srcarroll Jun 19, 2025
20b25f3
format and add test
srcarroll Jun 19, 2025
8e471a7
fix typo in test
srcarroll Jun 19, 2025
d723913
add same test for other fusion pass -linalg-fuse-elementwise-ops
srcarroll Jun 19, 2025
5280b87
fix bug with no output bb args and add test
srcarroll Jun 19, 2025
c2f52bc
add linalg.elementwise test
srcarroll Jun 19, 2025
8d2e8e0
fix formatting
srcarroll Jun 19, 2025
58582bf
use getElementTypeOrSelf for cleanup
srcarroll Jun 19, 2025
cf67ab6
switch elementwise test to broadcast version
srcarroll Jun 19, 2025
7d402c1
remove block args that were added (hacky)
srcarroll Jun 20, 2025
b1d15b2
add requested tests
srcarroll Jun 21, 2025
459bcb4
add checks for nontrivial map cases
srcarroll Jun 21, 2025
f7e164b
revert unintended change
srcarroll Jun 21, 2025
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4 changes: 2 additions & 2 deletions mlir/include/mlir/Dialect/Linalg/Transforms/Transforms.h
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Expand Up @@ -529,8 +529,8 @@ fuseElementwiseOps(RewriterBase &rewriter, OpOperand *fusedOperand);
/// * There is a chance that the implementation of the transformation does not
/// agree with the result of this method. This function gives a prediction based
/// on an optimized fusion.
llvm::SmallDenseSet<int> getPreservedProducerResults(GenericOp producer,
GenericOp consumer,
llvm::SmallDenseSet<int> getPreservedProducerResults(LinalgOp producer,
LinalgOp consumer,
OpOperand *fusedOperand);

/// Try to peel and canonicalize loop `op` and return the new result.
Expand Down
69 changes: 48 additions & 21 deletions mlir/lib/Dialect/Linalg/Transforms/ElementwiseOpFusion.cpp
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Expand Up @@ -77,11 +77,11 @@ static AffineMap getIndexingMapOfProducerOperandsInCoordinatesOfFusedOp(
// of the fused producer & consumer after the fusion can still compute the
// bounds of the op.
static bool isOpOperandCanBeDroppedAfterFusedLinalgs(
GenericOp producer, GenericOp consumer,
LinalgOp producer, LinalgOp consumer,
ArrayRef<OpOperand *> opOperandsToIgnore) {
SmallVector<AffineMap> indexingMaps;

SmallVector<GenericOp> ops = {producer, consumer};
SmallVector<LinalgOp> ops = {producer, consumer};
for (auto &op : ops) {
for (auto &opOperand : op->getOpOperands()) {
if (llvm::is_contained(opOperandsToIgnore, &opOperand)) {
Expand Down Expand Up @@ -109,8 +109,9 @@ static bool isOpOperandCanBeDroppedAfterFusedLinalgs(
/// * There is a chance that the implementation of the transformation does not
/// agree with the result of this method. This function gives a prediction based
/// on an optimized fusion.
llvm::SmallDenseSet<int> mlir::linalg::getPreservedProducerResults(
GenericOp producer, GenericOp consumer, OpOperand *fusedOperand) {
llvm::SmallDenseSet<int>
mlir::linalg::getPreservedProducerResults(LinalgOp producer, LinalgOp consumer,
OpOperand *fusedOperand) {
llvm::SmallDenseSet<int> preservedProducerResults;
llvm::SmallVector<OpOperand *> opOperandsToIgnore;

Expand Down Expand Up @@ -140,8 +141,8 @@ bool mlir::linalg::areElementwiseOpsFusable(OpOperand *fusedOperand) {
if (!fusedOperand)
return false;

auto producer = fusedOperand->get().getDefiningOp<GenericOp>();
auto consumer = dyn_cast<GenericOp>(fusedOperand->getOwner());
auto producer = fusedOperand->get().getDefiningOp<LinalgOp>();
auto consumer = dyn_cast<LinalgOp>(fusedOperand->getOwner());

