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|
// RUN: mlir-opt %s | mlir-opt | FileCheck %s --check-prefix=CHECK-ROUND
// RUN: mlir-opt %s --sparse-tensor-conversion --cse --canonicalize | FileCheck %s --check-prefix=CHECK-CONV
// RUN: mlir-opt %s --sparse-tensor-rewrite=enable-runtime-library=false --cse --canonicalize | FileCheck %s --check-prefix=CHECK-RWT
#SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#SparseMatrix = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
//
// roundtrip:
//
// CHECK-ROUND-LABEL: func.func @sparse_expand(
// CHECK-ROUND-SAME: %[[A:.*]]: tensor<100xf64, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<10x10xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-ROUND: %[[E:.*]] = tensor.expand_shape %[[A]] {{\[\[}}0, 1]] : tensor<100xf64, #sparse_tensor.encoding<{{{.*}}}>> into tensor<10x10xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-ROUND: return %[[E]] : tensor<10x10xf64, #sparse_tensor.encoding<{{{.*}}}>>
//
// conversion:
//
// CHECK-CONV-LABEL: func.func @sparse_expand(
// CHECK-CONV-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-CONV-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-CONV-DAG: %[[C10:.*]] = arith.constant 10 : index
// CHECK-CONV-DAG: call @newSparseTensor
// CHECK-CONV-DAG: call @newSparseTensor
// CHECK-CONV: scf.while : () -> () {
// CHECK-CONV: call @getNextF64
// CHECK-CONV: scf.condition
// CHECK-CONV: } do {
// CHECK-CONV: %[[X:.*]] = memref.load %{{.*}}[%[[C0]]] : memref<1xindex>
// CHECK-CONV: %[[D:.*]] = arith.divui %[[X]], %[[C10]] : index
// CHECK-CONV: %[[R:.*]] = arith.remui %[[X]], %[[C10]] : index
// CHECK-CONV: memref.store %[[D]], %{{.*}}[%[[C0]]] : memref<2xindex>
// CHECK-CONV: memref.store %[[R]], %{{.*}}[%[[C1]]] : memref<2xindex>
// CHECK-CONV: call @addEltF64
// CHECK-CONV: scf.yield
// CHECK-CONV: }
// CHECK-CONV: %[[N:.*]] = call @newSparseTensor
// CHECK-CONV: call @delSparseTensorCOOF64
// CHECK-CONV: call @delSparseTensorCOOF64
// CHECK-CONV: return %[[N]] : !llvm.ptr<i8>
//
// rewrite for codegen:
//
// CHECK-RWT-LABEL: func.func @sparse_expand(
// CHECK-RWT-SAME: %[[S:.*]]:
// CHECK-RWT-DAG: %[[C10:.*]] = arith.constant 10 : index
// CHECK-RWT-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-RWT-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-RWT: %[[B:.*]] = bufferization.alloc_tensor()
// CHECK-RWT: %[[P0:.*]] = sparse_tensor.pointers %[[S]] {dimension = 0 : index}
// CHECK-RWT: %[[I0:.*]] = sparse_tensor.indices %[[S]] {dimension = 0 : index}
// CHECK-RWT: %[[V:.*]] = sparse_tensor.values %[[S]]
// CHECK-RWT: %[[S0:.*]] = memref.load %[[P0]]{{\[}}%[[C0]]] : memref<?xindex>
// CHECK-RWT: %[[E0:.*]] = memref.load %[[P0]]{{\[}}%[[C1]]] : memref<?xindex>
// CHECK-RWT: scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] {
// CHECK-RWT: %[[SI:.*]] = memref.load %[[I0]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT: %[[SV:.*]] = memref.load %[[V]]{{\[}}%[[I]]] : memref<?xf64>
// CHECK-RWT: %[[DI0:.*]] = arith.divui %[[SI]], %[[C10]] : index
// CHECK-RWT: %[[DI1:.*]] = arith.remui %[[SI]], %[[C10]] : index
