gaspersic/triton
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[Triton-MLIR][BACKEND] adapt DotOp layout for FMADot (#872)

Browse Source
This commit is contained in:
Yan Chunwei
2022-11-14 16:56:30 +08:00
committed by GitHub
parent 516a241234
commit 1eedaf7bec
4 changed files with 482 additions and 253 deletions
Download Patch File Download Diff File Expand all files Collapse all files

584
lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVM.cpp
View File

@@ -457,6 +457,25 @@ struct ConvertTritonGPUOpToLLVMPatternBase {
}
return results;
}
static SharedMemoryObject
getSharedMemoryObjectFromStruct(Location loc, Value llvmStruct,
ConversionPatternRewriter &rewriter) {
auto elems = getElementsFromStruct(loc, llvmStruct, rewriter);
return SharedMemoryObject(/*base=*/elems[0],
/*strides=*/{elems.begin() + 1, elems.end()});
}
static Value
getStructFromSharedMemoryObject(Location loc,
const SharedMemoryObject &smemObj,
ConversionPatternRewriter &rewriter) {
auto elems = smemObj.getElems();
auto types = smemObj.getTypes();
auto structTy =
LLVM::LLVMStructType::getLiteral(rewriter.getContext(), types);
return getStructFromElements(loc, elems, rewriter, structTy);
}
};
template <typename SourceOp>
@@ -830,25 +849,6 @@ public:
return base;
}
static SharedMemoryObject
getSharedMemoryObjectFromStruct(Location loc, Value llvmStruct,
ConversionPatternRewriter &rewriter) {
auto elems = getElementsFromStruct(loc, llvmStruct, rewriter);
return SharedMemoryObject(/*base=*/elems[0],
/*strides=*/{elems.begin() + 1, elems.end()});
}
static Value
getStructFromSharedMemoryObject(Location loc,
const SharedMemoryObject &smemObj,
ConversionPatternRewriter &rewriter) {
auto elems = smemObj.getElems();
auto types = smemObj.getTypes();
auto structTy =
LLVM::LLVMStructType::getLiteral(rewriter.getContext(), types);
return getStructFromElements(loc, elems, rewriter, structTy);
}
protected:
const Allocation *allocation;
Value smem;
@@ -3566,7 +3566,8 @@ struct DotOpConversion : public ConvertTritonGPUOpToLLVMPattern<triton::DotOp> {
}
if (op.getType().cast<RankedTensorType>().getElementType().isF32() &&
A.getType().cast<RankedTensorType>().getElementType().isF32())
A.getType().cast<RankedTensorType>().getElementType().isF32() &&
!op.allowTF32())
return convertFMADot(op, adaptor, rewriter);
llvm::report_fatal_error(
@@ -4385,6 +4386,90 @@ private:
}
};
// Helper for conversion of FMA DotOp.
struct DotOpFMAConversionHelper {
Attribute layout;
MLIRContext *ctx{};
using ValueTable = std::map<std::pair<int, int>, Value>;
explicit DotOpFMAConversionHelper(Attribute layout)
: layout(layout), ctx(layout.getContext()) {}
SmallVector<Value> getThreadIds(Value threadId,
ArrayRef<unsigned> shapePerCTA,
ArrayRef<unsigned> order,
ConversionPatternRewriter &rewriter,
Location loc) const;
Value loadA(Value A, Value llA, BlockedEncodingAttr dLayout, Value thread,
Location loc, ConversionPatternRewriter &rewriter) const;
Value loadB(Value B, Value llB, BlockedEncodingAttr dLayout, Value thread,
Location loc, ConversionPatternRewriter &rewriter) const;
ValueTable getValueTableFromStruct(Value val, int K, int n0, int shapePerCTA,
int sizePerThread,
ConversionPatternRewriter &rewriter,
Location loc) const;
Value getStructFromValueTable(ValueTable vals,
ConversionPatternRewriter &rewriter,
Location loc) const {
SmallVector<Type> elemTypes(vals.size(), f32_ty);
SmallVector<Value> elems;
elems.reserve(vals.size());
for (auto &item : vals) {
elems.push_back(item.second);
}
Type structTy = struct_ty(elemTypes);
return getStructFromElements(loc, elems, rewriter, structTy);
}
// get number of elements per thread for $a or $b.