// Check producer and consumer are generic ops.
if (!producer || !consumer)
Expand Down Expand Up @@ -215,16 +216,37 @@ bool mlir::linalg::areElementwiseOpsFusable(OpOperand *fusedOperand) {
/// Generate the region of the fused tensor operation. The region of the fused
/// op must be empty.
static void generateFusedElementwiseOpRegion(
RewriterBase &rewriter, GenericOp fusedOp,
RewriterBase &rewriter, LinalgOp fusedOp,
AffineMap consumerToProducerLoopsMap, OpOperand *fusedOperand,
unsigned nloops, llvm::SmallDenseSet<int> &preservedProducerResults) {
auto producer = cast<GenericOp>(fusedOperand->get().getDefiningOp());
auto consumer = cast<GenericOp>(fusedOperand->getOwner());
auto producer = cast<LinalgOp>(fusedOperand->get().getDefiningOp());
auto consumer = cast<LinalgOp>(fusedOperand->getOwner());
// Build the region of the fused op.

// Since some ops, like `linalg.map`, do not have block arguments for init
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This looks like a missed opportunity. IIRC, neither contract and elementwise have that problem. Perhaps we can update map to behave like the new ones first? @javedabsar1 @shahidact

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I agree that it needs to be updated, or even better if we deprecate it in favour of linalg.elementwise as linalg.map is same semantically with linalg.elementwise, in fact linalg.elementwise seems more general, IIRC.

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@srcarroll srcarroll Jun 20, 2025 •
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@rengolin and I had a discussion of that here. seems like we want to keep map and it does support multi-op regions whereas elementwise does not. So elementwise isn't quite more general. They each have something the other doesn't. But yes i agree that this is an odd feature of map, so if we do keep it would be nice to canonicalize this kind of thing

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so i actually found another bug and i think it's because of what i'm doing here. i'm modifying the blocks for producers and consumers. as long as they don't stick around, this works fine. but if the producer has more than one user so that it has to stick around, then this logic here converts it to an invalid op. i'm in the process of confirming this. In any event, it's probably not a good idea to modify the blocks in place like this

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@srcarroll srcarroll Jun 20, 2025 •
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Perhaps we can update map to behave like the new ones first?

i'm fine with waiting for that change so we don't have to figure out special logic here

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@srcarroll srcarroll Jun 20, 2025 •
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i fixed the bug here. the problem was what i expected. i made note here that this is a hack and should be changed before merging. So I'll either come up with something better or wait for the map op change (if we decide to go with that).

I can't actually reproduce the bug with builtin passes. I only discovered it when I applied my own control function for populateElementwiseOpsFusionPatterns

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I can't actually reproduce the bug with builtin passes. I only discovered it when I applied my own control function for populateElementwiseOpsFusionPatterns

If there's a bug present is the current fuser, it'd be worth adding such test case to the TestLinalgElementwiseFusion. You could extend it with another option flag that adds necessary custom control logic.

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sure. however, since the bug was an artifact of my own changes that I noted are hacky, and those changes will go away before this merges anyway, it would be irrelevant by that point.

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@rengolin do you know if anyone will make the change to map, or should I do it?

// operands then we first "generalize" the block by adding arguments for init
// operands when they aren't present. We detect this case by checking if
// `getOpOperandsMatchingBBargs() == getDpsInputOperands()`.
// TODO: This is hacky and should not be merged. Keeping for now for testing
// purposes in the meantime, but need a better way
Block &producerBlock = producer->getRegion(0).front();
bool addOutputArgsProducer =
producer.getOpOperandsMatchingBBargs() == producer.getDpsInputOperands();
if (addOutputArgsProducer) {
for (auto init : producer.getDpsInits())
producerBlock.addArgument(getElementTypeOrSelf(init.getType()),
producer.getLoc());
}
Block &consumerBlock = consumer->getRegion(0).front();
bool addOutputArgsConsumer =
consumer.getOpOperandsMatchingBBargs() == consumer.getDpsInputOperands();
if (addOutputArgsConsumer) {
for (auto init : consumer.getDpsInits())
consumerBlock.addArgument(getElementTypeOrSelf(init.getType()),
consumer.getLoc());
}
OpBuilder::InsertionGuard guard(rewriter);
Block *fusedBlock = rewriter.createBlock(&fusedOp.getRegion());
Block *fusedBlock = rewriter.createBlock(&fusedOp->getRegion(0));
IRMapping mapper;

