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Integrate LLVM at llvm/llvm-project@13c761789753
Updates LLVM usage to match [13c761789753](llvm/llvm-project@13c761789753) PiperOrigin-RevId: 717293402
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,127 @@ | ||
|
|
||
| --- a/include/triton/Conversion/MLIRTypes.h 2024-07-03 07:14:55.000000000 -0700 | ||
| +++ b/include/triton/Conversion/MLIRTypes.h 2025-01-19 13:19:21.000000000 -0800 | ||
| @@ -21,10 +21,10 @@ | ||
| } | ||
|
|
||
| // Float types | ||
| -inline Type f16Ty(MLIRContext *ctx) { return FloatType::getF16(ctx); } | ||
| -inline Type f32Ty(MLIRContext *ctx) { return FloatType::getF32(ctx); } | ||
| -inline Type f64Ty(MLIRContext *ctx) { return FloatType::getF64(ctx); } | ||
| -inline Type bf16Ty(MLIRContext *ctx) { return FloatType::getBF16(ctx); } | ||
| +inline Type f16Ty(MLIRContext *ctx) { return Float16Type::get(ctx); } | ||
| +inline Type f32Ty(MLIRContext *ctx) { return Float32Type::get(ctx); } | ||
| +inline Type f64Ty(MLIRContext *ctx) { return Float64Type::get(ctx); } | ||
| +inline Type bf16Ty(MLIRContext *ctx) { return BFloat16Type::get(ctx); } | ||
|
|
||
| inline bool isFloat(Type type) { | ||
| return type.isF32() || type.isF64() || type.isF16() || type.isF128() || | ||
|
|
||
| --- a/lib/Dialect/TritonGPU/IR/Ops.cpp 2025-01-15 12:52:52.000000000 -0800 | ||
| +++ b/lib/Dialect/TritonGPU/IR/Ops.cpp 2025-01-19 13:19:21.000000000 -0800 | ||
| @@ -15,7 +15,7 @@ | ||
| auto xTy = getSrc().getType(); | ||
| auto scaleTy = getScale().getType(); | ||
|
|
||
| - if (xTy.getElementType() != FloatType::getBF16(getContext()) && | ||
| + if (xTy.getElementType() != BFloat16Type::get(getContext()) && | ||
| xTy.getElementType() != IntegerType::get(getContext(), 8)) { | ||
| return emitOpError("element type of the first operand must be bf16 or i8"); | ||
| } | ||
| @@ -111,7 +111,7 @@ | ||
| auto newShape = SmallVector<int64_t>(xShape); | ||
| if (!encoding) { | ||
| newShape.back() *= 2; | ||
| - retTy = RankedTensorType::get(xShape, FloatType::getBF16(ctx)); | ||
| + retTy = RankedTensorType::get(xShape, BFloat16Type::get(ctx)); | ||
| } else { | ||
| auto oldEncoding = cast<DotOperandEncodingAttr>(encoding); | ||
| auto newVEncoding = DotOperandEncodingAttr::get( | ||
| @@ -123,7 +123,7 @@ | ||
| const bool hasBatch = xShape.size() == 3; | ||
| const int kIdx = (opIdx == 0 ? 1 : 0) + hasBatch; | ||
| newShape[kIdx] *= 2; | ||
| - retTy = RankedTensorType::get(newShape, FloatType::getBF16(ctx), | ||
| + retTy = RankedTensorType::get(newShape, BFloat16Type::get(ctx), | ||
| newVEncoding); | ||
| } | ||
| inferredReturnTypes.push_back(retTy); | ||
|
|
||
| --- a/third_party/nvidia/lib/NVGPUToLLVM/NVGPUToLLVMPass.cpp 2025-01-15 12:52:52.000000000 -0800 | ||
| +++ b/third_party/nvidia/lib/NVGPUToLLVM/NVGPUToLLVMPass.cpp 2025-01-19 13:19:22.000000000 -0800 | ||
| @@ -56,9 +56,9 @@ | ||
| else if (constraint == 'l') | ||
| ty = IntegerType::get(rewriter.getContext(), 64); | ||
| else if (constraint == 'f') | ||
| - ty = FloatType::getF32(rewriter.getContext()); | ||
| + ty = Float32Type::get(rewriter.getContext()); | ||
| else if (constraint == 'd') | ||
| - ty = FloatType::getF64(rewriter.getContext()); | ||
