[Mlir-commits] [mlir] [MLIR][XeGPU] Introduce `xegpu::uArch` usage in target-sensitive passes (PR #163801)
Artem Kroviakov
llvmlistbot at llvm.org
Wed Oct 29 07:57:39 PDT 2025
https://github.com/akroviakov updated https://github.com/llvm/llvm-project/pull/163801
>From 62be14dc1779771b08a9bd3f4ddc827fba6cf463 Mon Sep 17 00:00:00 2001
From: Artem Kroviakov <artem.kroviakov at intel.com>
Date: Tue, 28 Oct 2025 09:09:40 +0000
Subject: [PATCH 1/3] Update PR to use the new uArch
---
.../mlir/Dialect/XeGPU/IR/XeGPUAttrs.td | 22 +-
.../mlir/Dialect/XeGPU/IR/XeGPUTargetInfo.h | 30 ---
.../mlir/Dialect/XeGPU/Transforms/Passes.td | 7 +-
mlir/lib/Dialect/XeGPU/IR/XeGPUDialect.cpp | 16 +-
.../XeGPU/Transforms/XeGPUPropagateLayout.cpp | 230 +++++++++++++-----
.../Transforms/XeGPUSubgroupDistribute.cpp | 27 +-
.../XeGPU/move-gpu-func-to-warp-op.mlir | 2 +-
.../XeGPU/propagate-layout-inst-data.mlir | 89 +++++++
mlir/test/Dialect/XeGPU/propagate-layout.mlir | 82 +++++--
9 files changed, 359 insertions(+), 146 deletions(-)
delete mode 100644 mlir/include/mlir/Dialect/XeGPU/IR/XeGPUTargetInfo.h
create mode 100644 mlir/test/Dialect/XeGPU/propagate-layout-inst-data.mlir
diff --git a/mlir/include/mlir/Dialect/XeGPU/IR/XeGPUAttrs.td b/mlir/include/mlir/Dialect/XeGPU/IR/XeGPUAttrs.td
index 19a52317956d2..40352b44b6441 100644
--- a/mlir/include/mlir/Dialect/XeGPU/IR/XeGPUAttrs.td
+++ b/mlir/include/mlir/Dialect/XeGPU/IR/XeGPUAttrs.td
@@ -379,28 +379,28 @@ def XeGPU_LayoutAttr : XeGPUAttr<"Layout", "layout", [DistributeLayoutAttr]> {
);
let builders = [
- AttrBuilder<(ins "llvm::ArrayRef<int32_t>": $lane_layout,
+ AttrBuilder<(ins "llvm::ArrayRef<int32_t>": $inst_data,
+ "llvm::ArrayRef<int32_t>": $lane_layout,
"llvm::ArrayRef<int32_t>": $lane_data),
[{
auto sg_layout = DenseI32ArrayAttr();
auto sg_data = DenseI32ArrayAttr();
- auto inst_data = DenseI32ArrayAttr();
auto order = DenseI32ArrayAttr();
- return $_get($_ctxt, sg_layout, sg_data, inst_data,
+ return $_get($_ctxt, sg_layout, sg_data,
+ DenseI32ArrayAttr::get($_ctxt, inst_data),
DenseI32ArrayAttr::get($_ctxt, lane_layout),
DenseI32ArrayAttr::get($_ctxt, lane_data), order);
}]>,
AttrBuilder<(ins "llvm::ArrayRef<int32_t>": $lane_layout,
- "llvm::ArrayRef<int32_t>": $lane_data,
- "llvm::ArrayRef<int32_t>": $order),
+ "llvm::ArrayRef<int32_t>": $lane_data),
[{
- return $_get($_ctxt,
- /*sg_layout =*/ nullptr,
- /*sg_data =*/ nullptr,
- /*inst_data =*/ nullptr,
+ auto sg_layout = DenseI32ArrayAttr();
+ auto sg_data = DenseI32ArrayAttr();
+ auto inst_data = DenseI32ArrayAttr();
+ auto order = DenseI32ArrayAttr();
+ return $_get($_ctxt, sg_layout, sg_data, inst_data,
DenseI32ArrayAttr::get($_ctxt, lane_layout),
- DenseI32ArrayAttr::get($_ctxt, lane_data),
- DenseI32ArrayAttr::get($_ctxt, order));
+ DenseI32ArrayAttr::get($_ctxt, lane_data), order);
}]>,
AttrBuilder<(ins "DenseI32ArrayAttr": $lane_layout,
"DenseI32ArrayAttr": $lane_data,
diff --git a/mlir/include/mlir/Dialect/XeGPU/IR/XeGPUTargetInfo.h b/mlir/include/mlir/Dialect/XeGPU/IR/XeGPUTargetInfo.h
deleted file mode 100644
index 8aa9536cb67c1..0000000000000
--- a/mlir/include/mlir/Dialect/XeGPU/IR/XeGPUTargetInfo.h
+++ /dev/null
@@ -1,30 +0,0 @@
-//===- XeGPUTargetInfo.h - Target constants ---------------------*- C++ -*-===//
-//
-// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
-// See https://llvm.org/LICENSE.txt for license information.
-// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
-//
-//===----------------------------------------------------------------------===//
-
-#ifndef MLIR_DIALECT_XEGPU_IR_XEGPUTARGETINFO_H_
-#define MLIR_DIALECT_XEGPU_IR_XEGPUTARGETINFO_H_
-
-namespace mlir {
-namespace xegpu {
-/// HW dependent constants.
-/// TODO: These constants should be queried from the target information.
-namespace targetinfo {
-constexpr unsigned subgroupSize = 16; // How many lanes in a subgroup.
-/// If DPAS A or B operands have low precision element types they must be packed
-/// according to the following sizes.
-constexpr unsigned packedSizeInBitsForDefault =
- 16; // Minimum packing size per register for DPAS A.
-constexpr unsigned packedSizeInBitsForDpasB =
- 32; // Minimum packing size per register for DPAS B.
-constexpr unsigned packedSizeInBitsForGatherScatter =
- 32; // Minimum packing size per register for Gather and Scatter ops.
-} // namespace targetinfo
-} // namespace xegpu
-} // namespace mlir
-
-#endif // MLIR_DIALECT_XEGPU_IR_XEGPUTARGETINFO_H_
diff --git a/mlir/include/mlir/Dialect/XeGPU/Transforms/Passes.td b/mlir/include/mlir/Dialect/XeGPU/Transforms/Passes.td
index 564d9c4d5422b..b7af5413669c9 100644
--- a/mlir/include/mlir/Dialect/XeGPU/Transforms/Passes.td
+++ b/mlir/include/mlir/Dialect/XeGPU/Transforms/Passes.td
@@ -43,7 +43,12 @@ def XeGPUPropagateLayout : Pass<"xegpu-propagate-layout"> {
let options = [Option<
"printOnly", "print-analysis-only", "bool",
/*default=*/"false",
- "Print the result of layout propagation analysis and exit.">];
+ "Print the result of layout propagation analysis and exit.">,
+ Option<
+ "layoutKind", "layout-kind", "std::string",
+ /*default=*/"\"lane\"",
+ "Propagate a `sg` / `inst` / `lane` level of xegpu layouts.">
+ ];
}
def XeGPUWgToSgDistribute : Pass<"xegpu-wg-to-sg-distribute"> {
diff --git a/mlir/lib/Dialect/XeGPU/IR/XeGPUDialect.cpp b/mlir/lib/Dialect/XeGPU/IR/XeGPUDialect.cpp
index f9aa28d5203db..ea9c872aae66f 100644
--- a/mlir/lib/Dialect/XeGPU/IR/XeGPUDialect.cpp
+++ b/mlir/lib/Dialect/XeGPU/IR/XeGPUDialect.cpp
@@ -11,7 +11,7 @@
#include "mlir/Dialect/Index/IR/IndexOps.h"
#include "mlir/Dialect/Utils/IndexingUtils.h"
#include "mlir/Dialect/XeGPU/IR/XeGPU.h"
-#include "mlir/Dialect/XeGPU/IR/XeGPUTargetInfo.h"
+#include "mlir/Dialect/XeGPU/Utils/XeGPUUtils.h"
#include "mlir/Dialect/XeGPU/uArch/IntelGpuXe2.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/DialectImplementation.h"
@@ -229,8 +229,10 @@ LayoutAttr::verify(llvm::function_ref<mlir::InFlightDiagnostic()> emitError,
}
if (inst_data && lane_layout && inst_data.size() != lane_layout.size()) {
- return emitError()
- << "expected inst_data and lane_layout to have the same rank";
+ return emitError() << "expected inst_data and lane_layout to have the same "
+ "rank, got inst_data "
+ << inst_data.size() << ", lane_layout "
+ << lane_layout.size();
}
// sg_data is optional for Workgroup layout, but its presence requires
@@ -568,10 +570,10 @@ TensorDescType::verify(llvm::function_ref<InFlightDiagnostic()> emitError,
// for gather and scatter ops, Low-precision types are packed in 32-bit units.
