[OPTIMIZER] Added kWidth attribute to DotOperandEncoding

include/triton/Dialect/TritonGPU/IR/TritonGPUAttrDefs.td

@@ -501,10 +501,22 @@ section 9.7.13.4.1 for more details.
   let parameters = (
     ins
     "unsigned":$opIdx,
-    "Attribute":$parent
+    "Attribute":$parent,
+    "unsigned":$MMAv2kWidth
   );
 
   let builders = [
+        // Specially for MMAV1(Volta)
+    AttrBuilder<(ins "unsigned":$opIdx,
+                     "Attribute":$parent,
+                     "Type":$eltTy), [{
+      MmaEncodingAttr parentAttr = parent.dyn_cast<MmaEncodingAttr>();
+      if (!parentAttr || !parentAttr.isAmpere())
+        return $_get(context, opIdx, parent, 0);
+      unsigned bitwidth = eltTy.getIntOrFloatBitWidth();
+      unsigned MMAv2kWidth = 32 / bitwidth;
+      return $_get(context, opIdx, parent, MMAv2kWidth);
+    }]>
   ];
 
   let hasCustomAssemblyFormat = 1;

lib/Conversion/TritonGPUToLLVM/ConvertLayoutOpToLLVM.cpp

@@ -325,7 +325,8 @@ struct ConvertLayoutOpConversion
 
       if (needTrans) {
         // do transpose
-        auto aEncoding = DotOperandEncodingAttr::get(mma.getContext(), 0, mma);
+        auto aEncoding =
+            DotOperandEncodingAttr::get(mma.getContext(), 0, mma, 0);
         int numM = aEncoding.getMMAv1NumOuter(shape);
         int numN = accumSizePerThread / numM;
 

lib/Conversion/TritonGPUToLLVM/ConvertLayoutOpToLLVM/SharedToDotOperandMMAv1.cpp

@@ -358,11 +358,11 @@ SmallVector<CoordTy> getMNCoords(Value thread,
   Value _fpw1 = i32_val(fpw[1]);
 
   // A info
-  auto aEncoding = DotOperandEncodingAttr::get(ctx, 0, mmaLayout);
+  auto aEncoding = DotOperandEncodingAttr::get(ctx, 0, mmaLayout, 0);
   auto aRep = aEncoding.getMMAv1Rep();
   auto aSpw = aEncoding.getMMAv1ShapePerWarp();
   // B info
-  auto bEncoding = DotOperandEncodingAttr::get(ctx, 1, mmaLayout);
+  auto bEncoding = DotOperandEncodingAttr::get(ctx, 1, mmaLayout, 0);
   auto bSpw = bEncoding.getMMAv1ShapePerWarp();
   auto bRep = bEncoding.getMMAv1Rep();
 

lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVMBase.h

@@ -714,11 +714,11 @@ class ConvertTritonGPUOpToLLVMPatternBase {
     Value _fpw1 = i32_val(fpw[1]);
 
     // A info
-    auto aEncoding = DotOperandEncodingAttr::get(ctx, 0, mmaLayout);
+    auto aEncoding = DotOperandEncodingAttr::get(ctx, 0, mmaLayout, 0);
     auto aRep = aEncoding.getMMAv1Rep();
     auto aSpw = aEncoding.getMMAv1ShapePerWarp();
     // B info
-    auto bEncoding = DotOperandEncodingAttr::get(ctx, 1, mmaLayout);
+    auto bEncoding = DotOperandEncodingAttr::get(ctx, 1, mmaLayout, 0);
     auto bSpw = bEncoding.getMMAv1ShapePerWarp();
     auto bRep = bEncoding.getMMAv1Rep();
 
@@ -775,12 +775,12 @@ class ConvertTritonGPUOpToLLVMPatternBase {
     // TODO: seems like the apttern below to get `rep`/`spw` appears quite often
     // A info
     auto aEncoding =
-        DotOperandEncodingAttr::get(type.getContext(), 0, mmaLayout);
+        DotOperandEncodingAttr::get(type.getContext(), 0, mmaLayout, 0);
     auto aRep = aEncoding.getMMAv1Rep();
     auto aSpw = aEncoding.getMMAv1ShapePerWarp();
     // B info
     auto bEncoding =
-        DotOperandEncodingAttr::get(type.getContext(), 1, mmaLayout);
+        DotOperandEncodingAttr::get(type.getContext(), 1, mmaLayout, 0);
     auto bSpw = bEncoding.getMMAv1ShapePerWarp();
     auto bRep = bEncoding.getMMAv1Rep();
 

