Updates to fVDB

fvdb/.gitignore

@@ -1,5 +1,4 @@
-build/*
-build/
+/build/*
 *.creator
 *.includes
 *.files

fvdb/README.md

@@ -93,7 +93,7 @@ pip install .
 
 To make sure that everything works by running tests:
 ```shell
-python setup.py test
+pytest tests/unit
 ```
 
 ### Building Documentation
@@ -118,7 +118,7 @@ docker run -it --gpus all --rm \
   --mount type=bind,source="$HOME/.ssh",target=/root/.ssh \
   --mount type=bind,source="$(pwd)",target=/fvdb \
   fvdb-dev:latest \
-  conda run -n fvdb_test --no-capture-output python setup.py test
+  conda run -n fvdb_test --no-capture-output python setup.py develop
 ```
 
 

fvdb/env/build_environment.yml

@@ -2,18 +2,20 @@ name: fvdb_build
 channels:
   - nvidia/label/cuda-12.1.0
   - pytorch
-  - conda-forge
 dependencies:
   - python=3.10
-  - pytorch=2.2
-  - pytorch-cuda=12.1
+  - pytorch::pytorch=2.2
+  - pytorch::pytorch-cuda=12.1
   - git
   - gitpython
   - ca-certificates
   - certifi
   - openssl
-  - cuda
-  - cuda-nvcc
+  - nvidia/label/cuda-12.1.0::cuda
+  - nvidia/label/cuda-12.1.0::cuda-tools
+  - nvidia/label/cuda-12.1.0::cuda-nvcc
+  - nvidia/label/cuda-12.1.0::cuda-cccl
+  - nvidia/label/cuda-12.1.0::cuda-libraries-static
   - gcc_linux-64=11
   - gxx_linux-64=11
   - setuptools

fvdb/env/cutlass.patch

@@ -7,7 +7,7 @@ index e22c8be3..a29e6067 100644
      storage = reinterpret_cast<uint16_t const &>(x);
      #else
 -    __half_raw raw(x);
-+    __half_raw raw(*(reinterpret_cast<const unsigned short*>(&x)));
++    __half_raw raw(*(reinterpret_cast<const __half_raw*>(&x)));
      std::memcpy(&storage, &raw.x, sizeof(storage));
      #endif
    }
@@ -16,7 +16,7 @@ index e22c8be3..a29e6067 100644
      storage = reinterpret_cast<uint16_t const &>(x);
      #else
 -    __half_raw raw(x);
-+    __half_raw raw(*(reinterpret_cast<const unsigned short*>(&x)));
++    __half_raw raw(*(reinterpret_cast<const __half_raw*>(&x)));
      std::memcpy(&storage, &raw.x, sizeof(storage));
      #endif
      return *this;

fvdb/env/test_environment.yml

@@ -3,20 +3,22 @@ channels:
   - pyg
   - nvidia/label/cuda-12.1.0
   - pytorch
-  - conda-forge
 dependencies:
   - python=3.10
-  - pytorch=2.2
-  - pytorch-cuda=12.1
+  - pytorch::pytorch=2.2
+  - pytorch::pytorch-cuda=12.1
   - tensorboard
   - pip
   - git
   - gitpython
   - ca-certificates
   - certifi
   - openssl
-  - cuda
-  - cuda-nvcc
+  - nvidia/label/cuda-12.1.0::cuda
+  - nvidia/label/cuda-12.1.0::cuda-tools
+  - nvidia/label/cuda-12.1.0::cuda-nvcc
+  - nvidia/label/cuda-12.1.0::cuda-cccl
+  - nvidia/label/cuda-12.1.0::cuda-libraries-static
   - parameterized
   - gcc_linux-64=11
   - gxx_linux-64=11

fvdb/fvdb/_Cpp.pyi

@@ -118,7 +118,9 @@ class JaggedTensor:
     @property
     def dtype(self) -> torch.dtype: ...
     @property
-    def jidx(self) -> torch.ShortTensor: ...
+    def jidx(self) -> torch.IntTensor: ...
+    @property
+    def jlidx(self) -> torch.IntTensor: ...
     @property
     def joffsets(self) -> torch.LongTensor: ...
     @property
@@ -134,6 +136,19 @@ class JaggedTensor:
     @property
     def requires_grad(self) -> bool: ...
 
+    @staticmethod
+    def from_data_and_indices(data: torch.Tensor, indices: torch.Tensor, num_tensors: int) -> JaggedTensor: ...
+
+    @staticmethod
