profile cub

tests/test_scan.py

@@ -175,9 +175,40 @@ def test_exclusive_prod():
     assert torch.allclose(outputs1, outputs3)
     assert torch.allclose(grad1, grad3)
 
+def profile():
+    import tqdm
+    from nerfacc.scan import inclusive_sum
+    from nerfacc.scan_cub import inclusive_sum_cub
+
+    torch.manual_seed(42)
+
+    data = torch.rand((8192, 8192), device=device, requires_grad=True)
+
+    chunk_starts = torch.arange(
+        0, data.numel(), data.shape[1], device=device, dtype=torch.long
+    )
+    chunk_cnts = torch.full(
+        (data.shape[0],), data.shape[1], dtype=torch.long, device=device
+    )
+    packed_info = torch.stack([chunk_starts, chunk_cnts], dim=-1)
+    flatten_data = data.flatten()
+    torch.cuda.synchronize()
+    for _ in tqdm.trange(2000):
+        outputs2 = inclusive_sum(flatten_data, packed_info=packed_info)
+        outputs2.sum().backward()
+
+    indices = torch.arange(data.shape[0], device=device, dtype=torch.long)
+    indices = indices.repeat_interleave(data.shape[1])
+    indices = indices.flatten()
+    torch.cuda.synchronize()
+    for _ in tqdm.trange(2000):
+        outputs3 = inclusive_sum_cub(flatten_data, indices)
+        outputs3.sum().backward()
+
 
 if __name__ == "__main__":
     test_inclusive_sum()
     test_exclusive_sum()
     test_inclusive_prod()
     test_exclusive_prod()
+    # profile()
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