unsigned bitWidth = elementType.getIntOrFloatBitWidth();
- int chunkAlignmentFactor =
- bitWidth < targetinfo::packedSizeInBitsForGatherScatter
- ? targetinfo::packedSizeInBitsForGatherScatter / bitWidth
- : 1;
+ constexpr int packingBitSizeGatherScatter{32};
+ int chunkAlignmentFactor = bitWidth < packingBitSizeGatherScatter
+ ? packingBitSizeGatherScatter / bitWidth
+ : 1;
auto scatterAttr = mlir::dyn_cast_if_present<ScatterTensorDescAttr>(encoding);
if (scatterAttr) {
int64_t chunkSize = scatterAttr.getChunkSizeAsInt();
diff --git a/mlir/lib/Dialect/XeGPU/Transforms/XeGPUPropagateLayout.cpp b/mlir/lib/Dialect/XeGPU/Transforms/XeGPUPropagateLayout.cpp
index 8fab255d6347f..070535bdf2049 100644
--- a/mlir/lib/Dialect/XeGPU/Transforms/XeGPUPropagateLayout.cpp
+++ b/mlir/lib/Dialect/XeGPU/Transforms/XeGPUPropagateLayout.cpp
@@ -14,7 +14,6 @@
#include "mlir/Dialect/MemRef/IR/MemRef.h"
#include "mlir/Dialect/Vector/IR/VectorOps.h"
#include "mlir/Dialect/XeGPU/IR/XeGPU.h"
-#include "mlir/Dialect/XeGPU/IR/XeGPUTargetInfo.h"
#include "mlir/Dialect/XeGPU/Transforms/Passes.h"
#include "mlir/Dialect/XeGPU/Utils/XeGPUUtils.h"
#include "mlir/IR/Attributes.h"
@@ -37,6 +36,8 @@
#include "llvm/Support/LogicalResult.h"
#include "llvm/Support/raw_ostream.h"
+#include "mlir/Dialect/XeGPU/uArch/IntelGpuXe2.h"
+
namespace mlir {
namespace xegpu {
#define GEN_PASS_DEF_XEGPUPROPAGATELAYOUT
@@ -104,6 +105,8 @@ struct LayoutInfo {
SmallVector<int> getLaneData() const;
+ SmallVector<int> getInstData() const;
+
bool isSliceLayout() const {
if (!isAssigned())
return false;
@@ -137,6 +140,13 @@ SmallVector<int> LayoutInfo::getLaneData() const {
[](int64_t val) { return static_cast<int>(val); });
}
+SmallVector<int> LayoutInfo::getInstData() const {
+ if (!isAssigned())
+ return {};
+ return llvm::map_to_vector(storage.getEffectiveInstDataAsInt(),
+ [](int64_t val) { return static_cast<int>(val); });
+}
+
void LayoutInfo::print(raw_ostream &os) const {
if (isAssigned()) {
os << storage;
@@ -174,12 +184,14 @@ LayoutInfo LayoutInfo::transpose(ArrayRef<int64_t> permutation) const {
SmallVector<int32_t> laneLayout;
SmallVector<int32_t> laneData;
+ SmallVector<int32_t> instData;
for (int64_t idx : permutation) {
laneLayout.push_back(static_cast<int32_t>(getLaneLayout()[idx]));
laneData.push_back(static_cast<int32_t>(getLaneData()[idx]));
+ instData.push_back(static_cast<int32_t>(getInstData()[idx]));
}
- return LayoutInfo(
- xegpu::LayoutAttr::get(storage.getContext(), laneLayout, laneData));
+ return LayoutInfo(xegpu::LayoutAttr::get(storage.getContext(), instData,
+ laneLayout, laneData));
}
//===----------------------------------------------------------------------===//
@@ -200,18 +212,32 @@ struct LayoutInfoLattice : public Lattice<LayoutInfo> {
/// For 1D vector, lane_layout is [subgroupSize] and lane_data is [1].
/// For 2D vector, lane_layout is [1, subgroupSize] and lane_data is [1, 1].
static LayoutInfo getDefaultSIMTLayoutInfo(mlir::MLIRContext *ctx,
- unsigned rank) {
+ unsigned rank,
+ const xegpu::uArch::uArch *uArch,
+ ArrayRef<int> instData) {
assert((rank == 1 || rank == 2) && "Expected 1D or 2D vector.");
if (rank == 1) {
return LayoutInfo(
- xegpu::LayoutAttr::get(ctx, {xegpu::targetinfo::subgroupSize}, {1}));
+ xegpu::LayoutAttr::get(ctx, instData, {uArch->getSubgroupSize()}, {1}));
}
return LayoutInfo(xegpu::LayoutAttr::get(
- ctx, {1, xegpu::targetinfo::subgroupSize}, {1, 1}));
+ ctx, instData, {1, uArch->getSubgroupSize()}, {1, 1}));
+}
+
+static LayoutInfo getDefaultSIMTLayoutInfo(mlir::MLIRContext *ctx,
+ unsigned rank, int subgroupSize) {
+ assert((rank == 1 || rank == 2) && "Expected 1D or 2D vector.");
+ if (rank == 1) {
+ return LayoutInfo(xegpu::LayoutAttr::get(ctx, {subgroupSize}, {1}));
+ }
+ return LayoutInfo(xegpu::LayoutAttr::get(ctx, {1, subgroupSize}, {1, 1}));
}
/// Helper to get the default layout for a vector type.
static LayoutInfo getDefaultSIMTLayoutInfo(VectorType vectorTy,
+ const xegpu::uArch::uArch *uArch,
+ ArrayRef<int> instData,
+ unsigned packingSize,
bool isScattered = false) {
// Expecting a 1D or 2D vector.
assert((vectorTy.getRank() == 1 || vectorTy.getRank() == 2) &&
@@ -221,28 +247,25 @@ static LayoutInfo getDefaultSIMTLayoutInfo(VectorType vectorTy,
"Expected int or float element type.");
// If the rank is 1, then return default layout for 1D vector.
if (vectorTy.getRank() == 1)
- return getDefaultSIMTLayoutInfo(vectorTy.getContext(), 1);
+ return getDefaultSIMTLayoutInfo(vectorTy.getContext(), 1, uArch, instData);
// Packing factor is determined by the element type bitwidth.
- int packingFactor = 1;
unsigned bitwidth = vectorTy.getElementType().getIntOrFloatBitWidth();
+ int packingFactor = bitwidth < packingSize ? packingSize / bitwidth : 1;
if (isScattered) {
- packingFactor =
- bitwidth < xegpu::targetinfo::packedSizeInBitsForGatherScatter
- ? xegpu::targetinfo::packedSizeInBitsForGatherScatter / bitwidth
- : 1;
- return LayoutInfo(xegpu::LayoutAttr::get(
- vectorTy.getContext(), {xegpu::targetinfo::subgroupSize, 1},
- {1, packingFactor}));
+ return LayoutInfo(xegpu::LayoutAttr::get(vectorTy.getContext(), instData,
+ {uArch->getSubgroupSize(), 1},
+ {1, packingFactor}));
}
- if (bitwidth < xegpu::targetinfo::packedSizeInBitsForDefault)
- packingFactor = xegpu::targetinfo::packedSizeInBitsForDefault / bitwidth;
- return LayoutInfo(xegpu::LayoutAttr::get(vectorTy.getContext(),
- {1, xegpu::targetinfo::subgroupSize},
+ return LayoutInfo(xegpu::LayoutAttr::get(vectorTy.getContext(), instData,
+ {1, uArch->getSubgroupSize()},
{1, packingFactor}));
}
/// Helper to get the default layout for a vector type.
static LayoutInfo getDefaultSIMTLayoutInfo(xegpu::TensorDescType tdescTy,
+ const xegpu::uArch::uArch *uArch,
+ ArrayRef<int> instData,
+ unsigned packingSize,
bool isScattered = false) {
// Expecting a 1D or 2D vector.
assert((tdescTy.getRank() == 1 || tdescTy.getRank() == 2) &&
@@ -252,27 +275,18 @@ static LayoutInfo getDefaultSIMTLayoutInfo(xegpu::TensorDescType tdescTy,
"Expected int or float element type.");
// If the rank is 1, then return default layout for 1D vector.
if (tdescTy.getRank() == 1)
- return getDefaultSIMTLayoutInfo(tdescTy.getContext(), 1);
+ return getDefaultSIMTLayoutInfo(tdescTy.getContext(), 1, uArch, instData);
// Packing factor is determined by the element type bitwidth.
unsigned bitwidth = tdescTy.getElementType().getIntOrFloatBitWidth();
-
+ int subgroupSize = uArch->getSubgroupSize();
+ int packingFactor = bitwidth < packingSize ? packingSize / bitwidth : 1;
if (isScattered) {
- int packingFactor =
- bitwidth < xegpu::targetinfo::packedSizeInBitsForGatherScatter
- ? xegpu::targetinfo::packedSizeInBitsForGatherScatter / bitwidth
- : 1;
return LayoutInfo(xegpu::LayoutAttr::get(
- tdescTy.getContext(), {xegpu::targetinfo::subgroupSize, 1},
- {1, packingFactor}));
+ tdescTy.getContext(), instData, {subgroupSize, 1}, {1, packingFactor}));
}
- int packingFactor =
- (bitwidth < xegpu::targetinfo::packedSizeInBitsForDefault)
- ? xegpu::targetinfo::packedSizeInBitsForDefault / bitwidth
- : 1;
- return LayoutInfo(xegpu::LayoutAttr::get(tdescTy.getContext(),
- {1, xegpu::targetinfo::subgroupSize},
- {1, packingFactor}));
+ return LayoutInfo(xegpu::LayoutAttr::get(
+ tdescTy.getContext(), instData, {1, subgroupSize}, {1, packingFactor}));
}
/// Helper Function to get the expected layouts for DPAS operands. `lane_data`
@@ -281,25 +295,25 @@ static LayoutInfo getDefaultSIMTLayoutInfo(xegpu::TensorDescType tdescTy,
/// `packedSizeInBitsForDefault`
/// * For B operand, the data must be packed in minimum
/// `packedSizeInBitsForDpasB`
-static LayoutInfo getSIMTLayoutInfoForDPASOperand(VectorType vectorTy,
- unsigned operandNum) {
+static LayoutInfo
+getSIMTLayoutInfoForDPASOperand(VectorType vectorTy, unsigned operandNum,
+ const xegpu::uArch::uArch *uArch,
+ ArrayRef<int> instData, unsigned packingSize) {
Type elementTy = vectorTy.getElementType();
assert(elementTy.isIntOrFloat() &&
"Expected int or float type in DPAS operands");
- SmallVector<int32_t, 2> layout({1, xegpu::targetinfo::subgroupSize});
+ SmallVector<int32_t, 2> layout({1, uArch->getSubgroupSize()});
// For B operand, data must be packed in minimum `packedDpasBSizeInBits` and
// must have the VNNI format.
