inclusive sum in one function

nerfacc/__init__.py

@@ -19,12 +19,6 @@ from .volrend import (
     render_weight_from_density,
     rendering,
 )
-from .scan_cub import (
-    exclusive_prod_cub,
-    exclusive_sum_cub,
-    inclusive_prod_cub,
-    inclusive_sum_cub,
-)
 
 __all__ = [
     "__version__",

nerfacc/cuda/__init__.py

@@ -30,12 +30,17 @@ inclusive_prod_backward = _make_lazy_cuda_func("inclusive_prod_backward")
 exclusive_prod_forward = _make_lazy_cuda_func("exclusive_prod_forward")
 exclusive_prod_backward = _make_lazy_cuda_func("exclusive_prod_backward")
 
+is_cub_available = _make_lazy_cuda_func("is_cub_available")
 inclusive_sum_cub = _make_lazy_cuda_func("inclusive_sum_cub")
 exclusive_sum_cub = _make_lazy_cuda_func("exclusive_sum_cub")
 inclusive_prod_cub_forward = _make_lazy_cuda_func("inclusive_prod_cub_forward")
-inclusive_prod_cub_backward = _make_lazy_cuda_func("inclusive_prod_cub_backward")
+inclusive_prod_cub_backward = _make_lazy_cuda_func(
+    "inclusive_prod_cub_backward"
+)
 exclusive_prod_cub_forward = _make_lazy_cuda_func("exclusive_prod_cub_forward")
-exclusive_prod_cub_backward = _make_lazy_cuda_func("exclusive_prod_cub_backward")
+exclusive_prod_cub_backward = _make_lazy_cuda_func(
+    "exclusive_prod_cub_backward"
+)
 
 # pdf
 importance_sampling = _make_lazy_cuda_func("importance_sampling")

nerfacc/cuda/csrc/nerfacc.cpp

@@ -38,6 +38,7 @@ torch::Tensor exclusive_prod_backward(
     torch::Tensor outputs,
     torch::Tensor grad_outputs);
 
+bool is_cub_available();
 torch::Tensor inclusive_sum_cub(
     torch::Tensor ray_indices,
     torch::Tensor inputs,
@@ -131,6 +132,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
     _REG_FUNC(exclusive_prod_forward);
     _REG_FUNC(exclusive_prod_backward);
 
+    _REG_FUNC(is_cub_available);
     _REG_FUNC(inclusive_sum_cub);
     _REG_FUNC(exclusive_sum_cub);
     _REG_FUNC(inclusive_prod_cub_forward);

nerfacc/cuda/csrc/scan_cub.cu

@@ -56,6 +56,14 @@ inline void inclusive_prod_by_key(
 }
 #endif
 
+bool is_cub_available() {
+#if CUB_SUPPORTS_SCAN_BY_KEY()
+    return true;
+#else
+    return false;
+#endif
+}
+
 torch::Tensor inclusive_sum_cub(
     torch::Tensor indices,
     torch::Tensor inputs,

nerfacc/scan.py

 """
 Copyright (c) 2022 Ruilong Li, UC Berkeley.
 """
+import warnings
 from typing import Optional
 
 import torch
 from torch import Tensor
 
 from . import cuda as _C
+from .pack import pack_info
 
 
 def inclusive_sum(
-    inputs: Tensor, packed_info: Optional[Tensor] = None
+    inputs: Tensor,
+    packed_info: Optional[Tensor] = None,
+    indices: Optional[Tensor] = None,
 ) -> Tensor:
     """Inclusive Sum that supports flattened tensor.
 
@@ -20,11 +24,12 @@ def inclusive_sum(
 
     Args:
         inputs: The tensor to be summed. Can be either a N-D tensor, or a flattened
-            tensor with `packed_info` specified.
+            tensor with either `packed_info` or `indices` specified.
         packed_info: A tensor of shape (n_rays, 2) that specifies the start and count
             of each chunk in the flattened input tensor, with in total n_rays chunks.
             If None, the input is assumed to be a N-D tensor and the sum is computed
             along the last dimension. Default is None.
+        indices: A flattened tensor with the same shape as `inputs`.
 
