diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/SparseGPUCodegen.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/SparseGPUCodegen.cpp --- a/mlir/lib/Dialect/SparseTensor/Transforms/SparseGPUCodegen.cpp +++ b/mlir/lib/Dialect/SparseTensor/Transforms/SparseGPUCodegen.cpp @@ -605,6 +605,125 @@ return success(); } +/// Match and rewrite SDDMM kernel. +static LogicalResult rewriteSDDMM(PatternRewriter &rewriter, linalg::GenericOp op, + bool enableRT) { + Location loc = op.getLoc(); + Value a = op.getOperand(1); + Value b = op.getOperand(2); + Value c = op.getOperand(0); // we have C = AB + SmallVector tokens; + + // Only admissible sparse matrix format and dense matrices. + bool isCOO = false; + SparseTensorType aTp = getSparseTensorType(a); + SparseTensorType bTp = getSparseTensorType(b); + SparseTensorType cTp = getSparseTensorType(c); + if (!areAdmissibleTypes(aTp, bTp, cTp, enableRT, isCOO)) + return failure(); + + // TODO: duplicate C and update its user later than this GenericOp line if it + // is used by other ops. + + // the following does the in-place operation. If the sparse matrix C is + // reused, we may need to copy it before the operation so that users could use + // the new copy instead. Start sparse kernel and copy data from host to + // device. + // TODO: assert no other uses of c is later than this GenericOp operator + + // a : bufA -> matA + // b : bufB -> matA + // c : memR/memC/memV -> rowC,colC,valC + Value nseC = rewriter.create(loc, a); + Value szm = linalg::createOrFoldDimOp(rewriter, loc, a, 0); + Value szk = linalg::createOrFoldDimOp(rewriter, loc, a, 1); + Value szn = linalg::createOrFoldDimOp(rewriter, loc, b, 1); + Value bufA = genTensorToMemref(rewriter, loc, a); + Value matA = genAllocCopy(rewriter, loc, bufA, tokens); + Value bufB = genTensorToMemref(rewriter, loc, b); + Value matB = genAllocCopy(rewriter, loc, bufB, tokens); + Value memR = genFirstPosOrCrds(rewriter, loc, c, isCOO, enableRT); + Value memC = genSecondCrds(rewriter, loc, c, isCOO, enableRT); + Value memV = genToValues(rewriter, loc, c); + Value rowC = genAllocCopy(rewriter, loc, memR, tokens); + Value colC = memC ? genAllocCopy(rewriter, loc, memC, tokens) : Value(); + Value valC = genAllocCopy(rewriter, loc, memV, tokens); + genBlockingWait(rewriter, loc, tokens); + tokens.clear(); + + // Create sparse environment and sparse matrix/dense matrix handles. + Type indexTp = rewriter.getIndexType(); + Type envHandleTp = rewriter.getType(); + Type dnMatHandleTp = rewriter.getType(); + Type spMatHandleTp = rewriter.getType(); + Type tokenTp = rewriter.getType(); + Value token = genFirstWait(rewriter, loc); + auto env = + rewriter.create(loc, envHandleTp, tokenTp, token); + Value handle = env.getResult(0); + token = env.getAsyncToken(); + + auto dmatA = rewriter.create(loc, dnMatHandleTp, tokenTp, + token, szm, szk, matA); + Value dnA = dmatA.getResult(0); + token = dmatA.getAsyncToken(); + auto dmatB = rewriter.create(loc, dnMatHandleTp, tokenTp, + token, szk, szn, matB); + Value dnB = dmatB.getResult(0); + token = dmatB.getAsyncToken(); + + Operation *spGenC = + genSpMat(rewriter, loc, spMatHandleTp, tokenTp, token, szm, szn, nseC, + rowC, colC, valC, isCOO, enableRT); + Value spMatC = spGenC->getResult(0); + token = spGenC->getResult(1); + + // Precompute buffersize for SDDMM. + auto bufferComp = rewriter.create( + loc, indexTp, tokenTp, token, handle, dnA, dnB, spMatC); + Value bufferSz = bufferComp.getResult(0); + token = bufferComp.getAsyncToken(); + auto buf = genAllocBuffer(rewriter, loc, bufferSz, token); + Value buffer = buf.getResult(0); + token = buf.getAsyncToken(); + + // Perform the SDDMM. + auto sddmmComp = rewriter.create(loc, tokenTp, token, handle, + dnA, dnB, spMatC, buffer); + token = sddmmComp.getAsyncToken(); + + // Copy data back to host and free all the resoures. + token = rewriter.create(loc, tokenTp, token, dnA) + .getAsyncToken(); + token = rewriter.create(loc, tokenTp, token, dnB) + .getAsyncToken(); + token = rewriter.create(loc, tokenTp, token, spMatC) + .getAsyncToken(); + token = rewriter.create(loc, tokenTp, token, handle) + .getAsyncToken(); + tokens.push_back(token); + genBlockingWait(rewriter, loc, tokens); + tokens.clear(); + token = genFirstWait(rewriter, loc); + token = genCopyMemRef(rewriter, loc, memR, rowC, token); + token = genCopyMemRef(rewriter, loc, memC, colC, token); + token = genCopyMemRef(rewriter, loc, memV, valC, token); + token = genDeallocMemRef(rewriter, loc, buffer, token); + token = genDeallocMemRef(rewriter, loc, matA, token); + token = genDeallocMemRef(rewriter, loc, matB, token); + token = genDeallocMemRef(rewriter, loc, rowC, token); + if (colC) + token = genDeallocMemRef(rewriter, loc, colC, token); + token = genDeallocMemRef(rewriter, loc, valC, token); + tokens.push_back(token); + genBlockingWait(rewriter, loc, tokens); + tokens.clear(); + + // Done. + rewriter.replaceOp(op, op.getDpsInitOperand(0)->get()); + return success(); +} + //===----------------------------------------------------------------------===// // Rewriting rules for direct code generation. //===----------------------------------------------------------------------===// diff --git a/mlir/test/Dialect/SparseTensor/GPU/gpu_sampled_matmul_lib.mlir b/mlir/test/Dialect/SparseTensor/GPU/gpu_sampled_matmul_lib.mlir new file mode 100644 --- /dev/null +++ b/mlir/test/Dialect/SparseTensor/GPU/gpu_sampled_matmul_lib.mlir @@ -0,0 +1,102 @@ +// RUN: mlir-opt %s --linalg-generalize-named-ops \ +// RUN: --sparsification="enable-gpu-libgen" | FileCheck %s + +#trait_sampled_dense_dense = { + indexing_maps = [ + affine_map<(i,j,k) -> (i,j)>, // S + affine_map<(i,j,k) -> (i,k)>, // A + affine_map<(i,j,k) -> (k,j)>, // B + affine_map<(i,j,k) -> (i,j)> // X (out) + ], + iterator_types = ["parallel", "parallel", "reduction"], + doc = "X(i,j) += S(i,j) SUM_k A(i,k) B(k,j)" +} + +#CSR = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }> + +module { + +// +// A kernel that computes a direct sampled matrix matrix multiplication +// (with sparse result). +// Compute SDDMM C = C\spy AB +// VAL_0 is C +// +// CHECK-LABEL: func.func @sampled_dense_dense_matmul( +// CHECK-SAME: %[[VAL_0:.*]]: tensor>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor, +// CHECK-SAME: %[[VAL_2:.*]]: tensor) -> tensor { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.number_of_entries %[[VAL_0]] : tensor> +// CHECK-DAG: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor> +// CHECK-DAG: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor> +// CHECK-DAG: %[[VAL_8:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor> to memref +// CHECK: %[[VAL_12:.*]] = gpu.wait async +// CHECK: %[[VAL_13:.*]] = memref.dim %[[VAL_9]], %[[VAL_3]] : memref +// CHECK: %[[VAL_14:.*]], %[[VAL_15:.*]] = gpu.alloc async {{\[}}%[[VAL_12]]] (%[[VAL_13]]) : memref +// CHECK: %[[VAL_16:.*]] = gpu.memcpy async {{\[}}%[[VAL_15]]] %[[VAL_14]], %[[VAL_9]] : memref, memref +// CHECK: %[[VAL_17:.*]] = gpu.wait async +// CHECK: %[[VAL_18:.*]] = memref.dim %[[VAL_10]], %[[VAL_3]] : memref +// CHECK: %[[VAL_19:.*]], %[[VAL_20:.*]] = gpu.alloc async {{\[}}%[[VAL_17]]] (%[[VAL_18]]) : memref +// CHECK: %[[VAL_21:.*]] = gpu.memcpy async {{\[}}%[[VAL_20]]] %[[VAL_19]], %[[VAL_10]] : memref, memref +// CHECK: %[[VAL_22:.*]] = gpu.wait async +// CHECK: %[[VAL_23:.*]] = memref.dim %[[VAL_11]], %[[VAL_3]] : memref +// CHECK: %[[VAL_24:.*]], %[[VAL_25:.*]] = gpu.alloc async {{\[}}%[[VAL_22]]] (%[[VAL_23]]) : memref +// CHECK: %[[VAL_26:.*]] = gpu.memcpy async {{\[}}%[[VAL_25]]] %[[VAL_24]], %[[VAL_11]] : memref, memref +// CHECK: %[[VAL_27:.*]] = bufferization.to_memref %[[VAL_1]] : memref +// CHECK: %[[VAL_28:.*]] = gpu.wait async +// CHECK: %[[VAL_29:.*]] = memref.dim %[[VAL_27]], %[[VAL_3]] : memref +// CHECK: %[[VAL_30:.*]] = memref.dim %[[VAL_27]], %[[VAL_4]] : memref +// CHECK: %[[VAL_31:.*]], %[[VAL_32:.*]] = gpu.alloc async {{\[}}%[[VAL_28]]] (%[[VAL_29]], %[[VAL_30]]) : memref +// CHECK: %[[VAL_33:.*]] = gpu.memcpy async {{\[}}%[[VAL_32]]] %[[VAL_31]], %[[VAL_27]] : memref, memref +// CHECK: %[[VAL_34:.*]] = bufferization.to_memref %[[VAL_2]] : memref +// CHECK: %[[VAL_35:.*]] = gpu.wait async +// CHECK: %[[VAL_36:.*]] = memref.dim %[[VAL_34]], %[[VAL_3]] : memref +// CHECK: %[[VAL_37:.*]] = memref.dim %[[VAL_34]], %[[VAL_4]] : memref +// CHECK: %[[VAL_38:.*]], %[[VAL_39:.*]] = gpu.alloc async {{\[}}%[[VAL_35]]] (%[[VAL_36]], %[[VAL_37]]) : memref +// CHECK: %[[VAL_40:.