ff6c84b803
Rationale: Narrower types for overhead storage yield a smaller memory footprint for sparse tensors and thus needs to be supported. Also, more value types need to be supported to deal with all kinds of kernels. Since the "one-size-fits-all" sparse storage scheme implementation is used instead of actual codegen, the library needs to be able to support all combinations of desired types. With some crafty templating and overloading, the actual code for this is kept reasonably sized though. Reviewed By: bixia Differential Revision: https://reviews.llvm.org/D96819
182 lines
12 KiB
MLIR
182 lines
12 KiB
MLIR
// RUN: mlir-opt %s -test-sparsification | \
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// RUN: FileCheck %s --check-prefix=CHECK-HIR
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//
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// RUN: mlir-opt %s -test-sparsification="lower" --convert-linalg-to-loops | \
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// RUN: FileCheck %s --check-prefix=CHECK-MIR
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//
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// RUN: mlir-opt %s -test-sparsification="lower" --convert-linalg-to-loops \
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// RUN: --func-bufferize --tensor-constant-bufferize \
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// RUN: --tensor-bufferize --finalizing-bufferize | \
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// RUN: FileCheck %s --check-prefix=CHECK-LIR
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//
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// RUN: mlir-opt %s -test-sparsification="lower fast-output" --convert-linalg-to-loops \
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// RUN: --func-bufferize --tensor-constant-bufferize \
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// RUN: --tensor-bufferize --finalizing-bufferize | \
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// RUN: FileCheck %s --check-prefix=CHECK-FAST
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#trait_matvec = {
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indexing_maps = [
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affine_map<(i,j) -> (i,j)>, // A
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affine_map<(i,j) -> (j)>, // b
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affine_map<(i,j) -> (i)> // x (out)
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],
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iterator_types = ["parallel","reduction"],
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sparse = [
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[ "D", "S" ], // A
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[ "D" ], // b
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[ "D" ] // x (out)
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],
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sparse_dim_map = [
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affine_map<(i,j) -> (j,i)>, // A: column-wise
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affine_map<(i) -> (i)>, // x
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affine_map<(i) -> (i)> // b
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],
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doc = "x(i) += A(i,j) * b(j)"
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}
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// CHECK-HIR-LABEL: func @matvec(
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// CHECK-HIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>,
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// CHECK-HIR-SAME: %[[VAL_1:.*]]: tensor<64xf64>,
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// CHECK-HIR-SAME: %[[VAL_2:.*]]: tensor<64xf64>) -> tensor<64xf64> {
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// CHECK-HIR: %[[VAL_3:.*]] = constant 64 : index
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// CHECK-HIR: %[[VAL_4:.*]] = constant 0 : index
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// CHECK-HIR: %[[VAL_5:.*]] = constant 1 : index
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// CHECK-HIR: %[[VAL_6:.*]] = linalg.sparse_tensor %[[VAL_0]] : !llvm.ptr<i8> to tensor<64x64xf64>
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// CHECK-HIR: %[[VAL_7:.*]] = linalg.sparse_pointers %[[VAL_6]], %[[VAL_5]] : tensor<64x64xf64> to memref<?xindex>
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// CHECK-HIR: %[[VAL_8:.*]] = linalg.sparse_indices %[[VAL_6]], %[[VAL_5]] : tensor<64x64xf64> to memref<?xindex>
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// CHECK-HIR: %[[VAL_9:.*]] = linalg.sparse_values %[[VAL_6]] : tensor<64x64xf64> to memref<?xf64>
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// CHECK-HIR: %[[VAL_10:.*]] = tensor_to_memref %[[VAL_1]] : memref<64xf64>
