[Feature] Add MLU support for Sparse Convolution op (#2589)

* [Feature] Add sparse convolution MLU API

* [Feature] update cpp code style

* end-of-file

* delete libext.a

* code style

* update ops.md

---------
Co-authored-by: budefei <budefei@cambricon.com>

docs/en/understand_mmcv/ops.md

@@ -52,7 +52,7 @@ We implement common ops used in detection, segmentation, etc.
 | SigmoidFocalLoss             |     | √    | √   |     | √      |
 | SoftmaxFocalLoss             |     | √    |     |     | √      |
 | SoftNMS                      |     | √    |     |     |        |
-| Sparse Convolution           |     | √    |     |     |        |
+| Sparse Convolution           |     | √    | √   |     |        |
 | Synchronized BatchNorm       |     | √    |     |     |        |
 | ThreeInterpolate             |     | √    |     |     |        |
 | ThreeNN                      |     | √    | √   |     |        |

docs/zh_cn/understand_mmcv/ops.md

@@ -52,7 +52,7 @@ MMCV 提供了检测、分割等任务中常用的算子
 | SigmoidFocalLoss             |     | √    | √   |     | √      |
 | SoftmaxFocalLoss             |     | √    |     |     | √      |
 | SoftNMS                      |     | √    |     |     |        |
-| Sparse Convolution           |     | √    |     |     |        |
+| Sparse Convolution           |     | √    | √   |     |        |
 | Synchronized BatchNorm       |     | √    |     |     |        |
 | ThreeInterpolate             |     | √    |     |     |        |
 | ThreeNN                      |     | √    | √   |     |        |

mmcv/ops/csrc/pytorch/mlu/mlu_common_helper.h

@@ -18,8 +18,8 @@
 #include "pytorch_device_registry.hpp"
 
 #define MLUOP_MAJOR 0
-#define MLUOP_MINOR 4
-#define MLUOP_PATCHLEVEL 2
+#define MLUOP_MINOR 5
+#define MLUOP_PATCHLEVEL 302
 
 mluOpDataType_t getMluOpDataType(const caffe2::TypeMeta& data_type);
 mluOpTensorLayout_t getMluOpSuggestLayout(const at::Tensor& input);

