haojin2 commented on a change in pull request #16865: [numpy]add op insert URL: https://github.com/apache/incubator-mxnet/pull/16865#discussion_r354589044
########## File path: src/operator/numpy/np_insert_op-inl.h ########## @@ -0,0 +1,632 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one + * or more contributor license agreements. See the NOTICE file + * distributed with this work for additional information + * regarding copyright ownership. The ASF licenses this file + * to you under the Apache License, Version 2.0 (the + * "License"); you may not use this file except in compliance + * with the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License is distributed on an + * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + * KIND, either express or implied. See the License for the + * specific language governing permissions and limitations + * under the License. + */ + +/*! + * Copyright (c) 2019 by Contributors + * \file np_insert_op-inl.h + * \brief Function definition of insert operators + */ +#ifndef MXNET_OPERATOR_NUMPY_NP_INSERT_OP_INL_H_ +#define MXNET_OPERATOR_NUMPY_NP_INSERT_OP_INL_H_ + +#include <vector> +#include <memory> +#include "../../common/utils.h" +#include "../tensor/sort_op.h" +#include "../operator_common.h" + +namespace mxnet { +namespace op { + +struct NumpyInsertParam : public dmlc::Parameter<NumpyInsertParam> { + dmlc::optional<int> start; + dmlc::optional<int> stop; + dmlc::optional<int> step; + dmlc::optional<int> int_ind; + dmlc::optional<int> axis; + DMLC_DECLARE_PARAMETER(NumpyInsertParam) { + DMLC_DECLARE_FIELD(start) + .set_default(dmlc::optional<int>()) + .describe("If 'obj' is slice, 'start' is one of it's arguments."); + DMLC_DECLARE_FIELD(stop) + .set_default(dmlc::optional<int>()) + .describe("If 'obj' is slice, 'stop' is one of it's arguments."); + DMLC_DECLARE_FIELD(step) + .set_default(dmlc::optional<int>()) + .describe("If 'obj' is slice, 'step' is one of it's arguments."); + DMLC_DECLARE_FIELD(int_ind) + .set_default(dmlc::optional<int>()) + .describe("If 'obj' is int, 'int_ind' is the index before which" + "'values' is inserted"); + DMLC_DECLARE_FIELD(axis) + .set_default(dmlc::optional<int>()) + .describe("Axis along which to insert `values`."); + } +}; + +namespace insert_ { + // insert 'values' to 'arr' according to 'obj' + enum InsertOpInputs {kArr, kValues, kObj}; + enum InsertOpOutputs {kOut}; +} // namespace insert_ + +template<int req> +struct InsertZeroNdimForward { + /*! + * \brief when axis is not None but arr.mdim == 0, output = values + */ + template<typename DType> + MSHADOW_XINLINE static void Map(int i, DType* out_data, const DType* in_data) { + KERNEL_ASSIGN(out_data[i], req, in_data[i]); + } +}; + +template<int req> +struct InsertSingleIndexForward { + /*! + * \brief insert when obj is 'scaler' or a 'slice' with only one element. + * \tparam xpu - cpu or gpu. + * \param out_data - output: insert 'value' to 'arr' according to 'index'. + * \param in_arr - input: 'arr', original array. + * \param index - input: it's the only element in 'obj' indicats insert position. + * \param in_val - input: 'value', insert to 'arr' according to 'index'. + * \param numnew - extra dim size in 'out_data' compared with 'arr' in 'axis'. + * \param axis - insert 'value' to 'arr' in 'axis'. + * \param ndim - both 'in_arr', 'in_val' and 'out_data' have same ndim before call this. + * \param moveaxis - If 'obj' is a scaler, moveaxis is true; + If 'obj' is a slice with one element, moveaxis is false. + */ + template<typename DType> + MSHADOW_XINLINE static void Map(int i, DType* out_data, + const DType* in_val, const DType* in_arr, + const mshadow::Shape<10> outshape, + const mshadow::Shape<10> valshape, + const int index, const int numnew, + const mshadow::Shape<10> val_stride, + const mshadow::Shape<10> old_val_stride, + const mshadow::Shape<10> arr_stride, + const mshadow::Shape<10> out_stride, + const int ndim, const int axis, + bool moveaxis) { + // i is the global flattened