5661647e85
The alloc->insert/compress->load chain needs to be properly represented with an SSA chain now in loops and if statements to properly reflect the modifying behavior (runtime support lib is forgiving on breaking this, but the new codegen is not). Reviewed By: Peiming Differential Revision: https://reviews.llvm.org/D136966
56 lines
3.1 KiB
MLIR
56 lines
3.1 KiB
MLIR
// RUN: mlir-opt %s --sparsification --canonicalize --cse | FileCheck %s
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#DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
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#SparseTensor = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>
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#trait = {
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indexing_maps = [
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affine_map<(d0, d1, d2) -> (d0, d2)>,
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affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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],
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iterator_types = ["parallel", "parallel", "parallel"]
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}
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// CHECK-LABEL: @main(
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// CHECK-SAME: %[[TMP_arg0:.*]]: tensor<4x5xi32,
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// CHECK-DAG: %[[TMP_c3:.*]] = arith.constant 3 : index
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// CHECK-DAG: %[[TMP_c0:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[TMP_c1:.*]] = arith.constant 1 : index
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// CHECK: %[[TMP_0:.*]] = bufferization.alloc_tensor()
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// CHECK: %[[TMP_1:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 0 : index}
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// CHECK: %[[TMP_2:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 0 : index}
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// CHECK: %[[TMP_3:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 1 : index}
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// CHECK: %[[TMP_4:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 1 : index}
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// CHECK: %[[TMP_5:.*]] = sparse_tensor.values %[[TMP_arg0]]
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// CHECK: %[[TMP_6:.*]] = memref.load %[[TMP_1]][%[[TMP_c0]]] : memref<?xindex>
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// CHECK: %[[TMP_7:.*]] = memref.load %[[TMP_1]][%[[TMP_c1]]] : memref<?xindex>
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// CHECK: %[[T:.*]] = scf.for %[[TMP_arg1:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]] {{.*}} {
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// CHECK: %[[TMP_9:.*]] = memref.load %[[TMP_2]][%[[TMP_arg1]]] : memref<?xindex>
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// CHECK: %[[L1:.*]] = scf.for %[[TMP_arg2:.*]] = %[[TMP_c0]] to %[[TMP_c3]] step %[[TMP_c1]] {{.*}} {
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// CHECK: %[[TMP_10:.*]] = memref.load %[[TMP_3]][%[[TMP_arg1]]] : memref<?xindex>
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// CHECK: %[[TMP_11:.*]] = arith.addi %[[TMP_arg1]], %[[TMP_c1]] : index
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// CHECK: %[[TMP_12:.*]] = memref.load %[[TMP_3]][%[[TMP_11]]] : memref<?xindex>
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// CHECK: %[[L2:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_10]] to %[[TMP_12]] step %[[TMP_c1]] {{.*}} {
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// CHECK: %[[TMP_13:.*]] = memref.load %[[TMP_4]][%[[TMP_arg3]]] : memref<?xindex>
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// CHECK: %[[TMP_14:.*]] = memref.load %[[TMP_5]][%[[TMP_arg3]]] : memref<?xi32>
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// CHECK: %[[Y:.*]] = sparse_tensor.insert %[[TMP_14]] into %{{.*}}[%[[TMP_9]], %[[TMP_arg2]], %[[TMP_13]]]
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// CHECK: scf.yield %[[Y]]
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// CHECK: }
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// CHECK: scf.yield %[[L2]]
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// CHECK: }
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// CHECK: scf.yield %[[L1]]
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// CHECK: }
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// CHECK: %[[TMP_8:.*]] = sparse_tensor.load %[[T]] hasInserts
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// CHECK: return %[[TMP_8]]
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module @func_sparse {
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func.func public @main(%arg0: tensor<4x5xi32, #DCSR>) -> tensor<4x3x5xi32, #SparseTensor> {
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%0 = bufferization.alloc_tensor() : tensor<4x3x5xi32, #SparseTensor>
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%1 = linalg.generic #trait
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ins(%arg0 : tensor<4x5xi32, #DCSR>) outs(%0 : tensor<4x3x5xi32, #SparseTensor>) {
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^bb0(%in: i32, %out: i32):
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linalg.yield %in : i32
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} -> tensor<4x3x5xi32, #SparseTensor>
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return %1 : tensor<4x3x5xi32, #SparseTensor>
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}
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}
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