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

+/*************************************************************************
+ * Copyright (C) 2022 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 <torch/script.h>
+
+#include <vector>
+
+#include "mlu_common_helper.h"
+#include "pytorch_device_registry.hpp"
+#include "pytorch_mlu_helper.hpp"
+
+template <unsigned NDim>
+std::vector<torch::Tensor> GetIndicePairsForwardMLUKernelLauncher(
+    torch::Tensor indices, int64_t batchSize,
+    std::vector<int64_t> outSpatialShape, std::vector<int64_t> spatialShape,
+    std::vector<int64_t> kernelSize, std::vector<int64_t> stride,
+    std::vector<int64_t> padding, std::vector<int64_t> dilation,
+    std::vector<int64_t> outPadding, int64_t _subM, int64_t _transpose) {
+  // The following code is copied from
+  // mmcv/ops/csrc/pytorch/cuda/spconv_ops_cuda.cu to ensure the output is
+  // available for network train. The outputs of this function have correct
+  // shape but wrong value.
+  auto numAct = indices.size(0);
+  auto kernelVolume = kernelSize[0];
+  int sub_m = (int)_subM;
+  int transpose = (int)_transpose;
+  int batch = (int)batchSize;
+  auto coorDim = indices.size(1) - 1;
+
+  for (int i = 1; i < kernelSize.size(); ++i) {
+    kernelVolume *= kernelSize[i];
+  }
+
+  auto outputVolume = outSpatialShape[0];
+  for (int i = 1; i < outSpatialShape.size(); ++i) {
+    outputVolume *= outSpatialShape[i];
+  }
+  torch::Tensor indicePairs = at::full({kernelVolume, 2, numAct}, -1,
+                                       indices.options().dtype(at::kInt));
+  torch::Tensor indiceNum =
+      at::zeros({kernelVolume}, indices.options().dtype(at::kInt));
+  int out_size = sub_m == 1
+                     ? numAct
+                     : std::min(numAct * kernelVolume, batch * outputVolume);
+  torch::Tensor out_indices =
+      at::zeros({out_size, coorDim + 1}, indices.options().dtype(at::kInt));
+  auto indices_contiguous = torch_mlu::cnnl::ops::cnnl_contiguous(
+      indices, at::MemoryFormat::Contiguous);
+  auto indicePairs_contiguous = torch_mlu::cnnl::ops::cnnl_contiguous(
+      indicePairs, at::MemoryFormat::Contiguous);
+  auto indiceNum_contiguous = torch_mlu::cnnl::ops::cnnl_contiguous(
+      indiceNum, at::MemoryFormat::Contiguous);
+  auto out_indices_contiguous = torch_mlu::cnnl::ops::cnnl_contiguous(
+      out_indices, at::MemoryFormat::Contiguous);
+
+  std::vector<int> input_space;
+  std::vector<int> filter_space;
+  std::vector<int> output_space;
+  std::vector<int> padding32;
+  std::vector<int> stride32;
+  std::vector<int> dilation32;
+  for (int i = 0; i < NDim; i++) {
+    input_space.push_back(spatialShape[i]);
+    filter_space.push_back(kernelSize[i]);
+    output_space.push_back(outSpatialShape[i]);
+    padding32.push_back(padding[i]);
+    stride32.push_back(stride[i]);
+    dilation32.push_back(dilation[i]);
+  }
+  MluOpTensorDescriptor indices_desc, out_indices_desc, indicePairs_desc,
+      indiceNum_desc;
+  indices_desc.set(indices_contiguous);
+  indicePairs_desc.set(indicePairs_contiguous);
+  indiceNum_desc.set(indiceNum_contiguous);
+  out_indices_desc.set(out_indices_contiguous);
+  {
+    mluOpTensorLayout_t layout = MLUOP_LAYOUT_ARRAY;
+    mluOpDataType_t dtype = MLUOP_DTYPE_INT32;
+    std::vector<int> dims;
+    dims = {numAct, coorDim + 1};
+    mluOpSetTensorDescriptor(indices_desc.desc(), layout, dtype, dims.size(),
+                             dims.data());
+    dims = {kernelVolume, 2, numAct};
+    mluOpSetTensorDescriptor(indicePairs_desc.desc(), layout, dtype,
+                             dims.size(), dims.data());
+    dims = {kernelVolume};
+    mluOpSetTensorDescriptor(indiceNum_desc.desc(), layout, dtype, dims.size(),
+                             dims.data());
+    dims = {out_size, coorDim + 1};
+    mluOpSetTensorDescriptor(out_indices_desc.desc(), layout, dtype,
