[mlir][sparse] fix CHECK test error on windows.

[aartbik]

great catch!

mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp

 //===----------------------------------------------------------------------===//
 
 /// Populates the given patterns list with conversion rules required for
 /// the sparsification of linear algebra operations.
 void mlir::populateSparseTensorCodegenPatterns(
     TypeConverter &typeConverter, RewritePatternSet &patterns,
     bool enableBufferInitialization) {
   patterns.add<SparseReturnConverter, SparseCallConverter, SparseDimOpConverter,
-               SparseCastConverter, SparseTensorAllocConverter,
-               SparseTensorDeallocConverter, SparseTensorLoadConverter,
-               SparseExpandConverter, SparseCompressConverter,
-               SparseInsertConverter, SparseToPointersConverter,
-               SparseToIndicesConverter, SparseToValuesConverter,
-               SparseConvertConverter, SparseNumberOfEntriesConverter>(
-      typeConverter, patterns.getContext());
+               SparseCastConverter, SparseTensorDeallocConverter,
+               SparseTensorLoadConverter, SparseExpandConverter,
+               SparseCompressConverter, SparseInsertConverter,
+               SparseToPointersConverter, SparseToIndicesConverter,
+               SparseToValuesConverter, SparseConvertConverter,
+               SparseNumberOfEntriesConverter>(typeConverter,
+                                               patterns.getContext());
   patterns.add<SparseTensorAllocConverter>(typeConverter, patterns.getContext(),
                                            enableBufferInitialization);
 }

mlir/test/Dialect/SparseTensor/codegen_buffer_initialization.mlir

 // RUN: mlir-opt %s --sparse-tensor-codegen=enable-buffer-initialization=true  --canonicalize --cse | FileCheck %s
 
 #SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
 
-// FIXME: windows fails on the "CH__ECK" tests below, non-deterministically
-
 // CHECK-LABEL: func @sparse_alloc_sparse_vector(
 //  CHECK-SAME: %[[A:.*]]: index) ->
 //  CHECK-SAME: memref<1xindex>, memref<3xindex>, memref<?xindex>, memref<?xindex>, memref<?xf64>
 //   CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
 //   CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
-// CH__ECK-DAG: %[[F0:.*]] = arith.constant 0.000000e+00 : f64
+//   CHECK-DAG: %[[F0:.*]] = arith.constant 0.000000e+00 : f64
 //       CHECK: %[[T0:.*]] = memref.alloc() : memref<1xindex>
 //       CHECK: %[[T1:.*]] = memref.alloc() : memref<3xindex>
 //       CHECK: %[[T2:.*]] = memref.alloc() : memref<16xindex>
 //       CHECK: %[[T3:.*]] = memref.cast %[[T2]] : memref<16xindex> to memref<?xindex>
 //       CHECK: linalg.fill ins(%[[C0]] : index) outs(%[[T2]] : memref<16xindex>)
 //       CHECK: %[[T4:.*]] = memref.alloc() : memref<16xindex>
 //       CHECK: %[[T5:.*]] = memref.cast %[[T4]] : memref<16xindex> to memref<?xindex>
 //       CHECK: linalg.fill ins(%[[C0]] : index) outs(%[[T4]] : memref<16xindex>)
 //       CHECK: %[[T6:.*]] = memref.alloc() : memref<16xf64>
 //       CHECK: %[[T7:.*]] = memref.cast %[[T6]] : memref<16xf64> to memref<?xf64>
-//     CH__ECK: linalg.fill ins(%[[F0]] : f64) outs(%[[T6]] : memref<16xf64>)
+//       CHECK: linalg.fill ins(%[[F0]] : f64) outs(%[[T6]] : memref<16xf64>)
 //       CHECK: linalg.fill ins(%[[C0]] : index) outs(%[[T1]] : memref<3xindex>)
 //       CHECK: memref.store %[[A]], %[[T0]][%[[C0]]] : memref<1xindex>
 //       CHECK: %[[P0:.*]] = sparse_tensor.push_back %[[T1]], %[[T3]]
 //       CHECK: %[[P1:.*]] = sparse_tensor.push_back %[[T1]], %[[P0]]
 //       CHECK: return %[[T0]], %[[T1]], %[[P1]], %[[T5]], %[[T7]] :
 func.func @sparse_alloc_sparse_vector(%arg0: index) -> tensor<?xf64, #SV> {
   %0 = bufferization.alloc_tensor(%arg0) : tensor<?xf64, #SV>
   %1 = sparse_tensor.load %0 : tensor<?xf64, #SV>
   return %1 : tensor<?xf64, #SV>
 }