forked from OSchip/llvm-project
258 lines
20 KiB
MLIR
258 lines
20 KiB
MLIR
// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
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// RUN: mlir-opt %s \
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// RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \
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// RUN: --sparsification | FileCheck %s
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#DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
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// CHECK-LABEL: func @matmul1(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30xf32>,
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// CHECK-SAME: %[[VAL_2:.*]]: tensor<10x30xf32>) -> tensor<10x30xf32> {
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
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// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 30 : index
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// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20x30xf32>
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// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x30xf32>
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// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<10x30xf32>
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// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<10x30xf32> to memref<10x30xf32>
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// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_4]] {
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// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex>
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// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>
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// CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_16]], %[[VAL_4]] : index
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// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_18]] to %[[VAL_20]] step %[[VAL_4]] {
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// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_21]]] : memref<?xindex>
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// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_21]]] : memref<?xf32>
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// CHECK: scf.for %[[VAL_24:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_4]] {
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// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_17]], %[[VAL_24]]] : memref<10x30xf32>
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// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]], %[[VAL_24]]] : memref<20x30xf32>
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// CHECK: %[[VAL_27:.*]] = arith.mulf %[[VAL_23]], %[[VAL_26]] : f32
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// CHECK: %[[VAL_28:.*]] = arith.addf %[[VAL_25]], %[[VAL_27]] : f32
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// CHECK: memref.store %[[VAL_28]], %[[VAL_13]]{{\[}}%[[VAL_17]], %[[VAL_24]]] : memref<10x30xf32>
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// CHECK: }
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_29:.*]] = bufferization.to_tensor %[[VAL_13]] : memref<10x30xf32>
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// CHECK: return %[[VAL_29]] : tensor<10x30xf32>
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// CHECK: }
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func @matmul1(%a: tensor<10x20xf32, #DCSR>,
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%b: tensor<20x30xf32>,
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%c: tensor<10x30xf32>) -> tensor<10x30xf32> {
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%0 = linalg.matmul
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ins(%a, %b: tensor<10x20xf32, #DCSR>, tensor<20x30xf32>)
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outs(%c: tensor<10x30xf32>) -> tensor<10x30xf32>
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return %0 : tensor<10x30xf32>
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}
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//
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// Computes C = A x B with all matrices sparse (SpMSpM) in DCSR.
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//
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// CHECK-LABEL: func @matmul2(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<4x8xf64, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<8x4xf64, #sparse_tensor.encoding<{{{.*}}}>> {
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// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 4 : index
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
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// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 2 : index
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// CHECK-DAG: %[[VAL_6:.*]] = arith.constant false
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// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.init{{\[}}%[[VAL_2]], %[[VAL_2]]] : tensor<4x4xf64, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<4x8xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<4x8xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<4x8xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<4x8xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<4x8xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
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// CHECK: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<8x4xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<8x4xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_16:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<8x4xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_17:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<8x4xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