lib/Conversion/TritonToTritonGPU/TritonToTritonGPUPass.cpp

@@ -268,6 +268,8 @@ struct TritonDotPattern : public OpConversionPattern<triton::DotOp> {
     // a & b must be of smem layout
     auto aType = adaptor.getA().getType().cast<RankedTensorType>();
     auto bType = adaptor.getB().getType().cast<RankedTensorType>();
+    Type aEltType = aType.getElementType();
+    Type bEltType = bType.getElementType();
     Attribute aEncoding = aType.getEncoding();
     Attribute bEncoding = bType.getEncoding();
     if (!aEncoding || !bEncoding)
@@ -276,17 +278,17 @@ struct TritonDotPattern : public OpConversionPattern<triton::DotOp> {
     Value b = adaptor.getB();
     Value c = adaptor.getC();
     if (!aEncoding.isa<triton::gpu::DotOperandEncodingAttr>()) {
-      Attribute encoding =
-          triton::gpu::DotOperandEncodingAttr::get(getContext(), 0, dEncoding);
-      auto dstType = RankedTensorType::get(aType.getShape(),
-                                           aType.getElementType(), encoding);
+      Attribute encoding = triton::gpu::DotOperandEncodingAttr::get(
+          getContext(), 0, dEncoding, aEltType);
+      auto dstType =
+          RankedTensorType::get(aType.getShape(), aEltType, encoding);
       a = rewriter.create<triton::gpu::ConvertLayoutOp>(a.getLoc(), dstType, a);
     }
     if (!bEncoding.isa<triton::gpu::DotOperandEncodingAttr>()) {
-      Attribute encoding =
-          triton::gpu::DotOperandEncodingAttr::get(getContext(), 1, dEncoding);
-      auto dstType = RankedTensorType::get(bType.getShape(),
-                                           bType.getElementType(), encoding);
+      Attribute encoding = triton::gpu::DotOperandEncodingAttr::get(
+          getContext(), 1, dEncoding, bEltType);
+      auto dstType =
+          RankedTensorType::get(bType.getShape(), bEltType, encoding);
       b = rewriter.create<triton::gpu::ConvertLayoutOp>(b.getLoc(), dstType, b);
     }
     c = rewriter.create<triton::gpu::ConvertLayoutOp>(c.getLoc(), retType, c);

lib/Dialect/TritonGPU/IR/Dialect.cpp

@@ -774,14 +774,27 @@ Attribute DotOperandEncodingAttr::parse(AsmParser &parser, Type type) {
     return {};
   unsigned opIdx = attrs.get("opIdx").cast<IntegerAttr>().getInt();
   Attribute parent = attrs.get("parent");
+  auto mmaParent = parent.dyn_cast<MmaEncodingAttr>();
+  unsigned kWidth = 0;
+  Attribute _kWidth = attrs.get("kWidth");
+  if (_kWidth) {
+    if (!mmaParent || mmaParent.isVolta()) {
+      auto loc = parser.getNameLoc();
+      parser.emitError(loc, "kWidth only supported for MMAv2+ parent");
+      return Attribute();
+    }
+    kWidth = _kWidth.cast<IntegerAttr>().getInt();
+  }
   return parser.getChecked<DotOperandEncodingAttr>(parser.getContext(), opIdx,
-                                                   parent);
+                                                   parent, kWidth);
 }
 
 void DotOperandEncodingAttr::print(mlir::AsmPrinter &printer) const {
+  auto mmaParent = getParent().dyn_cast<MmaEncodingAttr>();
   printer << "<{"
-          << "opIdx = " << getOpIdx() << ", "
-          << "parent = " << getParent();
+          << "opIdx = " << getOpIdx() << ", parent = " << getParent();
+  if (mmaParent && mmaParent.isAmpere())
+    printer << ", kWidth = " << getMMAv2kWidth();
   printer << "}>";
 }
 

lib/Dialect/TritonGPU/Transforms/AccelerateMatmul.cpp

@@ -170,14 +170,17 @@ class BlockedToMMA : public mlir::RewritePattern {
                          .cast<triton::gpu::BlockedEncodingAttr>()
                          .getOrder();
 