*]] = gpu.memcpy async {{\[}}%[[VAL_39]]] %[[VAL_38]], %[[VAL_34]] : memref, memref +// CHECK: gpu.wait {{\[}}%[[VAL_16]], %[[VAL_21]], %[[VAL_26]], %[[VAL_33]], %[[VAL_40]]] +// CHECK: %[[VAL_41:.*]] = gpu.wait async +// CHECK: %[[VAL_42:.*]], %[[VAL_43:.*]] = gpu.create_sparse_env async {{\[}}%[[VAL_41]]] +// CHECK: %[[VAL_44:.*]], %[[VAL_45:.*]] = gpu.create_csr async {{\[}}%[[VAL_43]]] %[[VAL_6]], %[[VAL_7]], %[[VAL_5]], %[[VAL_14]], %[[VAL_19]], %[[VAL_24]] : memref, memref, memref +// CHECK: %[[VAL_46:.*]], %[[VAL_47:.*]] = gpu.create_dn_mat async {{\[}}%[[VAL_45]]] %[[VAL_7]], %[[VAL_8]], %[[VAL_31]] : memref +// CHECK: %[[VAL_48:.*]], %[[VAL_49:.*]] = gpu.create_dn_mat async {{\[}}%[[VAL_47]]] %[[VAL_6]], %[[VAL_8]], %[[VAL_38]] : memref +// CHECK: %[[VAL_50:.*]], %[[VAL_51:.*]] = gpu.sddmm_buffer_size async {{\[}}%[[VAL_49]]] %[[VAL_42]], %[[VAL_44]], %[[VAL_46]], %[[VAL_48]] +// CHECK: %[[VAL_52:.*]], %[[VAL_53:.*]] = gpu.alloc async {{\[}}%[[VAL_51]]] (%[[VAL_50]]) : memref +// CHECK: %[[VAL_54:.*]] = gpu.sddmm async {{\[}}%[[VAL_53]]] %[[VAL_42]], %[[VAL_44]], %[[VAL_46]], %[[VAL_48]], %[[VAL_52]] : memref +// CHECK: %[[VAL_55:.*]] = gpu.destroy_sp_mat async {{\[}}%[[VAL_54]]] %[[VAL_44]] +// CHECK: %[[VAL_56:.*]] = gpu.destroy_dn_mat async {{\[}}%[[VAL_55]]] %[[VAL_46]] +// CHECK: %[[VAL_57:.*]] = gpu.destroy_dn_mat async {{\[}}%[[VAL_56]]] %[[VAL_48]] +// CHECK: %[[VAL_58:.*]] = gpu.destroy_sparse_env async {{\[}}%[[VAL_57]]] %[[VAL_42]] +// CHECK: %[[VAL_59:.*]] = gpu.dealloc async {{\[}}%[[VAL_58]]] %[[VAL_14]] : memref +// CHECK: %[[VAL_60:.*]] = gpu.dealloc async {{\[}}%[[VAL_59]]] %[[VAL_19]] : memref +// CHECK: %[[VAL_61:.*]] = gpu.dealloc async {{\[}}%[[VAL_60]]] %[[VAL_24]] : memref +// CHECK: %[[VAL_62:.*]] = gpu.dealloc async {{\[}}%[[VAL_61]]] %[[VAL_52]] : memref +// CHECK: %[[VAL_63:.*]] = gpu.dealloc async {{\[}}%[[VAL_62]]] %[[VAL_31]] : memref +// CHECK: %[[VAL_64:.*]] = gpu.memcpy async {{\[}}%[[VAL_63]]] %[[VAL_34]], %[[VAL_38]] : memref, memref +// CHECK: %[[VAL_65:.*]] = gpu.dealloc async {{\[}}%[[VAL_64]]] %[[VAL_38]] : memref +// CHECK: gpu.wait {{\[}}%[[VAL_65]]] +// CHECK: %[[VAL_66:.*]] = bufferization.to_tensor %[[VAL_34]] : memref +// CHECK: return %[[VAL_66]] : tensor +// CHECK: } +func.func @sparse_sampled_dd(%args: tensor<8x8xf64, #CSR>, + %arga: tensor<8x8xf64>, + %argb: tensor<8x8xf64>) -> tensor<8x8xf64, #CSR> { + %1 = bufferization.alloc_tensor() : tensor<8x8xf64, #CSR> + %2 = linalg.generic #trait_sampled_dense_dense + ins(%args, %arga, %argb: tensor<8x8xf64, #CSR>, + tensor<8x8xf64>, tensor<8x8xf64>) + outs(%1: tensor<8x8xf64, #CSR>) { + ^bb(%s: f64, %a: f64, %b: f64, %x: f64): + %p = arith.mulf %a, %b : f64 + %q = arith.mulf %s, %p : f64 + %r = arith.addf %x, %q : f64 + linalg.yield %r : f64 + } -> tensor<8x8xf64, #CSR> + return %2 : tensor<8x8xf64, #CSR> +} +}