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

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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
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532
lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVM.cpp
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@@ -457,6 +457,25 @@ struct ConvertTritonGPUOpToLLVMPatternBase {
} }
return results; 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> template <typename SourceOp>
@@ -830,25 +849,6 @@ public:
return base; 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: protected:
const Allocation *allocation; const Allocation *allocation;
Value smem; Value smem;
@@ -3566,7 +3566,8 @@ struct DotOpConversion : public ConvertTritonGPUOpToLLVMPattern<triton::DotOp> {
} }
if (op.getType().cast<RankedTensorType>().getElementType().isF32() && 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); return convertFMADot(op, adaptor, rewriter);
llvm::report_fatal_error( 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( Value ConvertLayoutOpConversion::lowerSharedToDotOperandMMA(
triton::gpu::ConvertLayoutOp op, OpAdaptor adaptor, triton::gpu::ConvertLayoutOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter, const MmaEncodingAttr &mmaLayout, ConversionPatternRewriter &rewriter, const MmaEncodingAttr &mmaLayout,
@@ -4393,14 +4478,15 @@ Value ConvertLayoutOpConversion::lowerSharedToDotOperandMMA(
Value src = op.src(); Value src = op.src();
Value dst = op.result(); Value dst = op.result();
auto dstTensorTy = dst.getType().cast<RankedTensorType>(); auto dstTensorTy = dst.getType().cast<RankedTensorType>();
// TODO[Superjomn]: the allowTF32 is not available in ConvertLayoutOp for it // TODO[Superjomn]: allowTF32 is not accessible here for it is an attribute of
// is an attribute of DotOp. // an Op instance.
bool allowTF32 = false; bool allowTF32 = false;
bool isHMMA = DotOpConversion::isDotHMMA(dstTensorTy, allowTF32, bool isHMMA = DotOpConversion::isDotHMMA(dstTensorTy, allowTF32,
mmaLayout.getVersion()); mmaLayout.getVersion());
auto smemObj = getSharedMemoryObjectFromStruct(loc, adaptor.src(), rewriter); auto smemObj = getSharedMemoryObjectFromStruct(loc, adaptor.src(), rewriter);
Value res; Value res;
if (!isOuter && mmaLayout.getVersion() == 2 && isHMMA) { // tensor core v2 if (!isOuter && mmaLayout.getVersion() == 2 && isHMMA) { // tensor core v2
MMA16816ConversionHelper mmaHelper(mmaLayout, getThreadId(rewriter, loc), MMA16816ConversionHelper mmaHelper(mmaLayout, getThreadId(rewriter, loc),
rewriter, getTypeConverter(), rewriter, getTypeConverter(),
@@ -4459,7 +4545,25 @@ LogicalResult ConvertLayoutOpConversion::lowerSharedToDotOperand(
} else if (auto blockedLayout = } else if (auto blockedLayout =
dotOperandLayout.getParent() dotOperandLayout.getParent()
.dyn_cast_or_null<BlockedEncodingAttr>()) { .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 { } else {
assert(false && "Unsupported dot operand layout found"); assert(false && "Unsupported dot operand layout found");
} }
@@ -4925,6 +5029,183 @@ DotOpMmaV1ConversionHelper::extractLoadedOperand(
return rcds; 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 LogicalResult
DotOpConversion::convertFMADot(triton::DotOp op, OpAdaptor adaptor, DotOpConversion::convertFMADot(triton::DotOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const { ConversionPatternRewriter &rewriter) const {
@@ -4948,120 +5229,68 @@ DotOpConversion::convertFMADot(triton::DotOp op, OpAdaptor adaptor,
auto bShape = bTensorTy.getShape(); auto bShape = bTensorTy.getShape();
auto cShape = cTensorTy.getShape(); auto cShape = cTensorTy.getShape();
