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Jun 19, 2025
Merged

Handle mix of structured and unstructured accesses in loops #290

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ad91c70
No longer lower triton pointer to unranked memref in loops
nhat-nguyen Apr 7, 2025
3f1e5a2
Fix
nhat-nguyen Jun 18, 2025
65a3d21
update
nhat-nguyen Jun 18, 2025
dc9bf1a
typo
nhat-nguyen Jun 18, 2025
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90 changes: 14 additions & 76 deletions lib/Conversion/StructuredToMemref/StructuredToMemrefPass.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@
#include "triton/Dialect/Triton/IR/Dialect.h"

#include "triton-shared/Conversion/StructuredToMemref/StructuredToMemref.h"
#include "triton-shared/Dialect/TPtr/IR/TPtrDialect.h"
#include "triton-shared/Dialect/TritonStructured/IR/TritonStructuredDialect.h"
#include "triton-shared/Dialect/TritonTilingExt/IR/TritonTilingExtDialect.h"

Expand All @@ -24,12 +25,7 @@
#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
#include "mlir/Dialect/Linalg/IR/Linalg.h"
#include "mlir/Dialect/SCF/Transforms/Patterns.h"
#include "mlir/Pass/PassManager.h"
#include "triton/Dialect/Triton/IR/Types.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/Support/Casting.h"

#include <optional>

#define DEBUG_TYPE "structured-to-memref"

Expand All @@ -45,67 +41,6 @@ namespace triton {

namespace {

class LoopTypeConverter : public TypeConverter {
public:
LoopTypeConverter(MLIRContext *context) {
// The order of type conversion is important: later ones are tried earlier.
addConversion([](Type type) { return type; });
// addConversion([context](triton::PointerType ptrType) {
// SmallVector<int64_t> strides{1};
// auto layout =
// StridedLayoutAttr::get(context, ShapedType::kDynamic, strides);

// auto elemType = ptrType.getPointeeType();
// auto memrefType = MemRefType::get({1}, elemType, layout);
// return memrefType;
// });

// A tensor of pointers can be passed in as scf.for's init-args, in such
// cases, we convert the type to a memref with dynamic offsets and
// strides.
addConversion(
[context](RankedTensorType tensorType) -> std::optional<MemRefType> {
if (auto ptrType = llvm::dyn_cast<triton::PointerType>(
tensorType.getElementType())) {
auto layout = StridedLayoutAttr::get(
context, ShapedType::kDynamic,
SmallVector<int64_t>(tensorType.getRank(),
ShapedType::kDynamic));
Type elemType = ptrType.getPointeeType();
return MemRefType::get(tensorType.getShape(), elemType, layout);
}

return std::nullopt;
});

// Convert the current memref type to a memref type with dynamic offsets and
// strides through another reinterpret_cast with the same offsets.
// Canonicalization will simplify this sequence by removing the inital
// reinterpret_cast.
addTargetMaterialization([&](OpBuilder &builder, MemRefType memrefType,
ValueRange inputs,
Location loc) -> Value {
auto reinterpretCast =
inputs[0].getDefiningOp<memref::ReinterpretCastOp>();
if (!reinterpretCast) {
return builder
.create<UnrealizedConversionCastOp>(loc, memrefType, inputs)
.getResult(0);
}
return builder.create<memref::ReinterpretCastOp>(
loc, memrefType, inputs[0], reinterpretCast.getMixedOffsets()[0],
reinterpretCast.getMixedSizes(), reinterpretCast.getMixedStrides());
});

addSourceMaterialization([&](OpBuilder &builder, Type resultType,
ValueRange inputs,
Location loc) -> Value {
return builder.create<UnrealizedConversionCastOp>(loc, resultType, inputs)
.getResult(0);
});
}
};

class PtrToUnrankedMemrefConverter : public TypeConverter {
public:
PtrToUnrankedMemrefConverter() {
Expand All @@ -120,6 +55,13 @@ class PtrToUnrankedMemrefConverter : public TypeConverter {
return builder.create<UnrealizedConversionCastOp>(loc, resultType, inputs)
.getResult(0);
});

addSourceMaterialization([&](OpBuilder &builder, Type resultType,
ValueRange inputs,
Location loc) -> Value {
return builder.create<UnrealizedConversionCastOp>(loc, resultType, inputs)
.getResult(0);
});
}
};