static int getNumElemsPerThread(ArrayRef<int64_t> shape,
DotOperandEncodingAttr dotOpLayout) {
auto blockedLayout = dotOpLayout.getParent().cast<BlockedEncodingAttr>();
auto shapePerCTA = getShapePerCTA(blockedLayout);
auto sizePerThread = getSizePerThread(blockedLayout);
auto order = blockedLayout.getOrder();
// TODO[Superjomn]: we assume the k aixs is fixed for $a and $b here, fix it
// if not.
int K = dotOpLayout.getOpIdx() == 0 ? shape[1] : shape[0];
int otherDim = dotOpLayout.getOpIdx() == 1 ? shape[1] : shape[0];
bool isM = dotOpLayout.getOpIdx() == 0;
int shapePerCTAMN = getShapePerCTAForMN(blockedLayout, isM);
int sizePerThreadMN = getsizePerThreadForMN(blockedLayout, isM);
return K * std::max<int>(otherDim / shapePerCTAMN, 1) * sizePerThreadMN;
}
// Get shapePerCTA for M or N axis.
static int getShapePerCTAForMN(BlockedEncodingAttr layout, bool isM) {
auto order = layout.getOrder();
auto shapePerCTA = getShapePerCTA(layout);
int mShapePerCTA =
order[0] == 1 ? shapePerCTA[order[1]] : shapePerCTA[order[0]];
int nShapePerCTA =
order[0] == 0 ? shapePerCTA[order[1]] : shapePerCTA[order[0]];
return isM ? mShapePerCTA : nShapePerCTA;
}
// Get sizePerThread for M or N axis.
static int getsizePerThreadForMN(BlockedEncodingAttr layout, bool isM) {
auto order = layout.getOrder();
auto sizePerThread = getSizePerThread(layout);
int mSizePerThread =
order[0] == 1 ? sizePerThread[order[1]] : sizePerThread[order[0]];
int nSizePerThread =
order[0] == 0 ? sizePerThread[order[1]] : sizePerThread[order[0]];
return isM ? mSizePerThread : nSizePerThread;
}
};
Value ConvertLayoutOpConversion::lowerSharedToDotOperandMMA(
triton::gpu::ConvertLayoutOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter, const MmaEncodingAttr &mmaLayout,
@@ -4393,14 +4478,15 @@ Value ConvertLayoutOpConversion::lowerSharedToDotOperandMMA(
Value src = op.src();
Value dst = op.result();
auto dstTensorTy = dst.getType().cast<RankedTensorType>();
// TODO[Superjomn]: the allowTF32 is not available in ConvertLayoutOp for it
// is an attribute of DotOp.
// TODO[Superjomn]: allowTF32 is not accessible here for it is an attribute of
// an Op instance.
bool allowTF32 = false;
bool isHMMA = DotOpConversion::isDotHMMA(dstTensorTy, allowTF32,
mmaLayout.getVersion());
auto smemObj = getSharedMemoryObjectFromStruct(loc, adaptor.src(), rewriter);
Value res;
if (!isOuter && mmaLayout.getVersion() == 2 && isHMMA) { // tensor core v2
MMA16816ConversionHelper mmaHelper(mmaLayout, getThreadId(rewriter, loc),
rewriter, getTypeConverter(),
@@ -4459,7 +4545,25 @@ LogicalResult ConvertLayoutOpConversion::lowerSharedToDotOperand(
} else if (auto blockedLayout =
dotOperandLayout.getParent()
.dyn_cast_or_null<BlockedEncodingAttr>()) {
assert(false && "Blocked layout is not supported yet");
// TODO[Superjomn]: the allowTF32 is not available in ConvertLayoutOp for it
// is an attribute of DotOp.
