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330 lines
14 KiB
330 lines
14 KiB
/* Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include <cmath>
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#include <fstream>
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#include <set>
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#include <string>
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#include <utility>
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#include <vector>
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#include "gflags/gflags.h"
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#include "paddle/fluid/framework/mixed_vector.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/sampler.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using Sampler = math::Sampler;
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using DDim = framework::DDim;
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using LoD = framework::LoD;
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using LoDTensor = framework::LoDTensor;
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using LoDAndOffset = std::pair<LoD, std::pair<size_t, size_t>>;
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template <typename T, typename TreeT = int, typename OutT = int>
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void TDMSamplerInner(const framework::ExecutionContext &context,
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const LoDTensor &input_tensor,
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const LoDTensor &travel_lod_tensor,
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const LoDTensor &layer_lod_tensor, LoDTensor *out_tensor,
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LoDTensor *label_tensor, LoDTensor *mask_tensor) {
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auto neg_samples_num_vec =
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context.Attr<std::vector<int>>("neg_samples_num_list");
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auto layer_offset_lod = context.Attr<std::vector<int>>("layer_offset_lod");
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auto output_positive_flag = context.Attr<bool>("output_positive");
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// get dimension
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int input_ids_num = input_tensor.numel();
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VLOG(3) << "TDM: input ids nums: " << input_ids_num;
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auto layer_nums = neg_samples_num_vec.size();
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VLOG(3) << "TDM: tree layer nums: " << layer_nums;
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int sample_res_length = 0;
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for (size_t layer_idx = 0; layer_idx < layer_nums; ++layer_idx) {
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sample_res_length += (neg_samples_num_vec[layer_idx] +
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static_cast<int>(output_positive_flag));
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}
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VLOG(3) << "TDM: sample res length: " << sample_res_length;
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auto travel_dim = travel_lod_tensor.dims();
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auto total_sample_nums = input_ids_num * sample_res_length;
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// get all data
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auto *input_data = input_tensor.data<T>();
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auto *travel_data = travel_lod_tensor.data<TreeT>();
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auto *layer_data = layer_lod_tensor.data<TreeT>();
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OutT zero = 0;
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OutT one = 1;
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std::vector<OutT> output_vec(total_sample_nums, zero);
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std::vector<OutT> label_vec(total_sample_nums, zero);
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std::vector<OutT> mask_vec(total_sample_nums, one);
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VLOG(3) << "End get input & output data";
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// generate uniform sampler
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auto seed = context.Attr<int>("seed");
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std::vector<Sampler *> sampler_vec{};
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for (size_t layer_index = 0; layer_index < layer_nums; layer_index++) {
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int layer_node_nums =
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layer_offset_lod[layer_index + 1] - layer_offset_lod[layer_index];
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Sampler *sampler = new math::UniformSampler(layer_node_nums - 1, seed);
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sampler_vec.push_back(sampler);
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}
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VLOG(3) << "TDM: get sampler ";
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for (int i = 0; i < input_ids_num; ++i) {
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// find leaf node travel path
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T input_id = input_data[i];
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PADDLE_ENFORCE_LT(
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-1, input_id,
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platform::errors::InvalidArgument(
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"Variable value (input) of OP(fluid.layers.tdm_sampler) "
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"expected >= 0 and < %ld, but got %ld. Please check input "
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"value.",
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travel_dim[0], input_id));
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PADDLE_ENFORCE_LT(
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input_id, travel_dim[0],
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platform::errors::InvalidArgument(
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"Variable value (input) of OP(fluid.layers.tdm_sampler) "
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"expected >= 0 and < %ld, but got %ld. Please check input "
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"value.",
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travel_dim[0], input_id));
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VLOG(3) << "TDM: input id: " << input_id;
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int start_offset = static_cast<int>(input_id * layer_nums);
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VLOG(3) << "TDM: Start offset(input_id * layer_nums): " << start_offset;
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// nce sample, layer by layer
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int offset = 0;
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for (size_t layer_idx = 0; layer_idx < layer_nums; ++layer_idx) {
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int sample_num = neg_samples_num_vec[layer_idx];
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VLOG(3) << "TDM: Sample num: " << sample_num;
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int node_nums =
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layer_offset_lod[layer_idx + 1] - layer_offset_lod[layer_idx];
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VLOG(3) << "TDM: layer - " << layer_idx + 1
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<< " - has node_nums: " << node_nums;
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PADDLE_ENFORCE_LE(
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sample_num, node_nums - 1,
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platform::errors::InvalidArgument(
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"Neg sample nums id of OP(fluid.layers.tdm_sampler) at layer %ld "
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"expected <= %ld - 1 (positive included), but got %ld. Please "
