D-Roberts commented on a change in pull request #17505: [numpy]Add numpy.random.weibull operator URL: https://github.com/apache/incubator-mxnet/pull/17505#discussion_r373843648
########## File path: src/operator/numpy/random/np_weibull_op.h ########## @@ -0,0 +1,146 @@ +/* + * 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_weibull_op.h + * \brief Operator for numpy sampling from weibull distribution. + */ + +#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_WEIBULL_OP_H_ +#define MXNET_OPERATOR_NUMPY_RANDOM_NP_WEIBULL_OP_H_ + +#include <mxnet/operator_util.h> +#include <algorithm> +#include <string> +#include <vector> +#include <cmath> +#include "../../elemwise_op_common.h" +#include "../../mshadow_op.h" +#include "../../mxnet_op.h" +#include "../../operator_common.h" +#include "../../tensor/elemwise_binary_broadcast_op.h" +#include "./dist_common.h" + +namespace mxnet { +namespace op { + +struct NumpyWeibullParam : public dmlc::Parameter<NumpyWeibullParam> { + dmlc::optional<float> a; + dmlc::optional<mxnet::Tuple<int>> size; + DMLC_DECLARE_PARAMETER(NumpyWeibullParam) { + DMLC_DECLARE_FIELD(a) + .set_default(dmlc::optional<float>()); + DMLC_DECLARE_FIELD(size) + .set_default(dmlc::optional<mxnet::Tuple<int>>()) + .describe("Output shape. If the given shape is, " + "e.g., (m, n, k), then m * n * k samples are drawn. " + "Default is None, in which case a single value is returned."); + } +}; + +template <typename DType> +struct scalar_weibull_kernel { + MSHADOW_XINLINE static void Map(index_t i, float a, float *threshold, + DType *out) { + out[i] = powf(threshold[i], DType(1.0/a)); + } +}; + +namespace mxnet_op { + +template <typename IType> +struct check_legal_a_kernel { + MSHADOW_XINLINE static void Map(index_t i, IType *a, float* flag) { + if (a[i] < 0.0) { + flag[0] = -1.0; + } + } +}; + + +template <int ndim, typename IType, typename OType> +struct weibull_kernel { + MSHADOW_XINLINE static void Map(index_t i, + const Shape<ndim> &stride, + const Shape<ndim> &oshape, + IType *aparams, float* threshold, OType *out) { + Shape<ndim> coord = unravel(i, oshape); + auto idx = static_cast<index_t>(dot(coord, stride)); + out[i] = powf(threshold[i], IType(1.0/aparams[idx])); + } +}; + +} // namespace mxnet_op + +template <typename xpu> +void NumpyWeibullForward(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; + const NumpyWeibullParam ¶m = nnvm::get<NumpyWeibullParam>(attrs.parsed); + Stream<xpu> *s = ctx.get_stream<xpu>(); + index_t output_len = outputs[0].Size(); + Random<xpu, float> *prnd = ctx.requested[0].get_random<xpu, float>(s); + Tensor<xpu, 1, float> workspace = + ctx.requested[1].get_space_typed<xpu, 1, float>(Shape1(output_len + 1), s); + Tensor<xpu, 1, float> exponential_tensor = workspace.Slice(0, output_len); + Tensor<xpu, 1, float> indicator_device = workspace.Slice(output_len, output_len + 1); + float indicator_host = 1.0; + float *indicator_device_ptr = indicator_device.dptr_; + Kernel<set_zero, xpu>::Launch(s, 1, indicator_device_ptr); + prnd->SampleExponential(&workspace, 1.0); Review comment: @xidulu Yes that was my plan :) ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