bool allowTF32 = false;
bool isFMADot = dstTensorTy.getElementType().isF32() && !allowTF32;
if (isFMADot) {
auto dotOpLayout =
dstTensorTy.getEncoding().cast<DotOperandEncodingAttr>();
auto blockedLayout = dotOpLayout.getParent().cast<BlockedEncodingAttr>();
DotOpFMAConversionHelper helper(blockedLayout);
auto thread = getThreadId(rewriter, loc);
if (dotOpLayout.getOpIdx() == 0) { // $a
res = helper.loadA(src, adaptor.src(), blockedLayout, thread, loc,
rewriter);
} else { // $b
res = helper.loadB(src, adaptor.src(), blockedLayout, thread, loc,
rewriter);
}
} else
assert(false && "Unsupported dot operand layout found");
} else {
assert(false && "Unsupported dot operand layout found");
}
@@ -4925,6 +5029,183 @@ DotOpMmaV1ConversionHelper::extractLoadedOperand(
return rcds;
}
Value DotOpFMAConversionHelper::loadA(
Value A, Value llA, BlockedEncodingAttr dLayout, Value thread, Location loc,
ConversionPatternRewriter &rewriter) const {
auto aTensorTy = A.getType().cast<RankedTensorType>();
auto aLayout = aTensorTy.getEncoding().cast<SharedEncodingAttr>();
auto aShape = aTensorTy.getShape();
auto aOrder = aLayout.getOrder();
auto order = dLayout.getOrder();
bool isARow = aOrder[0] == 1;
int strideAM = isARow ? aShape[1] : 1;
int strideAK = isARow ? 1 : aShape[0];
int strideA0 = isARow ? strideAK : strideAM;
int strideA1 = isARow ? strideAM : strideAK;
int lda = isARow ? strideAM : strideAK;
int aNumPtr = 8;
int bNumPtr = 8;
int NK = aShape[1];
auto shapePerCTA = getShapePerCTA(dLayout);
auto sizePerThread = getSizePerThread(dLayout);
Value _0 = i32_val(0);
Value mContig = i32_val(sizePerThread[order[1]]);
Value nContig = i32_val(sizePerThread[order[0]]);
// threadId in blocked layout
auto threadIds = getThreadIds(thread, shapePerCTA, order, rewriter, loc);
Value threadIdM = threadIds[0];
Value threadIdN = threadIds[1];
Value offA0 = isARow ? _0 : mul(threadIdM, mContig);
Value offA1 = isARow ? mul(threadIdM, mContig) : _0;
SmallVector<Value> aOff(aNumPtr);
for (int i = 0; i < aNumPtr; ++i) {
aOff[i] = add(mul(offA0, i32_val(strideA0)), mul(offA1, i32_val(strideA1)));
}
auto aSmem =
ConvertTritonGPUOpToLLVMPatternBase::getSharedMemoryObjectFromStruct(
loc, llA, rewriter);
Type f32PtrTy = ptr_ty(f32_ty);
SmallVector<Value> aPtrs(aNumPtr);
for (int i = 0; i < aNumPtr; ++i)
aPtrs[i] = gep(f32PtrTy, aSmem.base, aOff[i]);
ValueTable has;
int M = aShape[aOrder[1]];
int mShapePerCTA = getShapePerCTAForMN(dLayout, true /*isM*/);
int mSizePerThread = getsizePerThreadForMN(dLayout, true /*isM*/);
for (unsigned k = 0; k < NK; ++k) {
for (unsigned m = 0; m < M; m += mShapePerCTA)
for (unsigned mm = 0; mm < mSizePerThread; ++mm)
if (!has.count({m + mm, k})) {
Value pa = gep(f32PtrTy, aPtrs[0],
i32_val((m + mm) * strideAM + k * strideAK));
Value va = load(pa);
has[{m + mm, k}] = va;
}
}
return getStructFromValueTable(has, rewriter, loc);
}
Value DotOpFMAConversionHelper::loadB(
Value B, Value llB, BlockedEncodingAttr dLayout, Value thread, Location loc,
ConversionPatternRewriter &rewriter) const {
