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
56 lines
3.0 KiB
MLIR
56 lines
3.0 KiB
MLIR
// RUN: mlir-opt %s --sparsification --canonicalize --cse | FileCheck %s
|
|
|
|
#DCSR = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed" ] }>
|
|
#SparseTensor = #sparse_tensor.encoding<{ lvlTypes = [ "compressed", "compressed", "compressed" ] }>
|
|
|
|
#trait = {
|
|
indexing_maps = [
|
|
affine_map<(d0, d1, d2) -> (d0, d2)>,
|
|
affine_map<(d0, d1, d2) -> (d0, d1, d2)>
|
|
],
|
|
iterator_types = ["parallel", "parallel", "parallel"]
|
|
}
|
|
|
|
// CHECK-LABEL: @main(
|
|
// CHECK-SAME: %[[TMP_arg0:.*]]: tensor<4x5xi32,
|
|
// CHECK-DAG: %[[TMP_c3:.*]] = arith.constant 3 : index
|
|
// CHECK-DAG: %[[TMP_c0:.*]] = arith.constant 0 : index
|
|
// CHECK-DAG: %[[TMP_c1:.*]] = arith.constant 1 : index
|
|
// CHECK: %[[TMP_0:.*]] = bufferization.alloc_tensor()
|
|
// CHECK: %[[TMP_1:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 0 : index}
|
|
// CHECK: %[[TMP_2:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 0 : index}
|
|
// CHECK: %[[TMP_3:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 1 : index}
|
|
// CHECK: %[[TMP_4:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 1 : index}
|
|
// CHECK: %[[TMP_5:.*]] = sparse_tensor.values %[[TMP_arg0]]
|
|
// CHECK: %[[TMP_6:.*]] = memref.load %[[TMP_1]][%[[TMP_c0]]] : memref<?xindex>
|
|
// CHECK: %[[TMP_7:.*]] = memref.load %[[TMP_1]][%[[TMP_c1]]] : memref<?xindex>
|
|
// CHECK: %[[T:.*]] = scf.for %[[TMP_arg1:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]] {{.*}} {
|
|
// CHECK: %[[TMP_9:.*]] = memref.load %[[TMP_2]][%[[TMP_arg1]]] : memref<?xindex>
|
|
// CHECK: %[[L1:.*]] = scf.for %[[TMP_arg2:.*]] = %[[TMP_c0]] to %[[TMP_c3]] step %[[TMP_c1]] {{.*}} {
|
|
// CHECK: %[[TMP_10:.*]] = memref.load %[[TMP_3]][%[[TMP_arg1]]] : memref<?xindex>
|
|
// CHECK: %[[TMP_11:.*]] = arith.addi %[[TMP_arg1]], %[[TMP_c1]] : index
|
|
// CHECK: %[[TMP_12:.*]] = memref.load %[[TMP_3]][%[[TMP_11]]] : memref<?xindex>
|
|
// CHECK: %[[L2:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_10]] to %[[TMP_12]] step %[[TMP_c1]] {{.*}} {
|
|
// CHECK: %[[TMP_13:.*]] = memref.load %[[TMP_4]][%[[TMP_arg3]]] : memref<?xindex>
|
|
// CHECK: %[[TMP_14:.*]] = memref.load %[[TMP_5]][%[[TMP_arg3]]] : memref<?xi32>
|
|
// CHECK: %[[Y:.*]] = sparse_tensor.insert %[[TMP_14]] into %{{.*}}[%[[TMP_9]], %[[TMP_arg2]], %[[TMP_13]]]
|
|
// CHECK: scf.yield %[[Y]]
|
|
// CHECK: }
|
|
// CHECK: scf.yield %[[L2]]
|
|
// CHECK: }
|
|
// CHECK: scf.yield %[[L1]]
|
|
// CHECK: }
|
|
// CHECK: %[[TMP_8:.*]] = sparse_tensor.load %[[T]] hasInserts
|
|
// CHECK: return %[[TMP_8]]
|
|
module @func_sparse {
|
|
func.func public @main(%arg0: tensor<4x5xi32, #DCSR>) -> tensor<4x3x5xi32, #SparseTensor> {
|
|
%0 = bufferization.alloc_tensor() : tensor<4x3x5xi32, #SparseTensor>
|
|
%1 = linalg.generic #trait
|
|
ins(%arg0 : tensor<4x5xi32, #DCSR>) outs(%0 : tensor<4x3x5xi32, #SparseTensor>) {
|
|
^bb0(%in: i32, %out: i32):
|
|
linalg.yield %in : i32
|
|
} -> tensor<4x3x5xi32, #SparseTensor>
|
|
return %1 : tensor<4x3x5xi32, #SparseTensor>
|
|
}
|
|
}
|