OpenXLA-specific changes

lib/Analysis/AxisInfo.cpp

@@ -937,7 +937,7 @@ class ShROpAxisInfoVisitor final : public BinaryOpVisitorImpl<OpTy> {
       // Treat [2^n,2^n+1,...]'s divisibility as 1 instead of 2^n
       lhsDivisibility = 1;
     }
-    return std::max<int64_t>(1, lhsDivisibility / (1 << shift));
+    return std::max<int64_t>(1, lhsDivisibility / (int64_t(1) << shift));
   }
 
   int64_t getConstancy(OpTy op, const AxisInfo &lhs, const AxisInfo &rhs,
@@ -1084,8 +1084,8 @@ LogicalResult AxisInfoAnalysis::visitOperation(
 
 void AxisInfoAnalysis::visitForOpInductionVar(
     scf::ForOp op, ArrayRef<dataflow::Lattice<AxisInfo> *> argLattices) {
-  auto lb = getLatticeElementFor(op, op.getLowerBound())->getValue();
-  auto step = getLatticeElementFor(op, op.getStep())->getValue();
+  auto lb = getLatticeElementFor(getProgramPointAfter(op), op.getLowerBound())->getValue();
+  auto step = getLatticeElementFor(getProgramPointAfter(op), op.getStep())->getValue();
 
   AxisInfo::DimVectorT knownContiguity(1, 1);
   AxisInfo::DimVectorT knownDivisibility(1, 1);

lib/Analysis/Utility.cpp

@@ -896,15 +896,15 @@ class ConstantAnalysis : public DataFlowAnalysis {
 
   LogicalResult initialize(Operation *top) override {
     WalkResult result = top->walk([&](Operation *op) {
-      if (failed(visit(op)))
+      if (failed(visit(getProgramPointAfter(op))))
         return WalkResult::interrupt();
       return WalkResult::advance();
     });
     return success(!result.wasInterrupted());
   }
 
-  LogicalResult visit(ProgramPoint point) override {
-    Operation *op = point.get<Operation *>();
+  LogicalResult visit(ProgramPoint* point) override {
+    Operation *op = point->getPrevOp();
     Attribute value;
     if (matchPattern(op, m_Constant(&value))) {
       auto *constant = getOrCreate<dataflow::Lattice<dataflow::ConstantValue>>(

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]);
@@ -54,7 +55,8 @@ SmallVector<Value> reorderValues(const SmallVector<Value> &values, Type inType,
     }
     return ret;
   }
-  if (inBitWidth == 8 && ouBitWidth == 16) {
+  if ((inBitWidth == 8 && ouBitWidth == 16) ||
+      (inBitWidth == 16 && ouBitWidth == 8)) {
     SmallVector<Value> ret;
     for (unsigned i = 0; i < values.size(); i += 16) {
       ret.push_back(values[i + 0]);

lib/Conversion/TritonToTritonGPU/TritonGPUConversion.cpp

@@ -56,20 +56,20 @@ TritonGPUTypeConverter::TritonGPUTypeConverter(MLIRContext *context,
   // This will create newArg, and map(origArg, newArg)
   addArgumentMaterialization([&](OpBuilder &builder,
                                  RankedTensorType tensorType, ValueRange inputs,
-                                 Location loc) -> std::optional<Value> {
+                                 Location loc) -> Value {
     llvm_unreachable("Argument rematerialization should not happen in Triton "
                      "-> TritonGPU conversion");
-    return std::nullopt;
+    return Value();
   });
 
   // If the origValue still has live user(s), use this to
   // convert origValue to newValue
   addSourceMaterialization([&](OpBuilder &builder, RankedTensorType tensorType,
                                ValueRange inputs,
-                               Location loc) -> std::optional<Value> {
+                               Location loc) -> Value {
     llvm_unreachable("Source rematerialization should not happen in Triton -> "
                      "TritonGPU Conversion");
-    return std::nullopt;
+    return Value();
   });
 
   // This will be called when (desiredType != newOperandType)
@@ -79,7 +79,7 @@ TritonGPUTypeConverter::TritonGPUTypeConverter(MLIRContext *context,
                                ValueRange inputs, Location loc) {
     auto cast =
         builder.create<triton::gpu::ConvertLayoutOp>(loc, tensorType, inputs);
-    return std::optional<Value>(cast.getResult());
+    return Value(cast.getResult());
   });
 }
 

lib/Dialect/TritonGPU/IR/Dialect.cpp

@@ -2770,6 +2770,11 @@ 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) ||

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

@@ -116,7 +116,7 @@ Value Prefetcher::generatePrefetch(Value v, unsigned opIdx, bool isPrologue,
   // opIdx: 0 => a, 1 => b
   auto type = cast<triton::MemDescType>(v.getType());
   SmallVector<int64_t> shape{type.getShape().begin(), type.getShape().end()};
-  SmallVector<int64_t> offset{0, 0};
+  SmallVector<int64_t> offset(shape.size(), 0);
   Type elementType = type.getElementType();
 
