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[AMD][Navi31] Convert WMMA dot op to LLVM #3199
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,19 @@ | ||
| // RUN: triton-opt %s --split-input-file --convert-triton-amdgpu-to-llvm | FileCheck %s | ||
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| // CHECK-LABEL: wmma_dot | ||
| #blocked = #triton_gpu.blocked<{sizePerThread = [1, 8], threadsPerWarp = [4, 8], warpsPerCTA = [4, 1], order = [1, 0]}> | ||
| #mma = #triton_gpu.amd_wmma<{warpsPerCTA = [2, 2]}> | ||
| module attributes {"triton_gpu.compute-capability" = 90 : i32, "triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 : i32, "triton_gpu.threads-per-warp" = 32 : i32} { | ||
| tt.func @wmma_dot(%arg0: tensor<16x16xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma}>>, %arg1: tensor<16x16xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma}>>, %arg2: tensor<16x16xf16, #mma>) { | ||
| // CHECK-COUNT-2: llvm.extractvalue %{{.*}} : !llvm.struct<(f16)> | ||
| // CHECK-COUNT-8: llvm.extractvalue %{{.*}} : !llvm.struct<(f16, f16, f16, f16, f16, f16, f16, f16)> | ||
| // CHECK: llvm.mlir.undef : vector<16xf16> | ||
| // CHECK-COUNT-8: llvm.insertelement {{.*}} : vector<16xf16> | ||
| // CHECK: rocdl.wmma.f16.16x16x16.f16 {{.*}} : (f16, f16, vector<16xf16>, i1) -> vector<16xf16> | ||
| %0 = tt.dot %arg0, %arg1, %arg2 {allowTF32 = false, maxNumImpreciseAcc = 0 : i32} : tensor<16x16xf16, #triton_gpu.dot_op<{opIdx = 0, parent = #mma}>> * tensor<16x16xf16, #triton_gpu.dot_op<{opIdx = 1, parent = #mma}>> -> tensor<16x16xf16, #mma> | ||
| // CHECK-COUNT-8: llvm.extractelement {{.*}} : vector<16xf16> | ||
| // CHECK: llvm.mlir.undef : !llvm.struct<(f16, f16, f16, f16, f16, f16, f16, f16)> | ||
| // CHECK-COUNT-8: llvm.insertvalue {{.*}} : !llvm.struct<(f16, f16, f16, f16, f16, f16, f16, f16)> | ||
| tt.return | ||
| } | ||
| } |
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245 changes: 245 additions & 0 deletions
245
third_party/amd/lib/TritonAMDGPUToLLVM/DotOpToLLVM/WMMA.cpp
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,245 @@ | ||
| /* | ||
| * Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved. | ||
| * | ||
| * Permission is hereby granted, free of charge, to any person obtaining | ||
| * a copy of this software and associated documentation files | ||
| * (the "Software"), to deal in the Software without restriction, | ||
| * including without limitation the rights to use, copy, modify, merge, | ||
| * publish, distribute, sublicense, and/or sell copies of the Software, | ||
| * and to permit persons to whom the Software is furnished to do so, | ||
| * subject to the following conditions: | ||
| * | ||
| * The above copyright notice and this permission notice shall be | ||
| * included in all copies or substantial portions of the Software. | ||
| * | ||
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, | ||
| * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF | ||
| * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. | ||
| * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY | ||
| * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, | ||
| * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE | ||
| * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. | ||
| */ | ||
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| #include "../DotOpToLLVM.h" | ||
| #include "Utility.h" | ||
