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FastDeploy/fastdeploy/runtime/backends/paddle/ops/voxelize_op.cu
DefTruth 99c2b6592d [Backend] refactor paddle custom ops -> fastdeploy::paddle_custom_ops (#2101)
* [cmake] upgrade windows paddle inference -> 2.5.0

* [cmake] upgrade windows paddle inference -> 2.5.0

* fix paddle custom ops bug on windows

* [Backend] refactor paddle custom ops
2023-07-13 09:00:03 +08:00

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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#if defined(PADDLEINFERENCE_API_COMPAT_2_4_x)
#include "paddle/include/experimental/ext_all.h"
#elif defined(PADDLEINFERENCE_API_COMPAT_2_5_x)
#include "paddle/include/paddle/extension.h"
#else
#include "paddle/extension.h"
#endif
namespace fastdeploy {
namespace paddle_custom_ops {
#define CHECK_INPUT_CUDA(x) \
PD_CHECK(x.is_gpu() || x.is_gpu_pinned(), #x " must be a GPU Tensor.")
#define CUDA_KERNEL_LOOP(i, n) \
for (auto i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
i += blockDim.x * gridDim.x)
template <typename T, typename T_int>
__global__ void init_num_point_grid(
const T *points, const float point_cloud_range_x_min,
const float point_cloud_range_y_min, const float point_cloud_range_z_min,
const float voxel_size_x, const float voxel_size_y,
const float voxel_size_z, const int grid_size_x, const int grid_size_y,
const int grid_size_z, const int64_t num_points, const int num_point_dim,
T_int *num_points_in_grid, int *points_valid) {
int64_t point_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (point_idx > num_points || point_idx == num_points) {
return;
}
int coord_x =
floor((points[point_idx * num_point_dim + 0] - point_cloud_range_x_min) /
voxel_size_x);
int coord_y =
floor((points[point_idx * num_point_dim + 1] - point_cloud_range_y_min) /
voxel_size_y);
int coord_z =
floor((points[point_idx * num_point_dim + 2] - point_cloud_range_z_min) /
voxel_size_z);
if (coord_x < 0 || coord_x > grid_size_x || coord_x == grid_size_x) {
return;
}
if (coord_y < 0 || coord_y > grid_size_y || coord_y == grid_size_y) {
return;
}
if (coord_z < 0 || coord_z > grid_size_z || coord_z == grid_size_z) {
return;
}
int grid_idx =
coord_z * grid_size_y * grid_size_x + coord_y * grid_size_x + coord_x;
num_points_in_grid[grid_idx] = 0;
points_valid[grid_idx] = num_points;
}
template <typename T, typename T_int>
__global__ void map_point_to_grid_kernel(
const T *points, const float point_cloud_range_x_min,
const float point_cloud_range_y_min, const float point_cloud_range_z_min,
const float voxel_size_x, const float voxel_size_y,
const float voxel_size_z, const int grid_size_x, const int grid_size_y,
const int grid_size_z, const int64_t num_points, const int num_point_dim,
const int max_num_points_in_voxel, T_int *points_to_grid_idx,
T_int *points_to_num_idx, T_int *num_points_in_grid, int *points_valid) {
int64_t point_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (point_idx > num_points || point_idx == num_points) {
return;
}
int coord_x =
floor((points[point_idx * num_point_dim + 0] - point_cloud_range_x_min) /
voxel_size_x);
int coord_y =
floor((points[point_idx * num_point_dim + 1] - point_cloud_range_y_min) /
voxel_size_y);
int coord_z =
floor((points[point_idx * num_point_dim + 2] - point_cloud_range_z_min) /
voxel_size_z);
if (coord_x < 0 || coord_x > grid_size_x || coord_x == grid_size_x) {
return;
}
if (coord_y < 0 || coord_y > grid_size_y || coord_y == grid_size_y) {
return;
}
if (coord_z < 0 || coord_z > grid_size_z || coord_z == grid_size_z) {
return;
}
int grid_idx =
coord_z * grid_size_y * grid_size_x + coord_y * grid_size_x + coord_x;
T_int num = atomicAdd(num_points_in_grid + grid_idx, 1);
if (num < max_num_points_in_voxel) {
points_to_num_idx[point_idx] = num;
points_to_grid_idx[point_idx] = grid_idx;
atomicMin(points_valid + grid_idx, static_cast<int>(point_idx));
}
}
template <typename T_int>
__global__ void update_points_flag(const int *points_valid,
const T_int *points_to_grid_idx,
const int num_points, int *points_flag) {
int tid = threadIdx.x + blockIdx.x * blockDim.x;
for (int i = tid; i < num_points; i += gridDim.x * blockDim.x) {
