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[mlir][sparse] fix bug in permuting data structure 

Reviewed By: bixia

Differential Revision: https://reviews.llvm.org/D107379
This commit is contained in:
Aart Bik 2021-08-03 11:09:31 -07:00
parent e64e6924b8
commit 817303ef34
2 changed files with 99 additions and 3 deletions
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10
mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp
View File

@ -282,14 +282,18 @@ static bool genBuffers(Merger &merger, CodeGen &codegen,
codegen.indices[tensor][idx] =
rewriter.create<ToIndicesOp>(loc, indTp, t->get(), dim);
}
// Find lower and upper bound in current dimension.
// Find lower and upper bound in current dimension. Note that a
// permuted encoding queries static type dimensions accordingly,
// but queries dynamic type dimensions in the generated order.
Value up;
if (shape[d] == MemRefType::kDynamicSize) {
unsigned p = perm(enc, d);
if (shape[p] == MemRefType::kDynamicSize) {
up = rewriter.create<tensor::DimOp>(loc, t->get(), d);
args.push_back(up);
} else {
up = rewriter.create<ConstantIndexOp>(loc, shape[d]);
up = rewriter.create<ConstantIndexOp>(loc, shape[p]);
}
assert(codegen.highs[tensor][idx] == nullptr);
codegen.sizes[idx] = codegen.highs[tensor][idx] = up;
}
// Perform the required bufferization. Dense inputs materialize

92
mlir/test/Dialect/SparseTensor/sparse_perm.mlir Normal file
View File

@ -0,0 +1,92 @@
// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
// RUN: mlir-opt %s -sparsification | FileCheck %s
#X = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "dense", "dense" ],
dimOrdering = affine_map<(i,j,k) -> (k,i,j)>
}>
#trait = {
indexing_maps = [
affine_map<(i,j,k) -> (k,i,j)>, // A (in)
affine_map<(i,j,k) -> (i,j,k)> // X (out)
],
iterator_types = ["parallel", "parallel", "parallel"]
}
// CHECK-LABEL: builtin.func @sparse_static_dims(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30x10xf32>) -> tensor<20x30x10xf32> {
// CHECK: %[[VAL_2:.*]] = constant 20 : index
// CHECK: %[[VAL_3:.*]] = constant 30 : index
// CHECK: %[[VAL_4:.*]] = constant 10 : index
// CHECK: %[[VAL_5:.*]] = constant 0 : index
// CHECK: %[[VAL_6:.*]] = constant 1 : index
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_8:.*]] = memref.buffer_cast %[[VAL_1]] : memref<20x30x10xf32>
// CHECK: %[[VAL_9:.*]] = memref.alloc() : memref<20x30x10xf32>
// CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<20x30x10xf32> to memref<20x30x10xf32>
// CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] {
// CHECK: %[[VAL_12:.*]] = muli %[[VAL_10]], %[[VAL_4]] : index
// CHECK: %[[VAL_13:.*]] = addi %[[VAL_12]], %[[VAL_11]] : index
// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_2]] step %[[VAL_6]] {
// CHECK: %[[VAL_15:.*]] = muli %[[VAL_13]], %[[VAL_2]] : index
// CHECK: %[[VAL_16:.*]] = addi %[[VAL_15]], %[[VAL_14]] : index
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xf32>
// CHECK: memref.store %[[VAL_17]], %[[VAL_9]]{{\[}}%[[VAL_14]], %[[VAL_10]], %[[VAL_11]]] : memref<20x30x10xf32>
// CHECK: }
// CHECK: }
// CHECK: }
// CHECK: %[[VAL_18:.*]] = memref.tensor_load %[[VAL_9]] : memref<20x30x10xf32>
// CHECK: return %[[VAL_18]] : tensor<20x30x10xf32>
// CHECK: }
func @sparse_static_dims(%arga: tensor<10x20x30xf32, #X>,
%argx: tensor<20x30x10xf32>) -> tensor<20x30x10xf32> {
%0 = linalg.generic #trait
ins(%arga: tensor<10x20x30xf32, #X>)
outs(%argx: tensor<20x30x10xf32>) {
^bb(%a : f32, %x: f32):
linalg.yield %a : f32
} -> tensor<20x30x10xf32>
return %0 : tensor<20x30x10xf32>
}
// CHECK-LABEL: builtin.func @sparse_dynamic_dims(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
// CHECK: %[[VAL_2:.*]] = constant 2 : index
// CHECK: %[[VAL_3:.*]] = constant 0 : index
// CHECK: %[[VAL_4:.*]] = constant 1 : index
// CHECK: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_6:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor<?x?x?xf32>
// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor<?x?x?xf32>
// CHECK: %[[VAL_8:.*]] = tensor.dim %[[VAL_1]], %[[VAL_2]] : tensor<?x?x?xf32>
// CHECK: %[[VAL_9:.*]] = memref.buffer_cast %[[VAL_1]] : memref<?x?x?xf32>
// CHECK: %[[VAL_10:.*]] = memref.alloc(%[[VAL_6]], %[[VAL_7]], %[[VAL_8]]) : memref<?x?x?xf32>
// CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<?x?x?xf32> to memref<?x?x?xf32>
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_3]] to %[[VAL_7]] step %[[VAL_4]] {
// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_3]] to %[[VAL_8]] step %[[VAL_4]] {
// CHECK: %[[VAL_13:.*]] = muli %[[VAL_8]], %[[VAL_11]] : index
// CHECK: %[[VAL_14:.*]] = addi %[[VAL_13]], %[[VAL_12]] : index
// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_3]] to %[[VAL_6]] step %[[VAL_4]] {
// CHECK: %[[VAL_16:.*]] = muli %[[VAL_6]], %[[VAL_14]] : index
// CHECK: %[[VAL_17:.*]] = addi %[[VAL_16]], %[[VAL_15]] : index
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_17]]] : memref<?xf32>
// CHECK: memref.store %[[VAL_18]], %[[VAL_10]]{{\[}}%[[VAL_15]], %[[VAL_11]], %[[VAL_12]]] : memref<?x?x?xf32>
// CHECK: }
// CHECK: }
// CHECK: }
// CHECK: %[[VAL_19:.*]] = memref.tensor_load %[[VAL_10]] : memref<?x?x?xf32>
// CHECK: return %[[VAL_19]] : tensor<?x?x?xf32>
// CHECK: }
func @sparse_dynamic_dims(%arga: tensor<?x?x?xf32, #X>,
%argx: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
%0 = linalg.generic #trait
ins(%arga: tensor<?x?x?xf32, #X>)
outs(%argx: tensor<?x?x?xf32>) {
^bb(%a : f32, %x: f32):
linalg.yield %a : f32
} -> tensor<?x?x?xf32>
return %0 : tensor<?x?x?xf32>
}