blob: 66e61afd897dd13747427eecba8206b8f978c871 [file] [log] [blame]
// RUN: mlir-opt %s -split-input-file | mlir-opt -split-input-file | FileCheck %s
#SV = #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }>
// CHECK: #[[$SV:.*]] = #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }>
// CHECK-LABEL: func private @sparse_1d_tensor(
// CHECK-SAME: tensor<32xf64, #[[$SV]]>)
func.func private @sparse_1d_tensor(tensor<32xf64, #SV>)
// -----
#CSR = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : dense, d1 : compressed),
posWidth = 64,
crdWidth = 64
}>
// CHECK: #[[$CSR:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed), posWidth = 64, crdWidth = 64 }>
// CHECK-LABEL: func private @sparse_csr(
// CHECK-SAME: tensor<?x?xf32, #[[$CSR]]>)
func.func private @sparse_csr(tensor<?x?xf32, #CSR>)
// -----
#BCSR = #sparse_tensor.encoding<{
map = (d0, d1, d2) -> (d0 : batch, d1: dense, d2 : compressed),
}>
// CHECK: #[[$BCSR:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : batch, d1 : dense, d2 : compressed) }>
// CHECK-LABEL: func private @sparse_bcsr(
// CHECK-SAME: tensor<?x?x?xf32, #[[$BCSR]]>)
func.func private @sparse_bcsr(tensor<?x?x?xf32, #BCSR>)
// -----
#CSR_explicit = #sparse_tensor.encoding<{
map = {l0, l1} (d0 = l0, d1 = l1) -> (l0 = d0 : dense, l1 = d1 : compressed)
}>
// CHECK: #[[$CSR_EXPLICIT:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed) }>
// CHECK-LABEL: func private @CSR_explicit(
// CHECK-SAME: tensor<?x?xf64, #[[$CSR_EXPLICIT]]>
func.func private @CSR_explicit(%arg0: tensor<?x?xf64, #CSR_explicit>) {
return
}
// -----
#CSC = #sparse_tensor.encoding<{
map = (d0, d1) -> (d1 : dense, d0 : compressed),
posWidth = 0,
crdWidth = 0
}>
// CHECK-DAG: #[[$CSC:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d1 : dense, d0 : compressed) }>
// CHECK-LABEL: func private @sparse_csc(
// CHECK-SAME: tensor<?x?xf32, #[[$CSC]]>)
func.func private @sparse_csc(tensor<?x?xf32, #CSC>)
// -----
#DCSC = #sparse_tensor.encoding<{
map = (d0, d1) -> (d1 : compressed, d0 : compressed),
posWidth = 0,
crdWidth = 64
}>
// CHECK-DAG: #[[$DCSC:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d1 : compressed, d0 : compressed), crdWidth = 64 }>
// CHECK-LABEL: func private @sparse_dcsc(
// CHECK-SAME: tensor<?x?xf32, #[[$DCSC]]>)
func.func private @sparse_dcsc(tensor<?x?xf32, #DCSC>)
// -----
#COO = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed(nonunique, nonordered), d1 : singleton(nonordered))
}>
// CHECK-DAG: #[[$COO:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed(nonunique, nonordered), d1 : singleton(nonordered)) }>
// CHECK-LABEL: func private @sparse_coo(
// CHECK-SAME: tensor<?x?xf32, #[[$COO]]>)
func.func private @sparse_coo(tensor<?x?xf32, #COO>)
// -----
#BCOO = #sparse_tensor.encoding<{
map = (d0, d1, d2) -> (d0 : dense, d1 : loose_compressed(nonunique), d2 : singleton)
}>
// CHECK-DAG: #[[$BCOO:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : dense, d1 : loose_compressed(nonunique), d2 : singleton) }>
