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1// RUN: mlir-opt %s --test-tensor-copy-insertion --pre-sparsification-rewrite --sparse-reinterpret-map --sparsification --cse | FileCheck %s2 3#SM = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed, d1 : compressed) }>4 5#trait_matmul = {6 indexing_maps = [7 affine_map<(d0, d1, d2) -> (d1, d0)>,8 affine_map<(d0, d1, d2) -> (d0, d2)>,9 affine_map<(d0, d1, d2) -> (d1, d2)>10 ],11 iterator_types = ["reduction", "parallel", "parallel"]12}13 14#trait_scale = {15 indexing_maps = [16 affine_map<(d0, d1) -> (d0, d1)>,17 affine_map<(d0, d1) -> (d0, d1)>,18 affine_map<(d0, d1) -> (d0, d1)>19 ],20 iterator_types = ["parallel", "parallel"]21}22 23// CHECK-LABEL: func.func @fold_yield_arg_zero() -> tensor<1024x1024xf64> {24// CHECK: %[[C0:.*]] = arith.constant dense<0.000000e+00> : tensor<1024x1024xf64>25// CHECK: return %[[C0]] : tensor<1024x1024xf64>26// CHECK: }27func.func @fold_yield_arg_zero() -> tensor<1024x1024xf64> {28 %cst = arith.constant 0.000000e+00 : f6429 %0 = tensor.empty() : tensor<1024x1024xf64>30 %1 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> ()>,31 affine_map<(d0, d1) -> (d0, d1)>],32 iterator_types = ["parallel", "parallel"]}33 ins(%cst : f64)34 outs(%0 : tensor<1024x1024xf64>) {35 ^bb0(%a: f64, %x: f64):36 linalg.yield %a : f6437 } -> tensor<1024x1024xf64>38 return %1 : tensor<1024x1024xf64>39}40 41// CHECK-LABEL: func.func @fold_yield_direct_zero() -> tensor<32xf64> {42// CHECK: %[[C0:.*]] = arith.constant dense<0.000000e+00> : tensor<32xf64>43// CHECK: return %[[C0]] : tensor<32xf64>44// CHECK: }45func.func @fold_yield_direct_zero() -> tensor<32xf64> {46 %cst = arith.constant 0.000000e+00 : f6447 %0 = tensor.empty() : tensor<32xf64>48 %1 = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>],49 iterator_types = ["parallel"]}50 outs(%0 : tensor<32xf64>) {51 ^bb0(%x: f64):52 linalg.yield %cst : f6453 } -> tensor<32xf64>54 return %1 : tensor<32xf64>55}56 57// CHECK-LABEL: func.func @sampled_dd_unfused(58// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xf64, #sparse{{[0-9]*}}>,59// CHECK-SAME: %[[VAL_1:.*]]: tensor<8x8xf64>,60// CHECK-SAME: %[[VAL_2:.*]]: tensor<8x8xf64>) -> tensor<8x8xf64> {61// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index62// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index63// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index64// CHECK-DAG: %[[VAL_6:.*]] = arith.constant dense<0.000000e+00> : tensor<8x8xf64>65// CHECK-DAG: %[[VAL_7:.*]] = bufferization.alloc_tensor() copy(%[[VAL_6]]) : tensor<8x8xf64>66// CHECK-DAG: %[[VAL_8:.*]] = bufferization.alloc_tensor() copy(%[[VAL_6]]) : tensor<8x8xf64>67// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_buffer %[[VAL_1]] : tensor<8x8xf64> to memref<8x8xf64>68// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_buffer %[[VAL_2]] : tensor<8x8xf64> to memref<8x8xf64>69// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse{{[0-9]*}}> to memref<?xindex>70// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse{{[0-9]*}}> to memref<?xindex>71// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse{{[0-9]*}}> to memref<?xindex>72// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse{{[0-9]*}}> to memref<?xindex>73// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8x8xf64, #sparse{{[0-9]*}}> to memref<?xf64>74// CHECK-DAG: %[[VAL_16:.*]] = bufferization.to_buffer %[[VAL_8]] : tensor<8x8xf64> to memref<8x8xf64>75// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_4]]] : memref<?xindex>76// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_5]]] : memref<?xindex>77// CHECK: scf.for %[[VAL_19:.*]] = %[[VAL_17]] to %[[VAL_18]] step %[[VAL_5]] {78// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_19]]] : memref<?xindex>79// CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {80// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]], %[[VAL_21]]] : memref<8x8xf64>81// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_19]]] : memref<?xindex>82// CHECK: %[[VAL_24:.