T_int grid_idx = points_to_grid_idx[i];
if (grid_idx >= 0) {
int id = points_valid[grid_idx];
if (id != num_points && id == i) {
points_flag[i] = 1;
}
}
}
}
template <typename T_int>
__global__ void get_voxel_idx_kernel(const int *points_flag,
const T_int *points_to_grid_idx,
const int *points_flag_prefix_sum,
const int num_points, const int max_voxels,
T_int *num_voxels,
T_int *grid_idx_to_voxel_idx) {
int tid = threadIdx.x + blockIdx.x * blockDim.x;
for (int i = tid; i < num_points; i += gridDim.x * blockDim.x) {
if (points_flag[i] == 1) {
T_int grid_idx = points_to_grid_idx[i];
int num = points_flag_prefix_sum[i];
if (num < max_voxels) {
grid_idx_to_voxel_idx[grid_idx] = num;
}
}
if (i == num_points - 1) {
int num = points_flag_prefix_sum[i] + points_flag[i];
if (num < max_voxels) {
num_voxels[0] = num;
} else {
num_voxels[0] = max_voxels;
}
}
}
}
template <typename T>
__global__ void init_voxels_kernel(const int64_t num, T *voxels) {
int64_t idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx > num || idx == num) {
return;
}
voxels[idx] = static_cast<T>(0);
}
template <typename T, typename T_int>
__global__ void assign_voxels_kernel(
const T *points, const T_int *points_to_grid_idx,
const T_int *points_to_num_idx, const T_int *grid_idx_to_voxel_idx,
const int64_t num_points, const int num_point_dim,
const int max_num_points_in_voxel, T *voxels) {
int64_t point_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (point_idx > num_points || point_idx == num_points) {
return;
}
T_int grid_idx = points_to_grid_idx[point_idx];
T_int num_idx = points_to_num_idx[point_idx];
if (grid_idx > -1 && num_idx > -1) {
T_int voxel_idx = grid_idx_to_voxel_idx[grid_idx];
if (voxel_idx > -1) {
for (int64_t i = 0; i < num_point_dim; ++i) {
voxels[voxel_idx * max_num_points_in_voxel * num_point_dim +
num_idx * num_point_dim + i] =
points[point_idx * num_point_dim + i];
}
}
}
}
template <typename T, typename T_int>
__global__ void assign_coords_kernel(const T_int *grid_idx_to_voxel_idx,
const T_int *num_points_in_grid,
const int num_grids, const int grid_size_x,
const int grid_size_y,
const int grid_size_z,
const int max_num_points_in_voxel,
T *coords, T *num_points_per_voxel) {
int64_t grid_idx = blockIdx.x * blockDim.x + threadIdx.x;
if (grid_idx > num_grids || grid_idx == num_grids) {
return;
}
T_int voxel_idx = grid_idx_to_voxel_idx[grid_idx];
if (voxel_idx > -1) {
T_int coord_z = grid_idx / grid_size_x / grid_size_y;
T_int coord_y =
(grid_idx - coord_z * grid_size_x * grid_size_y) / grid_size_x;
T_int coord_x =
grid_idx - coord_z * grid_size_x * grid_size_y - coord_y * grid_size_x;
coords[voxel_idx * 3 + 0] = coord_z;
coords[voxel_idx * 3 + 1] = coord_y;
coords[voxel_idx * 3 + 2] = coord_x;
num_points_per_voxel[voxel_idx] =
min(num_points_in_grid[grid_idx], max_num_points_in_voxel);
}
}
std::vector<paddle::Tensor> hard_voxelize_cuda(
const paddle::Tensor &points, const std::vector<float> &voxel_size,
const std::vector<float> &point_cloud_range, int max_num_points_in_voxel,
int max_voxels) {
// check device
CHECK_INPUT_CUDA(points);
int64_t num_points = points.shape()[0];
int64_t num_point_dim = points.shape()[1];
const float voxel_size_x = voxel_size[0];
const float voxel_size_y = voxel_size[1];
const float voxel_size_z = voxel_size[2];
const float point_cloud_range_x_min = point_cloud_range[0];
const float point_cloud_range_y_min = point_cloud_range[1];
const float point_cloud_range_z_min = point_cloud_range[2];
int grid_size_x = static_cast<int>(
round((point_cloud_range[3] - point_cloud_range[0]) / voxel_size_x));
int grid_size_y = static_cast<int>(
round((point_cloud_range[4] - point_cloud_range[1]) / voxel_size_y));
int grid_size_z = static_cast<int>(
round((point_cloud_range[5] - point_cloud_range[2]) / voxel_size_z));
int num_grids = grid_size_x * grid_size_y * grid_size_z;
auto voxels =
paddle::empty({max_voxels, max_num_points_in_voxel, num_point_dim},
paddle::DataType::FLOAT32, paddle::GPUPlace());
auto coords = paddle::full({max_voxels, 3}, 0, paddle::DataType::INT32,
paddle::GPUPlace());
auto *coords_data = coords.data<int>();
auto num_points_per_voxel = paddle::full(