- if (operandNum == 1 && elementTy.getIntOrFloatBitWidth() <
- xegpu::targetinfo::packedSizeInBitsForDpasB) {
+ if (operandNum == 1 && elementTy.getIntOrFloatBitWidth() < packingSize) {
SmallVector<int32_t, 2> data(
- {static_cast<int32_t>(xegpu::targetinfo::packedSizeInBitsForDpasB /
- elementTy.getIntOrFloatBitWidth()),
+ {static_cast<int32_t>(packingSize / elementTy.getIntOrFloatBitWidth()),
1});
return LayoutInfo(
- xegpu::LayoutAttr::get(vectorTy.getContext(), layout, data));
+ xegpu::LayoutAttr::get(vectorTy.getContext(), instData, layout, data));
}
// Otherwise, return the default layout for the vector type.
- return getDefaultSIMTLayoutInfo(vectorTy);
+ return getDefaultSIMTLayoutInfo(vectorTy, uArch, instData, packingSize);
}
//===----------------------------------------------------------------------===//
@@ -456,7 +470,26 @@ void LayoutInfoPropagation::visitPrefetchNdOp(
// Here we assign the default layout to the tensor descriptor operand of
// prefetch.
auto tdescTy = prefetch.getTensorDescType();
- auto prefetchLayout = getDefaultSIMTLayoutInfo(tdescTy);
+
+ auto uArch = getUArch(getChipStr(prefetch).value_or(""));
+ const auto *uArchInstruction =
+ dyn_cast<xegpu::uArch::Subgroup2DBlockPrefetchInstruction>(
+ uArch->getInstruction(
+ xegpu::uArch::InstructionKind::Subgroup2DBlockPrefetch));
+
+ auto blockWHC =
+ uArchInstruction->getBlockWidthHeightCount(tdescTy.getElementType());
+ if (!blockWHC)
+ prefetch.emitWarning("No known block params found for the element type.");
+ auto [bWidth, bHeight, bCount] = blockWHC.value();
+
+ SmallVector<int> instData;
+ if (tdescTy.getRank() == 1)
+ instData = {bWidth.back() * bCount.back()};
+ else
+ instData = {bHeight.back(), bWidth.back() * bCount.back()};
+ auto prefetchLayout = getDefaultSIMTLayoutInfo(
+ tdescTy, uArch, instData, uArchInstruction->getPackedFormatBitSize());
// Propagate the layout to the source tensor descriptor.
propagateIfChanged(operands[0], operands[0]->meet(prefetchLayout));
}
@@ -475,10 +508,11 @@ void LayoutInfoPropagation::visitVectorMultiReductionOp(
reduction.emitWarning("Expecting output type to be 1D vector.");
return;
}
+ auto uArch = getUArch(xegpu::getChipStr(reduction).value_or(""));
// Given that the result is 1D, the layout of the operand should be 2D with
// default layout.
- LayoutInfo operandLayout =
- getDefaultSIMTLayoutInfo(reduction->getContext(), 2);
+ LayoutInfo operandLayout = getDefaultSIMTLayoutInfo(
+ reduction->getContext(), 2, uArch->getSubgroupSize());
propagateIfChanged(operands[0], operands[0]->meet(operandLayout));
// Accumulator should have the same layout as the result.
propagateIfChanged(operands[1], operands[1]->meet(resultLayout));
@@ -557,15 +591,36 @@ void LayoutInfoPropagation::visitDpasOp(
ArrayRef<const LayoutInfoLattice *> results) {
VectorType aTy = dpas.getLhsType();
VectorType bTy = dpas.getRhsType();
- propagateIfChanged(
- operands[0], operands[0]->meet(getSIMTLayoutInfoForDPASOperand(aTy, 0)));
- propagateIfChanged(
- operands[1], operands[1]->meet(getSIMTLayoutInfoForDPASOperand(bTy, 1)));
+
+ auto uArch = getUArch(getChipStr(dpas).value_or(""));
+ const int subgroupSize = uArch->getSubgroupSize();
+ const auto *uArchInstruction =
+ dyn_cast<xegpu::uArch::SubgroupMatrixMultiplyAcc>(uArch->getInstruction(
+ xegpu::uArch::InstructionKind::SubgroupMatrixMultiplyAcc));
+ const int maxALen =
+ uArchInstruction->getSupportedM(aTy.getElementType()).back();
+ const int maxBLen =
+ uArchInstruction->getSupportedK(bTy.getElementType()).back();
+ SmallVector<int> instDataA = {maxALen, subgroupSize};
+ SmallVector<int> instDataB = {subgroupSize, maxBLen};
+
+ propagateIfChanged(operands[0],
+ operands[0]->meet(getSIMTLayoutInfoForDPASOperand(
+ aTy, 0, uArch, instDataA,
+ uArchInstruction->getPackedFormatBitSizeA())));
+ propagateIfChanged(operands[1],
+ operands[1]->meet(getSIMTLayoutInfoForDPASOperand(
+ bTy, 1, uArch, instDataB,
+ uArchInstruction->getPackedFormatBitSizeB())));
if (operands.size() > 2) {
VectorType cTy = dpas.getAccType();
- propagateIfChanged(
- operands[2],
- operands[2]->meet(getSIMTLayoutInfoForDPASOperand(cTy, 2)));
+ const int maxCLen =
+ uArchInstruction->getSupportedN(bTy.getElementType()).back();
+ SmallVector<int> instDataC = {maxALen, maxCLen};
+ propagateIfChanged(operands[2],
+ operands[2]->meet(getSIMTLayoutInfoForDPASOperand(
+ cTy, 2, uArch, instDataC,
+ uArchInstruction->getPackedFormatBitSizeB())));
}
}
@@ -573,7 +628,25 @@ void LayoutInfoPropagation::visitDpasOp(
void LayoutInfoPropagation::visitStoreNdOp(
xegpu::StoreNdOp store, ArrayRef<LayoutInfoLattice *> operands,
ArrayRef<const LayoutInfoLattice *> results) {
- LayoutInfo storeLayout = getDefaultSIMTLayoutInfo(store.getValueType());
+
+ auto uArch = getUArch(getChipStr(store).value_or(""));
+ const auto *uArchInstruction =
+ dyn_cast<xegpu::uArch::Subgroup2DBlockStoreInstruction>(
+ uArch->getInstruction(
+ xegpu::uArch::InstructionKind::Subgroup2DBlockStore));
+ auto blockWHC = uArchInstruction->getBlockWidthHeightCount(
+ store.getValueType().getElementType());
+ if (!blockWHC)
+ store.emitWarning("No known block params found for the element type.");
+ auto [bWidth, bHeight, bCount] = blockWHC.value();
+ SmallVector<int> instData;
+ if (store.getValueType().getRank() == 1)
+ instData = {bWidth.back() * bCount.back()};
+ else
+ instData = {bHeight.back(), bWidth.back() * bCount.back()};
+ LayoutInfo storeLayout =
+ getDefaultSIMTLayoutInfo(store.getValueType(), uArch, instData,
+ uArchInstruction->getPackedFormatBitSize());
// Both operands should have the same layout
for (LayoutInfoLattice *operand : operands)
propagateIfChanged(operand, operand->meet(storeLayout));
@@ -694,10 +767,23 @@ void LayoutInfoPropagation::visitLoadGatherOp(
load.emitWarning("Not propagating, non-vector payload supplied.");
return;
}
- LayoutInfo layout = getDefaultSIMTLayoutInfo(payloadTy, /*scattered*/ true);
+ auto uArch = getUArch(getChipStr(load).value_or(""));
+ const int subgroupSize = uArch->getSubgroupSize();
+ SmallVector<int> instData{subgroupSize};
+ if (auto chunkSize = load.getChunkSize().value_or(0); chunkSize > 1)
+ instData.push_back(chunkSize);
+ else if (auto srcTdescTy =
+ dyn_cast<xegpu::TensorDescType>(load.getSourceType())) {
+ if (srcTdescTy.getChunkSizeAsInt() > 1)
+ instData.push_back(chunkSize);
+ }
+ LayoutInfo layout = getDefaultSIMTLayoutInfo(
+ payloadTy, uArch, instData, uArch->getGeneralPackedFormatBitSize(),
+ /*scattered*/ true);
// Mask operand should have 1D default layout.
- LayoutInfo maskLayout = getDefaultSIMTLayoutInfo(load->getContext(), 1);
+ LayoutInfo maskLayout =
+ getDefaultSIMTLayoutInfo(load->getContext(), 1, subgroupSize);
// Propagate the new layout to the tensor descriptor operand.
if (isa<xegpu::TensorDescType>(load.getSourceType()))
@@ -717,8 +803,10 @@ void LayoutInfoPropagation::visitCreateDescOp(
// Need the layout of the descriptor to propagate to the operands.
if (!descLayout.isAssigned())
return;
+ auto uArch = getUArch(getChipStr(createDesc).value_or(""));
// For offset operand propagate 1D default layout.