*]] = arith.addi %[[VAL_19]], %[[VAL_5]] : index83// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_24]]] : memref<?xindex>84// CHECK: scf.for %[[VAL_26:.*]] = %[[VAL_23]] to %[[VAL_25]] step %[[VAL_5]] {85// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_26]]] : memref<?xindex>86// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_20]], %[[VAL_27]]] : memref<8x8xf64>87// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_21]], %[[VAL_27]]] : memref<8x8xf64>88// CHECK: %[[VAL_30:.*]] = arith.mulf %[[VAL_22]], %[[VAL_29]] : f6489// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_26]]] : memref<?xf64>90// CHECK: %[[VAL_32:.*]] = arith.mulf %[[VAL_30]], %[[VAL_31]] : f6491// CHECK: %[[VAL_33:.*]] = arith.addf %[[VAL_28]], %[[VAL_32]] : f6492// CHECK: memref.store %[[VAL_33]], %[[VAL_16]]{{\[}}%[[VAL_20]], %[[VAL_27]]] : memref<8x8xf64>93// CHECK: }94// CHECK: }95// CHECK: }96// CHECK: %[[VAL_34:.*]] = bufferization.to_tensor %[[VAL_16]] : memref<8x8xf64>97// CHECK: return %[[VAL_34]] : tensor<8x8xf64>98// CHECK: }99func.func @sampled_dd_unfused(%args: tensor<8x8xf64, #SM>,100 %arga: tensor<8x8xf64>,101 %argb: tensor<8x8xf64>) -> tensor<8x8xf64> {102 // Perform dense-dense matrix matrix multiplication.103 %1 = arith.constant dense<0.0> : tensor<8x8xf64>104 %2 = linalg.generic #trait_matmul105 ins(%arga, %argb : tensor<8x8xf64>, tensor<8x8xf64>)106 outs(%1 : tensor<8x8xf64>) {107 ^bb0(%a: f64, %b: f64, %x: f64):108 %p = arith.mulf %a, %b : f64109 %q = arith.addf %x, %p : f64110 linalg.yield %q : f64111 } -> tensor<8x8xf64>112 // Sample the result with elements-wise multiplication with sparse matrix.113 %3 = linalg.generic #trait_scale114 ins(%2, %args : tensor<8x8xf64>, tensor<8x8xf64, #SM>)115 outs(%1 : tensor<8x8xf64>) {116 ^bb0(%t: f64, %s: f64, %x: f64):117 %r = arith.mulf %t, %s : f64118 linalg.yield %r : f64119 } -> tensor<8x8xf64>120 return %3 : tensor<8x8xf64>121}122 123// CHECK-LABEL: func.func @sparse_sampled_dd_unfused(124// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xf64, #sparse{{[0-9]*}}>,125// CHECK-SAME: %[[VAL_1:.*]]: tensor<8x8xf64>,126// CHECK-SAME: %[[VAL_2:.*]]: tensor<8x8xf64>) -> tensor<8x8xf64, #sparse{{[0-9]*}}> {127// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index128// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index129// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index130// CHECK-DAG: %[[VAL_6:.*]] = arith.constant false131// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true132// CHECK-DAG: %[[VAL_8:.*]] = arith.constant dense<0.000000e+00> : tensor<8x8xf64>133// CHECK-DAG: %[[VAL_9:.*]] = bufferization.alloc_tensor() copy(%[[VAL_8]]) : tensor<8x8xf64>134// CHECK-DAG: %[[VAL_10:.*]] = tensor.empty() : tensor<8x8xf64, #sparse{{[0-9]*}}>135// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_buffer %[[VAL_1]] : tensor<8x8xf64> to memref<8x8xf64>136// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_buffer %[[VAL_2]] : tensor<8x8xf64> to memref<8x8xf64>137// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse{{[0-9]*}}> to memref<?xindex>138// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse{{[0-9]*}}> to memref<?xindex>139// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse{{[0-9]*}}> to memref<?xindex>140// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse{{[0-9]*}}> to memref<?xindex>141// CHECK-DAG: %[[VAL_17:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8x8xf64, #sparse{{[0-9]*}}> to memref<?xf64>142// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_4]]] : memref<?xindex>143// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_5]]] : memref<?xindex>144// CHECK: %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_18]] to %[[VAL_19]] step %[[VAL_5]] iter_args(%[[VAL_22:.