[Feature] Support Voxelization with cambricon MLU device (#2500)

* [Feature] Support hard_voxelize with cambricon MLU backend

* [Feature](bangc-ops): add voxelization op

* [Feature](bangc-ops): add voxelization op

* [Feature](bangc-ops): add voxelization op

* [Feature](bangc-ops): add voxelization op

* [Feature](bangc-ops): add voxelization op

* [Feature](bangc-ops): add voxelization op

* [Feature](bangc-ops): add voxelization op

* [Feature](bangc-ops): add voxelization op

docs/en/understand_mmcv/ops.md

@@ -58,5 +58,5 @@ We implement common ops used in detection, segmentation, etc.
 | ThreeNN                      |     | √    | √   |     |        |
 | TINShift                     |     | √    | √   |     |        |
 | UpFirDn2d                    |     | √    |     |     |        |
-| Voxelization                 | √   | √    |     |     |        |
+| Voxelization                 | √   | √    | √   |     |        |
 | PrRoIPool                    |     | √    |     |     |        |

docs/zh_cn/understand_mmcv/ops.md

@@ -58,5 +58,5 @@ MMCV 提供了检测、分割等任务中常用的算子
 | ThreeNN                      |     | √    | √   |     |        |
 | TINShift                     |     | √    | √   |     |        |
 | UpFirDn2d                    |     | √    |     |     |        |
-| Voxelization                 | √   | √    |     |     |        |
+| Voxelization                 | √   | √    | √   |     |        |
 | PrRoIPool                    |     | √    |     |     |        |