- LayoutInfo layout = getDefaultSIMTLayoutInfo(createDesc->getContext(), 1);
+ LayoutInfo layout = getDefaultSIMTLayoutInfo(createDesc->getContext(), 1,
+ uArch->getSubgroupSize());
propagateIfChanged(operands[1], operands[1]->meet(layout));
}
@@ -735,18 +823,30 @@ void LayoutInfoPropagation::visitStoreScatterOp(
storeScatter.emitWarning("Not propagating, non-vector payload supplied.");
return;
}
+ auto uArch = getUArch(getChipStr(storeScatter).value_or(""));
+ const int subgroupSize = uArch->getSubgroupSize();
+
auto payloadShape = payloadTy.getShape();
if (payloadShape.size() > 1)
assert(
- payloadShape[0] == xegpu::targetinfo::subgroupSize &&
+ payloadShape[0] == subgroupSize &&
"Expected the first dimension of 2D tensor descriptor to be equal to "
"subgroup size.");
- LayoutInfo payloadLayout =
- getDefaultSIMTLayoutInfo(payloadTy, /*scattered=*/true);
+ SmallVector<int> instData{subgroupSize};
+ if (auto chunkSize = storeScatter.getChunkSize().value_or(0); chunkSize > 1)
+ instData.push_back(chunkSize);
+ else if (auto dstTdescTy =
+ dyn_cast<xegpu::TensorDescType>(storeScatter.getDestType())) {
+ if (dstTdescTy.getChunkSizeAsInt() > 1)
+ instData.push_back(chunkSize);
+ }
+ LayoutInfo payloadLayout = getDefaultSIMTLayoutInfo(
+ payloadTy, uArch, instData, uArch->getGeneralPackedFormatBitSize(),
+ /*scattered=*/true);
LayoutInfo maskLayout =
- getDefaultSIMTLayoutInfo(storeScatter->getContext(), 1);
+ getDefaultSIMTLayoutInfo(storeScatter->getContext(), 1, subgroupSize);
// Propagate the payload operand layout
propagateIfChanged(operands[0], operands[0]->meet(payloadLayout));
// Propagate the destination (if tdesc) operand layout
@@ -1023,9 +1123,13 @@ void XeGPUPropagateLayoutPass::runOnOperation() {
LayoutInfo layout = analysis.getLayoutInfo(val);
if (!layout.isAssigned())
return {};
+ xegpu::DistributeLayoutAttr layoutAttr =
+ cast<xegpu::DistributeLayoutAttr>(layout.get());
+ if (this->layoutKind == "lane")
+ layoutAttr = layoutAttr.dropInstData();
if (layout.isSliceLayout())
- return cast<xegpu::SliceAttr>(layout.get());
- return cast<xegpu::LayoutAttr>(layout.get());
+ return cast<xegpu::SliceAttr>(layoutAttr);
+ return cast<xegpu::LayoutAttr>(layoutAttr);
};
mlir::OpBuilder builder(&getContext());
diff --git a/mlir/lib/Dialect/XeGPU/Transforms/XeGPUSubgroupDistribute.cpp b/mlir/lib/Dialect/XeGPU/Transforms/XeGPUSubgroupDistribute.cpp
index d09dc196c0bf7..1c5b7e6e86a6c 100644
--- a/mlir/lib/Dialect/XeGPU/Transforms/XeGPUSubgroupDistribute.cpp
+++ b/mlir/lib/Dialect/XeGPU/Transforms/XeGPUSubgroupDistribute.cpp
@@ -11,10 +11,10 @@
#include "mlir/Dialect/Vector/IR/VectorOps.h"
#include "mlir/Dialect/Vector/Transforms/VectorDistribution.h"
#include "mlir/Dialect/XeGPU/IR/XeGPU.h"
-#include "mlir/Dialect/XeGPU/IR/XeGPUTargetInfo.h"
#include "mlir/Dialect/XeGPU/Transforms/Passes.h"
#include "mlir/Dialect/XeGPU/Transforms/Transforms.h"
#include "mlir/Dialect/XeGPU/Utils/XeGPUUtils.h"
+#include "mlir/Dialect/XeGPU/uArch/IntelGpuXe2.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/IR/Attributes.h"
#include "mlir/IR/Builders.h"
@@ -159,17 +159,18 @@ static bool requirePacked(const xegpu::LayoutAttr layout) {
/// Helper function to check if the layout requires a transpose effect.
static bool requireTranspose(const xegpu::LayoutAttr layout,
- const std::string &chipStr) {
+ const xegpu::uArch::uArch *uArch) {
// Return false for unsupported targets.
// TODO: Add more support or move to target info.
- if (chipStr != "pvc" && chipStr != "bmg")
+ if (uArch->getName().equals_insensitive("pvc") &&
+ uArch->getName().equals_insensitive("bmg"))
return false;
if (!layout)
return false;
auto laneLayout = layout.getEffectiveLaneLayoutAsInt();
if (laneLayout.size() != 2)
return false;
- return laneLayout[0] == xegpu::targetinfo::subgroupSize && laneLayout[1] == 1;
+ return laneLayout[0] == uArch->getSubgroupSize() && laneLayout[1] == 1;
}
/// Given a GPUFuncOp, this pattern creates a new GPUFuncOp and moves the body
@@ -228,9 +229,14 @@ struct MoveFuncBodyToWarpOp : public OpRewritePattern<gpu::GPUFuncOp> {
rewriter, newGpuFunc.getLoc(), rewriter.getIndexType(),
/** upperBound = **/ mlir::IntegerAttr());
ArrayRef<Type> gpuFuncResultType = gpuFuncOp.getFunctionType().getResults();
+ auto uArch = getUArch(xegpu::getChipStr(gpuFuncOp).value_or(""));
+ if (!uArch)
+ return rewriter.notifyMatchFailure(
+ gpuFuncOp, "Subgroup distribution requires target attribute attached "
+ "to set the warp size");
auto warpOp = gpu::WarpExecuteOnLane0Op::create(
rewriter, laneId.getLoc(), gpuFuncResultType, laneId,
- xegpu::targetinfo::subgroupSize, newGpuFunc.getArguments(),
+ uArch->getSubgroupSize(), newGpuFunc.getArguments(),
newGpuFunc.getArgumentTypes());
Block &warpBodyBlock = warpOp.getBodyRegion().front();
// Replace the ReturnOp of the original gpu function with a YieldOp.
@@ -498,11 +504,12 @@ struct LoadNdDistribution final : public gpu::WarpDistributionPattern {
// Chip information is required to decide if the layout requires transpose
// effect.
auto chipStr = xegpu::getChipStr(loadOp);
- if (!chipStr)
+ auto uArch = getUArch(chipStr.value_or(""));
+ if (!uArch)
return rewriter.notifyMatchFailure(
- loadOp,
- "xegpu::LoadNdOp require chip information to determine transpose "
- "requirement");
+ loadOp, "xegpu::LoadNdOp require target attribute attached to "
+ "determine transpose "
+ "requirement");
// Expecting offsets to be present.
SmallVector<OpFoldResult> offsets = loadOp.getMixedOffsets();
if (offsets.empty())
@@ -556,7 +563,7 @@ struct LoadNdDistribution final : public gpu::WarpDistributionPattern {
// Set the packed attribute if the layout requires it.
newLoadOp.setPacked(requirePacked(layout));
// Set the transpose attribute if the layout requires it.
- if (requireTranspose(layout, chipStr.value()))
+ if (requireTranspose(layout, uArch))
newLoadOp.setTranspose(
DenseI64ArrayAttr::get(rewriter.getContext(), {1, 0}));
Value distributedVal = newWarpOp.getResult(operandIdx);
diff --git a/mlir/test/Dialect/XeGPU/move-gpu-func-to-warp-op.mlir b/mlir/test/Dialect/XeGPU/move-gpu-func-to-warp-op.mlir
index d289d73e863c7..2780212d2917f 100644
--- a/mlir/test/Dialect/XeGPU/move-gpu-func-to-warp-op.mlir
+++ b/mlir/test/Dialect/XeGPU/move-gpu-func-to-warp-op.mlir
@@ -1,4 +1,4 @@
-// RUN: mlir-opt -test-xegpu-move-func-to-warp-op -split-input-file --allow-unregistered-dialect %s | FileCheck %s
+// RUN: mlir-opt -xevm-attach-target='chip=pvc' -test-xegpu-move-func-to-warp-op -split-input-file --allow-unregistered-dialect %s | FileCheck %s
gpu.module @test {
gpu.func @empty() {
diff --git a/mlir/test/Dialect/XeGPU/propagate-layout-inst-data.mlir b/mlir/test/Dialect/XeGPU/propagate-layout-inst-data.mlir
new file mode 100644
index 0000000000000..ad7a0343a2ef1
--- /dev/null
+++ b/mlir/test/Dialect/XeGPU/propagate-layout-inst-data.mlir
@@ -0,0 +1,89 @@
+// RUN: mlir-opt -xevm-attach-target='chip=pvc' -xegpu-propagate-layout="layout-kind=inst" -split-input-file %s | FileCheck %s
+
+// CHECK-LABEL: func.func @dpas_f16(
+// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<8x16xf16>, %[[ARG1:[0-9a-zA-Z]+]]: memref<16x16xf16>, %[[ARG2:[0-9a-zA-Z]+]]: memref<8x16xf32>) {
+// CHECK: %[[CST:.*]] = arith.constant {layout_result_0 = #xegpu.layout<inst_data = [8, 16], lane_layout = [1, 16], lane_data = [1, 1]>} dense<0.000000e+00> : vector<8x16xf32>
+// CHECK: %[[T0:.*]] = xegpu.create_nd_tdesc %[[ARG0]][{{.*}}] : memref<8x16xf16> -> !xegpu.tensor_desc<8x16xf16, #xegpu.layout<inst_data = [8, 16], lane_layout = [1, 16], lane_data = [1, 1]>>
