| // NOTE: Assertions have been autogenerated by utils/generate-test-checks.py |
| // RUN: mlir-opt %s -sparsification | FileCheck %s |
| |
| #SpVec = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }> |
| #CSR = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }> |
| |
| #trait1 = { |
| indexing_maps = [ |
| affine_map<(i) -> (i)>, // a |
| affine_map<(i) -> (3)>, // b |
| affine_map<(i) -> (i)> // x (out) |
| ], |
| iterator_types = ["parallel"], |
| doc = "x(i) += a(i) * b(3)" |
| } |
| |
| // CHECK-LABEL: func @mul_inv_dense1d( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<4xf32>, |
| // CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 3 : index |
| // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index |
| // CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<4xf32> |
| // CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> |
| // CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32xf32> |
| // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf32> to memref<32xf32> |
| // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_4]]] : memref<4xf32> |
| // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex> |
| // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex> |
| // CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_13]] to %[[VAL_14]] step %[[VAL_5]] { |
| // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex> |
| // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_16]]] : memref<32xf32> |
| // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xf32> |
| // CHECK: %[[VAL_19:.*]] = arith.mulf %[[VAL_18]], %[[VAL_12]] : f32 |
| // CHECK: %[[VAL_20:.*]] = arith.addf %[[VAL_17]], %[[VAL_19]] : f32 |
| // CHECK: memref.store %[[VAL_20]], %[[VAL_11]]{{\[}}%[[VAL_16]]] : memref<32xf32> |
| // CHECK: } |
| // CHECK: %[[VAL_21:.*]] = bufferization.to_tensor %[[VAL_11]] : memref<32xf32> |
| // CHECK: return %[[VAL_21]] : tensor<32xf32> |
| // CHECK: } |
| func @mul_inv_dense1d(%arga: tensor<32xf32, #SpVec>, |
| %argb: tensor<4xf32>, |
| %argx: tensor<32xf32>) -> tensor<32xf32> { |
| %0 = linalg.generic #trait1 |
| ins(%arga, %argb: tensor<32xf32, #SpVec>, tensor<4xf32>) |
| outs(%argx: tensor<32xf32>) { |
| ^bb(%a: f32, %b: f32, %x: f32): |
| %0 = arith.mulf %a, %b : f32 |
| %1 = arith.addf %x, %0 : f32 |
| linalg.yield %1 : f32 |
| } -> tensor<32xf32> |
| return %0 : tensor<32xf32> |
| } |
| |
| #trait2 = { |
| indexing_maps = [ |
| affine_map<(i) -> (i)>, // a |
| affine_map<(i) -> (i+2)>, // b |
| affine_map<(i) -> (i)> // x (out) |
| ], |
| iterator_types = ["parallel"], |
| doc = "x(i) = a(i) & b(i+2)" |
| } |
| |
| // CHECK-LABEL: func @and_affine_dense1d( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<34xi32>, |
| // CHECK-SAME: %[[VAL_2:.*]]: tensor<32xi32>) -> tensor<32xi32> { |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 2 : index |
| // CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<34xi32> |
| // CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xi32> |
| // CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32xi32> |
| // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xi32> to memref<32xi32> |
| // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex> |
| // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex> |
| // CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_4]] { |
| // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_14]]] : memref<?xindex> |
| // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xi32> |
| // CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_15]], %[[VAL_5]] : index |
| // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<34xi32> |
| // CHECK: %[[VAL_19:.*]] = arith.andi %[[VAL_16]], %[[VAL_18]] : i32 |