auto aLayout = aTensorTy.getEncoding().cast<SharedEncodingAttr>(); ValueTable has, hbs;
auto bLayout = bTensorTy.getEncoding().cast<SharedEncodingAttr>(); int mShapePerCTA{-1}, nShapePerCTA{-1};
auto cLayout = cTensorTy.getEncoding().cast<BlockedEncodingAttr>(); int mSizePerThread{-1}, nSizePerThread{-1};
auto dLayout = dTensorTy.getEncoding().cast<BlockedEncodingAttr>(); ArrayRef<unsigned> aOrder, bOrder;
Value llA, llB;
auto aOrder = aLayout.getOrder(); BlockedEncodingAttr dLayout =
auto bOrder = bLayout.getOrder(); dTensorTy.getEncoding().cast<BlockedEncodingAttr>();
auto order = dLayout.getOrder(); auto order = dLayout.getOrder();
auto cc = getElementsFromStruct(loc, adaptor.c(), rewriter);
bool isARow = aOrder[0] == 1; DotOpFMAConversionHelper helper(dLayout);
bool isBRow = bOrder[0] == 1; 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; assert(bLayout);
int strideAK = isARow ? 1 : aShape[0]; llA = adaptor.a();
int strideBN = isBRow ? 1 : bShape[0]; llB = adaptor.b();
int strideBK = isBRow ? bShape[1] : 1; } else if (auto aLayout =
int strideA0 = isARow ? strideAK : strideAM; aTensorTy.getEncoding()
int strideA1 = isARow ? strideAM : strideAK; .dyn_cast<SharedEncodingAttr>()) { // load input from smem
int strideB0 = isBRow ? strideBN : strideBK; auto bLayout = bTensorTy.getEncoding().dyn_cast<SharedEncodingAttr>();
int strideB1 = isBRow ? strideBK : strideBN; assert(bLayout);
int lda = isARow ? strideAM : strideAK; Value thread = getThreadId(rewriter, loc);
int ldb = isBRow ? strideBK : strideBN; llA = helper.loadA(A, adaptor.a(), dLayout, thread, loc, rewriter);
int aPerPhase = aLayout.getPerPhase(); llB = helper.loadB(B, adaptor.b(), dLayout, thread, loc, rewriter);
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);
auto sizePerThread = getSizePerThread(dLayout); 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]]); mShapePerCTA = order[0] == 1 ? shapePerCTA[order[1]] : shapePerCTA[order[0]];
Value nContig = i32_val(sizePerThread[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 has = helper.getValueTableFromStruct(llA, K, M, mShapePerCTA, mSizePerThread,
SmallVector<Value> threadIds; rewriter, loc);
{ hbs = helper.getValueTableFromStruct(llB, K, N, nShapePerCTA, nSizePerThread,
int dim = cShape.size(); rewriter, loc);
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);
}
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; SmallVector<Value> ret = cc;
// is this compatible with blocked layout? for (unsigned k = 0; k < K; k++) {
for (unsigned k = 0; k < NK; k++) {
int z = 0; int z = 0;
for (unsigned i = 0; i < cShape[order[1]]; i += shapePerCTA[order[1]]) for (unsigned m = 0; m < M; m += mShapePerCTA)
for (unsigned j = 0; j < cShape[order[0]]; j += shapePerCTA[order[0]]) for (unsigned n = 0; n < N; n += nShapePerCTA)
for (unsigned ii = 0; ii < sizePerThread[order[1]]; ++ii) for (unsigned mm = 0; mm < mSizePerThread; ++mm)
for (unsigned jj = 0; jj < sizePerThread[order[0]]; ++jj) { for (unsigned nn = 0; nn < nSizePerThread; ++nn) {
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;
}
ret[z] = rewriter.create<LLVM::FMulAddOp>(loc, has[{m + mm, k}], ret[z] = rewriter.create<LLVM::FMulAddOp>(loc, has[{m + mm, k}],
hbs[{n + nn, k}], ret[z]); hbs[{n + nn, k}], ret[z]);
++z; ++z;
} }
} }
@@ -5138,6 +5367,13 @@ public:
auto ctx = type.getContext(); auto ctx = type.getContext();
Attribute layout = type.getEncoding(); Attribute layout = type.getEncoding();
auto shape = type.getShape(); 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 && if (layout &&
(layout.isa<BlockedEncodingAttr>() || layout.isa<SliceEncodingAttr>() || (layout.isa<BlockedEncodingAttr>() || layout.isa<SliceEncodingAttr>() ||
layout.isa<MmaEncodingAttr>())) { layout.isa<MmaEncodingAttr>())) {