+// CHECK: %[[T1:.*]] = xegpu.create_nd_tdesc %[[ARG1]][{{.*}}] : memref<16x16xf16> -> !xegpu.tensor_desc<16x16xf16, #xegpu.layout<inst_data = [16, 16], lane_layout = [1, 16], lane_data = [2, 1]>>
+// CHECK: %[[T2:.*]] = xegpu.load_nd %[[T0]] {layout_result_0 = #xegpu.layout<inst_data = [8, 16], lane_layout = [1, 16], lane_data = [1, 1]>} :
+// CHECK-SAME: !xegpu.tensor_desc<8x16xf16, #xegpu.layout<inst_data = [8, 16], lane_layout = [1, 16], lane_data = [1, 1]>> -> vector<8x16xf16>
+// CHECK: %[[T3:.*]] = xegpu.load_nd %[[T1]] {layout_result_0 = #xegpu.layout<inst_data = [16, 16], lane_layout = [1, 16], lane_data = [2, 1]>} :
+// CHECK-SAME: !xegpu.tensor_desc<16x16xf16, #xegpu.layout<inst_data = [16, 16], lane_layout = [1, 16], lane_data = [2, 1]>> -> vector<16x16xf16>
+// CHECK: %[[T4:.*]] = xegpu.dpas %[[T2]], %[[T3]], %[[CST]] {layout_result_0 = #xegpu.layout<inst_data = [8, 16], lane_layout = [1, 16], lane_data = [1, 1]>} :
+// CHECK-SAME: vector<8x16xf16>, vector<16x16xf16>, vector<8x16xf32> -> vector<8x16xf32>
+// CHECK: %[[T5:.*]] = xegpu.create_nd_tdesc %[[ARG2]][{{.*}}] : memref<8x16xf32> -> !xegpu.tensor_desc<8x16xf32, #xegpu.layout<inst_data = [8, 16], lane_layout = [1, 16], lane_data = [1, 1]>>
+// CHECK: xegpu.store_nd %[[T4]], %[[T5]] : vector<8x16xf32>, !xegpu.tensor_desc<8x16xf32, #xegpu.layout<inst_data = [8, 16], lane_layout = [1, 16], lane_data = [1, 1]>>
+gpu.module @test {
+
+func.func @dpas_f16(%arg0: memref<8x16xf16>, %arg1: memref<16x16xf16>, %arg2: memref<8x16xf32>) {
+ %c0 = arith.constant 0 : index
+ %cst = arith.constant dense<0.000000e+00> : vector<8x16xf32>
+ %0 = xegpu.create_nd_tdesc %arg0[%c0, %c0] : memref<8x16xf16> -> !xegpu.tensor_desc<8x16xf16>
+ %1 = xegpu.create_nd_tdesc %arg1[%c0, %c0] : memref<16x16xf16> -> !xegpu.tensor_desc<16x16xf16>
+ %2 = xegpu.load_nd %0 : !xegpu.tensor_desc<8x16xf16> -> vector<8x16xf16>
+ %3 = xegpu.load_nd %1 : !xegpu.tensor_desc<16x16xf16> -> vector<16x16xf16>
+ %4 = xegpu.dpas %2, %3, %cst : vector<8x16xf16>, vector<16x16xf16>, vector<8x16xf32> -> vector<8x16xf32>
+ %5 = xegpu.create_nd_tdesc %arg2[%c0, %c0] : memref<8x16xf32> -> !xegpu.tensor_desc<8x16xf32>
+ xegpu.store_nd %4, %5 : vector<8x16xf32>, !xegpu.tensor_desc<8x16xf32>
+ return
+}
+}
+
+// -----
+gpu.module @test_kernel {
+ gpu.func @elementwise_with_inst_data_only(%A: memref<1024x1024xf16>, %B: memref<1024x1024xf16>, %C: memref<1024x1024xf16>) {
+ %c0 = arith.constant 0 : index
+ %c32 = arith.constant 32 : index
+ %c1024 = arith.constant 1024 : index
+ %block_id_x = gpu.block_id x
+ %block_id_y = gpu.block_id y
+ %m = arith.muli %block_id_x, %c32 : index
+
+ %a_tdesc = xegpu.create_nd_tdesc %A[%m, %c0] : memref<1024x1024xf16> -> !xegpu.tensor_desc<16x32xf16>
+ %b_tdesc = xegpu.create_nd_tdesc %B[%m, %c0] : memref<1024x1024xf16> -> !xegpu.tensor_desc<16x32xf16>
+ %c_tdesc = xegpu.create_nd_tdesc %C[%m, %c0] : memref<1024x1024xf16> -> !xegpu.tensor_desc<16x32xf16>
+
+ %out:3 = scf.for %k = %c0 to %c1024 step %c32
+ iter_args(%arg0 = %a_tdesc, %arg1 = %b_tdesc, %arg2 = %c_tdesc)
+ -> (!xegpu.tensor_desc<16x32xf16>, !xegpu.tensor_desc<16x32xf16>, !xegpu.tensor_desc<16x32xf16>) {
+ //CHECK: xegpu.load_nd {{.*}} {layout_result_0 = #xegpu.layout<inst_data = [8, 16], lane_layout = [1, 16], lane_data = [1, 1]>} :
+ //CHECK-SAME: !xegpu.tensor_desc<16x32xf16, #xegpu.layout<inst_data = [8, 16], lane_layout = [1, 16], lane_data = [1, 1]>> -> vector<16x32xf16>
+ %a = xegpu.load_nd %arg0 : !xegpu.tensor_desc<16x32xf16> -> vector<16x32xf16>
+ %b = xegpu.load_nd %arg1 : !xegpu.tensor_desc<16x32xf16> -> vector<16x32xf16>
+
+ //CHECK-COUNT: arith.addf {{.*}} {layout_result_0 = #xegpu.layout<inst_data = [8, 16], lane_layout = [1, 16], lane_data = [1, 1]>} : vector<16x32xf16>
+ %c = arith.addf %a, %b : vector<16x32xf16>
+
+ //CHECK-COUNT: xegpu.store_nd {{.*}} : vector<16x32xf16>, !xegpu.tensor_desc<16x32xf16, #xegpu.layout<inst_data = [8, 16], lane_layout = [1, 16], lane_data = [1, 1]>>>
+ xegpu.store_nd %c, %arg2: vector<16x32xf16>, !xegpu.tensor_desc<16x32xf16>
+
+ //CHECK-COUNT: xegpu.update_nd_offset {{.*}} : !xegpu.tensor_desc<16x32xf16, #xegpu.layout<inst_data = [8, 16], lane_layout = [1, 16], lane_data = [1, 1]>>
+ %a_next_tdesc = xegpu.update_nd_offset %arg0, [%c0, %c32] : !xegpu.tensor_desc<16x32xf16>
+ %b_next_tdesc = xegpu.update_nd_offset %arg1, [%c0, %c32] : !xegpu.tensor_desc<16x32xf16>
+ %c_next_tdesc = xegpu.update_nd_offset %arg2, [%c0, %c32] : !xegpu.tensor_desc<16x32xf16>
+ scf.yield %a_next_tdesc, %b_next_tdesc, %c_next_tdesc
+ : !xegpu.tensor_desc<16x32xf16>, !xegpu.tensor_desc<16x32xf16>, !xegpu.tensor_desc<16x32xf16>
+ }
+ gpu.return
+ }
+}
+
+// -----
+gpu.module @test {
+// CHECK-LABEL: func.func @scatter_ops_chunksize(
+// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<256xf16>) {
+// CHECK: %{{.*}} = arith.constant {layout_result_0 = #xegpu.layout<lane_layout = [16], lane_data = [1]>} dense<true> : vector<16xi1>
+// CHECK: %{{.*}} = arith.constant {layout_result_0 = #xegpu.layout<lane_layout = [16], lane_data = [1]>} dense<12> : vector<16xindex>
+// CHECK: %{{.*}} = xegpu.load %[[ARG0]][%{{.*}}], %{{.*}} <{chunk_size = 8 : i64}>
+// CHECK-SAME: {layout_result_0 = #xegpu.layout<inst_data = [16, 8], lane_layout = [16, 1], lane_data = [1, 2]>} : memref<256xf16>, vector<16xindex>, vector<16xi1> -> vector<16x8xf16>
+// CHECK: xegpu.store %0, %[[ARG0]][%{{.*}}], %{{.*}} <{chunk_size = 8 : i64}> : vector<16x8xf16>, memref<256xf16>, vector<16xindex>, vector<16xi1>
+func.func @scatter_ops_chunksize(%src: memref<256xf16>) {
+ %1 = arith.constant dense<1>: vector<16xi1>
+ %offset = arith.constant dense<12> : vector<16xindex>
+ %3 = xegpu.load %src[%offset], %1 <{chunk_size=8}>
+ : memref<256xf16>, vector<16xindex>, vector<16xi1> -> vector<16x8xf16>
+ xegpu.store %3, %src[%offset], %1 <{chunk_size=8}>
+ : vector<16x8xf16>, memref<256xf16>, vector<16xindex>, vector<16xi1>
+ return
+}
+}
diff --git a/mlir/test/Dialect/XeGPU/propagate-layout.mlir b/mlir/test/Dialect/XeGPU/propagate-layout.mlir
index 30f785ded975a..543e119d81d88 100644
--- a/mlir/test/Dialect/XeGPU/propagate-layout.mlir
+++ b/mlir/test/Dialect/XeGPU/propagate-layout.mlir
@@ -1,5 +1,6 @@
-// RUN: mlir-opt -xegpu-propagate-layout -split-input-file %s | FileCheck %s
+// RUN: mlir-opt -xevm-attach-target='chip=pvc' -xegpu-propagate-layout -split-input-file %s | FileCheck %s
+gpu.module @test {
// CHECK-LABEL: func.func @dpas_f16(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<8x16xf16>, %[[ARG1:[0-9a-zA-Z]+]]: memref<16x16xf16>, %[[ARG2:[0-9a-zA-Z]+]]: memref<8x16xf32>) {
// CHECK: %[[CST:.*]] = arith.constant {layout_result_0 = #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>} dense<0.000000e+00> : vector<8x16xf32>
@@ -25,8 +26,10 @@ func.func @dpas_f16(%arg0: memref<8x16xf16>, %arg1: memref<16x16xf16>, %arg2: me
xegpu.store_nd %4, %5 : vector<8x16xf32>, !xegpu.tensor_desc<8x16xf32>
return
}
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @dpas_i8(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: vector<8x32xi8>, %[[ARG1:[0-9a-zA-Z]+]]: vector<32x16xi8>, %[[ARG2:[0-9a-zA-Z]+]]: memref<8x16xi32>) {
// CHECK: %[[T0:.*]] = xegpu.dpas %[[ARG0]], %[[ARG1]] {layout_result_0 = #xegpu.layout<lane_layout = [1, 16],
@@ -37,8 +40,10 @@ func.func @dpas_i8(%arg0: vector<8x32xi8>, %arg1: vector<32x16xi8>, %arg2: memre
xegpu.store_nd %0, %1 : vector<8x16xi32>, !xegpu.tensor_desc<8x16xi32>
return
}