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// CHECK: %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<8x4xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
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// CHECK: %[[VAL_19:.*]] = memref.alloca(%[[VAL_5]]) : memref<?xindex>
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// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_4]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_22:.*]] = %[[VAL_20]] to %[[VAL_21]] step %[[VAL_4]] {
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// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_22]]] : memref<?xindex>
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// CHECK: memref.store %[[VAL_23]], %[[VAL_19]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: %[[VAL_24:.*]], %[[VAL_25:.*]], %[[VAL_26:.*]], %[[VAL_27:.*]] = sparse_tensor.expand %[[VAL_8]] : tensor<4x4xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>, memref<?xi1>, memref<?xindex>, index
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// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]]] : memref<?xindex>
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// CHECK: %[[VAL_29:.*]] = arith.addi %[[VAL_22]], %[[VAL_4]] : index
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// CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_29]]] : memref<?xindex>
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// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_4]]] : memref<?xindex>
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// CHECK: %[[VAL_33:.*]]:3 = scf.while (%[[VAL_34:.*]] = %[[VAL_28]], %[[VAL_35:.*]] = %[[VAL_31]], %[[VAL_36:.*]] = %[[VAL_27]]) : (index, index, index) -> (index, index, index) {
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// CHECK: %[[VAL_37:.*]] = arith.cmpi ult, %[[VAL_34]], %[[VAL_30]] : index
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// CHECK: %[[VAL_38:.*]] = arith.cmpi ult, %[[VAL_35]], %[[VAL_32]] : index
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// CHECK: %[[VAL_39:.*]] = arith.andi %[[VAL_37]], %[[VAL_38]] : i1
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// CHECK: scf.condition(%[[VAL_39]]) %[[VAL_34]], %[[VAL_35]], %[[VAL_36]] : index, index, index
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// CHECK: } do {
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// CHECK: ^bb0(%[[VAL_40:.*]]: index, %[[VAL_41:.*]]: index, %[[VAL_42:.*]]: index):
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// CHECK: %[[VAL_43:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_40]]] : memref<?xindex>
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// CHECK: %[[VAL_44:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_41]]] : memref<?xindex>
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// CHECK: %[[VAL_45:.*]] = arith.cmpi ult, %[[VAL_44]], %[[VAL_43]] : index
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// CHECK: %[[VAL_46:.*]] = arith.select %[[VAL_45]], %[[VAL_44]], %[[VAL_43]] : index
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// CHECK: %[[VAL_47:.*]] = arith.cmpi eq, %[[VAL_43]], %[[VAL_46]] : index
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// CHECK: %[[VAL_48:.*]] = arith.cmpi eq, %[[VAL_44]], %[[VAL_46]] : index
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// CHECK: %[[VAL_49:.*]] = arith.andi %[[VAL_47]], %[[VAL_48]] : i1
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// CHECK: %[[VAL_50:.*]] = scf.if %[[VAL_49]] -> (index) {
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// CHECK: %[[VAL_51:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_40]]] : memref<?xf64>
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// CHECK: %[[VAL_52:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_41]]] : memref<?xindex>
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// CHECK: %[[VAL_53:.*]] = arith.addi %[[VAL_41]], %[[VAL_4]] : index
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// CHECK: %[[VAL_54:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_53]]] : memref<?xindex>
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// CHECK: %[[VAL_55:.*]] = scf.for %[[VAL_56:.*]] = %[[VAL_52]] to %[[VAL_54]] step %[[VAL_4]] iter_args(%[[VAL_57:.*]] = %[[VAL_42]]) -> (index) {
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// CHECK: %[[VAL_58:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_56]]] : memref<?xindex>
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// CHECK: %[[VAL_59:.*]] = memref.load %[[VAL_24]]{{\[}}%[[VAL_58]]] : memref<?xf64>
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// CHECK: %[[VAL_60:.*]] = memref.load %[[VAL_18]]{{\[}}%[[VAL_56]]] : memref<?xf64>
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// CHECK: %[[VAL_61:.*]] = arith.mulf %[[VAL_51]], %[[VAL_60]] : f64
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// CHECK: %[[VAL_62:.*]] = arith.addf %[[VAL_59]], %[[VAL_61]] : f64
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// CHECK: %[[VAL_63:.*]] = memref.load %[[VAL_25]]{{\[}}%[[VAL_58]]] : memref<?xi1>
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// CHECK: %[[VAL_64:.*]] = arith.cmpi eq, %[[VAL_63]], %[[VAL_6]] : i1
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// CHECK: %[[VAL_65:.*]] = scf.if %[[VAL_64]] -> (index) {
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// CHECK: memref.store %[[VAL_7]], %[[VAL_25]]{{\[}}%[[VAL_58]]] : memref<?xi1>
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// CHECK: memref.store %[[VAL_58]], %[[VAL_26]]{{\[}}%[[VAL_57]]] : memref<?xindex>
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// CHECK: %[[VAL_66:.*]] = arith.addi %[[VAL_57]], %[[VAL_4]] : index
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// CHECK: scf.yield %[[VAL_66]] : index