@@ -5158,30 +5394,17 @@ public:
types.push_back(IntegerType::get(ctx, 32)); types.push_back(IntegerType::get(ctx, 32));
} }
return LLVM::LLVMStructType::getLiteral(ctx, types); return LLVM::LLVMStructType::getLiteral(ctx, types);
} else if (auto mmaLayout = layout.dyn_cast_or_null<MmaEncodingAttr>()) { } else if (auto dotOpLayout =
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 =
layout.dyn_cast_or_null<DotOperandEncodingAttr>()) { layout.dyn_cast_or_null<DotOperandEncodingAttr>()) {
auto mmaLayout = dot_op_layout.getParent().cast<MmaEncodingAttr>(); if (isFMADot) { // for parent is blocked layout
int numElemsPerThread =
DotOpFMAConversionHelper::getNumElemsPerThread(shape, dotOpLayout);
return LLVM::LLVMStructType::getLiteral(
ctx, SmallVector<Type>(numElemsPerThread, type::f32Ty(ctx)));
} else { // for parent is MMA layout
auto mmaLayout = dotOpLayout.getParent().cast<MmaEncodingAttr>();
auto wpt = mmaLayout.getWarpsPerCTA(); auto wpt = mmaLayout.getWarpsPerCTA();
Type elemTy = convertType(type.getElementType()); Type elemTy = convertType(type.getElementType());
auto vecSize = 1; auto vecSize = 1;
@@ -5194,13 +5417,13 @@ public:
} }
Type vecTy = vec_ty(elemTy, vecSize); Type vecTy = vec_ty(elemTy, vecSize);
if (mmaLayout.getVersion() == 2) { if (mmaLayout.getVersion() == 2) {
if (dot_op_layout.getOpIdx() == 0) { // $a if (dotOpLayout.getOpIdx() == 0) { // $a
int elems = int elems =
MMA16816ConversionHelper::getANumElemsPerThread(type, wpt); MMA16816ConversionHelper::getANumElemsPerThread(type, wpt);
return LLVM::LLVMStructType::getLiteral( return LLVM::LLVMStructType::getLiteral(
ctx, SmallVector<Type>(elems, vecTy)); ctx, SmallVector<Type>(elems, vecTy));
} }
if (dot_op_layout.getOpIdx() == 1) { // $b if (dotOpLayout.getOpIdx() == 1) { // $b
int elems = int elems =
MMA16816ConversionHelper::getBNumElemsPerThread(type, wpt); MMA16816ConversionHelper::getBNumElemsPerThread(type, wpt);
return struct_ty(SmallVector<Type>(elems, vecTy)); return struct_ty(SmallVector<Type>(elems, vecTy));
@@ -5210,13 +5433,16 @@ public:
if (mmaLayout.getVersion() == 1) { if (mmaLayout.getVersion() == 1) {
DotOpMmaV1ConversionHelper helper(mmaLayout); DotOpMmaV1ConversionHelper helper(mmaLayout);
if (dot_op_layout.getOpIdx() == 0) { // $a if (dotOpLayout.getOpIdx() == 0) { // $a
int elems = helper.numElemsPerThreadA(type); int elems = helper.numElemsPerThreadA(type);
return struct_ty(SmallVector<Type>(elems, vecTy)); Type x2Ty = vec_ty(elemTy, 2);
return struct_ty(SmallVector<Type>(elems, x2Ty));
} }
if (dot_op_layout.getOpIdx() == 1) { // $b if (dotOpLayout.getOpIdx() == 1) { // $b
int elems = helper.numElemsPerThreadB(type); int elems = helper.numElemsPerThreadB(type);
return struct_ty(SmallVector<Type>(elems, vecTy)); Type x2Ty = vec_ty(elemTy, 2);
return struct_ty(SmallVector<Type>(elems, x2Ty));
}
} }
} }

6
lib/Dialect/TritonGPU/IR/Dialect.cpp
View File

@@ -64,8 +64,7 @@ unsigned getElemsPerThread(Attribute layout, ArrayRef<int64_t> shape) {
} }
unsigned getElemsPerThread(Type type) { unsigned getElemsPerThread(Type type) {
if (type.isIntOrIndexOrFloat() || if (type.isIntOrIndexOrFloat() || type.isa<triton::Float8Type>() ||
type.isa<triton::Float8Type>() ||
type.isa<triton::PointerType>()) type.isa<triton::PointerType>())
return 1; return 1;
auto tensorType = type.cast<RankedTensorType>(); auto tensorType = type.cast<RankedTensorType>();
@@ -372,6 +371,9 @@ unsigned SharedEncodingAttr::getElemsPerThread(ArrayRef<int64_t> shape) const {
unsigned unsigned
DotOperandEncodingAttr::getElemsPerThread(ArrayRef<int64_t> shape) const { DotOperandEncodingAttr::getElemsPerThread(ArrayRef<int64_t> shape) const {
