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[Triton-MLIR][BACKEND] Fix masked load store op vector size (#785)

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Correct the Load/Store Op's vector size with the mask's alignment
correctly considered.

Some cases:

```mlir
// num_warp = 2
// block_size = 128
func @vecadd_mask_align_16(%a_ptr: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %b_ptr: !tt.ptr<f32> {tt.divisibility = 16 : i32}, 
  %out_ptr: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %n_elements: i32 {tt.divisibility = 16 : i32}) {
    // mask = make_range(128) < n_element
}
```
This should get the vec=2 `ld`/`st` instructions.

While the following example

```mlir
// num_warp = 2
// block_size = 128
func @vecadd_mask_align_16(%a_ptr: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %b_ptr: !tt.ptr<f32> {tt.divisibility = 16 : i32}, 
  %out_ptr: !tt.ptr<f32> {tt.divisibility = 16 : i32}, %n_elements: i32) {
    // mask = make_range(128) < n_element
}
```
it should get the vec=1 `ld`/`st` instructions.
This commit is contained in:
Yan Chunwei
2022-10-18 11:43:50 +08:00
committed by GitHub
parent 38a80664b5
commit 4464646efb
6 changed files with 383 additions and 227 deletions
Download Patch File Download Diff File Expand all files Collapse all files

30
lib/Analysis/AxisInfo.cpp
View File

@@ -209,6 +209,33 @@ ChangeResult AxisInfoAnalysis::visitOperation(
}
curr = AxisInfo(contiguity, divisibility, constancy);
}
// CmpI
if ((llvm::dyn_cast<arith::CmpIOp>(op) ||
llvm::dyn_cast<triton::gpu::CmpIOp>(op)) &&
op->getResult(0).getType().dyn_cast<TensorType>()) {
auto resTy = op->getResult(0).getType().cast<TensorType>();
short rank = resTy.getRank();
auto lhsInfo = operands[0]->getValue();
auto rhsInfo = operands[1]->getValue();
auto shape = resTy.getShape();
AxisInfo::DimVectorT contiguity, divisibility, constancy;
for (short d = 0; d < rank; ++d) {
if (rhsInfo.getConstancy(d) % lhsInfo.getContiguity(d) == 0 ||
rhsInfo.getConstancy(d) % lhsInfo.getConstancy(d))
constancy.push_back(
gcd(lhsInfo.getDivisibility(d), rhsInfo.getDivisibility(d)));
else
constancy.push_back(1);
divisibility.push_back(shape[d]);
contiguity.push_back(1);
}
curr = AxisInfo(contiguity, divisibility, constancy);
}
// UnrealizedConversionCast
// This is needed by TritonGPUToLLVM, to get AxisInfo when the graph is
// in the process of a PartialConversion, where UnrealizedConversionCast
@@ -219,7 +246,8 @@ ChangeResult AxisInfoAnalysis::visitOperation(
if (curr.getRank() == 0) {
return markAllPessimisticFixpoint(op->getResults());
}
// join all latice elements
// join all lattice elements
ChangeResult result = ChangeResult::NoChange;
for (Value value : op->getResults()) {
result |= getLatticeElement(value).join(curr);

447
lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVM.cpp
View File

@@ -759,6 +759,17 @@ struct LoadStoreConversionBase : public ConvertTritonGPUOpToLLVMPatternBase {
return vec;
}
unsigned getMaskAlignment(Value mask) const {
auto maskOrder = mask.getType()
.cast<RankedTensorType>()
.getEncoding()
.cast<BlockedEncodingAttr>()
.getOrder();
auto maskAxis = getAxisInfo(mask);
return std::max<int>(maskAxis->getConstancy(maskOrder[0]), 1);
}
llvm::Optional<AxisInfo> getAxisInfo(Value val) const {
if (auto it = AxisAnalysisPass.lookupLatticeElement(val)) {
return it->getValue();
@@ -771,6 +782,208 @@ protected:
AxisInfoAnalysis &AxisAnalysisPass;
};
struct LoadOpConversion
: public ConvertTritonGPUOpToLLVMPattern<triton::LoadOp>,
public LoadStoreConversionBase {
using ConvertTritonGPUOpToLLVMPattern<
triton::LoadOp>::ConvertTritonGPUOpToLLVMPattern;