auto bTensorTy = B.getType().cast<RankedTensorType>();
auto bLayout = bTensorTy.getEncoding().cast<SharedEncodingAttr>();
auto bShape = bTensorTy.getShape();
auto bOrder = bLayout.getOrder();
auto order = dLayout.getOrder();
bool isBRow = bOrder[0] == 1;
int strideBN = isBRow ? 1 : bShape[0];
int strideBK = isBRow ? bShape[1] : 1;
int strideB0 = isBRow ? strideBN : strideBK;
int strideB1 = isBRow ? strideBK : strideBN;
int ldb = isBRow ? strideBK : strideBN;
int bNumPtr = 8;
int NK = bShape[0];
auto shapePerCTA = getShapePerCTA(dLayout);
auto sizePerThread = getSizePerThread(dLayout);
Value _0 = i32_val(0);
Value mContig = i32_val(sizePerThread[order[1]]);
Value nContig = i32_val(sizePerThread[order[0]]);
// threadId in blocked layout
auto threadIds = getThreadIds(thread, shapePerCTA, order, rewriter, loc);
Value threadIdN = threadIds[1];
Value offB0 = isBRow ? mul(threadIdN, nContig) : _0;
Value offB1 = isBRow ? _0 : mul(threadIdN, nContig);
SmallVector<Value> bOff(bNumPtr);
for (int i = 0; i < bNumPtr; ++i) {
bOff[i] = add(mul(offB0, i32_val(strideB0)), mul(offB1, i32_val(strideB1)));
}
auto bSmem =
ConvertTritonGPUOpToLLVMPatternBase::getSharedMemoryObjectFromStruct(
loc, llB, rewriter);
Type f32PtrTy = ptr_ty(f32_ty);
SmallVector<Value> bPtrs(bNumPtr);
for (int i = 0; i < bNumPtr; ++i)
bPtrs[i] = gep(f32PtrTy, bSmem.base, bOff[i]);
int N = bShape[bOrder[0]];
ValueTable hbs;
int nShapePerCTA = getShapePerCTAForMN(dLayout, false /*isM*/);
int nSizePerThread = getsizePerThreadForMN(dLayout, false /*isM*/);
for (unsigned k = 0; k < NK; ++k)
for (unsigned n = 0; n < N; n += nShapePerCTA)
for (unsigned nn = 0; nn < nSizePerThread; ++nn) {
Value pb = gep(f32PtrTy, bPtrs[0],
i32_val((n + nn) * strideBN + k * strideBK));
Value vb = load(pb);
hbs[{n + nn, k}] = vb;
}
return getStructFromValueTable(hbs, rewriter, loc);
}
DotOpFMAConversionHelper::ValueTable
DotOpFMAConversionHelper::getValueTableFromStruct(
Value val, int K, int n0, int shapePerCTA, int sizePerThread,
ConversionPatternRewriter &rewriter, Location loc) const {
ValueTable res;
auto elems = ConvertTritonGPUOpToLLVMPatternBase::getElementsFromStruct(
loc, val, rewriter);
int id = 0;
std::set<std::pair<int, int>> keys; // ordered
for (unsigned k = 0; k < K; ++k) {
for (unsigned m = 0; m < n0; m += shapePerCTA)
for (unsigned mm = 0; mm < sizePerThread; ++mm) {
keys.insert({m + mm, k});
}
}
for (auto &key : llvm::enumerate(keys)) {
res[key.value()] = elems[key.index()];
}
return res;
}
SmallVector<Value> DotOpFMAConversionHelper::getThreadIds(
Value threadId, ArrayRef<unsigned int> shapePerCTA,
ArrayRef<unsigned int> order, ConversionPatternRewriter &rewriter,
Location loc) const {
int dim = order.size();
SmallVector<Value> threadIds(dim);
for (unsigned k = 0; k < dim - 1; k++) {
Value dimK = i32_val(shapePerCTA[order[k]]);
Value rem = urem(threadId, dimK);
threadId = udiv(threadId, dimK);
threadIds[order[k]] = rem;
}
Value dimK = i32_val(shapePerCTA[order[dim - 1]]);
threadIds[order[dim - 1]] = urem(threadId, dimK);
return threadIds;
}
LogicalResult