mmcv/ops/csrc/common/mlu/voxelization_mlu_kernel.mlu

+/*************************************************************************
+ * Copyright (C) 2022 by Cambricon.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+ * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+ * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+ * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+ * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+ * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+ * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ *************************************************************************/
+
+#include "common_mlu_helper.hpp"
+
+__nram__ char nram_buffer[MAX_NRAM_SIZE];
+
+#if __BANG_ARCH__ >= 322
+__mlu_func__ void computeDynamicVoxelize(
+    char *points_x, char *points_y, char *points_z, char *auxiliary_a,
+    char *auxiliary_b, char *auxiliary_c, const float coors_x_min,
+    const float coors_y_min, const float coors_z_min, const float voxel_x,
+    const float voxel_y, const float voxel_z, const int32_t grid_x,
+    const int32_t grid_y, const int32_t grid_z, const int32_t deal_num) {
+  // x - coors_x_min
+  __bang_sub_scalar((float *)points_x, (float *)points_x, coors_x_min,
+                    deal_num);
+  // y - coors_y_min
+  __bang_sub_scalar((float *)points_y, (float *)points_y, coors_y_min,
+                    deal_num);
+  // z - coors_z_min
+  __bang_sub_scalar((float *)points_z, (float *)points_z, coors_z_min,
+                    deal_num);
+  // (x - coors_x_min) / voxel_x
+  __bang_mul_scalar((float *)points_x, (float *)points_x, 1.0 / voxel_x,
+                    deal_num);
+  // (y - coors_y_min) / voxel_y
+  __bang_mul_scalar((float *)points_y, (float *)points_y, 1.0 / voxel_y,
+                    deal_num);
+  // (z - coors_z_min) / voxel_z
+  __bang_mul_scalar((float *)points_z, (float *)points_z, 1.0 / voxel_z,
+                    deal_num);
+  // c_x = floor((x - coors_x_min) / voxel_x)
+  __bang_floor((float *)auxiliary_a, (float *)points_x, deal_num);
+  __bang_float2int32((int32_t *)points_x, (float *)auxiliary_a, deal_num, 0);
+  // c_y = floor((y - coors_y_min) / voxel_y)
+  __bang_floor((float *)auxiliary_a, (float *)points_y, deal_num);
+  __bang_float2int32((int32_t *)points_y, (float *)auxiliary_a, deal_num, 0);
+  // c_z = floor((z - coors_z_min) / voxel_z)
+  __bang_floor((float *)auxiliary_a, (float *)points_z, deal_num);
+  __bang_float2int32((int32_t *)points_z, (float *)auxiliary_a, deal_num, 0);
+  // c_x >= 0
+  __bang_ge_scalar((int32_t *)auxiliary_b, (int32_t *)points_x, (int32_t)0,
+                   deal_num);
+  // c_x < grid_x
+  __bang_lt_scalar((int32_t *)auxiliary_c, (int32_t *)points_x, grid_x,
+                   deal_num);
+  // 0 <= c_x < grid_x
+  __bang_mul((int32_t *)auxiliary_a, (int32_t *)auxiliary_b,
+             (int32_t *)auxiliary_c, deal_num);
+  // c_y >= 0
+  __bang_ge_scalar((int32_t *)auxiliary_b, (int32_t *)points_y, (int32_t)0,
+                   deal_num);
+  // c_y < grid_y
+  __bang_lt_scalar((int32_t *)auxiliary_c, (int32_t *)points_y, grid_y,
+                   deal_num);
+  // 0 <= c_y < grid_y
+  __bang_mul((int32_t *)auxiliary_b, (int32_t *)auxiliary_b,
+             (int32_t *)auxiliary_c, deal_num);
+  // c_x >= 0 && c_x < grid_x && c_y >= 0 && c_y < grid_y
+  __bang_mul((int32_t *)auxiliary_a, (int32_t *)auxiliary_a,
+             (int32_t *)auxiliary_b, deal_num);
+  // c_z >= 0
+  __bang_ge_scalar((int32_t *)auxiliary_b, (int32_t *)points_z, (int32_t)0,
+                   deal_num);
+  // c_z < grid_z
+  __bang_lt_scalar((int32_t *)auxiliary_c, (int32_t *)points_z, grid_z,
+                   deal_num);
+  // 0 <= c_z < grid_z
+  __bang_mul((int32_t *)auxiliary_b, (int32_t *)auxiliary_b,
+             (int32_t *)auxiliary_c, deal_num);
+  // 0 <= c_x < grid_x && 0 <= c_y < grid_y && 0 <= c_z < grid_z
+  __bang_mul((int32_t *)auxiliary_a, (int32_t *)auxiliary_a,
+             (int32_t *)auxiliary_b, deal_num);
+  __bang_not((int32_t *)auxiliary_c, (int32_t *)auxiliary_a, deal_num);
+
+  __bang_mul((int32_t *)points_x, (int32_t *)points_x, (int32_t *)auxiliary_a,
+             deal_num);
+  __bang_mul_scalar((int32_t *)auxiliary_b, (int32_t *)auxiliary_c,
+                    (int32_t)(-1), deal_num);
+  __bang_add((int32_t *)points_x, (int32_t *)points_x, (int32_t *)auxiliary_b,
+             deal_num);
+  __bang_mul((int32_t *)points_y, (int32_t *)points_y, (int32_t *)auxiliary_a,
+             deal_num);
+  __bang_add((int32_t *)points_y, (int32_t *)points_y, (int32_t *)auxiliary_b,
+             deal_num);
+  __bang_mul((int32_t *)points_z, (int32_t *)points_z, (int32_t *)auxiliary_a,
+             deal_num);
+  __bang_add((int32_t *)points_z, (int32_t *)points_z, (int32_t *)auxiliary_b,
+             deal_num);
+}
+
+__mlu_func__ void computePoint2Voxel(char *coors_x, char *coors_y,
+                                     char *coors_z, const int32_t c_x,
+                                     const int32_t c_y, const int32_t c_z,
+                                     const int32_t max_points, int32_t *num,
+                                     int32_t *first_point,
+                                     const int32_t deal_idx,
+                                     const int32_t deal_num) {
+  __bang_eq_scalar((int32_t *)coors_x, (int32_t *)coors_x, c_x, deal_num);
+  __bang_eq_scalar((int32_t *)coors_y, (int32_t *)coors_y, c_y, deal_num);
+  __bang_eq_scalar((int32_t *)coors_z, (int32_t *)coors_z, c_z, deal_num);
+  __bang_mul((int32_t *)coors_x, (int32_t *)coors_x, (int32_t *)coors_y,
+             deal_num);
+  __bang_mul((int32_t *)coors_x, (int32_t *)coors_x, (int32_t *)coors_z,
+             deal_num);
+  if (*num == 0) {
+    *num = (int32_t)__bang_count((float *)coors_x, deal_num);
+    if (*num > 0) {
+      *first_point =
+          (int32_t)__bang_findfirst1((float *)coors_x, deal_num) + deal_idx;
+    }
+  } else {
+    *num += (int32_t)__bang_count((float *)coors_x, deal_num);
+  }
+}
+#endif
+
+__mlu_global__ void MLUUnion1KernelDynamicVoxelize(
+    const float *points, int32_t *coors, const float voxel_x,
+    const float voxel_y, const float voxel_z, const float coors_x_min,
