a0615d020a
This commit is part of the migration of towards the new STEA syntax/design. In particular, this commit includes the following changes:
* Renaming compiler-internal functions/methods:
* `SparseTensorEncodingAttr::{getDimLevelType => getLvlTypes}`
* `Merger::{getDimLevelType => getLvlType}` (for consistency)
* `sparse_tensor::{getDimLevelType => buildLevelType}` (to help reduce confusion vs actual getter methods)
* Renaming external facets to match:
* the STEA parser and printer
* the C and Python bindings
* PyTACO
However, the actual renaming of the `DimLevelType` itself (along with all the "dlt" names) will be handled in a separate commit.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D150330
133 lines
4.6 KiB
MLIR
133 lines
4.6 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<{ lvlTypes = ["compressed"] }>>)
|
|
|
|
// -----
|
|
|
|
#CSR = #sparse_tensor.encoding<{
|
|
lvlTypes = [ "dense", "compressed" ],
|
|
dimOrdering = affine_map<(i,j) -> (i,j)>,
|
|
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>)
|
|
|
|
// -----
|
|
|
|
#CSC = #sparse_tensor.encoding<{
|
|
lvlTypes = [ "dense", "compressed" ],
|
|
dimOrdering = affine_map<(i,j) -> (j,i)>,
|
|
posWidth = 0,
|
|
crdWidth = 0
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @sparse_csc(
|
|
// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>)
|
|
func.func private @sparse_csc(tensor<?x?xf32, #CSC>)
|
|
|
|
// -----
|
|
|
|
#DCSC = #sparse_tensor.encoding<{
|
|
lvlTypes = [ "compressed", "compressed" ],
|
|
dimOrdering = affine_map<(i,j) -> (j,i)>,
|
|
posWidth = 0,
|
|
crdWidth = 64
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @sparse_dcsc(
|
|
// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, crdWidth = 64 }>>)
|
|
func.func private @sparse_dcsc(tensor<?x?xf32, #DCSC>)
|
|
|
|
// -----
|
|
|
|
#COO = #sparse_tensor.encoding<{
|
|
lvlTypes = [ "compressed-nu-no", "singleton-no" ]
|
|
}>
|
|
|
|
// 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<{
|
|
lvlTypes = [ "dense", "compressed-hi-nu", "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<{
|
|
lvlTypes = [ "compressed-nu", "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<{
|
|
lvlTypes = [ "compressed", "compressed", "dense", "dense" ],
|
|
dimOrdering = affine_map<(ii, jj, i, j) -> (ii, jj, i, j)>,
|
|
higherOrdering = affine_map<(i, j) -> (i floordiv 2, j floordiv 3, i mod 2, j mod 3)>
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @sparse_bcsr(
|
|
// CHECK-SAME: tensor<10x60xf64, #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "dense", "dense" ], higherOrdering = 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" ],
|
|
dimOrdering = affine_map<(ii, i, j) -> (ii, i, j)>,
|
|
higherOrdering = 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" ], higherOrdering = 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" ],
|
|
slice = [ (1, 4, 1), (1, 4, 2) ]
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @sparse_slice(
|
|
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], slice = [ (1, 4, 1), (1, 4, 2) ] }>>
|
|
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
|
|
|
|
// -----
|
|
|
|
#CSR_SLICE = #sparse_tensor.encoding<{
|
|
lvlTypes = [ "dense", "compressed" ],
|
|
slice = [ (1, 4, 1), (1, 4, 2) ]
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @sparse_slice(
|
|
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], slice = [ (1, 4, 1), (1, 4, 2) ] }>>
|
|
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
|
|
|
|
// -----
|
|
|
|
#CSR_SLICE = #sparse_tensor.encoding<{
|
|
lvlTypes = [ "dense", "compressed" ],
|
|
slice = [ (1, ?, 1), (?, 4, 2) ]
|
|
}>
|
|
|
|
// CHECK-LABEL: func private @sparse_slice(
|
|
// CHECK-SAME: tensor<?x?xf64, #sparse_tensor.encoding<{ lvlTypes = [ "dense", "compressed" ], slice = [ (1, ?, 1), (?, 4, 2) ] }>>
|
|
func.func private @sparse_slice(tensor<?x?xf64, #CSR_SLICE>)
|