index in the output + mshadow::Shape<10> out_idx; // i -> position in output's shape + for (int j = 0; j < ndim; ++j) { + const int64_t head = i / out_stride[j]; + const int64_t mid = head % outshape[j]; + out_idx[j] = mid; + } + int64_t dest_idx; + if (out_idx[axis] >= index && out_idx[axis] < index + numnew) { // from 'value' + int idx_val = out_idx[axis] - index; + mshadow::Shape<10> val_idx(out_idx); // i -> position in values's shape + val_idx[axis] = idx_val; + for (int j = ndim - 1; j >= 0; --j) { + if (valshape[j] == 1) { // broadcast + val_idx[j] = 0; + } + } + dest_idx = 0; + if (moveaxis) { // moveaxis(values, 0, axis) + for (int j = 0; j < axis; ++j) { + dest_idx += old_val_stride[j + 1] * val_idx[j]; + } + dest_idx += old_val_stride[0] * val_idx[axis]; + for (int j = axis + 1; j < ndim ; ++j) { + dest_idx += old_val_stride[j] *val_idx[j]; + } + } else { + for (int j =0; j < ndim; ++j) { + dest_idx += val_stride[j] * val_idx[j]; + } + } + KERNEL_ASSIGN(out_data[i], req, in_val[dest_idx]); + } else { // from 'arr' + int idx_arr = (out_idx[axis] < index) ? + out_idx[axis] : out_idx[axis] - numnew; + mshadow::Shape<10> arr_idx(out_idx); // i -> position in arr's shape + arr_idx[axis] = idx_arr; + dest_idx = 0; + for (int j =0; j < ndim; ++j) { + dest_idx += arr_stride[j] * arr_idx[j]; + } + KERNEL_ASSIGN(out_data[i], req, in_arr[dest_idx]); + } + } + /*! + * \brief insert when obj is 'tensor' with only one element. + * \tparam xpu - cpu or gpu. + * \param out_data - output: insert 'value' to 'arr' according to 'index'. + * \param in_arr - input: 'arr', original array. + * \param in_obj - input: It indicats insert position, ndim may equals to 0. + * \param in_val - input: 'value', insert to 'arr' according to 'index'. + * \param N - arr.shape_[axis] + * \param numnew - extra dim size in 'out_data' compared with 'arr' in 'axis'. + * \param axis - insert 'value' to 'arr' in 'axis'. + * \param ndim - both 'in_arr', 'in_val' and 'out_data' have same ndim before call this. + * \param moveaxis - If 'obj' is a tensor with ndim == 0, regard it as a scaler and moveaxis is true; + If 'obj' is a tensor with ndim > 0 but has only one element, moveaxis is false. + */ + template<typename DType, typename IType> + MSHADOW_XINLINE static void Map(int i, DType* out_data, + const DType* in_val, const DType* in_arr, + const mshadow::Shape<10> outshape, + const mshadow::Shape<10> valshape, + const int N, const IType* in_obj, const int numnew, + const mshadow::Shape<10> val_stride, + const mshadow::Shape<10> old_val_stride, + const mshadow::Shape<10> arr_stride, + const mshadow::Shape<10> out_stride, + const int ndim, const int axis, + bool moveaxis) { + // i is the global flattened index in the output + mshadow::Shape<10> out_idx; // i -> position in output's shape + for (int j = 0; j < ndim; ++j) { + const int64_t head = i / out_stride[j]; + const int64_t mid = head % outshape[j]; + out_idx[j] = mid; + } + int64_t dest_idx; + IType index = in_obj[0]; + if (static_cast<int64_t>(index) < 0) { + index += static_cast<IType>(N); + } + if (out_idx[axis] >= index && out_idx[axis] < index + numnew) { // from 'value' + int idx_val = out_idx[axis] - index; + mshadow::Shape<10> val_idx(out_idx); // i -> position in values's shape + val_idx[axis] = idx_val; + for (int j = ndim - 1; j >= 0; --j) { + if (valshape[j] == 1) { // broadcast + val_idx[j] = 0; + } + } + dest_idx = 0; + if (moveaxis) { // moveaxis(values, 0, axis) + for (int j = 0; j < axis; ++j) { + dest_idx += old_val_stride[j + 1] * val_idx[j]; + } + dest_idx += old_val_stride[0] * val_idx[axis]; + for (int j = axis + 1; j < ndim ; ++j) { + dest_idx += old_val_stride[j] *val_idx[j]; + } + } else { + for (int j =0; j < ndim; ++j) { + dest_idx += val_stride[j] * val_idx[j]; + } + } + KERNEL_ASSIGN(out_data[i], req, in_val[dest_idx]); + } else { // from 'arr' + int idx_arr = (out_idx[axis] < index) ? out_idx[axis] : out_idx[axis] - numnew; + mshadow::Shape<10> arr_idx(out_idx); // i -> position