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// CHECK-HIR: %[[VAL_11:.*]] = tensor_to_memref %[[VAL_2]] : memref<64xf64>
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// CHECK-HIR: %[[VAL_12:.*]] = alloc() : memref<64xf64>
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// CHECK-HIR: linalg.copy(%[[VAL_11]], %[[VAL_12]]) : memref<64xf64>, memref<64xf64>
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// CHECK-HIR: scf.for %[[VAL_13:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
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// CHECK-HIR: %[[VAL_14:.*]] = load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex>
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// CHECK-HIR: %[[VAL_15:.*]] = addi %[[VAL_13]], %[[VAL_5]] : index
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// CHECK-HIR: %[[VAL_16:.*]] = load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
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// CHECK-HIR: %[[VAL_17:.*]] = load %[[VAL_12]]{{\[}}%[[VAL_13]]] : memref<64xf64>
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// CHECK-HIR: %[[VAL_18:.*]] = scf.for %[[VAL_19:.*]] = %[[VAL_14]] to %[[VAL_16]] step %[[VAL_5]] iter_args(%[[VAL_20:.*]] = %[[VAL_17]]) -> (f64) {
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// CHECK-HIR: %[[VAL_21:.*]] = load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref<?xindex>
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// CHECK-HIR: %[[VAL_22:.*]] = load %[[VAL_9]]{{\[}}%[[VAL_19]]] : memref<?xf64>
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// CHECK-HIR: %[[VAL_23:.*]] = load %[[VAL_10]]{{\[}}%[[VAL_21]]] : memref<64xf64>
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// CHECK-HIR: %[[VAL_24:.*]] = mulf %[[VAL_22]], %[[VAL_23]] : f64
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// CHECK-HIR: %[[VAL_25:.*]] = addf %[[VAL_20]], %[[VAL_24]] : f64
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// CHECK-HIR: scf.yield %[[VAL_25]] : f64
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// CHECK-HIR: }
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// CHECK-HIR: store %[[VAL_26:.*]], %[[VAL_12]]{{\[}}%[[VAL_13]]] : memref<64xf64>
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// CHECK-HIR: }
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// CHECK-HIR: %[[VAL_27:.*]] = tensor_load %[[VAL_12]] : memref<64xf64>
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// CHECK-HIR: return %[[VAL_27]] : tensor<64xf64>
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// CHECK-HIR: }
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// CHECK-MIR-LABEL: func @matvec(
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// CHECK-MIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>,
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// CHECK-MIR-SAME: %[[VAL_1:.*]]: tensor<64xf64>,
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// CHECK-MIR-SAME: %[[VAL_2:.*]]: tensor<64xf64>) -> tensor<64xf64> {
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// CHECK-MIR: %[[VAL_3:.*]] = constant 64 : index
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// CHECK-MIR: %[[VAL_4:.*]] = constant 0 : index
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// CHECK-MIR: %[[VAL_5:.*]] = constant 1 : index
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// CHECK-MIR: %[[VAL_6:.*]] = call @sparsePointers64(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
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// CHECK-MIR: %[[VAL_7:.*]] = call @sparseIndices64(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
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// CHECK-MIR: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
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// CHECK-MIR: %[[VAL_9:.*]] = tensor_to_memref %[[VAL_1]] : memref<64xf64>
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// CHECK-MIR: %[[VAL_10:.*]] = tensor_to_memref %[[VAL_2]] : memref<64xf64>
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// CHECK-MIR: %[[VAL_11:.*]] = alloc() : memref<64xf64>
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// CHECK-MIR: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
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// CHECK-MIR: %[[VAL_13:.*]] = load %[[VAL_10]]{{\[}}%[[VAL_12]]] : memref<64xf64>
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// CHECK-MIR: store %[[VAL_13]], %[[VAL_11]]{{\[}}%[[VAL_12]]] : memref<64xf64>
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// CHECK-MIR: }
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// CHECK-MIR: scf.for %[[VAL_14:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
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// CHECK-MIR: %[[VAL_15:.*]] = load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex>
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// CHECK-MIR: %[[VAL_16:.*]] = addi %[[VAL_14]], %[[VAL_5]] : index