// 2. Add an index operation for every fused loop dimension and use the
Expand Down Expand Up @@ -330,8 +352,16 @@ static void generateFusedElementwiseOpRegion(
rewriter.create<YieldOp>(fusedOp.getLoc(), fusedYieldValues);

// Sanity checks.
assert(fusedBlock->getNumArguments() == fusedOp.getNumOperands() &&
assert(fusedBlock->getNumArguments() == fusedOp->getNumOperands() &&
"Ill-formed GenericOp region");
// Erase added args in case that the ops are still live after fusion.
// TODO: Remove along with hacky code above.
if (addOutputArgsProducer)
producerBlock.eraseArguments(producer.getNumDpsInputs(),
producer.getNumDpsInits());
if (addOutputArgsConsumer)
consumerBlock.eraseArguments(consumer.getNumDpsInputs(),
consumer.getNumDpsInits());
}

FailureOr<mlir::linalg::ElementwiseOpFusionResult>
Expand All @@ -340,8 +370,8 @@ mlir::linalg::fuseElementwiseOps(RewriterBase &rewriter,
assert(areElementwiseOpsFusable(fusedOperand) &&
"expected elementwise operation pre-conditions to pass");
auto producerResult = cast<OpResult>(fusedOperand->get());
auto producer = cast<GenericOp>(producerResult.getOwner());
auto consumer = cast<GenericOp>(fusedOperand->getOwner());
auto producer = cast<LinalgOp>(producerResult.getOwner());
auto consumer = cast<LinalgOp>(fusedOperand->getOwner());
// TODO: allow fusing the producer of an output operand.
assert(consumer.isDpsInput(fusedOperand) &&
"expected producer of input operand");
Expand Down Expand Up @@ -418,10 +448,7 @@ mlir::linalg::fuseElementwiseOps(RewriterBase &rewriter,
// Generate the fused op.
auto fusedOp = rewriter.create<GenericOp>(
consumer.getLoc(), fusedResultTypes, fusedInputOperands,
fusedOutputOperands, rewriter.getAffineMapArrayAttr(fusedIndexMaps),
consumer.getIteratorTypes(),
/*doc=*/nullptr,
/*library_call=*/nullptr);
fusedOutputOperands, fusedIndexMaps, consumer.getIteratorTypesArray());
if (!fusedOp.getShapesToLoopsMap()) {
// Fused op has invalid indexing maps. Typically this means something is off
// in the input, but going ahead here would result in verification errors.
Expand Down Expand Up @@ -460,14 +487,14 @@ mlir::linalg::fuseElementwiseOps(RewriterBase &rewriter,

namespace {
/// Patterns to fuse a generic op, with the producer of its operands.
class FuseElementwiseOps : public OpRewritePattern<GenericOp> {
class FuseElementwiseOps : public OpInterfaceRewritePattern<LinalgOp> {
public:
FuseElementwiseOps(MLIRContext *context, ControlFusionFn fun,
PatternBenefit benefit = 1)
: OpRewritePattern<GenericOp>(context, benefit),
: OpInterfaceRewritePattern<LinalgOp>(context, benefit),
controlFn(std::move(fun)) {}

