haojin2 commented on a change in pull request #16152: [Numpy] Random.gamma() implemented URL: https://github.com/apache/incubator-mxnet/pull/16152#discussion_r364402159
########## File path: src/operator/numpy/random/np_gamma_op.h ########## @@ -0,0 +1,422 @@ +/* + * 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_gamma_op.h + * \brief Operator for random sampling from gamma distribution + */ + +#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_GAMMA_OP_H_ +#define MXNET_OPERATOR_NUMPY_RANDOM_NP_GAMMA_OP_H_ + +#include <mxnet/operator_util.h> +#include <mshadow/base.h> +#include <vector> +#include <string> +#include <algorithm> +#include "./dist_common.h" +#include "../../elemwise_op_common.h" +#include "../../tensor/elemwise_binary_broadcast_op.h" +#include "../../mshadow_op.h" +#include "../../mxnet_op.h" +#include "../../operator_common.h" + +#define M 2 + +namespace mxnet { +namespace op { + +struct NumpyGammaParam : public dmlc::Parameter<NumpyGammaParam> { + dmlc::optional<float> shape; + dmlc::optional<float> scale; + std::string ctx; + int dtype; + dmlc::optional<mxnet::Tuple<int>> size; + DMLC_DECLARE_PARAMETER(NumpyGammaParam) { + DMLC_DECLARE_FIELD(shape); + DMLC_DECLARE_FIELD(scale); + 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."); + DMLC_DECLARE_FIELD(ctx) + .set_default("xpu") + .describe("Context of output, in format [xpu|xpu|xpu_pinned](n)." + " Only used for imperative calls."); + DMLC_DECLARE_FIELD(dtype) + .add_enum("float32", mshadow::kFloat32) + .add_enum("float64", mshadow::kFloat64) + .add_enum("float16", mshadow::kFloat16) + .set_default(mshadow::kFloat32) + .describe("DType of the output in case this can't be inferred. " + "Defaults to float32 if not defined (dtype=None)."); + } +}; + + +namespace mxnet_op { + +template <typename IType, typename FType> +MSHADOW_XINLINE void GammaTransform(IType a, IType b, + FType* uniforms, FType* normals) { + // start + FType d = a < 1 ? a + 2.0 / 3.0 : a - 1.0 / 3.0; + FType k = sqrt(9.0 * d); + FType c = 1.0 / k; + for (size_t i = 0; i < M - 1; i++) { + FType u = uniforms[i]; + FType n = normals[i]; + uniforms[i] = FType(-1); + FType ocn = 1+c*n; + FType v = ocn*ocn*ocn; + if (v > 0) { + if (u <= (1 - 0.0331 * (n * n) * (n * n))) { + // rejection sample. The second operation should be + // performed with low probability. This is the "squeeze" + uniforms[i] = FType(d * v * b); + } + if (logf(u) < 0.5 * (n * n) + d * (1 - v + logf(v))) { + // rejection sample. The second operation should be + // performed with low probability. This is the "squeeze" + uniforms[i] = FType(d * v * b); + } + } + } +} + + +template <typename IType, typename FType> +MSHADOW_XINLINE FType GammaReduce(IType a, FType* uniforms) { + FType u2 = uniforms[M - 1]; + for (size_t i = 0; i < M - 1; i++) { + FType sample = uniforms[i]; + if (sample > 0) { + return a < 1 ? sample * powf(u2, FType(1.0 / a)) : sample; + } + } + return -1; +} + +template<typename OType, typename FType> +struct CheckSuccessKernel { + MSHADOW_XINLINE static void Map(int i, OType* out, FType* flag) { + if (out[i] < 0) { + flag[0] = -1.0; + } + } +}; + +template <int ndim, typename IType, typename OType, typename FType> +struct gamma_kernel { + MSHADOW_XINLINE static void Map(index_t i, + const Shape <ndim> &lstride, const Shape <ndim> &hstride, + const Shape <ndim> &oshape, + IType *shape, IType *scale, + FType *uniforms, FType *normals, + OType *out) { + Shape<ndim> coord = unravel(i, oshape); + auto lidx = static_cast<index_t>(dot(coord, lstride)); + auto