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[MLIR] Determine contiguousness of memrefs with a dynamic dimension #140872

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[MLIR] Determine contiguousness of memrefs with a dynamic dimension #140872

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a55d425
[MLIR] Determine contiguousness of memrefs with a dynamic dimension
momchil-velikov May 21, 2025
d6dd4e9
[fixup] Fix formatting
momchil-velikov May 21, 2025
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7 changes: 5 additions & 2 deletions mlir/lib/IR/BuiltinTypes.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -649,7 +649,10 @@ bool MemRefType::areTrailingDimsContiguous(int64_t n) {
if (!isLastDimUnitStride())
return false;

auto memrefShape = getShape().take_back(n);
if (n == 1)
return true;

auto memrefShape = getShape().take_back(n - 1);
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[nit] Could you add comments explaining what makes n == 1 and the last n-1 dims special? Alternatively, rename n to e.g. numTrailingDimsToCheck (or something else self-documenting).

if (ShapedType::isDynamicShape(memrefShape))
return false;

Expand All @@ -668,7 +671,7 @@ bool MemRefType::areTrailingDimsContiguous(int64_t n) {
// Check whether strides match "flattened" dims.
SmallVector<int64_t> flattenedDims;
auto dimProduct = 1;
for (auto dim : llvm::reverse(memrefShape.drop_front(1))) {
for (auto dim : llvm::reverse(memrefShape)) {
dimProduct *= dim;
flattenedDims.push_back(dimProduct);
}
Expand Down
90 changes: 81 additions & 9 deletions mlir/test/Dialect/Vector/vector-transfer-flatten.mlir
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Expand Up @@ -188,18 +188,20 @@ func.func @transfer_read_leading_dynamic_dims(

// -----

// One of the dims to be flattened is dynamic - not supported ATM.
// One of the dims to be flattened is dynamic and not the leftmost - not
// possible to reason whether the memref is contiguous as the dynamic dimension
// could be one and the corresponding stride could be arbitrary.
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From https://mlir.llvm.org/docs/Dialects/Builtin/#memreftype:

In absence of an explicit layout, a memref is considered to have a multi-dimensional identity affine map layout.

To me that reads as: without an explicit layout, it's an identity (i.e. a contiguous MemRef), no? If my reading is correct then the logic in areTrailingDimsContiguous should be relaxed (no need to check for dynamic dims).

However, in the context of "flattening", the dynamic dims are significant, yes. So one should check for dynamic dims, but probably somewhere in Vector dialect transforms.


func.func @negative_transfer_read_dynamic_dim_to_flatten(
%idx_1: index,
%idx_2: index,
%mem: memref<1x?x4x6xi32>) -> vector<1x2x6xi32> {
%mem: memref<1x4x?x6xi32>) -> vector<1x2x6xi32> {

%c0 = arith.constant 0 : index
%c0_i32 = arith.constant 0 : i32
%res = vector.transfer_read %mem[%c0, %idx_1, %idx_2, %c0], %c0_i32 {
in_bounds = [true, true, true]
} : memref<1x?x4x6xi32>, vector<1x2x6xi32>
} : memref<1x4x?x6xi32>, vector<1x2x6xi32>
return %res : vector<1x2x6xi32>
}

Expand All @@ -212,6 +214,41 @@ func.func @negative_transfer_read_dynamic_dim_to_flatten(

// -----

// One of the dims to be flattened is dynamic and leftmost.

func.func @transfer_read_dynamic_leftmost_dim_to_flatten(
%idx_1: index,
%idx_2: index,
%mem: memref<1x?x4x6xi32>) -> vector<1x2x6xi32> {

%c0 = arith.constant 0 : index
%c0_i32 = arith.constant 0 : i32
%res = vector.transfer_read %mem[%c0, %idx_1, %idx_2, %c0], %c0_i32 {
in_bounds = [true, true, true]
} : memref<1x?x4x6xi32>, vector<1x2x6xi32>
return %res : vector<1x2x6xi32>
}