DotOpConversion::convertFMADot(triton::DotOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
@@ -4948,120 +5229,68 @@ DotOpConversion::convertFMADot(triton::DotOp op, OpAdaptor adaptor,
auto bShape = bTensorTy.getShape();
auto cShape = cTensorTy.getShape();
auto aLayout = aTensorTy.getEncoding().cast<SharedEncodingAttr>();
auto bLayout = bTensorTy.getEncoding().cast<SharedEncodingAttr>();
auto cLayout = cTensorTy.getEncoding().cast<BlockedEncodingAttr>();
auto dLayout = dTensorTy.getEncoding().cast<BlockedEncodingAttr>();
auto aOrder = aLayout.getOrder();
auto bOrder = bLayout.getOrder();
ValueTable has, hbs;
int mShapePerCTA{-1}, nShapePerCTA{-1};
int mSizePerThread{-1}, nSizePerThread{-1};
ArrayRef<unsigned> aOrder, bOrder;
Value llA, llB;
BlockedEncodingAttr dLayout =
dTensorTy.getEncoding().cast<BlockedEncodingAttr>();
auto order = dLayout.getOrder();
auto cc = getElementsFromStruct(loc, adaptor.c(), rewriter);
bool isARow = aOrder[0] == 1;
bool isBRow = bOrder[0] == 1;
DotOpFMAConversionHelper helper(dLayout);
if (auto aDotOpLayout =
aTensorTy.getEncoding()
.dyn_cast<DotOperandEncodingAttr>()) { // get input from
// convert_layout
auto bDotOpLayout =
bTensorTy.getEncoding().dyn_cast<DotOperandEncodingAttr>();
auto aLayout = aDotOpLayout.getParent().cast<BlockedEncodingAttr>();
auto bLayout = bDotOpLayout.getParent().cast<BlockedEncodingAttr>();
int strideAM = isARow ? aShape[1] : 1;
int strideAK = isARow ? 1 : aShape[0];
int strideBN = isBRow ? 1 : bShape[0];
int strideBK = isBRow ? bShape[1] : 1;
int strideA0 = isARow ? strideAK : strideAM;
int strideA1 = isARow ? strideAM : strideAK;
int strideB0 = isBRow ? strideBN : strideBK;
int strideB1 = isBRow ? strideBK : strideBN;
int lda = isARow ? strideAM : strideAK;
int ldb = isBRow ? strideBK : strideBN;
int aPerPhase = aLayout.getPerPhase();
int aMaxPhase = aLayout.getMaxPhase();
int bPerPhase = bLayout.getPerPhase();
int bMaxPhase = bLayout.getMaxPhase();
int aNumPtr = 8;
int bNumPtr = 8;
int NK = aShape[1];
auto shapePerCTA = getShapePerCTA(dLayout);
assert(bLayout);
llA = adaptor.a();
llB = adaptor.b();
} else if (auto aLayout =
aTensorTy.getEncoding()
.dyn_cast<SharedEncodingAttr>()) { // load input from smem
auto bLayout = bTensorTy.getEncoding().dyn_cast<SharedEncodingAttr>();
assert(bLayout);
Value thread = getThreadId(rewriter, loc);
llA = helper.loadA(A, adaptor.a(), dLayout, thread, loc, rewriter);
llB = helper.loadB(B, adaptor.b(), dLayout, thread, loc, rewriter);
}
auto sizePerThread = getSizePerThread(dLayout);
auto shapePerCTA = getShapePerCTA(dLayout);
Value _0 = i32_val(0);
int K = aShape[1];
int M = aShape[0];
int N = bShape[1];
Value mContig = i32_val(sizePerThread[order[1]]);
Value nContig = i32_val(sizePerThread[order[0]]);
mShapePerCTA = order[0] == 1 ? shapePerCTA[order[1]] : shapePerCTA[order[0]];
mSizePerThread =
order[0] == 1 ? sizePerThread[order[1]] : sizePerThread[order[0]];
nShapePerCTA = order[0] == 0 ? shapePerCTA[order[1]] : shapePerCTA[order[0]];
nSizePerThread =
order[0] == 0 ? sizePerThread[order[1]] : sizePerThread[order[0]];
