FastDeploy/custom_ops/gpu_ops/save_with_output.cc

// Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed 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.

#include <fstream>
#include <iostream>
#include <memory>
#include <stdexcept>
#include <string>

#include <dlfcn.h>  // dladdr
#include <stdio.h>
#include <sys/stat.h>
#include <sys/time.h>
#include "paddle/extension.h"
#include "stdlib.h"

#ifndef PD_BUILD_STATIC_OP
#define PD_BUILD_STATIC_OP(name) PD_BUILD_OP(static_op_##name)
#endif

constexpr char kSEP = '/';

std::string DirName(const std::string& filepath) {
    auto pos = filepath.rfind(kSEP);
    if (pos == std::string::npos) {
        return "";
    }
    return filepath.substr(0, pos);
}

bool FileExists(const std::string& filepath) {
    struct stat buffer;
    return (stat(filepath.c_str(), &buffer) == 0);
}

void MkDir(const char* path) {
    std::string path_error(path);
    path_error += " mkdir failed!";
    if (mkdir(path, 0755)) {
        if (errno != EEXIST) {
            throw std::runtime_error(path_error);
        }
    }
}

void MkDirRecursively(const char* fullpath) {
    if (*fullpath == '\0') return;  // empty string
    if (FileExists(fullpath)) return;
    MkDirRecursively(DirName(fullpath).c_str());
    MkDir(fullpath);
}

template <typename data_t>
void saveToFile(std::ostream& os,
                const void* x_data,
                std::vector<int64_t> shape,
                int64_t x_numel,
                const char type_id) {
    // 1.type
    os.write(reinterpret_cast<const char*>(&type_id), sizeof(type_id));
    // 2.data
    uint64_t size = x_numel * sizeof(data_t);
    os.write(static_cast<const char*>(x_data),
             static_cast<std::streamsize>(size));
}

template <typename data_t>
void save_with_output_kernel(const paddle::Tensor& x,
                             const paddle::Tensor& batch_idx,
                             const paddle::Tensor& step_idx,
                             std::string file_path,
                             int64_t rank_id,
                             char type_id) {
    std::vector<int64_t> x_shape = x.shape();

    if (rank_id >= 0) {
        file_path += "_rank_" + std::to_string(rank_id);
    }

    int batch_idx_data = -1, step_idx_data = -1;

    if (batch_idx.is_gpu()) {
        paddle::Tensor batch_idx_cpu =
            batch_idx.copy_to<int32_t>(paddle::CPUPlace());
        batch_idx_data = batch_idx_cpu.data<int32_t>()[0];
    } else {
        batch_idx_data = batch_idx.data<int32_t>()[0];
    }
    if (step_idx.is_gpu()) {
        paddle::Tensor step_idx_cpu =
            step_idx.copy_to<int64_t>(paddle::CPUPlace());
        step_idx_data = step_idx_cpu.data<int64_t>()[0];
    } else {
        step_idx_data = step_idx.data<int64_t>()[0];
    }
    auto x_data = x.data<data_t>();

    if (batch_idx_data >= 0) {
        file_path += "_batch_" + std::to_string(batch_idx_data);
    }
    if (step_idx_data >= 0) {
        file_path += "_step_" + std::to_string(step_idx_data);
    }
    MkDirRecursively(DirName(file_path).c_str());
    std::ofstream fout(file_path, std::ios::binary);
    fout.write("0", 1);
    saveToFile<data_t>(fout, x_data, x_shape, x.numel(), type_id);
    fout.seekp(std::ios::beg);
    fout.write("1", 1);
    fout.close();
}

void print_shape(const paddle::Tensor& tmp, char* tmp_str) {
    std::vector<int64_t> shape = tmp.shape();
    printf("%s's shape: \n", tmp_str);
    for (int i = 0; i < shape.size(); i++) {
        printf("%d ", (int)shape[i]);
    }
    printf("\n");
}

std::vector<paddle::Tensor> SaveWithOutputForward(
    const paddle::Tensor& x,
    const paddle::Tensor& batch_idx,
    const paddle::Tensor& step_idx,
    std::string file_path,
    int64_t rank_id) {
    auto out = x.copy_to(paddle::CPUPlace(), false);
    switch (x.type()) {
        case paddle::DataType::FLOAT32:
            save_with_output_kernel<float>(
                out, batch_idx, step_idx, file_path, rank_id, '0');
            break;
        case paddle::DataType::INT64:
            save_with_output_kernel<int64_t>(
                out, batch_idx, step_idx, file_path, rank_id, '1');
            break;
        case paddle::DataType::INT32:
            save_with_output_kernel<int32_t>(
                out, batch_idx, step_idx, file_path, rank_id, '2');
            break;
        default:
            PD_THROW(
                "function SaveWithOutputForward is not implemented for data "
                "type");
    }
    return {out};
}

std::vector<std::vector<int64_t>> SaveWithOutputInferShape(
    const std::vector<int64_t>& x_shape,
    const std::vector<int64_t>& batch_idx_shape,
    const std::vector<int64_t>& step_idx_shape) {
    return {x_shape};
}

std::vector<paddle::DataType> SaveWithOutputInferDtype(
    const paddle::DataType& x_dtype,
    const paddle::DataType& batch_idx_dtype,
    const paddle::DataType& step_idx_dtype) {
    return {x_dtype};
}

PD_BUILD_STATIC_OP(save_with_output)
    .Inputs({"x", "batch_idx", "step_idx"})
    .Attrs({"file_path: std::string", "rank_id: int64_t"})
    .Outputs({"out"})
    .SetKernelFn(PD_KERNEL(SaveWithOutputForward))
    .SetInferShapeFn(PD_INFER_SHAPE(SaveWithOutputInferShape))
    .SetInferDtypeFn(PD_INFER_DTYPE(SaveWithOutputInferDtype));