hidx = static_cast<index_t>(dot(coord, hstride)); + IType shape_value = shape[lidx]; + IType scale_value = scale[hidx]; + // map phase + GammaTransform<IType, FType>(shape_value, scale_value, + uniforms + i * M, normals + i * M); + // reduce phase + OType sample = (OType)GammaReduce<IType, FType>(shape_value, uniforms + i * M); + out[i] = sample; + } +}; + +template <int ndim, typename IType, typename OType, typename FType> +struct gamma_kernel_r { + MSHADOW_XINLINE static void Map(index_t i, + const Shape <ndim> &lstride, const Shape <ndim> &hstride, + const Shape <ndim> &oshape, + IType *shape, IType *scale, + FType *uniforms, FType *normals, + OType *out, FType* flag) { + flag[0] = 1; + Shape<ndim> coord = unravel(i, oshape); + auto lidx = static_cast<index_t>(dot(coord, lstride)); + auto hidx = static_cast<index_t>(dot(coord, hstride)); + IType shape_value = shape[lidx]; + IType scale_value = scale[hidx]; + if (out[i] < 0) { + // map phase + GammaTransform<IType, FType>(shape_value, scale_value, + uniforms + i * M, normals + i * M); + // reduce phase + OType sample = (OType)GammaReduce<IType, FType>(shape_value, uniforms + i * M); + out[i] = sample; + } + } +}; + +template <int ndim, typename IType, typename OType, typename FType> +struct gamma_one_scalar_kernel { + MSHADOW_XINLINE static void Map(index_t i, int scalar_pos, + const Shape <ndim> &stride, + const Shape <ndim> &oshape, + IType *array, float scalar, + FType *uniforms, FType *normals, + OType *out) { + Shape<ndim> coord = unravel(i, oshape); + auto idx = static_cast<index_t>(dot(coord, stride)); + IType shape_value; + IType scale_value; + if (scalar_pos == 0) { + shape_value = scalar; + scale_value = array[idx]; + } else { + shape_value = array[idx]; + scale_value = scalar; + } + // map phase + GammaTransform<IType, FType>(shape_value, scale_value, + uniforms + i * M, normals + i * M); + // reduce phase + OType sample = (OType)GammaReduce<IType, FType>(shape_value, uniforms + i * M); + out[i] = sample; + } +}; + +template <int ndim, typename IType, typename OType, typename FType> +struct gamma_one_scalar_kernel_r { + MSHADOW_XINLINE static void Map(index_t i, int scalar_pos, + const Shape <ndim> &stride, + const Shape <ndim> &oshape, + IType *array, float scalar, + FType *uniforms, FType *normals, + OType *out, FType *flag) { + flag[0] = 1; + Shape<ndim> coord = unravel(i, oshape); + auto idx = static_cast<index_t>(dot(coord, stride)); + IType shape_value; + IType scale_value; + if (scalar_pos == 0) { + shape_value = scalar; + scale_value = array[idx]; + } else { + shape_value = array[idx]; + scale_value = scalar; + } + if (out[i] < 0) { + // map phase + GammaTransform<IType, FType>(shape_value, scale_value, + uniforms + i * M, normals + i * M); + // reduce phase + OType sample = (OType)GammaReduce<IType, FType>(shape_value, uniforms + i * M); + out[i] = sample; + } + } +}; + +template <typename OType, typename FType> +struct gamma_two_scalar_kernel { + MSHADOW_XINLINE static void Map(index_t i, float shape_value, + float scale_value, FType *uniforms_origin, + FType *normals_origin, OType *out) { + // map phase + FType *uniforms = uniforms_origin + i * M; + FType *normals = normals_origin + i * M; + GammaTransform<float, FType>(shape_value, scale_value, uniforms, + normals); + // reduce phase + OType sample = + (OType)GammaReduce<float, FType>(shape_value, uniforms); + out[i] = sample; + } +}; + +template <typename OType, typename FType> +struct gamma_two_scalar_kernel_r { + MSHADOW_XINLINE static void Map(index_t i, float shape_value, + float scale_value, + FType *uniforms_origin, + FType *normals_origin, OType *out, FType* flag) { + flag[0] = 1; + // map phase + FType *uniforms = uniforms_origin + i * M; + FType *normals = normals_origin + i * M; + if (out[i] < 0) { + GammaTransform<float, FType>(shape_value, scale_value, uniforms, + normals); + // reduce phase + OType sample = + (OType)GammaReduce<float, FType>(shape_value, uniforms); + out[i] = sample; + } + } +}; +} // namespace mxnet_op + +template <typename xpu, typename FType> +void NumpyGammaForward(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 NumpyGammaParam ¶m = nnvm::get<NumpyGammaParam>(attrs.parsed); + CHECK_EQ(outputs.size(), 1); + Stream<xpu> *s = ctx.get_stream<xpu>(); + // Generate base random number. + Random<xpu, FType> *prnd = ctx.requested[0].get_random<xpu, FType>(s); + index_t output_len = outputs[0].Size(); + Tensor<xpu, 1, FType> random_tensor = + ctx.requested[1].get_space_typed<xpu, 1, FType>(Shape1(output_len * 2 * M + 1), s); + Tensor<xpu, 1, FType> uniform_tensor = random_tensor.Slice(0, output_len * M); + Tensor<xpu, 1, FType> normal_tensor = random_tensor.Slice(output_len * M, output_len * 2 * M); + prnd->SampleUniform(&uniform_tensor, 0, 1); + prnd->SampleGaussian(&normal_tensor, 0, 1); + mxnet::TShape new_lshape, new_hshape, new_oshape; + FType failure_indicator = 1.0; + Tensor<xpu, 1, FType> failure_indic_workspace = + random_tensor.Slice(output_len * 2 * M, output_len * 2 * M + 1); + FType *failure_indicator_device = failure_indic_workspace.dptr_; + // [scalar scalar] case + if (inputs.size() == 0U) { + MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, { + Kernel<gamma_two_scalar_kernel<OType, FType>, xpu>::Launch( + s, outputs[0].Size(), param.shape.value(), param.scale.value(), + uniform_tensor.dptr_, normal_tensor.dptr_, outputs[0].dptr<OType>()); + Kernel<CheckSuccessKernel<OType, FType>, xpu>::Launch( + s, outputs[0].Size(), outputs[0].dptr<OType>(), + failure_indicator_device); + _copy<xpu>(s, &failure_indicator, failure_indicator_device); + while (1) { + if (failure_indicator >= 0) { + break; + } else { + prnd->SampleUniform(&uniform_tensor, 0, 1); + prnd->SampleGaussian(&normal_tensor, 0, 1); + Kernel<gamma_two_scalar_kernel_r<OType, FType>, xpu>::Launch( + s, outputs[0].Size(), param.shape.value(), param.scale.value(), + uniform_tensor.dptr_, normal_tensor.dptr_, + outputs[0].dptr<OType>(), failure_indicator_device); + failure_indicator = 1.0; + Kernel<CheckSuccessKernel<OType, FType>, xpu>::Launch( + s, outputs[0].Size(), outputs[0].dptr<OType>(), + failure_indicator_device); + _copy<xpu>(s, &failure_indicator, failure_indicator_device); + } + } + }); + } else if (inputs.size() == 1U) { + // [scalar tensor], [tensor scalar] case + int ndim = FillShape(inputs[0].shape_, inputs[0].shape_, outputs[0].shape_, + &new_lshape, &new_lshape, &new_oshape); + int scalar_pos; + float scalar_value; + // int type_flag = param.t; + if (param.shape.has_value()) { + scalar_pos = 0; + scalar_value = param.shape.value(); + } else { + scalar_pos = 1; + scalar_value = param.scale.value(); + } + MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, { + MSHADOW_TYPE_SWITCH(outputs[0].type_flag_, OType, { Review comment: Seems like your output type can only be floating-point numbers, so you can use `MSHADOW_REAL_TYPE_SWITCH` instead. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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