mirror of
https://github.com/capstone-engine/llvm-capstone.git
synced 2025-02-18 16:58:23 +00:00
[mlir][sparse] fix bug in custom reduction scalarization code (#74898)
Bug found with BSR of "spy" SDDMM method
This commit is contained in:
Aart Bik
GitHub
parent
40e2bb5330
commit
d96f46dd20
@ -673,25 +673,35 @@ static void genInvariants(CodegenEnv &env, OpBuilder &builder, ExprId exp,
|
||||
// All exhausted at current level.
|
||||
if (!isCurrentLoop)
|
||||
return;
|
||||
// Generate code for a scalarized reduction or invariant. Note that
|
||||
// because custom reduction lhs may occur several times in the IR,
|
||||
// we have a built-in safety for only initializing and wrapping-up
|
||||
// the scalarized reduction once.
|
||||
OpOperand *lhs = op.getDpsInitOperand(0);
|
||||
if (lhs == &t) {
|
||||
// Start or end a scalarized reduction.
|
||||
if (isStart) {
|
||||
Value load = env.isCustomReduc() ? env.getCustomRedId()
|
||||
: genTensorLoad(env, builder, exp);
|
||||
env.startReduc(exp, load);
|
||||
if (env.isCustomReduc()) {
|
||||
if (!env.isReduc())
|
||||
env.startReduc(exp, env.getCustomRedId());
|
||||
} else {
|
||||
env.startReduc(exp, genTensorLoad(env, builder, exp));
|
||||
}
|
||||
if (env.hasSparseOutput())
|
||||
env.setValidLexInsert(constantI1(builder, env.op().getLoc(), false));
|
||||
} else {
|
||||
genTensorStore(env, builder, exp, env.endReduc());
|
||||
env.clearValidLexInsert();
|
||||
if (!env.isCustomReduc() || env.isReduc())
|
||||
genTensorStore(env, builder, exp, env.endReduc());
|
||||
if (env.hasSparseOutput())
|
||||
env.clearValidLexInsert();
|
||||
}
|
||||
} else {
|
||||
// Start or end loop invariant hoisting of a tensor load.
|
||||
if (isStart)
|
||||
if (isStart) {
|
||||
env.merger().setExprValue(exp, genTensorLoad(env, builder, exp));
|
||||
else
|
||||
} else {
|
||||
env.merger().clearExprValue(exp);
|
||||
}
|
||||
}
|
||||
} else if (env.exp(exp).kind != TensorExp::Kind::kInvariant &&
|
||||
env.exp(exp).kind != TensorExp::Kind::kLoopVar &&
|
||||
|
||||
103
mlir/test/Dialect/SparseTensor/spy_sddmm_bsr.mlir
Executable file
103
mlir/test/Dialect/SparseTensor/spy_sddmm_bsr.mlir
Executable file
@ -0,0 +1,103 @@
|
||||
// RUN: mlir-opt %s --sparse-reinterpret-map -sparsification | FileCheck %s
|
||||
|
||||
//
|
||||
// A SDDMM implementation with "spy" function and
|
||||
// in-place update of the sampling sparse matrix.
|
||||
//
|
||||
|
||||
#BSR = #sparse_tensor.encoding<{
|
||||
map = (i, j) -> (
|
||||
i floordiv 2 : dense,
|
||||
j floordiv 2 : compressed,
|
||||
i mod 2 : dense,
|
||||
j mod 2 : dense)
|
||||
}>
|
||||
|
||||
#trait_SDDMM = {
|
||||
indexing_maps = [
|
||||
affine_map<(i,j,k) -> (i,k)>, // A
|
||||
affine_map<(i,j,k) -> (k,j)>, // B
|
||||
affine_map<(i,j,k) -> (i,j)> // S (in/out)
|
||||
],
|
||||
iterator_types = ["parallel", "parallel", "reduction"],
|
||||
doc = "S(i,j) += spy[S(i,j)] x SUM_k A(i,k) B(k,j)"
|
||||
}
|
||||
|
||||
//
|
||||
// CHECK: #[[$BSR:.+]] = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 floordiv 2 : dense, d1 floordiv 2 : compressed, d0 mod 2 : dense, d1 mod 2 : dense) }>
|
||||
// CHECK: #[[$MAP:.+]] = #sparse_tensor.encoding<{ map = (d0, d1, d2, d3) -> (d0 : dense, d1 : compressed, d2 : dense, d3 : dense) }>
|
||||
//
|
||||
// CHECK-LABEL: func.func @SDDMM_block(
|
||||
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32, #[[$BSR]]>,
|
||||
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xf32>,
|
||||
// CHECK-SAME: %[[VAL_2:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32, #[[$BSR]]> {
|
||||
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
|
||||
// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index
|
||||
// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 2 : index
|
||||
// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0.000000e+00 : f32
|
||||
// CHECK: %[[VAL_7:.*]] = sparse_tensor.reinterpret_map %[[VAL_0]] : tensor<?x?xf32, #[[$BSR]]> to tensor<?x?x2x2xf32, #[[$MAP]]>
|
||||
// CHECK: %[[VAL_8:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor<?x?xf32>
|
||||
// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<?x?xf32>