in arr's shape + arr_idx[axis] = idx_arr; + dest_idx = 0; + for (int j =0; j < ndim; ++j) { + dest_idx += arr_stride[j] * arr_idx[j]; + } + KERNEL_ASSIGN(out_data[i], req, in_arr[dest_idx]); + } + } +}; + +template<int ndim> +inline mshadow::Shape<ndim> GetStride(const mxnet::TShape& shape) { + mshadow::Shape<ndim>stride; + size_t tmp = 1; + for (int i = shape.ndim() - 1; i >= 0; --i) { + stride[i] = tmp; + tmp *= shape[i]; + } + return stride; +} + +template<int ndim> +inline mshadow::Shape<ndim> GetKernelShape(const mxnet::TShape& shape) { + mshadow::Shape<ndim>k_shape; + for (int i = 0 ; i < shape.ndim() ; ++i) { + k_shape[i] = shape[i]; + } + return k_shape; +} + +template<int req> +struct InsertSeqForward { + /*! + * \brief insert when obj is 'tensor' or 'slice' with more than one element. + * \tparam xpu - cpu or gpu. + * \param out_data - output: insert 'value' to 'arr' according to 'index'. + * \param in_arr - input: 'arr', original array. + * \param in_obj - input: It indicats insert position, ndim may equals to 0. + * \param in_val - input: 'value', insert to 'arr' according to 'index'. + * \param is_insert - if is_insert[out_idx[axis]] is true, it's from 'values', else from 'arr'. + * \param origin_idx - indicate the original position in 'arr' or 'values' in 'axis'. + * \param axis - insert 'value' to 'arr' in 'axis'. + * \param ndim - both 'in_arr', 'in_val' and 'out_data' have same ndim before call this. + */ + template<typename DType> + MSHADOW_XINLINE static void Map(int i, DType* out_data, + const DType* in_val, const DType* in_arr, + const mshadow::Shape<10> outshape, + const mshadow::Shape<10> valshape, + const int* is_insert, + const int* origin_idx, + const mshadow::Shape<10> val_stride, + const mshadow::Shape<10> arr_stride, + const mshadow::Shape<10> out_stride, + const int ndim, const int axis) { + // i is the global flattened index in the output + mshadow::Shape<10> out_idx; // i -> position in output's shape + for (int j = 0; j < ndim; ++j) { + const int64_t head = i / out_stride[j]; + const int64_t mid = head % outshape[j]; + out_idx[j] = mid; + } + int64_t dest_idx; + if (is_insert[out_idx[axis]]) { // from 'values' + int idx_val = origin_idx[out_idx[axis]]; + mshadow::Shape<10> insert_idx(out_idx); // i -> position in insert's shape + insert_idx[axis] = idx_val; + mshadow::Shape<10> val_idx(insert_idx); // i -> position in values's shape + for (int j = ndim - 1; j >= 0; --j) { // broadcast + if (valshape[j] == 1) { + val_idx[j] = 0; + } + } + dest_idx = 0; + for (int j =0; j < ndim; ++j) { + dest_idx += val_stride[j] * val_idx[j]; + } + KERNEL_ASSIGN(out_data[i], req, in_val[dest_idx]); + } else { // from 'arr' + int idx_arr = origin_idx[out_idx[axis]]; + mshadow::Shape<10> arr_idx(out_idx); // i -> position in arr's shape + arr_idx[axis] = idx_arr; + dest_idx = 0; + for (int j =0; j < ndim; ++j) { + dest_idx += arr_stride[j] * arr_idx[j]; + } + out_data[i] = in_arr[dest_idx]; + KERNEL_ASSIGN(out_data[i], req, in_arr[dest_idx]); + } + } +}; + +struct SliceToIndices { + template<typename IType> + MSHADOW_XINLINE static void Map(int i, IType* indices, int N, + int start, int step) { + indices[i] = start + i * step; + if (static_cast<int64_t>(indices[i]) < 0) { + indices[i] += static_cast<IType>(N); + } + } +}; + +struct ObjToIndices { + template<typename IType> + MSHADOW_XINLINE static void Map(int i, IType* indices, + int N, const IType* obj) { + indices[i] = obj[i]; + if (static_cast<int64_t>(indices[i]) < 0) { + indices[i] += static_cast<IType>(N); + } + } +}; + +struct AssignId { + MSHADOW_XINLINE static void Map(int i, int* order) { + order[i] = i; + } +}; + +struct IndicesModify { + template<typename IType> + MSHADOW_XINLINE static void Map(int i, IType* indices, const int* order) { + indices[order[i]] += i; + } +}; + +struct AssignInsertZero { Review comment: just use existing `set_zero` kernel ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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