[mlir][sparse] reject sparsity annotation in "scalar" tensors

[aartbik]

mlir/test/Dialect/SparseTensor/sparse_vector_peeled.mlir

1

note: not really related to this revision, but I noticed we were splitting a single test file for nothing....

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

LogicalResult SparseTensorEncodingAttr::verifyEncoding(
     function_ref<InFlightDiagnostic()> emitError) const {
   // Check structural integrity.
   if (failed(verify(emitError, getDimLevelType(), getDimOrdering(),
                     getPointerBitWidth(), getIndexBitWidth())))
     return failure();
   // Check integrity with tensor type specifics. Dimension ordering is optional,
   // but we always should have dimension level types for the full rank.
   unsigned size = shape.size();
+  if (size == 0)
+    return emitError() << "expected non-scalar sparse tensor";
   if (getDimOrdering() && getDimOrdering().getNumResults() != size)
     return emitError() << "expected an affine map of size " << size
                        << " for dimension ordering";
   if (getDimLevelType().size() != size)
     return emitError() << "expected an array of size " << size
                        << " for dimension level types";
   return success();
 }

mlir/test/Dialect/SparseTensor/invalid_encoding.mlir

 // RUN: mlir-opt %s -split-input-file -verify-diagnostics
 
+#a = #sparse_tensor.encoding<{dimLevelType = []}>
+func private @scalar(%arg0: tensor<f64, #a>) -> () // expected-error {{expected non-scalar sparse tensor}}
+
 // -----
 
 #a = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
 func private @tensor_size_mismatch(%arg0: tensor<8xi32, #a>) -> () // expected-error {{expected an array of size 1 for dimension level types}}
 
 // -----
 
 #a = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"], dimOrdering = affine_map<(i) -> (i)>}> // expected-error {{unexpected mismatch in ordering and dimension level types size}}

mlir/test/Dialect/SparseTensor/sparse_vector_peeled.mlir

-// RUN: mlir-opt %s -sparsification="vectorization-strategy=2 vl=16" -for-loop-peeling -canonicalize -split-input-file | \
+// RUN: mlir-opt %s -sparsification="vectorization-strategy=2 vl=16" -for-loop-peeling -canonicalize | \
 // RUN:   FileCheck %s
 
 #SparseVector = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed" ],
   pointerBitWidth = 32,
   indexBitWidth = 32
 }>