*]] = %[[VAL_10]]) -> (tensor<8x8xf64, #sparse{{[0-9]*}}>) {145// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_21]]] : memref<?xindex>146// CHECK: %[[VAL_24:.*]], %[[VAL_25:.*]], %[[VAL_26:.*]], %[[VAL_27:.*]] = sparse_tensor.expand %[[VAL_10]] : tensor<8x8xf64, #sparse{{[0-9]*}}> to memref<?xf64>, memref<?xi1>, memref<?xindex>147// CHECK: %[[VAL_28:.*]] = scf.for %[[VAL_29:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] iter_args(%[[VAL_30:.*]] = %[[VAL_27]]) -> (index) {148// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_23]], %[[VAL_29]]] : memref<8x8xf64>149// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_21]]] : memref<?xindex>150// CHECK: %[[VAL_33:.*]] = arith.addi %[[VAL_21]], %[[VAL_5]] : index151// CHECK: %[[VAL_34:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_33]]] : memref<?xindex>152// CHECK: %[[VAL_35:.*]] = scf.for %[[VAL_36:.*]] = %[[VAL_32]] to %[[VAL_34]] step %[[VAL_5]] iter_args(%[[VAL_37:.*]] = %[[VAL_30]]) -> (index) {153// CHECK: %[[VAL_38:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_36]]] : memref<?xindex>154// CHECK: %[[VAL_39:.*]] = memref.load %[[VAL_24]]{{\[}}%[[VAL_38]]] : memref<?xf64>155// CHECK: %[[VAL_40:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_29]], %[[VAL_38]]] : memref<8x8xf64>156// CHECK: %[[VAL_41:.*]] = arith.mulf %[[VAL_31]], %[[VAL_40]] : f64157// CHECK: %[[VAL_42:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_36]]] : memref<?xf64>158// CHECK: %[[VAL_43:.*]] = arith.mulf %[[VAL_41]], %[[VAL_42]] : f64159// CHECK: %[[VAL_44:.*]] = arith.addf %[[VAL_39]], %[[VAL_43]] : f64160// CHECK: %[[VAL_45:.*]] = memref.load %[[VAL_25]]{{\[}}%[[VAL_38]]] : memref<?xi1>161// CHECK: %[[VAL_46:.*]] = arith.cmpi eq, %[[VAL_45]], %[[VAL_6]] : i1162// CHECK: %[[VAL_47:.*]] = scf.if %[[VAL_46]] -> (index) {163// CHECK: memref.store %[[VAL_7]], %[[VAL_25]]{{\[}}%[[VAL_38]]] : memref<?xi1>164// CHECK: memref.store %[[VAL_38]], %[[VAL_26]]{{\[}}%[[VAL_37]]] : memref<?xindex>165// CHECK: %[[VAL_48:.*]] = arith.addi %[[VAL_37]], %[[VAL_5]] : index166// CHECK: scf.yield %[[VAL_48]] : index167// CHECK: } else {168// CHECK: scf.yield %[[VAL_37]] : index169// CHECK: }170// CHECK: memref.store %[[VAL_44]], %[[VAL_24]]{{\[}}%[[VAL_38]]] : memref<?xf64>171// CHECK: scf.yield %[[VAL_47]] : index172// CHECK: }173// CHECK: scf.yield %[[VAL_35]] : index174// CHECK: }175// CHECK: %[[VAL_49:.*]] = sparse_tensor.compress %[[VAL_24]], %[[VAL_25]], %[[VAL_26]], %[[VAL_28]] into %[[VAL_22]]{{\[}}%[[VAL_23]]] : memref<?xf64>, memref<?xi1>, memref<?xindex>, tensor<8x8xf64, #sparse{{[0-9]*}}>176// CHECK: scf.yield %[[VAL_49]] : tensor<8x8xf64, #sparse{{[0-9]*}}>177// CHECK: }178// CHECK: %[[VAL_50:.*]] = sparse_tensor.load %[[VAL_20]] hasInserts : tensor<8x8xf64, #sparse{{[0-9]*}}>179// CHECK: return %[[VAL_50]] : tensor<8x8xf64, #sparse{{[0-9]*}}>180// CHECK: }181func.func @sparse_sampled_dd_unfused(%args: tensor<8x8xf64, #SM>,182 %arga: tensor<8x8xf64>,183 %argb: tensor<8x8xf64>) -> tensor<8x8xf64, #SM> {184 // Perform dense-dense matrix matrix multiplication.185 %1 = arith.constant dense<0.0> : tensor<8x8xf64>186 %2 = linalg.generic #trait_matmul187 ins(%arga, %argb : tensor<8x8xf64>, tensor<8x8xf64>)188 outs(%1 : tensor<8x8xf64>) {189 ^bb0(%a: f64, %b: f64, %x: f64):190 %p = arith.mulf %a, %b : f64191 %q = arith.addf %x, %p : f64192 linalg.yield %q : f64193 } -> tensor<8x8xf64>194 // Sample the result with elements-wise multiplication with sparse matrix.195 %3 = tensor.empty() : tensor<8x8xf64, #SM>196 %4 = linalg.generic #trait_scale197 ins(%2, %args : tensor<8x8xf64>, tensor<8x8xf64, #SM>)198 outs(%3 : tensor<8x8xf64, #SM>) {199 ^bb0(%t: f64, %s: f64, %x: f64):200 %r = arith.mulf %t, %s : f64201 linalg.yield %r : f64202 } -> tensor<8x8xf64, #SM>203 return %4 : tensor<8x8xf64, #SM>204}205