Expand All @@ -129,11 +71,12 @@ class StructuredToMemrefPass

public:
void getDependentDialects(DialectRegistry &registry) const override {
registry.insert<func::FuncDialect, arith::ArithDialect, math::MathDialect,
linalg::LinalgDialect, affine::AffineDialect,
scf::SCFDialect, tensor::TensorDialect,
bufferization::BufferizationDialect, triton::TritonDialect,
ttx::TritonTilingExtDialect, memref::MemRefDialect>();
registry
.insert<tptr::TPtrDialect, func::FuncDialect, arith::ArithDialect,
math::MathDialect, linalg::LinalgDialect, affine::AffineDialect,
scf::SCFDialect, tensor::TensorDialect,
bufferization::BufferizationDialect, triton::TritonDialect,
ttx::TritonTilingExtDialect, memref::MemRefDialect>();
}

void runOnOperation() override {
Expand All @@ -158,11 +101,6 @@ class StructuredToMemrefPass
triton::populateStructuredToMemrefConversionPatterns(patterns,
typeConverter);

LoopTypeConverter loopTypeConverter(patterns.getContext());

mlir::scf::populateSCFStructuralTypeConversionsAndLegality(
loopTypeConverter, patterns, target);

if (failed(applyPartialConversion(moduleOp, target, std::move(patterns)))) {
signalPassFailure();
}
Expand Down
13 changes: 4 additions & 9 deletions lib/Conversion/TritonToLinalgExperimental/TritonToLinalgExperimentalPass.cpp
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Expand Up @@ -60,22 +60,17 @@ class TritonToLinalgExperimentalPass
pm.addPass(createTritonToUnstructuredPass());
pm.addPass(createTritonArithToLinalgPass(/*tensorPtrToLinalg=*/true));

// TODO: structured-to-memref converts the loop iter-args to memref, while
// triton-to-ptr converts the loop iter-args to ptr. These two passes might
// end up conflicting with each other in cases where we have a mixed of
// structured and unstructured accesses. Fortunately, the structured ops do
// not need to use the loop iter-args at all (see code in PtrAnalysis.cpp),
// so if we run remove-dead-values after structured-to-memref, the memref
// iter-args that are used in structured loads and stores should be removed.
// Running this now may be too invasive and cause many IR changes, so
// leave as a TODO for now.
pm.addPass(createStructuredToMemrefPass());
pm.addPass(createUnstructuredToMemrefPass());
pm.addPass(createTritonPtrToMemrefPass());
pm.addPass(createTritonToPtrPass());
pm.addPass(createReconcileUnrealizedCastsPass());
pm.addPass(createReconcilePtrCastsPass());

// Now that remove-dead-values fully works with linalg ops, clean up the IR
// again, particularly unused loop iter-args that were created
// during triton-to-structured.
pm.addPass(createRemoveDeadValuesPass());
pm.addPass(createCSEPass());
pm.addPass(createCanonicalizerPass());

