Skip to content

[mlir][gpu] Support extf before contract when converting to MMA ops #91988

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 3 commits into from
May 13, 2024
Merged

[mlir][gpu] Support extf before contract when converting to MMA ops #91988

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
6406abc
[mlir][gpu] Support extf before contract when converting to MMA ops
antiagainst May 13, 2024
269e008
Address comments
antiagainst May 13, 2024
83ab750
Merge remote-tracking branch 'origin/main' into HEAD
antiagainst May 13, 2024
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
15 changes: 11 additions & 4 deletions mlir/lib/Conversion/VectorToGPU/VectorToGPU.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -515,6 +515,14 @@ struct CombineTransferReadOpTranspose final
// TODO: Change the GPU dialect to abstract the layout at the this level and
// only care about it during lowering to NVVM.
static const char *inferFragType(Operation *op) {
// We can have arith.ext ops before reaching contract ops. See through them
// and other kinds of elementwise ops.
if (op->hasOneUse()) {
Operation *userOp = *op->user_begin();
if (userOp->hasTrait<OpTrait::Elementwise>())
return inferFragType(userOp);
}

for (Operation *users : op->getUsers()) {
auto contract = dyn_cast<vector::ContractionOp>(users);
if (!contract)
Expand Down Expand Up @@ -560,13 +568,12 @@ convertTransferReadOp(RewriterBase &rewriter, vector::TransferReadOp op,
if (op->hasOneUse()) {
auto *user = *op->user_begin();
// Infer the signedness of the mma type from the integer extend.
bool isSignedExtend = isa<arith::ExtSIOp>(user);
if (isSignedExtend || isa<arith::ExtUIOp>(user)) {
if (isa<arith::ExtSIOp, arith::ExtUIOp>(user)) {
elType = IntegerType::get(
op.getContext(), cast<IntegerType>(elType).getWidth(),
isSignedExtend ? IntegerType::Signed : IntegerType::Unsigned);
isa<arith::ExtSIOp>(user) ? IntegerType::Signed
: IntegerType::Unsigned);
mappingResult = user->getResult(0);
fragType = inferFragType(user);
}
}
gpu::MMAMatrixType type =
Expand Down
27 changes: 27 additions & 0 deletions mlir/test/Conversion/VectorToGPU/vector-to-mma-ops.mlir
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -490,3 +490,30 @@ func.func @fold_transpose_into_transfer_read(%alloc: memref<64x128xf16>, %vector
}

// -----

#map1 = affine_map<(d0, d1, d2) -> (d0, d2)>
#map2 = affine_map<(d0, d1, d2) -> (d1, d2)>
#map3 = affine_map<(d0, d1, d2) -> (d0, d1)>

// CHECK-LABEL: func @cast_f16_to_f32_read
// CHECK: %[[A:.+]] = gpu.subgroup_mma_load_matrix {{.+}} {leadDimension = 16 : index} : memref<16x16xf16> -> !gpu.mma_matrix<16x16xf16, "AOp">
// CHECK: %[[C:.+]] = gpu.subgroup_mma_load_matrix {{.+}} {leadDimension = 16 : index} : memref<16x16xf16> -> !gpu.mma_matrix<16x16xf16, "COp">
// CHECK: %[[AE:.+]] = gpu.subgroup_mma_elementwise extf %[[A]] : (!gpu.mma_matrix<16x16xf16, "AOp">) -> !gpu.mma_matrix<16x16xf32, "AOp">
// CHECK: %[[CE:.+]] = gpu.subgroup_mma_elementwise extf %[[C]] : (!gpu.mma_matrix<16x16xf16, "COp">) -> !gpu.mma_matrix<16x16xf32, "COp">
// CHECK: %[[B:.+]] = gpu.subgroup_mma_load_matrix {{.+}} {leadDimension = 16 : index, transpose} : memref<16x16xf16> -> !gpu.mma_matrix<16x16xf16, "BOp">
// CHECK: %[[BE:.+]] = gpu.subgroup_mma_elementwise extf %[[B]] : (!gpu.mma_matrix<16x16xf16, "BOp">) -> !gpu.mma_matrix<16x16xf32, "BOp">
// CHECK: gpu.subgroup_mma_compute %[[AE]], %[[BE]], %[[CE]]
func.func @cast_f16_to_f32_read(%arg0: memref<16x16xf16>, %arg1: memref<16x16xf16>, %arg2: memref<16x16xf16>, %arg3: memref<16x16xf32>) {
%c0 = arith.constant 0 : index
%cst = arith.constant 0.000000e+00 : f16
%A = vector.transfer_read %arg0[%c0, %c0], %cst {in_bounds = [true, true]} : memref<16x16xf16>, vector<16x16xf16>
%B = vector.transfer_read %arg1[%c0, %c0], %cst {in_bounds = [true, true]} : memref<16x16xf16>, vector<16x16xf16>
%C = vector.transfer_read %arg2[%c0, %c0], %cst {in_bounds = [true, true]} : memref<16x16xf16>, vector<16x16xf16>
%Aext = arith.extf %A : vector<16x16xf16> to vector<16x16xf32>
%Bext = arith.extf %B : vector<16x16xf16> to vector<16x16xf32>
%Cext = arith.extf %C : vector<16x16xf16> to vector<16x16xf32>
%D = vector.contract {indexing_maps = [#map1, #map2, #map3], iterator_types = ["parallel", "parallel", "reduction"], kind = #vector.kind<add>}
%Aext, %Bext, %Cext : vector<16x16xf32>, vector<16x16xf32> into vector<16x16xf32>
vector.transfer_write %D, %arg3[%c0, %c0] {in_bounds = [true, true]} : vector<16x16xf32>, memref<16x16xf32>
return
}