a3610359b5
The indices for insert/compress were previously provided as a memref<?xindex> with proper rank, since that matched the argument for the runtime support libary better. However, with proper codegen coming, providing the indices as SSA values is much cleaner. This also brings the sparse_tensor.insert closer to unification with tensor.insert, planned in the longer run. Reviewed By: Peiming Differential Revision: https://reviews.llvm.org/D134404
311 lines
26 KiB
MLIR
311 lines
26 KiB
MLIR
// 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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#SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
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#DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
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// CHECK-LABEL: func.func @matmul1(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
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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 30 : index
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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: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
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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.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
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// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_13]] to %[[VAL_14]] step %[[VAL_5]] {
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// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
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// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xindex>
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// CHECK: %[[VAL_18:.*]] = arith.addi %[[VAL_15]], %[[VAL_5]] : index
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// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_20:.*]] = %[[VAL_17]] to %[[VAL_19]] step %[[VAL_5]] {
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// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xindex>
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// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_20]]] : memref<?xf32>
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// CHECK: scf.for %[[VAL_23:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
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// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_16]], %[[VAL_23]]] : memref<10x30xf32>
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// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_21]], %[[VAL_23]]] : memref<20x30xf32>
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// CHECK: %[[VAL_26:.*]] = arith.mulf %[[VAL_22]], %[[VAL_25]] : f32
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// CHECK: %[[VAL_27:.*]] = arith.addf %[[VAL_24]], %[[VAL_26]] : f32
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// CHECK: memref.store %[[VAL_27]], %[[VAL_12]]{{\[}}%[[VAL_16]], %[[VAL_23]]] : memref<10x30xf32>
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// CHECK: }
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_28:.*]] = bufferization.to_tensor %[[VAL_12]] : memref<10x30xf32>
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// CHECK: return %[[VAL_28]] : tensor<10x30xf32>
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// CHECK: }
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func.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.func @matmul2(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
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// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
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// CHECK-DAG: %[[VAL_4:.*]] = arith.constant false
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// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true
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// CHECK: %[[VAL_6:.*]] = bufferization.alloc_tensor() : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
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// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>
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// CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>
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// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_2]]] : memref<?xindex>
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// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_19:.*]] = %[[VAL_17]] to %[[VAL_18]] step %[[VAL_3]] {
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// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref<?xindex>
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// CHECK: %[[VAL_21:.*]], %[[VAL_22:.*]], %[[VAL_23:.*]], %[[VAL_24:.*]] = sparse_tensor.expand %[[VAL_6]] : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>, memref<?xi1>, memref<?xindex>
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// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_19]]] : memref<?xindex>
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// CHECK: %[[VAL_26:.*]] = arith.addi %[[VAL_19]], %[[VAL_3]] : index
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// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_26]]] : memref<?xindex>
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// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_2]]] : memref<?xindex>
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// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: %[[VAL_30:.*]]:3 = scf.while (%[[VAL_31:.*]] = %[[VAL_25]], %[[VAL_32:.*]] = %[[VAL_28]], %[[VAL_33:.*]] = %[[VAL_24]]) : (index, index, index) -> (index, index, index) {
