OpenXLA-specific changes

include/triton/Analysis/Alias.h

@@ -85,10 +85,9 @@ class SharedMemoryAliasAnalysis
   }
 
   /// Computes if the alloc set of the results are changed.
-  void
-  visitOperation(Operation *op,
-                 ArrayRef<const dataflow::Lattice<AliasInfo> *> operands,
-                 ArrayRef<dataflow::Lattice<AliasInfo> *> results) override;
+  LogicalResult visitOperation(
+      Operation *op, ArrayRef<const dataflow::Lattice<AliasInfo> *> operands,
+      ArrayRef<dataflow::Lattice<AliasInfo> *> results) override;
 };
 
 } // namespace mlir

include/triton/Dialect/Triton/IR/TritonTypes.td

@@ -15,7 +15,7 @@ class TritonTypeDef<string name, string _mnemonic, list<Trait> traits = []>
 }
 
 // Floating-point Type
-def TT_Float : AnyTypeOf<[F8E4M3FNUZ, F8E5M2, F8E5M2FNUZ, F16, BF16, F32, F64], "floating-point">;
+def TT_Float : AnyTypeOf<[F8E4M3FN, F8E4M3FNUZ, F8E5M2, F8E5M2FNUZ, F16, BF16, F32, F64], "floating-point">;
 def TT_FloatTensor : RankedTensorOf<[TT_Float]>;
 def TT_FloatLike : AnyTypeOf<[TT_Float, TT_FloatTensor]>;
 

include/triton/Dialect/Triton/IR/Utility.h

@@ -31,7 +31,11 @@ template <typename Int> Int ceil(Int m, Int n) { return (m + n - 1) / n; }
 
 /// Get the highest power of 2 divisor of an integer.
 template <typename T> T highestPowOf2Divisor(T n) {
-  if (n == 0) {
+  // When n is 0 or min, return the highest power of 2. The min case is handled
+  // separately to avoid underflow when T is a signed integer. Technically
+  // in that case the correct divisor is -n, but this value is outside the
+  // range of possible values, so we take the next best alternative.
+  if (n == 0 || n == std::numeric_limits<T>::min()) {
     return (static_cast<T>(1) << (sizeof(T) * 8 - 2));
   }
   return (n & (~(n - 1)));

lib/Analysis/Alias.cpp

@@ -21,7 +21,7 @@ AliasInfo AliasInfo::join(const AliasInfo &lhs, const AliasInfo &rhs) {
   return ret;
 }
 
-void SharedMemoryAliasAnalysis::visitOperation(
+LogicalResult SharedMemoryAliasAnalysis::visitOperation(
     Operation *op, ArrayRef<const dataflow::Lattice<AliasInfo> *> operands,
     ArrayRef<dataflow::Lattice<AliasInfo> *> results) {
   AliasInfo aliasInfo;
@@ -31,7 +31,7 @@ void SharedMemoryAliasAnalysis::visitOperation(
   if (auto memdescTy = dyn_cast<triton::MemDescType>(result.getType())) {
     if (!isa_and_nonnull<triton::gpu::SharedMemorySpaceAttr>(
             memdescTy.getMemorySpace()))
-      return;
+      return mlir::success();
   }
 
   // Only LocalAllocOp creates a new buffer.
@@ -49,11 +49,13 @@ void SharedMemoryAliasAnalysis::visitOperation(
   }
 
   if (pessimistic) {
-    return setAllToEntryStates(results);
+    setAllToEntryStates(results);
+    return mlir::success();
   }
   // Join all lattice elements
   for (auto *result : results)
     propagateIfChanged(result, result->join(aliasInfo));
+  return mlir::success();
 }
 
 AliasResult SharedMemoryAliasAnalysis::alias(Value lhs, Value rhs) {

lib/Analysis/AxisInfo.cpp

@@ -195,9 +195,9 @@ class AxisInfoAnalysis : public dataflow::SparseForwardDataFlowAnalysis<
       dataflow::Lattice<AxisInfo>>::getLatticeElement;
   using FuncAxisInfoMapT = DenseMap<FunctionOpInterface, AxisInfo>;
 
-  void visitOperation(Operation *op,
-                      ArrayRef<const dataflow::Lattice<AxisInfo> *> operands,
-                      ArrayRef<dataflow::Lattice<AxisInfo> *> results) override;
+  LogicalResult visitOperation(
+      Operation *op, ArrayRef<const dataflow::Lattice<AxisInfo> *> operands,
+      ArrayRef<dataflow::Lattice<AxisInfo> *> results) override;
   void
   visitForOpInductionVar(scf::ForOp op,
                          ArrayRef<dataflow::Lattice<AxisInfo> *> argLattices);
@@ -1039,7 +1039,7 @@ AxisInfoAnalysis::AxisInfoAnalysis(DataFlowSolver &solver)
   visitors.append<LoadOpAxisInfoVisitor>();
 }
 