   // k => (prefetchWidth, k - prefetchWidth)
@@ -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);
@@ -190,6 +196,22 @@ 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;
+      }
+      // Propagation through ExpandDims is currently not supported. This blindly
+      // replaces the encoding with dot encoding & but ExpandDims requires a
+      // SliceEncoding. This could be rewritten to support it somehow, but I
+      // don't think it's trivial & it's currently crashing.
+      if (isa<ExpandDimsOp>(op)) {
+        break;
+      }
       rets.push_back(op->getOperand(0));
       if (auto cvt = dyn_cast<triton::gpu::LocalLoadOp>(op)) {
         foundConvertFromShared = true;

lib/Target/LLVMIR/LLVMDIScope.cpp

@@ -104,7 +104,8 @@ struct LLVMDIScopePass : public LLVMDIScopeBase<LLVMDIScopePass> {
     auto subprogramAttr = LLVM::DISubprogramAttr::get(
         context, distinctId, compileUnitAttr, fileAttr, funcNameAttr,
         funcNameAttr, fileAttr, /*line=*/line, /*scopeline=*/line,
-        subprogramFlags, subroutineTypeAttr, /*retainNodes=*/{});
+        subprogramFlags, subroutineTypeAttr, /*retainNodes=*/{},
+        /*annotations=*/{});
     funcOp->setLoc(FusedLoc::get(context, {loc}, subprogramAttr));
   }
 

python/BUILD

@@ -0,0 +1,77 @@
+# NOTE: Do not depend on any targets from this directory,
+# but use //third_party/py/triton instead.
+
+load("@pybind11_bazel//:build_defs.bzl", "pybind_extension")
+
+package(
+    default_applicable_licenses = ["//:license"],
+    default_visibility = [
+        "//third_party/py/triton:__pkg__",
+        "@triton//python:__subpackages__",
+    ],
+)
+
+cc_library(
+    name = "passes",
+    hdrs = ["src/passes.h"],
+    includes = ["src"],
+    visibility = ["@triton//third_party:__subpackages__"],
+)
+
+pybind_extension(
+    name = "libtriton",
+    srcs = [
+        "src/interpreter.cc",
+        "src/ir.cc",
+        "src/llvm.cc",
+        "src/main.cc",
+        "src/passes.cc",
+    ],
+    copts = ["-DTRITON_BACKENDS_TUPLE=(nvidia)"],
+    deps = [
+        ":passes",
+        "@llvm-project//llvm:Core",
+        "@llvm-project//llvm:IPO",
+        "@llvm-project//llvm:IRReader",
+        "@llvm-project//llvm:InstCombine",
+        "@llvm-project//llvm:Linker",
+        "@llvm-project//llvm:MC",
+        "@llvm-project//llvm:Passes",
+        "@llvm-project//llvm:Support",
+        "@llvm-project//llvm:Target",
+        "@llvm-project//mlir:BuiltinToLLVMIRTranslation",
+        "@llvm-project//mlir:BytecodeWriter",
+        "@llvm-project//mlir:ControlFlowDialect",
+        "@llvm-project//mlir:ConversionPasses",
+        "@llvm-project//mlir:IR",
+        "@llvm-project//mlir:IndexDialect",
+        "@llvm-project//mlir:LLVMDialect",
+        "@llvm-project//mlir:LLVMIRTransforms",
+        "@llvm-project//mlir:LLVMToLLVMIRTranslation",
+        "@llvm-project//mlir:NVVMToLLVMIRTranslation",
+        "@llvm-project//mlir:Parser",
+        "@llvm-project//mlir:Pass",
+        "@llvm-project//mlir:Support",
+        "@llvm-project//mlir:ToLLVMIRTranslation",
+        "@llvm-project//mlir:Transforms",
+        "//:TritonAnalysis",
+        "//:TritonDialects",
+        "//:TritonGPUToLLVM",
+        "//:TritonGPUTransforms",
+        "//:TritonHSACO",
+        "//:TritonLLVMIR",
+        "//:TritonNvidiaGPUTransforms",
+        "//:TritonPTX",
+        "//:TritonToTritonGPU",
+        "//:TritonTools",
+        "//:TritonTransforms",
+        "@triton//third_party/nvidia:triton_nvidia",
+    ],
+)
+
+filegroup(
+    name = "files",
+    srcs = glob(
+        include = ["triton/**/*.py"],
+    ),
+)

python/test/regression/BUILD

@@ -0,0 +1,26 @@
+load("//third_party/py/pytest:pytest_defs.bzl", "pytest_multi_tests")
+
+package(
+    default_applicable_licenses = ["//:license"],
+)
+
+pytest_multi_tests(
+    name = "tests",
+    size = "large",
+    srcs = ["conftest.py"],
+    shard_count = 10,
+    tags = [
+        "config-cuda-only",
+        "requires-gpu-sm80",
+    ],
+    tests = glob(
+        include = ["test_*.py"],
+        exclude = [
+            "test_performance.py",  #TODO(b/321005767): fix failing test
+        ],
+    ),
+    deps = [
+        "//third_party/py/torch:pytorch",
+        "//third_party/py/triton",
+    ],
+)

python/test/regression/conftest.py

@@ -0,0 +1,12 @@
+# content of conftest.py
+
+import pytest
+
+
+def pytest_addoption(parser):
+    parser.addoption("--device", action="store", default='cuda')
+
+
+@pytest.fixture
+def device(request):
+    return request.config.getoption("--device")