// CHECK-RWT: sparse_tensor.insert %[[SV]] into %[[B]]{{\[}}%[[DI0]], %[[DI1]]]
// CHECK-RWT: }
// CHECK-RWT: %[[T:.*]] = sparse_tensor.convert %[[B]]
// CHECK-RWT: return %[[T]] : tensor<10x10xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
//
func.func @sparse_expand(%arg0: tensor<100xf64, #SparseVector>) -> tensor<10x10xf64, #SparseMatrix> {
%0 = tensor.expand_shape %arg0 [[0, 1]] :
tensor<100xf64, #SparseVector> into tensor<10x10xf64, #SparseMatrix>
return %0 : tensor<10x10xf64, #SparseMatrix>
}
//
// roundtrip:
//
// CHECK-ROUND-LABEL: func.func @sparse_collapse(
// CHECK-ROUND-SAME: %[[A:.*]]: tensor<10x10xf64, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<100xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-ROUND: %[[C:.*]] = tensor.collapse_shape %[[A]] {{\[\[}}0, 1]] : tensor<10x10xf64, #sparse_tensor.encoding<{{{.*}}}>> into tensor<100xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-ROUND: return %[[C]] : tensor<100xf64, #sparse_tensor.encoding<{{{.*}}}>>
//
// conversion:
//
// CHECK-CONV-LABEL: func.func @sparse_collapse(
// CHECK-CONV-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-CONV-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-CONV-DAG: %[[C10:.*]] = arith.constant 10 : index
// CHECK-CONV-DAG: call @newSparseTensor
// CHECK-CONV-DAG: call @newSparseTensor
// CHECK-CONV: scf.while : () -> () {
// CHECK-CONV: call @getNextF64
// CHECK-CONV: scf.condition
// CHECK-CONV: } do {
// CHECK-CONV: %[[X:.*]] = memref.load %{{.*}}[%[[C0]]] : memref<2xindex>
// CHECK-CONV: %[[Y:.*]] = memref.load %{{.*}}[%[[C1]]] : memref<2xindex>
// CHECK-CONV: %[[M:.*]] = arith.muli %[[X]], %[[C10]] : index
// CHECK-CONV: %[[A:.*]] = arith.addi %[[M]], %[[Y]] : index
// CHECK-CONV: memref.store %[[A]], %{{.*}}[%[[C0]]] : memref<1xindex>
// CHECK-CONV: call @addEltF64
// CHECK-CONV: scf.yield
// CHECK-CONV: }
// CHECK-CONV: %[[N:.*]] = call @newSparseTensor
// CHECK-CONV: call @delSparseTensorCOOF64
// CHECK-CONV: call @delSparseTensorCOOF64
// CHECK-CONV: return %[[N]] : !llvm.ptr<i8>
//
// rewrite for codegen:
//
// CHECK-RWT-LABEL: func.func @sparse_collapse(
// CHECK-RWT-SAME: %[[S:.*]]:
// CHECK-RWT-DAG: %[[C10:.*]] = arith.constant 10 : index
// CHECK-RWT-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-RWT-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-RWT: %[[B:.*]] = bufferization.alloc_tensor()
// CHECK-RWT: %[[P0:.*]] = sparse_tensor.pointers %[[S]] {dimension = 0 : index}
// CHECK-RWT: %[[I0:.*]] = sparse_tensor.indices %[[S]] {dimension = 0 : index}
// CHECK-RWT: %[[P1:.*]] = sparse_tensor.pointers %[[S]] {dimension = 1 : index}
// CHECK-RWT: %[[I1:.*]] = sparse_tensor.indices %[[S]] {dimension = 1 : index}
// CHECK-RWT: %[[V:.*]] = sparse_tensor.values %[[S]]
// CHECK-RWT: %[[S0:.*]] = memref.load %[[P0]]{{\[}}%[[C0]]] : memref<?xindex>
// CHECK-RWT: %[[E0:.*]] = memref.load %[[P0]]{{\[}}%[[C1]]] : memref<?xindex>
// CHECK-RWT: scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] {