LoadOpConversion(LLVMTypeConverter &converter,
AxisInfoAnalysis &axisAnalysisPass, PatternBenefit benefit)
: ConvertTritonGPUOpToLLVMPattern<triton::LoadOp>(converter, benefit),
LoadStoreConversionBase(axisAnalysisPass) {}
LogicalResult
matchAndRewrite(triton::LoadOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value ptr = op.ptr();
Value mask = op.mask();
Value other = op.other();
Value llPtr = adaptor.ptr();
Value llMask = adaptor.mask();
Value llOther = adaptor.other();
auto loc = op->getLoc();
MLIRContext *ctx = rewriter.getContext();
auto valueTy = op.getResult().getType().dyn_cast<RankedTensorType>();
if (!valueTy)
return failure();
Type valueElemTy =
getTypeConverter()->convertType(valueTy.getElementType());
auto [layout, numElems] = getLayout(ptr);
auto ptrElems = getLLVMElems(ptr, llPtr, layout, rewriter, loc);
assert(ptrElems.size() == numElems);
// Determine the vectorization size
size_t vec = getVectorizeSize(ptr, layout);
SmallVector<Value> maskElems;
if (llMask) {
unsigned maskAlignment = getMaskAlignment(mask);
maskElems = getLLVMElems(mask, llMask, layout, rewriter, loc);
assert(ptrElems.size() == maskElems.size());
size_t maskAlign = getMaskAlignment(mask);
vec = std::min(vec, maskAlign);
}
const size_t dtsize =
std::max<int>(1, valueElemTy.getIntOrFloatBitWidth() / 8);
const size_t valueElemNbits = dtsize * 8;
const int numVecs = numElems / vec;
// TODO: (goostavz) handle when other is const but not splat, which
// should be rarely seen
bool otherIsSplatConstInt = false;
DenseElementsAttr constAttr;
int64_t splatVal = 0;
if (valueElemTy.isa<IntegerType>() &&
matchPattern(op.other(), m_Constant(&constAttr)) &&
constAttr.isSplat()) {
otherIsSplatConstInt = true;
splatVal = constAttr.getSplatValue<APInt>().getSExtValue();
}
auto otherElems = getLLVMElems(other, llOther, layout, rewriter, loc);
SmallVector<Value> loadedVals;
for (size_t vecStart = 0; vecStart < numElems; vecStart += vec) {
// TODO: optimization when ptr is GEP with constant offset
size_t in_off = 0;
const int maxWordWidth = std::max<int>(32, valueElemNbits);
const int totalWidth = valueElemNbits * vec;
const int width = std::min(totalWidth, maxWordWidth);
const int nWords = std::max(1, totalWidth / width);
const int wordNElems = width / valueElemNbits;
const int vecNElems = totalWidth / valueElemNbits;
assert(wordNElems * nWords * numVecs == numElems);
// TODO(Superjomn) Add cache policy fields to StoreOp.
// TODO(Superjomn) Deal with cache policy here.
const bool hasL2EvictPolicy = false;
PTXBuilder ptxBuilder;
auto &ld = *ptxBuilder.create<PTXIOInstr>("ld");
Value pred = mask ? maskElems[vecStart] : int_val(1, 1);
const std::string readConstraint =
(width == 64) ? "l" : ((width == 32) ? "r" : "c");
const std::string writeConstraint =
(width == 64) ? "=l" : ((width == 32) ? "=r" : "=c");
// prepare asm operands
auto *dstsOpr = ptxBuilder.newListOperand();
for (int wordIdx = 0; wordIdx < nWords; ++wordIdx) {
auto *opr = ptxBuilder.newOperand(writeConstraint); // =r operations
dstsOpr->listAppend(opr);
}
auto *addrOpr =
ptxBuilder.newAddrOperand(ptrElems[vecStart], "l", in_off);
// Define the instruction opcode
ld.o("volatile", op.isVolatile())
.global()
.o("ca", op.cache() == triton::CacheModifier::CA)
.o("cg", op.cache() == triton::CacheModifier::CG)
.o("L1::evict_first",
op.evict() == triton::EvictionPolicy::EVICT_FIRST)
.o("L1::evict_last", op.evict() == triton::EvictionPolicy::EVICT_LAST)
.o("L1::cache_hint", hasL2EvictPolicy)
.v(nWords)
.b(width);
PTXBuilder::Operand *evictOpr{};
// Here lack a mlir::Value to bind to this operation, so disabled.