// threadId in blocked layout
SmallVector<Value> threadIds;
{
int dim = cShape.size();
threadIds.resize(dim);
for (unsigned k = 0; k < dim - 1; k++) {
Value dimK = i32_val(shapePerCTA[order[k]]);
Value rem = urem(threadId, dimK);
threadId = udiv(threadId, dimK);
threadIds[order[k]] = rem;
}
Value dimK = i32_val(shapePerCTA[order[dim - 1]]);
threadIds[order[dim - 1]] = urem(threadId, dimK);
}
has = helper.getValueTableFromStruct(llA, K, M, mShapePerCTA, mSizePerThread,
rewriter, loc);
hbs = helper.getValueTableFromStruct(llB, K, N, nShapePerCTA, nSizePerThread,
rewriter, loc);
Value threadIdM = threadIds[0];
Value threadIdN = threadIds[1];
Value offA0 = isARow ? _0 : mul(threadIdM, mContig);
Value offA1 = isARow ? mul(threadIdM, mContig) : _0;
SmallVector<Value> aOff(aNumPtr);
for (int i = 0; i < aNumPtr; ++i) {
aOff[i] = add(mul(offA0, i32_val(strideA0)), mul(offA1, i32_val(strideA1)));
}
Value offB0 = isBRow ? mul(threadIdN, nContig) : _0;
Value offB1 = isBRow ? _0 : mul(threadIdN, nContig);
SmallVector<Value> bOff(bNumPtr);
for (int i = 0; i < bNumPtr; ++i) {
bOff[i] = add(mul(offB0, i32_val(strideB0)), mul(offB1, i32_val(strideB1)));
}
auto aSmem = getSharedMemoryObjectFromStruct(loc, adaptor.a(), rewriter);
auto bSmem = getSharedMemoryObjectFromStruct(loc, adaptor.b(), rewriter);
Type f32PtrTy = ptr_ty(f32_ty);
SmallVector<Value> aPtrs(aNumPtr);
for (int i = 0; i < aNumPtr; ++i)
aPtrs[i] = gep(f32PtrTy, aSmem.base, aOff[i]);
SmallVector<Value> bPtrs(bNumPtr);
for (int i = 0; i < bNumPtr; ++i)
bPtrs[i] = gep(f32PtrTy, bSmem.base, bOff[i]);
ValueTable has, hbs;
auto cc = getElementsFromStruct(loc, adaptor.c(), rewriter);
SmallVector<Value> ret = cc;
// is this compatible with blocked layout?
for (unsigned k = 0; k < NK; k++) {
for (unsigned k = 0; k < K; k++) {
int z = 0;
for (unsigned i = 0; i < cShape[order[1]]; i += shapePerCTA[order[1]])
for (unsigned j = 0; j < cShape[order[0]]; j += shapePerCTA[order[0]])
for (unsigned ii = 0; ii < sizePerThread[order[1]]; ++ii)
for (unsigned jj = 0; jj < sizePerThread[order[0]]; ++jj) {
unsigned m = order[0] == 1 ? i : j;
unsigned n = order[0] == 1 ? j : i;
unsigned mm = order[0] == 1 ? ii : jj;
unsigned nn = order[0] == 1 ? jj : ii;
if (!has.count({m + mm, k})) {
Value pa = gep(f32PtrTy, aPtrs[0],
i32_val((m + mm) * strideAM + k * strideAK));
Value va = load(pa);
has[{m + mm, k}] = va;
}
if (!hbs.count({n + nn, k})) {
Value pb = gep(f32PtrTy, bPtrs[0],
i32_val((n + nn) * strideBN + k * strideBK));
Value vb = load(pb);
hbs[{n + nn, k}] = vb;
}
for (unsigned m = 0; m < M; m += mShapePerCTA)
for (unsigned n = 0; n < N; n += nShapePerCTA)
for (unsigned mm = 0; mm < mSizePerThread; ++mm)
for (unsigned nn = 0; nn < nSizePerThread; ++nn) {
ret[z] = rewriter.create<LLVM::FMulAddOp>(loc, has[{m + mm, k}],
hbs[{n + nn, k}], ret[z]);
++z;
}
}
@@ -5138,6 +5367,13 @@ public:
auto ctx = type.getContext();
Attribute layout = type.getEncoding();
auto shape = type.getShape();
// TODO[Keren, Superjomn]: fix it, allowTF32 is not accessible here for it
// is bound to an Op instance.