// CHECK-LABEL: func.func @transfer_read_dynamic_leftmost_dim_to_flatten
// CHECK-SAME: %[[IDX_1:arg0]]: index
// CHECK-SAME: %[[IDX_2:arg1]]: index
// CHECK-SAME: %[[MEM:arg2]]: memref<1x?x4x6xi32>
// CHECK-NEXT: %[[C0_I32:.+]] = arith.constant 0 : i32
// CHECK-NEXT: %[[C0:.+]] = arith.constant 0 : index
// CHECK-NEXT: %[[COLLAPSED:.+]] = memref.collapse_shape %[[MEM]] {{\[}}[0], [1, 2, 3]{{\]}}
// CHECK-SAME: : memref<1x?x4x6xi32> into memref<1x?xi32>
// CHECK-NEXT: %[[TMP:.+]] = affine.apply #map{{.*}}()[%[[IDX_1]], %[[IDX_2]]]
// CHECK-NEXT: %[[VEC1D:.+]] = vector.transfer_read %[[COLLAPSED]]
// CHECK-SAME: [%[[C0]], %[[TMP]]], %[[C0_I32]]
// CHECK-SAME: {in_bounds = [true]} : memref<1x?xi32>, vector<12xi32>
// CHECK-NEXT: %[[RES:.+]] = vector.shape_cast %[[VEC1D]] : vector<12xi32> to vector<1x2x6xi32>
// CHECK-NEXT: return %[[RES]] : vector<1x2x6xi32>

// CHECK-128B-LABEL: func @transfer_read_dynamic_leftmost_dim_to_flatten
// CHECK-128B-NOT: memref.collapse_shape

// -----

// The vector to be read represents a _non-contiguous_ slice of the input
// memref.

Expand Down Expand Up @@ -451,26 +488,61 @@ func.func @transfer_write_leading_dynamic_dims(

// -----

// One of the dims to be flattened is dynamic - not supported ATM.
// One of the dims to be flattened is dynamic and not leftmost.

func.func @negative_transfer_write_dynamic_to_flatten(
func.func @negative_transfer_write_dynamic_dim_to_flatten(
%idx_1: index,
%idx_2: index,
%vec : vector<1x2x6xi32>,
%mem: memref<1x?x4x6xi32>) {
%mem: memref<1x4x?x6xi32>) {

%c0 = arith.constant 0 : index
%c0_i32 = arith.constant 0 : i32
vector.transfer_write %vec, %mem[%c0, %idx_1, %idx_2, %c0] {in_bounds = [true, true, true]} :
vector<1x2x6xi32>, memref<1x?x4x6xi32>
vector<1x2x6xi32>, memref<1x4x?x6xi32>
return
}

// CHECK-LABEL: func.func @negative_transfer_write_dynamic_to_flatten
// CHECK-LABEL: func.func @negative_transfer_write_dynamic_dim_to_flatten
// CHECK-NOT: memref.collapse_shape
// CHECK-NOT: vector.shape_cast

// CHECK-128B-LABEL: func @negative_transfer_write_dynamic_to_flatten
// CHECK-128B-LABEL: func @negative_transfer_write_dynamic_dim_to_flatten
// CHECK-128B-NOT: memref.collapse_shape

// -----

// One of the dims to be flattened is dynamic and leftmost.

func.func @transfer_write_dynamic_leftmost_dim_to_flatten(
%idx_1: index,
%idx_2: index,
%vec : vector<1x2x6xi32>,
%mem: memref<1x?x4x6xi32>) {

%c0 = arith.constant 0 : index
%c0_i32 = arith.constant 0 : i32
vector.transfer_write %vec, %mem[%c0, %idx_1, %idx_2, %c0] {in_bounds = [true, true, true]} :
vector<1x2x6xi32>, memref<1x?x4x6xi32>
return
}

// CHECK-LABEL: func.func @transfer_write_dynamic_leftmost_dim_to_flatten
// CHECK-SAME: %[[IDX_1:arg0]]: index
// CHECK-SAME: %[[IDX_2:arg1]]: index
// CHECK-SAME: %[[VEC:arg2]]: vector<1x2x6xi32>,
// CHECK-SAME: %[[MEM:arg3]]: memref<1x?x4x6xi32>
// CHECK-NEXT: %[[C0:.+]] = arith.constant 0 : index
// CHECK-NEXT: %[[COLLAPSED:.+]] = memref.collapse_shape %[[MEM]] {{\[}}[0], [1, 2, 3]{{\]}}
// CHECK-SAME: : memref<1x?x4x6xi32> into memref<1x?xi32>
// CHECK-NEXT: %[[TMP:.+]] = affine.apply #map{{.*}}()[%[[IDX_1]], %[[IDX_2]]]
// CHECK-NEXT: %[[VEC1D:.+]] = vector.shape_cast %[[VEC]] : vector<1x2x6xi32> to vector<12xi32>
// CHECK-NEXT: vector.transfer_write %[[VEC1D]], %[[COLLAPSED]]
// CHECK-SAME: [%[[C0]], %[[TMP]]]
// CHECK-SAME: {in_bounds = [true]} : vector<12xi32>, memref<1x?xi32>
// CHECK-NEXT: return

// CHECK-128B-LABEL: func @transfer_write_dynamic_leftmost_dim_to_flatten
// CHECK-128B-NOT: memref.collapse_shape

// -----
Expand Down
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