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// CHECK: } else {
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// CHECK: scf.yield %[[VAL_57]] : index
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// CHECK: }
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// CHECK: memref.store %[[VAL_62]], %[[VAL_24]]{{\[}}%[[VAL_58]]] : memref<?xf64>
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// CHECK: scf.yield %[[VAL_67:.*]] : index
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// CHECK: }
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// CHECK: scf.yield %[[VAL_68:.*]] : index
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// CHECK: } else {
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// CHECK: scf.yield %[[VAL_42]] : index
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// CHECK: }
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// CHECK: %[[VAL_69:.*]] = arith.cmpi eq, %[[VAL_43]], %[[VAL_46]] : index
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// CHECK: %[[VAL_70:.*]] = arith.addi %[[VAL_40]], %[[VAL_4]] : index
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// CHECK: %[[VAL_71:.*]] = arith.select %[[VAL_69]], %[[VAL_70]], %[[VAL_40]] : index
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// CHECK: %[[VAL_72:.*]] = arith.cmpi eq, %[[VAL_44]], %[[VAL_46]] : index
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// CHECK: %[[VAL_73:.*]] = arith.addi %[[VAL_41]], %[[VAL_4]] : index
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// CHECK: %[[VAL_74:.*]] = arith.select %[[VAL_72]], %[[VAL_73]], %[[VAL_41]] : index
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// CHECK: scf.yield %[[VAL_71]], %[[VAL_74]], %[[VAL_75:.*]] : index, index, index
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// CHECK: }
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// CHECK: sparse_tensor.compress %[[VAL_8]], %[[VAL_19]], %[[VAL_24]], %[[VAL_25]], %[[VAL_26]], %[[VAL_76:.*]]#2 : tensor<4x4xf64, #sparse_tensor.encoding<{{{.*}}}>>, memref<?xindex>, memref<?xf64>, memref<?xi1>, memref<?xindex>, index
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// CHECK: }
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// CHECK: %[[VAL_77:.*]] = sparse_tensor.load %[[VAL_8]] hasInserts : tensor<4x4xf64, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: return %[[VAL_77]] : tensor<4x4xf64, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: }
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func @matmul2(%A: tensor<4x8xf64, #DCSR>,
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%B: tensor<8x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> {
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%c4 = arith.constant 4 : index
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%C = sparse_tensor.init [%c4, %c4] : tensor<4x4xf64, #DCSR>
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%D = linalg.matmul
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ins(%A, %B: tensor<4x8xf64, #DCSR>, tensor<8x4xf64, #DCSR>)
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outs(%C: tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR>
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return %D: tensor<4x4xf64, #DCSR>
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}
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// CHECK-LABEL: func @conv2d(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x8xi32>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-SAME: %[[VAL_2:.*]]: tensor<6x6xi32>) -> tensor<6x6xi32> {
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
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// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 6 : index
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// CHECK: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<8x8xi32>
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// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<6x6xi32>
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// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<6x6xi32>
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// CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<6x6xi32> to memref<6x6xi32>
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// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_4]] {
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// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>
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// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_16]]] : memref<?xindex>
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// CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_16]], %[[VAL_4]] : index
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// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_19]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_18]] to %[[VAL_20]] step %[[VAL_4]] {
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// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_21]]] : memref<?xindex>
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// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_21]]] : memref<?xi32>
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// CHECK: scf.for %[[VAL_24:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_4]] {
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// CHECK: scf.for %[[VAL_25:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_4]] {
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// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_25]], %[[VAL_24]]] : memref<6x6xi32>
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// CHECK: %[[VAL_27:.*]] = arith.addi %[[VAL_25]], %[[VAL_17]] : index
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// CHECK: %[[VAL_28:.*]] = arith.addi %[[VAL_24]], %[[VAL_22]] : index
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// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_27]], %[[VAL_28]]] : memref<8x8xi32>
