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Differential D111743

[mlir][sparse] emergency fix after constant -> arith.constant change
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Authored by aartbik on Oct 13 2021, 10:14 AM.

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Mogball
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rGa652e5b53a1a: [mlir][sparse] emergency fix after constant -> arith.constant change

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aartbik created this revision.Oct 13 2021, 10:14 AM
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Mogball accepted this revision.Oct 13 2021, 10:14 AM
This revision is now accepted and ready to land.Oct 13 2021, 10:14 AM
This revision was landed with ongoing or failed builds.Oct 13 2021, 10:26 AM
Closed by commit rGa652e5b53a1a: [mlir][sparse] emergency fix after constant -> arith.constant change (authored by aartbik). · Explain Why
This revision was automatically updated to reflect the committed changes.
aartbik added a commit: rGa652e5b53a1a: [mlir][sparse] emergency fix after constant -> arith.constant change.
Harbormaster completed remote builds in B128669: Diff 379453.Oct 13 2021, 10:56 AM

Revision Contents

PathSize
mlir/
lib/
Dialect/
SparseTensor/
IR/
4 lines
test/
Dialect/
SparseTensor/
4 lines
8 lines

Diff 379453

mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp

Show First 20 Lines • Show All 219 Lines • ▼ Show 20 Linesstatic LogicalResult verify(InitOp op) {
unsigned rank = ttp.getRank(); unsigned rank = ttp.getRank();
if (rank != op.sizes().size()) if (rank != op.sizes().size())
return op.emitError("unexpected mismatch between tensor rank and sizes: ") return op.emitError("unexpected mismatch between tensor rank and sizes: ")
<< rank << " vs. " << op.sizes().size(); << rank << " vs. " << op.sizes().size();
auto shape = ttp.getShape(); auto shape = ttp.getShape();
for (unsigned i = 0; i < rank; i++) { for (unsigned i = 0; i < rank; i++) {
if (shape[i] == ShapedType::kDynamicSize) if (shape[i] == ShapedType::kDynamicSize)
continue; continue;
auto constantOp = op.sizes()[i].getDefiningOp<ConstantOp>(); auto constantOp = op.sizes()[i].getDefiningOp<arith::ConstantOp>();
if (!constantOp || if (!constantOp ||
constantOp.getValue().cast<IntegerAttr>().getInt() != shape[i]) constantOp.value().cast<IntegerAttr>().getInt() != shape[i])
return op.emitError("unexpected mismatch with static dimension size ") return op.emitError("unexpected mismatch with static dimension size ")
<< shape[i]; << shape[i];
} }
return success(); return success();
} }
static LogicalResult verify(ConvertOp op) { static LogicalResult verify(ConvertOp op) {
if (auto tp1 = op.source().getType().dyn_cast<RankedTensorType>()) { if (auto tp1 = op.source().getType().dyn_cast<RankedTensorType>()) {
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mlir/test/Dialect/SparseTensor/invalid.mlir

Show All 31 Linesfunc @invalid_init_rank(%arg0: index) -> tensor<?xf32, #SparseVector> {
return %0 : tensor<?xf32, #SparseVector> return %0 : tensor<?xf32, #SparseVector>
} }
// ----- // -----
#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}> #SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
func @invalid_init_size() -> tensor<?x10xf32, #SparseMatrix> { func @invalid_init_size() -> tensor<?x10xf32, #SparseMatrix> {
%c10 = constant 10 : index %c10 = arith.constant 10 : index
%c20 = constant 20 : index %c20 = arith.constant 20 : index
// expected-error@+1 {{unexpected mismatch with static dimension size 10}} // expected-error@+1 {{unexpected mismatch with static dimension size 10}}
%0 = sparse_tensor.init [%c10, %c20] : tensor<?x10xf32, #SparseMatrix> %0 = sparse_tensor.init [%c10, %c20] : tensor<?x10xf32, #SparseMatrix>
return %0 : tensor<?x10xf32, #SparseMatrix> return %0 : tensor<?x10xf32, #SparseMatrix>
} }
// ----- // -----
func @invalid_pointers_dense(%arg0: tensor<128xf64>) -> memref<?xindex> { func @invalid_pointers_dense(%arg0: tensor<128xf64>) -> memref<?xindex> {
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mlir/test/Dialect/SparseTensor/roundtrip.mlir

Show All 10 Linesfunc @sparse_new(%arg0: !llvm.ptr<i8>) -> tensor<128xf64, #SparseVector> {
return %0 : tensor<128xf64, #SparseVector> return %0 : tensor<128xf64, #SparseVector>
} }
// ----- // -----
#SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}> #SparseMatrix = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
// CHECK-LABEL: func @sparse_init() // CHECK-LABEL: func @sparse_init()
// CHECK-DAG: %[[C16:.*]] = constant 16 : index // CHECK-DAG: %[[C16:.*]] = arith.constant 16 : index
// CHECK-DAG: %[[C32:.*]] = constant 32 : index // CHECK-DAG: %[[C32:.*]] = arith.constant 32 : index
// CHECK: %[[T:.*]] = sparse_tensor.init[%[[C16]], %[[C32]]] : tensor<?x32xf64, #{{.*}}> // CHECK: %[[T:.*]] = sparse_tensor.init[%[[C16]], %[[C32]]] : tensor<?x32xf64, #{{.*}}>
// CHECK: return %[[T]] : tensor<?x32xf64, #{{.*}}> // CHECK: return %[[T]] : tensor<?x32xf64, #{{.*}}>
func @sparse_init() -> tensor<?x32xf64, #SparseMatrix> { func @sparse_init() -> tensor<?x32xf64, #SparseMatrix> {
%d1 = constant 16 : index %d1 = arith.constant 16 : index
%d2 = constant 32 : index %d2 = arith.constant 32 : index
%0 = sparse_tensor.init [%d1, %d2] : tensor<?x32xf64, #SparseMatrix> %0 = sparse_tensor.init [%d1, %d2] : tensor<?x32xf64, #SparseMatrix>
return %0 : tensor<?x32xf64, #SparseMatrix> return %0 : tensor<?x32xf64, #SparseMatrix>
} }
// ----- // -----
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}> #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
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