a54f4eae0e
Precursor: https://reviews.llvm.org/D110200 Removed redundant ops from the standard dialect that were moved to the `arith` or `math` dialects. Renamed all instances of operations in the codebase and in tests. Reviewed By: rriddle, jpienaar Differential Revision: https://reviews.llvm.org/D110797
93 lines
5.8 KiB
MLIR
93 lines
5.8 KiB
MLIR
// RUN: mlir-opt %s -sparsification --canonicalize | FileCheck %s --check-prefix=CHECK-HIR
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//
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// RUN: mlir-opt %s -sparsification --sparse-tensor-conversion --canonicalize | \
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// RUN: FileCheck %s --check-prefix=CHECK-MIR
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#X = #sparse_tensor.encoding<{
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dimLevelType = [ "dense", "dense", "dense" ],
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dimOrdering = affine_map<(i,j,k) -> (k,i,j)>
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}>
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#trait = {
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indexing_maps = [
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affine_map<(i,j,k) -> (k,i,j)>, // A (in)
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affine_map<(i,j,k) -> ()> // X (out)
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],
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iterator_types = ["reduction", "reduction", "reduction"]
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}
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// CHECK-HIR-LABEL: func @sparse_dynamic_dims(
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// CHECK-HIR-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
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// CHECK-HIR-DAG: %[[C0:.*]] = arith.constant 0 : index
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// CHECK-HIR-DAG: %[[C1:.*]] = arith.constant 1 : index
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// CHECK-HIR-DAG: %[[C2:.*]] = arith.constant 2 : index
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// CHECK-HIR: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[C2]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK-HIR: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[C0]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK-HIR: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[C1]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK-HIR: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK-HIR: %[[VAL_9:.*]] = memref.buffer_cast %[[VAL_1]] : memref<f32>
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// CHECK-HIR: %[[VAL_10:.*]] = memref.alloc() : memref<f32>
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// CHECK-HIR: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<f32> to memref<f32>
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// CHECK-HIR: scf.for %[[VAL_11:.*]] = %[[C0]] to %[[VAL_5]] step %[[C1]] {
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// CHECK-HIR: scf.for %[[VAL_12:.*]] = %[[C0]] to %[[VAL_6]] step %[[C1]] {
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// CHECK-HIR: %[[VAL_13:.*]] = arith.muli %[[VAL_6]], %[[VAL_11]] : index
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// CHECK-HIR: %[[VAL_14:.*]] = arith.addi %[[VAL_13]], %[[VAL_12]] : index
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// CHECK-HIR: %[[VAL_15:.*]] = memref.load %[[VAL_10]][] : memref<f32>
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// CHECK-HIR: %[[VAL_16:.*]] = scf.for %[[VAL_17:.*]] = %[[C0]] to %[[VAL_7]] step %[[C1]] iter_args(%[[VAL_18:.*]] = %[[VAL_15]]) -> (f32) {
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// CHECK-HIR: %[[VAL_19:.*]] = arith.muli %[[VAL_7]], %[[VAL_14]] : index
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// CHECK-HIR: %[[VAL_20:.*]] = arith.addi %[[VAL_19]], %[[VAL_17]] : index
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// CHECK-HIR: %[[VAL_21:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xf32>
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// CHECK-HIR: %[[VAL_22:.*]] = arith.addf %[[VAL_18]], %[[VAL_21]] : f32
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// CHECK-HIR: scf.yield %[[VAL_22]] : f32
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// CHECK-HIR: }
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// CHECK-HIR: memref.store %[[VAL_23:.*]], %[[VAL_10]][] : memref<f32>
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// CHECK-HIR: }
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// CHECK-HIR: }
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// CHECK-HIR: %[[VAL_24:.*]] = memref.tensor_load %[[VAL_10]] : memref<f32>
