forked from OSchip/llvm-project
[mlir][sparse] refine simply dynamic sparse tensor outputs
Proper test for sparse tensor outputs is a single condition throughout the whole tensor index expression (not a general conjunction, since this may include other conditions that cause cancellation). Reviewed By: bixia Differential Revision: https://reviews.llvm.org/D114810
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Aart Bik
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cbd445e4a3
commit
0e85232fa3
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@ -192,10 +192,11 @@ public:
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/// Returns true if any set bit corresponds to queried dim.
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bool hasAnyDimOf(const llvm::BitVector &bits, Dim d) const;
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/// Returns true if given tensor co-iterates with conjunction only in the
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/// given tensor expression. For the output tensor, this defines a "simply
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/// dynamic" operation [Bik96]. For instance: a(i) *= b(i) * c(i)
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bool isConjunction(unsigned t, unsigned e) const;
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/// Returns true if given tensor iterates *only* in the given tensor
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/// expression. For the output tensor, this defines a "simply dynamic"
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/// operation [Bik96]. For instance: a(i) *= 2.0 or a(i) += a(i) for
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/// sparse vector a.
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bool isSingleCondition(unsigned t, unsigned e) const;
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/// Dimension setter.
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void setDim(unsigned t, unsigned i, Dim d) { dims[t][i] = d; }
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@ -321,7 +321,7 @@ static bool isAdmissableTensorExp(Merger &merger, linalg::GenericOp op,
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// but not its nonzero structure, an operation called "simply dynamic" in
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// [Bik96,Ch9], is also admissable without special codegen, provided
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// the tensor's underlying sparse storage scheme can be modified in place.
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if (merger.isConjunction(tensor, exp) && isInPlace(lhs->get()))
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if (merger.isSingleCondition(tensor, exp) && isInPlace(lhs->get()))
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return true;
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// Accept "truly dynamic" if the output tensor materializes uninitialized
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// into the computation and insertions occur in lexicographic index order.
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@ -213,7 +213,7 @@ bool Merger::hasAnyDimOf(const llvm::BitVector &bits, Dim d) const {
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return false;
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}
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bool Merger::isConjunction(unsigned t, unsigned e) const {
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bool Merger::isSingleCondition(unsigned t, unsigned e) const {
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switch (tensorExps[e].kind) {
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case kTensor:
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return tensorExps[e].tensor == t;
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@ -232,22 +232,30 @@ bool Merger::isConjunction(unsigned t, unsigned e) const {
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case kCastU:
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case kTruncI:
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case kBitCast:
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return isConjunction(t, tensorExps[e].children.e0);
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return isSingleCondition(t, tensorExps[e].children.e0);
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case kDivF: // note: x / c only
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case kDivS:
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case kDivU:
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assert(!maybeZero(tensorExps[e].children.e1));
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return isConjunction(t, tensorExps[e].children.e0);
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return isSingleCondition(t, tensorExps[e].children.e0);
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case kShrS: // note: x >> inv only
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case kShrU:
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case kShlI:
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assert(isInvariant(tensorExps[e].children.e1));
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return isConjunction(t, tensorExps[e].children.e0);
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return isSingleCondition(t, tensorExps[e].children.e0);
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case kMulF:
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case kMulI:
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case kAndI:
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return isConjunction(t, tensorExps[e].children.e0) ||
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isConjunction(t, tensorExps[e].children.e1);
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if (isSingleCondition(t, tensorExps[e].children.e0))
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return isSingleCondition(t, tensorExps[e].children.e1) ||
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isInvariant(tensorExps[e].children.e1);
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if (isSingleCondition(t, tensorExps[e].children.e1))
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return isInvariant(tensorExps[e].children.e0);
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return false;
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case kAddF:
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case kAddI:
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return isSingleCondition(t, tensorExps[e].children.e0) &&
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isSingleCondition(t, tensorExps[e].children.e1);
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default:
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return false;
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}
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@ -20,7 +20,7 @@
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affine_map<(i,j) -> (i,j)> // X (out)
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],
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iterator_types = ["parallel", "parallel"],
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doc = "X(i,j) = X(i,j) * 2"
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doc = "X(i,j) *= 2 or X(i,j) += X(i,j)"
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}
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// CHECK-LABEL: func @sparse_simply_dynamic1(
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@ -57,78 +57,34 @@ func @sparse_simply_dynamic1(%argx: tensor<32x16xf32, #DCSR> {linalg.inplaceable
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return %0 : tensor<32x16xf32, #DCSR>
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}
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#trait_elt_wise_mult = {
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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) -> (i,j)> // X (out)
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],
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iterator_types = ["parallel", "parallel"],
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doc = "X(i,j) = A(i,j) * X(i,j)"
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}
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// CHECK-LABEL: func @sparse_simply_dynamic2(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> {
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// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
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// CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xf32>