     Returns:
         The inclusive sum with the same shape as the input tensor.
@@ -39,22 +44,43 @@ def inclusive_sum(
         tensor([ 1.,  3.,  3.,  7., 12.,  6., 13., 21., 30.], device='cuda:0')
 
     """
-    if packed_info is None:
-        # Batched inclusive sum on the last dimension.
-        outputs = torch.cumsum(inputs, dim=-1)
+    if indices is not None and packed_info is not None:
+        raise ValueError(
+            "Only one of `indices` and `packed_info` can be specified."
+        )
+
+    if indices is not None:
+        assert (
+            indices.dim() == 1 and indices.shape == inputs.shape
+        ), "indices must be 1-D with the same shape as inputs."
+        if _C.is_cub_available():
+            # Use CUB if available
+            outputs = _InclusiveSumCUB.apply(indices, inputs)
         else:
-        # Flattened inclusive sum.
+            warnings.warn(
+                "Passing in `indices` without CUB available is slow. Considering passing in `packed_info` instead."
+            )
+            packed_info = pack_info(ray_indices=indices)
+
+    if packed_info is not None:
         assert inputs.dim() == 1, "inputs must be flattened."
         assert (
             packed_info.dim() == 2 and packed_info.shape[-1] == 2
         ), "packed_info must be 2-D with shape (B, 2)."
         chunk_starts, chunk_cnts = packed_info.unbind(dim=-1)
         outputs = _InclusiveSum.apply(chunk_starts, chunk_cnts, inputs, False)
+
+    if indices is None and packed_info is None:
+        # Batched inclusive sum on the last dimension.
+        outputs = torch.cumsum(inputs, dim=-1)
+
     return outputs
 
 
 def exclusive_sum(
-    inputs: Tensor, packed_info: Optional[Tensor] = None
+    inputs: Tensor,
+    packed_info: Optional[Tensor] = None,
+    indices: Optional[Tensor] = None,
 ) -> Tensor:
     """Exclusive Sum that supports flattened tensor.
 
@@ -62,11 +88,12 @@ def exclusive_sum(
 
     Args:
         inputs: The tensor to be summed. Can be either a N-D tensor, or a flattened
-            tensor with `packed_info` specified.
+            tensor with either `packed_info` or `indices` specified.
         packed_info: A tensor of shape (n_rays, 2) that specifies the start and count
             of each chunk in the flattened input tensor, with in total n_rays chunks.
             If None, the input is assumed to be a N-D tensor and the sum is computed
             along the last dimension. Default is None.
+        indices: A flattened tensor with the same shape as `inputs`.
 
     Returns:
         The exclusive sum with the same shape as the input tensor.
@@ -81,27 +108,47 @@ def exclusive_sum(
         tensor([ 0.,  1.,  0.,  3.,  7.,  0.,  6., 13., 21.], device='cuda:0')
 
     """
-    if packed_info is None:
-        # Batched exclusive sum on the last dimension.
-        outputs = torch.cumsum(
-            torch.cat(
-                [torch.zeros_like(inputs[..., :1]), inputs[..., :-1]], dim=-1
-            ),
-            dim=-1,
+    if indices is not None and packed_info is not None:
+        raise ValueError(
+            "Only one of `indices` and `packed_info` can be specified."
         )
+
+    if indices is not None:
+        assert (
+            indices.dim() == 1 and indices.shape == inputs.shape
+        ), "indices must be 1-D with the same shape as inputs."
+        if _C.is_cub_available():
+            # Use CUB if available
+            outputs = _ExclusiveSumCUB.apply(indices, inputs)
         else:
-        # Flattened exclusive sum.
+            warnings.warn(
+                "Passing in `indices` without CUB available is slow. Considering passing in `packed_info` instead."
+            )
+            packed_info = pack_info(ray_indices=indices)
+
+    if packed_info is not None:
         assert inputs.dim() == 1, "inputs must be flattened."
         assert (
             packed_info.dim() == 2 and packed_info.shape[-1] == 2
         ), "packed_info must be 2-D with shape (B, 2)."
         chunk_starts, chunk_cnts = packed_info.unbind(dim=-1)
         outputs = _ExclusiveSum.apply(chunk_starts, chunk_cnts, inputs, False)
+
+    if indices is None and packed_info is None:
+        # Batched exclusive sum on the last dimension.
+        outputs = torch.cumsum(
+            torch.cat(
+                [torch.zeros_like(inputs[..., :1]), inputs[..., :-1]], dim=-1
+            ),
+            dim=-1,
+        )
     return outputs
 
 
 def inclusive_prod(
-    inputs: Tensor, packed_info: Optional[Tensor] = None
+    inputs: Tensor,
+    packed_info: Optional[Tensor] = None,
+    indices: Optional[Tensor] = None,
 ) -> Tensor:
     """Inclusive Product that supports flattened tensor.
 
