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[mlir][linalg] LinalgOp: Disallow mixed tensor/buffer semantics #80660

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Merged
merged 1 commit into from
Feb 23, 2024
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[mlir][linalg] LinalgOp: Disallow mixed tensor/buffer semantics #80660

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5 changes: 5 additions & 0 deletions mlir/lib/Dialect/Linalg/IR/LinalgInterfaces.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1041,6 +1041,11 @@ int64_t LinalgOp::getIndexingMapIndex(OpOperand *opOperand) {
LogicalResult mlir::linalg::detail::verifyStructuredOpInterface(Operation *op) {
LinalgOp linalgOp = cast<LinalgOp>(op);

// Mixed tensor/buffer operands are not allowed.
if (!linalgOp.hasPureTensorSemantics() &&
!linalgOp.hasPureBufferSemantics() && op->getNumOperands() > 0)
return op->emitOpError("expected to have pure tensor or buffer semantics");

// Before checking indexing maps, we need to make sure the attributes
// referenced by it are valid.
if (linalgOp.hasDynamicIndexingMaps())
Expand Down
55 changes: 14 additions & 41 deletions mlir/test/Dialect/Linalg/canonicalize.mlir
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Expand Up @@ -102,17 +102,16 @@ func.func @tensor.cast.unranked(%a : tensor<*xf32>, %b : tensor<*xf32>, %c : ten
// -----

// CHECK-LABEL: func @linalg_effects(
// CHECK-SAME: %[[A:[a-z0-9]*]]: tensor<?x?xf32>
// CHECK-SAME: %[[B:[a-z0-9]*]]: memref<?x?xf32>
// CHECK-SAME: %[[C:[a-z0-9]*]]: tensor<?x?xf32>
func.func @linalg_effects(%a : tensor<?x?xf32>, %b : memref<?x?xf32>, %c : tensor<?x?xf32>) {
func.func @linalg_effects(
%a : tensor<?x?xf32>, %b : tensor<?x?xf32>, %c : tensor<?x?xf32>,
%d : memref<?x?xf32>, %e : memref<?x?xf32>, %f : memref<?x?xf32>) {
// CHECK-NOT: %{{.*}} = linalg.matmul
%t = linalg.matmul ins(%a, %b : tensor<?x?xf32>, memref<?x?xf32>)
%t = linalg.matmul ins(%a, %b : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%c : tensor<?x?xf32>) -> tensor<?x?xf32>

// CHECK: linalg.matmul
linalg.matmul ins(%a, %c : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%b : memref<?x?xf32>)
linalg.matmul ins(%d, %e : memref<?x?xf32>, memref<?x?xf32>)
outs(%f : memref<?x?xf32>)
return
}

Expand Down Expand Up @@ -889,39 +888,38 @@ func.func @fold_multi_use_generic_op_with_consumer(%arg0 : tensor<?x?x?xf32>) ->
// -----

#map = affine_map<(d0) -> (d0)>
func.func @identity_mixed(%arg0 : tensor<?xf32>, %arg1: memref<?xf32>) {
func.func @identity_buffer(%arg0 : memref<?xf32>, %arg1: memref<?xf32>) {
linalg.generic {
indexing_maps = [#map, #map],
iterator_types = ["parallel"]
} ins(%arg0 : tensor<?xf32>)
} ins(%arg0 : memref<?xf32>)
outs(%arg1 : memref<?xf32>) {
^bb0(%arg2 : f32, %arg3 : f32):
linalg.yield %arg2 : f32
}
return
}

// There was a crash in EraseIdentityGenericOp for generic with mixed semantics.
// For now, check generic remained unchanged.
// CHECK-LABEL: func @identity_mixed
// CHECK-SAME: (%[[ARG1:.*]]: tensor<?xf32>, %[[ARG2:.*]]: memref<?xf32>)
// Do not erase ops with buffer semantics.
// CHECK-LABEL: func @identity_buffer
// CHECK-SAME: (%[[ARG1:.*]]: memref<?xf32>, %[[ARG2:.*]]: memref<?xf32>)
// CHECK: linalg.generic {
// CHECK-SAME: indexing_maps = [#map, #map],
// CHECK-SAME: iterator_types = ["parallel"]
// CHECK-SAME: } ins(%[[ARG1]] : tensor<?xf32>)
// CHECK-SAME: } ins(%[[ARG1]] : memref<?xf32>)
// CHECK-SAME: outs(%[[ARG2]] : memref<?xf32>) {

// -----

// Just make sure that we don't crash.