+    auto newAEncoding = triton::gpu::DotOperandEncodingAttr::get(
+        oldAType.getContext(), 0, newRetType.getEncoding(),
+        oldAType.getElementType());
+    auto newBEncoding = triton::gpu::DotOperandEncodingAttr::get(
+        oldBType.getContext(), 1, newRetType.getEncoding(),
+        oldBType.getElementType());
+
     auto newAType = RankedTensorType::get(
-        oldAType.getShape(), oldAType.getElementType(),
-        triton::gpu::DotOperandEncodingAttr::get(oldAType.getContext(), 0,
-                                                 newRetType.getEncoding()));
+        oldAType.getShape(), oldAType.getElementType(), newAEncoding);
     auto newBType = RankedTensorType::get(
-        oldBType.getShape(), oldBType.getElementType(),
-        triton::gpu::DotOperandEncodingAttr::get(oldBType.getContext(), 1,
-                                                 newRetType.getEncoding()));
+        oldBType.getShape(), oldBType.getElementType(), newBEncoding);
 
     a = rewriter.create<triton::gpu::ConvertLayoutOp>(a.getLoc(), newAType, a);
     b = rewriter.create<triton::gpu::ConvertLayoutOp>(b.getLoc(), newBType, b);

lib/Dialect/TritonGPU/Transforms/Pipeline.cpp

@@ -1,3 +1,4 @@
+#include "Utility.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/IR/IRMapping.h"
 #include "mlir/IR/TypeUtilities.h"
@@ -42,6 +43,9 @@ class LoopPipeliner {
 
   /// Loads to be pipelined
   SetVector<Value> loads;
+  /// Smallest data-type for each load (used to optimize swizzle and
+  /// (create DotOpEncoding layout)
+  DenseMap<Value, Type> loadsSmallestType;
   /// The value that each load will be mapped to (after layout conversion)
   DenseMap<Value, Value> loadsMapping;
   /// load => buffer
@@ -256,33 +260,62 @@ LogicalResult LoopPipeliner::initialize() {
         use = *use->getResult(0).getUsers().begin();
       }
 
-      if (auto convertLayout = llvm::dyn_cast<ttg::ConvertLayoutOp>(use)) {
-        if (auto tensorType = convertLayout.getResult()
-                                  .getType()
-                                  .dyn_cast<RankedTensorType>()) {
-          if (auto dotOpEnc = tensorType.getEncoding()
-                                  .dyn_cast<ttg::DotOperandEncodingAttr>()) {
-            isCandidate = true;
-            loadsMapping[loadOp] = convertLayout;
-            auto ty = loadOp.getType().cast<RankedTensorType>();
-            SmallVector<int64_t> bufferShape(ty.getShape().begin(),
-                                             ty.getShape().end());
-            bufferShape.insert(bufferShape.begin(), numStages);
-            auto sharedEnc = ttg::SharedEncodingAttr::get(
-                ty.getContext(), dotOpEnc, ty.getShape(),
-                triton::gpu::getOrder(ty.getEncoding()), ty.getElementType());
-            loadsBufferType[loadOp] = RankedTensorType::get(
-                bufferShape, ty.getElementType(), sharedEnc);
-          }
-        }
-      }
-    } else
+      auto convertLayout = llvm::dyn_cast<ttg::ConvertLayoutOp>(use);
+      if (!convertLayout)
+        continue;
+      auto tensorType =
+          convertLayout.getResult().getType().dyn_cast<RankedTensorType>();
+      if (!tensorType)
+        continue;
+      auto dotOpEnc =
+          tensorType.getEncoding().dyn_cast<ttg::DotOperandEncodingAttr>();
+      if (!dotOpEnc)
+        continue;
+      isCandidate = true;
+      loadsMapping[loadOp] = convertLayout;
+    }
+
+    else
       isCandidate = false;
 