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// CHECK: %[[VAL_30:.*]] = arith.muli %[[VAL_29]], %[[VAL_23]] : i32
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// CHECK: %[[VAL_31:.*]] = arith.addi %[[VAL_26]], %[[VAL_30]] : i32
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// CHECK: memref.store %[[VAL_31]], %[[VAL_13]]{{\[}}%[[VAL_25]], %[[VAL_24]]] : memref<6x6xi32>
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// CHECK: }
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// CHECK: }
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_32:.*]] = bufferization.to_tensor %[[VAL_13]] : memref<6x6xi32>
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// CHECK: return %[[VAL_32]] : tensor<6x6xi32>
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// CHECK: }
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func @conv2d(%input: tensor<8x8xi32>,
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%filter: tensor<3x3xi32, #DCSR>,
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%output: tensor<6x6xi32>) -> tensor<6x6xi32> {
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%0 = linalg.conv_2d
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ins (%input, %filter: tensor<8x8xi32>, tensor<3x3xi32, #DCSR>)
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outs (%output: tensor<6x6xi32>) -> tensor<6x6xi32>
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return %0 : tensor<6x6xi32>
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}
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// CHECK-LABEL: func @quantized_matmul(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<5x3xi8>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-SAME: %[[VAL_2:.*]]: tensor<5x6xi64>) -> tensor<5x6xi64> {
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : i64
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// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
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// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 5 : index
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// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<5x3xi8>
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_5]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_5]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<5x6xi64>
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// CHECK: %[[VAL_14:.*]] = memref.alloc() : memref<5x6xi64>
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// CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<5x6xi64> to memref<5x6xi64>
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// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
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// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_15]] to %[[VAL_16]] step %[[VAL_5]] {
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// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<?xindex>
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// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_17]]] : memref<?xindex>
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// CHECK: %[[VAL_20:.*]] = arith.addi %[[VAL_17]], %[[VAL_5]] : index
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// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_20]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_22:.*]] = %[[VAL_19]] to %[[VAL_21]] step %[[VAL_5]] {
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// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]]] : memref<?xindex>
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// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_22]]] : memref<?xi8>
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// CHECK: scf.for %[[VAL_25:.*]] = %[[VAL_4]] to %[[VAL_6]] step %[[VAL_5]] {
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// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_25]], %[[VAL_23]]] : memref<5x6xi64>
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// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_25]], %[[VAL_18]]] : memref<5x3xi8>
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// CHECK: %[[VAL_28:.*]] = arith.extsi %[[VAL_27]] : i8 to i64
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// CHECK: %[[VAL_29:.*]] = arith.subi %[[VAL_28]], %[[VAL_3]] : i64
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// CHECK: %[[VAL_30:.*]] = arith.extsi %[[VAL_24]] : i8 to i64
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// CHECK: %[[VAL_31:.*]] = arith.muli %[[VAL_29]], %[[VAL_30]] : i64
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// CHECK: %[[VAL_32:.*]] = arith.addi %[[VAL_26]], %[[VAL_31]] : i64
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// CHECK: memref.store %[[VAL_32]], %[[VAL_14]]{{\[}}%[[VAL_25]], %[[VAL_23]]] : memref<5x6xi64>
|
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// CHECK: }
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|
// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_33:.*]] = bufferization.to_tensor %[[VAL_14]] : memref<5x6xi64>
|
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// CHECK: return %[[VAL_33]] : tensor<5x6xi64>
|
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// CHECK: }
|
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func @quantized_matmul(%input1: tensor<5x3xi8>,
|
|
%input2: tensor<3x6xi8, #DCSR>,
|
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%output: tensor<5x6xi64>) -> tensor<5x6xi64> {
|
|
%c0 = arith.constant 0 : i32
|
|
%c2 = arith.constant 2 : i32
|
|
%0 = linalg.quantized_matmul
|
|
ins(%input1, %input2, %c2, %c0 : tensor<5x3xi8>, tensor<3x6xi8, #DCSR>, i32, i32)
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outs(%output : tensor<5x6xi64>) -> tensor<5x6xi64>
|
|
return %0: tensor<5x6xi64>
|
|
}
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