// CHECK-RWT: %[[SI0:.*]] = memref.load %[[I0]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT: %[[PE1:.*]] = arith.addi %[[I]], %[[C1]] : index
// CHECK-RWT: %[[S1:.*]] = memref.load %[[P1]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT: %[[E1:.*]] = memref.load %[[P1]]{{\[}}%[[PE1]]] : memref<?xindex>
// CHECK-RWT: scf.for %[[J:.*]] = %[[S1]] to %[[E1]] step %[[C1]] {
// CHECK-RWT: %[[SI1:.*]] = memref.load %[[I1]]{{\[}}%[[J]]] : memref<?xindex>
// CHECK-RWT: %[[SV:.*]] = memref.load %[[V]]{{\[}}%[[J]]] : memref<?xf64>
// CHECK-RWT: %[[T:.*]] = arith.muli %[[SI0]], %[[C10]] : index
// CHECK-RWT: %[[DI:.*]] = arith.addi %[[T]], %[[SI1]] : index
// CHECK-RWT: sparse_tensor.insert %[[SV]] into %[[B]]{{\[}}%[[DI]]]
// CHECK-RWT }
// CHECK-RWT: }
// CHECK-RWT: %[[T:.*]] = sparse_tensor.convert %[[B]]
// CHECK-RWT: return %[[T]] : tensor<100xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
//
func.func @sparse_collapse(%arg0: tensor<10x10xf64, #SparseMatrix>) -> tensor<100xf64, #SparseVector> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] :
tensor<10x10xf64, #SparseMatrix> into tensor<100xf64, #SparseVector>
return %0 : tensor<100xf64, #SparseVector>
}
//
// roundtrip:
//
// CHECK-ROUND-LABEL: func.func @dynamic_sparse_expand(
// CHECK-ROUND-SAME: %[[A:.*]]: tensor<?xf64, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<?x10xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-ROUND: %[[E:.*]] = tensor.expand_shape %[[A]] {{\[\[}}0, 1]] : tensor<?xf64, #sparse_tensor.encoding<{{{.*}}}>> into tensor<?x10xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-ROUND: return %[[E]] : tensor<?x10xf64, #sparse_tensor.encoding<{{{.*}}}>>
//
// conversion:
//
// CHECK-CONV-LABEL: func.func @dynamic_sparse_expand(
// CHECK-CONV-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-CONV-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-CONV-DAG: %[[C10:.*]] = arith.constant 10 : index
// CHECK-CONV-DAG: %[[D1:.*]] = arith.divui %{{.*}}, %[[C10]] : index
// CHECK-CONV-DAG: call @newSparseTensor
// CHECK-CONV-DAG: call @newSparseTensor
// CHECK-CONV: scf.while : () -> () {
// CHECK-CONV: call @getNextF64
// CHECK-CONV: scf.condition
// CHECK-CONV: } do {
// CHECK-CONV: %[[L:.*]] = memref.load %{{.*}}[%[[C0]]] : memref<1xindex>
// CHECK-CONV: %[[M:.*]] = arith.muli %[[D1]], %[[C10]] : index
// CHECK-CONV: %[[D2:.*]] = arith.divui %[[M]], %[[D1]] : index
// CHECK-CONV: %[[D3:.*]] = arith.divui %[[L]], %[[D2]] : index
// CHECK-CONV: %[[R:.*]] = arith.remui %[[L]], %[[D2]] : index
// CHECK-CONV: %[[D4:.*]] = arith.divui %[[D2]], %[[C10]] : index
// CHECK-CONV: %[[D5:.*]] = arith.divui %[[R]], %[[D4]] : index
// CHECK-CONV: memref.store %[[D3]], %{{.*}}[%[[C0]]] : memref<2xindex>
// CHECK-CONV: memref.store %[[D5]], %{{.*}}[%[[C1]]] : memref<2xindex>
// CHECK-CONV: call @addEltF64
// CHECK-CONV: scf.yield
// CHECK-CONV: }
// CHECK-CONV: %[[N:.*]] = call @newSparseTensor
// CHECK-CONV: call @delSparseTensorCOOF64
// CHECK-CONV: call @delSparseTensorCOOF64
// CHECK-CONV: return %[[N]] : !llvm.ptr<i8>
//
// rewrite for codegen:
//
// CHECK-RWT-LABEL: func.func @dynamic_sparse_expand(