+                             dims.size(), dims.data());
+  }
+
+  mluOpSparseConvolutionDescriptor_t sparse_conv_desc;
+  mluOpCreateSparseConvolutionDescriptor(&sparse_conv_desc);
+  mluOpSetSparseConvolutionDescriptor(
+      sparse_conv_desc, NDim + 2, batch, padding32.data(), stride32.data(),
+      dilation32.data(), input_space.data(), filter_space.data(),
+      output_space.data(), sub_m, transpose, 0);
+
+  auto handle = mluOpGetCurrentHandle();
+  size_t workspace_size = 0;
+  mluOpGetIndicePairsWorkspaceSize(
+      handle, sparse_conv_desc, indices_desc.desc(), indicePairs_desc.desc(),
+      out_indices_desc.desc(), indiceNum_desc.desc(), &workspace_size);
+  auto indice_workspace_size =
+      at::empty(workspace_size, indices.options().dtype(at::kByte));
+
+  auto indices_impl = torch_mlu::getMluTensorImpl(indices_contiguous);
+  auto out_indices_impl = torch_mlu::getMluTensorImpl(out_indices_contiguous);
+  auto indicePairs_impl = torch_mlu::getMluTensorImpl(indicePairs_contiguous);
+  auto indiceNum_impl = torch_mlu::getMluTensorImpl(indiceNum_contiguous);
+  auto indice_workspace_impl =
+      torch_mlu::getMluTensorImpl(indice_workspace_size);
+
+  auto indices_ptr = indices_impl->cnnlMalloc();
+  auto out_indices_ptr = out_indices_impl->cnnlMalloc();
+  auto indicePairs_ptr = indicePairs_impl->cnnlMalloc();
+  auto indiceNum_ptr = indiceNum_impl->cnnlMalloc();
+  auto indice_workspace_ptr = indice_workspace_impl->cnnlMalloc();
+
+  mluOpGetIndicePairs(handle, sparse_conv_desc, indices_desc.desc(),
+                      indices_ptr, indice_workspace_ptr, workspace_size,
+                      indicePairs_desc.desc(), indicePairs_ptr,
+                      out_indices_desc.desc(), out_indices_ptr,
+                      indiceNum_desc.desc(), indiceNum_ptr);
+  int num_act_out = 0;
+  mluOpGetSparseConvolutionNumActOut(sparse_conv_desc, &num_act_out);
+  mluOpDestroySparseConvolutionDescriptor(sparse_conv_desc);
+  if (!sub_m) {
+    return {out_indices.slice(0, 0, num_act_out), indicePairs, indiceNum};
+  } else {
+    return {indices, indicePairs, indiceNum};
+  }
+}
+
+torch::Tensor IndiceConvForwardMLUKernelLauncher(
+    torch::Tensor features, torch::Tensor filters, torch::Tensor indicePairs,
+    torch::Tensor indiceNum, int64_t numActOut, int64_t _inverse,
+    int64_t _subM) {
+  auto indice_num_cpu = indiceNum.to({torch::kCPU});
+  auto indice_num_cpu_64 = indice_num_cpu.data_ptr<int>();
+  int indice_num_len = indiceNum.numel();
+  int64_t indice_num[indice_num_len];
+  for (int i = 0; i < indice_num_len; ++i) {
+    indice_num[i] = (int64_t)(((int *)indice_num_cpu_64)[i]);
+  }
+
+  // generate empty output
+  int C = filters.dim() == 4 ? filters.size(3) : filters.size(4);
+  torch::Tensor output =
+      at::zeros({numActOut, C}, features.options().dtype(at::kFloat));
+  // generate descriptor
+  auto features_contiguous = torch_mlu::cnnl::ops::cnnl_contiguous(
+      features, at::MemoryFormat::Contiguous);
+  auto filters_contiguous = torch_mlu::cnnl::ops::cnnl_contiguous(
+      filters, at::MemoryFormat::Contiguous);
+  auto indice_pairs_contiguous = torch_mlu::cnnl::ops::cnnl_contiguous(
+      indicePairs, at::MemoryFormat::Contiguous);
+  auto output_contiguous = torch_mlu::cnnl::ops::cnnl_contiguous(
+      output, at::MemoryFormat::Contiguous);
+
+  MluOpTensorDescriptor features_desc, filters_desc, indice_pairs_desc,
+      output_desc;
+  features_desc.set(features_contiguous);
+  filters_desc.set(filters_contiguous);
+  indice_pairs_desc.set(indice_pairs_contiguous);
+  output_desc.set(output_contiguous);
+
+  // set layout
+  {
+    mluOpTensorLayout_t layout;
+    mluOpDataType_t dtype;
+    int dim;
+    int dims[8];
+
+    // features_desc
+    mluOpGetTensorDescriptor(features_desc.desc(), &layout, &dtype, &dim, dims);
+    mluOpSetTensorDescriptor(features_desc.desc(), MLUOP_LAYOUT_ARRAY, dtype,
+                             dim, dims);