@@ -111,11 +158,12 @@ def inclusive_prod(
 
     Args:
         inputs: The tensor to be producted. Can be either a N-D tensor, or a flattened
-            tensor with `packed_info` specified.
+            tensor with either `packed_info` or `indices` specified.
         packed_info: A tensor of shape (n_rays, 2) that specifies the start and count
             of each chunk in the flattened input tensor, with in total n_rays chunks.
             If None, the input is assumed to be a N-D tensor and the product is computed
             along the last dimension. Default is None.
+        indices: A flattened tensor with the same shape as `inputs`.
 
     Returns:
         The inclusive product with the same shape as the input tensor.
@@ -130,22 +178,43 @@ def inclusive_prod(
         tensor([1., 2., 3., 12., 60., 6., 42., 336., 3024.], device='cuda:0')
 
     """
-    if packed_info is None:
-        # Batched inclusive product on the last dimension.
-        outputs = torch.cumprod(inputs, dim=-1)
+    if indices is not None and packed_info is not None:
+        raise ValueError(
+            "Only one of `indices` and `packed_info` can be specified."
+        )
+
+    if indices is not None:
+        assert (
+            indices.dim() == 1 and indices.shape == inputs.shape
+        ), "indices must be 1-D with the same shape as inputs."
+        if _C.is_cub_available():
+            # Use CUB if available
+            outputs = _InclusiveProdCUB.apply(indices, inputs)
         else:
-        # Flattened inclusive product.
+            warnings.warn(
+                "Passing in `indices` without CUB available is slow. Considering passing in `packed_info` instead."
+            )
+            packed_info = pack_info(ray_indices=indices)
+
+    if packed_info is not None:
         assert inputs.dim() == 1, "inputs must be flattened."
         assert (
             packed_info.dim() == 2 and packed_info.shape[-1] == 2
         ), "packed_info must be 2-D with shape (B, 2)."
         chunk_starts, chunk_cnts = packed_info.unbind(dim=-1)
         outputs = _InclusiveProd.apply(chunk_starts, chunk_cnts, inputs)
+
+    if indices is None and packed_info is None:
+        # Batched inclusive product on the last dimension.
+        outputs = torch.cumprod(inputs, dim=-1)
+
     return outputs
 
 
 def exclusive_prod(
-    inputs: Tensor, packed_info: Optional[Tensor] = None
+    inputs: Tensor,
+    packed_info: Optional[Tensor] = None,
+    indices: Optional[Tensor] = None,
 ) -> Tensor:
     """Exclusive Product that supports flattened tensor.
 
@@ -153,11 +222,12 @@ def exclusive_prod(
 
     Args:
         inputs: The tensor to be producted. Can be either a N-D tensor, or a flattened
-            tensor with `packed_info` specified.
+            tensor with either `packed_info` or `indices` specified.
         packed_info: A tensor of shape (n_rays, 2) that specifies the start and count
             of each chunk in the flattened input tensor, with in total n_rays chunks.
             If None, the input is assumed to be a N-D tensor and the product is computed
             along the last dimension. Default is None.
+        indices: A flattened tensor with the same shape as `inputs`.
 
     Returns:
         The exclusive product with the same shape as the input tensor.
@@ -173,16 +243,42 @@ def exclusive_prod(
         tensor([1., 1., 1., 3., 12., 1., 6., 42., 336.], device='cuda:0')
 
     """
-    if packed_info is None:
+
+    if indices is not None and packed_info is not None:
+        raise ValueError(
+            "Only one of `indices` and `packed_info` can be specified."
+        )
+
+    if indices is not None:
+        assert (
+            indices.dim() == 1 and indices.shape == inputs.shape
+        ), "indices must be 1-D with the same shape as inputs."
+        if _C.is_cub_available():
+            # Use CUB if available
+            outputs = _ExclusiveProdCUB.apply(indices, inputs)
+        else:
+            warnings.warn(
+                "Passing in `indices` without CUB available is slow. Considering passing in `packed_info` instead."
+            )
+            packed_info = pack_info(ray_indices=indices)
+
+    if packed_info is not None:
+        assert inputs.dim() == 1, "inputs must be flattened."
+        assert (
+            packed_info.dim() == 2 and packed_info.shape[-1] == 2
+        ), "packed_info must be 2-D with shape (B, 2)."
+        chunk_starts, chunk_cnts = packed_info.unbind(dim=-1)
+        outputs = _ExclusiveProd.apply(chunk_starts, chunk_cnts, inputs)
+
+    if indices is None and packed_info is None:
+        # Batched exclusive product on the last dimension.
         outputs = torch.cumprod(
             torch.cat(
                 [torch.ones_like(inputs[..., :1]), inputs[..., :-1]], dim=-1
             ),
             dim=-1,
         )
-    else:
-        chunk_starts, chunk_cnts = packed_info.unbind(dim=-1)
-        outputs = _ExclusiveProd.apply(chunk_starts, chunk_cnts, inputs)
+
     return outputs
 