if (auto blockedLayout = getParent().dyn_cast<BlockedEncodingAttr>()) {
return blockedLayout.getElemsPerThread(shape);
}
assert(0 && "DotOperandEncodingAttr::getElemsPerThread not implemented"); assert(0 && "DotOperandEncodingAttr::getElemsPerThread not implemented");
return 0; return 0;
} }

121
python/tests/test_gemm.py
View File

@@ -171,65 +171,64 @@ def test_gemm(SIZE_M, SIZE_N, SIZE_K, NUM_WARPS, BLOCK_SIZE_M, BLOCK_SIZE_N, BLO
assert_close(c, golden, rtol=max(1e-4, 1.5 * golden_rel_err), atol=max(1e-4, 1.5 * golden_abs_err), check_dtype=False) assert_close(c, golden, rtol=max(1e-4, 1.5 * golden_rel_err), atol=max(1e-4, 1.5 * golden_abs_err), check_dtype=False)
# XXX(Keren): Temporarily disable this test until we have shared -> dot conversion implemented @pytest.mark.parametrize('M,N,K,num_warps,block_M,block_N,block_K', [
#@pytest.mark.parametrize('M,N,K,num_warps,block_M,block_N,block_K', [ [32, 32, 16, 4, 32, 32, 16],
# [32, 32, 16, 4, 32, 32, 16], [32, 16, 16, 4, 32, 32, 16],
# [32, 16, 16, 4, 32, 32, 16], [128, 8, 8, 4, 32, 32, 16],
# [128, 8, 8, 4, 32, 32, 16], # TODO[Superjomn]: fix it later
# [127, 41, 43, 4, 32, 32, 16], # [127, 41, 43, 4, 32, 32, 16],
#]) ])
#def test_gemm_fmadot(M, N, K, num_warps, block_M, block_N, block_K): def test_gemm_fmadot(M, N, K, num_warps, block_M, block_N, block_K):
# @triton.jit @triton.jit
# def matmul_kernel( def matmul_kernel(
# a_ptr, b_ptr, c_ptr, a_ptr, b_ptr, c_ptr,
# M, N, K, M, N, K,
# stride_am, stride_ak, stride_am, stride_ak,
# stride_bk, stride_bn, stride_bk, stride_bn,
# stride_cm, stride_cn, stride_cm, stride_cn,
# BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
# ): ):
# pid = tl.program_id(axis=0) pid = tl.program_id(axis=0)
# # num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) # num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
# num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
# pid_m = pid // num_pid_n pid_m = pid // num_pid_n
# pid_n = pid % num_pid_n pid_n = pid % num_pid_n
#
# offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
# offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
# offs_k = tl.arange(0, BLOCK_SIZE_K) offs_k = tl.arange(0, BLOCK_SIZE_K)
# a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
# b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn) b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
#
# accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
# for k in range(0, K, BLOCK_SIZE_K): for k in range(0, K, BLOCK_SIZE_K):
# a_mask = (offs_am[:, None] < M) & (offs_k[None, :] < K) a_mask = (offs_am[:, None] < M) & (offs_k[None, :] < K)
# b_mask = (offs_k[:, None] < K) & (offs_bn[None, :] < N) b_mask = (offs_k[:, None] < K) & (offs_bn[None, :] < N)
# a = tl.load(a_ptrs, a_mask) a = tl.load(a_ptrs, a_mask)
# b = tl.load(b_ptrs, b_mask) b = tl.load(b_ptrs, b_mask)
# # NOTE the allow_tf32 should be false to force the dot op to do fmadot lowering # NOTE the allow_tf32 should be false to force the dot op to do fmadot lowering
# accumulator += tl.dot(a, b, allow_tf32=False) accumulator += tl.dot(a, b, allow_tf32=False)
# a_ptrs += BLOCK_SIZE_K * stride_ak a_ptrs += BLOCK_SIZE_K * stride_ak
# b_ptrs += BLOCK_SIZE_K * stride_bk b_ptrs += BLOCK_SIZE_K * stride_bk
# offs_k += BLOCK_SIZE_K offs_k += BLOCK_SIZE_K
#
# offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
# offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
# c_ptrs = c_ptr + offs_cm[:, None] * stride_cm + offs_cn[None, :] * stride_cn c_ptrs = c_ptr + offs_cm[:, None] * stride_cm + offs_cn[None, :] * stride_cn
# c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N) c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
