fix long to int64_t for Windows

nerfacc/cuda/csrc/pack.cu

@@ -9,7 +9,7 @@ __global__ void unpack_info_kernel(
     const int n_rays,
     const int *packed_info,
     // output
-    long *ray_indices)
+    int64_t *ray_indices)
 {
     CUDA_GET_THREAD_ID(i, n_rays);
 
@@ -97,7 +97,7 @@ torch::Tensor unpack_info(const torch::Tensor packed_info, const int n_samples)
     unpack_info_kernel<<<blocks, threads, 0, at::cuda::getCurrentCUDAStream()>>>(
         n_rays,
         packed_info.data_ptr<int>(),
-        ray_indices.data_ptr<long>());
+        ray_indices.data_ptr<int64_t>());
     return ray_indices;
 }
 

nerfacc/cuda/csrc/ray_marching.cu

@@ -97,7 +97,7 @@ __global__ void ray_marching_kernel(
     // first round outputs
     int *num_steps,
     // second round outputs
-    long *ray_indices,
+    int64_t *ray_indices,
     float *t_starts,
     float *t_ends)
 {
@@ -281,7 +281,7 @@ std::vector<torch::Tensor> ray_marching(
         packed_info.data_ptr<int>(),
         // outputs
         nullptr, /* num_steps */
-        ray_indices.data_ptr<long>(),
+        ray_indices.data_ptr<int64_t>(),
         t_starts.data_ptr<float>(),
         t_ends.data_ptr<float>());
 

nerfacc/cuda/csrc/render_transmittance_cub.cu

@@ -20,7 +20,7 @@ template <typename KeysInputIteratorT, typename ValuesInputIteratorT, typename V
 inline void exclusive_sum_by_key(
     KeysInputIteratorT keys, ValuesInputIteratorT input, ValuesOutputIteratorT output, int64_t num_items)
 {
-    TORCH_CHECK(num_items <= std::numeric_limits<long>::max(),
+    TORCH_CHECK(num_items <= std::numeric_limits<int64_t>::max(),
                 "cub ExclusiveSumByKey does not support more than LONG_MAX elements");
     CUB_WRAPPER(cub::DeviceScan::ExclusiveSumByKey, keys, input, output,
                 num_items, cub::Equality(), at::cuda::getCurrentCUDAStream());
@@ -30,7 +30,7 @@ template <typename KeysInputIteratorT, typename ValuesInputIteratorT, typename V
 inline void exclusive_prod_by_key(
     KeysInputIteratorT keys, ValuesInputIteratorT input, ValuesOutputIteratorT output, int64_t num_items)
 {
-    TORCH_CHECK(num_items <= std::numeric_limits<long>::max(),
+    TORCH_CHECK(num_items <= std::numeric_limits<int64_t>::max(),
                 "cub ExclusiveScanByKey does not support more than LONG_MAX elements");
     CUB_WRAPPER(cub::DeviceScan::ExclusiveScanByKey, keys, input, output, Product(), 1.0f,
                 num_items, cub::Equality(), at::cuda::getCurrentCUDAStream());
@@ -60,7 +60,7 @@ torch::Tensor transmittance_from_sigma_forward_cub(
     torch::Tensor sigmas_dt_cumsum = torch::empty_like(sigmas);
 #if CUB_SUPPORTS_SCAN_BY_KEY()
     exclusive_sum_by_key(
-        ray_indices.data_ptr<long>(),
+        ray_indices.data_ptr<int64_t>(),
         sigmas_dt.data_ptr<float>(),
         sigmas_dt_cumsum.data_ptr<float>(),
         n_samples);
@@ -97,7 +97,7 @@ torch::Tensor transmittance_from_sigma_backward_cub(
     torch::Tensor sigmas_dt_grad = torch::empty_like(transmittance_grad);
 #if CUB_SUPPORTS_SCAN_BY_KEY()
     exclusive_sum_by_key(
-        thrust::make_reverse_iterator(ray_indices.data_ptr<long>() + n_samples),
+        thrust::make_reverse_iterator(ray_indices.data_ptr<int64_t>() + n_samples),
         thrust::make_reverse_iterator(sigmas_dt_cumsum_grad.data_ptr<float>() + n_samples),
         thrust::make_reverse_iterator(sigmas_dt_grad.data_ptr<float>() + n_samples),
         n_samples);
@@ -123,7 +123,7 @@ torch::Tensor transmittance_from_alpha_forward_cub(
     torch::Tensor transmittance = torch::empty_like(alphas);
 #if CUB_SUPPORTS_SCAN_BY_KEY()
     exclusive_prod_by_key(
-        ray_indices.data_ptr<long>(),
+        ray_indices.data_ptr<int64_t>(),
         (1.0f - alphas).data_ptr<float>(),
         transmittance.data_ptr<float>(),
         n_samples);
@@ -154,7 +154,7 @@ torch::Tensor transmittance_from_alpha_backward_cub(
     torch::Tensor sigmas_dt_grad = torch::empty_like(transmittance_grad);
 #if CUB_SUPPORTS_SCAN_BY_KEY()
     exclusive_sum_by_key(
-        thrust::make_reverse_iterator(ray_indices.data_ptr<long>() + n_samples),
+        thrust::make_reverse_iterator(ray_indices.data_ptr<int64_t>() + n_samples),
         thrust::make_reverse_iterator(sigmas_dt_cumsum_grad.data_ptr<float>() + n_samples),
         thrust::make_reverse_iterator(sigmas_dt_grad.data_ptr<float>() + n_samples),
         n_samples);