+    const float coors_y_min, const float coors_z_min, const float coors_x_max,
+    const float coors_y_max, const float coors_z_max, const int32_t grid_x,
+    const int32_t grid_y, const int32_t grid_z, const int32_t num_points,
+    const int32_t num_features) {
+#if __BANG_ARCH__ >= 322
+  if (coreId == 0x80) {
+    return;
+  }
+
+  const int32_t points_rem = num_points % taskDim;
+  const int32_t points_per_core =
+      taskId < points_rem ? num_points / taskDim + 1 : num_points / taskDim;
+  const int32_t points_start = taskId < points_rem
+                                   ? taskId * points_per_core
+                                   : taskId * points_per_core + points_rem;
+
+  const int32_t split_num = 9;
+  const int32_t deal_num =
+      PAD_DOWN(MAX_NRAM_SIZE / split_num / sizeof(float), NFU_ALIGN_SIZE);
+  const int32_t repeat = points_per_core / deal_num;
+  const int32_t rem = points_per_core % deal_num;
+  const int32_t ping_pong_gap = 3 * deal_num * sizeof(float);
+
+  char *points_x = nram_buffer;
+  char *points_y = points_x + deal_num * sizeof(float);
+  char *points_z = points_y + deal_num * sizeof(float);
+  char *auxiliary_a = points_x + 2 * ping_pong_gap;
+  char *auxiliary_b = auxiliary_a + deal_num * sizeof(float);
+  char *auxiliary_c = auxiliary_b + deal_num * sizeof(float);
+
+  int32_t *coors_z_start = coors + points_start;
+  int32_t *coors_y_start = coors + num_points + points_start;
+  int32_t *coors_x_start = coors + num_points * 2 + points_start;
+
+  if (repeat > 0) {
+    __memcpy_async(points_x, points + points_start * num_features,
+                   sizeof(float), GDRAM2NRAM, sizeof(float),
+                   num_features * sizeof(float), deal_num - 1);
+    __memcpy_async(points_y, points + points_start * num_features + 1,
+                   sizeof(float), GDRAM2NRAM, sizeof(float),
+                   num_features * sizeof(float), deal_num - 1);
+    __memcpy_async(points_z, points + points_start * num_features + 2,
+                   sizeof(float), GDRAM2NRAM, sizeof(float),
+                   num_features * sizeof(float), deal_num - 1);
+    __asm__ volatile("sync;");
+  }
+  if (repeat > 1) {
+    __memcpy_async(points_x + ping_pong_gap,
+                   points + (points_start + deal_num) * num_features,
+                   sizeof(float), GDRAM2NRAM, sizeof(float),
+                   num_features * sizeof(float), deal_num - 1);
+    __memcpy_async(points_y + ping_pong_gap,
+                   points + (points_start + deal_num) * num_features + 1,
+                   sizeof(float), GDRAM2NRAM, sizeof(float),
+                   num_features * sizeof(float), deal_num - 1);
+    __memcpy_async(points_z + ping_pong_gap,
+                   points + (points_start + deal_num) * num_features + 2,
+                   sizeof(float), GDRAM2NRAM, sizeof(float),
+                   num_features * sizeof(float), deal_num - 1);
+    computeDynamicVoxelize(points_x, points_y, points_z, auxiliary_a,
+                           auxiliary_b, auxiliary_c, coors_x_min, coors_y_min,
+                           coors_z_min, voxel_x, voxel_y, voxel_z, grid_x,
+                           grid_y, grid_z, deal_num);
+    __asm__ volatile("sync;");
+  }
+
+  for (int32_t i = 0; i < repeat - 2; ++i) {
+    __memcpy_async(coors_x_start + i * deal_num,
+                   points_x + (i % 2) * ping_pong_gap,
+                   deal_num * sizeof(int32_t), NRAM2GDRAM);
+    __memcpy_async(coors_y_start + i * deal_num,
+                   points_y + (i % 2) * ping_pong_gap,
+                   deal_num * sizeof(int32_t), NRAM2GDRAM);
+    __memcpy_async(coors_z_start + i * deal_num,
+                   points_z + (i % 2) * ping_pong_gap,
+                   deal_num * sizeof(int32_t), NRAM2GDRAM);
+    __memcpy_async(points_x + (i % 2) * ping_pong_gap,
+                   points + (points_start + (i + 2) * deal_num) * num_features,
+                   sizeof(float), GDRAM2NRAM, sizeof(float),
+                   num_features * sizeof(float), deal_num - 1);
+    __memcpy_async(
+        points_y + (i % 2) * ping_pong_gap,
+        points + (points_start + (i + 2) * deal_num) * num_features + 1,
+        sizeof(float), GDRAM2NRAM, sizeof(float), num_features * sizeof(float),
+        deal_num - 1);
+    __memcpy_async(
+        points_z + (i % 2) * ping_pong_gap,
+        points + (points_start + (i + 2) * deal_num) * num_features + 2,
+        sizeof(float), GDRAM2NRAM, sizeof(float), num_features * sizeof(float),
+        deal_num - 1);
+    computeDynamicVoxelize(points_x + ((i + 1) % 2) * ping_pong_gap,
+                           points_y + ((i + 1) % 2) * ping_pong_gap,
+                           points_z + ((i + 1) % 2) * ping_pong_gap,
+                           auxiliary_a, auxiliary_b, auxiliary_c, coors_x_min,
+                           coors_y_min, coors_z_min, voxel_x, voxel_y, voxel_z,
+                           grid_x, grid_y, grid_z, deal_num);
+    __asm__ volatile("sync;");
+  }
+
+  if (repeat >= 2) {
+    __memcpy_async(coors_x_start + (repeat - 2) * deal_num,
+                   points_x + (repeat % 2) * ping_pong_gap,
+                   deal_num * sizeof(int32_t), NRAM2GDRAM);
+    __memcpy_async(coors_y_start + (repeat - 2) * deal_num,
+                   points_y + (repeat % 2) * ping_pong_gap,
+                   deal_num * sizeof(int32_t), NRAM2GDRAM);
+    __memcpy_async(coors_z_start + (repeat - 2) * deal_num,
+                   points_z + (repeat % 2) * ping_pong_gap,
+                   deal_num * sizeof(int32_t), NRAM2GDRAM);
+  }
+  if (rem > 0) {
+    __memcpy_async(points_x + (repeat % 2) * ping_pong_gap,
+                   points + (points_start + repeat * deal_num) * num_features,
+                   sizeof(float), GDRAM2NRAM, sizeof(float),
+                   num_features * sizeof(float), rem - 1);
+    __memcpy_async(
+        points_y + (repeat % 2) * ping_pong_gap,
+        points + (points_start + repeat * deal_num) * num_features + 1,
+        sizeof(float), GDRAM2NRAM, sizeof(float), num_features * sizeof(float),
+        rem - 1);
+    __memcpy_async(
+        points_z + (repeat % 2) * ping_pong_gap,
