[SparseCompressed] support csc layout for add sparse/dense. (#115433)

`add` when passed one sparse and one dense argument  will error if the
sparse argument does not have  csr layout. This PR modifies the
underlying algorithm to be generic on the compressed dimension handling
both csr and csc. The functions are renamed to use the
`sparse_compressed` qualifier rather than `sparse_csr`

Fixes: #114807

Pull Request resolved: https://github.com/pytorch/pytorch/pull/115433
Approved by: https://github.com/cpuhrsch, https://github.com/pearu
ghstack dependencies: #115432

test/test_sparse_csr.py

@@ -2109,13 +2109,19 @@ class TestSparseCSR(TestCase):
                 with self.assertRaisesRegex(RuntimeError, re.escape(str(msg))):
                     test(is_sparse=True)
 
+    @sparse_compressed_nonblock_layouts()
     @dtypes(torch.float, torch.double)
-    def test_add(self, device, dtype):
+    def test_add(self, device, layout, dtype):
         def _test_spadd_shape(nnz, shape):
             # sparse.to_dense() uses torch.add internally so if torch.add is wrong,
             # the dense tensor will be wrong but this test would still pass
             # there's a separate test that checks for the correctness of the .to_dense() call
-            x = self.genSparseCSRTensor(shape, nnz, dtype=dtype, device=device, index_dtype=torch.int32)
+            x = self.genSparseCompressedTensor(shape, nnz,
+                                               dtype=dtype,
+                                               device=device,
+                                               index_dtype=torch.int32,
+                                               layout=layout,
+                                               blocksize=())
             y = torch.randn(*shape, dtype=dtype, device=device)
             r = random.random()
 
@@ -2140,6 +2146,7 @@ class TestSparseCSR(TestCase):
             self.assertEqual(res, expected)
             self.assertEqual(res_perm, expected)
 
+
         ns = [2, 5]
         batch_shapes = [(), (2,), (2, 3)]
         for b, m, n in itertools.product(batch_shapes, ns, ns):