| // RUN: mlir-opt %s -sparsification --canonicalize | FileCheck %s --check-prefix=CHECK-HIR |
| // |
| // RUN: mlir-opt %s -sparsification --sparse-tensor-conversion --canonicalize | \ |
| // RUN: FileCheck %s --check-prefix=CHECK-MIR |
| |
| #X = #sparse_tensor.encoding<{ |
| dimLevelType = [ "dense", "dense", "dense" ], |
| dimOrdering = affine_map<(i,j,k) -> (k,i,j)> |
| }> |
| |
| #trait = { |
| indexing_maps = [ |
| affine_map<(i,j,k) -> (k,i,j)>, // A (in) |
| affine_map<(i,j,k) -> ()> // X (out) |
| ], |
| iterator_types = ["reduction", "reduction", "reduction"] |
| } |
| |
| // CHECK-HIR-LABEL: func @sparse_dynamic_dims( |
| // CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>, |
| // CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> { |
| // CHECK-HIR-DAG: %[[VAL_2:.*]] = arith.constant 1 : index |
| // CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 0 : index |
| // CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 2 : index |
| // CHECK-HIR: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK-HIR: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK-HIR: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK-HIR: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK-HIR: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32> |
| // CHECK-HIR: %[[VAL_10:.*]] = memref.alloc() : memref<f32> |
| // CHECK-HIR: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<f32> to memref<f32> |
| // CHECK-HIR: %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f32> |
| // CHECK-HIR: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_2]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) { |
| // CHECK-HIR: %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_3]] to %[[VAL_6]] step %[[VAL_2]] iter_args(%[[VAL_17:.*]] = %[[VAL_14]]) -> (f32) { |
| // CHECK-HIR: %[[VAL_18:.*]] = arith.muli %[[VAL_6]], %[[VAL_13]] : index |
| // CHECK-HIR: %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[VAL_16]] : index |
| // CHECK-HIR: %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_3]] to %[[VAL_7]] step %[[VAL_2]] iter_args(%[[VAL_22:.*]] = %[[VAL_17]]) -> (f32) { |
| // CHECK-HIR: %[[VAL_23:.*]] = arith.muli %[[VAL_7]], %[[VAL_19]] : index |
| // CHECK-HIR: %[[VAL_24:.*]] = arith.addi %[[VAL_23]], %[[VAL_21]] : index |
| // CHECK-HIR: %[[VAL_25:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_24]]] : memref<?xf32> |
| // CHECK-HIR: %[[VAL_26:.*]] = arith.addf %[[VAL_22]], %[[VAL_25]] : f32 |
| // CHECK-HIR: scf.yield %[[VAL_26]] : f32 |
| // CHECK-HIR: } |
| // CHECK-HIR: scf.yield %[[VAL_20]] : f32 |
| // CHECK-HIR: } |
| // CHECK-HIR: scf.yield %[[VAL_15]] : f32 |
| // CHECK-HIR: } |
| // CHECK-HIR: memref.store %[[VAL_12]], %[[VAL_10]][] : memref<f32> |
| // CHECK-HIR: %[[VAL_30:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<f32> |
| // CHECK-HIR: return %[[VAL_30]] : tensor<f32> |
| // CHECK-HIR: } |
| // |
| // CHECK-MIR-LABEL: func @sparse_dynamic_dims( |
| // CHECK-MIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>, |
| // CHECK-MIR-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> { |
| // CHECK-MIR-DAG: %[[VAL_2:.*]] = arith.constant 2 : index |
| // CHECK-MIR-DAG: %[[VAL_3:.*]] = arith.constant 1 : index |
| // CHECK-MIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index |
| // CHECK-MIR: %[[VAL_5:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_4]]) : (!llvm.ptr<i8>, index) -> index |
| // CHECK-MIR: %[[VAL_6:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_3]]) : (!llvm.ptr<i8>, index) -> index |
| // CHECK-MIR: %[[VAL_7:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_2]]) : (!llvm.ptr<i8>, index) -> index |
| // CHECK-MIR: %[[VAL_8:.*]] = call @sparseValuesF32(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf32> |
| // CHECK-MIR: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32> |
| // CHECK-MIR: %[[VAL_10:.*]] = memref.alloc() : memref<f32> |
| // CHECK-MIR: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<f32> to memref<f32> |
| // CHECK-MIR: %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f32> |
| // CHECK-MIR: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_4]] to %[[VAL_5]] step %[[VAL_3]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) { |
| // CHECK-MIR: %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_4]] to %[[VAL_6]] step %[[VAL_3]] iter_args(%[[VAL_17:.*]] = %[[VAL_14]]) -> (f32) { |
| // CHECK-MIR: %[[VAL_18:.*]] = arith.muli %[[VAL_6]], %[[VAL_13]] : index |
| // CHECK-MIR: %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[VAL_16]] : index |
| // CHECK-MIR: %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_4]] to %[[VAL_7]] step %[[VAL_3]] iter_args(%[[VAL_22:.*]] = %[[VAL_17]]) -> (f32) { |
| // CHECK-MIR: %[[VAL_23:.*]] = arith.muli %[[VAL_7]], %[[VAL_19]] : index |
| // CHECK-MIR: %[[VAL_24:.*]] = arith.addi %[[VAL_23]], %[[VAL_21]] : index |
| // CHECK-MIR: %[[VAL_25:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_24]]] : memref<?xf32> |
| // CHECK-MIR: %[[VAL_26:.*]] = arith.addf %[[VAL_22]], %[[VAL_25]] : f32 |
| // CHECK-MIR: scf.yield %[[VAL_26]] : f32 |
| // CHECK-MIR: } |
| // CHECK-MIR: scf.yield %[[VAL_20]] : f32 |
| // CHECK-MIR: } |
| // CHECK-MIR: scf.yield %[[VAL_15]] : f32 |
| // CHECK-MIR: } |
| // CHECK-MIR: memref.store %[[VAL_12]], %[[VAL_10]][] : memref<f32> |
| // CHECK-MIR: %[[VAL_30:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<f32> |
| // CHECK-MIR: return %[[VAL_30]] : tensor<f32> |
| // CHECK-MIR: } |
| func @sparse_dynamic_dims(%arga: tensor<?x?x?xf32, #X>, |
| %argx: tensor<f32>) -> tensor<f32> { |
| %0 = linalg.generic #trait |
| ins(%arga: tensor<?x?x?xf32, #X>) |
| outs(%argx: tensor<f32>) { |
| ^bb(%a : f32, %x: f32): |
| %0 = arith.addf %x, %a : f32 |
| linalg.yield %0 : f32 |
| } -> tensor<f32> |
| return %0 : tensor<f32> |
| } |