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"check neg_samples_num_list.",
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layer_idx, node_nums, sample_num));
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int node_id_min = layer_offset_lod[layer_idx];
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int node_id_max = layer_offset_lod[layer_idx + 1];
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OutT positive_node_id =
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static_cast<OutT>(travel_data[start_offset + layer_idx]);
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if (positive_node_id == 0) {
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// skip padding
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VLOG(3) << "TDM: Skip padding ";
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for (int sample_index = 0;
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sample_index < sample_num + static_cast<int>(output_positive_flag);
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sample_index++) {
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output_vec[i * sample_res_length + offset] = 0;
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label_vec[i * sample_res_length + offset] = 0;
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mask_vec[i * sample_res_length + offset] = 0;
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VLOG(3) << "TDM: Res append positive "
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<< output_vec[i * sample_res_length + offset]
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<< " Label append positive "
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<< label_vec[i * sample_res_length + offset]
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<< " Mask append value "
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<< mask_vec[i * sample_res_length + offset];
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offset += 1;
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}
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continue;
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}
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PADDLE_ENFORCE_LE(
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positive_node_id, node_id_max,
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platform::errors::InvalidArgument(
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"Positive node id of OP(fluid.layers.tdm_sampler) at layer %ld "
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"expected >= %ld and <= %ld, but got %ld. Please check input "
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"value.",
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layer_idx, node_id_min, node_id_max, positive_node_id));
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PADDLE_ENFORCE_LE(
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node_id_min, positive_node_id,
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platform::errors::InvalidArgument(
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"Positive node id of OP(fluid.layers.tdm_sampler) at layer %ld "
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"expected >= %ld and <= %ld, but got %ld. Please check input "
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"value.",
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layer_idx, node_id_min, node_id_max, positive_node_id));
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// If output positive, add itself
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if (output_positive_flag) {
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output_vec[i * sample_res_length + offset] = positive_node_id;
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label_vec[i * sample_res_length + offset] = 1;
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mask_vec[i * sample_res_length + offset] = 1;
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VLOG(3) << "TDM: node id: " << positive_node_id << " Res append "
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<< output_vec[i * sample_res_length + offset]
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<< " Label append "
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<< label_vec[i * sample_res_length + offset] << " Mask append "
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<< mask_vec[i * sample_res_length + offset];
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offset += 1;
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}
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std::vector<int> sample_res_vec{};
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// Sampling at layer, until samples enough
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for (int sample_index = 0; sample_index < sample_num; ++sample_index) {
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// Avoid sampling to positive samples
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int sample_res = 0;
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do {
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sample_res = sampler_vec[layer_idx]->Sample();
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} while (positive_node_id ==
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layer_data[layer_offset_lod[layer_idx] + sample_res] ||
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find(sample_res_vec.begin(), sample_res_vec.end(),
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sample_res) != sample_res_vec.end());
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sample_res_vec.push_back(sample_res);
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output_vec[i * sample_res_length + offset] = static_cast<OutT>(
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layer_data[layer_offset_lod[layer_idx] + sample_res]);
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label_vec[i * sample_res_length + offset] = 0;
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mask_vec[i * sample_res_length + offset] = 1;
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VLOG(3) << "TDM: node id: " << travel_data[start_offset + layer_idx]
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<< " Res append negitive "
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<< output_vec[i * sample_res_length + offset]
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<< " Label append negitive "
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<< label_vec[i * sample_res_length + offset]
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<< " Mask append value "
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<< mask_vec[i * sample_res_length + offset];
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PADDLE_ENFORCE_LE(
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layer_data[layer_offset_lod[layer_idx] + sample_res], node_id_max,
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platform::errors::InvalidArgument(
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"Negative node id of OP(fluid.layers.tdm_sampler) at layer %ld"
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"expected >= %ld and <= %ld, but got %ld. Please check input "
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"tdm tree structure and tdm travel info.",
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layer_idx, node_id_min, node_id_max,
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layer_data[layer_offset_lod[layer_idx] + sample_res]));
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offset += 1;
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} // end layer nce
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} // end one input nce
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} // end all input nce
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auto *output_data = out_tensor->mutable_data<OutT>(context.GetPlace());
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auto *label_data = label_tensor->mutable_data<OutT>(context.GetPlace());
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auto *mask_data = mask_tensor->mutable_data<OutT>(context.GetPlace());
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memcpy(output_data, &output_vec[0], sizeof(OutT) * total_sample_nums);
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memcpy(label_data, &label_vec[0], sizeof(OutT) * total_sample_nums);
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memcpy(mask_data, &mask_vec[0], sizeof(OutT) * total_sample_nums);