LogicalResult matchAndRewrite(GenericOp genericOp,
LogicalResult matchAndRewrite(LinalgOp genericOp,
PatternRewriter &rewriter) const override {
// Find the first operand that is defined by another generic op on tensors.
for (OpOperand &opOperand : genericOp->getOpOperands()) {
Expand All @@ -494,7 +521,7 @@ class FuseElementwiseOps : public OpRewritePattern<GenericOp> {
rewriter.eraseOp(genericOp);
return success();
}
return failure();
return rewriter.notifyMatchFailure(genericOp, "no fusable operands");
}

private:
Expand Down
21 changes: 21 additions & 0 deletions mlir/test/Dialect/Linalg/fusion-elementwise-ops.mlir
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Expand Up @@ -1014,3 +1014,24 @@ module {
// CHECK-DAG: %[[T3:.+]] = arith.addf %[[T2]], %[[B1]]
// CHECK: linalg.yield %[[T3]] : f32
// CHECK: return %[[GENERIC]]

// -----

func.func @map_ops(%in1: tensor<8xf32>, %in2: tensor<8xf32>) -> tensor<8xf32> {
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Could you also add a test where the map operations each have more than one region ops? The fuser should be able to cope with it.

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sure. I initially didn't go that far with tests because at the time my reasoning was that the logic is unchanged so should generalize. but then I ran across that one issue with the bb args, so I'm less convinced of that.

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done

%fill = tensor.empty() : tensor<8xf32>
%add = linalg.map {arith.addf} ins(%in1, %in2: tensor<8xf32>, tensor<8xf32>) outs(%fill: tensor<8xf32>)
%mapped_65 = linalg.map { math.sqrt } ins(%add : tensor<8xf32>) outs(%fill : tensor<8xf32>)
return %mapped_65 : tensor<8xf32>
}

// CHECK-LABEL: func @map_ops
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<8xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<8xf32>
// CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<8xf32>
// CHECK: %[[FUSED_OP:.+]] = linalg.generic
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : {{.*}}) outs(%[[EMPTY]] :
// CHECK-NEXT: ^bb0(%[[IN0:.*]]: f32, %[[IN1:.*]]: f32, %[[OUT:.*]]: f32):
// CHECK-NEXT: %[[ADD:.*]] = arith.addf %[[IN0]], %[[IN1]]
// CHECK-NEXT: %[[SQRT:.*]] = math.sqrt %[[ADD]]
// CHECK-NEXT: linalg.yield %[[SQRT]]
// CHECK-NOT: linalg.generic
151 changes: 151 additions & 0 deletions mlir/test/Dialect/Linalg/fusion-elementwise.mlir
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Expand Up @@ -59,3 +59,154 @@ func.func @handle_unused_operands(%arg0: tensor<8xf32>, %arg1: tensor<8xf32>) ->
// CHECK: %[[FUSED_OP:.+]] = linalg.generic
// CHECK-SAME: outs(%[[EMPTY]] :
// CHECK-NOT: linalg.generic

// -----

func.func @map_ops(%in1: tensor<8xf32>, %in2: tensor<8xf32>) -> tensor<8xf32> {
%fill = tensor.empty() : tensor<8xf32>
%add = linalg.map {arith.addf} ins(%in1, %in2: tensor<8xf32>, tensor<8xf32>) outs(%fill: tensor<8xf32>)
%sqrt = linalg.map { math.sqrt } ins(%add : tensor<8xf32>) outs(%fill : tensor<8xf32>)
return %sqrt : tensor<8xf32>
}

// CHECK-LABEL: func @map_ops
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<8xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<8xf32>
// CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<8xf32>
// CHECK: %[[FUSED_OP:.+]] = linalg.generic
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Maybe not for this PR, but we have discussed keeping the named ops for as long as possible. Here, since they're both maps, we could fuse into a map still. Technically, they're the same (as discussed in the forum), but if I have a chain of matches and fusers, I'd have to match against all possible representations.

