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|
// RUN: mlir-opt %s --post-sparsification-rewrite="enable-runtime-library=false enable-foreach=true" --canonicalize | FileCheck %s
// CHECK-LABEL: func.func @sparse_foreach_constant
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[V1:.*]] = arith.constant 5.000000e+00 : f32
// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[V3:.*]] = arith.constant 1.000000e+00 : f32
// CHECK-DAG: %[[V4:.*]] = arith.constant 6.000000e+00 : f32
// (1, 1) -> (2, 1) -> (2, 2)
// CHECK-NEXT: "test.use"(%[[C1]], %[[C1]], %[[V1]])
// CHECK-NEXT: "test.use"(%[[C2]], %[[C1]], %[[V3]])
// CHECK-NEXT: "test.use"(%[[C1]], %[[C2]], %[[V4]])
// (1, 1) -> (1, 2) -> (2, 1)
// CHECK-NEXT: "test.use"(%[[C1]], %[[C1]], %[[V1]])
// CHECK-NEXT: "test.use"(%[[C1]], %[[C2]], %[[V4]])
// CHECK-NEXT: "test.use"(%[[C2]], %[[C1]], %[[V3]])
func.func @sparse_foreach_constant() -> () {
%cst = arith.constant sparse<[[2, 1], [1, 1], [1, 2]], [1.0, 5.0, 6.0]> : tensor<8x7xf32>
// Make use the sparse constant are properly sorted based on the requested order.
sparse_tensor.foreach in %cst { order = affine_map<(d0, d1) -> (d1, d0)> } : tensor<8x7xf32> do {
^bb0(%arg0: index, %arg1: index, %arg2: f32):
"test.use" (%arg0, %arg1, %arg2): (index,index,f32)->()
}
sparse_tensor.foreach in %cst : tensor<8x7xf32> do {
^bb0(%arg0: index, %arg1: index, %arg2: f32):
"test.use" (%arg0, %arg1, %arg2): (index,index,f32)->()
}
return
}
#CSR_SLICE = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "compressed" ],
dimSlices = [ (0, 4, 1), (2, 4, 1) ]
}>
#CSR_SLICE_DYN = #sparse_tensor.encoding<{
lvlTypes = [ "compressed", "compressed" ],
dimSlices = [ (?, ?, ?), (?, ?, ?) ]
}>
// CHECK-LABEL: func.func @foreach_print_slice_dyn(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf64,
// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_3:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf64,
// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xf64,
// CHECK-DAG: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_1]] : tensor<?x?xf64,
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.slice.offset %[[VAL_0]] at 0 : tensor<?x?xf64,
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.slice.stride %[[VAL_0]] at 0 : tensor<?x?xf64,
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xf64,
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xf64,
// CHECK-DAG: %[[VAL_10:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?xf64,
// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.slice.offset %[[VAL_0]] at 1 : tensor<?x?xf64,
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.slice.stride %[[VAL_0]] at 1 : tensor<?x?xf64,
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf64,
// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xindex>
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_2]] {
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: %[[VAL_18:.*]] = arith.subi %[[VAL_17]], %[[VAL_6]] : index
// CHECK: %[[VAL_19:.*]] = arith.remui %[[VAL_18]], %[[VAL_7]] : index
// CHECK: %[[VAL_20:.*]] = arith.divui %[[VAL_18]], %[[VAL_7]] : index
// CHECK: %[[VAL_21:.*]] = arith.cmpi uge, %[[VAL_17]], %[[VAL_6]] : index
// CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_5]] : index
// CHECK: %[[VAL_23:.*]] = arith.cmpi eq, %[[VAL_19]], %[[VAL_1]] : index
// CHECK: %[[VAL_24:.*]] = arith.andi %[[VAL_21]], %[[VAL_22]] : i1
// CHECK: %[[VAL_25:.*]] = arith.andi %[[VAL_24]], %[[VAL_23]] : i1
// CHECK: scf.if %[[VAL_25]] {
// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: %[[VAL_27:.*]] = arith.addi %[[VAL_16]], %[[VAL_2]] : index
// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_27]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_29:.*]] = %[[VAL_26]] to %[[VAL_28]] step %[[VAL_2]] {
// CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_29]]] : memref<?xindex>
// CHECK: %[[VAL_31:.*]] = arith.subi %[[VAL_30]], %[[VAL_11]] : index
// CHECK: %[[VAL_32:.*]] = arith.remui %[[VAL_31]], %[[VAL_12]] : index
