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@ -26,6 +26,42 @@ namespace operators {
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using Tensor = framework::Tensor;
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using platform::PADDLE_CUDA_NUM_THREADS;
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__global__ void ComputeTargetIdx(const int64_t* in_dims, int dims_size,
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int axis, int64_t n, int64_t* trg_idx,
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int64_t* med_ids) {
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int64_t index = threadIdx.x + blockDim.x * blockIdx.x;
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if (index < n) {
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int64_t* shape_out_axis = new int64_t[dims_size - 1];
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int64_t* dims_out_axis = new int64_t[dims_size - 1];
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int64_t tmp = index;
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int64_t pos_in_axis = 0;
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int64_t i = dims_size - 2;
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int64_t dim_axis = 0;
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for (int64_t j = dims_size - 1; j >= 0; --j) {
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int64_t dim = in_dims[j];
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if (j != axis) {
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shape_out_axis[i] = tmp % dim;
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dims_out_axis[i] = dim;
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i--;
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} else {
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dim_axis = dim;
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pos_in_axis = tmp % dim_axis;
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}
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tmp /= dim;
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}
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int64_t group = (dims_size > 1) ? shape_out_axis[0] : 0;
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for (int64_t j = 0; j < dims_size - 2; ++j) {
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group = group * dims_out_axis[j + 1] + shape_out_axis[j + 1];
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}
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int64_t traget_idx = group * dim_axis + pos_in_axis;
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trg_idx[index] = traget_idx;
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med_ids[traget_idx] = pos_in_axis;
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delete[] shape_out_axis;
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delete[] dims_out_axis;
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}
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}
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template <typename T>
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__global__ void PermuteInData(const T* in, const int64_t* trg_idx, int64_t n,
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T* med_out) {
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@ -76,50 +112,27 @@ class ArgsortOpCUDAKernel : public framework::OpKernel<T> {
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int64_t numel = input->numel();
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int64_t groups = numel / in_dims[axis];
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// Mediate tensor for sorting
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Tensor mediate_output;
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std::vector<int64_t> in_dims_vec = vectorize(in_dims);
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thrust::device_vector<int64_t> in_dims_dev(in_dims_vec.begin(),
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in_dims_vec.end());
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int64_t* in_dims_data = thrust::raw_pointer_cast(in_dims_dev.data());
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// Mediate tensor for sorting data and indices
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Tensor mediate_output, mediate_indices;
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T* med_out_data =
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mediate_output.mutable_data<T>(input->dims(), ctx.GetPlace());
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// The target index of each elemement in mediate tensor
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std::vector<int64_t> target_idx(numel, 0);
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// To record the index along the given axis for the data in mediate tensor
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std::vector<int64_t> mediate_indices(numel, 0);
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std::vector<int64_t> in_dims_out_axis = vectorize(in_dims);
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in_dims_out_axis.erase(in_dims_out_axis.begin() + axis);
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for (int64_t index = 0; index < numel; ++index) {
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int64_t tmp = index;
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int64_t pos_in_axis = 0;
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std::vector<int64_t> shape;
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for (int64_t j = in_dims.size() - 1; j >= 0; --j) {
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if (j != axis) {
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shape.push_back(tmp % in_dims[j]);
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} else {
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pos_in_axis = tmp % in_dims[j];
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}
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tmp /= in_dims[j];
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}
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std::reverse(shape.begin(), shape.end());
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int64_t group = (shape.size() > 0) ? shape[0] : 0;
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for (size_t j = 0; j < shape.size() - 1; ++j) {
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group = group * in_dims_out_axis[j + 1] + shape[j + 1];
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}
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target_idx[index] = group * in_dims[axis] + pos_in_axis;
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mediate_indices[target_idx[index]] = pos_in_axis;
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}
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thrust::device_vector<int64_t> med_ids_dev(mediate_indices.begin(),
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mediate_indices.end());
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int64_t* med_ids_data = thrust::raw_pointer_cast(med_ids_dev.data());
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thrust::device_vector<int64_t> trg_idx_dev(target_idx.begin(),
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target_idx.end());
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int64_t* trg_idx = thrust::raw_pointer_cast(trg_idx_dev.data());
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int64_t* med_ids_data =
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mediate_indices.mutable_data<int64_t>(in_dims, ctx.GetPlace());
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// Target index of each element along the given axis in the mediate tensors
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Tensor trg_idx_t;
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int64_t* trg_idx = trg_idx_t.mutable_data<int64_t>(in_dims, ctx.GetPlace());
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auto stream = reinterpret_cast<const platform::CUDADeviceContext&>(
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ctx.device_context())
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.stream();
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auto num_threads = PADDLE_CUDA_NUM_THREADS;
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int num_threads = PADDLE_CUDA_NUM_THREADS;
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ComputeTargetIdx<<<(numel - 1) / num_threads + 1, num_threads, 0, stream>>>(
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in_dims_data, in_dims.size(), axis, numel, trg_idx, med_ids_data);
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PermuteInData<<<(numel - 1) / num_threads + 1, num_threads, 0, stream>>>(
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in_data, trg_idx, numel, med_out_data);
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