+
+    // filters_desc
+    mluOpGetTensorDescriptor(filters_desc.desc(), &layout, &dtype, &dim, dims);
+    mluOpSetTensorDescriptor(filters_desc.desc(), MLUOP_LAYOUT_ARRAY, dtype,
+                             dim, dims);
+
+    // indice_pairs_desc
+    mluOpGetTensorDescriptor(indice_pairs_desc.desc(), &layout, &dtype, &dim,
+                             dims);
+    mluOpSetTensorDescriptor(indice_pairs_desc.desc(), MLUOP_LAYOUT_ARRAY,
+                             dtype, dim, dims);
+
+    // output_desc
+    mluOpGetTensorDescriptor(output_desc.desc(), &layout, &dtype, &dim, dims);
+    mluOpSetTensorDescriptor(output_desc.desc(), MLUOP_LAYOUT_ARRAY, dtype, dim,
+                             dims);
+  }
+
+  auto handle = mluOpGetCurrentHandle();
+  size_t workspace_size = 0;
+  mluOpGetIndiceConvolutionForwardWorkspaceSize(
+      handle, features_desc.desc(), filters_desc.desc(),
+      indice_pairs_desc.desc(), output_desc.desc(), indice_num, numActOut,
+      _inverse, _subM, &workspace_size);
+
+  auto workspace =
+      at::empty(workspace_size, features.options().dtype(at::kByte));
+
+  auto features_impl = torch_mlu::getMluTensorImpl(features_contiguous);
+  auto filters_impl = torch_mlu::getMluTensorImpl(filters_contiguous);
+  auto indice_pairs_impl = torch_mlu::getMluTensorImpl(indice_pairs_contiguous);
+  auto workspace_impl = torch_mlu::getMluTensorImpl(workspace);
+
+  auto features_ptr = features_impl->cnnlMalloc();
+  auto filters_ptr = filters_impl->cnnlMalloc();
+  auto indice_pairs_ptr = indice_pairs_impl->cnnlMalloc();
+  auto workspace_ptr = workspace_impl->cnnlMalloc();
+
+  //  outputs
+  auto output_impl = torch_mlu::getMluTensorImpl(output);
+  auto output_ptr = output_impl->cnnlMalloc();
+  mluOpIndiceConvolutionForward(
+      handle, features_desc.desc(), features_ptr, filters_desc.desc(),
+      filters_ptr, indice_pairs_desc.desc(), indice_pairs_ptr, indice_num,
+      numActOut, _inverse, _subM, workspace_ptr, workspace_size,
+      output_desc.desc(), output_ptr);
+
+  return output;
+}
+
+std::vector<torch::Tensor> IndiceConvBackwardMLUKernelLauncher(
+    torch::Tensor features, torch::Tensor filters, torch::Tensor outGrad,
+    torch::Tensor indicePairs, torch::Tensor indiceNum, int64_t _inverse,
+    int64_t _subM) {
+  auto indice_num_cpu = indiceNum.to({torch::kCPU});
+  auto indice_num_cpu_64 = indice_num_cpu.data_ptr<int>();
+  int indice_num_len = indiceNum.numel();
+  int64_t indice_num[indice_num_len];
+  for (int i = 0; i < indice_num_len; ++i) {
+    indice_num[i] = (int64_t)(((int *)(indice_num_cpu_64))[i]);
+  }
+
+  // generate empty input_grad
+  torch::Tensor input_grad = at::zeros({features.size(0), features.size(1)},
+                                       features.options().dtype(at::kFloat));
+  torch::Tensor filters_grad;
+  if (filters.dim() == 4) {
+    int h = filters.size(0);
+    int w = filters.size(1);
+    int c = filters.size(2);
+    int n = filters.size(3);
+    filters_grad = at::zeros({h, w, c, n}, filters.options().dtype(at::kFloat));
+  } else if (filters.dim() == 5) {
+    int d = filters.size(0);
+    int h = filters.size(1);
+    int w = filters.size(2);
+    int c = filters.size(3);
+    int n = filters.size(4);
+    filters_grad =
+        at::zeros({d, h, w, c, n}, filters.options().dtype(at::kFloat));
+  }
+
+  auto features_contiguous = torch_mlu::cnnl::ops::cnnl_contiguous(
+      features, at::MemoryFormat::Contiguous);
+  auto filters_contiguous = torch_mlu::cnnl::ops::cnnl_contiguous(
+      filters, at::MemoryFormat::Contiguous);
+  auto output_grad_contiguous = torch_mlu::cnnl::ops::cnnl_contiguous(
+      outGrad, at::MemoryFormat::Contiguous);
+  auto indice_pairs_contiguous = torch_mlu::cnnl::ops::cnnl_contiguous(
+      indicePairs, at::MemoryFormat::Contiguous);
+  auto input_grad_contiguous = torch_mlu::cnnl::ops::cnnl_contiguous(
