wehub-resource-sync
371 lines
15 KiB
C++
371 lines
15 KiB
C++
// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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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#include <cmath>
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#include <vector>
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#include "glog/logging.h"
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#include "paddle/common/flags.h"
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#include "paddle/phi/backends/cpu/cpu_context.h"
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#include "paddle/phi/core/enforce.h"
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#include "paddle/phi/core/generator.h"
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#include "paddle/phi/core/kernel_registry.h"
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#include "paddle/phi/core/utils/data_type.h"
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#include "paddle/phi/kernels/funcs/math/sampler.h"
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namespace phi {
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using Sampler = math::Sampler;
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template <typename T,
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typename Context,
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typename TreeT = int,
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typename OutT = int>
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void TDMSamplerInner(const Context &dev_ctx,
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const DenseTensor &input_tensor,
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const DenseTensor &travel_dense_tensor,
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const DenseTensor &layer_dense_tensor,
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bool output_positive,
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std::vector<int> neg_samples_num_list,
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std::vector<int> layer_offset,
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int seed,
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DenseTensor *out,
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DenseTensor *label,
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DenseTensor *mask) {
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// get dimension
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int64_t 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_list.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 +=
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(neg_samples_num_list[layer_idx] + static_cast<int>(output_positive));
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}
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VLOG(3) << "TDM: sample res length: " << sample_res_length;
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auto travel_dim = vectorize<int>(travel_dense_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_dense_tensor.data<TreeT>();
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auto *layer_data = layer_dense_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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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[layer_index + 1] - layer_offset[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 (int64_t 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,
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input_id,
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common::errors::InvalidArgument(
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"Variable value (input) of OP(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],
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input_id));
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PADDLE_ENFORCE_LT(
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input_id,
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travel_dim[0],
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common::errors::InvalidArgument(
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"Variable value (input) of OP(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],
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input_id));
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VLOG(3) << "TDM: input id: " << input_id;
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// TODO(large-tensor): array index not support int64
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int64_t start_offset_val = input_id * layer_nums;
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PADDLE_ENFORCE_LE_INT_MAX(start_offset_val, "input_id * layer_nums");
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int start_offset = static_cast<int>(start_offset_val);
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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_list[layer_idx];
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VLOG(3) << "TDM: Sample num: " << sample_num;
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int node_nums = layer_offset[layer_idx + 1] - layer_offset[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,
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node_nums - 1,
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common::errors::InvalidArgument(
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"Neg sample nums id of OP(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,
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node_nums,
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sample_num));
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int node_id_min = layer_offset[layer_idx];
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int node_id_max = layer_offset[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);
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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,
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node_id_max,
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common::errors::InvalidArgument(
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"Positive node id of OP(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,
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node_id_min,
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node_id_max,
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positive_node_id));
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PADDLE_ENFORCE_LE(
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node_id_min,
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positive_node_id,
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common::errors::InvalidArgument(
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"Positive node id of OP(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,
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node_id_min,
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node_id_max,
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positive_node_id));
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// If output positive, add itself
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if (output_positive) {
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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<int64_t> 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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int64_t 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[layer_idx] + sample_res] ||
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find(sample_res_vec.begin(),
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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] =
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static_cast<OutT>(layer_data[layer_offset[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 negative "
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<< output_vec[i * sample_res_length + offset]
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<< " Label append negative "
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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[layer_idx] + sample_res],
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node_id_max,
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common::errors::InvalidArgument(
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"Negative node id of OP(tdm_sampler) at layer "
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"%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,
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node_id_min,
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node_id_max,
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layer_data[layer_offset[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 = dev_ctx.template Alloc<OutT>(out);
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auto *label_data = dev_ctx.template Alloc<OutT>(label);
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auto *mask_data = dev_ctx.template Alloc<OutT>(mask);
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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 T, typename Context>
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void TDMSamplerKernel(const Context &dev_ctx,
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const DenseTensor &x,
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const DenseTensor &travel,
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const DenseTensor &layer,
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bool output_positive,
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const std::vector<int> &neg_samples_num_list,
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const std::vector<int> &layer_offset,
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int seed,
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int dtype,
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DenseTensor *out,
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DenseTensor *labels,
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DenseTensor *mask) {
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const auto &input_type = x.dtype();
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bool input_type_match =
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input_type == DataType::INT32 || input_type == DataType::INT64;
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PADDLE_ENFORCE_EQ(input_type_match,
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true,
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common::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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DataTypeToString(x.dtype()),
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DataTypeToString(DataType::INT32),
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DataTypeToString(DataType::INT64)));
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const auto &travel_type = travel.dtype();
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bool travel_type_match =
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travel_type == DataType::INT32 || travel_type == DataType::INT64;
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PADDLE_ENFORCE_EQ(travel_type_match,
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true,
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common::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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DataTypeToString(travel.dtype()),
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DataTypeToString(DataType::INT32),
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DataTypeToString(DataType::INT64)));
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const auto &layer_type = layer.dtype();
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bool layer_type_match =
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layer_type == DataType::INT32 || layer_type == DataType::INT64;
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PADDLE_ENFORCE_EQ(layer_type_match,
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true,
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common::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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DataTypeToString(layer.dtype()),
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DataTypeToString(DataType::INT32),
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DataTypeToString(DataType::INT64)));
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PADDLE_ENFORCE_EQ(travel_type,
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layer_type,
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common::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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DataTypeToString(travel.dtype()),
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DataTypeToString(layer.dtype())));
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auto output_type = TransToPhiDataType(dtype);
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if (travel_type == DataType::INT32 && output_type == DataType::INT32) {
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TDMSamplerInner<T, Context, int, int>(dev_ctx,
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x,
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travel,
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layer,
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output_positive,
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neg_samples_num_list,
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layer_offset,
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seed,
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out,
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labels,
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mask);
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} else if (travel_type == DataType::INT64 && output_type == DataType::INT32) {
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TDMSamplerInner<T, Context, int64_t, int>(dev_ctx,
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x,
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travel,
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layer,
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output_positive,
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neg_samples_num_list,
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layer_offset,
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seed,
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out,
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labels,
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mask);
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} else if (travel_type == DataType::INT32 && output_type == DataType::INT64) {
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TDMSamplerInner<T, Context, int, int64_t>(dev_ctx,
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x,
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travel,
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layer,
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output_positive,
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neg_samples_num_list,
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layer_offset,
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seed,
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out,
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labels,
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mask);
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} else if (travel_type == DataType::INT64 && output_type == DataType::INT64) {
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TDMSamplerInner<T, Context, int64_t, int64_t>(dev_ctx,
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x,
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travel,
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layer,
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output_positive,
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neg_samples_num_list,
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layer_offset,
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seed,
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out,
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labels,
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mask);
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}
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}
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} // namespace phi
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PD_REGISTER_KERNEL(tdm_sampler,
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CPU,
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ALL_LAYOUT,
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phi::TDMSamplerKernel,
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float,
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double,
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int,
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int64_t) {}
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