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Yah I mentioned in a comment that I wanted to try to do that, but don't know of a clean way to do that yet. I've done something like that before by just using clone and modifying as a way to generalizing a transform from a named op to that same named op. It seems more complicated here, but maybe not

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Also that wouldn't help with elementwise+elementwise -> map anyway

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Also that wouldn't help with elementwise+elementwise -> map anyway

Right, the idea is that ew + ew -> map is still better than to generic. So we only walk up the tree when needed (assuming generic -> map -> ew is the branch we're walking).

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@srcarroll srcarroll Jun 21, 2025 •
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one thing to note. ew + ew -> map only works if maps for both ew are same rank identity on all operands since indexing maps for map are limited

// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : {{.*}}) outs(%[[EMPTY]] :
// CHECK-NEXT: ^bb0(%[[IN0:.*]]: f32, %[[IN1:.*]]: f32, %[[OUT:.*]]: f32):
// CHECK-NEXT: %[[ADD:.*]] = arith.addf %[[IN0]], %[[IN1]]
// CHECK-NEXT: %[[SQRT:.*]] = math.sqrt %[[ADD]]
// CHECK-NEXT: linalg.yield %[[SQRT]]
// CHECK-NOT: linalg.map

// -----

func.func @map_ops_mixed_types(%arg0: tensor<8xf32>, %arg1: tensor<8xf32>) -> tensor<8xf32> {
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Is it possible to fuse map and generic together, if their affine maps and iterator types are compatible? If yes, we should have a quick test on it. If not, this should eventually be supported (separate PR), so a FIXME in the code would help.

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my thinking is that if it works for the generic form it should work for map form. but again, that block arg oddity kinda ruins that. nevertheless I expect that to work, but will add a test

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done

%init = tensor.empty() : tensor<8xi1>
%initf = tensor.empty() : tensor<8xf32>
%0 = linalg.map {math.sqrt} ins(%arg0 : tensor<8xf32>) outs(%initf : tensor<8xf32>)
%1 = linalg.map {math.exp} ins(%arg1 : tensor<8xf32>) outs(%initf : tensor<8xf32>)
%2 = linalg.map ins(%0, %1 : tensor<8xf32>, tensor<8xf32>) outs (%init : tensor<8xi1>)
(%in0 : f32, %in1 : f32) {
%cmp = arith.cmpf olt, %in0, %in1 : f32
linalg.yield %cmp : i1
}
%3 = linalg.map { arith.select } ins(%2, %0, %1 : tensor<8xi1>, tensor<8xf32>, tensor<8xf32>) outs(%initf : tensor<8xf32>)
return %3 : tensor<8xf32>
}

// CHECK-LABEL: func @map_ops_mixed_types
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<8xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<8xf32>
// CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<8xf32>
// CHECK: %[[FUSED_OP:.+]] = linalg.generic
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : {{.*}}) outs(%[[EMPTY]] :
// CHECK-NEXT: ^bb0(%[[IN0:.*]]: f32, %[[IN1:.*]]: f32, %[[OUT:.*]]: f32):
// CHECK-NEXT: %[[EXP0:.*]] = math.exp %[[IN1]]
// CHECK-NEXT: %[[SQRT0:.*]] = math.sqrt %[[IN0]]
// CHECK-NEXT: %[[EXP1:.*]] = math.exp %[[IN1]]
// CHECK-NEXT: %[[SQRT1:.*]] = math.sqrt %[[IN0]]
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i think the duplicate computations are an old artifact. these do go away with cse but let me know if this is something that should be looked at

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This is indeed odd. Looks like a bug in the fuser. Could be related to the map vs generic issue you've seen above.