+      features, at::MemoryFormat::Contiguous);
+  auto filters_grad_contiguous = torch_mlu::cnnl::ops::cnnl_contiguous(
+      filters, at::MemoryFormat::Contiguous);
+
+  MluOpTensorDescriptor features_desc, output_grad_desc, filters_desc,
+      indice_pairs_desc, input_grad_desc, filters_grad_desc;
+  features_desc.set(features_contiguous);
+  filters_desc.set(filters_contiguous);
+  output_grad_desc.set(output_grad_contiguous);
+  indice_pairs_desc.set(indice_pairs_contiguous);
+  input_grad_desc.set(input_grad_contiguous);
+  filters_grad_desc.set(filters_grad_contiguous);
+
+  // need to set desc layout with mluOp functions
+  {
+    mluOpTensorLayout_t layout;
+    mluOpDataType_t dtype;
+    int dim;
+    int dims[8];
+
+    // features_desc
+    mluOpGetTensorDescriptor(features_desc.desc(), &layout, &dtype, &dim, dims);
+    mluOpSetTensorDescriptor(features_desc.desc(), MLUOP_LAYOUT_ARRAY, dtype,
+                             dim, dims);
+
+    // filters_desc
+    mluOpGetTensorDescriptor(filters_desc.desc(), &layout, &dtype, &dim, dims);
+    if (dim == 4) {
+      mluOpSetTensorDescriptor(filters_desc.desc(), MLUOP_LAYOUT_HWCN, dtype,
+                               dim, dims);
+    } else {
+      mluOpSetTensorDescriptor(filters_desc.desc(), MLUOP_LAYOUT_ARRAY, dtype,
+                               dim, dims);
+    }
+
+    // output_grad_desc
+    mluOpGetTensorDescriptor(output_grad_desc.desc(), &layout, &dtype, &dim,
+                             dims);
+    mluOpSetTensorDescriptor(output_grad_desc.desc(), MLUOP_LAYOUT_ARRAY, dtype,
+                             dim, dims);
+
+    // indice_pairs_desc
+    mluOpGetTensorDescriptor(indice_pairs_desc.desc(), &layout, &dtype, &dim,
+                             dims);
+    mluOpSetTensorDescriptor(indice_pairs_desc.desc(), MLUOP_LAYOUT_ARRAY,
+                             dtype, dim, dims);
+
+    // input_grad_desc
+    mluOpGetTensorDescriptor(input_grad_desc.desc(), &layout, &dtype, &dim,
+                             dims);
+    mluOpSetTensorDescriptor(input_grad_desc.desc(), MLUOP_LAYOUT_ARRAY, dtype,
+                             dim, dims);
+  }
+
+  auto handle = mluOpGetCurrentHandle();
+  size_t data_workspace_size = 0;
+  mluOpGetIndiceConvolutionBackwardDataWorkspaceSize(
+      handle, output_grad_desc.desc(), filters_desc.desc(),
+      indice_pairs_desc.desc(), input_grad_desc.desc(), indice_num, _inverse,
+      &data_workspace_size);
+
+  size_t filters_workspace_size = 0;
+  mluOpGetIndiceConvolutionBackwardFilterWorkspaceSize(
+      handle, features_desc.desc(), output_grad_desc.desc(),
+      indice_pairs_desc.desc(), filters_grad_desc.desc(), indice_num, _inverse,
+      _subM, &filters_workspace_size);
+
+  auto indice_convbpdata_workspace =
+      at::empty(data_workspace_size, features.options().dtype(at::kByte));
+  auto indice_convbpfilter_workspace =
+      at::empty(filters_workspace_size, filters.options().dtype(at::kByte));
+
+  auto features_impl = torch_mlu::getMluTensorImpl(features_contiguous);
+  auto filters_impl = torch_mlu::getMluTensorImpl(filters_contiguous);
+  auto output_grad_impl = torch_mlu::getMluTensorImpl(output_grad_contiguous);
+  auto indice_pairs_impl = torch_mlu::getMluTensorImpl(indice_pairs_contiguous);
+  auto indice_convbpdata_workspace_impl =
+      torch_mlu::getMluTensorImpl(indice_convbpdata_workspace);
+  auto indice_convbpfilter_workspace_impl =
+      torch_mlu::getMluTensorImpl(indice_convbpfilter_workspace);
+
+  auto features_ptr = features_impl->cnnlMalloc();
+  auto filters_ptr = filters_impl->cnnlMalloc();
+  auto output_grad_ptr = output_grad_impl->cnnlMalloc();
+  auto indice_pairs_ptr = indice_pairs_impl->cnnlMalloc();
+  auto indice_convbpdata_workspace_ptr =
+      indice_convbpdata_workspace_impl->cnnlMalloc();
+  auto indice_convbpfilter_workspace_ptr =
+      indice_convbpfilter_workspace_impl->cnnlMalloc();
+
+  // outputs