// if (has_l2_evict_policy)
// evictOpr = ptxBuilder.newOperand(l2Evict, "l");
if (!evictOpr)
ld(dstsOpr, addrOpr).predicate(pred, "b");
else
ld(dstsOpr, addrOpr, evictOpr).predicate(pred, "b");
if (other) {
for (size_t ii = 0; ii < nWords; ++ii) {
PTXInstr &mov = *ptxBuilder.create<>("mov");
mov.o("u", width);
size_t size = width / valueElemNbits;
auto vecTy = LLVM::getFixedVectorType(valueElemTy, size);
Value v = rewriter.create<LLVM::UndefOp>(loc, vecTy);
for (size_t s = 0; s < size; ++s) {
Value falseVal = otherElems[vecStart + ii * size + s];
Value sVal = createIndexAttrConstant(
rewriter, loc, this->getTypeConverter()->getIndexType(), s);
v = insert_element(vecTy, v, falseVal, sVal);
}
v = bitcast(IntegerType::get(getContext(), width), v);
PTXInstr::Operand *opr{};
if (otherIsSplatConstInt)
opr = ptxBuilder.newConstantOperand(splatVal);
else
opr = ptxBuilder.newOperand(v, readConstraint);
mov(dstsOpr->listGet(ii), opr).predicateNot(pred, "b");
}
}
// ---
// create inline ASM signature
// ---
SmallVector<Type> retTys(nWords, IntegerType::get(getContext(), width));
Type retTy = retTys.size() > 1
? LLVM::LLVMStructType::getLiteral(getContext(), retTys)
: retTys[0];
// TODO: if (has_l2_evict_policy)
auto asmDialectAttr = LLVM::AsmDialectAttr::get(rewriter.getContext(),
LLVM::AsmDialect::AD_ATT);
Value ret = ptxBuilder.launch(rewriter, loc, retTy);
// ---
// extract and store return values
// ---
SmallVector<Value> rets;
for (unsigned int ii = 0; ii < nWords; ++ii) {
Value curr;
if (retTy.isa<LLVM::LLVMStructType>()) {
curr = extract_val(IntegerType::get(getContext(), width), ret,
rewriter.getI64ArrayAttr(ii));
} else {
curr = ret;
}
curr = bitcast(
LLVM::getFixedVectorType(valueElemTy, width / valueElemNbits),
curr);
rets.push_back(curr);
}
int tmp = width / valueElemNbits;
for (size_t ii = 0; ii < vec; ++ii) {
Value vecIdx = createIndexAttrConstant(
rewriter, loc, this->getTypeConverter()->getIndexType(), ii % tmp);
Value loaded = extract_element(valueElemTy, rets[ii / tmp], vecIdx);
loadedVals.push_back(loaded);
}
} // end vec
Type llvmResultStructTy = getTypeConverter()->convertType(valueTy);
Value resultStruct =
getStructFromElements(loc, loadedVals, rewriter, llvmResultStructTy);
rewriter.replaceOp(op, {resultStruct});
return success();
}
};
struct StoreOpConversion
: public ConvertTritonGPUOpToLLVMPattern<triton::StoreOp>,
public LoadStoreConversionBase {
@@ -814,14 +1027,8 @@ struct StoreOpConversion
if (llMask) {
maskElems = getLLVMElems(mask, llMask, layout, rewriter, loc);
assert(valueElems.size() == maskElems.size());
auto maskOrder = mask.getType()
.cast<RankedTensorType>()
.getEncoding()
.cast<BlockedEncodingAttr>()
.getOrder();
auto maskAxis = getAxisInfo(mask);
size_t maskAlign = std::max<int>(maskAxis->getConstancy(maskOrder[0]), 1);
size_t maskAlign = getMaskAlignment(mask);
vec = std::min(vec, maskAlign);
}
@@ -846,15 +1053,10 @@ struct StoreOpConversion
// TODO(Superjomn) Deal with cache policy here.