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// CHECK: %[[VAL_34:.*]] = arith.cmpi ult, %[[VAL_31]], %[[VAL_27]] : index
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// CHECK: %[[VAL_35:.*]] = arith.cmpi ult, %[[VAL_32]], %[[VAL_29]] : index
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// CHECK: %[[VAL_36:.*]] = arith.andi %[[VAL_34]], %[[VAL_35]] : i1
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// CHECK: scf.condition(%[[VAL_36]]) %[[VAL_31]], %[[VAL_32]], %[[VAL_33]] : index, index, index
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// CHECK: } do {
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// CHECK: ^bb0(%[[VAL_37:.*]]: index, %[[VAL_38:.*]]: index, %[[VAL_39:.*]]: index):
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// CHECK: %[[VAL_40:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_37]]] : memref<?xindex>
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// CHECK: %[[VAL_41:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_38]]] : memref<?xindex>
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// CHECK: %[[VAL_42:.*]] = arith.cmpi ult, %[[VAL_41]], %[[VAL_40]] : index
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// CHECK: %[[VAL_43:.*]] = arith.select %[[VAL_42]], %[[VAL_41]], %[[VAL_40]] : index
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// CHECK: %[[VAL_44:.*]] = arith.cmpi eq, %[[VAL_40]], %[[VAL_43]] : index
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// CHECK: %[[VAL_45:.*]] = arith.cmpi eq, %[[VAL_41]], %[[VAL_43]] : index
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// CHECK: %[[VAL_46:.*]] = arith.andi %[[VAL_44]], %[[VAL_45]] : i1
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// CHECK: %[[VAL_47:.*]] = scf.if %[[VAL_46]] -> (index) {
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// CHECK: %[[VAL_48:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_37]]] : memref<?xf64>
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// CHECK: %[[VAL_49:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_38]]] : memref<?xindex>
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// CHECK: %[[VAL_50:.*]] = arith.addi %[[VAL_38]], %[[VAL_3]] : index
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// CHECK: %[[VAL_51:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_50]]] : memref<?xindex>
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// CHECK: %[[VAL_52:.*]] = scf.for %[[VAL_53:.*]] = %[[VAL_49]] to %[[VAL_51]] step %[[VAL_3]] iter_args(%[[VAL_54:.*]] = %[[VAL_39]]) -> (index) {
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// CHECK: %[[VAL_55:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_53]]] : memref<?xindex>
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// CHECK: %[[VAL_56:.*]] = memref.load %[[VAL_21]]{{\[}}%[[VAL_55]]] : memref<?xf64>
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// CHECK: %[[VAL_57:.*]] = memref.load %[[VAL_16]]{{\[}}%[[VAL_53]]] : memref<?xf64>
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// CHECK: %[[VAL_58:.*]] = arith.mulf %[[VAL_48]], %[[VAL_57]] : f64
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// CHECK: %[[VAL_59:.*]] = arith.addf %[[VAL_56]], %[[VAL_58]] : f64
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// CHECK: %[[VAL_60:.*]] = memref.load %[[VAL_22]]{{\[}}%[[VAL_55]]] : memref<?xi1>
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// CHECK: %[[VAL_61:.*]] = arith.cmpi eq, %[[VAL_60]], %[[VAL_4]] : i1
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// CHECK: %[[VAL_62:.*]] = scf.if %[[VAL_61]] -> (index) {
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// CHECK: memref.store %[[VAL_5]], %[[VAL_22]]{{\[}}%[[VAL_55]]] : memref<?xi1>
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// CHECK: memref.store %[[VAL_55]], %[[VAL_23]]{{\[}}%[[VAL_54]]] : memref<?xindex>
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// CHECK: %[[VAL_63:.*]] = arith.addi %[[VAL_54]], %[[VAL_3]] : index
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// CHECK: scf.yield %[[VAL_63]] : index
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// CHECK: } else {
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// CHECK: scf.yield %[[VAL_54]] : index
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// CHECK: }
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// CHECK: memref.store %[[VAL_59]], %[[VAL_21]]{{\[}}%[[VAL_55]]] : memref<?xf64>
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// CHECK: scf.yield %[[VAL_64:.*]] : index
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// CHECK: }
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// CHECK: scf.yield %[[VAL_65:.*]] : index
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// CHECK: } else {
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// CHECK: scf.yield %[[VAL_39]] : index
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// CHECK: }
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// CHECK: %[[VAL_66:.*]] = arith.cmpi eq, %[[VAL_40]], %[[VAL_43]] : index
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// CHECK: %[[VAL_67:.*]] = arith.addi %[[VAL_37]], %[[VAL_3]] : index
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// CHECK: %[[VAL_68:.*]] = arith.select %[[VAL_66]], %[[VAL_67]], %[[VAL_37]] : index
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// CHECK: %[[VAL_69:.*]] = arith.cmpi eq, %[[VAL_41]], %[[VAL_43]] : index
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// CHECK: %[[VAL_70:.*]] = arith.addi %[[VAL_38]], %[[VAL_3]] : index