+  auto input_grad_impl = torch_mlu::getMluTensorImpl(input_grad);
+  auto input_grad_ptr = input_grad_impl->cnnlMalloc();
+  auto filters_grad_impl = torch_mlu::getMluTensorImpl(filters_grad);
+  auto filters_grad_ptr = filters_grad_impl->cnnlMalloc();
+
+  mluOpIndiceConvolutionBackwardData(
+      handle, output_grad_desc.desc(), output_grad_ptr, filters_desc.desc(),
+      filters_ptr, indice_pairs_desc.desc(), indice_pairs_ptr, indice_num,
+      _inverse, _subM, indice_convbpdata_workspace_ptr, data_workspace_size,
+      input_grad_desc.desc(), input_grad_ptr);
+
+  mluOpIndiceConvolutionBackwardFilter(
+      handle, features_desc.desc(), features_ptr, output_grad_desc.desc(),
+      output_grad_ptr, indice_pairs_desc.desc(), indice_pairs_ptr, indice_num,
+      _inverse, _subM, indice_convbpfilter_workspace_ptr,
+      filters_workspace_size, filters_grad_desc.desc(), filters_grad_ptr);
+
+  std::vector<torch::Tensor> result;
+  result.push_back(input_grad);
+  result.push_back(filters_grad);
+  return result;
+}
+
+torch::Tensor indice_conv_forward_mlu(torch::Tensor features,
+                                      torch::Tensor filters,
+                                      torch::Tensor indicePairs,
+                                      torch::Tensor indiceNum,
+                                      int64_t numActOut, int64_t _inverse,
+                                      int64_t _subM) {
+  return IndiceConvForwardMLUKernelLauncher(
+      features, filters, indicePairs, indiceNum, numActOut, _inverse, _subM);
+}
+
+std::vector<torch::Tensor> indice_conv_backward_mlu(
+    torch::Tensor features, torch::Tensor filters, torch::Tensor outGrad,
+    torch::Tensor indicePairs, torch::Tensor indiceNum, int64_t _inverse,
+    int64_t _subM) {
+  return IndiceConvBackwardMLUKernelLauncher(
+      features, filters, outGrad, indicePairs, indiceNum, _inverse, _subM);
+}
+
+torch::Tensor indice_conv_forward_impl(torch::Tensor features,
+                                       torch::Tensor filters,
+                                       torch::Tensor indicePairs,
+                                       torch::Tensor indiceNum,
+                                       int64_t numActOut, int64_t _inverse,
+                                       int64_t _subM);
+
+std::vector<torch::Tensor> indice_conv_backward_impl(
+    torch::Tensor features, torch::Tensor filters, torch::Tensor outGrad,
+    torch::Tensor indicePairs, torch::Tensor indiceNum, int64_t _inverse,
+    int64_t _subM);
+
+REGISTER_DEVICE_IMPL(indice_conv_forward_impl, MLU, indice_conv_forward_mlu);
+REGISTER_DEVICE_IMPL(indice_conv_backward_impl, MLU, indice_conv_backward_mlu);
+
+template std::vector<torch::Tensor> GetIndicePairsForwardMLUKernelLauncher<2>(
+    torch::Tensor indices, int64_t batchSize,
+    std::vector<int64_t> outSpatialShape, std::vector<int64_t> spatialShape,
+    std::vector<int64_t> kernelSize, std::vector<int64_t> stride,
+    std::vector<int64_t> padding, std::vector<int64_t> dilation,
+    std::vector<int64_t> outPadding, int64_t _subM, int64_t _transpose);
+
+template std::vector<torch::Tensor> GetIndicePairsForwardMLUKernelLauncher<3>(
+    torch::Tensor indices, int64_t batchSize,
+    std::vector<int64_t> outSpatialShape, std::vector<int64_t> spatialShape,
+    std::vector<int64_t> kernelSize, std::vector<int64_t> stride,
+    std::vector<int64_t> padding, std::vector<int64_t> dilation,
+    std::vector<int64_t> outPadding, int64_t _subM, int64_t _transpose);
+
+template std::vector<torch::Tensor> GetIndicePairsForwardMLUKernelLauncher<4>(
+    torch::Tensor indices, int64_t batchSize,
+    std::vector<int64_t> outSpatialShape, std::vector<int64_t> spatialShape,
+    std::vector<int64_t> kernelSize, std::vector<int64_t> stride,
+    std::vector<int64_t> padding, std::vector<int64_t> dilation,
+    std::vector<int64_t> outPadding, int64_t _subM, int64_t _transpose);