Expand Down
10 changes: 4 additions & 6 deletions test/Conversion/StructuredToMemref/addptr_scalar_for.mlir
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
// RUN: triton-shared-opt --split-input-file --triton-to-linalg-experimental %s | FileCheck %s
// RUN: triton-shared-opt --triton-to-linalg-experimental %s | FileCheck %s
module {
tt.func @kernel (%arg0: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg2: i32, %arg3: i32, %arg4: i32) {
%0 = tt.get_program_id x : i32
Expand Down Expand Up @@ -47,7 +47,7 @@ module {
// CHECK-DAG: [[VAR_1_:%.+]] = linalg.fill ins([[CST_0_dot_000000_]] : f32) outs([[VAR_0_]] : tensor<1024xf32>) -> tensor<1024xf32>
// CHECK-DAG: [[VAR_2_:%.+]] = arith.muli [[PARAM_8_]], [[PARAM_2_]] : i32
// CHECK: [[VAR_3_:%.+]] = arith.index_cast [[VAR_2_]] : i32 to index
// CHECK-DAG: [[VAR_4_:%.+]]:3 = scf.for [[VAR_arg11_:%.+]] = [[CST_0_]] to [[CST_12_]] step [[CST_3_]] iter_args([[VAR_arg12_:%.+]] = [[VAR_2_]], [[VAR_arg13_:%.+]] = [[VAR_3_]], [[VAR_arg14_:%.+]] = [[VAR_1_]]) -> (i32, index, tensor<1024xf32>) {
// CHECK-DAG: [[VAR_4_:%.+]]:2 = scf.for [[VAR_arg11_:%.+]] = [[CST_0_]] to [[CST_12_]] step [[CST_3_]] iter_args([[VAR_arg13_:%.+]] = [[VAR_3_]], [[VAR_arg14_:%.+]] = [[VAR_1_]]) -> (index, tensor<1024xf32>) {
// CHECK-DAG: [[VAR_reinterpret_cast_0_:%.+]] = memref.reinterpret_cast [[PARAM_1_]] to offset: {{.}}[[VAR_arg13_]]{{.}}, sizes: [1024], strides: [1] : memref<*xf32> to memref<1024xf32, strided<[1], offset: ?>>
// CHECK-DAG: [[RES_:%.+]] = memref.alloc() : memref<1024xf32>
// CHECK: memref.copy [[VAR_reinterpret_cast_0_]], [[RES_]] : memref<1024xf32, strided<[1], offset: ?>> to memref<1024xf32>
Expand All @@ -62,14 +62,12 @@ module {
// CHECK: [[VAR_13_1_:%.+]] = arith.addf [[IN_2_]], [[IN_3_]] : f32
// CHECK: linalg.yield [[VAR_13_1_]] : f32
// CHECK: } -> tensor<1024xf32>
// CHECK-DAG: [[VAR_10_:%.+]] = arith.index_cast [[VAR_arg11_]] : index to i32
// CHECK-DAG: [[VAR_11_:%.+]] = arith.addi [[VAR_arg13_]], [[VAR_arg11_]] : index
// CHECK: [[VAR_12_:%.+]] = arith.addi [[VAR_arg12_]], [[VAR_10_]] : i32
// CHECK: scf.yield [[VAR_12_]], [[VAR_11_]], [[VAR_9_]] : i32, index, tensor<1024xf32>
// CHECK: scf.yield [[VAR_11_]], [[VAR_9_]] : index, tensor<1024xf32>
// CHECK: }
// CHECK: [[VAR_5_:%.+]] = arith.muli [[PARAM_8_]], [[PARAM_3_]] : i32
// CHECK: [[VAR_6_:%.+]] = arith.index_cast [[VAR_5_]] : i32 to index
// CHECK: [[VAR_reinterpret_cast_:%.+]] = memref.reinterpret_cast [[PARAM_0_]] to offset: {{.}}[[VAR_6_]]{{.}}, sizes: [1024], strides: [1] : memref<*xf32> to memref<1024xf32, strided<[1], offset: ?>>
// CHECK: bufferization.materialize_in_destination [[VAR_4_]]#2 in writable [[VAR_reinterpret_cast_]] : (tensor<1024xf32>, memref<1024xf32, strided<[1], offset: ?>>) -> ()