// CHECK-RWT-SAME: %[[S:.*]]:
// CHECK-RWT-DAG: %[[C10:.*]] = arith.constant 10 : index
// CHECK-RWT-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-RWT-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-RWT: %[[SD:.*]] = tensor.dim %[[S]], %[[C0]]
// CHECK-RWT: %[[DD0:.*]] = arith.divui %[[SD]], %[[C10]] : index
// CHECK-RWT: %[[B:.*]] = bufferization.alloc_tensor(%[[DD0]])
// CHECK-RWT: %[[P0:.*]] = sparse_tensor.pointers %[[S]] {dimension = 0 : index}
// CHECK-RWT: %[[I0:.*]] = sparse_tensor.indices %[[S]] {dimension = 0 : index}
// CHECK-RWT: %[[V:.*]] = sparse_tensor.values %[[S]]
// CHECK-RWT: %[[S0:.*]] = memref.load %[[P0]]{{\[}}%[[C0]]] : memref<?xindex>
// CHECK-RWT: %[[E0:.*]] = memref.load %[[P0]]{{\[}}%[[C1]]] : memref<?xindex>
// CHECK-RWT: scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] {
// CHECK-RWT: %[[SI:.*]] = memref.load %[[I0]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT: %[[SV:.*]] = memref.load %[[V]]{{\[}}%[[I]]] : memref<?xf64>
// CHECK-RWT: %[[T1:.*]] = arith.muli %[[DD0]], %[[C10]] : index
// CHECK-RWT: %[[T2:.*]] = arith.divui %[[T1]], %[[DD0]] : index
// CHECK-RWT: %[[DI0:.*]] = arith.divui %[[SI]], %[[T2]] : index
// CHECK-RWT: %[[T3:.*]] = arith.remui %[[SI]], %[[T2]] : index
// CHECK-RWT: %[[T4:.*]] = arith.divui %[[T2]], %[[C10]] : index
// CHECK-RWT: %[[DI1:.*]] = arith.divui %[[T3]], %[[T4]] : index
// CHECK-RWT: sparse_tensor.insert %[[SV]] into %[[B]]{{\[}}%[[DI0]], %[[DI1]]]
// CHECK-RWT: }
// CHECK-RWT: %[[T:.*]] = sparse_tensor.convert %[[B]]
// CHECK-RWT: return %[[T]] : tensor<?x10xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
//
func.func @dynamic_sparse_expand(%arg0: tensor<?xf64, #SparseVector>) -> tensor<?x10xf64, #SparseMatrix> {
%0 = tensor.expand_shape %arg0 [[0, 1]] :
tensor<?xf64, #SparseVector> into tensor<?x10xf64, #SparseMatrix>
return %0 : tensor<?x10xf64, #SparseMatrix>
}
//
// roundtrip:
//
// CHECK-ROUND-LABEL: func.func @dynamic_sparse_collapse(
// CHECK-ROUND-SAME: %[[A:.*]]: tensor<10x?xf64, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<?xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-ROUND: %[[C:.*]] = tensor.collapse_shape %[[A]] {{\[\[}}0, 1]] : tensor<10x?xf64, #sparse_tensor.encoding<{{{.*}}}>> into tensor<?xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK-ROUND: return %[[C]] : tensor<?xf64, #sparse_tensor.encoding<{{{.*}}}>>
//
// conversion:
//
// CHECK-CONV-LABEL: func.func @dynamic_sparse_collapse(
// CHECK-CONV-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-CONV-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-CONV-DAG: %[[C10:.*]] = arith.constant 10 : index
// CHECK-CONV-DAG: %[[M1:.*]] = arith.muli %{{.*}}, %[[C10]] : index
// CHECK-CONV-DAG: call @newSparseTensor
// CHECK-CONV-DAG: call @newSparseTensor
// CHECK-CONV: scf.while : () -> () {
// CHECK-CONV: call @getNextF64
// CHECK-CONV: scf.condition
// CHECK-CONV: } do {
// CHECK-CONV: %[[X:.*]] = memref.load %{{.*}}[%[[C0]]] : memref<2xindex>
// CHECK-CONV: %[[Y:.*]] = memref.load %{{.*}}[%[[C1]]] : memref<2xindex>
