caio/clang-p2996
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
41a94de75caacb979070ec7a010dfe3c4e9f116f
clang-p2996/mlir/test/Dialect/SparseTensor/vectorize_reduction.mlir
Christopher Bate ced2fc7819 [mlir][bufferization] Fix OneShotBufferize when defaultMemorySpaceFn is used (#91524) 
As described in issue llvm/llvm-project#91518, a previous PR
llvm/llvm-project#78484 introduced the `defaultMemorySpaceFn` into
bufferization options, allowing one to inform OneShotBufferize that it
should use a specified function to derive the memory space attribute
from the encoding attribute attached to tensor types.

However, introducing this feature exposed unhandled edge cases,
examples of which are introduced by this change in the new test under

`test/Dialect/Bufferization/Transforms/one-shot-bufferize-encodings.mlir`.

Fixing the inconsistencies introduced by `defaultMemorySpaceFn` is
pretty simple. This change:

- Updates the `bufferization.to_memref` and `bufferization.to_tensor`
  operations to explicitly include operand and destination types,
  whereas previously they relied on type inference to deduce the
  tensor types. Since the type inference cannot recover the correct
  tensor encoding/memory space, the operand and result types must be
  explicitly included. This is a small assembly format change, but it
  touches a large number of test files.

- Makes minor updates to other bufferization functions to handle the
  changes in building the above ops.

- Updates bufferization of `tensor.from_elements` to handle memory
  space.


Integration/upgrade guide:

In downstream projects, if you have tests or MLIR files that explicitly
use
`bufferization.to_tensor` or `bufferization.to_memref`, then update
them to the new assembly format as follows:

```
%1 = bufferization.to_memref %0 : memref<10xf32>
%2 = bufferization.to_tensor %1 : memref<10xf32>
```

becomes

```
%1 = bufferization.to_memref %0 : tensor<10xf32> to memref<10xf32>
%2 = bufferization.to_tensor %0 : memref<10xf32> to tensor<10xf32> 
```
2024-11-26 09:45:57 -07:00