const bool hasL2EvictPolicy = false;
PTXBuilder ptxBuilder;
auto &ptxStoreInstr = *ptxBuilder.create<PTXIOInstr>("st");
llvm::SmallVector<std::string> asmArgs;
Type valArgTy = IntegerType::get(ctx, width);
auto wordTy = vec_ty(valueElemTy, wordNElems);
auto *asmArgList = ptxBuilder.newListOperand();
SmallVector<std::pair<Value, std::string>> asmArgs;
for (int wordIdx = 0; wordIdx < nWords; ++wordIdx) {
// llWord is a width-len composition
Value llWord = rewriter.create<LLVM::UndefOp>(loc, wordTy);
@@ -876,23 +1078,25 @@ struct StoreOpConversion
llWord = bitcast(valArgTy, llWord);
std::string constraint =
(width == 64) ? "l" : ((width == 32) ? "r" : "c");
asmArgList->listAppend(ptxBuilder.newOperand(llWord, constraint));
asmArgs.emplace_back(llWord, constraint);
}
// TODO(Superjomn) Need to check masks before vectorize the load for
// the values share one predicate? Here assume all the mask values are
// the same.
// Prepare the PTX inline asm.
PTXBuilder ptxBuilder;
auto *asmArgList = ptxBuilder.newListOperand(asmArgs);
Value maskVal = llMask ? maskElems[vecStart] : int_val(1, 1);
ptxStoreInstr.global().b(width).v(nWords);
auto *asmAddr =
ptxBuilder.newAddrOperand(ptrElems[vecStart], "l", in_off);
auto &ptxStoreInstr =
ptxBuilder.create<PTXIOInstr>("st")->global().b(width).v(nWords);
ptxStoreInstr(asmAddr, asmArgList).predicate(maskVal, "b");
Type boolTy = getTypeConverter()->convertType(rewriter.getIntegerType(1));
llvm::SmallVector<Type> argTys({boolTy, ptr.getType()});
for (int i = 0; i < nWords; ++i)
argTys.push_back(valArgTy);
argTys.insert(argTys.end(), nWords, valArgTy);
auto ASMReturnTy = LLVM::LLVMVoidType::get(ctx);
@@ -1065,209 +1269,6 @@ struct MakeRangeOpConversion
}
};
struct LoadOpConversion
: public ConvertTritonGPUOpToLLVMPattern<triton::LoadOp>,
public LoadStoreConversionBase {
using ConvertTritonGPUOpToLLVMPattern<
triton::LoadOp>::ConvertTritonGPUOpToLLVMPattern;
LoadOpConversion(LLVMTypeConverter &converter,
AxisInfoAnalysis &axisAnalysisPass, PatternBenefit benefit)
: ConvertTritonGPUOpToLLVMPattern<triton::LoadOp>(converter, benefit),
LoadStoreConversionBase(axisAnalysisPass) {}
LogicalResult
matchAndRewrite(triton::LoadOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value ptr = op.ptr();
Value mask = op.mask();
Value other = op.other();
Value llPtr = adaptor.ptr();
Value llMask = adaptor.mask();
Value llOther = adaptor.other();
auto loc = op->getLoc();
MLIRContext *ctx = rewriter.getContext();
auto valueTy = op.getResult().getType().dyn_cast<RankedTensorType>();
if (!valueTy)
return failure();
Type valueElemTy =
getTypeConverter()->convertType(valueTy.getElementType());
auto [layout, numElems] = getLayout(ptr);
auto ptrElems = getLLVMElems(ptr, llPtr, layout, rewriter, loc);
assert(ptrElems.size() == numElems);