 
@@ -286,3 +382,87 @@ class _ExclusiveProd(torch.autograd.Function):
             chunk_starts, chunk_cnts, inputs, outputs, grad_outputs
         )
         return None, None, grad_inputs
+
+
+class _InclusiveSumCUB(torch.autograd.Function):
+    """Inclusive Sum on a Flattened Tensor with CUB."""
+
+    @staticmethod
+    def forward(ctx, indices, inputs):
+        indices = indices.contiguous()
+        inputs = inputs.contiguous()
+        outputs = _C.inclusive_sum_cub(indices, inputs, False)
+        if ctx.needs_input_grad[1]:
+            ctx.save_for_backward(indices)
+        return outputs
+
+    @staticmethod
+    def backward(ctx, grad_outputs):
+        grad_outputs = grad_outputs.contiguous()
+        (indices,) = ctx.saved_tensors
+        grad_inputs = _C.inclusive_sum_cub(indices, grad_outputs, True)
+        return None, grad_inputs
+
+
+class _ExclusiveSumCUB(torch.autograd.Function):
+    """Exclusive Sum on a Flattened Tensor with CUB."""
+
+    @staticmethod
+    def forward(ctx, indices, inputs):
+        indices = indices.contiguous()
+        inputs = inputs.contiguous()
+        outputs = _C.exclusive_sum_cub(indices, inputs, False)
+        if ctx.needs_input_grad[1]:
+            ctx.save_for_backward(indices)
+        return outputs
+
+    @staticmethod
+    def backward(ctx, grad_outputs):
+        grad_outputs = grad_outputs.contiguous()
+        (indices,) = ctx.saved_tensors
+        grad_inputs = _C.exclusive_sum_cub(indices, grad_outputs, True)
+        return None, grad_inputs
+
+
+class _InclusiveProdCUB(torch.autograd.Function):
+    """Inclusive Product on a Flattened Tensor with CUB."""
+
+    @staticmethod
+    def forward(ctx, indices, inputs):
+        indices = indices.contiguous()
+        inputs = inputs.contiguous()
+        outputs = _C.inclusive_prod_cub_forward(indices, inputs)
+        if ctx.needs_input_grad[1]:
+            ctx.save_for_backward(indices, inputs, outputs)
+        return outputs
+
+    @staticmethod
+    def backward(ctx, grad_outputs):
+        grad_outputs = grad_outputs.contiguous()
+        indices, inputs, outputs = ctx.saved_tensors
+        grad_inputs = _C.inclusive_prod_cub_backward(
+            indices, inputs, outputs, grad_outputs
+        )
+        return None, grad_inputs
+
+
+class _ExclusiveProdCUB(torch.autograd.Function):
+    """Exclusive Product on a Flattened Tensor with CUB."""
+
+    @staticmethod
+    def forward(ctx, indices, inputs):
+        indices = indices.contiguous()
+        inputs = inputs.contiguous()
+        outputs = _C.exclusive_prod_cub_forward(indices, inputs)
+        if ctx.needs_input_grad[1]:
+            ctx.save_for_backward(indices, inputs, outputs)
+        return outputs
+
+    @staticmethod
+    def backward(ctx, grad_outputs):
+        grad_outputs = grad_outputs.contiguous()
+        indices, inputs, outputs = ctx.saved_tensors
+        grad_inputs = _C.exclusive_prod_cub_backward(
+            indices, inputs, outputs, grad_outputs
+        )
+        return None, grad_inputs