+    def from_data_indices_and_list_ids(data: torch.Tensor, indices: torch.Tensor, list_ids: torch.Tensor, num_tensors: int) -> JaggedTensor: ...
+
+    @staticmethod
+    def from_data_and_offsets(data: torch.Tensor, offsets: torch.Tensor) -> JaggedTensor: ...
+
+    @staticmethod
+    def from_data_offsets_and_list_ids(data: torch.Tensor, offsets: torch.Tensor, list_ids: torch.Tensor) -> JaggedTensor: ...
+
+
 JaggedTensorOrTensor = Union[torch.Tensor, JaggedTensor]
 
 class GridBatch:
@@ -243,8 +258,8 @@ class GridBatch:
     def cubes_in_grid(self, cube_centers: JaggedTensorOrTensor, cube_min: Vec3dOrScalar = 0.0, cube_max: Vec3dOrScalar = 0.0, ignore_disabled: bool = False) -> JaggedTensor: ...
     def cubes_intersect_grid(self, cube_centers: JaggedTensorOrTensor, cube_min: Vec3dOrScalar = 0.0, cube_max: Vec3dOrScalar = 0.0, ignore_disabled: bool = False) -> JaggedTensor: ...
 
-    def ijk_to_index(self, ijk: JaggedTensorOrTensor) -> JaggedTensor: ...
-    def ijk_to_inv_index(self, ijk: JaggedTensorOrTensor) -> JaggedTensor: ...
+    def ijk_to_index(self, ijk: JaggedTensorOrTensor, cumulative: bool = False) -> JaggedTensor: ...
+    def ijk_to_inv_index(self, ijk: JaggedTensorOrTensor, cumulative: bool = False) -> JaggedTensor: ...
     def neighbor_indexes(self, ijk: JaggedTensorOrTensor, extent: int, bitshift: int = 0) -> JaggedTensor: ...
 
     def splat_bezier(self,  points: JaggedTensorOrTensor, points_data: JaggedTensorOrTensor) -> JaggedTensor: ...
@@ -256,7 +271,7 @@ class GridBatch:
 
 
     def segments_along_rays(self, ray_origins: JaggedTensorOrTensor, ray_directions: JaggedTensorOrTensor, max_segments: int, eps: float = 0.0, ignore_masked: bool = False) -> JaggedTensor: ...
-    def voxels_along_rays(self, ray_origins: JaggedTensorOrTensor, ray_directions: JaggedTensorOrTensor, max_voxels: int, eps: float = 0.0, return_ijk: bool = True) -> Tuple[JaggedTensor, JaggedTensor]: ...
+    def voxels_along_rays(self, ray_origins: JaggedTensorOrTensor, ray_directions: JaggedTensorOrTensor, max_voxels: int, eps: float = 0.0, return_ijk: bool = True, cumulative: bool = False) -> Tuple[JaggedTensor, JaggedTensor]: ...
     def uniform_ray_samples(self, ray_origins: JaggedTensorOrTensor, ray_directions: JaggedTensorOrTensor, t_min: JaggedTensorOrTensor, t_max: JaggedTensorOrTensor, step_size: float, cone_angle: float = 0.0, include_end_segments : bool = True, return_midpoints: bool = False, eps: float = 0.0) -> JaggedTensor: ...
     def ray_implicit_intersection(self, ray_origins: JaggedTensorOrTensor, ray_directions: JaggedTensorOrTensor, grid_scalars: JaggedTensorOrTensor, eps: float = 0.0) -> JaggedTensor: ...
 

fvdb/fvdb/nn/vdbtensor.py

@@ -71,7 +71,7 @@ def _feature_ops(op, other: List[Union["VDBTensor", JaggedTensor, Any]]):
                 raw_features.append(o.feature.jdata)
             elif isinstance(o, JaggedTensor):
                 assert pivot_tensor.total_voxels == o.jdata.size(0), "All tensors should have the same voxels"
-                assert pivot_tensor.grid.grid_count == len(o.joffsets), "All tensors should have the same batch size"
+                assert pivot_tensor.grid.grid_count == o.num_tensors, "All tensors should have the same batch size"
                 raw_features.append(o.jdata)
             else:
                 raw_features.append(o)