use workaround for points_normals

Summary:
Use existing workaround for batched 3x3 symeig because it is faster than torch.symeig.

Added benchmark showing speedup. True = workaround.
```
Benchmark                Avg Time(μs)      Peak Time(μs) Iterations
--------------------------------------------------------------------------------
normals_True_3000            16237           17233             31
normals_True_6000            33028           33391             16
normals_False_3000        18623069        18623069              1
normals_False_6000        36535475        36535475              1
```

Should help https://github.com/facebookresearch/pytorch3d/issues/988

Reviewed By: nikhilaravi

Differential Revision: D33660585

fbshipit-source-id: d1162b277f5d61ed67e367057a61f25e03888dce

tests/benchmarks/bm_points_normals.py

@@ -0,0 +1,47 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import itertools
+
+import torch
+from fvcore.common.benchmark import benchmark
+from pytorch3d.ops import estimate_pointcloud_normals
+from test_points_normals import TestPCLNormals
+
+
+def to_bm(num_points, use_symeig_workaround):
+    device = torch.device("cuda:0")
+    points_padded, _normals = TestPCLNormals.init_spherical_pcl(
+        num_points=num_points, device=device, use_pointclouds=False
+    )
+    torch.cuda.synchronize()
+
+    def run():
+        estimate_pointcloud_normals(
+            points_padded, use_symeig_workaround=use_symeig_workaround
+        )
+        torch.cuda.synchronize()
+
+    return run
+
+
+def bm_points_normals() -> None:
+    case_grid = {
+        "use_symeig_workaround": [True, False],
+        "num_points": [3000, 6000],
+    }
+    test_cases = itertools.product(*case_grid.values())
+    kwargs_list = [dict(zip(case_grid.keys(), case)) for case in test_cases]
+    benchmark(
+        to_bm,
+        "normals",
+        kwargs_list,
+        warmup_iters=1,
+    )
+
+
+if __name__ == "__main__":
+    bm_points_normals()