+        points + (points_start + repeat * deal_num) * num_features + 2,
+        sizeof(float), GDRAM2NRAM, sizeof(float), num_features * sizeof(float),
+        rem - 1);
+  }
+  if (repeat > 0) {
+    computeDynamicVoxelize(points_x + ((repeat - 1) % 2) * ping_pong_gap,
+                           points_y + ((repeat - 1) % 2) * ping_pong_gap,
+                           points_z + ((repeat - 1) % 2) * ping_pong_gap,
+                           auxiliary_a, auxiliary_b, auxiliary_c, coors_x_min,
+                           coors_y_min, coors_z_min, voxel_x, voxel_y, voxel_z,
+                           grid_x, grid_y, grid_z, deal_num);
+  }
+  __asm__ volatile("sync;");
+
+  if (repeat > 0) {
+    __memcpy_async(coors_x_start + (repeat - 1) * deal_num,
+                   points_x + ((repeat - 1) % 2) * ping_pong_gap,
+                   deal_num * sizeof(int32_t), NRAM2GDRAM);
+    __memcpy_async(coors_y_start + (repeat - 1) * deal_num,
+                   points_y + ((repeat - 1) % 2) * ping_pong_gap,
+                   deal_num * sizeof(int32_t), NRAM2GDRAM);
+    __memcpy_async(coors_z_start + (repeat - 1) * deal_num,
+                   points_z + ((repeat - 1) % 2) * ping_pong_gap,
+                   deal_num * sizeof(int32_t), NRAM2GDRAM);
+  }
+  if (rem > 0) {
+    computeDynamicVoxelize(points_x + (repeat % 2) * ping_pong_gap,
+                           points_y + (repeat % 2) * ping_pong_gap,
+                           points_z + (repeat % 2) * ping_pong_gap, auxiliary_a,
+                           auxiliary_b, auxiliary_c, coors_x_min, coors_y_min,
+                           coors_z_min, voxel_x, voxel_y, voxel_z, grid_x,
+                           grid_y, grid_z, rem);
+    __asm__ volatile("sync;");
+    __memcpy_async(coors_x_start + repeat * deal_num,
+                   points_x + (repeat % 2) * ping_pong_gap,
+                   rem * sizeof(int32_t), NRAM2GDRAM);
+    __memcpy_async(coors_y_start + repeat * deal_num,
+                   points_y + (repeat % 2) * ping_pong_gap,
+                   rem * sizeof(int32_t), NRAM2GDRAM);
+    __memcpy_async(coors_z_start + repeat * deal_num,
+                   points_z + (repeat % 2) * ping_pong_gap,
+                   rem * sizeof(int32_t), NRAM2GDRAM);
+  }
+#endif
+}
+
+__mlu_global__ void MLUUnion1KernelPoint2Voxel(int32_t *coors,
+                                               int32_t *point_to_pointidx,
+                                               int32_t *point_to_voxelidx,
+                                               const int32_t num_points,
+                                               const int32_t max_points) {
+#if __BANG_ARCH__ >= 322
+  if (coreId == 0x80) {
+    return;
+  }
+
+  const int32_t split_num = 6;
+  const int32_t deal_num =
+      PAD_DOWN(MAX_NRAM_SIZE / split_num / sizeof(int32_t), NFU_ALIGN_SIZE);
+  const int32_t ping_pong_gap = 3 * deal_num * sizeof(int32_t);
+
+  char *coors_x = nram_buffer;
+  char *coors_y = coors_x + deal_num * sizeof(int32_t);
+  char *coors_z = coors_y + deal_num * sizeof(int32_t);
+
+  int32_t *coors_z_start = coors;
+  int32_t *coors_y_start = coors + num_points;
+  int32_t *coors_x_start = coors + num_points * 2;
+
+  for (int32_t point_idx = taskId; point_idx < num_points;
+       point_idx += taskDim) {
+    if (coors_x_start[point_idx] == -1) {
+      point_to_pointidx[point_idx] = -1;
+      point_to_voxelidx[point_idx] = -1;
+      continue;
+    }
+
+    int32_t c_x = coors_x_start[point_idx];
+    int32_t c_y = coors_y_start[point_idx];
+    int32_t c_z = coors_z_start[point_idx];
+
+    int32_t deal_total_num = point_idx;
+    int32_t repeat = deal_total_num / deal_num;
+    int32_t rem = deal_total_num % deal_num;
+    int32_t num = 0;
+    int32_t first_point = -1;
+
+    if (repeat > 0) {
+      __memcpy_async(coors_x, coors_x_start, deal_num * sizeof(int32_t),
+                     GDRAM2NRAM);
+      __memcpy_async(coors_y, coors_y_start, deal_num * sizeof(int32_t),
+                     GDRAM2NRAM);
+      __memcpy_async(coors_z, coors_z_start, deal_num * sizeof(int32_t),
+                     GDRAM2NRAM);
+      __asm__ volatile("sync;");
+    }
+
+    for (int32_t i = 0; i < repeat - 1; ++i) {
+      __memcpy_async(coors_x + ((i + 1) % 2) * ping_pong_gap,
+                     coors_x_start + (i + 1) * deal_num,
+                     deal_num * sizeof(int32_t), GDRAM2NRAM);
+      __memcpy_async(coors_y + ((i + 1) % 2) * ping_pong_gap,
+                     coors_y_start + (i + 1) * deal_num,
+                     deal_num * sizeof(int32_t), GDRAM2NRAM);
+      __memcpy_async(coors_z + ((i + 1) % 2) * ping_pong_gap,
+                     coors_z_start + (i + 1) * deal_num,
+                     deal_num * sizeof(int32_t), GDRAM2NRAM);
+      computePoint2Voxel(
+          coors_x + (i % 2) * ping_pong_gap, coors_y + (i % 2) * ping_pong_gap,
+          coors_z + (i % 2) * ping_pong_gap, c_x, c_y, c_z, max_points, &num,
+          &first_point, i * deal_num, deal_num);
+      __asm__ volatile("sync;");
+    }
+
+    if (rem > 0) {
+      __memcpy_async(coors_x + (repeat % 2) * ping_pong_gap,
+                     coors_x_start + repeat * deal_num, rem * sizeof(int32_t),
+                     GDRAM2NRAM);
+      __memcpy_async(coors_y + (repeat % 2) * ping_pong_gap,
+                     coors_y_start + repeat * deal_num, rem * sizeof(int32_t),
+                     GDRAM2NRAM);
+      __memcpy_async(coors_z + (repeat % 2) * ping_pong_gap,
+                     coors_z_start + repeat * deal_num, rem * sizeof(int32_t),
+                     GDRAM2NRAM);
+    }
+    if (repeat > 0) {
+      computePoint2Voxel(coors_x + ((repeat - 1) % 2) * ping_pong_gap,
+                         coors_y + ((repeat - 1) % 2) * ping_pong_gap,
+                         coors_z + ((repeat - 1) % 2) * ping_pong_gap, c_x, c_y,
+                         c_z, max_points, &num, &first_point,
+                         (repeat - 1) * deal_num, deal_num);
+    }
+    __asm__ volatile("sync;");
+
+    if (rem > 0) {
+      computePoint2Voxel(coors_x + (repeat % 2) * ping_pong_gap,
+                         coors_y + (repeat % 2) * ping_pong_gap,