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @load_with_transpose_effect(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<8x16xf16>, %[[ARG0:[0-9a-zA-Z]+]]: memref<16x16xf16>, %[[ARG0:[0-9a-zA-Z]+]]: memref<8x16xf32>) {
// CHECK: %{{.*}} = xegpu.load_nd %{{.*}} <{transpose = array<i64: 1, 0>}> {layout_result_0 = #xegpu.layout<lane_layout = [1, 16], lane_data = [2, 1]>} :
@@ -55,8 +60,10 @@ func.func @load_with_transpose_effect(%arg0: memref<8x16xf16>, %arg1: memref<16x
xegpu.store_nd %4, %5 : vector<8x16xf32>, !xegpu.tensor_desc<8x16xf32>
return
}
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @vector_transpose(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<8x16xf16>, %[[ARG1:[0-9a-zA-Z]+]]: memref<16x16xf16>, %[[ARG2:[0-9a-zA-Z]+]]: memref<8x16xf32>) {
// CHECK: %{{.*}} = vector.transpose %{{.*}}, [1, 0] {layout_result_0 = #xegpu.layout<lane_layout = [1, 16], lane_data = [2, 1]>} : vector<16x16xf16> to vector<16x16xf16>
@@ -73,8 +80,10 @@ func.func @vector_transpose(%arg0: memref<8x16xf16>, %arg1: memref<16x16xf16>, %
xegpu.store_nd %5, %6 : vector<8x16xf32>, !xegpu.tensor_desc<8x16xf32>
return
}
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @extf_truncf(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<8x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>>, %[[ARG1:[0-9a-zA-Z]+]]:
// CHECK-SAME: !xegpu.tensor_desc<16x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [2, 1]>>) -> vector<8x16xf32> {
@@ -88,8 +97,10 @@ func.func @extf_truncf(%arg0: !xegpu.tensor_desc<8x16xf16>, %arg1: !xegpu.tensor
%4 = xegpu.dpas %0, %3 : vector<8x16xf16>, vector<16x16xf16> -> vector<8x16xf32>
return %4 : vector<8x16xf32>
}
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @load_gather_with_chunksize(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<8x16xf16>, %[[ARG1:[0-9a-zA-Z]+]]: memref<256xf16>, %[[ARG2:[0-9a-zA-Z]+]]: memref<8x16xf32>) {
// CHECK: %[[CST:.*]] = arith.constant {layout_result_0 = #xegpu.layout<lane_layout = [16], lane_data = [1]>}
@@ -113,8 +124,10 @@ func.func @load_gather_with_chunksize(%arg0: memref<8x16xf16>, %arg1: memref<256
xegpu.store_nd %5, %6 : vector<8x16xf32>, !xegpu.tensor_desc<8x16xf32>
return
}
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @load_gather_1d(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<256xf32>, %[[ARG1:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<16xf32, #xegpu.layout<lane_layout = [16], lane_data = [1]>>) {
// CHECK: %[[CST:.*]] = arith.constant {layout_result_0 = #xegpu.layout<lane_layout = [16], lane_data = [1]>}
@@ -132,8 +145,9 @@ func.func @load_gather_1d(%arg0: memref<256xf32>, %arg1: !xegpu.tensor_desc<16xf
xegpu.store_nd %1, %arg1 : vector<16xf32>, !xegpu.tensor_desc<16xf32>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @store_scatter_with_chunksize(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<128xf32>) {
// CHECK: %[[T0:.*]] = xegpu.create_tdesc %[[ARG0]], %{{.*}} : memref<128xf32>, vector<16xindex> ->
@@ -148,8 +162,9 @@ func.func @store_scatter_with_chunksize(%arg0: memref<128xf32>) {
xegpu.store %cst, %0, %cst_0 : vector<16x8xf32>, !xegpu.tensor_desc<16x8xf32, #xegpu.scatter_tdesc_attr<chunk_size = 8 : i64>>, vector<16xi1>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @store_scatter_1d(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: vector<16xf32>, %[[ARG1:[0-9a-zA-Z]+]]: memref<256xf32>) {
// CHECK: xegpu.store %[[ARG0]], %{{.*}}, %{{.*}} : vector<16xf32>, !xegpu.tensor_desc<16xf32, #xegpu.scatter_tdesc_attr<>,
@@ -161,8 +176,9 @@ func.func @store_scatter_1d(%arg0: vector<16xf32>, %arg1: memref<256xf32>) {
xegpu.store %arg0, %0, %cst_0 : vector<16xf32>, !xegpu.tensor_desc<16xf32, #xegpu.scatter_tdesc_attr<>>, vector<16xi1>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @scatter_ops_chunksize(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<256xf16>) {
// CHECK: %[[MASK:.*]] = arith.constant {layout_result_0 = #xegpu.layout<lane_layout = [16], lane_data = [1]>} dense<true> : vector<16xi1>
@@ -179,8 +195,9 @@ func.func @scatter_ops_chunksize(%src: memref<256xf16>) {
: vector<16x8xf16>, memref<256xf16>, vector<16xindex>, vector<16xi1>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @scatter_ops(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<256xf16>) {
// CHECK: %[[MASK:.*]] = arith.constant {layout_result_0 = #xegpu.layout<lane_layout = [16], lane_data = [1]>} dense<true> : vector<16xi1>
@@ -195,8 +212,9 @@ func.func @scatter_ops(%src: memref<256xf16>) {
xegpu.store %3, %src[%offset], %1 : vector<16xf16>, memref<256xf16>, vector<16xindex>, vector<16xi1>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @vector_bitcast_i16_to_f16(
// CHECK: %[[LOAD0:.*]] = xegpu.load_nd %{{.*}} {layout_result_0 = #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>}
// CHECK-SAME: !xegpu.tensor_desc<8x16xi16, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>> -> vector<8x16xi16>
@@ -219,8 +237,9 @@ func.func @vector_bitcast_i16_to_f16(%arg0: memref<8x16xi16>, %arg1: memref<16x1
xegpu.store_nd %6, %7 : vector<8x16xf32>, !xegpu.tensor_desc<8x16xf32>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @vector_bitcast_i32_to_f16(
// CHECK: %[[LOAD:.*]] = xegpu.load_nd %{{.*}} {layout_result_0 = #xegpu.layout<lane_layout = [16, 1], lane_data = [1, 1]>}
// CHECK-SAME: !xegpu.tensor_desc<16x8xi32, #xegpu.layout<lane_layout = [16, 1], lane_data = [1, 1]>> -> vector<16x8xi32>
@@ -239,8 +258,9 @@ func.func @vector_bitcast_i32_to_f16(%arg0: memref<8x16xf16>, %arg1: memref<16x8
xegpu.store_nd %6, %7 : vector<8x16xf32>, !xegpu.tensor_desc<8x16xf32>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @vector_bitcast_i16_to_i32(
// CHECK: %[[LOAD:.*]] = xegpu.load_nd %{{.*}} {layout_result_0 = #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 2]>}
// CHECK-SAME: !xegpu.tensor_desc<8x32xi16, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 2]>> -> vector<8x32xi16>
@@ -255,8 +275,9 @@ func.func @vector_bitcast_i16_to_i32(%arg0: memref<8x32xi16>, %arg1: memref<8x16
xegpu.store_nd %3, %1 : vector<8x16xi32>, !xegpu.tensor_desc<8x16xi32>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @vector_bitcast_require_cross_lane_shuffle(
// CHECK: %[[LOAD:.*]] = xegpu.load_nd %{{.*}} : !xegpu.tensor_desc<8x16xi32> -> vector<8x16xi32>
// CHECK: %{{.*}} = vector.bitcast %[[LOAD]] {layout_result_0 = #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>}
@@ -270,9 +291,10 @@ func.func @vector_bitcast_require_cross_lane_shuffle(%arg0: memref<8x16xi32>, %a
xegpu.store_nd %3, %1 : vector<8x32xi16>, !xegpu.tensor_desc<8x32xi16>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @binary_op_one_use(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<8x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>>,
// CHECK-SAME: %[[ARG1:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<16x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [2, 1]>>,
@@ -291,8 +313,9 @@ func.func @binary_op_one_use(%arg0: !xegpu.tensor_desc<8x16xf16>, %arg1: !xegpu.
xegpu.store_nd %4, %arg2 : vector<8x16xf32>, !xegpu.tensor_desc<8x16xf32>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @binary_op_multiple_uses(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<8x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>>,
// CHECK-SAME: %[[ARG1:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<16x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>>,
@@ -312,8 +335,9 @@ func.func @binary_op_multiple_uses(%arg0: !xegpu.tensor_desc<8x16xf16>, %arg1: !