// CHECK: bufferization.materialize_in_destination [[VAR_4_]]#1 in writable [[VAR_reinterpret_cast_]] : (tensor<1024xf32>, memref<1024xf32, strided<[1], offset: ?>>) -> ()
// CHECK: return
// CHECK: }
8 changes: 3 additions & 5 deletions test/Conversion/StructuredToMemref/addptr_scalar_for_2d.mlir
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
// RUN: triton-shared-opt --split-input-file --triton-to-linalg-experimental %s | FileCheck %s
// RUN: triton-shared-opt --triton-to-linalg-experimental %s | FileCheck %s
module {
tt.func @kernel (%arg0: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg1: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %arg2: i32, %arg3: i32, %arg4: i32) {
%0 = tt.get_program_id x : i32
Expand Down Expand Up @@ -67,7 +67,7 @@ module {
// CHECK-DAG: [[VAR_1_:%.+]] = linalg.fill ins([[CST_0_dot_000000_]] : f32) outs([[VAR_0_]] : tensor<128x128xf32>) -> tensor<128x128xf32>
// CHECK-DAG: [[VAR_2_:%.+]] = arith.muli [[PARAM_8_]], [[PARAM_2_]] : i32
// CHECK: [[VAR_3_:%.+]] = arith.index_cast [[VAR_2_]] : i32 to index
// CHECK-DAG: [[VAR_4_:%.+]]:3 = scf.for [[VAR_arg11_:%.+]] = [[CST_0_]] to [[CST_12_]] step [[CST_3_]] iter_args([[VAR_arg12_:%.+]] = [[VAR_1_]], [[VAR_arg13_:%.+]] = [[VAR_2_]], [[VAR_arg14_:%.+]] = [[VAR_3_]]) -> (tensor<128x128xf32>, i32, index) {
// CHECK-DAG: [[VAR_4_:%.+]]:2 = scf.for [[VAR_arg11_:%.+]] = [[CST_0_]] to [[CST_12_]] step [[CST_3_]] iter_args([[VAR_arg12_:%.+]] = [[VAR_1_]], [[VAR_arg14_:%.+]] = [[VAR_3_]]) -> (tensor<128x128xf32>, index) {
// CHECK-DAG: [[VAR_8_:%.+]] = arith.addi [[VAR_arg14_]], [[CST_128_]] : index
// CHECK-NOT: separator of consecutive DAGs
// CHECK-DAG: [[VAR_reinterpret_cast_0_:%.+]] = memref.reinterpret_cast [[PARAM_1_]] to offset: {{.}}[[VAR_8_]]{{.}}, sizes: [128, 128], strides: [1, 1] : memref<*xf32> to memref<128x128xf32, strided<[1, 1], offset: ?>>
Expand All @@ -84,10 +84,8 @@ module {
// CHECK: [[VAR_15_1_:%.+]] = arith.addf [[IN_2_]], [[IN_3_]] : f32
// CHECK: linalg.yield [[VAR_15_1_]] : f32
// CHECK: } -> tensor<128x128xf32>
// CHECK-DAG: [[VAR_12_:%.+]] = arith.index_cast [[VAR_arg11_]] : index to i32
// CHECK-DAG: [[VAR_13_:%.+]] = arith.addi [[VAR_arg14_]], [[VAR_arg11_]] : index
// CHECK: [[VAR_14_:%.+]] = arith.addi [[VAR_arg13_]], [[VAR_12_]] : i32
// CHECK: scf.yield [[VAR_11_]], [[VAR_14_]], [[VAR_13_]] : tensor<128x128xf32>, i32, index
// CHECK: scf.yield [[VAR_11_]], [[VAR_13_]] : tensor<128x128xf32>, index
// CHECK: }
// CHECK: [[VAR_5_:%.+]] = arith.muli [[PARAM_8_]], [[PARAM_3_]] : i32
// CHECK: [[VAR_6_:%.+]] = arith.index_cast [[VAR_5_]] : i32 to index
Expand Down
4 changes: 1 addition & 3 deletions test/Conversion/StructuredToMemref/nested_loops.mlir
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Expand Up @@ -182,6 +182,4 @@ module {
// CHECK-NOT: tt.addptr
// CHECK-NOT: tt.load
// CHECK-NOT: tt.store

