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[Feature] Add the implementation of dynamic_scatter with mlu-ops (ope…
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/************************************************************************* | ||
* Copyright (C) 2023 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 "mlu_common_helper.h" | ||
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std::vector<Tensor> dynamic_point_to_voxel_forward_mlu(const Tensor &feats, | ||
const Tensor &coors, | ||
const reduce_t reduce_type) { | ||
// params check | ||
TORCH_CHECK(feats.scalar_type() == at::kFloat, | ||
"feats type should be Float, got ", feats.scalar_type()); | ||
TORCH_CHECK(coors.scalar_type() == at::kInt, | ||
"coors type should be Int32, got ", coors.scalar_type()); | ||
TORCH_CHECK(feats.size(0) == coors.size(0), | ||
"feats.dim(0) and coors.dim(0) should be same, got ", feats.size(0), " vs ", coors.size(0)); | ||
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const int num_input = feats.size(0); | ||
const int num_feats = feats.size(1); | ||
// zero-element check | ||
if (num_input == 0) | ||
return {feats.clone().detach(), coors.clone().detach(), | ||
coors.new_empty({0}, torch::kInt32), | ||
coors.new_empty({0}, torch::kInt32)}; | ||
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auto mlu_reduce_type = getMluOpReduceMode(reduce_type); | ||
auto reduced_feats = at::empty({num_input, num_feats}, feats.options()); | ||
auto out_coors = at::empty({num_input, 3}, coors.options()); | ||
auto coors_map = at::empty({num_input}, coors.options()); | ||
auto reduce_count = at::empty({num_input}, coors.options()); | ||
auto voxel_num = at::empty({1}, coors.options()); | ||
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INITIAL_MLU_PARAM_WITH_TENSOR(feats); | ||
INITIAL_MLU_PARAM_WITH_TENSOR(coors); | ||
INITIAL_MLU_PARAM_WITH_TENSOR(reduced_feats); | ||
INITIAL_MLU_PARAM_WITH_TENSOR(out_coors); | ||
INITIAL_MLU_PARAM_WITH_TENSOR(coors_map); | ||
INITIAL_MLU_PARAM_WITH_TENSOR(reduce_count); | ||
INITIAL_MLU_PARAM_WITH_TENSOR(voxel_num); | ||
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// get compute handle | ||
auto handle = mluOpGetCurrentHandle(); | ||
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size_t workspace_size; | ||
mluOpGetDynamicPointToVoxelForwardWorkspaceSize(handle, | ||
feats_desc.desc(), | ||
coors_desc.desc(), | ||
&workspace_size); | ||
auto workspace_tensor = | ||
at::empty(workspace_size, feats.options().dtype(at::kByte)); | ||
INITIAL_MLU_PARAM_WITH_TENSOR(workspace_tensor); | ||
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// launch kernel | ||
mluOpDynamicPointToVoxelForward(handle, | ||
mlu_reduce_type, | ||
feats_desc.desc(), | ||
feats_ptr, | ||
coors_desc.desc(), | ||
coors_ptr, | ||
workspace_tensor_ptr, | ||
workspace_size, | ||
reduced_feats_desc.desc(), | ||
reduced_feats_ptr, | ||
out_coors_desc.desc(), | ||
out_coors_ptr, | ||
coors_map_desc.desc(), | ||
coors_map_ptr, | ||
reduce_count_desc.desc(), | ||
reduce_count_ptr, | ||
voxel_num_desc.desc(), | ||
voxel_num_ptr); | ||
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int voxel_num_value = *static_cast<int *>(voxel_num.cpu().data_ptr()); | ||
TORCH_CHECK(voxel_num_value <= feats.size(0), | ||
"voxel_num should be less than or equal to feats_num, got ", voxel_num_value, " vs ", feats.size(0)); | ||
return {reduced_feats.slice(0, 0, voxel_num_value), out_coors.slice(0, 0, voxel_num_value), | ||
coors_map, reduce_count.slice(0, 0, voxel_num_value)}; | ||
} | ||
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void dynamic_point_to_voxel_backward_mlu(Tensor &grad_feats, | ||
const Tensor &grad_reduced_feats, | ||
const Tensor &feats, | ||
const Tensor &reduced_feats, | ||
const Tensor &coors_idx, | ||
const Tensor &reduce_count, | ||
const reduce_t reduce_type) { | ||
// params check | ||
TORCH_CHECK(grad_reduced_feats.scalar_type() == at::kFloat, | ||
