eric-haibin-lin commented on a change in pull request #11024: [MXNET-394]
concat of CSR NDArrays on first dimension
URL: https://github.com/apache/incubator-mxnet/pull/11024#discussion_r194189532
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File path: src/operator/nn/concat-inl.h
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@@ -154,6 +154,92 @@ void ConcatGradCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
});
}
+/*!
+ * \brief concat CSRNDArray on the first dimension.
+ */
+struct concat_csr_first_dim {
+ /*!
+ * \param i the i-th row of the input ndarray
+ * \param out_idx output csr ndarray column indices
+ * \param out_data output csr ndarray data
+ * \param out_indptr output csr ndarray row index pointer
+ * \param in_idx input csr ndarray column indices
+ * \param in_data input csr ndarray data
+ * \param in_indptr input csr ndarray row index pointer
+ * \param indptr_offset offset for ouput ndarray row index pointer
+ * \param idx_offset offset for ouput ndarray column indices
+ */
+ template<typename DType, typename RType, typename IType>
+ MSHADOW_XINLINE static void Map(int i, const OpReqType req,
+ DType* out_data, const DType* in_data,
+ RType* out_indptr, const RType* in_indptr,
+ IType* out_idx, const IType* in_idx,
+ const nnvm::dim_t indptr_offset,
+ const nnvm::dim_t idx_offset) {
+ if (i == 0) out_indptr[0] = 0;
+ out_indptr[i+1+indptr_offset] = in_indptr[i+1] + idx_offset;
+ for (nnvm::dim_t j = in_indptr[i]; j < in_indptr[i+1]; ++j) {
+ KERNEL_ASSIGN(out_idx[j+idx_offset], req, in_idx[j]);
+ KERNEL_ASSIGN(out_data[j+idx_offset], req, in_data[j]);
+ }
+ }
+};
+
+template<typename xpu>
+void ConcatRspImpl(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<NDArray>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<NDArray>& outputs) {
+ using namespace mshadow;
+ using namespace mxnet_op;
+ using namespace csr;
+ const ConcatParam& param = nnvm::get<ConcatParam>(attrs.parsed);
+ int num_args = param.num_args;
+ int concat_dim = param.dim;
+ CHECK_EQ(inputs.size(), num_args);
+ CHECK_EQ(outputs.size(), 1);
+ int axis = CheckAxis(concat_dim, inputs[0].shape().ndim());
+ CHECK_EQ(axis, 0);
+ if (req[0] == kNullOp) return;
+ Stream<xpu>* s = ctx.get_stream<xpu>();
+ nnvm::dim_t nnz = 0;
+ for (int i=0; i < num_args; i++) {
+ nnz += inputs[i].aux_shape(kIdx)[0];
+ }
+ const NDArray& out = outputs[0];
+ const nnvm::dim_t num_rows = out.shape()[0];
+ out.CheckAndAllocAuxData(kIndPtr, Shape1(num_rows+1));
+
+ MSHADOW_IDX_TYPE_SWITCH(inputs[0].aux_type(kIndPtr), RType, {
+ MSHADOW_IDX_TYPE_SWITCH(inputs[0].aux_type(kIdx), IType, {
+ MSHADOW_TYPE_SWITCH(inputs[0].dtype(), DType, {
+ RType* out_indptr = out.aux_data(kIndPtr).dptr<RType>();
+ if (nnz == 0) {
+ out.set_aux_shape(kIdx, Shape1(0));
+ Kernel<set_zero, xpu>::Launch(s, num_rows+1, out_indptr);
+ return;
+ }
+ out.CheckAndAllocAuxData(kIdx, Shape1(nnz));
+ out.CheckAndAllocData(Shape1(nnz));
+ IType* out_idx = out.aux_data(kIdx).dptr<IType>();
+ DType* out_data = out.data().dptr<DType>();
+ nnvm::dim_t indptr_offset = 0;
+ nnvm::dim_t idx_offset = 0;
+ for (const auto& in : inputs) {
+ RType* in_indptr = in.aux_data(kIndPtr).dptr<RType>();
Review comment:
nit: const
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