536 lines
32 KiB
MLIR
Raw Blame History

// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification -cse -sparse-vectorization="vl=8" -cse -split-input-file | \
// RUN: FileCheck %s --check-prefix=CHECK-ON
// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification -cse -split-input-file | \
// RUN: FileCheck %s --check-prefix=CHECK-OFF
// -----
// Check that we vectorize reductions with ori.
// CHECK-ON-LABEL: func.func @sparse_reduction_ori(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i13>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse{{[0-9]*}}>) -> tensor<i13> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi13>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-ON-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-ON-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xi13>
// CHECK-ON-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<i13> to memref<i13>
// CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i13>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK-ON: %[[VAL_12:.*]] = vector.broadcast %[[VAL_9]] : i13 to vector<8xi13>
// CHECK-ON: %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi13>) {
// CHECK-ON: %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]]
// CHECK-ON: %[[VAL_17:.*]] = vector.create_mask %[[VAL_16]] : vector<8xi1>
// CHECK-ON: %[[VAL_18:.*]] = vector.maskedload %[[VAL_7]]{{\[}}%[[VAL_14]]], %[[VAL_17]], %[[VAL_3]] : memref<?xi13>, vector<8xi1>, vector<8xi13> into vector<8xi13>
// CHECK-ON: %[[VAL_19:.*]] = arith.ori %[[VAL_15]], %[[VAL_18]] : vector<8xi13>
// CHECK-ON: %[[VAL_20:.*]] = arith.select %[[VAL_17]], %[[VAL_19]], %[[VAL_15]] : vector<8xi1>, vector<8xi13>
// CHECK-ON: scf.yield %[[VAL_20]] : vector<8xi13>
// CHECK-ON: } {"Emitted from" = "linalg.generic"}
// CHECK-ON: %[[VAL_21:.*]] = vector.reduction <or>, %[[VAL_22:.*]] : vector<8xi13> into i13
// CHECK-ON: memref.store %[[VAL_21]], %[[VAL_8]][] : memref<i13>
// CHECK-ON: %[[VAL_23:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<i13>
// CHECK-ON: return %[[VAL_23]] : tensor<i13>
// CHECK-ON: }
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_ori(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i13>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse{{[0-9]*}}>) -> tensor<i13> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-OFF-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xi13>
// CHECK-OFF-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<i13> to memref<i13>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i13>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK-OFF: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK-OFF: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i13) {
// CHECK-OFF: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi13>
// CHECK-OFF: %[[VAL_14:.*]] = arith.ori %[[VAL_12]], %[[VAL_13]] : i13
// CHECK-OFF: scf.yield %[[VAL_14]] : i13
// CHECK-OFF: } {"Emitted from" = "linalg.generic"}
// CHECK-OFF: memref.store %[[VAL_15:.*]], %[[VAL_6]][] : memref<i13>
// CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<i13>
// CHECK-OFF: return %[[VAL_16]] : tensor<i13>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}>
#trait = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> ()> // x (out)
],
iterator_types = ["reduction"]
}
func.func @sparse_reduction_ori(%argx: tensor<i13>,
%arga: tensor<?xi13, #SparseVector>)
-> tensor<i13> {
%0 = linalg.generic #trait
ins(%arga: tensor<?xi13, #SparseVector>)
outs(%argx: tensor<i13>) {
^bb(%a: i13, %x: i13):
%t = arith.ori %x, %a: i13
linalg.yield %t : i13
} -> tensor<i13>
return %0 : tensor<i13>
}
// -----
// Same test as sparse_reduction_ori except that the accumulator is on the
// rhs of the operation. This checks that we can recognize a reduction
// irrespective to where the accumulator appears on commutative operations.
// CHECK-ON-LABEL: func.func @sparse_reduction_ori_accumulator_on_rhs(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i13>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse{{[0-9]*}}>) -> tensor<i13> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi13>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-ON-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-ON-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xi13>
// CHECK-ON-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<i13> to memref<i13>
// CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i13>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK-ON: %[[VAL_12:.*]] = vector.broadcast %[[VAL_9]] : i13 to vector<8xi13>
// CHECK-ON: %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi13>) {
// CHECK-ON: %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]]
// CHECK-ON: %[[VAL_17:.*]] = vector.create_mask %[[VAL_16]] : vector<8xi1>