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// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_2]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_2]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
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// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xf32>
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// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_2]]] : memref<?xindex>
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// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_3]] {
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// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xindex>
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// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_15]]] : memref<?xindex>
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// CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_15]], %[[VAL_3]] : index
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// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_17]]] : memref<?xindex>
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// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_14]]] : memref<?xindex>
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// CHECK: %[[VAL_20:.*]] = arith.addi %[[VAL_14]], %[[VAL_3]] : index
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// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xindex>
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// CHECK: %[[VAL_22:.*]]:2 = scf.while (%[[VAL_23:.*]] = %[[VAL_16]], %[[VAL_24:.*]] = %[[VAL_19]]) : (index, index) -> (index, index) {
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// CHECK: %[[VAL_25:.*]] = arith.cmpi ult, %[[VAL_23]], %[[VAL_18]] : index
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// CHECK: %[[VAL_26:.*]] = arith.cmpi ult, %[[VAL_24]], %[[VAL_21]] : index
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// CHECK: %[[VAL_27:.*]] = arith.andi %[[VAL_25]], %[[VAL_26]] : i1
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// CHECK: scf.condition(%[[VAL_27]]) %[[VAL_23]], %[[VAL_24]] : index, index
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// CHECK: } do {
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// CHECK: ^bb0(%[[VAL_28:.*]]: index, %[[VAL_29:.*]]: index):
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// CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_28]]] : memref<?xindex>
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// CHECK: %[[VAL_31:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_29]]] : memref<?xindex>
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// CHECK: %[[VAL_32:.*]] = arith.cmpi ult, %[[VAL_31]], %[[VAL_30]] : index
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// CHECK: %[[VAL_33:.*]] = select %[[VAL_32]], %[[VAL_31]], %[[VAL_30]] : index
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// CHECK: %[[VAL_34:.*]] = arith.cmpi eq, %[[VAL_30]], %[[VAL_33]] : index
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// CHECK: %[[VAL_35:.*]] = arith.cmpi eq, %[[VAL_31]], %[[VAL_33]] : index
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// CHECK: %[[VAL_36:.*]] = arith.andi %[[VAL_34]], %[[VAL_35]] : i1
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// CHECK: scf.if %[[VAL_36]] {
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// CHECK: %[[VAL_37:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_29]]] : memref<?xf32>
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// CHECK: %[[VAL_38:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_28]]] : memref<?xf32>
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// CHECK: %[[VAL_39:.*]] = arith.mulf %[[VAL_37]], %[[VAL_38]] : f32
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// CHECK: memref.store %[[VAL_39]], %[[VAL_11]]{{\[}}%[[VAL_29]]] : memref<?xf32>
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// CHECK: } else {
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// CHECK: }
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// CHECK: %[[VAL_40:.*]] = arith.cmpi eq, %[[VAL_30]], %[[VAL_33]] : index
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// CHECK: %[[VAL_41:.*]] = arith.addi %[[VAL_28]], %[[VAL_3]] : index
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// CHECK: %[[VAL_42:.*]] = select %[[VAL_40]], %[[VAL_41]], %[[VAL_28]] : index
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// CHECK: %[[VAL_43:.*]] = arith.cmpi eq, %[[VAL_31]], %[[VAL_33]] : index
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// CHECK: %[[VAL_44:.*]] = arith.addi %[[VAL_29]], %[[VAL_3]] : index
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// CHECK: %[[VAL_45:.*]] = select %[[VAL_43]], %[[VAL_44]], %[[VAL_29]] : index
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// CHECK: scf.yield %[[VAL_42]], %[[VAL_45]] : index, index
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>
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// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index
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// CHECK: %[[VAL_3:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>
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// CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>
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// CHECK: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>
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// CHECK: %[[VAL_6:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xindex>
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// CHECK: %[[VAL_7:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_2]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_8:.*]] = %[[VAL_6]] to %[[VAL_7]] step %[[VAL_2]] {
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// CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_8]]] : memref<?xindex>
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// CHECK: %[[VAL_10:.*]] = arith.addi %[[VAL_8]], %[[VAL_2]] : index
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// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_10]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_9]] to %[[VAL_11]] step %[[VAL_2]] {
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// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref<?xf32>
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// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref<?xf32>
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// CHECK: %[[VAL_15:.*]] = arith.addf %[[VAL_13]], %[[VAL_14]] : f32
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// CHECK: memref.store %[[VAL_15]], %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref<?xf32>
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_46:.*]] = sparse_tensor.load %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>
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// CHECK: return %[[VAL_46]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>
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// CHECK: %[[VAL_16:.*]] = sparse_tensor.load %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>
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// CHECK: return %[[VAL_16]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>
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// CHECK: }
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func @sparse_simply_dynamic2(%arga: tensor<32x16xf32, #CSR>,
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%argx: tensor<32x16xf32, #DCSR> {linalg.inplaceable = true}) -> tensor<32x16xf32, #DCSR> {
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%0 = linalg.generic #trait_elt_wise_mult
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ins(%arga: tensor<32x16xf32, #CSR>)
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func @sparse_simply_dynamic2(%argx: tensor<32x16xf32, #DCSR> {linalg.inplaceable = true}) -> tensor<32x16xf32, #DCSR> {
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%0 = linalg.generic #trait_scale_inpl
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outs(%argx: tensor<32x16xf32, #DCSR>) {
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^bb(%a: f32, %x: f32):
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%1 = arith.mulf %x, %a : f32
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^bb(%x: f32):
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%1 = arith.addf %x, %x : f32
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linalg.yield %1 : f32
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} -> tensor<32x16xf32, #DCSR>
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return %0 : tensor<32x16xf32, #DCSR>
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