nerfacc/scan_cub.py

-"""
-Copyright (c) 2022 Ruilong Li, UC Berkeley.
-"""
-import torch
-from torch import Tensor
-
-from . import cuda as _C
-
-
-def inclusive_sum_cub(inputs: Tensor, indices: Tensor) -> Tensor:
-    """Inclusive Sum that supports flattened tensor with CUB."""
-    # Flattened inclusive sum.
-    assert inputs.dim() == 1, "inputs must be flattened."
-    assert (
-        indices.dim() == 1 and indices.shape[0] == inputs.shape[0]
-    ), "indices must be 1-D with the same shape as inputs."
-    outputs = _InclusiveSum.apply(indices, inputs)
-    return outputs
-
-def exclusive_sum_cub(inputs: Tensor, indices: Tensor) -> Tensor:
-    """Exclusive Sum that supports flattened tensor with CUB."""
-    # Flattened inclusive sum.
-    assert inputs.dim() == 1, "inputs must be flattened."
-    assert (
-        indices.dim() == 1 and indices.shape[0] == inputs.shape[0]
-    ), "indices must be 1-D with the same shape as inputs."
-    outputs = _ExclusiveSum.apply(indices, inputs)
-    return outputs
-
-def inclusive_prod_cub(inputs: Tensor, indices: Tensor) -> Tensor:
-    """Inclusive Prod that supports flattened tensor with CUB."""
-    # Flattened inclusive prod.
-    assert inputs.dim() == 1, "inputs must be flattened."
-    assert (
-        indices.dim() == 1 and indices.shape[0] == inputs.shape[0]
-    ), "indices must be 1-D with the same shape as inputs."
-    outputs = _InclusiveProd.apply(indices, inputs)
-    return outputs
-
-def exclusive_prod_cub(inputs: Tensor, indices: Tensor) -> Tensor:
-    """Exclusive Prod that supports flattened tensor with CUB."""
-    # Flattened inclusive prod.
-    assert inputs.dim() == 1, "inputs must be flattened."
-    assert (
-        indices.dim() == 1 and indices.shape[0] == inputs.shape[0]
-    ), "indices must be 1-D with the same shape as inputs."
-    outputs = _ExclusiveProd.apply(indices, inputs)
-    return outputs
-
-
-class _InclusiveSum(torch.autograd.Function):
-    """Inclusive Sum on a Flattened Tensor with CUB."""
-
-    @staticmethod
-    def forward(ctx, indices, inputs):
-        indices = indices.contiguous()
-        inputs = inputs.contiguous()
-        outputs = _C.inclusive_sum_cub(indices, inputs, False)
-        if ctx.needs_input_grad[1]:
-            ctx.save_for_backward(indices)
-        return outputs
-
-    @staticmethod
-    def backward(ctx, grad_outputs):
-        grad_outputs = grad_outputs.contiguous()
-        (indices,) = ctx.saved_tensors
-        grad_inputs = _C.inclusive_sum_cub(indices, grad_outputs, True)
-        return None, grad_inputs
-
-
-class _ExclusiveSum(torch.autograd.Function):
-    """Exclusive Sum on a Flattened Tensor with CUB."""
-
-    @staticmethod
-    def forward(ctx, indices, inputs):
-        indices = indices.contiguous()
-        inputs = inputs.contiguous()
-        outputs = _C.exclusive_sum_cub(indices, inputs, False)
-        if ctx.needs_input_grad[1]:
-            ctx.save_for_backward(indices)
-        return outputs
-
-    @staticmethod
-    def backward(ctx, grad_outputs):
-        grad_outputs = grad_outputs.contiguous()
-        (indices,) = ctx.saved_tensors
-        grad_inputs = _C.exclusive_sum_cub(indices, grad_outputs, True)
-        return None, grad_inputs
-
-
-class _InclusiveProd(torch.autograd.Function):
-    """Inclusive Product on a Flattened Tensor with CUB."""
-
-    @staticmethod
-    def forward(ctx, indices, inputs):
-        indices = indices.contiguous()
-        inputs = inputs.contiguous()
-        outputs = _C.inclusive_prod_cub_forward(indices, inputs)
-        if ctx.needs_input_grad[1]:
-            ctx.save_for_backward(indices, inputs, outputs)
-        return outputs
-
-    @staticmethod
-    def backward(ctx, grad_outputs):
-        grad_outputs = grad_outputs.contiguous()
-        indices, inputs, outputs = ctx.saved_tensors
-        grad_inputs = _C.inclusive_prod_cub_backward(indices, inputs, outputs, grad_outputs)
-        return None, grad_inputs
-
-
-class _ExclusiveProd(torch.autograd.Function):
-    """Exclusive Product on a Flattened Tensor with CUB."""
-
-    @staticmethod
-    def forward(ctx, indices, inputs):
-        indices = indices.contiguous()
-        inputs = inputs.contiguous()
-        outputs = _C.exclusive_prod_cub_forward(indices, inputs)
-        if ctx.needs_input_grad[1]:
-            ctx.save_for_backward(indices, inputs, outputs)
-        return outputs
-
-    @staticmethod
-    def backward(ctx, grad_outputs):
-        grad_outputs = grad_outputs.contiguous()
-        indices, inputs, outputs = ctx.saved_tensors
-        grad_inputs = _C.exclusive_prod_cub_backward(indices, inputs, outputs, grad_outputs)
-        return None, grad_inputs