// CHECK-CONV: %[[D1:.*]] = arith.divui %[[M1]], %[[C10]] : index
// CHECK-CONV: %[[M2:.*]] = arith.muli %[[X]], %[[D1]] : index
// CHECK-CONV: %[[D2:.*]] = arith.divui %[[D1]], %{{.*}} : index
// CHECK-CONV: %[[M3:.*]] = arith.muli %[[Y]], %[[D2]] : index
// CHECK-CONV: %[[A:.*]] = arith.addi %[[M2]], %[[M3]] : index
// CHECK-CONV: memref.store %[[A]], %{{.*}}[%[[C0]]] : memref<1xindex>
// CHECK-CONV: call @addEltF64
// CHECK-CONV: scf.yield
// CHECK-CONV: }
// CHECK-CONV: %[[N:.*]] = call @newSparseTensor
// CHECK-CONV: call @delSparseTensorCOOF64
// CHECK-CONV: call @delSparseTensorCOOF64
// CHECK-CONV: return %[[N]] : !llvm.ptr<i8>
//
// rewrite for codegen:
//
// CHECK-RWT-LABEL: func.func @dynamic_sparse_collapse(
// CHECK-RWT-SAME: %[[S:.*]]:
// CHECK-RWT-DAG: %[[C10:.*]] = arith.constant 10 : index
// CHECK-RWT-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-RWT-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-RWT: %[[SD1:.*]] = tensor.dim %[[S]], %[[C1]]
// CHECK-RWT: %[[DD0:.*]] = arith.muli %[[SD1]], %[[C10]] : index
// CHECK-RWT: %[[B:.*]] = bufferization.alloc_tensor(%[[DD0]])
// CHECK-RWT: %[[P0:.*]] = sparse_tensor.pointers %[[S]] {dimension = 0 : index}
// CHECK-RWT: %[[I0:.*]] = sparse_tensor.indices %[[S]] {dimension = 0 : index}
// CHECK-RWT: %[[P1:.*]] = sparse_tensor.pointers %[[S]] {dimension = 1 : index}
// CHECK-RWT: %[[I1:.*]] = sparse_tensor.indices %[[S]] {dimension = 1 : index}
// CHECK-RWT: %[[V:.*]] = sparse_tensor.values %[[S]]
// CHECK-RWT: %[[S0:.*]] = memref.load %[[P0]]{{\[}}%[[C0]]] : memref<?xindex>
// CHECK-RWT: %[[E0:.*]] = memref.load %[[P0]]{{\[}}%[[C1]]] : memref<?xindex>
// CHECK-RWT: scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] {
// CHECK-RWT: %[[SI0:.*]] = memref.load %[[I0]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT: %[[PE1:.*]] = arith.addi %[[I]], %[[C1]] : index
// CHECK-RWT: %[[S1:.*]] = memref.load %[[P1]]{{\[}}%[[I]]] : memref<?xindex>
// CHECK-RWT: %[[E1:.*]] = memref.load %[[P1]]{{\[}}%[[PE1]]] : memref<?xindex>
// CHECK-RWT: scf.for %[[J:.*]] = %[[S1]] to %[[E1]] step %[[C1]] {
// CHECK-RWT: %[[SI1:.*]] = memref.load %[[I1]]{{\[}}%[[J]]] : memref<?xindex>
// CHECK-RWT: %[[SV:.*]] = memref.load %[[V]]{{\[}}%[[J]]] : memref<?xf64>
// CHECK-RWT: %[[T1:.*]] = arith.divui %[[DD0]], %[[C10]] : index
// CHECK-RWT: %[[T2:.*]] = arith.muli %[[SI0]], %[[T1]] : index
// CHECK-RWT: %[[T3:.*]] = arith.divui %[[T1]], %[[SD1]] : index
// CHECK-RWT: %[[T4:.*]] = arith.muli %[[SI1]], %[[T3]] : index
// CHECK-RWT: %[[DI:.*]] = arith.addi %[[T2]], %[[T4]] : index
// CHECK-RWT: sparse_tensor.insert %[[SV]] into %[[B]]{{\[}}%[[DI]]]
// CHECK-RWT }
// CHECK-RWT: }
// CHECK-RWT: %[[T:.*]] = sparse_tensor.convert %[[B]]
// CHECK-RWT: return %[[T]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
//
func.func @dynamic_sparse_collapse(%arg0: tensor<10x?xf64, #SparseMatrix>) -> tensor<?xf64, #SparseVector> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] :
tensor<10x?xf64, #SparseMatrix> into tensor<?xf64, #SparseVector>
return %0 : tensor<?xf64, #SparseVector>
}
|