-void AxisInfoAnalysis::visitOperation(
+LogicalResult AxisInfoAnalysis::visitOperation(
     Operation *op, ArrayRef<const dataflow::Lattice<AxisInfo> *> operands,
     ArrayRef<dataflow::Lattice<AxisInfo> *> results) {
   // TODO: For sure not the right way to do this
@@ -1048,8 +1048,10 @@ void AxisInfoAnalysis::visitOperation(
     if (op->getValue().getRank() == 0)
       setToEntryState((dataflow::Lattice<AxisInfo> *)op);
   AxisInfo curr = visitors.apply(op, operands);
-  if (curr.getRank() == 0)
-    return setAllToEntryStates(results);
+  if (curr.getRank() == 0) {
+    setAllToEntryStates(results);
+    return mlir::success();
+  }
   // override with hint
   auto newContiguity = curr.getContiguity();
   auto newDivisibility = curr.getDivisibility();
@@ -1071,6 +1073,7 @@ void AxisInfoAnalysis::visitOperation(
   // join all lattice elements
   for (auto *result : results)
     propagateIfChanged(result, result->join(curr));
+  return mlir::success();
 }
 
 void AxisInfoAnalysis::visitForOpInductionVar(

lib/Analysis/Utility.cpp

@@ -425,6 +425,7 @@ bool supportMFMATypes(Type a, Type b) {
   if (a.getIntOrFloatBitWidth() != b.getIntOrFloatBitWidth())
     return false;
 
+  auto F8E4M3FN = TypeID::get<Float8E4M3FNType>();
   auto F8E5M2 = TypeID::get<Float8E5M2Type>();
   auto F8E4M3FNUZ = TypeID::get<Float8E4M3FNUZType>();
   auto F8E5M2FNUZ = TypeID::get<Float8E5M2FNUZType>();
@@ -436,6 +437,7 @@ bool supportMFMATypes(Type a, Type b) {
       {F32, F32},
       {F16, F16},
       {BF16, BF16},
+      {F8E4M3FN, F8E4M3FN},
       {F8E5M2, F8E5M2},
       {F8E4M3FNUZ, F8E4M3FNUZ},
       {F8E4M3FNUZ, F8E5M2FNUZ},
@@ -495,14 +497,14 @@ bool supportMMA(triton::DotOp op, int version) {
       return false;
     if (!(numWarps % 4 == 0 && retShapePerCTA[rank - 2] % 64 == 0 &&
           retShapePerCTA[rank - 1] % 8 == 0 &&
-          (aElemTy.isFloat8E5M2() || aElemTy.isFloat8E4M3FNUZ() ||
+          (aElemTy.isFloat8E5M2() || aElemTy.isFloat8E4M3FN() ||
            aElemTy.isInteger(8) || aElemTy.isF16() || aElemTy.isBF16() ||
            aElemTy.isF32()))) {
       return false;
     }
     // We cannot use MMA_V3 if we need to accumulate in F32 within the MMA op.
     if (op.getMaxNumImpreciseAcc() < 32 &&
-        (aElemTy.isFloat8E5M2() || aElemTy.isFloat8E4M3FNUZ()) &&
+        (aElemTy.isFloat8E5M2() || aElemTy.isFloat8E4M3FN()) &&
         cast<RankedTensorType>(op.getType()).getElementType().isF32()) {
       return false;
     }

lib/Conversion/TritonGPUToLLVM/ElementwiseOpToLLVM.cpp

@@ -40,7 +40,8 @@ SmallVector<Value> reorderValues(const SmallVector<Value> &values, Type inType,
   auto ouEltTy = ouTensorTy.getElementType();
   if (inBitWidth == ouBitWidth)
     return values;
-  if (inBitWidth == 16 && ouBitWidth == 32) {
+  if ((inBitWidth == 16 && ouBitWidth == 32) ||
+      (inBitWidth == 32 && ouBitWidth == 16)) {
     SmallVector<Value> ret;
     for (unsigned i = 0; i < values.size(); i += 8) {
       ret.push_back(values[i]);