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// CHECK: %[[VAL_71:.*]] = arith.select %[[VAL_69]], %[[VAL_70]], %[[VAL_38]] : index
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// CHECK: scf.yield %[[VAL_68]], %[[VAL_71]], %[[VAL_72:.*]] : index, index, index
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// CHECK: }
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// CHECK: sparse_tensor.compress %[[VAL_21]], %[[VAL_22]], %[[VAL_23]], %[[VAL_73:.*]]#2 into %[[VAL_6]]{{\[}}%[[VAL_20]]] : memref<?xf64>, memref<?xi1>, memref<?xindex>, tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
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// CHECK: }
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// CHECK: %[[VAL_74:.*]] = sparse_tensor.load %[[VAL_6]] hasInserts : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
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// CHECK: return %[[VAL_74]] : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
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// CHECK: }
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func.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 = bufferization.alloc_tensor() : 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.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<{ dimLevelType = [ "compressed", "compressed" ] }>>,
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// CHECK-SAME: %[[VAL_2:.*]]: tensor<6x6xi32>) -> tensor<6x6xi32> {
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 6 : index
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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: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<8x8xi32>
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// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
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// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xi32>
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// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<6x6xi32>
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// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
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// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_13]] to %[[VAL_14]] step %[[VAL_5]] {
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// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xindex>
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// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_15]]] : memref<?xindex>
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// CHECK: %[[VAL_18:.*]] = arith.addi %[[VAL_15]], %[[VAL_5]] : index
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// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_18]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_20:.*]] = %[[VAL_17]] to %[[VAL_19]] step %[[VAL_5]] {
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// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_20]]] : memref<?xindex>
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// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_20]]] : memref<?xi32>
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// CHECK: scf.for %[[VAL_23:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
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// CHECK: scf.for %[[VAL_24:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
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// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_24]], %[[VAL_23]]] : memref<6x6xi32>
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// CHECK: %[[VAL_26:.*]] = arith.addi %[[VAL_24]], %[[VAL_16]] : index
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// CHECK: %[[VAL_27:.*]] = arith.addi %[[VAL_23]], %[[VAL_21]] : index
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// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_26]], %[[VAL_27]]] : memref<8x8xi32>
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// CHECK: %[[VAL_29:.*]] = arith.muli %[[VAL_28]], %[[VAL_22]] : i32
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// CHECK: %[[VAL_30:.*]] = arith.addi %[[VAL_25]], %[[VAL_29]] : i32
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// CHECK: memref.store %[[VAL_30]], %[[VAL_12]]{{\[}}%[[VAL_24]], %[[VAL_23]]] : 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_31:.*]] = bufferization.to_tensor %[[VAL_12]] : memref<6x6xi32>
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// CHECK: return %[[VAL_31]] : tensor<6x6xi32>
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// CHECK: }
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func.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.func @quantized_matmul(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<5x3xi8>,
|
|
// CHECK-SAME: %[[VAL_1:.*]]: tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
|
|
// CHECK-SAME: %[[VAL_2:.*]]: tensor<5x6xi64>) -> tensor<5x6xi64> {
|
|
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 5 : index
|
|
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
|
|
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
|
|
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 2 : i64
|
|
// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<5x3xi8>
|
|
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
|
|
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
|
|
// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
|
|
// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
|
|
// CHECK: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xi8>
|
|
// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<5x6xi64>
|
|
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xindex>
|
|
// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_5]] {
|
|
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_16]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_16]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_19:.*]] = arith.addi %[[VAL_16]], %[[VAL_5]] : index
|
|
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_19]]] : memref<?xindex>
|
|
// CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_18]] to %[[VAL_20]] step %[[VAL_5]] {
|
|
// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_21]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_21]]] : memref<?xi8>
|
|
// CHECK: scf.for %[[VAL_24:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
|
|
// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_24]], %[[VAL_22]]] : memref<5x6xi64>
|
|
// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_24]], %[[VAL_17]]] : memref<5x3xi8>
|
|
// CHECK: %[[VAL_27:.*]] = arith.extsi %[[VAL_26]] : i8 to i64
|
|
// CHECK: %[[VAL_28:.*]] = arith.subi %[[VAL_27]], %[[VAL_6]] : i64
|
|
// CHECK: %[[VAL_29:.*]] = arith.extsi %[[VAL_23]] : i8 to i64
|
|
// CHECK: %[[VAL_30:.*]] = arith.muli %[[VAL_28]], %[[VAL_29]] : i64
|
|
// CHECK: %[[VAL_31:.*]] = arith.addi %[[VAL_25]], %[[VAL_30]] : i64
|
|
// CHECK: memref.store %[[VAL_31]], %[[VAL_13]]{{\[}}%[[VAL_24]], %[[VAL_22]]] : memref<5x6xi64>
|
|
// CHECK: }
|
|
// CHECK: }
|
|
// CHECK: }
|
|
// CHECK: %[[VAL_32:.*]] = bufferization.to_tensor %[[VAL_13]] : memref<5x6xi64>
|
|
// CHECK: return %[[VAL_32]] : tensor<5x6xi64>
|
|
// CHECK: }
|
|
func.func @quantized_matmul(%input1: tensor<5x3xi8>,
|
|
%input2: tensor<3x6xi8, #DCSR>,
|
|
%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)
|
|
outs(%output : tensor<5x6xi64>) -> tensor<5x6xi64>
|
|
return %0: tensor<5x6xi64>
|
|
}
|
|
|
|
// CHECK-LABEL: func.func @sparse_dot(
|
|
// CHECK-SAME: %[[VAL_0:.*0]]: tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
|
|
// CHECK-SAME: %[[VAL_1:.*1]]: tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
|
|
// CHECK-SAME: %[[VAL_2:.*2]]: tensor<f32>) -> tensor<f32> {
|
|
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
|
|
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
|
|
// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
|
|
// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
|
|
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
|
|
// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
|
|
// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
|
|
// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
|
|
// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<f32>
|
|
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_11]][] : memref<f32>
|
|
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_17:.*]]:3 = scf.while (%[[VAL_18:.*]] = %[[VAL_13]], %[[VAL_19:.*]] = %[[VAL_15]], %[[VAL_20:.*]] = %[[VAL_12]]) : (index, index, f32) -> (index, index, f32) {
|
|
// CHECK: %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_18]], %[[VAL_14]] : index
|
|
// CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_16]] : index
|
|
// CHECK: %[[VAL_23:.*]] = arith.andi %[[VAL_21]], %[[VAL_22]] : i1
|
|
// CHECK: scf.condition(%[[VAL_23]]) %[[VAL_18]], %[[VAL_19]], %[[VAL_20]] : index, index, f32
|
|
// CHECK: } do {
|
|
// CHECK: ^bb0(%[[VAL_24:.*]]: index, %[[VAL_25:.*]]: index, %[[VAL_26:.*]]: f32):
|
|
// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_24]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_25]]] : memref<?xindex>
|
|
// CHECK: %[[VAL_29:.*]] = arith.cmpi ult, %[[VAL_28]], %[[VAL_27]] : index
|
|
// CHECK: %[[VAL_30:.*]] = arith.select %[[VAL_29]], %[[VAL_28]], %[[VAL_27]] : index
|
|
// CHECK: %[[VAL_31:.*]] = arith.cmpi eq, %[[VAL_27]], %[[VAL_30]] : index
|
|
// CHECK: %[[VAL_32:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_30]] : index
|
|
// CHECK: %[[VAL_33:.*]] = arith.andi %[[VAL_31]], %[[VAL_32]] : i1
|
|
// CHECK: %[[VAL_34:.*]] = scf.if %[[VAL_33]] -> (f32) {
|
|
// CHECK: %[[VAL_35:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_24]]] : memref<?xf32>
|
|
// CHECK: %[[VAL_36:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_25]]] : memref<?xf32>
|
|
// CHECK: %[[VAL_37:.*]] = arith.mulf %[[VAL_35]], %[[VAL_36]] : f32
|
|
// CHECK: %[[VAL_38:.*]] = arith.addf %[[VAL_26]], %[[VAL_37]] : f32
|
|
// CHECK: scf.yield %[[VAL_38]] : f32
|
|
// CHECK: } else {
|
|
// CHECK: scf.yield %[[VAL_26]] : f32
|
|
// CHECK: }
|
|
// CHECK: %[[VAL_39:.*]] = arith.cmpi eq, %[[VAL_27]], %[[VAL_30]] : index
|
|
// CHECK: %[[VAL_40:.*]] = arith.addi %[[VAL_24]], %[[VAL_4]] : index
|
|
// CHECK: %[[VAL_41:.*]] = arith.select %[[VAL_39]], %[[VAL_40]], %[[VAL_24]] : index
|
|
// CHECK: %[[VAL_42:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_30]] : index
|
|
// CHECK: %[[VAL_43:.*]] = arith.addi %[[VAL_25]], %[[VAL_4]] : index
|
|
// CHECK: %[[VAL_44:.*]] = arith.select %[[VAL_42]], %[[VAL_43]], %[[VAL_25]] : index
|
|
// CHECK: scf.yield %[[VAL_41]], %[[VAL_44]], %[[VAL_45:.*]] : index, index, f32
|
|
// CHECK: }
|
|
// CHECK: memref.store %[[VAL_46:.*]]#2, %[[VAL_11]][] : memref<f32>
|
|
// CHECK: %[[VAL_47:.*]] = bufferization.to_tensor %[[VAL_11]] : memref<f32>
|
|
// CHECK: return %[[VAL_47]] : tensor<f32>
|
|
// CHECK: }
|
|
func.func @sparse_dot(%a: tensor<1024xf32, #SparseVector>,
|
|
%b: tensor<1024xf32, #SparseVector>,
|
|
%x: tensor<f32>) -> tensor<f32> {
|
|
%dot = linalg.dot ins(%a, %b: tensor<1024xf32, #SparseVector>,
|
|
tensor<1024xf32, #SparseVector>)
|
|
outs(%x: tensor<f32>) -> tensor<f32>
|
|
return %dot : tensor<f32>
|
|
}
|