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// CHECK-MIR: %[[VAL_17:.*]] = load %[[VAL_6]]{{\[}}%[[VAL_16]]] : memref<?xindex>
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// CHECK-MIR: %[[VAL_18:.*]] = load %[[VAL_11]]{{\[}}%[[VAL_14]]] : memref<64xf64>
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// CHECK-MIR: %[[VAL_19:.*]] = scf.for %[[VAL_20:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_5]] iter_args(%[[VAL_21:.*]] = %[[VAL_18]]) -> (f64) {
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// CHECK-MIR: %[[VAL_22:.*]] = load %[[VAL_7]]{{\[}}%[[VAL_20]]] : memref<?xindex>
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// CHECK-MIR: %[[VAL_23:.*]] = load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xf64>
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// CHECK-MIR: %[[VAL_24:.*]] = load %[[VAL_9]]{{\[}}%[[VAL_22]]] : memref<64xf64>
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// CHECK-MIR: %[[VAL_25:.*]] = mulf %[[VAL_23]], %[[VAL_24]] : f64
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// CHECK-MIR: %[[VAL_26:.*]] = addf %[[VAL_21]], %[[VAL_25]] : f64
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// CHECK-MIR: scf.yield %[[VAL_26]] : f64
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// CHECK-MIR: }
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// CHECK-MIR: store %[[VAL_27:.*]], %[[VAL_11]]{{\[}}%[[VAL_14]]] : memref<64xf64>
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// CHECK-MIR: }
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// CHECK-MIR: %[[VAL_28:.*]] = tensor_load %[[VAL_11]] : memref<64xf64>
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// CHECK-MIR: return %[[VAL_28]] : tensor<64xf64>
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// CHECK-MIR: }
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// CHECK-LIR-LABEL: func @matvec(
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// CHECK-LIR-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>,
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// CHECK-LIR-SAME: %[[VAL_1:.*]]: memref<64xf64>,
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// CHECK-LIR-SAME: %[[VAL_2:.*]]: memref<64xf64>) -> memref<64xf64> {
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// CHECK-LIR: %[[VAL_3:.*]] = constant 64 : index
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// CHECK-LIR: %[[VAL_4:.*]] = constant 0 : index
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// CHECK-LIR: %[[VAL_5:.*]] = constant 1 : index
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// CHECK-LIR: %[[VAL_6:.*]] = call @sparsePointers64(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
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// CHECK-LIR: %[[VAL_7:.*]] = call @sparseIndices64(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
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// CHECK-LIR: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
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// CHECK-LIR: %[[VAL_9:.*]] = alloc() : memref<64xf64>
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// CHECK-LIR: scf.for %[[VAL_10:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
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// CHECK-LIR: %[[VAL_11:.*]] = load %[[VAL_2]]{{\[}}%[[VAL_10]]] : memref<64xf64>
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// CHECK-LIR: store %[[VAL_11]], %[[VAL_9]]{{\[}}%[[VAL_10]]] : memref<64xf64>
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// CHECK-LIR: }
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// CHECK-LIR: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
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// CHECK-LIR: %[[VAL_13:.*]] = load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
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// CHECK-LIR: %[[VAL_14:.*]] = addi %[[VAL_12]], %[[VAL_5]] : index
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// CHECK-LIR: %[[VAL_15:.*]] = load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex>
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// CHECK-LIR: %[[VAL_16:.*]] = load %[[VAL_9]]{{\[}}%[[VAL_12]]] : memref<64xf64>
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// CHECK-LIR: %[[VAL_17:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_5]] iter_args(%[[VAL_19:.*]] = %[[VAL_16]]) -> (f64) {
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// CHECK-LIR: %[[VAL_20:.*]] = load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex>
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// CHECK-LIR: %[[VAL_21:.*]] = load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xf64>
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// CHECK-LIR: %[[VAL_22:.*]] = load %[[VAL_1]]{{\[}}%[[VAL_20]]] : memref<64xf64>
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// CHECK-LIR: %[[VAL_23:.*]] = mulf %[[VAL_21]], %[[VAL_22]] : f64
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// CHECK-LIR: %[[VAL_24:.*]] = addf %[[VAL_19]], %[[VAL_23]] : f64