// CHECK-LABEL: func @dedeplicate_regression_test
func.func @dedeplicate_regression_test(%0: tensor<4xf32>, %1: memref<4xf32>) {
func.func @dedeplicate_regression_test(%0: tensor<4xf32>, %1: tensor<4xf32>) {
%36 = linalg.generic
{indexing_maps = [affine_map<(d0) -> (d0)>,
affine_map<(d0) -> (d0)>, affine_map<(d0) -> (d0)>],
iterator_types = ["parallel"]}
ins(%1, %1 : memref<4xf32>, memref<4xf32>)
ins(%1, %1 : tensor<4xf32>, tensor<4xf32>)
outs(%0 : tensor<4xf32>) {
^bb0(%in: f32, %in_24: f32, %out: f32):
linalg.yield %in : f32
Expand All @@ -937,31 +935,6 @@ func.func @dedeplicate_regression_test(%0: tensor<4xf32>, %1: memref<4xf32>) {

// -----

#map = affine_map<(d0) -> (d0)>
func.func @cast_producer_mixed(%arg0 : tensor<5xf32>, %arg1: memref<?xf32>) {
%0 = tensor.cast %arg0 : tensor<5xf32> to tensor<?xf32>
linalg.generic {
indexing_maps = [#map, #map],
iterator_types = ["parallel"]
} ins(%0 : tensor<?xf32>)
outs(%arg1 : memref<?xf32>) {
^bb0(%arg2 : f32, %arg3 : f32):
linalg.yield %arg2 : f32
}
return
}

// We need a mixed linalg as a bridge between tensor and memref worlds.
// CHECK-LABEL: func @cast_producer_mixed
// CHECK-SAME: (%[[ARG1:.*]]: tensor<5xf32>, %[[ARG2:.*]]: memref<?xf32>)
// CHECK: linalg.generic {
// CHECK-SAME: indexing_maps = [#map, #map],
// CHECK-SAME: iterator_types = ["parallel"]
// CHECK-SAME: } ins(%[[ARG1]] : tensor<5xf32>)
// CHECK-SAME: outs(%[[ARG2]] : memref<?xf32>) {

// -----

// CHECK-LABEL: dead_softmax
func.func @dead_softmax(%arg0: tensor<16x64x256xf32>) -> tensor<16x64x256xf32> {
%0 = tensor.empty() : tensor<16x64x256xf32>
Expand Down
40 changes: 0 additions & 40 deletions mlir/test/Dialect/Linalg/fusion-elementwise-ops.mlir
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Expand Up @@ -1110,43 +1110,3 @@ module {
// CHECK-DAG: %[[T3:.+]] = arith.addf %[[T2]], %[[B1]]
// CHECK: linalg.yield %[[T3]] : f32
// CHECK: return %[[GENERIC]]

// -----

// CHECK-DAG: [[$MAP0:#[a-zA-Z0-9_]*]] = affine_map<(d0, d1) -> (d0, d1)>
#map0 = affine_map<(d0, d1) -> (d0, d1)>

// CHECK-LABEL: @mixed_fusion
func.func @mixed_fusion(%arg0: tensor<?x?xf32>, %arg1 : tensor<?x?xf32>, %arg2 : tensor<?x?xf32>, %arg8 : memref<?x?xf32>)
{
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
%1 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
%2 = tensor.empty(%0, %1) : tensor<?x?xf32>
%3 = linalg.generic {indexing_maps = [#map0, #map0, #map0], iterator_types = ["parallel", "parallel"]}
ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%2 : tensor<?x?xf32>) {
^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
%4 = arith.addf %arg3, %arg4 : f32
linalg.yield %4 : f32
} -> tensor<?x?xf32>
// CHECK: linalg.generic {
// CHECK-SAME: indexing_maps = {{\[}}[[$MAP0]], [[$MAP0]], [[$MAP0]], [[$MAP0]]{{\]}}
linalg.generic {indexing_maps = [#map0, #map0, #map0], iterator_types = ["parallel", "parallel"]}
ins(%3, %arg2 : tensor<?x?xf32>, tensor<?x?xf32>)
outs(%arg8 : memref<?x?xf32>) {
// CHECK: ^{{[a-zA-Z0-9_]*}}
// CHECK-SAME: [[ARG0:%[a-zA-Z0-9_]*]]
// CHECK-SAME: [[ARG1:%[a-zA-Z0-9_]*]]
// CHECK-SAME: [[ARG2:%[a-zA-Z0-9_]*]]
^bb0(%arg5: f32, %arg6: f32, %arg7: f32):
// CHECK: [[T1:%[a-zA-Z0-9_]*]] = arith.addf [[ARG0]], [[ARG1]]
// CHECK-NOT: linalg.yield
// CHECK: arith.mulf [[T1]], [[ARG2]]
// CHECK: linalg.yield
%5 = arith.mulf %arg5, %arg6 : f32
linalg.yield %5 : f32
}
return
}
10 changes: 10 additions & 0 deletions mlir/test/Dialect/Linalg/invalid.mlir
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Expand Up @@ -770,3 +770,13 @@ func.func @mmt4d_rank_mismatch(%A: tensor<16x16x8x1xf32>,
-> tensor<8x8xf32>
return %res : tensor<8x8xf32>
}

// -----

func.func @mixed_semantics(%a: tensor<?x?xf32>, %b: tensor<?x?xf32>, %c: memref<?x?xf32>) {
// expected-error @+1 {{expected to have pure tensor or buffer semantics}}
linalg.matmul ins(%a, %b: tensor<?x?xf32>, tensor<?x?xf32>)
outs(%c: memref<?x?xf32>)
return
}