// CHECK-ON: %[[VAL_18:.*]] = vector.maskedload %[[VAL_7]]{{\[}}%[[VAL_14]]], %[[VAL_17]], %[[VAL_3]] : memref<?xi13>, vector<8xi1>, vector<8xi13> into vector<8xi13>
// CHECK-ON: %[[VAL_19:.*]] = arith.ori %[[VAL_18]], %[[VAL_15]] : vector<8xi13>
// CHECK-ON: %[[VAL_20:.*]] = arith.select %[[VAL_17]], %[[VAL_19]], %[[VAL_15]] : vector<8xi1>, vector<8xi13>
// CHECK-ON: scf.yield %[[VAL_20]] : vector<8xi13>
// CHECK-ON: } {"Emitted from" = "linalg.generic"}
// CHECK-ON: %[[VAL_21:.*]] = vector.reduction <or>, %[[VAL_22:.*]] : vector<8xi13> into i13
// CHECK-ON: memref.store %[[VAL_21]], %[[VAL_8]][] : memref<i13>
// CHECK-ON: %[[VAL_23:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<i13>
// CHECK-ON: return %[[VAL_23]] : tensor<i13>
// CHECK-ON: }
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_ori_accumulator_on_rhs(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i13>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi13, #sparse{{[0-9]*}}>) -> tensor<i13> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-OFF-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse{{[0-9]*}}> to memref<?xi13>
// CHECK-OFF-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<i13> to memref<i13>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i13>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK-OFF: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK-OFF: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i13) {
// CHECK-OFF: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi13>
// CHECK-OFF: %[[VAL_14:.*]] = arith.ori %[[VAL_13]], %[[VAL_12]] : i13
// CHECK-OFF: scf.yield %[[VAL_14]] : i13
// CHECK-OFF: } {"Emitted from" = "linalg.generic"}
// CHECK-OFF: memref.store %[[VAL_15:.*]], %[[VAL_6]][] : memref<i13>
// CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<i13>
// CHECK-OFF: return %[[VAL_16]] : tensor<i13>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}>
#trait = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> ()> // x (out)
],
iterator_types = ["reduction"]
}
func.func @sparse_reduction_ori_accumulator_on_rhs(%argx: tensor<i13>,
%arga: tensor<?xi13, #SparseVector>)
-> tensor<i13> {
%0 = linalg.generic #trait
ins(%arga: tensor<?xi13, #SparseVector>)
outs(%argx: tensor<i13>) {
^bb(%a: i13, %x: i13):
%t = arith.ori %a, %x: i13
linalg.yield %t : i13
} -> tensor<i13>
return %0 : tensor<i13>
}
// -----
// Check that we vectorize reductions with subi.
//
// CHECK-ON-LABEL: func.func @sparse_reduction_subi(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse{{[0-9]*}}>) -> tensor<i32> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant dense<0> : vector<8xi32>
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-ON-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-ON-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xi32>
// CHECK-ON-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<i32> to memref<i32>
// CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i32>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK-ON: %[[VAL_12:.*]] = vector.insertelement %[[VAL_9]], %[[VAL_4]]{{\[}}%[[VAL_3]] : index] : vector<8xi32>
// CHECK-ON: %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi32>) {
// CHECK-ON: %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]]
// CHECK-ON: %[[VAL_17:.*]] = vector.create_mask %[[VAL_16]] : vector<8xi1>
// CHECK-ON: %[[VAL_18:.*]] = vector.maskedload %[[VAL_7]]{{\[}}%[[VAL_14]]], %[[VAL_17]], %[[VAL_4]] : memref<?xi32>, vector<8xi1>, vector<8xi32> into vector<8xi32>
// CHECK-ON: %[[VAL_19:.*]] = arith.subi %[[VAL_15]], %[[VAL_18]] : vector<8xi32>
// CHECK-ON: %[[VAL_20:.*]] = arith.select %[[VAL_17]], %[[VAL_19]], %[[VAL_15]] : vector<8xi1>, vector<8xi32>
// CHECK-ON: scf.yield %[[VAL_20]] : vector<8xi32>
// CHECK-ON: } {"Emitted from" = "linalg.generic"}
// CHECK-ON: %[[VAL_21:.*]] = vector.reduction <add>, %[[VAL_22:.*]] : vector<8xi32> into i32
// CHECK-ON: memref.store %[[VAL_21]], %[[VAL_8]][] : memref<i32>
// CHECK-ON: %[[VAL_23:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<i32>
// CHECK-ON: return %[[VAL_23]] : tensor<i32>
// CHECK-ON: }
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_subi(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse{{[0-9]*}}>) -> tensor<i32> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-OFF-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xi32>
// CHECK-OFF-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<i32> to memref<i32>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK-OFF: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK-OFF: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i32) {
// CHECK-OFF: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi32>
// CHECK-OFF: %[[VAL_14:.*]] = arith.subi %[[VAL_12]], %[[VAL_13]] : i32
