gguf_parser.cpp File Reference

#include "gguf_parser.h"
#include <iomanip>
#include <iostream>
#include <numeric>
#include <sstream>
#include <stdexcept>
#include <vector>
#include <cstring>
#include <cerrno>
#include <sys/mman.h>
#include <sys/stat.h>
#include <fcntl.h>
#include <unistd.h>
#include "logger.h"
#include "quantization.h"

Include dependency graph for gguf_parser.cpp:

Go to the source code of this file.

Functions
size_t	gguf_value_type_size (GGUFValueType type)
template<typename T >
void	read_raw (std::ifstream &file, T &dest)
	Reads raw binary data from a file stream.
template void	read_raw< uint8_t > (std::ifstream &, uint8_t &)
template void	read_raw< int8_t > (std::ifstream &, int8_t &)
template void	read_raw< uint16_t > (std::ifstream &, uint16_t &)
template void	read_raw< int16_t > (std::ifstream &, int16_t &)
template void	read_raw< uint32_t > (std::ifstream &, uint32_t &)
template void	read_raw< int32_t > (std::ifstream &, int32_t &)
template void	read_raw< float > (std::ifstream &, float &)
template void	read_raw< uint64_t > (std::ifstream &, uint64_t &)
template void	read_raw< int64_t > (std::ifstream &, int64_t &)
template void	read_raw< double > (std::ifstream &, double &)
template void	read_raw< GGUFValueType > (std::ifstream &, GGUFValueType &)
std::string	read_gguf_string (std::ifstream &file)
	Reads a string from a GGUF format file.
GGUFData	load_gguf_meta (const std::string &filename, bool use_mmap)
	Loads GGUF metadata and optionally memory-maps tensor data.

Function Documentation

gguf_value_type_size()

size_t gguf_value_type_size

(

GGUFValueType

type

)

Definition at line 28 of file gguf_parser.cpp.

                                                {
  switch (type) {
    case GGUFValueType::UINT8:
      return sizeof(uint8_t);
    case GGUFValueType::INT8:
      return sizeof(int8_t);
    case GGUFValueType::UINT16:
      return sizeof(uint16_t);
    case GGUFValueType::INT16:
      return sizeof(int16_t);
    case GGUFValueType::UINT32:
      return sizeof(uint32_t);
    case GGUFValueType::INT32:
      return sizeof(int32_t);
    case GGUFValueType::FLOAT32:
      return sizeof(float);
    case GGUFValueType::BOOL:
      return sizeof(uint8_t);
    case GGUFValueType::UINT64:
      return sizeof(uint64_t);
    case GGUFValueType::INT64:
      return sizeof(int64_t);
    case GGUFValueType::FLOAT64:
      return sizeof(double);
    case GGUFValueType::STRING:
      return 0;
    case GGUFValueType::ARRAY:
      return 0;
    default:
      return 0;
  }
}

References ARRAY, BOOL, FLOAT32, FLOAT64, INT16, INT32, INT64, INT8, j, STRING, type, UINT16, UINT32, UINT64, and UINT8.

Referenced by load_gguf_meta().

load_gguf_meta()

GGUFData load_gguf_meta	(	const std::string &	filename,
		bool	use_mmap
	)

Loads GGUF metadata and optionally memory-maps tensor data.

Parses the header, metadata, and tensor information from a GGUF file. If mmap is enabled, it will also memory-map the tensor data region. If mmap is disabled, tensor data pointers will be null and fd will be -1.

Parameters

filename	Path to the GGUF file.
use_mmap	Whether to memory-map the tensor data block.

Returns

GGUFData structure containing loaded information.

Exceptions

std::runtime_error

on file I/O or format errors.

Definition at line 123 of file gguf_parser.cpp.

                                                                  {
  Logger::info("Attempting to load GGUF file: " + filename + (use_mmap ? " with mmap" : " without mmap"));
  std::ifstream metadata_file(filename, std::ios::binary);
  if (!metadata_file.is_open()) {
    throw std::runtime_error("Failed to open file for metadata: " + filename);
  }
 
  GGUFData result;
  // The file_descriptor for mmap will be opened separately and stored in result.
  // The GGUFData destructor will handle closing this fd and munmap.
 
  read_raw(metadata_file, result.header.magic);
  read_raw(metadata_file, result.header.version);
  read_raw(metadata_file, result.header.tensor_count);
  read_raw(metadata_file, result.header.metadata_kv_count);
 
  {
    std::stringstream ss;
    ss << "Read Header:\n"
       << "    Magic: 0x" << std::hex << result.header.magic << std::dec << "\n"
       << "    Version: " << result.header.version << "\n"
       << "    Tensor Count: " << result.header.tensor_count << "\n"
       << "    Metadata KV Count: " << result.header.metadata_kv_count;
    Logger::info(ss.str());
  }
 
  if (result.header.magic != GGUF_MAGIC) {
    throw std::runtime_error("Not a valid GGUF file (magic number mismatch).");
  }
 