// CHECK: %[[VAL_33:.*]] = arith.divui %[[VAL_31]], %[[VAL_12]] : index
// CHECK: %[[VAL_34:.*]] = arith.cmpi uge, %[[VAL_30]], %[[VAL_11]] : index
// CHECK: %[[VAL_35:.*]] = arith.cmpi ult, %[[VAL_33]], %[[VAL_10]] : index
// CHECK: %[[VAL_36:.*]] = arith.cmpi eq, %[[VAL_32]], %[[VAL_1]] : index
// CHECK: %[[VAL_37:.*]] = arith.andi %[[VAL_34]], %[[VAL_35]] : i1
// CHECK: %[[VAL_38:.*]] = arith.andi %[[VAL_37]], %[[VAL_36]] : i1
// CHECK: scf.if %[[VAL_38]] {
// CHECK: %[[VAL_39:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_29]]] : memref<?xf64>
// CHECK: "test.use"(%[[VAL_39]]) : (f64) -> ()
// CHECK: }
// CHECK: }
// CHECK: }
// CHECK: }
// CHECK: return
//
func.func @foreach_print_slice_dyn(%A: tensor<?x?xf64, #CSR_SLICE_DYN>) {
sparse_tensor.foreach in %A : tensor<?x?xf64, #CSR_SLICE_DYN> do {
^bb0(%1: index, %2: index, %v: f64) :
"test.use" (%v) : (f64) -> ()
}
return
}
// CHECK-LABEL: func.func @foreach_print_slice(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<4x4xf64,
// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 4 : index
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<4x4xf64,
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<4x4xf64,
// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<4x4xf64,
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<4x4xf64,
// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<4x4xf64,
// CHECK-DAG: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_4]] {
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
// CHECK: %[[VAL_14:.*]] = arith.cmpi ult, %[[VAL_13]], %[[VAL_1]] : index
// CHECK: scf.if %[[VAL_14]] {
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_12]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]] = arith.addi %[[VAL_12]], %[[VAL_4]] : index
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_18:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_4]] {
// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]] = arith.subi %[[VAL_19]], %[[VAL_2]] : index
// CHECK: %[[VAL_21:.*]] = arith.cmpi uge, %[[VAL_19]], %[[VAL_2]] : index
// CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_1]] : index
// CHECK: %[[VAL_23:.*]] = arith.andi %[[VAL_21]], %[[VAL_22]] : i1
// CHECK: scf.if %[[VAL_23]] {
// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_18]]] : memref<?xf64>
// CHECK: "test.use"(%[[VAL_24]]) : (f64) -> ()
// CHECK: }
// CHECK: }
// CHECK: }
// CHECK: }
// CHECK: return
//
func.func @foreach_print_slice(%A: tensor<4x4xf64, #CSR_SLICE>) {
sparse_tensor.foreach in %A : tensor<4x4xf64, #CSR_SLICE> do {
^bb0(%1: index, %2: index, %v: f64) :
"test.use" (%v) : (f64) -> ()
}
return
}
#BCOO = #sparse_tensor.encoding<{
lvlTypes = [ "dense", "compressed-hi-nu", "singleton" ],
}>
// CHECK-LABEL: func.func @foreach_bcoo(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<4x4x4xf64, #{{.*}}>>) {
// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 4 : index
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<4x4x4xf64, #{{.*}}>> to memref<?xindex>
// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<4x4x4xf64, #{{.*}}>> to memref<?xf64>
// CHECK: scf.for %[[VAL_7:.*]] = %[[VAL_2]] to %[[VAL_1]] step %[[VAL_3]] {
// CHECK: %[[VAL_8:.*]] = arith.muli %[[VAL_7]], %[[VAL_4]] : index
// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_8]]] : memref<?xindex>
// CHECK: %[[VAL_10:.*]] = arith.addi %[[VAL_8]], %[[VAL_3]] : index
// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_10]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_9]] to %[[VAL_11]] step %[[VAL_3]] {
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xf64>
// CHECK: "test.use"(%[[VAL_13]]) : (f64) -> ()
// CHECK: } {"Emitted from" = "sparse_tensor.foreach"}
// CHECK: } {"Emitted from" = "sparse_tensor.foreach"}
// CHECK: return
// CHECK: }
func.func @foreach_bcoo(%A: tensor<4x4x4xf64, #BCOO>) {
sparse_tensor.foreach in %A : tensor<4x4x4xf64, #BCOO> do {
^bb0(%1: index, %2: index, %3: index, %v: f64) :
"test.use" (%v) : (f64) -> ()
}
return
}
|