+                         coors_z + (repeat % 2) * ping_pong_gap, c_x, c_y, c_z,
+                         max_points, &num, &first_point, repeat * deal_num,
+                         rem);
+      __asm__ volatile("sync;");
+    }
+
+    if (num == 0) {
+      point_to_pointidx[point_idx] = point_idx;
+    } else if (num > 0) {
+      point_to_pointidx[point_idx] = first_point;
+    }
+
+    if (num < max_points) {
+      point_to_voxelidx[point_idx] = num;
+    } else {
+      point_to_voxelidx[point_idx] = -1;
+    }
+  }
+#endif
+}
+
+__mlu_global__ void MLUUnion1KernelCalcPointsPerVoxel(
+    int32_t *point_to_pointidx, int32_t *point_to_voxelidx,
+    int32_t *coor_to_voxelidx, int32_t *num_points_per_voxel,
+    int32_t *voxel_num, const int32_t max_voxels, const int32_t num_points) {
+#if __BANG_ARCH__ >= 322
+  if (coreId == 0) {
+    int32_t voxel_num_temp = 0;
+    for (int32_t point_idx = 0; point_idx < num_points; ++point_idx) {
+      int32_t point_pos_in_voxel = point_to_voxelidx[point_idx];
+      coor_to_voxelidx[point_idx] = -1;
+      if (point_pos_in_voxel == -1) {
+        continue;
+      } else if (point_pos_in_voxel == 0) {
+        int32_t voxel_idx = voxel_num_temp;
+        if (voxel_num_temp >= max_voxels) {
+          continue;
+        }
+        voxel_num_temp += 1;
+        coor_to_voxelidx[point_idx] = voxel_idx;
+        num_points_per_voxel[voxel_idx] = 1;
+      } else {
+        int32_t point_idx_temp = point_to_pointidx[point_idx];
+        int32_t voxel_idx = coor_to_voxelidx[point_idx_temp];
+        if (voxel_idx != -1) {
+          coor_to_voxelidx[point_idx] = voxel_idx;
+          num_points_per_voxel[voxel_idx] += 1;
+        }
+      }
+    }
+    *voxel_num = voxel_num_temp;
+  }
+#endif
+}
+
+__mlu_global__ void MLUUnion1KernelAssignVoxelsCoors(
+    const float *points, int32_t *temp_coors, int32_t *point_to_voxelidx,
+    int32_t *coor_to_voxelidx, float *voxels, int32_t *coors,
+    const int32_t max_points, const int32_t num_points,
+    const int32_t num_features) {
+#if __BANG_ARCH__ >= 322
+  if (coreId == 0x80) {
+    return;
+  }
+
+  int32_t points_per_core = num_points / taskDim;
+  int32_t points_rem = num_points % taskDim;
+  int32_t points_start = taskId < points_rem
+                             ? taskId * (points_per_core + 1)
+                             : taskId * points_per_core + points_rem;
+  int32_t points_end = taskId < points_rem ? points_start + points_per_core + 1
+                                           : points_start + points_per_core;
+
+  for (int32_t point_idx = points_start; point_idx < points_end; ++point_idx) {
+    int32_t num = point_to_voxelidx[point_idx];
+    int32_t voxel_idx = coor_to_voxelidx[point_idx];
+    if (num > -1 && voxel_idx > -1) {
+      float *voxels_offset =
+          voxels + voxel_idx * max_points * num_features + num * num_features;
+      const float *points_offset = points + point_idx * num_features;
+      __memcpy_async(voxels_offset, points_offset, num_features * sizeof(float),
+                     GDRAM2GDRAM);
+
+      if (num == 0) {
+        int32_t *coors_offset = coors + voxel_idx * 3;
+        __memcpy_async(coors_offset, temp_coors + point_idx, sizeof(int32_t),
+                       GDRAM2GDRAM, sizeof(int32_t),
+                       num_points * sizeof(int32_t), 2);
+      }
+    }
+  }
+  __asm__ volatile("sync;");
+#endif
+}
+
+void KernelDynamicVoxelize(cnrtDim3_t k_dim, cnrtFunctionType_t k_type,
+                           cnrtQueue_t queue, const void *points, void *coors,
+                           const float voxel_x, const float voxel_y,
+                           const float voxel_z, const float coors_x_min,
+                           const float coors_y_min, const float coors_z_min,
+                           const float coors_x_max, const float coors_y_max,
+                           const float coors_z_max, const int32_t grid_x,
+                           const int32_t grid_y, const int32_t grid_z,
+                           const int32_t num_points,
+                           const int32_t num_features) {
+  MLUUnion1KernelDynamicVoxelize<<<k_dim, k_type, queue>>>(
+      (float *)points, (int32_t *)coors, voxel_x, voxel_y, voxel_z, coors_x_min,
+      coors_y_min, coors_z_min, coors_x_max, coors_y_max, coors_z_max, grid_x,
+      grid_y, grid_z, num_points, num_features);
+}
+
+void KernelPoint2Voxel(cnrtDim3_t k_dim, cnrtFunctionType_t k_type,
+                       cnrtQueue_t queue, void *coors, void *point_to_pointidx,
+                       void *point_to_voxelidx, const int32_t num_points,
+                       const int32_t max_points) {
+  MLUUnion1KernelPoint2Voxel<<<k_dim, k_type, queue>>>(
+      (int32_t *)coors, (int32_t *)point_to_pointidx,
+      (int32_t *)point_to_voxelidx, num_points, max_points);
+}
+
+void KernelCalcPointsPerVoxel(cnrtDim3_t k_dim, cnrtFunctionType_t k_type,
+                              cnrtQueue_t queue, void *point_to_pointidx,
+                              void *point_to_voxelidx, void *coor_to_voxelidx,
+                              void *num_points_per_voxel, void *voxel_num,
+                              const int32_t max_voxels,
+                              const int32_t num_points) {
+  MLUUnion1KernelCalcPointsPerVoxel<<<k_dim, k_type, queue>>>(
+      (int32_t *)point_to_pointidx, (int32_t *)point_to_voxelidx,
+      (int32_t *)coor_to_voxelidx, (int32_t *)num_points_per_voxel,
+      (int32_t *)voxel_num, max_voxels, num_points);
+}
+
+void KernelAssignVoxelsCoors(cnrtDim3_t k_dim, cnrtFunctionType_t k_type,
+                             cnrtQueue_t queue, const void *points,
+                             void *temp_coors, void *point_to_voxelidx,
+                             void *coor_to_voxelidx, void *voxels, void *coors,
+                             const int32_t max_points, const int32_t num_points,
+                             const int32_t num_features) {
+  MLUUnion1KernelAssignVoxelsCoors<<<k_dim, k_type, queue>>>(
+      (float *)points, (int32_t *)temp_coors, (int32_t *)point_to_voxelidx,
+      (int32_t *)coor_to_voxelidx, (float *)voxels, (int32_t *)coors,
+      max_points, num_points, num_features);
+}