  Logger::info("Reading Metadata (" +
               std::to_string(result.header.metadata_kv_count) + " pairs)...");
  for (uint64_t i = 0; i < result.header.metadata_kv_count; ++i) {
    std::string key;
    GGUFValueType value_type_enum;
    try {
      key = read_gguf_string(metadata_file);
      read_raw(metadata_file, value_type_enum);
 
      switch (value_type_enum) {
        case GGUFValueType::UINT8: {
          uint8_t val;
          read_raw(metadata_file, val);
          result.metadata[key] = val;
          break;
        }
        case GGUFValueType::INT8: {
          int8_t val;
          read_raw(metadata_file, val);
          result.metadata[key] = val;
          break;
        }
        case GGUFValueType::UINT16: {
          uint16_t val;
          read_raw(metadata_file, val);
          result.metadata[key] = val;
          break;
        }
        case GGUFValueType::INT16: {
          int16_t val;
          read_raw(metadata_file, val);
          result.metadata[key] = val;
          break;
        }
        case GGUFValueType::UINT32: {
          uint32_t val;
          read_raw(metadata_file, val);
          result.metadata[key] = val;
          break;
        }
        case GGUFValueType::INT32: {
          int32_t val;
          read_raw(metadata_file, val);
          result.metadata[key] = val;
          break;
        }
        case GGUFValueType::FLOAT32: {
          float val;
          read_raw(metadata_file, val);
          result.metadata[key] = val;
          break;
        }
        case GGUFValueType::BOOL: {
          uint8_t byte;
          read_raw(metadata_file, byte);
          result.metadata[key] = (byte != 0);
          break;
        }
        case GGUFValueType::STRING: {
          std::string val = read_gguf_string(metadata_file);
          result.metadata[key] = val;
          break;
        }
        case GGUFValueType::UINT64: {
          uint64_t val;
          read_raw(metadata_file, val);
          result.metadata[key] = val;
          break;
        }
        case GGUFValueType::INT64: {
          int64_t val;
          read_raw(metadata_file, val);
          result.metadata[key] = val;
          break;
        }
        case GGUFValueType::FLOAT64: {
          double val;
          read_raw(metadata_file, val);
          result.metadata[key] = val;
          break;
        }
        case GGUFValueType::ARRAY: {
          GGUFValueType array_type_enum;
          uint64_t count;
          read_raw(metadata_file, array_type_enum);
          read_raw(metadata_file, count);
 
          GGUFArray array_obj;
          array_obj.type = array_type_enum;
          array_obj.len = count;
          result.metadata[key] = array_obj;
          bool skipped_data = false;
          if (key == "tokenizer.ggml.tokens" &&
              array_type_enum == GGUFValueType::STRING) {
            Logger::info("Loading STRING array data ('" + key + "') with " +
                         std::to_string(count) + " elements...");
            result.tokenizer_tokens.reserve(static_cast<size_t>(count));
            for (uint64_t arr_i = 0; arr_i < count; ++arr_i) {
              result.tokenizer_tokens.push_back(read_gguf_string(metadata_file));
            }
            Logger::info("Loaded tokenizer_tokens. Size: " +
                         std::to_string(result.tokenizer_tokens.size()));
          } else if (key == "tokenizer.ggml.scores" &&
                     array_type_enum == GGUFValueType::FLOAT32) {
            Logger::info("Loading FLOAT32 array data ('" + key + "') with " +
                         std::to_string(count) + " elements...");
            result.tokenizer_scores.resize(static_cast<size_t>(count));
            metadata_file.read(reinterpret_cast<char*>(result.tokenizer_scores.data()),
                      static_cast<std::streamsize>(count * sizeof(float)));
            if (!metadata_file) {
              throw std::runtime_error(
                  "GGUF Error: Failed to read scores array data.");
            }
            Logger::info("Loaded tokenizer_scores. Size: " +
                         std::to_string(result.tokenizer_scores.size()));
          } else if (key == "tokenizer.ggml.token_type" &&
                     (array_type_enum == GGUFValueType::UINT32 || array_type_enum == GGUFValueType::INT32) ) {
            Logger::info("Loading " + std::string(array_type_enum == GGUFValueType::UINT32 ? "UINT32" : "INT32") + 
                         " array data ('" + key + "') with " +
                         std::to_string(count) + " elements...");
            result.tokenizer_token_types.resize(static_cast<size_t>(count));
            if (array_type_enum == GGUFValueType::UINT32) {
                metadata_file.read(
                    reinterpret_cast<char*>(result.tokenizer_token_types.data()),
                    static_cast<std::streamsize>(count * sizeof(uint32_t)));
            } else { // GGUFValueType::INT32
                std::vector<int32_t> temp_s32_types(static_cast<size_t>(count));
                metadata_file.read(
                    reinterpret_cast<char*>(temp_s32_types.data()),
                    static_cast<std::streamsize>(count * sizeof(int32_t)));
                for(size_t k=0; k < count; ++k) {
                    result.tokenizer_token_types[k] = static_cast<uint32_t>(temp_s32_types[k]);
                }
            }
            if (!metadata_file) {
              throw std::runtime_error(
                  "GGUF Error: Failed to read token_type array data.");
            }
            Logger::info("Loaded tokenizer_token_types. Size: " +
                         std::to_string(result.tokenizer_token_types.size()));
          } else if (key == "tokenizer.ggml.merges" &&
                     array_type_enum == GGUFValueType::STRING) {
            Logger::info("Loading STRING array data ('" + key + "') with " +
                         std::to_string(count) + " elements...");
            result.tokenizer_merges.reserve(static_cast<size_t>(count));
            for (uint64_t arr_i = 0; arr_i < count; ++arr_i) {
              result.tokenizer_merges.push_back(read_gguf_string(metadata_file));
            }
            Logger::info("Loaded tokenizer_merges. Size: " +
                         std::to_string(result.tokenizer_merges.size()));
          } else {
            skipped_data = true;
            Logger::info(
                "Skipping unhandled/non-tokenizer ARRAY data for key '" + key +
                "' (Type: " +
                std::to_string(static_cast<uint32_t>(array_type_enum)) +
                ", Count: " + std::to_string(count) + ")");
 