|
||||
// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<?x?xf32>
|
||||
// CHECK: %[[VAL_11:.*]] = sparse_tensor.lvl %[[VAL_7]], %[[VAL_4]] : tensor<?x?x2x2xf32, #[[$MAP]]>
|
||||
// CHECK: %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_7]] {level = 1 : index} : tensor<?x?x2x2xf32, #[[$MAP]]> to memref<?xindex>
|
||||
// CHECK: %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_7]] {level = 1 : index} : tensor<?x?x2x2xf32, #[[$MAP]]> to memref<?xindex>
|
||||
// CHECK: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_7]] : tensor<?x?x2x2xf32, #[[$MAP]]> to memref<?xf32>
|
||||
// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_4]] to %[[VAL_11]] step %[[VAL_3]] {
|
||||
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_15]]] : memref<?xindex>
|
||||
// CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_15]], %[[VAL_3]] : index
|
||||
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_17]]] : memref<?xindex>
|
||||
// CHECK: scf.for %[[VAL_19:.*]] = %[[VAL_16]] to %[[VAL_18]] step %[[VAL_3]] {
|
||||
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_19]]] : memref<?xindex>
|
||||
// CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_4]] to %[[VAL_5]] step %[[VAL_3]] {
|
||||
// CHECK: %[[VAL_22:.*]] = arith.muli %[[VAL_19]], %[[VAL_5]] : index
|
||||
// CHECK: %[[VAL_23:.*]] = arith.addi %[[VAL_22]], %[[VAL_21]] : index
|
||||
// CHECK: scf.for %[[VAL_24:.*]] = %[[VAL_4]] to %[[VAL_5]] step %[[VAL_3]] {
|
||||
// CHECK: %[[VAL_25:.*]] = arith.muli %[[VAL_23]], %[[VAL_5]] : index
|
||||
// CHECK: %[[VAL_26:.*]] = arith.addi %[[VAL_25]], %[[VAL_24]] : index
|
||||
// CHECK: %[[VAL_27:.*]] = scf.for %[[VAL_28:.*]] = %[[VAL_4]] to %[[VAL_8]] step %[[VAL_3]] iter_args(%[[VAL_29:.*]] = %[[VAL_6]]) -> (f32) {
|
||||
// CHECK: %[[VAL_30:.*]] = arith.muli %[[VAL_15]], %[[VAL_5]] : index
|
||||
// CHECK: %[[VAL_31:.*]] = arith.addi %[[VAL_30]], %[[VAL_21]] : index
|
||||
// CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_31]], %[[VAL_28]]] : memref<?x?xf32>
|
||||
// CHECK: %[[VAL_33:.*]] = arith.muli %[[VAL_20]], %[[VAL_5]] : index
|
||||
// CHECK: %[[VAL_34:.*]] = arith.addi %[[VAL_33]], %[[VAL_24]] : index
|
||||
// CHECK: %[[VAL_35:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_28]], %[[VAL_34]]] : memref<?x?xf32>
|
||||
// CHECK: %[[VAL_36:.*]] = arith.mulf %[[VAL_32]], %[[VAL_35]] : f32
|
||||
// CHECK: %[[VAL_37:.*]] = arith.addf %[[VAL_29]], %[[VAL_36]] : f32
|
||||
// CHECK: scf.yield %[[VAL_37]] : f32
|
||||
// CHECK: } {"Emitted from" = "linalg.generic"}
|
||||
// CHECK: memref.store %[[VAL_27]], %[[VAL_14]]{{\[}}%[[VAL_26]]] : memref<?xf32>
|
||||
// CHECK: } {"Emitted from" = "linalg.generic"}
|
||||
// CHECK: } {"Emitted from" = "linalg.generic"}
|
||||
// CHECK: } {"Emitted from" = "linalg.generic"}
|
||||
// CHECK: } {"Emitted from" = "linalg.generic"}
|
||||
// CHECK: %[[VAL_38:.*]] = sparse_tensor.load %[[VAL_7]] : tensor<?x?x2x2xf32, #[[$MAP]]>
|
||||
// CHECK: %[[VAL_39:.*]] = sparse_tensor.reinterpret_map %[[VAL_38]] : tensor<?x?x2x2xf32, #[[$MAP]]> to tensor<?x?xf32, #[[$BSR]]>
|
||||
// CHECK: return %[[VAL_39]] : tensor<?x?xf32, #[[$BSR]]>
|
||||
// CHECK: }
|
||||
module {
|
||||
func.func @SDDMM_block(%args: tensor<?x?xf32, #BSR>,
|
||||
%arga: tensor<?x?xf32>,
|
||||
%argb: tensor<?x?xf32>) -> tensor<?x?xf32, #BSR> {
|
||||
%result = linalg.generic #trait_SDDMM
|
||||
ins(%arga, %argb: tensor<?x?xf32>, tensor<?x?xf32>)
|
||||
outs(%args: tensor<?x?xf32, #BSR>) {
|
||||
^bb(%a: f32, %b: f32, %s: f32):
|
||||
%f0 = arith.constant 0.0 : f32
|
||||
%u = sparse_tensor.unary %s : f32 to f32
|
||||
present={
|
||||
^bb0(%p: f32):
|
||||
%mul = arith.mulf %a, %b : f32
|
||||
sparse_tensor.yield %mul : f32
|
||||
}
|
||||
absent={}
|
||||
%r = sparse_tensor.reduce %s, %u, %f0 : f32 {
|
||||
^bb0(%p: f32, %q: f32):
|
||||
%add = arith.addf %p, %q : f32
|
||||
sparse_tensor.yield %add : f32
|
||||
}
|
||||
linalg.yield %r : f32
|
||||
} -> tensor<?x?xf32, #BSR>
|
||||
return %result : tensor<?x?xf32, #BSR>
|
||||
}
|
||||
}
|
||||
Loading…
x
Reference in New Issue
Block a user