// CHECK-OFF: scf.yield %[[VAL_14]] : i32
// CHECK-OFF: } {"Emitted from" = "linalg.generic"}
// CHECK-OFF: memref.store %[[VAL_15:.*]], %[[VAL_6]][] : memref<i32>
// CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<i32>
// CHECK-OFF: return %[[VAL_16]] : tensor<i32>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}>
#trait = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> ()> // x (out)
],
iterator_types = ["reduction"]
}
func.func @sparse_reduction_subi(%argx: tensor<i32>,
%arga: tensor<?xi32, #SparseVector>)
-> tensor<i32> {
%0 = linalg.generic #trait
ins(%arga: tensor<?xi32, #SparseVector>)
outs(%argx: tensor<i32>) {
^bb(%a: i32, %x: i32):
%t = arith.subi %x, %a: i32
linalg.yield %t : i32
} -> tensor<i32>
return %0 : tensor<i32>
}
// -----
// Check that we vectorize reductions with xor.
// CHECK-ON-LABEL: func.func @sparse_reduction_xor(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse{{[0-9]*}}>) -> tensor<i32> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi32>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-ON-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-ON-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xi32>
// CHECK-ON-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<i32> to memref<i32>
// CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i32>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK-ON: %[[VAL_12:.*]] = vector.insertelement %[[VAL_9]], %[[VAL_3]]{{\[}}%[[VAL_4]] : index] : vector<8xi32>
// CHECK-ON: %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi32>) {
// CHECK-ON: %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]]
// CHECK-ON: %[[VAL_17:.*]] = vector.create_mask %[[VAL_16]] : vector<8xi1>
// CHECK-ON: %[[VAL_18:.*]] = vector.maskedload %[[VAL_7]]{{\[}}%[[VAL_14]]], %[[VAL_17]], %[[VAL_3]] : memref<?xi32>, vector<8xi1>, vector<8xi32> into vector<8xi32>
// CHECK-ON: %[[VAL_19:.*]] = arith.xori %[[VAL_15]], %[[VAL_18]] : vector<8xi32>
// CHECK-ON: %[[VAL_20:.*]] = arith.select %[[VAL_17]], %[[VAL_19]], %[[VAL_15]] : vector<8xi1>, vector<8xi32>
// CHECK-ON: scf.yield %[[VAL_20]] : vector<8xi32>
// CHECK-ON: } {"Emitted from" = "linalg.generic"}
// CHECK-ON: %[[VAL_21:.*]] = vector.reduction <xor>, %[[VAL_22:.*]] : vector<8xi32> into i32
// CHECK-ON: memref.store %[[VAL_21]], %[[VAL_8]][] : memref<i32>
// CHECK-ON: %[[VAL_23:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<i32>
// CHECK-ON: return %[[VAL_23]] : tensor<i32>
// CHECK-ON: }
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_xor(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse{{[0-9]*}}>) -> tensor<i32> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-OFF-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xi32>
// CHECK-OFF-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<i32> to memref<i32>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK-OFF: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK-OFF: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i32) {
// CHECK-OFF: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi32>
// CHECK-OFF: %[[VAL_14:.*]] = arith.xori %[[VAL_12]], %[[VAL_13]] : i32
// CHECK-OFF: scf.yield %[[VAL_14]] : i32
// CHECK-OFF: } {"Emitted from" = "linalg.generic"}
// CHECK-OFF: memref.store %[[VAL_15:.*]], %[[VAL_6]][] : memref<i32>
// CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<i32>
// CHECK-OFF: return %[[VAL_16]] : tensor<i32>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}>
#trait = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> ()> // x (out)
],
iterator_types = ["reduction"]
}
func.func @sparse_reduction_xor(%argx: tensor<i32>,
%arga: tensor<?xi32, #SparseVector>)
-> tensor<i32> {
%0 = linalg.generic #trait
ins(%arga: tensor<?xi32, #SparseVector>)
outs(%argx: tensor<i32>) {
^bb(%a: i32, %x: i32):
%t = arith.xori %x, %a: i32
linalg.yield %t : i32
} -> tensor<i32>
return %0 : tensor<i32>
}
// -----
// Check that we vectorize reductions with addi.
// CHECK-ON-LABEL: func.func @sparse_reduction_addi(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse{{[0-9]*}}>) -> tensor<i32> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi32>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-ON-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-ON-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xi32>
// CHECK-ON-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<i32> to memref<i32>
// CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i32>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK-ON: %[[VAL_12:.*]] = vector.insertelement %[[VAL_9]], %[[VAL_3]]{{\[}}%[[VAL_4]] : index] : vector<8xi32>
// CHECK-ON: %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi32>) {
// CHECK-ON: %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]]