            if (array_type_enum == GGUFValueType::STRING) {
              for (uint64_t arr_i = 0; arr_i < count; ++arr_i) {
                try {
                  std::string discarded_str = read_gguf_string(metadata_file);
                } catch (const std::exception& e) {
                  Logger::error("Error skipping string element " +
                                std::to_string(arr_i) + " for key '" + key +
                                "': " + e.what());
                  throw;
                }
              }
            } else {
              size_t element_size = gguf_value_type_size(array_type_enum);
              if (element_size == 0) {
                throw std::runtime_error(
                    "Cannot skip array for key '" + key +
                    "' with unsupported or variable-sized element type: " +
                    std::to_string(static_cast<uint32_t>(array_type_enum)));
              }
 
              if (count > 0 &&
                  element_size > std::numeric_limits<uint64_t>::max() / count) {
                throw std::overflow_error(
                    "Array size overflow calculating skip amount for key '" +
                    key + "'");
              }
              uint64_t total_size_to_skip = count * element_size;
              if (total_size_to_skip > 0) {
                metadata_file.seekg(static_cast<std::streamoff>(total_size_to_skip),
                           std::ios::cur);
                if (!metadata_file) {
                  throw std::runtime_error(
                      "GGUF Error: Failed to seek past array data for key '" +
                      key + "'");
                }
              }
            }
          }
          break;
        }
        default: {
          throw std::runtime_error(
              "Unknown metadata type encountered: " +
              std::to_string(static_cast<uint32_t>(value_type_enum)) +
              " for key: " + key);
        }
      }
    } catch (const std::exception& e) {
      std::string error_key =
          key.empty() ? "(unknown key, error during key read)" : key;
      Logger::error(
          "Error reading metadata for key: '" + error_key +
          "' (type: " + std::to_string(static_cast<uint32_t>(value_type_enum)) +
          ") - " + e.what());
      throw;
    }
  }
  Logger::info("Finished reading metadata.");
 
  result.tensor_infos.reserve(static_cast<size_t>(result.header.tensor_count));
  Logger::info("Reading Tensor Info (" +
               std::to_string(result.header.tensor_count) + " tensors)...");
  uint64_t accumulated_offset_debug = 0;
  for (uint64_t i = 0; i < result.header.tensor_count; ++i) {
    GGUFTensorInfo info;
    try {
      info.name = read_gguf_string(metadata_file);
 
      uint32_t n_dims;
      read_raw(metadata_file, n_dims);
      if (n_dims > GGUF_MAX_TENSOR_DIMS) {
        throw std::runtime_error("Tensor '" + info.name +
                                 "' has unsupported number of dimensions: " +
                                 std::to_string(n_dims));
      }
      info.shape.resize(n_dims);
      for (uint32_t d = 0; d < n_dims; ++d) {
        read_raw(metadata_file, info.shape[d]);
      }
 
      uint32_t ggml_type_u32;
      read_raw(metadata_file, ggml_type_u32);
      info.type = static_cast<GGMLType>(ggml_type_u32);
 