lib/Conversion/TritonGPUToLLVM/TypeConverter.cpp

@@ -34,6 +34,9 @@ TritonGPUToLLVMTypeConverter::TritonGPUToLLVMTypeConverter(
   addConversion([&](mlir::Float8E4M3FNUZType type) -> std::optional<Type> {
     return IntegerType::get(type.getContext(), 8);
   });
+  addConversion([&](mlir::Float8E4M3FNType type) -> std::optional<Type> {
+    return IntegerType::get(type.getContext(), 8);
+  });
   addConversion([&](mlir::Float8E5M2Type type) -> std::optional<Type> {
     return IntegerType::get(type.getContext(), 8);
   });

lib/Conversion/TritonGPUToLLVM/ViewOpToLLVM.cpp

@@ -87,8 +87,9 @@ struct ArithConstantSplatOpConversion
     // LLVM IR.
     if (type::isFloat8(elemType))
       elemType = rewriter.getIntegerType(8);
-    auto constOp = rewriter.create<LLVM::ConstantOp>(loc, elemType, val);
     auto typeConverter = getTypeConverter();
+    auto constOp = rewriter.create<LLVM::ConstantOp>(
+        loc, typeConverter->convertType(elemType), val);
     auto llStruct = SplatOpConversion::convertSplatLikeOp(
         elemType, op.getType(), constOp, typeConverter, rewriter, loc);
     rewriter.replaceOp(op, llStruct);

lib/Dialect/TritonGPU/IR/Dialect.cpp

@@ -2721,6 +2721,11 @@ struct CanonicalizeConvertFromAlloc
     auto convert = op.getSrc().getDefiningOp<ConvertLayoutOp>();
     if (!convert)
       return failure();
+    // LocalAllocOp lowering doesn't support going from DotOperandEncoding
+    // to SharedEncoding, so we want to keep this layout conversion.
+    if (mlir::isa<triton::gpu::DotOperandEncodingAttr>(
+            convert.getSrc().getType().getEncoding()))
+      return failure();
     rewriter.replaceOpWithNewOp<triton::gpu::LocalAllocOp>(
         op, op->getResult(0).getType(), convert.getSrc());
     return mlir::success();

lib/Dialect/TritonGPU/Transforms/AccelerateMatmul.cpp

@@ -153,6 +153,21 @@ static Value getSharedMemoryMMAOperand(Value v, mlir::PatternRewriter &rewriter,
   auto newType = MemDescType::get(argType.getShape(), argType.getElementType(),
                                   newLayout, SharedMemorySpace);
   rewriter.setInsertionPointAfterValue(arg);
+
+  // LocalAllocOp lowering doesn't support going from DotOperandEncoding
+  // to SharedEncoding.
+  if (auto dotOpEnc = mlir::dyn_cast<DotOperandEncodingAttr>(
+          argType.getEncoding())) {
+    // Create a layout conversion from DotOperandEncoding to BlockedEncoding
+    // then pass it to the LocalAllocOp.
+    auto newArgType = RankedTensorType::get(
+        argType.getShape(), argType.getElementType(), dotOpEnc.getParent());
+    auto dotOperandToBlockedCvt =
+        rewriter.create<ConvertLayoutOp>(arg.getLoc(), newArgType, arg);
+    return rewriter.create<LocalAllocOp>(arg.getLoc(), newType,
+                                              dotOperandToBlockedCvt);
+  }
+
   return rewriter.create<LocalAllocOp>(arg.getLoc(), newType, arg);
 }
 
@@ -162,6 +177,15 @@ class BlockedToMMA : public mlir::OpRewritePattern<DotOp> {
   mutable llvm::DenseMap<Operation *, unsigned> dotOpInstNs;
 
   static bool bwdFilter(Operation *op) {
+    // Dot operand layout assignment to Predicates are not currently supported
+    // during lowering from TritonGPU to LLVM in Triton for MMA cases. This
+    // condition limits visibility of the original bit-width so that predicate
+    // are not considered, hence, kwidth can never be = 32.
+    if (isa<arith::UIToFPOp>(op)) {
+      Type srcType = getElementTypeOrSelf(op->getOperand(0));
+      if (srcType.isInteger(1))
+        return false;
+    }
     return op->getNumOperands() == 1 &&
            (isa<FpToFpOp, BitcastOp, ConvertLayoutOp>(op) ||
             isPureUnaryInlineAsm(op) ||
@@ -357,7 +381,7 @@ static void decomposeMixedModeDotOp(ModuleOp mod, int computeCapability) {
     NvidiaMmaEncodingAttr mmaLayout =
         dyn_cast<NvidiaMmaEncodingAttr>(D.getType().getEncoding());
     if (mmaLayout) {
-      bool isNativeFP8 = AElType.isFloat8E5M2() || AElType.isFloat8E4M3FNUZ();
+      bool isNativeFP8 = AElType.isFloat8E5M2() || AElType.isFloat8E4M3FN();
       // promote operands for sm < 89 since fp8 mma is not natively supported
       // promote operands for sm >= 90 when mma is not v3
       if (!isNativeFP8 ||