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// CHECK-HIR: return %[[VAL_24]] : tensor<f32>
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// CHECK-HIR: }
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//
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// CHECK-MIR-LABEL: func @sparse_dynamic_dims(
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// CHECK-MIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>,
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// CHECK-MIR-SAME: %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
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// CHECK-MIR-DAG: %[[C0:.*]] = arith.constant 0 : index
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// CHECK-MIR-DAG: %[[C1:.*]] = arith.constant 1 : index
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// CHECK-MIR-DAG: %[[C2:.*]] = arith.constant 2 : index
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// CHECK-MIR: %[[VAL_5:.*]] = call @sparseDimSize(%[[VAL_0]], %[[C0]]) : (!llvm.ptr<i8>, index) -> index
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// CHECK-MIR: %[[VAL_6:.*]] = call @sparseDimSize(%[[VAL_0]], %[[C1]]) : (!llvm.ptr<i8>, index) -> index
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// CHECK-MIR: %[[VAL_7:.*]] = call @sparseDimSize(%[[VAL_0]], %[[C2]]) : (!llvm.ptr<i8>, index) -> index
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// CHECK-MIR: %[[VAL_8:.*]] = call @sparseValuesF32(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf32>
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// CHECK-MIR: %[[VAL_9:.*]] = memref.buffer_cast %[[VAL_1]] : memref<f32>
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// CHECK-MIR: %[[VAL_10:.*]] = memref.alloc() : memref<f32>
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// CHECK-MIR: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<f32> to memref<f32>
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// CHECK-MIR: scf.for %[[VAL_11:.*]] = %[[C0]] to %[[VAL_5]] step %[[C1]] {
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// CHECK-MIR: scf.for %[[VAL_12:.*]] = %[[C0]] to %[[VAL_6]] step %[[C1]] {
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// CHECK-MIR: %[[VAL_13:.*]] = arith.muli %[[VAL_6]], %[[VAL_11]] : index
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// CHECK-MIR: %[[VAL_14:.*]] = arith.addi %[[VAL_13]], %[[VAL_12]] : index
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// CHECK-MIR: %[[VAL_15:.*]] = memref.load %[[VAL_10]][] : memref<f32>
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// CHECK-MIR: %[[VAL_16:.*]] = scf.for %[[VAL_17:.*]] = %[[C0]] to %[[VAL_7]] step %[[C1]] iter_args(%[[VAL_18:.*]] = %[[VAL_15]]) -> (f32) {
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// CHECK-MIR: %[[VAL_19:.*]] = arith.muli %[[VAL_7]], %[[VAL_14]] : index
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// CHECK-MIR: %[[VAL_20:.*]] = arith.addi %[[VAL_19]], %[[VAL_17]] : index
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// CHECK-MIR: %[[VAL_21:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xf32>
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// CHECK-MIR: %[[VAL_22:.*]] = arith.addf %[[VAL_18]], %[[VAL_21]] : f32
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// CHECK-MIR: scf.yield %[[VAL_22]] : f32
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// CHECK-MIR: }
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// CHECK-MIR: memref.store %[[VAL_23:.*]], %[[VAL_10]][] : memref<f32>
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// CHECK-MIR: }
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// CHECK-MIR: }
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// CHECK-MIR: %[[VAL_24:.*]] = memref.tensor_load %[[VAL_10]] : memref<f32>
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// CHECK-MIR: return %[[VAL_24]] : tensor<f32>
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// CHECK-MIR: }
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func @sparse_dynamic_dims(%arga: tensor<?x?x?xf32, #X>,
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%argx: tensor<f32>) -> tensor<f32> {
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%0 = linalg.generic #trait
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ins(%arga: tensor<?x?x?xf32, #X>)
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outs(%argx: tensor<f32>) {
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^bb(%a : f32, %x: f32):
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%0 = arith.addf %x, %a : f32
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linalg.yield %0 : f32
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} -> tensor<f32>
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return %0 : tensor<f32>
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}
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