| // RUN: mlir-opt %s --sparsification-and-bufferization | FileCheck %s |
| |
| #map = affine_map<(d0, d1, d2) -> (d0, d1, d2)> |
| |
| #sparse = #sparse_tensor.encoding<{ |
| map = (d0, d1, d2) -> (d0 : dense, d1 : dense, d2 : compressed) |
| }> |
| |
| // |
| // Make sure a simple ReLU passes the sparsifier |
| // |
| // CHECK-LABEL: func.func @relu |
| // CHECK: scf.for |
| // CHECK: scf.for |
| // CHECK: scf.for |
| // CHECK: arith.cmpf ugt |
| // CHECK: arith.select |
| // |
| func.func @relu(%arg0: tensor<10x20x30xf64, #sparse>) -> tensor<10x20x30xf64, #sparse> { |
| %cst = arith.constant 0.000000e+00 : f64 |
| %0 = tensor.empty() : tensor<10x20x30xf64> |
| %1 = linalg.generic { |
| indexing_maps = [#map, #map], |
| iterator_types = ["parallel", "parallel", "parallel"]} |
| ins(%arg0 : tensor<10x20x30xf64, #sparse>) |
| outs(%0 : tensor<10x20x30xf64>) { |
| ^bb0(%in: f64, %out: f64): |
| %2 = arith.cmpf ugt, %in, %cst : f64 |
| %3 = arith.select %2, %in, %cst : f64 |
| linalg.yield %3 : f64 |
| } -> tensor<10x20x30xf64> |
| %cast = tensor.cast %1 : tensor<10x20x30xf64> to tensor<10x20x30xf64, #sparse> |
| return %cast : tensor<10x20x30xf64, #sparse> |
| } |