lib/Dialect/TritonGPU/Transforms/OptimizeDotOperands.cpp

@@ -111,7 +111,8 @@ class HoistLayoutConversion : public OpRewritePattern<ConvertLayoutOp> {
                                 PatternRewriter &rewriter) const override {
     // Only consider conversions to dot operand.
     auto cvtTy = cast<RankedTensorType>(cvt.getType());
-    if (!isa<DotOperandEncodingAttr>(cvtTy.getEncoding()))
+    auto dotOpEnc = dyn_cast<DotOperandEncodingAttr>(cvtTy.getEncoding());
+    if (!dotOpEnc)
       return failure();
 
     auto src = cvt.getSrc().getDefiningOp();
@@ -126,6 +127,12 @@ class HoistLayoutConversion : public OpRewritePattern<ConvertLayoutOp> {
                 [](Type ty) { return isa<RankedTensorType>(ty); }))
       return failure();
 
+    // Quick handling to fix loading issues when computing the original
+    // bitwidth is unable to realize that there is a mixed-precision dot
+    // (hence kWidth = 1) but wants to hoist through the type conversion.
+    if (isa<arith::ExtFOp>(src) && dotOpEnc.getKWidth() == 1)
+        return failure();
+
     // Only consider custom conversions or arith ops.
     // TODO(jlebar): Is this too restrictive?
     if (!isa<FpToFpOp, BitcastOp>(src) && !isPureUnaryInlineAsm(src) &&
@@ -138,6 +145,14 @@ class HoistLayoutConversion : public OpRewritePattern<ConvertLayoutOp> {
     if (isa<arith::TruncIOp, arith::TruncFOp, arith::SelectOp>(src))
       return failure();
 
+    // Don't hoist through u1 -> fp casts as they aren't supported in
+    // ElementwiseOpToLLVM::reorderValues().
+    if (isa<arith::UIToFPOp>(src)) {
+      Type srcType = getElementTypeOrSelf(src->getOperand(0));
+      if (srcType.isInteger(1))
+        return failure();
+    }
+
     // Check that the conversion is transitively dependent on a load, and all
     // operations between the load and the conversion are layout preserving.
     //

lib/Dialect/TritonGPU/Transforms/Prefetch.cpp

@@ -140,8 +140,14 @@ Value Prefetcher::generatePrefetch(Value v, unsigned opIdx, bool isPrologue,
                                type.getMemorySpace()),
       v, offsetsVal);
 
+  // We need to assign kwidth to zero in the case where the parent layout is
+  // Blocked, otherwise the verifier emits a failure. The parent layout is
+  // Blocked only when Tensor Cores are disabled.
+  int kwidth = dyn_cast<triton::gpu::BlockedEncodingAttr>(dotEncoding)
+                   ? 0
+                   : prefetchWidth / 8;
   auto dotOperandEnc = triton::gpu::DotOperandEncodingAttr::get(
-      builder.getContext(), opIdx, dotEncoding, prefetchWidth / 8);
+      builder.getContext(), opIdx, dotEncoding, kwidth);
   Value prefetchSlice = builder.create<triton::gpu::LocalLoadOp>(
       v.getLoc(), RankedTensorType::get(shape, elementType, dotOperandEnc),
       newSmem);
@@ -187,6 +193,15 @@ LogicalResult Prefetcher::initialize() {
         break;
       if (!op->getResult(0).hasOneUse())
         break;
+      // Similar to issues faced in HoistLayoutConversion pattern in
+      // OptimizeDotOperands.cpp, we can't propagate through type casts from
+      // predicates as they aren't supported in Triton when encoded with dot_op
+      // layout.
+      if (isa<arith::UIToFPOp>(op)) {
+        Type srcType = getElementTypeOrSelf(op->getOperand(0));
+        if (srcType.isInteger(1))
+          break;
+      }
       rets.push_back(op->getOperand(0));
       if (auto cvt = dyn_cast<triton::gpu::LocalLoadOp>(op)) {
         foundConvertFromShared = true;