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| #include "mlir/Dialect/LLVMIR/ROCDLDialect.h" | ||
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| using namespace mlir; | ||
| using namespace mlir::triton; | ||
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| namespace AMD { | ||
| namespace { | ||
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| using ::AMD::TritonGPUToLLVMTypeConverter; | ||
| using ::mlir::triton::gpu::AMDWmmaEncodingAttr; | ||
| using ::mlir::triton::gpu::DotOperandEncodingAttr; | ||
| using ::mlir::triton::gpu::SharedEncodingAttr; | ||
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| enum class WMMAInstrType : uint8_t { | ||
| // D = AB + C; | ||
| // typeof(D) == typeof(C) | ||
| // typeof(A) == typeof(B) | ||
| // typeof(D), typeof(A): | ||
| FP32_FP16, | ||
| FP32_BF16, | ||
| FP16_FP16, | ||
| BF16_BF16, | ||
| INT32_IU8, | ||
| INT32_IU4, | ||
| NOT_APPLICABLE, | ||
| }; | ||
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| using ValueTable = std::map<std::pair<unsigned, unsigned>, Value>; | ||
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| ValueTable | ||
| getValuesFromDotOperandLayoutStruct(ConversionPatternRewriter &rewriter, | ||
| TritonGPUToLLVMTypeConverter *typeConverter, | ||
| Value value, int n0, int n1, Type type, | ||
| Location loc) { | ||
| auto elems = typeConverter->unpackLLElements(loc, value, rewriter); | ||
| ValueTable vals; | ||
| for (int i = 0; i < n0; i++) { | ||
| for (int j = 0; j < n1; j++) { | ||
| vals[{i, j}] = elems[n1 * i + j]; | ||
| } | ||
| } | ||
| return vals; | ||
| } | ||
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| static WMMAInstrType getWMMAInstrTypeFromDot(DotOp op) { | ||
| auto aOperandTy = op.getA().getType(); | ||
| auto aTensorTy = aOperandTy.cast<RankedTensorType>(); | ||
| auto aElemTy = aTensorTy.getElementType(); | ||
| auto bOperandTy = op.getB().getType(); | ||
| auto bTensorTy = bOperandTy.cast<RankedTensorType>(); | ||
| auto bElemTy = bTensorTy.getElementType(); | ||
| assert(aElemTy == bElemTy); | ||
| auto cOperandTy = op.getC().getType(); | ||
| auto cTensorTy = cOperandTy.cast<RankedTensorType>(); | ||
| auto cElemTy = cTensorTy.getElementType(); | ||
| auto dOperandTy = op.getD().getType(); | ||
| auto dTensorTy = dOperandTy.cast<RankedTensorType>(); | ||
| auto dElemTy = dTensorTy.getElementType(); | ||
| assert(cElemTy == dElemTy); | ||
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| if (dElemTy.isF32() && aElemTy.isF16()) | ||
| return WMMAInstrType::FP32_FP16; | ||
| if (dElemTy.isF32() && aElemTy.isBF16()) | ||
| return WMMAInstrType::FP32_BF16; | ||
| if (dElemTy.isF16() && aElemTy.isF16()) | ||
| return WMMAInstrType::FP16_FP16; | ||
| if (dElemTy.isBF16() && aElemTy.isBF16()) | ||
| return WMMAInstrType::BF16_BF16; | ||
| if (dElemTy.isSignedInteger(32) && aElemTy.isUnsignedInteger(8)) | ||
| return WMMAInstrType::INT32_IU8; | ||
| if (dElemTy.isSignedInteger(32) && aElemTy.isUnsignedInteger(4)) | ||
| return WMMAInstrType::INT32_IU4; | ||
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| return WMMAInstrType::NOT_APPLICABLE; | ||
| } | ||
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| Value generateWMMAOp(ConversionPatternRewriter &rewriter, Location loc, | ||
| WMMAInstrType wmmaType, Value valA, Value valB, | ||
| Value valC) { | ||
| auto resType = valC.getType(); | ||