// CHECK-COUNT-20: memref.reinterpret_cast %arg{{[0-9]+}}
// CHECK-NOT: memref.reinterpret_cast %arg{{[0-9]+}}
// CHECK-NOT: unrealized_conversion_cast
4 changes: 1 addition & 3 deletions test/Conversion/StructuredToMemref/ridiculously_nested_loops.mlir
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Expand Up @@ -159,6 +159,4 @@ module {
// CHECK-NOT: tt.addptr
// CHECK-NOT: tt.load
// CHECK-NOT: tt.store

// CHECK-COUNT-43: memref.reinterpret_cast %arg{{[0-9]+}}
// CHECK-NOT: memref.reinterpret_cast %arg{{[0-9]+}}
// CHECK-NOT: unrealized_conversion_cast
8 changes: 2 additions & 6 deletions test/Conversion/StructuredToMemref/scalar_store_loop_iterargs.mlir
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Expand Up @@ -25,18 +25,14 @@ module {
// CHECK-DAG: [[CST_5_:%.+]] = arith.constant 5 : i32
// CHECK-DAG: [[CST_1_:%.+]] = arith.constant 1 : i32
// CHECK-DAG: [[CST_0_:%.+]] = arith.constant 0 : i32
// CHECK-DAG: [[VAR_0_:%.+]] = arith.index_cast [[PARAM_7_]] : i32 to index
// CHECK-DAG: [[VAR_1_:%.+]] = arith.sitofp [[PARAM_7_]] : i32 to f32
// CHECK-NOT: separator of consecutive DAGs
// CHECK-DAG: [[VAR_2_:%.+]]:3 = scf.for [[VAR_arg16_:%.+]] = [[CST_0_]] to [[CST_5_]] step [[CST_1_]] iter_args([[VAR_arg17_:%.+]] = [[PARAM_7_]], [[VAR_arg18_:%.+]] = [[VAR_0_]], [[VAR_arg19_:%.+]] = [[VAR_0_]]) -> (i32, index, index) : i32 {
// CHECK-DAG: [[VAR_2_:%.+]] = scf.for [[VAR_arg16_:%.+]] = [[CST_0_]] to [[CST_5_]] step [[CST_1_]] iter_args([[VAR_arg17_:%.+]] = [[PARAM_7_]]) -> (i32) : i32 {
// CHECK-DAG: [[VAR_3_:%.+]] = arith.index_cast [[VAR_arg17_]] : i32 to index
// CHECK: [[VAR_reinterpret_cast_:%.+]] = memref.reinterpret_cast [[PARAM_1_]] to offset: {{.}}[[VAR_3_]]{{.}}, sizes: [1], strides: [1] : memref<*xf32> to memref<1xf32, strided<[1], offset: ?>>
// CHECK: memref.store [[VAR_1_]], [[VAR_reinterpret_cast_]][%[[C0]]] : memref<1xf32, strided<[1], offset: ?>>
// CHECK: [[VAR_4_:%.+]] = arith.index_cast [[VAR_arg16_]] : i32 to index
// CHECK-DAG: [[VAR_5_:%.+]] = arith.addi [[VAR_arg18_]], [[VAR_4_]] : index
// CHECK-DAG: [[VAR_6_:%.+]] = arith.addi [[VAR_arg17_]], [[VAR_arg16_]] : i32
// CHECK-DAG: [[VAR_7_:%.+]] = arith.addi [[VAR_arg19_]], [[VAR_4_]] : index
// CHECK: scf.yield [[VAR_6_]], [[VAR_5_]], [[VAR_7_]] : i32, index, index
// CHECK: scf.yield [[VAR_6_]] : i32
// CHECK: }
// CHECK: return
// CHECK: }
14 changes: 5 additions & 9 deletions test/Conversion/StructuredToMemref/scalar_store_nested_loop.mlir
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Expand Up @@ -24,26 +24,22 @@ module {
// CHECK-LABEL: func.func @reduce_kernel_2d_0d
