haojin2 commented on a change in pull request #15258: Numpy Trace URL: https://github.com/apache/incubator-mxnet/pull/15258#discussion_r296149614
########## File path: src/operator/numpy/np_trace_op-inl.h ########## @@ -0,0 +1,258 @@ +/* + * 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_trace_op-inl.h + * \brief Function definition of matrix numpy-compatible trace operator + */ + +#ifndef MXNET_OPERATOR_NUMPY_NP_TRACE_OP_INL_H_ +#define MXNET_OPERATOR_NUMPY_NP_TRACE_OP_INL_H_ + +#include <dmlc/parameter.h> +#include <mxnet/operator_util.h> +#include <vector> +#include <utility> +#include <algorithm> +#include "../mxnet_op.h" +#include "../operator_common.h" +#include "../elemwise_op_common.h" +#include "../tensor/broadcast_reduce_op.h" + +namespace mxnet { +namespace op { + +struct NumpyTraceParam: public dmlc::Parameter<NumpyTraceParam> { + int offset, axis1, axis2; + DMLC_DECLARE_PARAMETER(NumpyTraceParam) { + DMLC_DECLARE_FIELD(offset) + .set_default(0) + .describe("Offset of the diagonal from the main diagonal. " + "Can be both positive and negative. Defaults to 0."); + DMLC_DECLARE_FIELD(axis1) + .set_default(0) + .describe("Axes to be used as the first axis of the 2-D sub-arrays " + "from which the diagonals should be taken. Defaults to 0."); + DMLC_DECLARE_FIELD(axis2) + .set_default(1) + .describe("Axes to be used as the second axis of the 2-D sub-arrays " + "from which the diagonals should be taken. Defaults to 1."); + } +}; + +template<int ndim, int req, bool back> +struct numpy_trace { + template<typename DType> + MSHADOW_XINLINE static void Map(index_t i, DType* out, const DType* a, + mshadow::Shape<ndim> oshape, + mshadow::Shape<ndim> ishape, + index_t stride, index_t offset, int dlength) { + using namespace mxnet_op; + using namespace mshadow; + index_t j = ravel(unravel(i, oshape), ishape) + offset; + if (back) { + for (index_t k = 0; k < dlength; ++k) { + KERNEL_ASSIGN(out[j], req, a[i]); + j += stride; + } + } else { + if (req == kWriteTo) { + out[i] = 0; + for (index_t k = 0; k < dlength; ++k) { + out[i] += a[j]; + j += stride; + } + } else if (req == kAddTo) { + for (index_t k = 0; k < dlength; ++k) { + out[i] += a[j]; + j += stride; + } + } + } + } +}; + +template<typename xpu, bool back> +void NumpyTraceOpProcess(const TBlob& in_data, + const TBlob& out_data, + const mxnet::TShape& ishape, + const mxnet::TShape& oshape, + index_t dsize, + const NumpyTraceParam& param, + mxnet_op::Stream<xpu> *s, + const std::vector<OpReqType>& req) { + using namespace mxnet_op; + using namespace mshadow; + if (dsize == 0) { + if (back) { + if (out_data.Size() != 0) { + MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, { + MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, { + if (req_type == kWriteTo) { + out_data.FlatTo1D<xpu, DType>(s) = 0; + } + }); + }); + } + } + return; + } else if (ishape.Size() == 0) { + if (!back) { + MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, { + MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, { + if (req_type == kWriteTo) { + out_data.FlatTo1D<xpu, DType>(s) = 0; + } + }); + }); + } + return; + } + uint32_t x1 = CheckAxis(param.axis1, ishape.ndim()); + uint32_t x2 = CheckAxis(param.axis2, ishape.ndim()); + + uint32_t idim = ishape.ndim(); + + uint32_t minx = x1, maxx = x2; + if (minx > maxx) { + std::swap(minx, maxx); + } + + // merges contiguous axes that are not separated + // by axis1 or axis2 since they can be directly + // mapped to the output and there is no need + // to distinguish them + // (After this the input will have no more than + // three axes, hence improving the rave and + // unravel efficiency) + + index_t oleading = 1, + obody = 1, + otrailing = 1; + + for (uint32_t i = 0; i < minx; ++i) { + oleading *= ishape[i]; + } + for (uint32_t i = minx + 1; i < maxx; ++i) { + obody *= ishape[i]; + } + for (uint32_t i = maxx + 1; i < idim; ++i) { + otrailing *= ishape[i]; + } + + index_t ileading = oleading, + ibody = obody * ishape[minx], + itrailing = otrailing * ishape[maxx]; + + index_t stride1 = itrailing * obody, + stride2 = otrailing; + // stride1 + stride2 is the stride for + // iterating over the diagonal in question + + if (x1 == maxx) { + std::swap(stride1, stride2); + } + + // the extra index offset introduced by offset + index_t offset; + if (param.offset > 0) { + offset = stride2 * param.offset; + } else if (param.offset < 0) { + offset = stride1 * -param.offset; + } else { + offset = 0; + } + + // number of elements in the offset diagonal + // may be negative + int dlength; + if (param.offset > 0) { + dlength = std::min(ishape[x1], ishape[x2] - param.offset); + } else if (param.offset < 0) { + dlength = std::min(ishape[x1] - (-param.offset), ishape[x2]); + } else { + dlength = std::min(ishape[x1], ishape[x2]); + } + + MSHADOW_TYPE_SWITCH(out_data.type_flag_, DType, { + MXNET_ASSIGN_REQ_SWITCH(req[0], req_type, { + if (back) { + out_data.FlatTo1D<xpu, DType>(s) = 0; + } + Kernel<numpy_trace<3, req_type, back>, xpu>::Launch(s, dsize, out_data.dptr<DType>(), + in_data.dptr<DType>(), + Shape3(oleading, obody, otrailing), + Shape3(ileading, ibody, itrailing), + stride1 + stride2, offset, dlength); + }); + }); +} + + +template<typename xpu> +void NumpyTraceOpForward(const nnvm::NodeAttrs& attrs, + const OpContext& ctx, + const std::vector<TBlob>& inputs, + const std::vector<OpReqType>& req, + const std::vector<TBlob>& outputs) { + using namespace mxnet_op; + using namespace mshadow; + CHECK_EQ(inputs.size(), 1U); + CHECK_EQ(outputs.size(), 1U); + CHECK_EQ(req.size(), 1U); + mshadow::Stream<xpu> *s = ctx.get_stream<xpu>(); + const TBlob& in_data = inputs[0]; + const TBlob& out_data = outputs[0]; + const mxnet::TShape& ishape = inputs[0].shape_; + const mxnet::TShape& oshape = outputs[0].shape_; + const NumpyTraceParam& param = nnvm::get<NumpyTraceParam>(attrs.parsed); + + NumpyTraceOpProcess<xpu, false>(in_data, out_data, ishape, oshape, + out_data.Size(), param, s, req); +} + Review comment: nit: Remove extra blank line here. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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