| Value falseFlag = int_val(1, false); | ||
| switch (wmmaType) { | ||
| case WMMAInstrType::FP32_FP16: | ||
| return rewriter.create<ROCDL::wmma_f32_16x16x16_f16>( | ||
| loc, TypeRange{resType}, ValueRange{valA, valB, valC}); | ||
| case WMMAInstrType::FP32_BF16: | ||
| return rewriter.create<ROCDL::wmma_f32_16x16x16_bf16>( | ||
| loc, TypeRange{resType}, ValueRange{valA, valB, valC}); | ||
| case WMMAInstrType::FP16_FP16: | ||
| return rewriter.create<ROCDL::wmma_f16_16x16x16_f16>( | ||
| loc, TypeRange{resType}, ValueRange{valA, valB, valC, falseFlag}); | ||
| case WMMAInstrType::BF16_BF16: | ||
| return rewriter.create<ROCDL::wmma_bf16_16x16x16_bf16>( | ||
| loc, TypeRange{resType}, ValueRange{valA, valB, valC, falseFlag}); | ||
| case WMMAInstrType::INT32_IU8: | ||
| return rewriter.create<ROCDL::wmma_i32_16x16x16_iu8>( | ||
| loc, TypeRange{resType}, ValueRange{valA, valB, valC, falseFlag}); | ||
| case WMMAInstrType::INT32_IU4: | ||
| return rewriter.create<ROCDL::wmma_i32_16x16x16_iu4>( | ||
| loc, TypeRange{resType}, ValueRange{valA, valB, valC, falseFlag}); | ||
| default: | ||
| llvm::report_fatal_error("WMMA data type not supported"); | ||
| } | ||
| return Value(); | ||
| } | ||
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| // Conduct the Dot conversion. | ||
| LogicalResult convertDot(DotOp op, DotOpAdaptor adaptor, | ||
| ConversionPatternRewriter &rewriter, | ||
| TritonGPUToLLVMTypeConverter *typeConverter) { | ||
| auto wmmaLayout = op.getResult() | ||
| .getType() | ||
| .cast<RankedTensorType>() | ||
| .getEncoding() | ||
| .cast<AMDWmmaEncodingAttr>(); | ||
| auto warpsPerCTA = wmmaLayout.getWarpsPerCTA(); | ||
| auto mnkDim = AMDWmmaEncodingAttr::getMNKDimPerWMMAInstr(); | ||
| auto wmmaInstrType = getWMMAInstrTypeFromDot(op); | ||
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| auto loc = op.getLoc(); | ||
| Value a = op.getA(); | ||
| Value b = op.getB(); | ||
| Value d = op.getD(); | ||
| auto aTensorTy = a.getType().cast<RankedTensorType>(); | ||
| auto bTensorTy = b.getType().cast<RankedTensorType>(); | ||
| auto dTensorTy = d.getType().cast<RankedTensorType>(); | ||
| auto elemTy = aTensorTy.getElementType(); | ||
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| auto aEncoding = aTensorTy.getEncoding().cast<DotOperandEncodingAttr>(); | ||
| auto bEncoding = bTensorTy.getEncoding().cast<DotOperandEncodingAttr>(); | ||
| int kWidth = aEncoding.getKWidth(); | ||
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| auto repA = | ||
| wmmaLayout.getWMMARepForOperands(aTensorTy.getShape(), elemTy, kWidth, 0); | ||
| auto repB = | ||
| wmmaLayout.getWMMARepForOperands(bTensorTy.getShape(), elemTy, kWidth, 1); | ||
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| assert(repA[1] == repB[0]); | ||
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| Value loadedA = adaptor.getA(); | ||
| Value loadedB = adaptor.getB(); | ||
| Value loadedC = adaptor.getC(); | ||
| auto numRepM = repA[0]; | ||
| auto numRepN = repB[1]; | ||
| auto numRepK = repA[1]; | ||
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| ValueTable ha = getValuesFromDotOperandLayoutStruct( | ||
| rewriter, typeConverter, loadedA, numRepM, numRepK, | ||
| aTensorTy.getElementType(), loc); | ||
| ValueTable hb = getValuesFromDotOperandLayoutStruct( | ||
| rewriter, typeConverter, loadedB, numRepN, numRepK, | ||
| aTensorTy.getElementType(), loc); | ||