SmallVector<Value> maskElems;
if (llMask) {
maskElems = getLLVMElems(mask, llMask, layout, rewriter, loc);
assert(ptrElems.size() == maskElems.size());
}
// Determine the vectorization size
size_t vec = getVectorizeSize(ptr, layout);
const size_t dtsize =
std::max<int>(1, valueElemTy.getIntOrFloatBitWidth() / 8);
const size_t valueElemNbits = dtsize * 8;
const int numVecs = numElems / vec;
// TODO: (goostavz) handle when other is const but not splat, which
// should be rarely seen
bool otherIsSplatConstInt = false;
DenseElementsAttr constAttr;
int64_t splatVal = 0;
if (valueElemTy.isa<IntegerType>() &&
matchPattern(op.other(), m_Constant(&constAttr)) &&
constAttr.isSplat()) {
otherIsSplatConstInt = true;
splatVal = constAttr.getSplatValue<APInt>().getSExtValue();
}
auto otherElems = getLLVMElems(other, llOther, layout, rewriter, loc);
SmallVector<Value> loadedVals;
for (size_t vecStart = 0; vecStart < numElems; vecStart += vec) {
// TODO: optimization when ptr is GEP with constant offset
size_t in_off = 0;
const int maxWordWidth = std::max<int>(32, valueElemNbits);
const int totalWidth = valueElemNbits * vec;
const int width = std::min(totalWidth, maxWordWidth);
const int nWords = std::max(1, totalWidth / width);
const int wordNElems = width / valueElemNbits;
const int vecNElems = totalWidth / valueElemNbits;
assert(wordNElems * nWords * numVecs == numElems);
// TODO(Superjomn) Add cache policy fields to StoreOp.
// TODO(Superjomn) Deal with cache policy here.
const bool hasL2EvictPolicy = false;
PTXBuilder ptxBuilder;
auto &ld = *ptxBuilder.create<PTXIOInstr>("ld");
// TODO(Superjomn) Need to check masks before vectorize the load for all
// the values share one predicate? Here assume all the mask values are
// the same.
Value pred = mask ? maskElems[vecStart] : int_val(1, 1);
const std::string readConstraint =
(width == 64) ? "l" : ((width == 32) ? "r" : "c");
const std::string writeConstraint =
(width == 64) ? "=l" : ((width == 32) ? "=r" : "=c");
// prepare asm operands
auto *dstsOpr = ptxBuilder.newListOperand();
for (int wordIdx = 0; wordIdx < nWords; ++wordIdx) {
auto *opr = ptxBuilder.newOperand(writeConstraint); // =r operations
dstsOpr->listAppend(opr);
}
auto *addrOpr =
ptxBuilder.newAddrOperand(ptrElems[vecStart], "l", in_off);
// Define the instruction opcode
ld.o("volatile", op.isVolatile())
.global()
.o("ca", op.cache() == triton::CacheModifier::CA)
.o("cg", op.cache() == triton::CacheModifier::CG)
.o("L1::evict_first",
op.evict() == triton::EvictionPolicy::EVICT_FIRST)
.o("L1::evict_last", op.evict() == triton::EvictionPolicy::EVICT_LAST)
.o("L1::cache_hint", hasL2EvictPolicy)
.v(nWords)
.b(width);
PTXBuilder::Operand *evictOpr{};
// Here lack a mlir::Value to bind to this operation, so disabled.