mmcv/ops/csrc/pytorch/mlu/voxelization_mlu.cpp

+/*************************************************************************
+ * Copyright (C) 2022 by Cambricon.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+ * OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+ * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+ * IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+ * CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+ * TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+ * SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+ *************************************************************************/
+#include "pytorch_device_registry.hpp"
+#include "pytorch_mlu_helper.hpp"
+
+#define MIN(a, b) (((a) < (b)) ? (a) : (b))
+
+void KernelDynamicVoxelize(
+    cnrtDim3_t k_dim, cnrtFunctionType_t k_type, cnrtQueue_t queue,
+    const void *points, void *coors, const float voxel_x, const float voxel_y,
+    const float voxel_z, const float coors_x_min, const float coors_y_min,
+    const float coors_z_min, const float coors_x_max, const float coors_y_max,
+    const float coors_z_max, const int32_t grid_x, const int32_t grid_y,
+    const int32_t grid_z, const int32_t num_points, const int32_t num_features);
+
+void KernelPoint2Voxel(cnrtDim3_t k_dim, cnrtFunctionType_t k_type,
+                       cnrtQueue_t queue, void *coors, void *point_to_pointidx,
+                       void *point_to_voxelidx, const int32_t num_points,
+                       const int32_t max_points);
+
+void KernelCalcPointsPerVoxel(cnrtDim3_t k_dim, cnrtFunctionType_t k_type,
+                              cnrtQueue_t queue, void *point_to_pointidx,
+                              void *point_to_voxelidx, void *coor_to_voxelidx,
+                              void *num_points_per_voxel, void *voxel_num,
+                              const int32_t max_voxels,
+                              const int32_t num_points);
+
+void KernelAssignVoxelsCoors(cnrtDim3_t k_dim, cnrtFunctionType_t k_type,
+                             cnrtQueue_t queue, const void *points,
+                             void *temp_coors, void *point_to_voxelidx,
+                             void *coor_to_voxelidx, void *voxels, void *coors,
+                             const int32_t max_points, const int32_t num_points,
+                             const int32_t num_features);
+
+// policy function
+static void policyFuncDefault(cnrtDim3_t *k_dim, cnrtFunctionType_t *k_type,
+                              const int num_points) {
+  k_dim->x = torch_mlu::getDeviceAttr(cnrtAttrMcorePerCluster);
+  k_dim->y = MIN((num_points + k_dim->x - 1) / k_dim->x,
+                 torch_mlu::getDeviceAttr(cnrtAttrClusterCount));
+  k_dim->z = 1;
+  *k_type = CNRT_FUNC_TYPE_UNION1;
+}
+
+// policy function
+static void policyFuncCalcPointsPerVoxel(cnrtDim3_t *k_dim,
+                                         cnrtFunctionType_t *k_type,
+                                         const int num_points) {
+  k_dim->x = 1;
+  k_dim->y = 1;
+  k_dim->z = 1;
+  *k_type = CNRT_FUNC_TYPE_BLOCK;
+}
+
+int HardVoxelizeForwardMLUKernelLauncher(
+    const at::Tensor &points, at::Tensor &voxels, at::Tensor &coors,
+    at::Tensor &num_points_per_voxel, const std::vector<float> voxel_size,
+    const std::vector<float> coors_range, const int max_points,
+    const int max_voxels, const int NDim = 3) {
+  // check datatype
+  TORCH_CHECK(points.scalar_type() == at::kFloat,
+              "points type should be Float, got ", points.scalar_type(), ".");
+  TORCH_CHECK(voxels.scalar_type() == at::kFloat,
+              "voxels type should be Float, got ", voxels.scalar_type(), ".");
+  TORCH_CHECK(coors.scalar_type() == at::kInt,
+              "coors type should be Float, got ", coors.scalar_type(), ".");
+  TORCH_CHECK(num_points_per_voxel.scalar_type() == at::kInt,
+              "num_points_per_voxel type should be Float, got ",
+              num_points_per_voxel.scalar_type(), ".");
+
+  // check shape
+  TORCH_CHECK(points.dim() == 2, "points should be a 2d tensor, got ",
+              points.dim(), "D.");
+  TORCH_CHECK(voxels.dim() == 3, "voxels should be a 3d tensor, got ",
+              voxels.dim(), "D.");
+  TORCH_CHECK(coors.dim() == 2, "coors should be a 2d tensor, got ",
+              coors.dim(), "D.");
+  TORCH_CHECK(num_points_per_voxel.dim() == 1,
+              "num_points_per_voxel should be a 1d tensor, got ",
+              num_points_per_voxel.dim(), "D.");
+
+  const int num_points = points.size(0);
+  const int num_features = points.size(1);
+
+  TORCH_CHECK(points.size(0) == num_points,
+              "the 1st dimensions of points should be num_points, got ",
+              points.size(0), ".");
+  TORCH_CHECK(points.size(1) == num_features,
+              "the 2nd dimensions of points should be num_features, got ",
+              points.size(1), ".");
+  TORCH_CHECK(voxels.size(0) == max_voxels,
+              "the 1st dimensions of voxels should be max_voxels, got ",
+              voxels.size(0), ".");
+  TORCH_CHECK(voxels.size(1) == max_points,
+              "the 2nd dimensions of voxels should be max_points, got ",
+              voxels.size(1), ".");
+  TORCH_CHECK(voxels.size(2) == num_features,
+              "the 3rd dimensions of voxels should be num_features, got ",
+              voxels.size(2), ".");
+  TORCH_CHECK(coors.size(0) == max_voxels,
+              "the 1st dimensions of coors should be max_voxels, got ",
+              coors.size(0), ".");
+  TORCH_CHECK(coors.size(1) == 3,
+              "the 2nd dimensions of coors should be 3, got ", coors.size(1),
+              ".");
+  TORCH_CHECK(num_points_per_voxel.size(0) == max_voxels,
+              "the 1st dimensions of num_points_per_voxel should be 3, got ",
+              num_points_per_voxel.size(0), ".");
+
+  // large tensor check
+  const size_t max_input_size = 2147483648;
+  TORCH_CHECK(points.numel() < max_input_size,
+              "points element num should be less than 2^31, got ",
+              points.numel(), ".");
+  TORCH_CHECK(voxels.numel() < max_input_size,
+              "voxels element num should be less than 2^31, got ",
+              voxels.numel(), ".");
+  TORCH_CHECK(coors.numel() < max_input_size,
