| // RUN: mlir-opt %s --canonicalize --pre-sparsification-rewrite | FileCheck %s |
| |
| #map = affine_map<(d0, d1, d2) -> (d0, d1, d2)> |
| |
| #sparse = #sparse_tensor.encoding<{ |
| map = (d0, d1, d2) -> |
| (d0 : compressed(nonunique), |
| d1 : singleton(nonunique, soa), |
| d2 : singleton(soa)), |
| posWidth = 64, |
| crdWidth = 64 |
| }> |
| |
| |
| module { |
| // |
| // This IR should not end up in an infinite loop trying to fold |
| // the linalg producer into the tensor cast consumer (even though |
| // static sizes can fold, the different encodings cannot). The |
| // cast was sloppy to begin with (but it has been observed by |
| // external sources) and can be easily repaired by the sparsifier. |
| // |
| // CHECK-LABEL: func @avoid_fold |
| // CHECK: arith.constant |
| // CHECK: tensor.empty() |
| // CHECK: linalg.generic |
| // CHECK: sparse_tensor.convert |
| // CHECK: return |
| // |
| func.func @avoid_fold(%0: tensor<10x20x30xf64, #sparse>) -> tensor<10x20x30xf64, #sparse> { |
| %1 = tensor.empty() : tensor<10x20x30xf64> |
| %2 = linalg.generic { indexing_maps = [#map, #map], |
| iterator_types = ["parallel", "parallel", "parallel"] |
| } |
| ins (%0 : tensor<10x20x30xf64, #sparse>) |
| outs(%1 : tensor<10x20x30xf64>) { |
| ^bb0(%in: f64, %out: f64): |
| %cst = arith.constant 0.000000e+00 : f64 |
| %4 = arith.cmpf ugt, %in, %cst : f64 |
| %5 = arith.select %4, %in, %cst : f64 |
| linalg.yield %5 : f64 |
| } -> tensor<10x20x30xf64> |
| %cast = tensor.cast %2 : tensor<10x20x30xf64> to tensor<10x20x30xf64, #sparse> |
| return %cast : tensor<10x20x30xf64, #sparse> |
| } |
| } |
| |
