haojin2 commented on a change in pull request #15906: [Numpy] Numpy operator diff URL: https://github.com/apache/incubator-mxnet/pull/15906#discussion_r317879913
########## File path: src/operator/numpy/np_diff-inl.h ########## @@ -0,0 +1,215 @@ +/* + * 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. + */ + +/*! + * \file np_diff-inl.h + * \brief Function definition of numpy-compatible diff operator + */ + +#ifndef MXNET_OPERATOR_NUMPY_NP_DIFF_INL_H_ +#define MXNET_OPERATOR_NUMPY_NP_DIFF_INL_H_ + +#include <mxnet/base.h> +#include <mxnet/operator_util.h> +#include <vector> +#include "../mxnet_op.h" +#include "../operator_common.h" +#include "../tensor/broadcast_reduce_op.h" + +namespace mxnet { +namespace op { + +struct DiffParam : public dmlc::Parameter<DiffParam> { + int n, axis; + dmlc::optional<mxnet::TShape> prepend; + dmlc::optional<mxnet::TShape> append; + DMLC_DECLARE_PARAMETER(DiffParam) { + DMLC_DECLARE_FIELD(n).set_default(1).describe( + "The number of times values are differenced." + " If zero, the input is returned as-is."); + DMLC_DECLARE_FIELD(axis).set_default(-1).describe( + "Axis along which the cumulative sum is computed." + " The default (None) is to compute the diff over the flattened array."); + } +}; + +inline void YanghuiTri(std::vector<int>* buffer, int n) { + // apply basic yanghui's triangular to calculate the factors + (*buffer)[0] = 1; + for (int i = 1; i <= n; ++i) { + (*buffer)[i] = 1; + for (int j = i - 1; j > 0; --j) { + (*buffer)[j] += (*buffer)[j - 1]; + } + } +} + +struct diff_forward { + template <typename IType, typename OType, int ndim> + MSHADOW_XINLINE static void Map(int i, int* diffFactor, OType* out, + const IType* in, const int n, + const int stride, + const mshadow::Shape<ndim> oshape, + const mshadow::Shape<ndim> ishape) { + using namespace broadcast; + + // j represent the memory index of the corresponding input entry + int j = ravel(unravel(i, oshape), ishape); + int indicator = 1; + out[i] = 0; + for (int k = n; k >= 0; --k) { + out[i] += in[j + stride * k] * indicator * diffFactor[k]; + indicator *= -1; + } + } +}; + +template <typename xpu> +void DiffForwardImpl(const OpContext& ctx, const TBlob& in, const TBlob& out, + const int n, const int axis) { + using namespace mshadow; + using namespace mxnet_op; + + CHECK_GE(n, 0); + int axis_checked = CheckAxis(axis, in.ndim()); + if (n >= in.shape_[axis_checked]) return; // nothing in the output + int stride = 1; + for (int i = in.ndim() - 1; i > axis_checked; --i) { + stride *= in.shape_[i]; + } + + Stream<xpu>* s = ctx.get_stream<xpu>(); + std::vector<int> buffer(n+1, 0); + YanghuiTri(&buffer, n); + Tensor<xpu, 1, int> diffFactor = + ctx.requested[0].get_space_typed<xpu, 1, int>(Shape1(n + 1), s); + Copy(diffFactor, Tensor<cpu, 1, int>(&buffer[0], Shape1(n + 1), 0), s); + + MSHADOW_TYPE_SWITCH(in.type_flag_, IType, { + MSHADOW_TYPE_SWITCH(out.type_flag_, OType, { + MXNET_NDIM_SWITCH(in.ndim(), ndim, { + Kernel<diff_forward, xpu>::Launch( + s, out.Size(), diffFactor.dptr_, + out.dptr<OType>(), in.dptr<IType>(), + n, stride, out.shape_.get<ndim>(), + in.shape_.get<ndim>()); + }); + }); + }); +} + +template <typename xpu> +void DiffForward(const nnvm::NodeAttrs& attrs, const OpContext& ctx, + const std::vector<TBlob>& inputs, + const std::vector<OpReqType>& req, + const std::vector<TBlob>& outputs) { + using namespace mshadow; + using namespace mxnet_op; + CHECK_EQ(inputs.size(), 1U); + CHECK_EQ(req.size(), 1U); + CHECK_EQ(outputs.size(), 1U); + const DiffParam& param = nnvm::get<DiffParam>(attrs.parsed); + + DiffForwardImpl<xpu>(ctx, inputs[0], outputs[0], param.n, param.axis); +} + +struct diff_backward { + template <typename IType, typename OType, int ndim> + MSHADOW_XINLINE static void Map(int i, int* diffFactor, OType* igrad, + const IType* ograd, const int n, + const int stride, const int axis, + const mshadow::Shape<ndim> oshape, + const mshadow::Shape<ndim> ishape) { + using namespace broadcast; + if (n == 0) { + igrad[i] = ograd[i]; + return; + } + + Shape<ndim> coor = unravel(i, oshape); + // one head thread for a whole sequence along the axis + if (coor[axis] != 0) return; + int j = ravel(coor, ishape); + // initialize the elements of output array + for (int k = 0; k < oshape[axis]; ++k) igrad[i + k * stride] = 0; + for (int k = 0; k < ishape[axis]; ++k) { + int indicator = 1; + for (int m = n; m >= 0; --m) { + igrad[i + (m + k) * stride] += + ograd[j + k * stride] * indicator * diffFactor[m]; + indicator *= -1; + } + } + } +}; + +template <typename xpu> +void DiffBackwardImpl(const OpContext& ctx, const TBlob& ograd, + const TBlob& igrad, const int n, const int axis) { + using namespace mshadow; + using namespace mxnet_op; + + CHECK_GE(n, 0); + int axis_checked = CheckAxis(axis, igrad.ndim()); + if (n >= igrad.shape_[axis_checked]) + return; // nothing in the ograd and igrad Review comment: for all one-line if-statements, do either: ```c++ if (<condition>) { // your code } ``` or ```c++ if (<condition>) // your code ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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