Skip to content

[mlir][vector] Don't treat memrefs with empty stride as non-contiguous #76848

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 1 commit into from
Jan 9, 2024
Merged

[mlir][vector] Don't treat memrefs with empty stride as non-contiguous #76848

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
27 changes: 16 additions & 11 deletions mlir/lib/Dialect/Vector/Utils/VectorUtils.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -264,26 +264,31 @@ bool vector::isContiguousSlice(MemRefType memrefType, VectorType vectorType) {
if (!succeeded(getStridesAndOffset(memrefType, stridesFull, offset)))
return false;
auto strides = ArrayRef<int64_t>(stridesFull).take_back(vecRank);
memrefType.getLayout().isIdentity();

// TODO: Add support for memref with trailing dynamic shapes. Memrefs
// with leading dynamic dimensions are already supported.
if (ShapedType::isDynamicShape(memrefShape))
return false;

// Cond 1: A contiguous memref will always have a unit trailing stride.
if (strides.empty() || strides.back() != 1)
return false;
// Cond 1: Check whether `memrefType` is contiguous.
if (!strides.empty()) {
// Cond 1.1: A contiguous memref will always have a unit trailing stride.
if (strides.back() != 1)
return false;

// Cond 2: Strides of a contiguous memref have to match the flattened dims.
strides = strides.drop_back(1);
SmallVector<int64_t> flattenedDims;
for (size_t i = 1; i < memrefShape.size(); i++)
flattenedDims.push_back(mlir::computeProduct(memrefShape.take_back(i)));
// Cond 1.2: Strides of a contiguous memref have to match the flattened
// dims.
strides = strides.drop_back(1);
SmallVector<int64_t> flattenedDims;
for (size_t i = 1; i < memrefShape.size(); i++)
flattenedDims.push_back(mlir::computeProduct(memrefShape.take_back(i)));

if (!llvm::equal(strides, llvm::reverse(flattenedDims)))
return false;
if (!llvm::equal(strides, llvm::reverse(flattenedDims)))
return false;
}

// Cond 3: Compare the dims of `vectorType` against `memrefType` (in reverse).
// Cond 2: Compare the dims of `vectorType` against `memrefType` (in reverse).
// In the most basic case, all dims will match.
auto firstNonMatchingDim =
std::mismatch(vectorShape.rbegin(), vectorShape.rend(),
Expand Down
48 changes: 33 additions & 15 deletions mlir/test/Dialect/Vector/vector-transfer-flatten.mlir
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,24 @@ func.func @transfer_read_dims_match_contiguous(

// -----

func.func @transfer_read_dims_match_contiguous_empty_stride(
%arg : memref<5x4x3x2xi8>) -> vector<5x4x3x2xi8> {
%c0 = arith.constant 0 : index
%cst = arith.constant 0 : i8
%v = vector.transfer_read %arg[%c0, %c0, %c0, %c0], %cst :
memref<5x4x3x2xi8>, vector<5x4x3x2xi8>
return %v : vector<5x4x3x2xi8>
}

// CHECK-LABEL: func @transfer_read_dims_match_contiguous_empty_stride
// CHECK-SAME: %[[ARG:[0-9a-zA-Z]+]]: memref<5x4x3x2xi8
// CHECK: %[[COLLAPSED:.+]] = memref.collapse_shape %[[ARG]] {{.}}[0, 1, 2, 3]
// CHECK: %[[READ1D:.+]] = vector.transfer_read %[[COLLAPSED]]
// CHECK: %[[VEC2D:.+]] = vector.shape_cast %[[READ1D]] : vector<120xi8> to vector<5x4x3x2xi8>
// CHECK: return %[[VEC2D]]

// -----

// The shape of the memref and the vector don't match, but the vector is a
// contiguous subset of the memref, so "flattenable".

Expand Down Expand Up @@ -114,6 +132,21 @@ func.func @transfer_read_dims_mismatch_non_contiguous(

// -----

func.func @transfer_read_dims_mismatch_non_contiguous_empty_stride(
%arg : memref<5x4x3x2xi8>) -> vector<2x1x2x2xi8> {
%c0 = arith.constant 0 : index
%cst = arith.constant 0 : i8
%v = vector.transfer_read %arg[%c0, %c0, %c0, %c0], %cst :
memref<5x4x3x2xi8>, vector<2x1x2x2xi8>
return %v : vector<2x1x2x2xi8>
}

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

// -----

func.func @transfer_write_dims_match_contiguous(
%arg : memref<5x4x3x2xi8, strided<[24, 6, 2, 1], offset: ?>>, %vec : vector<5x4x3x2xi8>) {
%c0 = arith.constant 0 : index
Expand Down Expand Up @@ -356,18 +389,3 @@ func.func @fold_unit_dims_entirely(%arg0 : vector<8xi32>,
// CHECK: %[[VAL_3:.*]] = arith.muli %[[VAL_0]], %[[VAL_1]] : vector<8xi32>
// CHECK: %[[VAL_4:.*]] = arith.addi %[[VAL_3]], %[[VAL_2]] : vector<8xi32>
// CHECK: return %[[VAL_4]] : vector<8xi32>

// -----

// This test is to make sure there is no crash for empty stride.
func.func @stride_empty_test(%1: memref<i16>) -> vector<32x256xi16> {
%c0_i16 = arith.constant 0 : i16
%3 = vector.transfer_read %1[], %c0_i16 {permutation_map = affine_map<() -> (0, 0)>} : memref<i16>, vector<32x256xi16>
return %3 : vector<32x256xi16>

// CHECK-LABEL: func.func @stride_empty_test
// CHECK: %[[VAL:.*]] = arith.constant 0 : i16
// CHECK: %[[RET:.*]] = vector.transfer_read {{.*}} vector<32x256xi16>
// CHECK: return %[[RET]]
// CHECK-NOT: empty()
}