     if (isCandidate)
       loads.insert(loadOp);
   }
 
+  // we need to find the smallest ocmmon dtype
+  // since this determines the layout of `mma.sync` operands
+  // in mixed-precision mode
+  Type smallestType;
+  for (auto loadCvt : loadsMapping) {
+    auto loadOp = loadCvt.first;
+    auto ty = loadOp.getType().cast<RankedTensorType>();
+    Type eltTy = ty.getElementType();
+    if (!smallestType ||
+        (eltTy.getIntOrFloatBitWidth() < smallestType.getIntOrFloatBitWidth()))
+      smallestType = eltTy;
+  }
+
+  for (auto loadCvt : loadsMapping)
+    loadsSmallestType[loadCvt.first] = smallestType;
+
+  for (auto loadCvt : loadsMapping) {
+    auto loadOp = loadCvt.first;
+    Value cvt = loadCvt.second;
+    auto dotOpEnc = cvt.getType()
+                        .cast<RankedTensorType>()
+                        .getEncoding()
+                        .cast<ttg::DotOperandEncodingAttr>();
+    auto ty = loadOp.getType().cast<RankedTensorType>();
+    SmallVector<int64_t> bufferShape(ty.getShape().begin(),
+                                     ty.getShape().end());
+    bufferShape.insert(bufferShape.begin(), numStages);
+    auto sharedEnc = ttg::SharedEncodingAttr::get(
+        ty.getContext(), dotOpEnc, ty.getShape(),
+        triton::gpu::getOrder(ty.getEncoding()), loadsSmallestType[loadOp]);
+    loadsBufferType[loadOp] =
+        RankedTensorType::get(bufferShape, ty.getElementType(), sharedEnc);
+  }
+
   // We have some loads to pipeline
   if (!loads.empty()) {
     // Update depArgs & depOps
@@ -551,8 +584,15 @@ scf::ForOp LoopPipeliner::createNewForOp() {
     }
     // we replace the use new load use with a convert layout
     size_t i = std::distance(loads.begin(), it);
+    auto cvtDstTy = op.getResult(0).getType().cast<RankedTensorType>();
+    auto cvtDstEnc = cvtDstTy.getEncoding().cast<ttg::DotOperandEncodingAttr>();
+    auto newDstTy = RankedTensorType::get(
+        cvtDstTy.getShape(), cvtDstTy.getElementType(),
+        ttg::DotOperandEncodingAttr::get(
+            cvtDstEnc.getContext(), cvtDstEnc.getOpIdx(), cvtDstEnc.getParent(),
+            loadsSmallestType[op.getOperand(0)]));
     auto cvt = builder.create<ttg::ConvertLayoutOp>(
-        op.getLoc(), op.getResult(0).getType(),
+        op.getResult(0).getLoc(), newDstTy,
         newForOp.getRegionIterArgs()[loadIdx + i]);
     mapping.map(op.getResult(0), cvt.getResult());
   }

lib/Dialect/TritonGPU/Transforms/Prefetch.cpp

@@ -110,7 +110,7 @@ Value Prefetcher::generatePrefetch(Value v, unsigned opIdx, bool isPrologue,
       SmallVector<OpFoldResult>{intAttr(1), intAttr(1)});
 
   auto dotOperandEnc = triton::gpu::DotOperandEncodingAttr::get(
-      builder.getContext(), opIdx, dotEncoding);
+      builder.getContext(), opIdx, dotEncoding, prefetchWidth / 8);
   Value prefetchSlice = builder.create<triton::gpu::ConvertLayoutOp>(
       v.getLoc(), RankedTensorType::get(shape, elementType, dotOperandEnc),
       newSmem);
@@ -156,12 +156,22 @@ LogicalResult Prefetcher::initialize() {
   };
 
   for (triton::DotOp dot : dotsInFor) {
-    auto kSize = dot.getA().getType().cast<RankedTensorType>().getShape()[1];
+    auto aType = dot.getA().getType().cast<RankedTensorType>();
+    auto bType = dot.getB().getType().cast<RankedTensorType>();
+    auto aEnc = aType.getEncoding().cast<triton::gpu::DotOperandEncodingAttr>();
+    auto bEnc = bType.getEncoding().cast<triton::gpu::DotOperandEncodingAttr>();
+    int aKWidth = aEnc.getMMAv2kWidth();
+    int bKWidth = bEnc.getMMAv2kWidth();
+    assert(aKWidth == bKWidth);
+
+    auto kSize = aType.getShape()[1];
 
     // works better with nvidia tensor cores
-    unsigned elementWidth =
-        dot.getA().getType().cast<RankedTensorType>().getElementTypeBitWidth();
-    prefetchWidth = 256 / elementWidth;
+    unsigned elementWidth = aType.getElementTypeBitWidth();
+    if (aKWidth == 0)
+      prefetchWidth = 256 / elementWidth;
+    else
+      prefetchWidth = 8 * aKWidth;
 
     // Skip prefetching if kSize is less than prefetchWidth
     if (kSize < prefetchWidth)

lib/Dialect/TritonGPU/Transforms/RemoveLayoutConversions.cpp

@@ -341,7 +341,10 @@ class RematerializeForward : public mlir::RewritePattern {
         cvt.getOperand().getType().cast<RankedTensorType>().getEncoding();
     auto dstEncoding =
         cvt.getResult().getType().cast<RankedTensorType>().getEncoding();
-    // XXX: why is this needed?
+    if (srcEncoding.isa<triton::gpu::SharedEncodingAttr>() ||
+        dstEncoding.isa<triton::gpu::SharedEncodingAttr>())
+      return failure();
+    // heuristics for flash attention
     if (srcEncoding.isa<triton::gpu::SliceEncodingAttr>())
       return failure();
     SetVector<Operation *> cvtSlices;