tests/test_scan.py

@@ -7,7 +7,6 @@ device = "cuda:0"
 @pytest.mark.skipif(not torch.cuda.is_available, reason="No CUDA device")
 def test_inclusive_sum():
     from nerfacc.scan import inclusive_sum
-    from nerfacc.scan_cub import inclusive_sum_cub
 
     torch.manual_seed(42)
 
@@ -34,7 +33,7 @@ def test_inclusive_sum():
     indices = torch.arange(data.shape[0], device=device, dtype=torch.long)
     indices = indices.repeat_interleave(data.shape[1])
     indices = indices.flatten()
-    outputs3 = inclusive_sum_cub(flatten_data, indices)
+    outputs3 = inclusive_sum(flatten_data, indices=indices)
     outputs3.sum().backward()
     grad3 = data.grad.clone()
     data.grad.zero_()
@@ -49,7 +48,6 @@ def test_inclusive_sum():
 @pytest.mark.skipif(not torch.cuda.is_available, reason="No CUDA device")
 def test_exclusive_sum():
     from nerfacc.scan import exclusive_sum
-    from nerfacc.scan_cub import exclusive_sum_cub
 
     torch.manual_seed(42)
 
@@ -76,7 +74,7 @@ def test_exclusive_sum():
     indices = torch.arange(data.shape[0], device=device, dtype=torch.long)
     indices = indices.repeat_interleave(data.shape[1])
     indices = indices.flatten()
-    outputs3 = exclusive_sum_cub(flatten_data, indices)
+    outputs3 = exclusive_sum(flatten_data, indices=indices)
     outputs3.sum().backward()
     grad3 = data.grad.clone()
     data.grad.zero_()
@@ -93,7 +91,6 @@ def test_exclusive_sum():
 @pytest.mark.skipif(not torch.cuda.is_available, reason="No CUDA device")
 def test_inclusive_prod():
     from nerfacc.scan import inclusive_prod
-    from nerfacc.scan_cub import inclusive_prod_cub
 
     torch.manual_seed(42)
 
@@ -120,7 +117,7 @@ def test_inclusive_prod():
     indices = torch.arange(data.shape[0], device=device, dtype=torch.long)
     indices = indices.repeat_interleave(data.shape[1])
     indices = indices.flatten()
-    outputs3 = inclusive_prod_cub(flatten_data, indices)
+    outputs3 = inclusive_prod(flatten_data, indices=indices)
     outputs3.sum().backward()
     grad3 = data.grad.clone()
     data.grad.zero_()
@@ -135,7 +132,6 @@ def test_inclusive_prod():
 @pytest.mark.skipif(not torch.cuda.is_available, reason="No CUDA device")
 def test_exclusive_prod():
     from nerfacc.scan import exclusive_prod
-    from nerfacc.scan_cub import exclusive_prod_cub
 
     torch.manual_seed(42)
 
@@ -162,7 +158,7 @@ def test_exclusive_prod():
     indices = torch.arange(data.shape[0], device=device, dtype=torch.long)
     indices = indices.repeat_interleave(data.shape[1])
     indices = indices.flatten()
-    outputs3 = exclusive_prod_cub(flatten_data, indices)
+    outputs3 = exclusive_prod(flatten_data, indices=indices)
     outputs3.sum().backward()
     grad3 = data.grad.clone()
     data.grad.zero_()
@@ -175,10 +171,11 @@ 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)
 
@@ -202,7 +199,7 @@ def profile():
     indices = indices.flatten()
     torch.cuda.synchronize()
     for _ in tqdm.trange(2000):
-        outputs3 = inclusive_sum_cub(flatten_data, indices)
+        outputs3 = inclusive_sum(flatten_data, indices=indices)
         outputs3.sum().backward()
 
 
@@ -211,4 +208,4 @@ if __name__ == "__main__":
     test_exclusive_sum()
     test_inclusive_prod()
     test_exclusive_prod()
-    # profile()
+    profile()
\ No newline at end of file