# tl.store(c_ptrs, accumulator, c_mask) tl.store(c_ptrs, accumulator, c_mask)
#
# a = torch.randn((M, K), device='cuda', dtype=torch.float32) a = torch.randn((M, K), device='cuda', dtype=torch.float32)
# b = torch.randn((K, N), device='cuda', dtype=torch.float32) b = torch.randn((K, N), device='cuda', dtype=torch.float32)
# c = torch.empty((M, N), device=a.device, dtype=torch.float32) c = torch.empty((M, N), device=a.device, dtype=torch.float32)
#
# grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),) grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),)
# matmul_kernel[grid](a, b, c, matmul_kernel[grid](a, b, c,
# M, N, K, M, N, K,
# stride_am=a.stride(0), stride_ak=a.stride(1), stride_am=a.stride(0), stride_ak=a.stride(1),
# stride_bk=b.stride(0), stride_bn=b.stride(1), stride_bk=b.stride(0), stride_bn=b.stride(1),
# stride_cm=c.stride(0), stride_cn=c.stride(1), stride_cm=c.stride(0), stride_cn=c.stride(1),
# BLOCK_SIZE_M=block_M, BLOCK_SIZE_N=block_N, BLOCK_SIZE_K=block_K) BLOCK_SIZE_M=block_M, BLOCK_SIZE_N=block_N, BLOCK_SIZE_K=block_K)
#
# golden = torch.matmul(a, b) golden = torch.matmul(a, b)
# torch.testing.assert_close(c, golden) torch.testing.assert_close(c, golden)
#

20
test/Conversion/tritongpu_to_llvm.mlir
View File

@@ -820,18 +820,20 @@ module attributes {"triton_gpu.num-warps" = 4 : i32} {
#blocked = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [2, 16], warpsPerCTA = [1, 4], order = [1, 0]}> #blocked = #triton_gpu.blocked<{sizePerThread = [1, 4], threadsPerWarp = [2, 16], warpsPerCTA = [1, 4], order = [1, 0]}>
#shared = #triton_gpu.shared<{vec = 1, perPhase = 1, maxPhase = 1, order = [1, 0]}> #shared = #triton_gpu.shared<{vec = 1, perPhase = 1, maxPhase = 1, order = [1, 0]}>
#mma = #triton_gpu.mma<{version = 2, warpsPerCTA = [2, 2]}> #dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#blocked}>
#dot_operand_a = #triton_gpu.dot_op<{opIdx=0, parent=#mma}> #dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#blocked}>
#dot_operand_b = #triton_gpu.dot_op<{opIdx=1, parent=#mma}>
module attributes {"triton_gpu.num-warps" = 4 : i32} { module attributes {"triton_gpu.num-warps" = 4 : i32} {
func @matmul_fmadot(%ptr:!tt.ptr<f32> {tt.divisibility = 16 : i32}, func @matmul_fmadot(%ptr:!tt.ptr<f32> {tt.divisibility = 16 : i32},
%a:tensor<32x16xf32, #shared>, %b:tensor<16x32xf32, #shared>) { %a:tensor<32x16xf32, #shared>, %b:tensor<16x32xf32, #shared>) {
// We are going to completely depracate using shared layout for operands of dot %cst = arith.constant dense<0.000000e+00> : tensor<32x32xf32, #blocked>
//%cst = arith.constant dense<0.000000e+00> : tensor<32x32xf32, #blocked> // CHECK: llvm.intr.fmuladd
//%28 = tt.dot %a, %b, %cst {allowTF32 = false, transA = false, transB = false} : tensor<32x16xf32, #shared> * tensor<16x32xf32, #shared> -> tensor<32x32xf32, #blocked> %a_mat = triton_gpu.convert_layout %a : (tensor<32x16xf32, #shared>) -> tensor<32x16xf32, #dot_operand_a>
//%30 = tt.splat %ptr : (!tt.ptr<f32>) -> tensor<32x1x!tt.ptr<f32>, #blocked> %b_mat = triton_gpu.convert_layout %b : (tensor<16x32xf32, #shared>) -> tensor<16x32xf32, #dot_operand_b>
//%36 = tt.broadcast %30 : (tensor<32x1x!tt.ptr<f32>, #blocked>) -> tensor<32x32x!tt.ptr<f32>, #blocked>
//tt.store %36, %28 : tensor<32x32xf32, #blocked> %28 = tt.dot %a_mat, %b_mat, %cst {allowTF32 = false, transA = false, transB = false} : tensor<32x16xf32, #dot_operand_a> * tensor<16x32xf32, #dot_operand_b> -> tensor<32x32xf32, #blocked>
%30 = tt.splat %ptr : (!tt.ptr<f32>) -> tensor<32x1x!tt.ptr<f32>, #blocked>
%36 = tt.broadcast %30 : (tensor<32x1x!tt.ptr<f32>, #blocked>) -> tensor<32x32x!tt.ptr<f32>, #blocked>
tt.store %36, %28 : tensor<32x32xf32, #blocked>
return return
} }
} }