+              "coors element num should be less than 2^31, got ", coors.numel(),
+              ".");
+
+  // check zero element
+  if (max_points == 0 || max_voxels == 0) {
+    return 0;
+  }
+
+  // get compute queue
+  auto queue = torch_mlu::getCurQueue();
+
+  // get ptr of tensors
+  auto points_ = points.contiguous();
+  auto points_impl = torch_mlu::getMluTensorImpl(points_);
+  auto points_ptr = points_impl->cnnlMalloc();
+  auto voxels_ = voxels.contiguous();
+  auto voxels_impl = torch_mlu::getMluTensorImpl(voxels_);
+  auto voxels_ptr = voxels_impl->cnnlMalloc();
+  auto coors_ = coors.contiguous();
+  auto coors_impl = torch_mlu::getMluTensorImpl(coors_);
+  auto coors_ptr = coors_impl->cnnlMalloc();
+  auto num_points_per_voxel_ = num_points_per_voxel.contiguous();
+  auto num_points_per_voxel_impl =
+      torch_mlu::getMluTensorImpl(num_points_per_voxel_);
+  auto num_points_per_voxel_ptr = num_points_per_voxel_impl->cnnlMalloc();
+
+  // calculate task dimension
+  cnrtDim3_t k_dim;
+  cnrtFunctionType_t k_type;
+  policyFuncDefault(&k_dim, &k_type, num_points);
+
+  // 1. link point to corresponding voxel coors
+  const float voxel_x = voxel_size[0];
+  const float voxel_y = voxel_size[1];
+  const float voxel_z = voxel_size[2];
+  const float coors_x_min = coors_range[0];
+  const float coors_y_min = coors_range[1];
+  const float coors_z_min = coors_range[2];
+  const float coors_x_max = coors_range[3];
+  const float coors_y_max = coors_range[4];
+  const float coors_z_max = coors_range[5];
+
+  const int grid_x = round((coors_x_max - coors_x_min) / voxel_x);
+  const int grid_y = round((coors_y_max - coors_y_min) / voxel_y);
+  const int grid_z = round((coors_z_max - coors_z_min) / voxel_z);
+
+  auto temp_coors =
+      at::zeros({NDim, num_points}, points.options().dtype(at::kInt))
+          .contiguous();
+  auto temp_coors_impl = torch_mlu::getMluTensorImpl(temp_coors);
+  auto temp_coors_ptr = temp_coors_impl->cnnlMalloc();
+
+  KernelDynamicVoxelize(k_dim, k_type, queue, points_ptr, temp_coors_ptr,
+                        voxel_x, voxel_y, voxel_z, coors_x_min, coors_y_min,
+                        coors_z_min, coors_x_max, coors_y_max, coors_z_max,
+                        grid_x, grid_y, grid_z, num_points, num_features);
+
+  // 2. map point to the idx of the corresponding voxel, find duplicate coor
+  auto point_to_pointidx = at::zeros(
+                               {
+                                   num_points,
+                               },
+                               points.options().dtype(at::kInt))
+                               .contiguous();
+  auto point_to_pointidx_impl = torch_mlu::getMluTensorImpl(point_to_pointidx);
+  auto point_to_pointidx_ptr = point_to_pointidx_impl->cnnlMalloc();
+  auto point_to_voxelidx = at::zeros(
+                               {
+                                   num_points,
+                               },
+                               points.options().dtype(at::kInt))
+                               .contiguous();
+  auto point_to_voxelidx_impl = torch_mlu::getMluTensorImpl(point_to_voxelidx);
+  auto point_to_voxelidx_ptr = point_to_voxelidx_impl->cnnlMalloc();
+
+  KernelPoint2Voxel(k_dim, k_type, queue, temp_coors_ptr, point_to_pointidx_ptr,
+                    point_to_voxelidx_ptr, num_points, max_points);
+
+  // calculate task dimension
+  cnrtDim3_t k_dim_calc_points_per_voxel;
+  cnrtFunctionType_t k_type_calc_points_per_voxel;
+  policyFuncCalcPointsPerVoxel(&k_dim_calc_points_per_voxel,
+                               &k_type_calc_points_per_voxel, num_points);
+
+  // 3. determine voxel num and voxel's coor index
+  auto coor_to_voxelidx = at::zeros(
+                              {
+                                  num_points,
+                              },
+                              points.options().dtype(at::kInt))
+                              .contiguous();
+  auto coor_to_voxelidx_impl = torch_mlu::getMluTensorImpl(coor_to_voxelidx);
+  auto coor_to_voxelidx_ptr = coor_to_voxelidx_impl->cnnlMalloc();
+  auto voxel_num = at::zeros(
+                       {
+                           1,
+                       },
+                       points.options().dtype(at::kInt))
+                       .contiguous();
+  auto voxel_num_impl = torch_mlu::getMluTensorImpl(voxel_num);
+  auto voxel_num_ptr = voxel_num_impl->cnnlMalloc();
+
+  KernelCalcPointsPerVoxel(
+      k_dim_calc_points_per_voxel, k_type_calc_points_per_voxel, queue,
+      point_to_pointidx_ptr, point_to_voxelidx_ptr, coor_to_voxelidx_ptr,
+      num_points_per_voxel_ptr, voxel_num_ptr, max_voxels, num_points);
+
+  // 4. copy point features and coors of each voxels to voxels
+  KernelAssignVoxelsCoors(k_dim, k_type, queue, points_ptr, temp_coors_ptr,
+                          point_to_voxelidx_ptr, coor_to_voxelidx_ptr,
+                          voxels_ptr, coors_ptr, max_points, num_points,
+                          num_features);
+
+  auto voxel_num_cpu = voxel_num.to(at::kCPU);
+  int voxel_num_int = voxel_num_cpu.data_ptr<int>()[0];
+
+  return voxel_num_int;
+}
+
+int hard_voxelize_forward_mlu(const at::Tensor &points, at::Tensor &voxels,
+                              at::Tensor &coors,
+                              at::Tensor &num_points_per_voxel,
+                              const std::vector<float> voxel_size,
+                              const std::vector<float> coors_range,
+                              const int max_points, const int max_voxels,
+                              const int NDim) {
+  return HardVoxelizeForwardMLUKernelLauncher(
+      points, voxels, coors, num_points_per_voxel, voxel_size, coors_range,
+      max_points, max_voxels, NDim);
+};
+
+int hard_voxelize_forward_impl(const at::Tensor &points, at::Tensor &voxels,
+                               at::Tensor &coors,
+                               at::Tensor &num_points_per_voxel,
+                               const std::vector<float> voxel_size,
+                               const std::vector<float> coors_range,
+                               const int max_points, const int max_voxels,
+                               const int NDim);
+
+REGISTER_DEVICE_IMPL(hard_voxelize_forward_impl, MLU,
+                     hard_voxelize_forward_mlu);

