3dc621124f
**COO**
`lvlTypes = [ "compressed_nu", "singleton" ]` to `map = (d0, d1) -> (d0
: compressed(nonunique), d1 : singleton)`
`lvlTypes = [ "compressed_nu_no", "singleton_no" ]` to `map = (d0, d1)
-> (d0 : compressed(nonunique, nonordered), d1 : singleton(nonordered))`
**SortedCOO**
`lvlTypes = [ "compressed_nu", "singleton" ]` to `map = (d0, d1) -> (d0
: compressed(nonunique), d1 : singleton)`
**BCOO**
`lvlTypes = [ "dense", "compressed_hi_nu", "singleton" ]` to `map = (d0,
d1, d2) -> (d0 : dense, d1 : compressed(nonunique, high), d2 :
singleton)`
**BCSR**
`lvlTypes = [ "compressed", "compressed", "dense", "dense" ], dimToLvl =
affine_map<(d0, d1) -> (d0 floordiv 2, d1 floordiv 3, d0 mod 2, d1 mod
3)>` to
`map = ( i, j ) ->
( i floordiv 2 : compressed,
j floordiv 3 : compressed,
i mod 2 : dense,
j mod 3 : dense
)`
**Tensor and other supported formats(e.g. CCC, CDC, CCCC)**
Currently, ELL and slice are not supported yet in the new syntax and the
CHECK tests will be updated once printing is set to output the new
syntax.
Previous PRs: #66146, #66309, #66443
211 lines
6.8 KiB
MLIR
211 lines
6.8 KiB
MLIR
// RUN: mlir-opt %s -split-input-file | mlir-opt | FileCheck %s
|
|
|
|
// CHECK-LABEL: func private @sparse_1d_tensor(
|
|
// CHECK-SAME: tensor<32xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>)
|
|
func.func private @sparse_1d_tensor(tensor<32xf64, #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }>>)
|
|
|
|
// -----
|
|
|
|
#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, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], posWidth = 64, crdWidth = 64 }>>)
|
|
func.func private @sparse_csr(tensor<?x?xf32, #CSR>)
|
|
|
|
// -----
|
|
|
|
#CSR_explicit = #sparse_tensor.encoding<{
|
|
map = {l0, l1} (d0 = l0, d1 = l1) -> (l0 = d0 : dense, l1 = d1 : compressed)
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @CSR_explicit(
|
|
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ] }>>
|
|
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-LABEL: func private @sparse_csc(
|
|
// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimToLvl = affine_map<(d0, d1) -> (d1, d0)> }>>)
|
|
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-LABEL: func private @sparse_dcsc(
|
|
// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ], dimToLvl = affine_map<(d0, d1) -> (d1, d0)>, crdWidth = 64 }>>)
|
|
func.func private @sparse_dcsc(tensor<?x?xf32, #DCSC>)
|
|
|
|
// -----
|
|
|
|
#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, #sparse_tensor.encoding<{ lvlTypes = [ "compressed_nu_no", "singleton_no" ] }>>)
|
|
func.func private @sparse_coo(tensor<?x?xf32, #COO>)
|
|
|
|
// -----
|
|
|
|
#BCOO = #sparse_tensor.encoding<{
|
|
map = (d0, d1, d2) -> (d0 : dense, d1 : compressed(nonunique, high), d2 : singleton)
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @sparse_bcoo(
|
|
// CHECK-SAME: tensor<?x?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed_hi_nu", "singleton" ] }>>)
|
|
func.func private @sparse_bcoo(tensor<?x?x?xf32, #BCOO>)
|
|
|
|
// -----
|
|
|
|
#SortedCOO = #sparse_tensor.encoding<{
|
|
map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @sparse_sorted_coo(
|
|
// CHECK-SAME: tensor<10x10xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed_nu", "singleton" ] }>>)
|
|
func.func private @sparse_sorted_coo(tensor<10x10xf64, #SortedCOO>)
|
|
|
|
// -----
|
|
|
|
#BCSR = #sparse_tensor.encoding<{
|
|
map = ( i, j ) ->
|
|
( i floordiv 2 : compressed,
|
|
j floordiv 3 : compressed,
|
|
i mod 2 : dense,
|
|
j mod 3 : dense
|
|
)
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @sparse_bcsr(
|
|
// CHECK-SAME: tensor<10x60xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense", "dense" ], dimToLvl = affine_map<(d0, d1) -> (d0 floordiv 2, d1 floordiv 3, d0 mod 2, d1 mod 3)> }>>