// CHECK-ON: %[[VAL_17:.*]] = vector.create_mask %[[VAL_16]] : vector<8xi1>
// CHECK-ON: %[[VAL_18:.*]] = vector.maskedload %[[VAL_7]]{{\[}}%[[VAL_14]]], %[[VAL_17]], %[[VAL_3]] : memref<?xi32>, vector<8xi1>, vector<8xi32> into vector<8xi32>
// CHECK-ON: %[[VAL_19:.*]] = arith.addi %[[VAL_15]], %[[VAL_18]] : vector<8xi32>
// CHECK-ON: %[[VAL_20:.*]] = arith.select %[[VAL_17]], %[[VAL_19]], %[[VAL_15]] : vector<8xi1>, vector<8xi32>
// CHECK-ON: scf.yield %[[VAL_20]] : vector<8xi32>
// CHECK-ON: } {"Emitted from" = "linalg.generic"}
// CHECK-ON: %[[VAL_21:.*]] = vector.reduction <add>, %[[VAL_22:.*]] : vector<8xi32> into i32
// CHECK-ON: memref.store %[[VAL_21]], %[[VAL_8]][] : memref<i32>
// CHECK-ON: %[[VAL_23:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<i32>
// CHECK-ON: return %[[VAL_23]] : tensor<i32>
// CHECK-ON: }
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_addi(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<i32>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse{{[0-9]*}}>) -> tensor<i32> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-OFF-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse{{[0-9]*}}> to memref<?xi32>
// CHECK-OFF-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<i32> to memref<i32>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK-OFF: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK-OFF: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i32) {
// CHECK-OFF: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi32>
// CHECK-OFF: %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_13]] : i32
// CHECK-OFF: scf.yield %[[VAL_14]] : i32
// CHECK-OFF: } {"Emitted from" = "linalg.generic"}
// CHECK-OFF: memref.store %[[VAL_15:.*]], %[[VAL_6]][] : memref<i32>
// CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<i32>
// CHECK-OFF: return %[[VAL_16]] : tensor<i32>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}>
#trait = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> ()> // x (out)
],
iterator_types = ["reduction"]
}
func.func @sparse_reduction_addi(%argx: tensor<i32>,
%arga: tensor<?xi32, #SparseVector>)
-> tensor<i32> {
%0 = linalg.generic #trait
ins(%arga: tensor<?xi32, #SparseVector>)
outs(%argx: tensor<i32>) {
^bb(%a: i32, %x: i32):
%t = arith.addi %x, %a: i32
linalg.yield %t : i32
} -> tensor<i32>
return %0 : tensor<i32>
}
// -----
// Check that we vectorize reductions with subf.
// CHECK-ON-LABEL: func.func @sparse_reduction_subf(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<f32>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse{{[0-9]*}}>) -> tensor<f32> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0.000000e+00> : vector<8xf32>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-ON-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-ON-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xf32>
// CHECK-ON-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<f32> to memref<f32>
// CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<f32>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK-ON: %[[VAL_12:.*]] = vector.insertelement %[[VAL_9]], %[[VAL_3]]{{\[}}%[[VAL_4]] : index] : vector<8xf32>
// CHECK-ON: %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xf32>) {
// CHECK-ON: %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]]
// CHECK-ON: %[[VAL_17:.*]] = vector.create_mask %[[VAL_16]] : vector<8xi1>
// CHECK-ON: %[[VAL_18:.*]] = vector.maskedload %[[VAL_7]]{{\[}}%[[VAL_14]]], %[[VAL_17]], %[[VAL_3]] : memref<?xf32>, vector<8xi1>, vector<8xf32> into vector<8xf32>
// CHECK-ON: %[[VAL_19:.*]] = arith.subf %[[VAL_15]], %[[VAL_18]] : vector<8xf32>
// CHECK-ON: %[[VAL_20:.*]] = arith.select %[[VAL_17]], %[[VAL_19]], %[[VAL_15]] : vector<8xi1>, vector<8xf32>
// CHECK-ON: scf.yield %[[VAL_20]] : vector<8xf32>
// CHECK-ON: } {"Emitted from" = "linalg.generic"}
// CHECK-ON: %[[VAL_21:.*]] = vector.reduction <add>, %[[VAL_22:.*]] : vector<8xf32> into f32
// CHECK-ON: memref.store %[[VAL_21]], %[[VAL_8]][] : memref<f32>
// CHECK-ON: %[[VAL_23:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<f32>
// CHECK-ON: return %[[VAL_23]] : tensor<f32>
// CHECK-ON: }
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_subf(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<f32>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse{{[0-9]*}}>) -> tensor<f32> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-OFF-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xf32>
// CHECK-OFF-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<f32> to memref<f32>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<f32>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK-OFF: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK-OFF: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (f32) {