mmcv/ops/csrc/pytorch/spconv_ops.cpp

@@ -35,6 +35,26 @@ std::vector<torch::Tensor> get_indice_pairs_forward_cuda(
       padding, dilation, outPadding, _subM, _transpose);
 };
 
+template <unsigned NDim>
+std::vector<torch::Tensor> GetIndicePairsForwardMLUKernelLauncher(
+    torch::Tensor indices, int64_t batchSize,
+    std::vector<int64_t> outSpatialShape, std::vector<int64_t> spatialShape,
+    std::vector<int64_t> kernelSize, std::vector<int64_t> stride,
+    std::vector<int64_t> padding, std::vector<int64_t> dilation,
+    std::vector<int64_t> outPadding, int64_t _subM, int64_t _transpose);
+
+template <unsigned NDim>
+std::vector<torch::Tensor> get_indice_pairs_forward_mlu(
+    torch::Tensor indices, int64_t batchSize,
+    std::vector<int64_t> outSpatialShape, std::vector<int64_t> spatialShape,
+    std::vector<int64_t> kernelSize, std::vector<int64_t> stride,
+    std::vector<int64_t> padding, std::vector<int64_t> dilation,
+    std::vector<int64_t> outPadding, int64_t _subM, int64_t _transpose) {
+  return GetIndicePairsForwardMLUKernelLauncher<NDim>(
+      indices, batchSize, outSpatialShape, spatialShape, kernelSize, stride,
+      padding, dilation, outPadding, _subM, _transpose);
+}
+
 template <unsigned NDim>
 std::vector<torch::Tensor> GetIndicePairsBackwardCUDAKernelLauncher(
     torch::Tensor indices, torch::Tensor gridOut, int64_t batchSize,
@@ -71,6 +91,12 @@ std::vector<torch::Tensor> get_indice_pairs_forward(
         padding, dilation, outPadding, _subM, _transpose);
 #else
     AT_ERROR("get_indice_pairs is not compiled with GPU support");
+#endif
+#ifdef MMCV_WITH_MLU
+  } else if (indices.device().type() == at::kMLU) {
+    return get_indice_pairs_forward_mlu<NDim>(
+        indices, batchSize, outSpatialShape, spatialShape, kernelSize, stride,
+        padding, dilation, outPadding, _subM, _transpose);
 #endif
   } else {
     AT_ERROR("get_indice_pairs is not implemented on CPU");

setup.py

@@ -388,7 +388,7 @@ def get_extensions():
                 glob.glob(
                     './mlu-ops/bangc-ops/kernels/**/*.mlu', recursive=True)
             extra_objects = glob.glob(
-                './mlu-ops/bangc-ops/kernels/*/x86_64/*.o')
+                './mlu-ops/bangc-ops/kernels/kernel_wrapper/*.o')
             extension = MLUExtension
             include_dirs.append(os.path.abspath('./mmcv/ops/csrc/common'))
             include_dirs.append(os.path.abspath('./mmcv/ops/csrc/common/mlu'))

tests/test_ops/test_spconv.py

@@ -10,6 +10,8 @@ from mmcv.ops import (SparseConvTensor, SparseInverseConv3d, SparseSequential,
 if torch.__version__ == 'parrots':
     pytest.skip('not supported in parrots now', allow_module_level=True)
 