| + ty = Float64Type::get(rewriter.getContext()); | ||
| else { | ||
| assert(false && "Unsupported constraint"); | ||
| } | ||
|
|
||
| --- a/unittest/Dialect/TritonGPU/DialectTest.cpp 2025-01-15 12:52:52.000000000 -0800 | ||
| +++ b/unittest/Dialect/TritonGPU/DialectTest.cpp 2025-01-19 13:19:23.000000000 -0800 | ||
| @@ -492,10 +492,10 @@ | ||
| llvm::to_vector(llvm::reverse(llvm::seq<unsigned>(rank)))); | ||
|
|
||
| auto srcTy = RankedTensorType::get( | ||
| - srcShape, FloatType::getF32(&ctx), | ||
| + srcShape, Float32Type::get(&ctx), | ||
| BlockedEncodingAttr::get(&ctx, sizePerThread, threadsPerWarp, | ||
| warpsPerCTA, order, ctaLayout)); | ||
| - auto dstTy = RankedTensorType::get(dstShape, FloatType::getF32(&ctx)); | ||
| + auto dstTy = RankedTensorType::get(dstShape, Float32Type::get(&ctx)); | ||
|
|
||
| bool couldReshape = false; | ||
| testReshape(srcTy, dstTy, /*expectedDstEnc=*/std::nullopt, | ||
| @@ -526,7 +526,7 @@ | ||
| ctx.getOrLoadDialect<TritonGPUDialect>(); | ||
| ctaLayout = | ||
| triton::gpu::CTALayoutAttr::get(&ctx, ctaPerCGA, ctaSplit, ctaOrder); | ||
| - f16Ty = FloatType::getF16(&ctx); | ||
| + f16Ty = Float16Type::get(&ctx); | ||
| } | ||
|
|
||
| triton::gpu::AMDMfmaEncodingAttr createMFMA(int mDim, int nDim, | ||
| @@ -692,7 +692,7 @@ | ||
| ASSERT_EQ(linearLayout, expandedLL); | ||
|
|
||
| // Test that methods of DistributedEncoding return the same values | ||
| - Type eltTy = FloatType::getF32(&ctx); | ||
| + Type eltTy = Float32Type::get(&ctx); | ||
|
|
||
| ASSERT_EQ(getOrder(distributedEncoding), linearEncoding.getRepOrder()); | ||
| ASSERT_EQ(cast<triton::gpu::TritonGPU_AttrTrait>(distributedEncoding) | ||
|
|
||
| --- a/unittest/Dialect/TritonGPU/DumpLayoutTest.cpp 2024-10-31 04:36:20.000000000 -0700 | ||
| +++ b/unittest/Dialect/TritonGPU/DumpLayoutTest.cpp 2025-01-19 13:19:23.000000000 -0800 | ||
| @@ -182,7 +182,7 @@ | ||
| {1}, /* ord, row-major */ | ||
| {1}); /* cOrd */ | ||
|
|
||
| - auto elemTy = FloatType::getF16(sharedLayout.getContext()); | ||
| + auto elemTy = Float16Type::get(sharedLayout.getContext()); | ||
| auto tensorType = RankedTensorType::get({32}, elemTy, sharedLayout); | ||
| std::string layout = getLayoutStr(tensorType, /*useHWPointOfView=*/false); | ||
| assertSameStr(refStr, layout); | ||
| @@ -237,7 +237,7 @@ | ||
| {1, 0}, /* ord, row-major */ | ||
| {1, 0}); /* cOrd */ | ||
|
|
||
| - auto elemTy = FloatType::getF16(sharedLayout.getContext()); | ||
| + auto elemTy = Float16Type::get(sharedLayout.getContext()); | ||
| auto tensorType = RankedTensorType::get({8, 32}, elemTy, sharedLayout); | ||
| std::string layout = getLayoutStr(tensorType, /*useHWPointOfView=*/false); | ||
| assertSameStr(refStr, layout); | ||
| @@ -510,7 +510,7 @@ | ||
| {1, 0}, /* ord, row-major */ | ||
| {1, 0}); /* cOrd */ | ||
|
|
||
| - auto elemTyHW = FloatType::getF16(sharedLayoutHW.getContext()); | ||
| + auto elemTyHW = Float16Type::get(sharedLayoutHW.getContext()); | ||
| auto tensorTypeHW = RankedTensorType::get({8, 32}, elemTyHW, sharedLayoutHW); | ||
|
|
||
| std::string layoutHW = getLayoutStr(tensorTypeHW, /*useHWPointOfView=*/true); |
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