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I did make a generic version of this and ran the old version of the pass and got same results to confirm it's a pre-existing thing

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here's the generic version

#map = affine_map<(d0)->(d0)>
func.func @map_ops_mixed_types(%arg0: tensor<8xf32>, %arg1: tensor<8xf32>, %arg3: tensor<8xf32>) -> tensor<8xf32> {
  %init = tensor.empty() : tensor<8xi1>
  %initf = tensor.empty() : tensor<8xf32>
  %0 = linalg.generic {
      indexing_maps = [#map, #map],
      iterator_types = ["parallel"]} ins(%arg0 : tensor<8xf32>) outs(%initf : tensor<8xf32>) {
    ^bb0(%in0 : f32, %out : f32):
        %sqrt = math.sqrt %in0 : f32
        linalg.yield %sqrt : f32
    } -> tensor<8xf32>
  %1 = linalg.generic {
      indexing_maps = [#map, #map],
      iterator_types = ["parallel"]} ins(%arg1 : tensor<8xf32>) outs(%initf : tensor<8xf32>) {
    ^bb0(%in0 : f32, %out : f32):
        %sqrt = math.exp %in0 : f32
        linalg.yield %sqrt : f32
    } -> tensor<8xf32>
  %2 = linalg.generic {
      indexing_maps = [#map, #map, #map],
      iterator_types = ["parallel"]} ins(%0, %1 : tensor<8xf32>, tensor<8xf32>) outs(%init : tensor<8xi1>) {
    ^bb0(%in0 : f32, %in1 : f32, %out: i1):
      %cmp = arith.cmpf olt, %in0, %in1 : f32
      linalg.yield %cmp : i1
  } -> tensor<8xi1>
  %3 = linalg.generic {
      indexing_maps = [#map, #map, #map, #map],
      iterator_types = ["parallel"]} ins(%2, %0, %1 : tensor<8xi1>, tensor<8xf32>, tensor<8xf32>) outs(%initf : tensor<8xf32>) { 
    ^bb0(%in0 : i1, %in1 : f32, %in2 : f32, %out: f32):
      %select = arith.select %in0, %in1, %in2 : f32
      linalg.yield %select : f32
  } -> tensor<8xf32>
  return %3 : tensor<8xf32>
}

// CHECK-NEXT: %[[CMP:.*]] = arith.cmpf olt, %[[SQRT1]], %[[EXP1]]
// CHECK-NEXT: %[[RES:.*]] = arith.select %[[CMP]], %[[SQRT0]], %[[EXP0]]
// CHECK-NEXT: linalg.yield %[[RES]]
// CHECK-NOT: linalg.map

// -----

#identity = affine_map<(d0, d1) -> (d0, d1)>
#bcast = affine_map<(d0, d1) -> (d0)>
func.func @elementwise_ops(%in1: tensor<8xf32>, %in2: tensor<8x10xf32>) -> tensor<8x10xf32> {
%fill = tensor.empty() : tensor<8x10xf32>
%add = linalg.elementwise
kind=#linalg.elementwise_kind<add>
indexing_maps = [#bcast, #identity, #identity]
ins(%in1, %in2: tensor<8xf32>, tensor<8x10xf32>) outs(%fill: tensor<8x10xf32>) -> tensor<8x10xf32>
%sqrt = linalg.elementwise
kind=#linalg.elementwise_kind<sqrt>
indexing_maps = [#identity, #identity]
ins(%add : tensor<8x10xf32>) outs(%fill : tensor<8x10xf32>) -> tensor<8x10xf32>
return %sqrt : tensor<8x10xf32>
}

// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1) -> (d0)>
// CHECK-LABEL: func @elementwise_ops
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<8xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<8x10xf32>
// CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<8x10xf32>
// CHECK: %[[FUSED_OP:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP1]], #[[MAP0]], #[[MAP0]]]
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : {{.*}}) outs(%[[EMPTY]] :
// CHECK-NEXT: ^bb0(%[[IN0:.*]]: f32, %[[IN1:.*]]: f32, %[[OUT:.*]]: f32):
// CHECK-NEXT: %[[ADD:.*]] = arith.addf %[[IN0]], %[[IN1]]
// CHECK-NEXT: %[[SQRT:.*]] = math.sqrt %[[ADD]]
// CHECK-NEXT: linalg.yield %[[SQRT]]
// CHECK-NOT: linalg.map