xegpu.store_nd %2, %arg3 : vector<16x16xf16>, !xegpu.tensor_desc<16x16xf16>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @for_op(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<8x128xf16>, %[[ARG1:[0-9a-zA-Z]+]]: memref<128x16xf16>, %[[ARG2:[0-9a-zA-Z]+]]: memref<8x16xf32>) {
// CHECK: %[[T0:.*]] = xegpu.create_nd_tdesc %[[ARG0]][%{{.*}}] : memref<8x128xf16> -> !xegpu.tensor_desc<8x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>>
@@ -353,8 +377,9 @@ func.func @for_op(%arg0: memref<8x128xf16>, %arg1: memref<128x16xf16>, %arg2: me
xegpu.store_nd %2#2, %3 : vector<8x16xf32>, !xegpu.tensor_desc<8x16xf32>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @if_single_use(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<8x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>>,
// CHECK-SAME: %[[ARG1:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<16x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [2, 1]>>,
@@ -381,8 +406,9 @@ func.func @if_single_use(%arg0: !xegpu.tensor_desc<8x16xf16>, %arg1: !xegpu.tens
xegpu.store_nd %2, %arg3 : vector<8x16xf32>, !xegpu.tensor_desc<8x16xf32>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @if_multiple_uses(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<8x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>>,
// CHECK-SAME: %[[ARG1:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<16x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>>,
@@ -411,8 +437,9 @@ func.func @if_multiple_uses(%arg0: !xegpu.tensor_desc<8x16xf16>, %arg1: !xegpu.t
xegpu.store_nd %1, %arg4 : vector<16x16xf16>, !xegpu.tensor_desc<16x16xf16>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @vector_outer_reduction(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: vector<16x16xf32>, %[[ARG1:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<16xf32, #xegpu.layout<lane_layout = [16], lane_data = [1]>>) {
// CHECK: %{{.*}} = vector.multi_reduction <add>, %[[ARG0]], %{{.*}} {layout_result_0 = #xegpu.layout<lane_layout = [16], lane_data = [1]>} [0] : vector<16x16xf32> to vector<16xf32>
@@ -422,8 +449,9 @@ func.func @vector_outer_reduction(%arg0: vector<16x16xf32>, %arg1: !xegpu.tensor
xegpu.store_nd %0, %arg1 : vector<16xf32>, !xegpu.tensor_desc<16xf32>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @vector_inner_reduction(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: vector<16x16xf32>, %[[ARG1:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<16xf32, #xegpu.layout<lane_layout = [16], lane_data = [1]>>) {
// CHECK: %{{.*}} = vector.multi_reduction <add>, %[[ARG0]], %{{.*}} {layout_result_0 = #xegpu.layout<lane_layout = [16], lane_data = [1]>} [1] : vector<16x16xf32> to vector<16xf32>
@@ -433,8 +461,9 @@ func.func @vector_inner_reduction(%arg0: vector<16x16xf32>, %arg1: !xegpu.tensor
xegpu.store_nd %0, %arg1 : vector<16xf32>, !xegpu.tensor_desc<16xf32>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @update_nd_offset_1d(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<256xf32>) {
// CHECK: %[[T0:.*]] = xegpu.create_nd_tdesc %[[ARG0]][%{{.*}}] : memref<256xf32> -> !xegpu.tensor_desc<16xf32, #xegpu.layout<lane_layout = [16], lane_data = [1]>>
@@ -448,8 +477,9 @@ func.func @update_nd_offset_1d(%arg0: memref<256xf32>){
xegpu.store_nd %1, %2 : vector<16xf32>, !xegpu.tensor_desc<16xf32>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @update_nd_offset_2d(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<256x256xf32>) {
// CHECK: %[[T0:.*]] = xegpu.create_nd_tdesc %[[ARG0]][%{{.*}}, %{{.*}}] : memref<256x256xf32> -> !xegpu.tensor_desc<16x16xf32, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>>
@@ -463,8 +493,9 @@ func.func @update_nd_offset_2d(%arg0: memref<256x256xf32>){
xegpu.store_nd %1, %2 : vector<16x16xf32>, !xegpu.tensor_desc<16x16xf32>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @prefetch_2d(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<256x256xf16>) {
// CHECK: %[[T0:.*]] = xegpu.create_nd_tdesc %[[ARG0]][%{{.*}}, %{{.*}}] : memref<256x256xf16> -> !xegpu.tensor_desc<16x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>>
@@ -475,8 +506,9 @@ func.func @prefetch_2d(%arg0: memref<256x256xf16>){
xegpu.prefetch_nd %0 <{l1_hint = #xegpu.cache_hint<cached>, l2_hint = #xegpu.cache_hint<uncached>}>: !xegpu.tensor_desc<16x16xf16>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @prefetch_1d(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<256xf16>) {
// CHECK: %[[T0:.*]] = xegpu.create_nd_tdesc %[[ARG0]][%{{.*}}] : memref<256xf16> -> !xegpu.tensor_desc<16xf16, #xegpu.layout<lane_layout = [16], lane_data = [1]>>
@@ -487,8 +519,9 @@ func.func @prefetch_1d(%arg0: memref<256xf16>){
xegpu.prefetch_nd %0 <{l1_hint = #xegpu.cache_hint<cached>, l2_hint = #xegpu.cache_hint<uncached>}>: !xegpu.tensor_desc<16xf16>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @scf_while_and_condition(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: memref<256xf32>, %[[ARG1:[0-9a-zA-Z]+]]: memref<256xf32>) {
// CHECK: %{{.*}}:3 = scf.while ({{.*}}) : (vector<16xf32>, i32, !xegpu.tensor_desc<16xf32, #xegpu.layout<lane_layout = [16], lane_data = [1]>>)
@@ -520,8 +553,9 @@ func.func @scf_while_and_condition(%arg0: memref<256xf32>, %arg1: memref<256xf32
}
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @vector_shape_cast_1d_to_2d_dim1_distributed(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<16x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>>,
// CHECK-SAME: %[[ARG1:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<16x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>>) {
@@ -541,8 +575,9 @@ func.func @vector_shape_cast_1d_to_2d_dim1_distributed(%arg0: !xegpu.tensor_desc
xegpu.store_nd %5, %arg1 : vector<16x16xf16>, !xegpu.tensor_desc<16x16xf16>
return
}
-
+}
// -----
+gpu.module @test {
// CHECK-LABEL: func.func @vector_shape_cast_1d_to_2d_dim0_broadcasted(
// CHECK-SAME: %[[ARG0:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<16x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>>,
// CHECK-SAME: %[[ARG1:[0-9a-zA-Z]+]]: !xegpu.tensor_desc<16x16xf16, #xegpu.layout<lane_layout = [1, 16], lane_data = [1, 1]>>) {
@@ -563,3 +598,4 @@ func.func @vector_shape_cast_1d_to_2d_dim0_broadcasted(%arg0: !xegpu.tensor_desc
xegpu.store_nd %5, %arg1 : vector<16x16xf16>, !xegpu.tensor_desc<16x16xf16>
return
}
+}
>From a6c9e49658f42e963d254e620b75a1ee1f5e139c Mon Sep 17 00:00:00 2001
From: Artem Kroviakov <artem.kroviakov at intel.com>
Date: Tue, 28 Oct 2025 09:30:53 +0000
Subject: [PATCH 2/3] Feedback part1
---
.../mlir/Dialect/XeGPU/uArch/IntelGpuXe2.h | 2 ++
.../Transforms/XeGPUSubgroupDistribute.cpp | 17 ++++++++---------
2 files changed, 10 insertions(+), 9 deletions(-)
diff --git a/mlir/include/mlir/Dialect/XeGPU/uArch/IntelGpuXe2.h b/mlir/include/mlir/Dialect/XeGPU/uArch/IntelGpuXe2.h
index dcb2ad5d67a25..b3231a173f33a 100644
--- a/mlir/include/mlir/Dialect/XeGPU/uArch/IntelGpuXe2.h
+++ b/mlir/include/mlir/Dialect/XeGPU/uArch/IntelGpuXe2.h
@@ -270,6 +270,8 @@ inline const uArch *getUArch(llvm::StringRef archName) {
return PVCuArch::getInstance();
else if (archName.equals_insensitive("bmg"))
return BMGuArch::getInstance();
+ else
+ llvm_unreachable("No matching uArch found");
return nullptr;
}
diff --git a/mlir/lib/Dialect/XeGPU/Transforms/XeGPUSubgroupDistribute.cpp b/mlir/lib/Dialect/XeGPU/Transforms/XeGPUSubgroupDistribute.cpp
index 1c5b7e6e86a6c..5a3b27ec6108e 100644
--- a/mlir/lib/Dialect/XeGPU/Transforms/XeGPUSubgroupDistribute.cpp
+++ b/mlir/lib/Dialect/XeGPU/Transforms/XeGPUSubgroupDistribute.cpp
@@ -200,6 +200,11 @@ struct MoveFuncBodyToWarpOp : public OpRewritePattern<gpu::GPUFuncOp> {
using OpRewritePattern<gpu::GPUFuncOp>::OpRewritePattern;
LogicalResult matchAndRewrite(gpu::GPUFuncOp gpuFuncOp,
PatternRewriter &rewriter) const override {
+ auto uArch = getUArch(xegpu::getChipStr(gpuFuncOp).value_or(""));
+ if (!uArch)
+ return rewriter.notifyMatchFailure(
+ gpuFuncOp, "Subgroup distribution requires target attribute attached "
+ "to set the warp size");
// If the function only contains a single void return, skip.
if (llvm::all_of(gpuFuncOp.getBody().getOps(), [](Operation &op) {
return isa<gpu::ReturnOp>(op) && !op.getNumOperands();
@@ -229,11 +234,6 @@ struct MoveFuncBodyToWarpOp : public OpRewritePattern<gpu::GPUFuncOp> {
rewriter, newGpuFunc.getLoc(), rewriter.getIndexType(),
/** upperBound = **/ mlir::IntegerAttr());
ArrayRef<Type> gpuFuncResultType = gpuFuncOp.getFunctionType().getResults();
- auto uArch = getUArch(xegpu::getChipStr(gpuFuncOp).value_or(""));
- if (!uArch)
- return rewriter.notifyMatchFailure(
- gpuFuncOp, "Subgroup distribution requires target attribute attached "
- "to set the warp size");
auto warpOp = gpu::WarpExecuteOnLane0Op::create(
rewriter, laneId.getLoc(), gpuFuncResultType, laneId,
uArch->getSubgroupSize(), newGpuFunc.getArguments(),
@@ -501,15 +501,14 @@ struct LoadNdDistribution final : public gpu::WarpDistributionPattern {
warpOp, "warp result is not a xegpu::LoadNd op");
auto loadOp = operand->get().getDefiningOp<xegpu::LoadNdOp>();
- // Chip information is required to decide if the layout requires transpose
- // effect.
- auto chipStr = xegpu::getChipStr(loadOp);
- auto uArch = getUArch(chipStr.value_or(""));
+ auto uArch = getUArch(xegpu::getChipStr(loadOp).value_or(""));
if (!uArch)
return rewriter.notifyMatchFailure(
loadOp, "xegpu::LoadNdOp require target attribute attached to "
"determine transpose "
"requirement");
+ // Chip information is required to decide if the layout requires transpose
+ // effect.