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for (size_t layer_index = 0; layer_index < layer_nums; layer_index++) {
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delete sampler_vec[layer_index];
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}
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}
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template <typename DeviceContext, typename T>
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class TDMSamplerKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext &context) const override {
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auto *input_var = context.InputVar("X");
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auto *travel_var = context.InputVar("Travel");
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auto *layer_var = context.InputVar("Layer");
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// get all tensor
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auto &input_tensor = input_var->Get<framework::LoDTensor>();
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auto &travel_lod_tensor = travel_var->Get<framework::LoDTensor>();
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auto &layer_lod_tensor = layer_var->Get<framework::LoDTensor>();
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const auto &input_type = input_tensor.type();
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bool input_type_match = input_type == framework::proto::VarType::INT32 ||
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input_type == framework::proto::VarType::INT64;
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PADDLE_ENFORCE_EQ(input_type_match, true,
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platform::errors::InvalidArgument(
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"Input(X) holds the wrong type, it holds %s, but "
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"desires to be %s or %s",
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paddle::framework::DataTypeToString(input_type),
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paddle::framework::DataTypeToString(
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framework::proto::VarType::INT32),
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paddle::framework::DataTypeToString(
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framework::proto::VarType::INT64)));
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const auto &travel_type = travel_lod_tensor.type();
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bool travel_type_match = travel_type == framework::proto::VarType::INT32 ||
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travel_type == framework::proto::VarType::INT64;
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PADDLE_ENFORCE_EQ(
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travel_type_match, true,
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platform::errors::InvalidArgument(
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"Input(Travel) holds the wrong type, it holds %s, but "
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"desires to be %s or %s",
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paddle::framework::DataTypeToString(travel_type),
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paddle::framework::DataTypeToString(
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framework::proto::VarType::INT32),
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paddle::framework::DataTypeToString(
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framework::proto::VarType::INT64)));
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const auto &layer_type = layer_lod_tensor.type();
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bool layer_type_match = layer_type == framework::proto::VarType::INT32 ||
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layer_type == framework::proto::VarType::INT64;
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PADDLE_ENFORCE_EQ(layer_type_match, true,
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platform::errors::InvalidArgument(
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"Input(Layer) holds the wrong type, it holds %s, but "
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"desires to be %s or %s",
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paddle::framework::DataTypeToString(layer_type),
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paddle::framework::DataTypeToString(
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framework::proto::VarType::INT32),
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paddle::framework::DataTypeToString(
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framework::proto::VarType::INT64)));
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PADDLE_ENFORCE_EQ(
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travel_type, layer_type,
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platform::errors::InvalidArgument(
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"Input(Travel) must holds the same type with "
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"Input(Layer), but Travel holds %s, and Layer holds %s",
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paddle::framework::DataTypeToString(travel_type),
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paddle::framework::DataTypeToString(layer_type)));
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auto *out_var = context.OutputVar("Out");
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auto *label_var = context.OutputVar("Labels");
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auto *mask_var = context.OutputVar("Mask");
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auto *out_tensor = out_var->GetMutable<framework::LoDTensor>();
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auto *label_tensor = label_var->GetMutable<framework::LoDTensor>();
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auto *mask_tensor = mask_var->GetMutable<framework::LoDTensor>();
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auto output_type = static_cast<framework::proto::VarType::Type>(
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context.Attr<int>("dtype"));
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if (travel_type == framework::proto::VarType::INT32 &&
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output_type == framework::proto::VarType::INT32) {
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TDMSamplerInner<T, int, int>(context, input_tensor, travel_lod_tensor,
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layer_lod_tensor, out_tensor, label_tensor,
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mask_tensor);
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} else if (travel_type == framework::proto::VarType::INT64 &&
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output_type == framework::proto::VarType::INT32) {
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TDMSamplerInner<T, int64_t, int>(context, input_tensor, travel_lod_tensor,
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layer_lod_tensor, out_tensor,
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label_tensor, mask_tensor);
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} else if (travel_type == framework::proto::VarType::INT32 &&
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output_type == framework::proto::VarType::INT64) {
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TDMSamplerInner<T, int, int64_t>(context, input_tensor, travel_lod_tensor,
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layer_lod_tensor, out_tensor,
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label_tensor, mask_tensor);
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} else if (travel_type == framework::proto::VarType::INT64 &&
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output_type == framework::proto::VarType::INT64) {
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TDMSamplerInner<T, int64_t, int64_t>(
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context, input_tensor, travel_lod_tensor, layer_lod_tensor,
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out_tensor, label_tensor, mask_tensor);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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