diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp --- a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp +++ b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp @@ -51,13 +51,15 @@ // Helper method to find zero/uninitialized allocation. static bool isAlloc(OpOperand *op, bool isZero) { Value val = op->get(); + // Check allocation, with zero alloc when required. if (auto alloc = val.getDefiningOp()) { Value copy = alloc.getCopy(); if (isZero) return copy && isZeroValue(copy); return !copy; } - return false; + // Last resort for zero alloc: the whole value is zero. + return isZero && isZeroValue(val); } // Helper to detect sampling operation. diff --git a/mlir/test/Dialect/SparseTensor/sparse_sddmm_org.mlir b/mlir/test/Dialect/SparseTensor/sparse_sddmm_org.mlir new file mode 100644 --- /dev/null +++ b/mlir/test/Dialect/SparseTensor/sparse_sddmm_org.mlir @@ -0,0 +1,102 @@ +// RUN: mlir-opt %s --pre-sparsification-rewrite --sparsification --cse | FileCheck %s + +#SM = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }> + +#trait_matmul = { + indexing_maps = [ + affine_map<(d0, d1, d2) -> (d1, d0)>, + affine_map<(d0, d1, d2) -> (d0, d2)>, + affine_map<(d0, d1, d2) -> (d1, d2)> + ], + iterator_types = ["reduction", "parallel", "parallel"] +} + +#trait_scale = { + indexing_maps = [ + affine_map<(d0, d1) -> (d0, d1)>, + affine_map<(d0, d1) -> (d0, d1)>, + affine_map<(d0, d1) -> (d0, d1)> + ], + iterator_types = ["parallel", "parallel"] +} + +// CHECK-LABEL: func.func @sparse_sampled_dd_unfused( +// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<8x8xf64>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<8x8xf64>) -> tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant false +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true +// CHECK: %[[VAL_8:.*]] = bufferization.alloc_tensor() : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<8x8xf64> +// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<8x8xf64> +// CHECK: %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK: %[[VAL_13:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK: %[[VAL_14:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK: %[[VAL_15:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref +// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_4]]] : memref +// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_5]]] : memref +// CHECK: %[[VAL_18:.*]] = scf.for %[[VAL_19:.*]] = %[[VAL_16]] to %[[VAL_17]] step %[[VAL_5]] iter_args(%[[VAL_20:.*]] = %[[VAL_8]]) -> (tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) { +// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_19]]] : memref +// CHECK: %[[VAL_22:.*]], %[[VAL_23:.*]], %[[VAL_24:.*]], %[[VAL_25:.*]] = sparse_tensor.expand %[[VAL_8]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref, memref, memref +// CHECK: %[[VAL_26:.*]] = scf.for %[[VAL_27:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] iter_args(%[[VAL_28:.*]] = %[[VAL_25]]) -> (index) { +// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_21]], %[[VAL_27]]] : memref<8x8xf64> +// CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_19]]] : memref +// CHECK: %[[VAL_31:.*]] = arith.addi %[[VAL_19]], %[[VAL_5]] : index +// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_31]]] : memref +// CHECK: %[[VAL_33:.*]] = scf.for %[[VAL_34:.*]] = %[[VAL_30]] to %[[VAL_32]] step %[[VAL_5]] iter_args(%[[VAL_35:.*]] = %[[VAL_28]]) -> (index) { +// CHECK: %[[VAL_36:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_34]]] : memref +// CHECK: %[[VAL_37:.*]] = memref.load %[[VAL_22]]{{\[}}%[[VAL_36]]] : memref +// CHECK: %[[VAL_38:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_27]], %[[VAL_36]]] : memref<8x8xf64> +// CHECK: %[[VAL_39:.*]] = arith.mulf %[[VAL_29]], %[[VAL_38]] : f64 +// CHECK: %[[VAL_40:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_34]]] : memref +// CHECK: %[[VAL_41:.*]] = arith.mulf %[[VAL_39]], %[[VAL_40]] : f64 +// CHECK: %[[VAL_42:.*]] = arith.addf %[[VAL_37]], %[[VAL_41]] : f64 +// CHECK: %[[VAL_43:.*]] = memref.load %[[VAL_23]]{{\[}}%[[VAL_36]]] : memref +// CHECK: %[[VAL_44:.*]] = arith.cmpi eq, %[[VAL_43]], %[[VAL_6]] : i1 +// CHECK: %[[VAL_45:.*]] = scf.if %[[VAL_44]] -> (index) { +// CHECK: memref.store %[[VAL_7]], %[[VAL_23]]{{\[}}%[[VAL_36]]] : memref +// CHECK: memref.store %[[VAL_36]], %[[VAL_24]]{{\[}}%[[VAL_35]]] : memref +// CHECK: %[[VAL_46:.*]] = arith.addi %[[VAL_35]], %[[VAL_5]] : index +// CHECK: scf.yield %[[VAL_46]] : index +// CHECK: } else { +// CHECK: scf.yield %[[VAL_35]] : index +// CHECK: } +// CHECK: memref.store %[[VAL_42]], %[[VAL_22]]{{\[}}%[[VAL_36]]] : memref +// CHECK: scf.yield %[[VAL_47:.*]] : index +// CHECK: } {"Emitted from" = "linalg.generic"} +// CHECK: scf.yield %[[VAL_48:.*]] : index +// CHECK: } {"Emitted from" = "linalg.generic"} +// CHECK: %[[VAL_49:.*]] = sparse_tensor.compress %[[VAL_22]], %[[VAL_23]], %[[VAL_24]], %[[VAL_50:.*]] into %[[VAL_20]]{{\[}}%[[VAL_21]]] : memref, memref, memref, tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: scf.yield %[[VAL_49]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: } {"Emitted from" = "linalg.generic"} +// CHECK: %[[VAL_51:.*]] = sparse_tensor.load %[[VAL_52:.*]] hasInserts : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: return %[[VAL_51]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> +// CHECK: } +func.func @sparse_sampled_dd_unfused(%args: tensor<8x8xf64, #SM>, + %arga: tensor<8x8xf64>, + %argb: tensor<8x8xf64>) -> tensor<8x8xf64, #SM> { + // Perform dense-dense matrix matrix multiplication. + %1 = arith.constant dense<0.0> : tensor<8x8xf64> + %2 = linalg.generic #trait_matmul + ins(%arga, %argb : tensor<8x8xf64>, tensor<8x8xf64>) + outs(%1 : tensor<8x8xf64>) { + ^bb0(%a: f64, %b: f64, %x: f64): + %p = arith.mulf %a, %b : f64 + %q = arith.addf %x, %p : f64 + linalg.yield %q : f64 + } -> tensor<8x8xf64> + // Sample the result with elements-wise multiplication with sparse matrix. + %3 = bufferization.alloc_tensor() : tensor<8x8xf64, #SM> + %4 = linalg.generic #trait_scale + ins(%2, %args : tensor<8x8xf64>, tensor<8x8xf64, #SM>) + outs(%3 : tensor<8x8xf64, #SM>) { + ^bb0(%t: f64, %s: f64, %x: f64): + %r = arith.mulf %t, %s : f64 + linalg.yield %r : f64 + } -> tensor<8x8xf64, #SM> + return %4 : tensor<8x8xf64, #SM> +}