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[XLA:GPU/TMA] Adding TMA related attributes to the function arguments. #23249

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[XLA:GPU/TMA] Adding TMA related attributes to the function arguments. #23249

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3 changes: 2 additions & 1 deletion xla/backends/gpu/codegen/triton/transforms/tests/triton_xla_extract_insert_to_triton.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,8 @@ func.func @lower_tile_extract_insert(%arg0: tensor<512x128xbf16>,
// CHECK: tt.return

// CHECK-TMA-LABEL:tt.func @lower_tile_extract_insert
// CHECK-TMA-SAME: %[[ARG_0:.*]]: !tt.ptr<bf16>, %[[ARG_1:.*]]: !tt.ptr<bf16>
// CHECK-TMA-SAME: %[[ARG_0:.*]]: !tt.ptr<bf16> {tt.nv_tma_desc = 1 : i32, tt.tma_descriptor = #triton_xla.tma_descriptor<global_shape = [512, 128], block_shape = [16, 64], element_byte_size = 2>},
// CHECK-TMA-SAME: %[[ARG_1:.*]]: !tt.ptr<bf16> {tt.nv_tma_desc = 1 : i32, tt.tma_descriptor = #triton_xla.tma_descriptor<global_shape = [256, 256], block_shape = [16, 64], element_byte_size = 2>}
// CHECK-TMA: %[[DESC_0:.*]] = tt.reinterpret_tensor_descriptor %[[ARG_0]]
// CHECK-TMA: %[[DESC_1:.*]] = tt.reinterpret_tensor_descriptor %[[ARG_1]]
// CHECK-TMA: %[[LOAD:.*]] = tt.experimental_descriptor_load %[[DESC_0]]
Expand Down
78 changes: 58 additions & 20 deletions xla/backends/gpu/codegen/triton/transforms/triton_xla_extract_insert_to_triton_pass.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@ limitations under the License.
#include <stdbool.h>

#include <cstdint>
#include <iterator>
#include <memory>
#include <numeric>
#include <optional>
Expand Down Expand Up @@ -137,12 +138,6 @@ void ComputeBoundaryChecks(std::vector<int32_t>& boundary_checks,
}

struct RewriteFuncOp : mlir::OpRewritePattern<func::FuncOp> {
RewriteFuncOp(mlir::MLIRContext* context,
const stream_executor::DeviceDescription* device_description,
bool tma_enabled)
: OpRewritePattern(context),
device_description(device_description),
tma_enabled(tma_enabled) {}
using OpRewritePattern::OpRewritePattern;

// Rewrite tensors<> to !tt.ptr<tensor>
Expand Down Expand Up @@ -183,6 +178,14 @@ struct RewriteFuncOp : mlir::OpRewritePattern<func::FuncOp> {
op.getContext(), new_operand_types, /*result_types=*/{});
auto new_func = rewriter.create<triton::FuncOp>(op.getLoc(), op.getName(),
new_function_type);
// Transfer the argument attributes from the old function to the new one.
if (op.getArgAttrs().has_value()) {
auto oldArgAttrsArray = op.getArgAttrs().value();
for (int i = 0; i < oldArgAttrsArray.size(); ++i) {
new_func.setArgAttrs(
i, mlir::cast<mlir::DictionaryAttr>(oldArgAttrsArray[i]));
}
}

rewriter.inlineRegionBefore(op.getRegion(), new_func.getFunctionBody(),
new_func.end());
Expand All @@ -195,9 +198,6 @@ struct RewriteFuncOp : mlir::OpRewritePattern<func::FuncOp> {

return mlir::success();
}

const stream_executor::DeviceDescription* device_description;
const bool tma_enabled;
};

struct RewriteTile : mlir::OpRewritePattern<TileOp> {
Expand All @@ -215,17 +215,44 @@ struct RewriteTile : mlir::OpRewritePattern<TileOp> {
TileOp op, mlir::PatternRewriter& rewriter) const override {
::xla::EmitterLocOpBuilder builder(op.getLoc(), rewriter);

// tensor -> !tt.ptr<>
auto cast_to_tensor_ptr_type =
builder
.create<mlir::UnrealizedConversionCastOp>(
GetTensorPtrType(builder,
op.getTensor().getType().getElementType()),
op.getTensor())
.getResult(0);

if (CanUseTMA(tma_enabled, *device_description,
op.getTiledTensor().getType())) {
// Add TMA attributes to the equivalent argument in the function.
if (auto block_arg = mlir::dyn_cast<BlockArgument>(op.getTensor())) {
mlir::Operation* parent_op = block_arg.getOwner()->getParentOp();
if (auto func_op = mlir::dyn_cast<func::FuncOp>(parent_op)) {
// TODO(manany): Revisit. This needs to be part of the eligibility
// check inside CanUseTMA itself. Should we enforce TileOp to be
// rewritten first and then have the other patterns check a flag?
if (std::distance(block_arg.getUsers().begin(),
block_arg.getUsers().end()) == 1) {
func_op.setArgAttr(block_arg.getArgNumber(), "tt.nv_tma_desc",
builder.getI32IntegerAttr(1));
// TODO(manany): We need to prefix the attribute name with "tt",
// otherwise tt.func will complain that it has an attribute that
// is not part of the dialect. Is there a better way to do this?
func_op.setArgAttr(
block_arg.getArgNumber(), "tt.tma_descriptor",
builder.getAttr<TmaDescriptorAttr>(
op.getTiledTensor().getType().getOriginalShape(),
op.getTiledTensor().getType().getTileShape(),
op.getTiledTensor()
.getType()
.getElementType()
.getIntOrFloatBitWidth() /
8));
}
}
}
// tensor -> !tt.ptr<>
auto cast_to_tensor_ptr_type =
builder
.create<mlir::UnrealizedConversionCastOp>(
GetTensorPtrType(builder,
op.getTensor().getType().getElementType()),
op.getTensor())
.getResult(0);

auto reinterpret_tensor_desc =
xg::EmitTmaDescriptor(builder, cast_to_tensor_ptr_type,
op.getTiledTensor().getType().getTileType());
Expand All @@ -238,13 +265,23 @@ struct RewriteTile : mlir::OpRewritePattern<TileOp> {
reinterpret_tensor_desc);

rewriter.replaceOp(op, cast_desc_ptr_to_tiled_tensor_ptr_type);

return mlir::success();
}

// Order is 0, 1, ..., rank - 1.
std::vector<int32_t> dim_order(op.getSizes().size());
std::iota(dim_order.begin(), dim_order.end(), 0);

// tensor -> !tt.ptr<>
auto cast_to_tensor_ptr_type =
builder
.create<mlir::UnrealizedConversionCastOp>(
GetTensorPtrType(builder,
op.getTensor().getType().getElementType()),
op.getTensor())
.getResult(0);

auto tensor_ptr =
builder
.create<MakeTensorPtrOp>(
Expand Down Expand Up @@ -473,10 +510,11 @@ struct TritonXLAExtractInsertToTritonPass
mlir::MLIRContext* mlir_context = &getContext();
mlir::RewritePatternSet patterns(mlir_context);
// clang-format off
patterns.add<RewriteScalarExtract, RewriteScalarInsert>(mlir_context);
patterns.add<RewriteFuncOp,
RewriteScalarExtract,
RewriteScalarInsert>(mlir_context);
patterns.add<
RewriteExtract,
RewriteFuncOp,
RewriteInsert,
RewriteTile
>(mlir_context, &device_description, tma_enabled);
Expand Down