{max_voxels}, 0, paddle::DataType::INT32, paddle::GPUPlace());
auto *num_points_per_voxel_data = num_points_per_voxel.data<int>();
auto points_to_grid_idx = paddle::full(
{num_points}, -1, paddle::DataType::INT32, paddle::GPUPlace());
auto *points_to_grid_idx_data = points_to_grid_idx.data<int>();
auto points_to_num_idx = paddle::full(
{num_points}, -1, paddle::DataType::INT32, paddle::GPUPlace());
auto *points_to_num_idx_data = points_to_num_idx.data<int>();
auto num_points_in_grid =
paddle::empty({grid_size_z, grid_size_y, grid_size_x},
paddle::DataType::INT32, paddle::GPUPlace());
auto *num_points_in_grid_data = num_points_in_grid.data<int>();
auto grid_idx_to_voxel_idx =
paddle::full({grid_size_z, grid_size_y, grid_size_x}, -1,
paddle::DataType::INT32, paddle::GPUPlace());
auto *grid_idx_to_voxel_idx_data = grid_idx_to_voxel_idx.data<int>();
auto num_voxels =
paddle::full({1}, 0, paddle::DataType::INT32, paddle::GPUPlace());
auto *num_voxels_data = num_voxels.data<int>();
auto points_valid =
paddle::empty({grid_size_z, grid_size_y, grid_size_x},
paddle::DataType::INT32, paddle::GPUPlace());
int *points_valid_data = points_valid.data<int>();
auto points_flag = paddle::full({num_points}, 0, paddle::DataType::INT32,
paddle::GPUPlace());
// 1. Find the grid index for each point, compute the
// number of points in each grid
int64_t threads = 512;
int64_t blocks = (num_points + threads - 1) / threads;
PD_DISPATCH_FLOATING_TYPES(
points.type(), "init_num_point_grid", ([&] {
init_num_point_grid<data_t, int>
<<<blocks, threads, 0, points.stream()>>>(
points.data<data_t>(), point_cloud_range_x_min,
point_cloud_range_y_min, point_cloud_range_z_min, voxel_size_x,
voxel_size_y, voxel_size_z, grid_size_x, grid_size_y,
grid_size_z, num_points, num_point_dim, num_points_in_grid_data,
points_valid_data);
}));
PD_DISPATCH_FLOATING_TYPES(
points.type(), "map_point_to_grid_kernel", ([&] {
map_point_to_grid_kernel<data_t, int>
<<<blocks, threads, 0, points.stream()>>>(
points.data<data_t>(), point_cloud_range_x_min,
point_cloud_range_y_min, point_cloud_range_z_min, voxel_size_x,
voxel_size_y, voxel_size_z, grid_size_x, grid_size_y,
grid_size_z, num_points, num_point_dim, max_num_points_in_voxel,
points_to_grid_idx_data, points_to_num_idx_data,
num_points_in_grid_data, points_valid_data);
}));
// 2. Find the number of non-zero voxels
int *points_flag_data = points_flag.data<int>();
threads = 512;
blocks = (num_points + threads - 1) / threads;
update_points_flag<int><<<blocks, threads, 0, points.stream()>>>(
points_valid_data, points_to_grid_idx_data, num_points, points_flag_data);
auto points_flag_prefix_sum =
paddle::experimental::cumsum(points_flag, 0, false, true, false);
int *points_flag_prefix_sum_data = points_flag_prefix_sum.data<int>();
get_voxel_idx_kernel<int><<<blocks, threads, 0, points.stream()>>>(
points_flag_data, points_to_grid_idx_data, points_flag_prefix_sum_data,
num_points, max_voxels, num_voxels_data, grid_idx_to_voxel_idx_data);
// 3. Store points to voxels coords and num_points_per_voxel
int64_t num = max_voxels * max_num_points_in_voxel * num_point_dim;
threads = 512;
blocks = (num + threads - 1) / threads;
PD_DISPATCH_FLOATING_TYPES(points.type(), "init_voxels_kernel", ([&] {
init_voxels_kernel<data_t>
<<<blocks, threads, 0, points.stream()>>>(
num, voxels.data<data_t>());
}));
threads = 512;
blocks = (num_points + threads - 1) / threads;
PD_DISPATCH_FLOATING_TYPES(
points.type(), "assign_voxels_kernel", ([&] {
assign_voxels_kernel<data_t, int>
<<<blocks, threads, 0, points.stream()>>>(
points.data<data_t>(), points_to_grid_idx_data,
points_to_num_idx_data, grid_idx_to_voxel_idx_data, num_points,
num_point_dim, max_num_points_in_voxel, voxels.data<data_t>());
}));
// 4. Store coords, num_points_per_voxel
blocks = (num_grids + threads - 1) / threads;
assign_coords_kernel<int><<<blocks, threads, 0, points.stream()>>>(
grid_idx_to_voxel_idx_data, num_points_in_grid_data, num_grids,
grid_size_x, grid_size_y, grid_size_z, max_num_points_in_voxel,
coords_data, num_points_per_voxel_data);
return {voxels, coords, num_points_per_voxel, num_voxels};
}
} // namespace fastdeploy
} // namespace paddle_custom_ops