// Expecting offsets to be present.
SmallVector<OpFoldResult> offsets = loadOp.getMixedOffsets();
if (offsets.empty())
>From c9bd5aa06350243547cbedea87e88751bcb5950d Mon Sep 17 00:00:00 2001
From: Artem Kroviakov <artem.kroviakov at intel.com>
Date: Wed, 29 Oct 2025 14:57:19 +0000
Subject: [PATCH 3/3] Find suitable multiple
---
.../mlir/Dialect/XeGPU/uArch/uArchBase.h | 2 +
mlir/lib/Dialect/XeGPU/IR/XeGPUDialect.cpp | 8 +-
.../XeGPU/Transforms/XeGPUPropagateLayout.cpp | 85 ++++++++++++++++---
3 files changed, 80 insertions(+), 15 deletions(-)
diff --git a/mlir/include/mlir/Dialect/XeGPU/uArch/uArchBase.h b/mlir/include/mlir/Dialect/XeGPU/uArch/uArchBase.h
index ea33e885c78ff..8f23b89134773 100644
--- a/mlir/include/mlir/Dialect/XeGPU/uArch/uArchBase.h
+++ b/mlir/include/mlir/Dialect/XeGPU/uArch/uArchBase.h
@@ -29,6 +29,8 @@ namespace mlir {
namespace xegpu {
namespace uArch {
+constexpr unsigned generalPackedFormatBitSize{32};
+
// An enum class to represent the scope of an instruction
enum class InstructionScope { Lane, Subgroup, Workgroup, Cluster };
enum class InstructionKind {
diff --git a/mlir/lib/Dialect/XeGPU/IR/XeGPUDialect.cpp b/mlir/lib/Dialect/XeGPU/IR/XeGPUDialect.cpp
index ea9c872aae66f..6b4c185d7d897 100644
--- a/mlir/lib/Dialect/XeGPU/IR/XeGPUDialect.cpp
+++ b/mlir/lib/Dialect/XeGPU/IR/XeGPUDialect.cpp
@@ -570,10 +570,10 @@ TensorDescType::verify(llvm::function_ref<InFlightDiagnostic()> emitError,
// for gather and scatter ops, Low-precision types are packed in 32-bit units.
unsigned bitWidth = elementType.getIntOrFloatBitWidth();
- constexpr int packingBitSizeGatherScatter{32};
- int chunkAlignmentFactor = bitWidth < packingBitSizeGatherScatter
- ? packingBitSizeGatherScatter / bitWidth
- : 1;
+ int chunkAlignmentFactor =
+ bitWidth < xegpu::uArch::generalPackedFormatBitSize
+ ? xegpu::uArch::generalPackedFormatBitSize / bitWidth
+ : 1;
auto scatterAttr = mlir::dyn_cast_if_present<ScatterTensorDescAttr>(encoding);
if (scatterAttr) {
int64_t chunkSize = scatterAttr.getChunkSizeAsInt();
diff --git a/mlir/lib/Dialect/XeGPU/Transforms/XeGPUPropagateLayout.cpp b/mlir/lib/Dialect/XeGPU/Transforms/XeGPUPropagateLayout.cpp
index 070535bdf2049..90eae871a5ef3 100644
--- a/mlir/lib/Dialect/XeGPU/Transforms/XeGPUPropagateLayout.cpp
+++ b/mlir/lib/Dialect/XeGPU/Transforms/XeGPUPropagateLayout.cpp
@@ -204,6 +204,28 @@ struct LayoutInfoLattice : public Lattice<LayoutInfo> {
using Lattice::Lattice;
};
+/// Helper Function to find a proper instruction multiple for the user-supplied
+/// sg-level data shape. `candidates` are uArch allowed shapes.
+/// `candidateMultiples` are uArch multiples of such shapes (e.g., block count).
+template <typename T>
+int getLargestDivisor(T dim, ArrayRef<T> candidates,
+ ArrayRef<T> candidateMultiples = {}) {
+ static_assert(std::is_integral<T>::value, "T must be an integer type");
+ int largest = -1;
+ SmallVector<T> multiples = {1};
+ if (!candidateMultiples.empty())
+ multiples =
+ SmallVector<T>(candidateMultiples.begin(), candidateMultiples.end());
+ for (T candidate : candidates) {
+ for (T multiple : multiples) {
+ int value = static_cast<int>(candidate * multiple);
+ if (value != 0 && dim % value == 0 && value > largest)
+ largest = value;
+ }
+ }
+ return largest;
+}
+
/// Helper Functions to get default layouts. A `default layout` is a layout that
/// is assigned to a value when the layout is not fixed by some anchor operation
/// (like DPAS).
@@ -482,12 +504,23 @@ void LayoutInfoPropagation::visitPrefetchNdOp(
if (!blockWHC)
prefetch.emitWarning("No known block params found for the element type.");
auto [bWidth, bHeight, bCount] = blockWHC.value();
-
SmallVector<int> instData;
+ int instWidth = getLargestDivisor(
+ static_cast<int>(tdescTy.getDimSize(tdescTy.getRank() - 1)), bWidth,
+ bCount);
+ if (instWidth == -1)
+ prefetch.emitWarning(
+ "No suitable instruction multiple found for the given shape.");
if (tdescTy.getRank() == 1)
- instData = {bWidth.back() * bCount.back()};
- else
- instData = {bHeight.back(), bWidth.back() * bCount.back()};
+ instData = {instWidth};
+ else {
+ int instHeight = getLargestDivisor(
+ static_cast<int>(tdescTy.getDimSize(tdescTy.getRank() - 2)), bHeight);
+ if (instHeight == -1)
+ prefetch.emitWarning(
+ "No suitable instruction multiple found for the given shape.");
+ instData = {instHeight, instWidth};
+ }
auto prefetchLayout = getDefaultSIMTLayoutInfo(
tdescTy, uArch, instData, uArchInstruction->getPackedFormatBitSize());
// Propagate the layout to the source tensor descriptor.
@@ -597,10 +630,22 @@ void LayoutInfoPropagation::visitDpasOp(
const auto *uArchInstruction =
dyn_cast<xegpu::uArch::SubgroupMatrixMultiplyAcc>(uArch->getInstruction(
xegpu::uArch::InstructionKind::SubgroupMatrixMultiplyAcc));
+
+ const unsigned dataALen = aTy.getShape().front();
+ auto supportedALen = uArchInstruction->getSupportedM(aTy.getElementType());
const int maxALen =
- uArchInstruction->getSupportedM(aTy.getElementType()).back();
+ getLargestDivisor(dataALen, ArrayRef<unsigned>(supportedALen));
+ if (maxALen == -1)
+ dpas.emitWarning(
+ "No suitable instruction multiple found for the given shape.");
+
+ const unsigned dataBLen = bTy.getShape().back();
+ auto supportedBLen = uArchInstruction->getSupportedK(bTy.getElementType());
const int maxBLen =
- uArchInstruction->getSupportedK(bTy.getElementType()).back();
+ getLargestDivisor(dataBLen, ArrayRef<unsigned>(supportedBLen));
+ if (maxBLen == -1)
+ dpas.emitWarning(
+ "No suitable instruction multiple found for the given shape.");
SmallVector<int> instDataA = {maxALen, subgroupSize};
SmallVector<int> instDataB = {subgroupSize, maxBLen};
@@ -614,8 +659,13 @@ void LayoutInfoPropagation::visitDpasOp(
uArchInstruction->getPackedFormatBitSizeB())));
if (operands.size() > 2) {
VectorType cTy = dpas.getAccType();
+ const unsigned dataCLen = bTy.getShape().back();
+ auto supportedCLen = uArchInstruction->getSupportedN(bTy.getElementType());
const int maxCLen =
- uArchInstruction->getSupportedN(bTy.getElementType()).back();
+ getLargestDivisor(dataCLen, ArrayRef<unsigned>(supportedCLen));
+ if (maxCLen == -1)
+ dpas.emitWarning(
+ "No suitable instruction multiple found for the given shape.");
SmallVector<int> instDataC = {maxALen, maxCLen};
propagateIfChanged(operands[2],
operands[2]->meet(getSIMTLayoutInfoForDPASOperand(
@@ -634,16 +684,29 @@ void LayoutInfoPropagation::visitStoreNdOp(
dyn_cast<xegpu::uArch::Subgroup2DBlockStoreInstruction>(
uArch->getInstruction(
xegpu::uArch::InstructionKind::Subgroup2DBlockStore));
+ VectorType dataTy = store.getValueType();
auto blockWHC = uArchInstruction->getBlockWidthHeightCount(
store.getValueType().getElementType());
if (!blockWHC)
store.emitWarning("No known block params found for the element type.");
auto [bWidth, bHeight, bCount] = blockWHC.value();
SmallVector<int> instData;
- if (store.getValueType().getRank() == 1)
- instData = {bWidth.back() * bCount.back()};
- else
- instData = {bHeight.back(), bWidth.back() * bCount.back()};
+ int instWidth = getLargestDivisor(
+ static_cast<int>(dataTy.getDimSize(dataTy.getRank() - 1)), bWidth,
+ bCount);
+ if (instWidth == -1)
+ store.emitWarning(
+ "No suitable instruction multiple found for the given shape.");
+ if (dataTy.getRank() == 1)
+ instData = {instWidth};
+ else {
+ int instHeight = getLargestDivisor(
+ static_cast<int>(dataTy.getDimSize(dataTy.getRank() - 2)), bHeight);
+ if (instHeight == -1)
+ store.emitWarning(
+ "No suitable instruction multiple found for the given shape.");
+ instData = {instHeight, instWidth};
+ }
LayoutInfo storeLayout =
getDefaultSIMTLayoutInfo(store.getValueType(), uArch, instData,
uArchInstruction->getPackedFormatBitSize());
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