tests/test_ops/test_voxelization.py

@@ -4,6 +4,7 @@ import pytest
 import torch
 
 from mmcv.ops import Voxelization
+from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE
 
 
 def _get_voxel_points_indices(points, coors, voxel):
@@ -16,7 +17,7 @@ def _get_voxel_points_indices(points, coors, voxel):
     pytest.param(
         'cuda:0',
         marks=pytest.mark.skipif(
-            not torch.cuda.is_available(), reason='requires CUDA support'))
+            not IS_CUDA_AVAILABLE, reason='requires CUDA support'))
 ])
 def test_voxelization(device_type):
     voxel_size = [0.5, 0.5, 0.5]
@@ -62,8 +63,7 @@ def test_voxelization(device_type):
         assert num_points_current_voxel == expected_num_points_per_voxel[i]
 
 
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason='requires CUDA support')
+@pytest.mark.skipif(not IS_CUDA_AVAILABLE, reason='requires CUDA support')
 def test_voxelization_nondeterministic():
     voxel_size = [0.5, 0.5, 0.5]
     point_cloud_range = [0, -40, -3, 70.4, 40, 1]
@@ -137,3 +137,38 @@ def test_voxelization_nondeterministic():
     coors_all_set = {tuple(c) for c in coors_all}
 
     assert len(coors_set) == len(coors) == len(coors_all_set)
+
+
+@pytest.mark.parametrize('device_type', [
+    pytest.param(
+        'mlu',
+        marks=pytest.mark.skipif(
+            not IS_MLU_AVAILABLE, reason='requires MLU support'))
+])
+def test_voxelization_mlu(device_type):
+    voxel_size = [0.5, 0.5, 0.5]
+    point_cloud_range = [0, -40, -3, 70.4, 40, 1]
+
+    voxel_dict = np.load(
+        'tests/data/for_3d_ops/test_voxel.npy', allow_pickle=True).item()
+    expected_coors = voxel_dict['coors']
+    expected_voxels = voxel_dict['voxels']
+    expected_num_points_per_voxel = voxel_dict['num_points_per_voxel']
+    points = voxel_dict['points']
+
+    points = torch.tensor(points)
+    max_num_points = 1000
+    hard_voxelization = Voxelization(voxel_size, point_cloud_range,
+                                     max_num_points)
+
+    device = torch.device(device_type)
+
+    # test hard_voxelization on mlu
+    points = points.contiguous().to(device)
+    coors, voxels, num_points_per_voxel = hard_voxelization.forward(points)
+    coors = coors.cpu().detach().numpy()
+    voxels = voxels.cpu().detach().numpy()
+    num_points_per_voxel = num_points_per_voxel.cpu().detach().numpy()
+    assert np.all(coors == expected_coors)
+    assert np.all(voxels == expected_voxels)
+    assert np.all(num_points_per_voxel == expected_num_points_per_voxel)