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// CHECK-LIR: scf.yield %[[VAL_24]] : f64
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// CHECK-LIR: }
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// CHECK-LIR: store %[[VAL_25:.*]], %[[VAL_9]]{{\[}}%[[VAL_12]]] : memref<64xf64>
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// CHECK-LIR: }
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// CHECK-LIR: return %[[VAL_9]] : memref<64xf64>
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// CHECK-LIR: }
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// CHECK-FAST-LABEL: func @matvec(
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// CHECK-FAST-SAME: %[[VAL_0:.*]]: !llvm.ptr<i8>,
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// CHECK-FAST-SAME: %[[VAL_1:.*]]: memref<64xf64>,
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// CHECK-FAST-SAME: %[[VAL_2:.*]]: memref<64xf64>) -> memref<64xf64> {
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// CHECK-FAST: %[[VAL_3:.*]] = constant 64 : index
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// CHECK-FAST: %[[VAL_4:.*]] = constant 0 : index
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// CHECK-FAST: %[[VAL_5:.*]] = constant 1 : index
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// CHECK-FAST: %[[VAL_6:.*]] = call @sparsePointers64(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
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// CHECK-FAST: %[[VAL_7:.*]] = call @sparseIndices64(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
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// CHECK-FAST: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
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// CHECK-FAST: scf.for %[[VAL_9:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
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// CHECK-FAST: %[[VAL_10:.*]] = load %[[VAL_6]]{{\[}}%[[VAL_9]]] : memref<?xindex>
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// CHECK-FAST: %[[VAL_11:.*]] = addi %[[VAL_9]], %[[VAL_5]] : index
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// CHECK-FAST: %[[VAL_12:.*]] = load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
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// CHECK-FAST: %[[VAL_13:.*]] = load %[[VAL_2]]{{\[}}%[[VAL_9]]] : memref<64xf64>
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// CHECK-FAST: %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_10]] to %[[VAL_12]] step %[[VAL_5]] iter_args(%[[VAL_16:.*]] = %[[VAL_13]]) -> (f64) {
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// CHECK-FAST: %[[VAL_17:.*]] = load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
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// CHECK-FAST: %[[VAL_18:.*]] = load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xf64>
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// CHECK-FAST: %[[VAL_19:.*]] = load %[[VAL_1]]{{\[}}%[[VAL_17]]] : memref<64xf64>
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// CHECK-FAST: %[[VAL_20:.*]] = mulf %[[VAL_18]], %[[VAL_19]] : f64
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// CHECK-FAST: %[[VAL_21:.*]] = addf %[[VAL_16]], %[[VAL_20]] : f64
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// CHECK-FAST: scf.yield %[[VAL_21]] : f64
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// CHECK-FAST: }
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// CHECK-FAST: store %[[VAL_22:.*]], %[[VAL_2]]{{\[}}%[[VAL_9]]] : memref<64xf64>
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// CHECK-FAST: }
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// CHECK-FAST: return %[[VAL_2]] : memref<64xf64>
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// CHECK-FAST: }
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!SparseTensor = type !llvm.ptr<i8>
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func @matvec(%argA: !SparseTensor, %argb: tensor<64xf64>, %argx: tensor<64xf64>) -> tensor<64xf64> {
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%arga = linalg.sparse_tensor %argA : !SparseTensor to tensor<64x64xf64>
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%0 = linalg.generic #trait_matvec
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ins(%arga, %argb : tensor<64x64xf64>, tensor<64xf64>)
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outs(%argx: tensor<64xf64>) {
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^bb(%A: f64, %b: f64, %x: f64):
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%0 = mulf %A, %b : f64
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%1 = addf %x, %0 : f64
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linalg.yield %1 : f64
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} -> tensor<64xf64>
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return %0 : tensor<64xf64>
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}
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