bool allowTF32 = false;
bool isFMADot = type.getElementType().isF32() && !allowTF32 &&
layout.dyn_cast_or_null<DotOperandEncodingAttr>();
if (layout &&
(layout.isa<BlockedEncodingAttr>() || layout.isa<SliceEncodingAttr>() ||
layout.isa<MmaEncodingAttr>())) {
@@ -5158,65 +5394,55 @@ public:
types.push_back(IntegerType::get(ctx, 32));
}
return LLVM::LLVMStructType::getLiteral(ctx, types);
} else if (auto mmaLayout = layout.dyn_cast_or_null<MmaEncodingAttr>()) {
if (mmaLayout.getVersion() == 2) {
auto [repM, repN] = DotOpMmaV2ConversionHelper::getRepMN(type);
size_t fcSize = 4 * repM * repN;
return LLVM::LLVMStructType::getLiteral(
ctx, SmallVector<Type>(fcSize, convertType(type.getElementType())));
}
if (mmaLayout.getVersion() == 1) {
DotOpMmaV1ConversionHelper helper(mmaLayout);
int repM = helper.getRepM(shape[0]);
int repN = helper.getRepN(shape[1]);
int elems = 8 * repM * repN;
return LLVM::LLVMStructType::getLiteral(
ctx, SmallVector<Type>(elems, convertType(type.getElementType())));
}
llvm::errs()
<< "Unexpected mma layout detected in TritonToLLVMTypeConverter";
return llvm::None;
} else if (auto dot_op_layout =
} else if (auto dotOpLayout =
layout.dyn_cast_or_null<DotOperandEncodingAttr>()) {
auto mmaLayout = dot_op_layout.getParent().cast<MmaEncodingAttr>();
auto wpt = mmaLayout.getWarpsPerCTA();
Type elemTy = convertType(type.getElementType());
auto vecSize = 1;
if (elemTy.getIntOrFloatBitWidth() == 16) {
vecSize = 2;
} else if (elemTy.getIntOrFloatBitWidth() == 8) {
vecSize = 4;
} else {
assert(false && "Unsupported element type");
}
Type vecTy = vec_ty(elemTy, vecSize);
if (mmaLayout.getVersion() == 2) {
if (dot_op_layout.getOpIdx() == 0) { // $a
int elems =
MMA16816ConversionHelper::getANumElemsPerThread(type, wpt);
return LLVM::LLVMStructType::getLiteral(
ctx, SmallVector<Type>(elems, vecTy));
}
if (dot_op_layout.getOpIdx() == 1) { // $b
int elems =
MMA16816ConversionHelper::getBNumElemsPerThread(type, wpt);
return struct_ty(SmallVector<Type>(elems, vecTy));
}
}
if (isFMADot) { // for parent is blocked layout
int numElemsPerThread =
DotOpFMAConversionHelper::getNumElemsPerThread(shape, dotOpLayout);
if (mmaLayout.getVersion() == 1) {
DotOpMmaV1ConversionHelper helper(mmaLayout);
return LLVM::LLVMStructType::getLiteral(
ctx, SmallVector<Type>(numElemsPerThread, type::f32Ty(ctx)));
if (dot_op_layout.getOpIdx() == 0) { // $a
int elems = helper.numElemsPerThreadA(type);
return struct_ty(SmallVector<Type>(elems, vecTy));
} else { // for parent is MMA layout
auto mmaLayout = dotOpLayout.getParent().cast<MmaEncodingAttr>();
auto wpt = mmaLayout.getWarpsPerCTA();
Type elemTy = convertType(type.getElementType());
auto vecSize = 1;
if (elemTy.getIntOrFloatBitWidth() == 16) {
vecSize = 2;
} else if (elemTy.getIntOrFloatBitWidth() == 8) {
vecSize = 4;
} else {
assert(false && "Unsupported element type");
}
if (dot_op_layout.getOpIdx() == 1) { // $b
int elems = helper.numElemsPerThreadB(type);
return struct_ty(SmallVector<Type>(elems, vecTy));
Type vecTy = vec_ty(elemTy, vecSize);
if (mmaLayout.getVersion() == 2) {
if (dotOpLayout.getOpIdx() == 0) { // $a
int elems =
MMA16816ConversionHelper::getANumElemsPerThread(type, wpt);
return LLVM::LLVMStructType::getLiteral(
ctx, SmallVector<Type>(elems, vecTy));
}
if (dotOpLayout.getOpIdx() == 1) { // $b
int elems =
MMA16816ConversionHelper::getBNumElemsPerThread(type, wpt);
return struct_ty(SmallVector<Type>(elems, vecTy));
}
}
if (mmaLayout.getVersion() == 1) {
DotOpMmaV1ConversionHelper helper(mmaLayout);
if (dotOpLayout.getOpIdx() == 0) { // $a
int elems = helper.numElemsPerThreadA(type);
Type x2Ty = vec_ty(elemTy, 2);
return struct_ty(SmallVector<Type>(elems, x2Ty));
}
if (dotOpLayout.getOpIdx() == 1) { // $b
int elems = helper.numElemsPerThreadB(type);
Type x2Ty = vec_ty(elemTy, 2);
return struct_ty(SmallVector<Type>(elems, x2Ty));
}
}
}