// CHECK-SAME: ([[PARAM_0_:%.+]]: memref<*xf32> {tt.divisibility = 16 : i32}, [[PARAM_1_:%.+]]: i32, [[PARAM_2_:%.+]]: i32, [[PARAM_3_:%.+]]: i32, [[PARAM_4_:%.+]]: i32, [[PARAM_5_:%.+]]: i32, [[PARAM_6_:%.+]]: i32, [[PARAM_7_:%.+]]: i32, [[PARAM_8_:%.+]]: i32, [[PARAM_9_:%.+]]: i32, [[PARAM_10_:%.+]]: i32, [[PARAM_11_:%.+]]: i32, [[PARAM_12_:%.+]]: i32) {
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: [[CST_1_:%.+]] = arith.constant 1 : index
// CHECK-DAG: [[CST_1_1_:%.+]] = arith.constant 1 : i32
// CHECK-DAG: [[CST_8_:%.+]] = arith.constant 8 : i32
// CHECK-DAG: [[CST_2_:%.+]] = arith.constant 2 : i32
// CHECK-DAG: [[CST_0_:%.+]] = arith.constant 0 : i32
// CHECK-DAG: [[VAR_0_:%.+]] = arith.index_cast [[PARAM_4_]] : i32 to index
// CHECK-NOT: separator of consecutive DAGs
// CHECK-DAG: [[VAR_1_:%.+]]:2 = scf.for [[VAR_arg13_:%.+]] = [[CST_0_]] to [[CST_8_]] step [[CST_1_1_]] iter_args([[VAR_arg14_:%.+]] = [[PARAM_4_]], [[VAR_arg15_:%.+]] = [[VAR_0_]]) -> (i32, index) : i32 {
// CHECK-DAG: [[VAR_2_:%.+]]:2 = scf.for [[VAR_arg16_:%.+]] = [[CST_0_]] to [[CST_2_]] step [[CST_1_1_]] iter_args([[VAR_arg17_:%.+]] = [[VAR_arg14_]], [[VAR_arg18_:%.+]] = [[VAR_arg15_]]) -> (i32, index) : i32 {
// CHECK-DAG: [[VAR_1_:%.+]] = scf.for [[VAR_arg13_:%.+]] = [[CST_0_]] to [[CST_8_]] step [[CST_1_1_]] iter_args([[VAR_arg14_:%.+]] = [[PARAM_4_]]) -> (i32) : i32 {
// CHECK-DAG: [[VAR_2_:%.+]] = scf.for [[VAR_arg16_:%.+]] = [[CST_0_]] to [[CST_2_]] step [[CST_1_1_]] iter_args([[VAR_arg17_:%.+]] = [[VAR_arg14_]]) -> (i32) : i32 {
// CHECK-DAG: [[VAR_3_:%.+]] = arith.muli [[VAR_arg13_]], [[VAR_arg16_]] : i32
// CHECK-NOT: separator of consecutive DAGs
// CHECK-DAG: [[VAR_4_:%.+]] = arith.sitofp [[VAR_3_]] : i32 to f32
// CHECK-DAG: [[VAR_5_:%.+]] = arith.index_cast [[VAR_arg17_]] : i32 to index
// CHECK: [[VAR_reinterpret_cast_:%.+]] = memref.reinterpret_cast [[PARAM_0_]] to offset: {{.}}[[VAR_5_]]{{.}}, sizes: [1], strides: [1] : memref<*xf32> to memref<1xf32, strided<[1], offset: ?>>
// CHECK: memref.store [[VAR_4_]], [[VAR_reinterpret_cast_]][%[[C0]]] : memref<1xf32, strided<[1], offset: ?>>
// CHECK-DAG: [[VAR_6_:%.+]] = arith.addi [[VAR_arg18_]], [[CST_1_]] : index
// CHECK-DAG: [[VAR_7_:%.+]] = arith.addi [[VAR_arg17_]], [[CST_1_1_]] : i32
// CHECK: scf.yield [[VAR_7_]], [[VAR_6_]] : i32, index
// CHECK: [[VAR_7_:%.+]] = arith.addi [[VAR_arg17_]], [[CST_1_1_]] : i32
// CHECK: scf.yield [[VAR_7_]] : i32
// CHECK: }
// CHECK: scf.yield [[VAR_2_]]#0, [[VAR_2_]]#1 : i32, index
// CHECK: scf.yield [[VAR_2_]] : i32
// CHECK: }
// CHECK: return
// CHECK: }
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