xidulu commented on a change in pull request #16638: [Numpy] Add sampling method for bernoulli URL: https://github.com/apache/incubator-mxnet/pull/16638#discussion_r344610882
########## File path: src/operator/numpy/random/np_bernoulli_op.h ########## @@ -0,0 +1,201 @@ +/* + * 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_bernoulli_op.h + * \brief Operator for numpy sampling from bernoulli distribution. + */ +#ifndef MXNET_OPERATOR_NUMPY_RANDOM_NP_BERNOULLI_OP_H_ +#define MXNET_OPERATOR_NUMPY_RANDOM_NP_BERNOULLI_OP_H_ + +#include <mxnet/operator_util.h> +#include <algorithm> +#include <string> +#include <vector> +#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 NumpyBernoulliParam : public dmlc::Parameter<NumpyBernoulliParam> { + dmlc::optional<float> prob; + dmlc::optional<float> logit; + std::string ctx; + int dtype; + bool is_logit; + dmlc::optional<mxnet::Tuple<int>> size; + DMLC_DECLARE_PARAMETER(NumpyBernoulliParam) { + DMLC_DECLARE_FIELD(prob); + DMLC_DECLARE_FIELD(logit); + 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("cpu").describe( + "Context of output, in format [cpu|gpu|cpu_pinned](n)." + " Only used for imperative calls."); + DMLC_DECLARE_FIELD(dtype) + .add_enum("uint8", mshadow::kUint8) + .add_enum("int32", mshadow::kInt32) + .add_enum("float32", mshadow::kFloat32) + .add_enum("float64", mshadow::kFloat64) + .add_enum("float16", mshadow::kFloat16) + .add_enum("bool", mshadow::kBool) + .set_default(mshadow::kFloat32) + .describe( + "DType of the output in case this can't be inferred. " + "Defaults to float32 if not defined (dtype=None)."); + DMLC_DECLARE_FIELD(is_logit); + } +}; + +inline bool NumpyBernoulliOpType(const nnvm::NodeAttrs &attrs, + std::vector<int> *in_attrs, + std::vector<int> *out_attrs) { + const NumpyBernoulliParam ¶m = nnvm::get<NumpyBernoulliParam>(attrs.parsed); + int otype = param.dtype; + (*out_attrs)[0] = otype; + return true; +} + +namespace mxnet_op { + +struct prob_to_logit { + MSHADOW_XINLINE static void Map(index_t i, float* uniforms) { + float prob = uniforms[i]; + uniforms[i] = log(prob) - log(1 - prob); + } +}; + +template <int ndim, typename IType, typename OType> +struct bernoulli_kernel { + MSHADOW_XINLINE static void Map(index_t i, + const Shape<ndim> &stride, + const Shape<ndim> &oshape, + IType *inputs, float* threshold, OType *out) { + Shape<ndim> coord = unravel(i, oshape); + auto idx = static_cast<index_t>(dot(coord, stride)); + out[i] = inputs[idx] > threshold[i] ? OType(1) : OType(0); + } +}; + +template <typename OType> +struct scalar_bernoulli_kernel { + MSHADOW_XINLINE static void Map(index_t i, float inputs, float *threshold, + OType *out) { + out[i] = inputs > threshold[i] ? OType(1) : OType(0); + } +}; + +template <typename IType> +struct check_legal_prob_kernel { + MSHADOW_XINLINE static void Map(index_t i, IType *scalar, float* flag) { + if (scalar[i] < 0.0 || scalar[i] > 1.0) { + flag[0] = -1.0; + } + } +}; + +} // namespace mxnet_op + +template <typename xpu> +void NumpyBernoulliForward(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 NumpyBernoulliParam ¶m = nnvm::get<NumpyBernoulliParam>(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> uniform_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->SampleUniform(&uniform_tensor, 0.0, 1.0); + if (param.prob.has_value()) { + // scalar prob input + CHECK_LE(param.prob.value(), 1.0) << "ValueError: expect probs >= 0 && probs <= 1"; + CHECK_GE(param.prob.value(), 0.0) << "ValueError: expect probs >= 0 && probs <= 1"; + MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, OType, { + Kernel<scalar_bernoulli_kernel<OType>, xpu>::Launch( + s, outputs[0].Size(), param.prob.value(), + uniform_tensor.dptr_, outputs[0].dptr<OType>()); + }); + } else if (param.logit.has_value()) { + // scalar logit input + // sigmoid(x) > u <=> x > logit(u) + Kernel<prob_to_logit, xpu>::Launch(s, outputs[0].Size(), + uniform_tensor.dptr_); + MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, OType, { + Kernel<scalar_bernoulli_kernel<OType>, xpu>::Launch( + s, outputs[0].Size(), param.logit.value(), + uniform_tensor.dptr_, outputs[0].dptr<OType>()); + }); + } else { + if (param.is_logit) { + // tensor logit input + Kernel<prob_to_logit, xpu>::Launch(s, outputs[0].Size(), + uniform_tensor.dptr_); + } else { + // tensor prob input + // sigmoid(x) > u <=> x > logit(u) + MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, { + Kernel<check_legal_prob_kernel<IType>, xpu>::Launch( + s, inputs[0].Size(), inputs[0].dptr<IType>(), indicator_device_ptr); + }); + _copy<xpu>(&indicator_host, indicator_device_ptr); + CHECK_GE(indicator_host, 0.0) + << "ValueError: expect probs >= 0 && probs <= 1"; + } + mxnet::TShape new_lshape, new_oshape; + int ndim = FillShape(inputs[0].shape_, inputs[0].shape_, outputs[0].shape_, + &new_lshape, &new_lshape, &new_oshape); + MSHADOW_TYPE_SWITCH(inputs[0].type_flag_, IType, { + MSHADOW_TYPE_SWITCH_WITH_BOOL(outputs[0].type_flag_, OType, { Review comment: Thx for pointing out, working on it ---------------------------------------------------------------- This is an automated message from the Apache Git Service. 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