| auto dstElemTy = dTensorTy.getElementType(); | ||
| auto fc = typeConverter->unpackLLElements(loc, loadedC, rewriter); | ||
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| unsigned warpSize = triton::gpu::getWarpSize(wmmaLayout); | ||
| // TODO get rid of magic numbers | ||
| unsigned vgprElemWidth = 32; | ||
| unsigned paddedOutputElemSize = | ||
| vgprElemWidth / dstElemTy.getIntOrFloatBitWidth(); | ||
| // compute number of output elements that each thread holds for one WMMA | ||
| // instruction. | ||
| auto elemsPerVec = mnkDim[0] * mnkDim[1] * paddedOutputElemSize / warpSize; | ||
| auto dElemsToStorePerThread = mnkDim[0] * mnkDim[1] / warpSize; | ||
| auto vecTy = vec_ty(dstElemTy, elemsPerVec); | ||
| for (int m = 0; m < numRepM; ++m) { | ||
| for (int n = 0; n < numRepN; ++n) { | ||
| Value acc = undef(vecTy); | ||
| for (unsigned v = 0; v < dElemsToStorePerThread; ++v) { | ||
| acc = insert_element(vecTy, acc, | ||
| fc[m * numRepN * dElemsToStorePerThread + | ||
| n * dElemsToStorePerThread + v], | ||
| i32_val(v * paddedOutputElemSize)); | ||
| } | ||
| for (size_t k = 0; k < numRepK; k++) { | ||
| acc = generateWMMAOp(rewriter, loc, wmmaInstrType, ha[{m, k}], | ||
| hb[{n, k}], acc); | ||
| } | ||
| for (unsigned v = 0; v < dElemsToStorePerThread; ++v) { | ||
| fc[m * numRepN * dElemsToStorePerThread + n * dElemsToStorePerThread + | ||
| v] = | ||
| extract_element(dstElemTy, acc, i32_val(v * paddedOutputElemSize)); | ||
| } | ||
| } | ||
| } | ||
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| // replace with new packed result | ||
| Type structTy = LLVM::LLVMStructType::getLiteral( | ||
| wmmaLayout.getContext(), SmallVector<Type>(fc.size(), dstElemTy)); | ||
| Value res = typeConverter->packLLElements(loc, fc, rewriter, structTy); | ||
| rewriter.replaceOp(op, res); | ||
| return success(); | ||
| } | ||
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| } // namespace | ||
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| LogicalResult convertWMMA(triton::DotOp op, triton::DotOp::Adaptor adaptor, | ||
| TritonGPUToLLVMTypeConverter *typeConverter, | ||
| ConversionPatternRewriter &rewriter) { | ||
| auto rankedTType = [](Value tensor) { | ||
| return tensor.getType().cast<RankedTensorType>(); | ||
| }; | ||
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| assert(rankedTType(op.getA()).getEncoding().isa<DotOperandEncodingAttr>() && | ||
| rankedTType(op.getB()).getEncoding().isa<DotOperandEncodingAttr>() && | ||
| "Both $a and %b should be DotOperand layout."); | ||
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| auto cTensorTy = rankedTType(op.getC()); | ||
| auto dTensorTy = rankedTType(op.getD()); | ||
| assert(cTensorTy.getEncoding().isa<AMDWmmaEncodingAttr>() && | ||
| "Currently, we only support $c with a wmma layout."); | ||
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| assert(cTensorTy.getShape()[0] == dTensorTy.getShape()[0] && | ||
| cTensorTy.getShape()[1] == dTensorTy.getShape()[1] && | ||
| "DotOp's $c operand should pass the same number of values as $d"); | ||
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| return convertDot(op, adaptor, rewriter, typeConverter); | ||
| } | ||
| } // namespace AMD | ||
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can you add a basic lit tests to make sure this codes runs.
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Yep. working on it