// if (has_l2_evict_policy)
// evictOpr = ptxBuilder.newOperand(l2Evict, "l");
if (!evictOpr)
ld(dstsOpr, addrOpr).predicate(pred, "b");
else
ld(dstsOpr, addrOpr, evictOpr).predicate(pred, "b");
if (other) {
for (size_t ii = 0; ii < nWords; ++ii) {
PTXInstr &mov = *ptxBuilder.create<>("mov");
mov.o("u", width);
size_t size = width / valueElemNbits;
auto vecTy = LLVM::getFixedVectorType(valueElemTy, size);
Value v = rewriter.create<LLVM::UndefOp>(loc, vecTy);
for (size_t s = 0; s < size; ++s) {
Value falseVal = otherElems[vecStart + ii * size + s];
Value sVal = createIndexAttrConstant(
rewriter, loc, this->getTypeConverter()->getIndexType(), s);
v = insert_element(vecTy, v, falseVal, sVal);
}
v = bitcast(IntegerType::get(getContext(), width), v);
PTXInstr::Operand *opr{};
if (otherIsSplatConstInt) {
opr = ptxBuilder.newConstantOperand(splatVal);
} else {
opr = ptxBuilder.newOperand(v, readConstraint);
}
mov(dstsOpr->listGet(ii), opr).predicateNot(pred, "b");
}
}
// ---
// create inline ASM signature
// ---
SmallVector<Type> retTys(nWords, IntegerType::get(getContext(), width));
Type retTy = retTys.size() > 1
? LLVM::LLVMStructType::getLiteral(getContext(), retTys)
: retTys[0];
// TODO: if (has_l2_evict_policy)
auto asmDialectAttr = LLVM::AsmDialectAttr::get(rewriter.getContext(),
LLVM::AsmDialect::AD_ATT);
Value ret = ptxBuilder.launch(rewriter, loc, retTy);
// ---
// extract and store return values
// ---
SmallVector<Value> rets;
for (unsigned int ii = 0; ii < nWords; ++ii) {
Value curr;
if (retTy.isa<LLVM::LLVMStructType>()) {
curr = extract_val(IntegerType::get(getContext(), width), ret,
rewriter.getI64ArrayAttr(ii));
} else {
curr = ret;
}
curr = bitcast(
LLVM::getFixedVectorType(valueElemTy, width / valueElemNbits),
curr);
rets.push_back(curr);
}
int tmp = width / valueElemNbits;
for (size_t ii = 0; ii < vec; ++ii) {
Value vecIdx = createIndexAttrConstant(
rewriter, loc, this->getTypeConverter()->getIndexType(), ii % tmp);
Value loaded = extract_element(valueElemTy, rets[ii / tmp], vecIdx);
loadedVals.push_back(loaded);
}
} // end vec
Type llvmResultStructTy = getTypeConverter()->convertType(valueTy);
Value resultStruct =
getStructFromElements(loc, loadedVals, rewriter, llvmResultStructTy);
rewriter.replaceOp(op, {resultStruct});
return success();
}
};
struct GetProgramIdOpConversion
: public ConvertTritonGPUOpToLLVMPattern<triton::GetProgramIdOp> {
using ConvertTritonGPUOpToLLVMPattern<

9
lib/Dialect/TritonGPU/Transforms/Coalesce.cpp
View File

@@ -1,4 +1,5 @@
#include "triton/Analysis/AxisInfo.h"
#include "triton/Analysis/Utility.h"
#include "triton/Dialect/TritonGPU/IR/Dialect.h"
#include "triton/Dialect/TritonGPU/Transforms/Passes.h"
#include <numeric>
@@ -23,6 +24,11 @@ struct CoalescePass : public TritonGPUCoalesceBase<CoalescePass> {
std::sort(order.begin(), order.end(), [&](unsigned x, unsigned y) {
return contiguity[x] > contiguity[y];
});
int numElems = product(origType.getShape());
int numThreads = numWarps * 32;
int numElemsPerThread = std::max(numElems / numThreads, 1);
// Thread tile size depends on memory alignment
SmallVector<unsigned, 4> sizePerThread(rank, 1);
PointerType ptrType = origType.getElementType().cast<PointerType>();
@@ -31,7 +37,8 @@ struct CoalescePass : public TritonGPUCoalesceBase<CoalescePass> {
unsigned maxContig = info.getContiguity(order[0]);
unsigned alignment = std::min(maxMultiple, maxContig);
unsigned perThread = std::min(alignment, 128 / numBits);
sizePerThread[order[0]] = perThread;
sizePerThread[order[0]] = std::min<int>(perThread, numElemsPerThread);
SmallVector<unsigned> dims(rank);
std::iota(dims.begin(), dims.end(), 0);
// create encoding