// CHECK-LABEL: func private @sparse_bcoo(
// CHECK-SAME: tensor<?x?x?xf32, #[[$BCOO]]>)
func.func private @sparse_bcoo(tensor<?x?x?xf32, #BCOO>)
// -----
#SortedCOO = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)
}>
// CHECK-DAG: #[[$SortedCOO:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton) }>
// CHECK-LABEL: func private @sparse_sorted_coo(
// CHECK-SAME: tensor<10x10xf64, #[[$SortedCOO]]>)
func.func private @sparse_sorted_coo(tensor<10x10xf64, #SortedCOO>)
// -----
#COO_SoA = #sparse_tensor.encoding<{
map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton(soa))
}>
// CHECK-DAG: #[[$COO_SoA:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton(soa)) }>
// CHECK-LABEL: func private @sparse_coo(
// CHECK-SAME: tensor<?x?xf32, #[[$COO_SoA]]>)
func.func private @sparse_coo(tensor<?x?xf32, #COO_SoA>)
// -----
#BSR = #sparse_tensor.encoding<{
map = ( i, j ) ->
( i floordiv 2 : dense,
j floordiv 3 : compressed,
i mod 2 : dense,
j mod 3 : dense
)
}>
// CHECK-DAG: #[[$BSR:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 floordiv 2 : dense, d1 floordiv 3 : compressed, d0 mod 2 : dense, d1 mod 3 : dense) }>
// CHECK-LABEL: func private @sparse_bsr(
// CHECK-SAME: tensor<10x60xf64, #[[$BSR]]>
func.func private @sparse_bsr(tensor<10x60xf64, #BSR>)
// -----
#ELL = #sparse_tensor.encoding<{
map = [s0](d0, d1) -> (d0 * (s0 * 4) : dense, d0 : dense, d1 : compressed)
}>
// CHECK-DAG: #[[$ELL:.*]] = #sparse_tensor.encoding<{ map = [s0](d0, d1) -> (d0 * (s0 * 4) : dense, d0 : dense, d1 : compressed) }>
// CHECK-LABEL: func private @sparse_ell(
// CHECK-SAME: tensor<?x?xf64, #[[$ELL]]>
func.func private @sparse_ell(tensor<?x?xf64, #ELL>)
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>
// CHECK-DAG: #[[$CSR_SLICE:.*]] = #sparse_tensor.encoding<{ map = (d0 : #sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) -> (d0 : dense, d1 : compressed) }>
// CHECK-LABEL: func private @sparse_slice(
// CHECK-SAME: tensor<?x?xf64, #[[$CSR_SLICE]]>
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
// -----
#CSR_SLICE = #sparse_tensor.encoding<{
map = (d0 : #sparse_tensor<slice(1, ?, 1)>, d1 : #sparse_tensor<slice(?, 4, 2)>) -> (d0 : dense, d1 : compressed)
}>
// CHECK-DAG: #[[$CSR_SLICE:.*]] = #sparse_tensor.encoding<{ map = (d0 : #sparse_tensor<slice(1, ?, 1)>, d1 : #sparse_tensor<slice(?, 4, 2)>) -> (d0 : dense, d1 : compressed) }>
// CHECK-LABEL: func private @sparse_slice(
// CHECK-SAME: tensor<?x?xf64, #[[$CSR_SLICE]]>
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
// -----
#BSR = #sparse_tensor.encoding<{
map = ( i, j ) ->
( i floordiv 2 : dense,
j floordiv 3 : compressed,
i mod 2 : dense,
j mod 3 : dense
)
}>
// CHECK-DAG: #[[$BSR:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 floordiv 2 : dense, d1 floordiv 3 : compressed, d0 mod 2 : dense, d1 mod 3 : dense) }>
// CHECK-LABEL: func private @BSR(
// CHECK-SAME: tensor<?x?xf64, #[[$BSR]]>
func.func private @BSR(%arg0: tensor<?x?xf64, #BSR>) {