+from mmcv.utils import IS_CUDA_AVAILABLE, IS_MLU_AVAILABLE
+
 
 def make_sparse_convmodule(in_channels,
                            out_channels,
@@ -76,21 +78,29 @@ def make_sparse_convmodule(in_channels,
     return layers
 
 
-@pytest.mark.skipif(
-    not torch.cuda.is_available(), reason='requires CUDA support')
-def test_make_sparse_convmodule():
+@pytest.mark.parametrize('device', [
+    pytest.param(
+        'cuda',
+        marks=pytest.mark.skipif(
+            not IS_CUDA_AVAILABLE, reason='requires CUDA support')),
+    pytest.param(
+        'mlu',
+        marks=pytest.mark.skipif(
+            not IS_MLU_AVAILABLE, reason='requires MLU support'))
+])
+def test_make_sparse_convmodule(device):
     torch.cuda.empty_cache()
     voxel_features = torch.tensor([[6.56126, 0.9648336, -1.7339306, 0.315],
                                    [6.8162713, -2.480431, -1.3616394, 0.36],
                                    [11.643568, -4.744306, -1.3580885, 0.16],
                                    [23.482342, 6.5036807, 0.5806964, 0.35]],
                                   dtype=torch.float32,
-                                  device='cuda')  # n, point_features
+                                  device=device)  # n, point_features
     coordinates = torch.tensor(
         [[0, 12, 819, 131], [0, 16, 750, 136], [1, 16, 705, 232],
          [1, 35, 930, 469]],
         dtype=torch.int32,
-        device='cuda')  # n, 4(batch, ind_x, ind_y, ind_z)
+        device=device)  # n, 4(batch, ind_x, ind_y, ind_z)
 
     # test
     input_sp_tensor = SparseConvTensor(voxel_features, coordinates,
@@ -105,7 +115,7 @@ def test_make_sparse_convmodule():
         padding=0,
         conv_type='SubMConv3d',
         norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.01),
-        order=('conv', 'norm', 'act')).cuda()
+        order=('conv', 'norm', 'act')).to(device)
     assert isinstance(sparse_block0[0], SubMConv3d)
     assert sparse_block0[0].in_channels == 4
     assert sparse_block0[0].out_channels == 16
@@ -118,16 +128,18 @@ def test_make_sparse_convmodule():
     out_features = sparse_block0(input_sp_tensor)
     assert out_features.features.shape == torch.Size([4, 16])
 
-    sparse_block1 = make_sparse_convmodule(
-        4,
-        16,
-        3,
-        'test1',
-        stride=1,
-        padding=0,
-        conv_type='SparseInverseConv3d',
-        norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.01),
-        order=('norm', 'act', 'conv')).cuda()
-    assert isinstance(sparse_block1[0], torch.nn.BatchNorm1d)
-    assert isinstance(sparse_block1[1], torch.nn.ReLU)
-    assert isinstance(sparse_block1[2], SparseInverseConv3d)
+    # device == mlu: not support inverse==1 yet
+    if device != 'mlu':
+        sparse_block1 = make_sparse_convmodule(
+            4,
+            16,
+            3,
+            'test1',
+            stride=1,
+            padding=0,
+            conv_type='SparseInverseConv3d',
+            norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.01),
+            order=('norm', 'act', 'conv')).to(device)
+        assert isinstance(sparse_block1[2], SparseInverseConv3d)
+        assert isinstance(sparse_block1[0], torch.nn.BatchNorm1d)
+        assert isinstance(sparse_block1[1], torch.nn.ReLU)