"grad_reduced_feats type should be Float, got ", grad_reduced_feats.scalar_type()); | ||
TORCH_CHECK(feats.scalar_type() == at::kFloat, | ||
"feats type should be Float, got ", feats.scalar_type()); | ||
TORCH_CHECK(reduced_feats.scalar_type() == at::kFloat, | ||
"reduced_feats type should be Float, got ", reduced_feats.scalar_type()); | ||
TORCH_CHECK(coors_idx.scalar_type() == at::kInt, | ||
"coors_idx type should be Int32, got ", coors_idx.scalar_type()); | ||
TORCH_CHECK(reduce_count.scalar_type() == at::kInt, | ||
"reduce_count type should be Int32, got ", reduce_count.scalar_type()); | ||
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const int num_input = feats.size(0); | ||
const int num_reduced = reduced_feats.size(0); | ||
const int num_feats = feats.size(1); | ||
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grad_feats.fill_(0); | ||
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// zero-element check | ||
if (num_input == 0 || num_reduced == 0) return; | ||
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// TODO(miaochen): remove this after mlu-ops supports other mode of reduce. | ||
TORCH_CHECK(reduce_type == reduce_t::MAX, | ||
"only supports max reduce in current version, got ", to_string(reduce_type)); | ||
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int voxel_num_value = reduced_feats.size(0); | ||
auto opts = torch::TensorOptions().dtype(torch::kInt32); | ||
auto voxel_num = torch::from_blob(&voxel_num_value, {1}, opts).clone().to(at::kMLU); | ||
auto mlu_reduce_type = getMluOpReduceMode(reduce_type); | ||
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INITIAL_MLU_PARAM_WITH_TENSOR(grad_feats); | ||
INITIAL_MLU_PARAM_WITH_TENSOR(grad_reduced_feats); | ||
INITIAL_MLU_PARAM_WITH_TENSOR(feats); | ||
INITIAL_MLU_PARAM_WITH_TENSOR(reduced_feats); | ||
INITIAL_MLU_PARAM_WITH_TENSOR(coors_idx); | ||
INITIAL_MLU_PARAM_WITH_TENSOR(reduce_count); | ||
INITIAL_MLU_PARAM_WITH_TENSOR(voxel_num); | ||
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// get compute handle | ||
auto handle = mluOpGetCurrentHandle(); | ||
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size_t workspace_size; | ||
mluOpGetDynamicPointToVoxelBackwardWorkspaceSize( | ||
handle, mlu_reduce_type, | ||
grad_feats_desc.desc(), | ||
feats_desc.desc(), | ||
grad_reduced_feats_desc.desc(), | ||
coors_idx_desc.desc(), | ||
reduce_count_desc.desc(), | ||
voxel_num_desc.desc(), | ||
&workspace_size); | ||
auto workspace_tensor = | ||
at::empty(workspace_size, feats.options().dtype(at::kByte)); | ||
INITIAL_MLU_PARAM_WITH_TENSOR(workspace_tensor); | ||
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// launch kernel | ||
mluOpDynamicPointToVoxelBackward( | ||
handle, mlu_reduce_type, | ||
grad_reduced_feats_desc.desc(), | ||
grad_reduced_feats_ptr, | ||
feats_desc.desc(), feats_ptr, | ||
reduced_feats_desc.desc(), reduced_feats_ptr, | ||
coors_idx_desc.desc(), coors_idx_ptr, | ||
reduce_count_desc.desc(), reduce_count_ptr, | ||
voxel_num_desc.desc(), voxel_num_ptr, | ||
workspace_tensor_ptr, workspace_size, | ||
grad_feats_desc.desc(), grad_feats_ptr); | ||
} | ||
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std::vector<Tensor> dynamic_point_to_voxel_forward_impl(const Tensor &feats, | ||
const Tensor &coors, | ||
const reduce_t reduce_type); | ||
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void dynamic_point_to_voxel_backward_impl(Tensor &grad_feats, | ||
const Tensor &grad_reduced_feats, | ||
const Tensor &feats, | ||
const Tensor &reduced_feats, | ||
const Tensor &coors_idx, | ||
const Tensor &reduce_count, | ||
const reduce_t reduce_type); | ||
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REGISTER_DEVICE_IMPL(dynamic_point_to_voxel_forward_impl, MLU, | ||
dynamic_point_to_voxel_forward_mlu); | ||
REGISTER_DEVICE_IMPL(dynamic_point_to_voxel_backward_impl, MLU, | ||
dynamic_point_to_voxel_backward_mlu); |
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