// -----

func.func @map_multi_ops(%arg0: tensor<8xf32>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>) -> tensor<8xf32> {
%fill = tensor.empty() : tensor<8xf32>
%add_exp = linalg.map ins(%arg0, %arg1: tensor<8xf32>, tensor<8xf32>) outs(%fill: tensor<8xf32>)
(%in0 : f32, %in1 : f32) {
%add = arith.addf %in0, %in1 : f32
%exp = math.exp %add : f32
linalg.yield %exp : f32
}
%sqrt_mul = linalg.map ins(%add_exp, %arg2 : tensor<8xf32>, tensor<8xf32>) outs(%fill : tensor<8xf32>)
(%in0 : f32, %in1 : f32) {
%sqrt = math.sqrt %in0 : f32
%mul = arith.mulf %sqrt, %in1 : f32
linalg.yield %mul : f32
}
return %sqrt_mul : tensor<8xf32>
}

// CHECK-LABEL: func @map_multi_ops
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<8xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<8xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<8xf32>
// CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<8xf32>
// CHECK: %[[FUSED_OP:.+]] = linalg.generic
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]], %[[ARG2]] : {{.*}}) outs(%[[EMPTY]] :
// CHECK-NEXT: ^bb0(%[[IN0:.*]]: f32, %[[IN1:.*]]: f32, %[[IN2:.*]]: f32, %[[OUT:.*]]: f32):
// CHECK-NEXT: %[[ADD:.*]] = arith.addf %[[IN0]], %[[IN1]]
// CHECK-NEXT: %[[EXP:.*]] = math.exp %[[ADD]]
// CHECK-NEXT: %[[SQRT:.*]] = math.sqrt %[[EXP]]
// CHECK-NEXT: %[[MUL:.*]] = arith.mulf %[[SQRT]], %[[IN2]]
// CHECK-NEXT: linalg.yield %[[MUL]]
// CHECK-NOT: linalg.map

// -----

#identity = affine_map<(d0, d1) -> (d0, d1)>
#bcast = affine_map<(d0, d1) -> (d0)>
func.func @map_genric_ops(%arg0: tensor<8xf32>, %arg1: tensor<8x10xf32>) -> tensor<8x10xf32> {
%fill = tensor.empty() : tensor<8x10xf32>
%add = linalg.generic
{indexing_maps = [#bcast, #identity, #identity], iterator_types = ["parallel", "parallel"]}
ins(%arg0, %arg1: tensor<8xf32>, tensor<8x10xf32>) outs(%fill: tensor<8x10xf32>) {
^bb0(%in0: f32, %in1: f32, %out: f32):
%add = arith.addf %in0, %in1 : f32
linalg.yield %add : f32
} -> tensor<8x10xf32>
%sqrt = linalg.map { math.sqrt } ins(%add : tensor<8x10xf32>) outs(%fill : tensor<8x10xf32>)
return %sqrt : tensor<8x10xf32>
}

// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1) -> (d0)>
// CHECK-LABEL: func @map_genric_ops
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<8xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<8x10xf32>
// CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<8x10xf32>
// CHECK: %[[FUSED_OP:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#[[MAP1]], #[[MAP0]], #[[MAP0]]]
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : {{.*}}) outs(%[[EMPTY]] :
// CHECK-NEXT: ^bb0(%[[IN0:.*]]: f32, %[[IN1:.*]]: f32, %[[OUT:.*]]: f32):
// CHECK-NEXT: %[[ADD:.*]] = arith.addf %[[IN0]], %[[IN1]]
// CHECK-NEXT: %[[SQRT:.*]] = math.sqrt %[[ADD]]
// CHECK-NEXT: linalg.yield %[[SQRT]]
// CHECK-NOT: linalg.map
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