|
|
func.func private @sparse_bcsr(tensor<10x60xf64, #BCSR>)
|
|
|
|
|
|
// -----
|
|
|
|
#ELL = #sparse_tensor.encoding<{
|
|
lvlTypes = [ "dense", "dense", "compressed" ],
|
|
dimToLvl = affine_map<(i,j)[c] -> (c*4*i, i, j)>
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @sparse_ell(
|
|
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed" ], dimToLvl = affine_map<(d0, d1)[s0] -> (d0 * (s0 * 4), d0, d1)> }>>
|
|
func.func private @sparse_ell(tensor<?x?xf64, #ELL>)
|
|
|
|
// -----
|
|
|
|
#CSR_SLICE = #sparse_tensor.encoding<{
|
|
lvlTypes = [ "dense", "compressed" ],
|
|
dimSlices = [ (1, 4, 1), (1, 4, 2) ]
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @sparse_slice(
|
|
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimSlices = [ (1, 4, 1), (1, 4, 2) ] }>>
|
|
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
|
|
|
|
// -----
|
|
|
|
#CSR_SLICE = #sparse_tensor.encoding<{
|
|
lvlTypes = [ "dense", "compressed" ],
|
|
dimSlices = [ (1, 4, 1), (1, 4, 2) ]
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @sparse_slice(
|
|
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimSlices = [ (1, 4, 1), (1, 4, 2) ] }>>
|
|
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
|
|
|
|
// -----
|
|
|
|
#CSR_SLICE = #sparse_tensor.encoding<{
|
|
lvlTypes = [ "dense", "compressed" ],
|
|
dimSlices = [ (1, ?, 1), (?, 4, 2) ]
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @sparse_slice(
|
|
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimSlices = [ (1, ?, 1), (?, 4, 2) ] }>>
|
|
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
|
|
|
|
// -----
|
|
|
|
// TODO: It is probably better to use [dense, dense, 2:4] (see NV_24 defined using new syntax
|
|
// below) to encode a 2D matrix, but it would require dim2lvl mapping which is not ready yet.
|
|
// So we take the simple path for now.
|
|
#NV_24= #sparse_tensor.encoding<{
|
|
map = (d0, d1) -> (d0 : dense, d1 : compressed(block2_4))
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @sparse_2_out_of_4(
|
|
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed24" ] }>>
|
|
func.func private @sparse_2_out_of_4(tensor<?x?xf64, #NV_24>)
|
|
|
|
// -----
|
|
|
|
#BCSR = #sparse_tensor.encoding<{
|
|
map = ( i, j ) ->
|
|
( i floordiv 2 : compressed,
|
|
j floordiv 3 : compressed,
|
|
i mod 2 : dense,
|
|
j mod 3 : dense
|
|
)
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @BCSR(
|
|
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense", "dense" ], dimToLvl = affine_map<(d0, d1) -> (d0 floordiv 2, d1 floordiv 3, d0 mod 2, d1 mod 3)> }>>
|
|
func.func private @BCSR(%arg0: tensor<?x?xf64, #BCSR>) {
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
#BCSR_explicit = #sparse_tensor.encoding<{
|
|
map =
|
|
{il, jl, ii, jj}
|
|
( i = il * 2 + ii,
|
|
j = jl * 3 + jj
|
|
) ->
|
|
( il = i floordiv 2 : compressed,
|
|
jl = j floordiv 3 : compressed,
|
|
ii = i mod 2 : dense,
|
|
jj = j mod 3 : dense
|
|
)
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @BCSR_explicit(
|
|
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense", "dense" ], dimToLvl = affine_map<(d0, d1) -> (d0 floordiv 2, d1 floordiv 3, d0 mod 2, d1 mod 3)> }>>
|
|
func.func private @BCSR_explicit(%arg0: tensor<?x?xf64, #BCSR_explicit>) {
|
|
return
|
|
}
|
|
|
|
// -----
|
|
|
|
#NV_24 = #sparse_tensor.encoding<{
|
|
map = ( i, j ) ->
|
|
( i : dense,
|
|
j floordiv 4 : dense,
|
|
j mod 4 : compressed(block2_4)
|
|
)
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @NV_24(
|
|
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense", "compressed24" ], dimToLvl = affine_map<(d0, d1) -> (d0, d1 floordiv 4, d1 mod 4)> }>>
|
|
func.func private @NV_24(%arg0: tensor<?x?xf64, #NV_24>) {
|
|
return
|
|
}
|