[Mlir-commits] [mlir] 817303e - [mlir][sparse] fix bug in permuting data structure
Aart Bik
llvmlistbot at llvm.org
Tue Aug 3 14:27:59 PDT 2021
Author: Aart Bik
Date: 2021-08-03T14:27:43-07:00
New Revision: 817303ef345f35bd693d71f87c4dd95a11c341c4
URL: https://github.com/llvm/llvm-project/commit/817303ef345f35bd693d71f87c4dd95a11c341c4
DIFF: https://github.com/llvm/llvm-project/commit/817303ef345f35bd693d71f87c4dd95a11c341c4.diff
LOG: [mlir][sparse] fix bug in permuting data structure
Reviewed By: bixia
Differential Revision: https://reviews.llvm.org/D107379
Added:
mlir/test/Dialect/SparseTensor/sparse_perm.mlir
Modified:
mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp
Removed:
################################################################################
diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp
index 8847189c3779..b2c64e450a84 100644
--- a/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp
+++ b/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp
@@ -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
diff --git a/mlir/test/Dialect/SparseTensor/sparse_perm.mlir b/mlir/test/Dialect/SparseTensor/sparse_perm.mlir
new file mode 100644
index 000000000000..7cfb95b76e68
--- /dev/null
+++ b/mlir/test/Dialect/SparseTensor/sparse_perm.mlir
@@ -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>
+}
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