lib/Dialect/TritonGPU/Transforms/Utility.cpp

@@ -206,11 +206,15 @@ int simulateBackwardRematerialization(
 
 //
 
-Operation *cloneWithInferType(mlir::PatternRewriter &rewriter, Operation *op,
+Operation *cloneWithInferType(mlir::OpBuilder &rewriter, Operation *op,
                               IRMapping &mapping) {
   Operation *newOp = rewriter.clone(*op, mapping);
-  auto origType = op->getResult(0).getType().cast<RankedTensorType>();
-  auto argType = newOp->getOperand(0).getType().cast<RankedTensorType>();
+  if (newOp->getNumResults() == 0)
+    return newOp;
+  auto origType = op->getResult(0).getType().dyn_cast<RankedTensorType>();
+  auto argType = newOp->getOperand(0).getType().dyn_cast<RankedTensorType>();
+  if (!origType || !argType)
+    return newOp;
   auto newType = RankedTensorType::get(
       origType.getShape(), origType.getElementType(), argType.getEncoding());
   newOp->getResult(0).setType(newType);

lib/Dialect/TritonGPU/Transforms/Utility.h

@@ -21,7 +21,7 @@ int simulateBackwardRematerialization(
     SetVector<Attribute> &layout, llvm::MapVector<Value, Attribute> &toConvert,
     Attribute targetEncoding);
 
-Operation *cloneWithInferType(mlir::PatternRewriter &rewriter, Operation *op,
+Operation *cloneWithInferType(mlir::OpBuilder &rewriter, Operation *op,
                               IRMapping &mapping);
 
 void rematerializeConversionChain(

test/Analysis/test-alias.mlir

@@ -6,8 +6,8 @@
 #A_SHARED_T = #triton_gpu.shared<{vec = 2, perPhase = 2, maxPhase = 4, order = [0, 1]}>
 #B_SHARED = #triton_gpu.shared<{vec = 2, perPhase = 2, maxPhase = 4, order = [1, 0]}>
 #C = #triton_gpu.mma<{versionMajor = 2, warpsPerCTA = [4, 1]}>
-#A_DOT = #triton_gpu.dot_op<{opIdx = 0, parent = #C}>
-#B_DOT = #triton_gpu.dot_op<{opIdx = 1, parent = #C}>
+#A_DOT = #triton_gpu.dot_op<{opIdx = 0, parent = #C, kWidth=2}>
+#B_DOT = #triton_gpu.dot_op<{opIdx = 1, parent = #C, kWidth=2}>
 
 // CHECK-LABEL: matmul_loop
 // There shouldn't be any aliasing with the dot op encoding.

test/Analysis/test-allocation.mlir

@@ -7,8 +7,8 @@
 #A_SHARED_T = #triton_gpu.shared<{vec = 2, perPhase = 2, maxPhase = 4, order = [0, 1]}>
 #B_SHARED = #triton_gpu.shared<{vec = 2, perPhase = 2, maxPhase = 4, order = [1, 0]}>
 #C = #triton_gpu.mma<{versionMajor = 2, warpsPerCTA = [4, 1]}>
-#A_DOT = #triton_gpu.dot_op<{opIdx = 0, parent = #C}>
-#B_DOT = #triton_gpu.dot_op<{opIdx = 1, parent = #C}>
+#A_DOT = #triton_gpu.dot_op<{opIdx = 0, parent = #C, kWidth=2}>
+#B_DOT = #triton_gpu.dot_op<{opIdx = 1, parent = #C, kWidth=2}>
 
 module attributes {"triton_gpu.num-warps" = 4 : i32} {
 

test/Analysis/test-membar.mlir

@@ -7,8 +7,8 @@
 #A_SHARED_T = #triton_gpu.shared<{vec = 2, perPhase = 2, maxPhase = 4, order = [0, 1]}>
 #B_SHARED = #triton_gpu.shared<{vec = 2, perPhase = 2, maxPhase = 4, order = [1, 0]}>
 #C = #triton_gpu.mma<{versionMajor = 2, warpsPerCTA = [4, 1]}>
-#A_DOT = #triton_gpu.dot_op<{opIdx = 0, parent = #C}>
-#B_DOT = #triton_gpu.dot_op<{opIdx = 1, parent = #C}>
+#A_DOT = #triton_gpu.dot_op<{opIdx = 0, parent = #C, kWidth=2}>
+#B_DOT = #triton_gpu.dot_op<{opIdx = 1, parent = #C, kWidth=2}>
 
 module attributes {"triton_gpu.num-warps" = 4 : i32} {