// CHECK-OFF: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xf32>
// CHECK-OFF: %[[VAL_14:.*]] = arith.subf %[[VAL_12]], %[[VAL_13]] : f32
// CHECK-OFF: scf.yield %[[VAL_14]] : f32
// CHECK-OFF: } {"Emitted from" = "linalg.generic"}
// CHECK-OFF: memref.store %[[VAL_15:.*]], %[[VAL_6]][] : memref<f32>
// CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<f32>
// CHECK-OFF: return %[[VAL_16]] : tensor<f32>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}>
#trait = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> ()> // x (out)
],
iterator_types = ["reduction"]
}
func.func @sparse_reduction_subf(%argx: tensor<f32>,
%arga: tensor<?xf32, #SparseVector>)
-> tensor<f32> {
%0 = linalg.generic #trait
ins(%arga: tensor<?xf32, #SparseVector>)
outs(%argx: tensor<f32>) {
^bb(%a: f32, %x: f32):
%t = arith.subf %x, %a: f32
linalg.yield %t : f32
} -> tensor<f32>
return %0 : tensor<f32>
}
// -----
// Check that we vectorize reductions with addf.
// CHECK-ON-LABEL: func.func @sparse_reduction_addf(
// CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<f32>,
// CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse{{[0-9]*}}>) -> tensor<f32> {
// CHECK-ON-DAG: %[[VAL_2:.*]] = arith.constant 8 : index
// CHECK-ON-DAG: %[[VAL_3:.*]] = arith.constant dense<0.000000e+00> : vector<8xf32>
// CHECK-ON-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK-ON-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK-ON-DAG: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-ON-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xf32>
// CHECK-ON-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<f32> to memref<f32>
// CHECK-ON: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<f32>
// CHECK-ON: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK-ON: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK-ON: %[[VAL_12:.*]] = vector.insertelement %[[VAL_9]], %[[VAL_3]]{{\[}}%[[VAL_4]] : index] : vector<8xf32>
// CHECK-ON: %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xf32>) {
// CHECK-ON: %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]]
// CHECK-ON: %[[VAL_17:.*]] = vector.create_mask %[[VAL_16]] : vector<8xi1>
// CHECK-ON: %[[VAL_18:.*]] = vector.maskedload %[[VAL_7]]{{\[}}%[[VAL_14]]], %[[VAL_17]], %[[VAL_3]] : memref<?xf32>, vector<8xi1>, vector<8xf32> into vector<8xf32>
// CHECK-ON: %[[VAL_19:.*]] = arith.addf %[[VAL_15]], %[[VAL_18]] : vector<8xf32>
// CHECK-ON: %[[VAL_20:.*]] = arith.select %[[VAL_17]], %[[VAL_19]], %[[VAL_15]] : vector<8xi1>, vector<8xf32>
// CHECK-ON: scf.yield %[[VAL_20]] : vector<8xf32>
// CHECK-ON: } {"Emitted from" = "linalg.generic"}
// CHECK-ON: %[[VAL_21:.*]] = vector.reduction <add>, %[[VAL_22:.*]] : vector<8xf32> into f32
// CHECK-ON: memref.store %[[VAL_21]], %[[VAL_8]][] : memref<f32>
// CHECK-ON: %[[VAL_23:.*]] = bufferization.to_tensor %[[VAL_8]] : memref<f32>
// CHECK-ON: return %[[VAL_23]] : tensor<f32>
// CHECK-ON: }
//
// CHECK-OFF-LABEL: func.func @sparse_reduction_addf(
// CHECK-OFF-SAME: %[[VAL_0:.*]]: tensor<f32>,
// CHECK-OFF-SAME: %[[VAL_1:.*]]: tensor<?xf32, #sparse{{[0-9]*}}>) -> tensor<f32> {
// CHECK-OFF-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK-OFF-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK-OFF-DAG: %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xindex>
// CHECK-OFF-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse{{[0-9]*}}> to memref<?xf32>
// CHECK-OFF-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : tensor<f32> to memref<f32>
// CHECK-OFF: %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<f32>
// CHECK-OFF: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK-OFF: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK-OFF: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (f32) {
// CHECK-OFF: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xf32>
// CHECK-OFF: %[[VAL_14:.*]] = arith.addf %[[VAL_12]], %[[VAL_13]] : f32
// CHECK-OFF: scf.yield %[[VAL_14]] : f32
// CHECK-OFF: } {"Emitted from" = "linalg.generic"}
// CHECK-OFF: memref.store %[[VAL_15:.*]], %[[VAL_6]][] : memref<f32>
// CHECK-OFF: %[[VAL_16:.*]] = bufferization.to_tensor %[[VAL_6]] : memref<f32>
// CHECK-OFF: return %[[VAL_16]] : tensor<f32>
// CHECK-OFF: }
#SparseVector = #sparse_tensor.encoding<{map = (d0) -> (d0 : compressed)}>
#trait = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> ()> // x (out)
],
iterator_types = ["reduction"]
}
func.func @sparse_reduction_addf(%argx: tensor<f32>,
%arga: tensor<?xf32, #SparseVector>)
-> tensor<f32> {
%0 = linalg.generic #trait
ins(%arga: tensor<?xf32, #SparseVector>)
outs(%argx: tensor<f32>) {
^bb(%a: f32, %x: f32):
%t = arith.addf %x, %a: f32
linalg.yield %t : f32
} -> tensor<f32>
return %0 : tensor<f32>
}
Reference in New Issue View Git Blame Copy Permalink