return
}
// -----
#BCSR = #sparse_tensor.encoding<{
map = ( i, j, k ) ->
( i floordiv 2 : dense,
j floordiv 3 : dense,
k floordiv 4 : compressed,
i mod 2 : dense,
j mod 3 : dense,
k mod 4 : dense
)
}>
// CHECK-DAG: #[[$BCSR:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 floordiv 2 : dense, d1 floordiv 3 : dense, d2 floordiv 4 : compressed, d0 mod 2 : dense, d1 mod 3 : dense, d2 mod 4 : dense) }>
// CHECK-LABEL: func private @BCSR(
// CHECK-SAME: tensor<?x?x?xf64, #[[$BCSR]]>
func.func private @BCSR(%arg0: tensor<?x?x?xf64, #BCSR>) {
return
}
// -----
#BSR_explicit = #sparse_tensor.encoding<{
map =
{il, jl, ii, jj}
( i = il * 2 + ii,
j = jl * 3 + jj
) ->
( il = i floordiv 2 : dense,
jl = j floordiv 3 : compressed,
ii = i mod 2 : dense,
jj = j mod 3 : dense
)
}>
// CHECK-DAG: #[[$BSR_explicit:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 floordiv 2 : dense, d1 floordiv 3 : compressed, d0 mod 2 : dense, d1 mod 3 : dense) }>
// CHECK-LABEL: func private @BSR_explicit(
// CHECK-SAME: tensor<?x?xf64, #[[$BSR_explicit]]>
func.func private @BSR_explicit(%arg0: tensor<?x?xf64, #BSR_explicit>) {
return
}
// -----
#NV_24 = #sparse_tensor.encoding<{
map = ( i, j ) ->
( i : dense,
j floordiv 4 : dense,
j mod 4 : structured[2, 4]
),
crdWidth = 8 // we would even like just 2-bits
}>
// CHECK-DAG: #[[$NV_24:.*]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 floordiv 4 : dense, d1 mod 4 : structured[2, 4]), crdWidth = 8 }>
// CHECK-LABEL: func private @NV_24(
// CHECK-SAME: tensor<?x?xf64, #[[$NV_24]]>
func.func private @NV_24(%arg0: tensor<?x?xf64, #NV_24>) {
return
}
// -----
#NV_24 = #sparse_tensor.encoding<{
map = ( i, j, k ) ->
( i : dense,
j : dense,
k floordiv 4 : dense,
k mod 4 : structured[2, 4]
)
}>
// CHECK-DAG: #[[$NV_24:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : dense, d1 : dense, d2 floordiv 4 : dense, d2 mod 4 : structured[2, 4]) }>
// CHECK-LABEL: func private @NV_24(
// CHECK-SAME: tensor<?x?x?xf64, #[[$NV_24]]>
func.func private @NV_24(%arg0: tensor<?x?x?xf64, #NV_24>) {
return
}
// -----
#NV_24 = #sparse_tensor.encoding<{
map = ( i, j, k ) ->
( i : dense,
k floordiv 4 : dense,
j : dense,
k mod 4 : structured[2, 4]
)
}>
// CHECK-DAG: #[[$NV_24:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : dense, d2 floordiv 4 : dense, d1 : dense, d2 mod 4 : structured[2, 4]) }>
// CHECK-LABEL: func private @NV_24(
// CHECK-SAME: tensor<?x?x?xf64, #[[$NV_24]]>
func.func private @NV_24(%arg0: tensor<?x?x?xf64, #NV_24>) {
return
}
// -----
#NOutOfM = #sparse_tensor.encoding<{
map = ( i, j, k ) ->
( i : dense,
k floordiv 8 : dense,
j : dense,
k mod 8 : structured[5, 8]
)
}>
// CHECK-DAG: #[[$NOutOfM:.*]] = #sparse_tensor.encoding<{ map = (d0, d1, d2) -> (d0 : dense, d2 floordiv 8 : dense, d1 : dense, d2 mod 8 : structured[5, 8]) }>
// CHECK-LABEL: func private @NOutOfM(
// CHECK-SAME: tensor<?x?x?xf64, #[[$NOutOfM]]>
func.func private @NOutOfM(%arg0: tensor<?x?x?xf64, #NOutOfM>) {
return
}