| // CHECK: memref.store %[[VAL_19]], %[[VAL_11]]{{\[}}%[[VAL_15]]] : memref<32xi32> |
| // CHECK: } |
| // CHECK: %[[VAL_20:.*]] = bufferization.to_tensor %[[VAL_11]] : memref<32xi32> |
| // CHECK: return %[[VAL_20]] : tensor<32xi32> |
| // CHECK: } |
| func @and_affine_dense1d(%arga: tensor<32xi32, #SpVec>, |
| %argb: tensor<34xi32>, |
| %argx: tensor<32xi32>) -> tensor<32xi32> { |
| %0 = linalg.generic #trait2 |
| ins(%arga, %argb: tensor<32xi32, #SpVec>, tensor<34xi32>) |
| outs(%argx: tensor<32xi32>) { |
| ^bb(%a: i32, %b: i32, %x: i32): |
| %0 = arith.andi %a, %b : i32 |
| linalg.yield %0 : i32 |
| } -> tensor<32xi32> |
| return %0 : tensor<32xi32> |
| } |
| |
| #trait3 = { |
| indexing_maps = [ |
| affine_map<(i,j) -> (i,j)>, // a |
| affine_map<(i,j) -> (i+2,j+3)>, // b |
| affine_map<(i,j) -> (i,j)> // x (out) |
| ], |
| iterator_types = ["parallel","parallel"], |
| doc = "x(i,j) += a(i,j) * b(i+2,j+3)" |
| } |
| |
| // CHECK-LABEL: func @mul_affine_dense2d( |
| // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>, |
| // CHECK-SAME: %[[VAL_1:.*]]: tensor<34x19xf64>, |
| // CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf64>) -> tensor<32x16xf64> { |
| // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index |
| // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index |
| // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index |
| // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 2 : index |
| // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 3 : index |
| // CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> |
| // CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<34x19xf64> |
| // CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf64> |
| // CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf64> |
| // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16xf64> to memref<32x16xf64> |
| // CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_3]] { |
| // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xindex> |
| // CHECK: %[[VAL_16:.*]] = arith.addi %[[VAL_14]], %[[VAL_3]] : index |
| // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex> |
| // CHECK: scf.for %[[VAL_18:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_3]] { |
| // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_18]]] : memref<?xindex> |
| // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_14]], %[[VAL_19]]] : memref<32x16xf64> |
| // CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_18]]] : memref<?xf64> |
| // CHECK: %[[VAL_22:.*]] = arith.addi %[[VAL_14]], %[[VAL_6]] : index |
| // CHECK: %[[VAL_23:.*]] = arith.addi %[[VAL_19]], %[[VAL_7]] : index |
| // CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]], %[[VAL_23]]] : memref<34x19xf64> |
| // CHECK: %[[VAL_25:.*]] = arith.mulf %[[VAL_21]], %[[VAL_24]] : f64 |
| // CHECK: %[[VAL_26:.*]] = arith.addf %[[VAL_20]], %[[VAL_25]] : f64 |
| // CHECK: memref.store %[[VAL_26]], %[[VAL_13]]{{\[}}%[[VAL_14]], %[[VAL_19]]] : memref<32x16xf64> |
| // CHECK: } |
| // CHECK: } |
| // CHECK: %[[VAL_27:.*]] = bufferization.to_tensor %[[VAL_13]] : memref<32x16xf64> |
| // CHECK: return %[[VAL_27]] : tensor<32x16xf64> |
| // CHECK: } |
| func @mul_affine_dense2d(%arga: tensor<32x16xf64, #CSR>, |
| %argb: tensor<34x19xf64>, |
| %argx: tensor<32x16xf64>) -> tensor<32x16xf64> { |
| %0 = linalg.generic #trait3 |
| ins(%arga, %argb: tensor<32x16xf64, #CSR>, tensor<34x19xf64>) |
| outs(%argx: tensor<32x16xf64>) { |
| ^bb(%a: f64, %b: f64, %x: f64): |
| %0 = arith.mulf %a, %b : f64 |
| %1 = arith.addf %x, %0 : f64 |
| linalg.yield %1 : f64 |
| } -> tensor<32x16xf64> |
| return %0 : tensor<32x16xf64> |
| } |