      uint64_t pos_before_offset_read = metadata_file.tellg();
 
      read_raw(metadata_file, info.offset);
 
      std::stringstream ss_offset_log;
      ss_offset_log
          << "[GGUF_TENSOR_INFO] Tensor " << i << " ('" << info.name
          << "'):" << "\n  Raw offset from file: " << info.offset
          << "\n  File pos before offset read: " << pos_before_offset_read
          << "\n  Calculated accumulated_offset_debug (before this tensor): "
          << accumulated_offset_debug;
 
      info.num_elements = 1;
      for (uint64_t dim : info.shape) {
        if (dim > 0 &&
            info.num_elements > std::numeric_limits<uint64_t>::max() / dim) {
          throw std::overflow_error(
              "Tensor dimension overflow calculating num_elements for tensor "
              "'" +
              info.name + "'");
        }
        info.num_elements *= dim;
      }
 
      size_t type_size = ggml_type_size(info.type);
      size_t block_size = ggml_type_block_size(info.type);
 
      if (block_size == 0 && info.num_elements > 0) {
        throw std::runtime_error(
            "Tensor '" + info.name +
            "' has unknown or unsupported type: " + std::to_string(info.type));
      }
 
      if (block_size > 1) {
        if (info.num_elements % block_size != 0) {
          throw std::runtime_error("Tensor '" + info.name + "' num_elements (" +
                                   std::to_string(info.num_elements) +
                                   ") not divisible by block_size (" +
                                   std::to_string(block_size) + ") for type " +
                                   ggml_type_name(info.type));
        }
        uint64_t num_blocks = info.num_elements / block_size;
        if (type_size > 0 &&
            num_blocks > std::numeric_limits<uint64_t>::max() / type_size) {
          throw std::overflow_error(
              "Tensor size overflow calculating size_in_bytes for tensor '" +
              info.name + "'");
        }
        info.size_in_bytes = static_cast<size_t>(num_blocks * type_size);
      } else {
        if (type_size > 0 &&
            info.num_elements >
                std::numeric_limits<uint64_t>::max() / type_size) {
          throw std::overflow_error(
              "Tensor size overflow calculating size_in_bytes for tensor '" +
              info.name + "'");
        }
        info.size_in_bytes = static_cast<size_t>(info.num_elements * type_size);
      }
 
      ss_offset_log << "\n  Calculated size_in_bytes for this tensor: "
                    << info.size_in_bytes;
      Logger::info(ss_offset_log.str());
      accumulated_offset_debug += info.size_in_bytes;
 
      result.tensor_infos.push_back(info);
      {
        std::stringstream ss_tensor;
        ss_tensor << "Tensor " << i << ": Name='" << info.name
                  << "', Type=" << ggml_type_name(info.type) << ", Shape=[ ";
        for (size_t d = 0; d < info.shape.size(); ++d)
          ss_tensor << info.shape[d]
                    << (d == info.shape.size() - 1 ? "" : ", ");
        ss_tensor << " ], Offset=" << info.offset
                  << ", Size=" << info.size_in_bytes << " bytes";
        Logger::info(ss_tensor.str());
      }
    } catch (const std::exception& e) {
      std::string tensor_name =
          info.name.empty() ? ("(unknown, index " + std::to_string(i) + ")")
                            : info.name;
      Logger::error("Error reading tensor info for tensor " + tensor_name +
                    ": " + e.what());
      throw;
    }
  }
  Logger::info("Finished reading tensor info.");
 
  Logger::info("Populating tensor_infos_map...");
  for (const auto& tinfo : result.tensor_infos) {
    if (result.tensor_infos_map.count(tinfo.name)) {
      Logger::warning("Duplicate tensor name found in GGUF: '" + tinfo.name +
                      "'. Overwriting entry in map.");
    }
    result.tensor_infos_map[tinfo.name] = tinfo;
  }
  Logger::info("Finished populating tensor_infos_map. Map size: " +
               std::to_string(result.tensor_infos_map.size()));
 
  uint64_t alignment = GGUF_DEFAULT_ALIGNMENT;
  try {
    if (result.metadata.count("general.alignment")) {
      uint32_t align_val =
          std::get<uint32_t>(result.metadata["general.alignment"]);
      if (align_val > 0) {
        alignment = align_val;
      }
      Logger::info("Using alignment value from metadata: " +
                   std::to_string(alignment));
    } else {
      Logger::info(
          "Metadata key 'general.alignment' not found. Using default "
          "alignment: " +
          std::to_string(alignment));
    }
  } catch (const std::bad_variant_access& e) {
    Logger::warning(
        "Could not read 'general.alignment' metadata as uint32. Using default "
        "alignment: " +
        std::to_string(alignment));
  } catch (const std::exception& e) {
    Logger::warning("Error accessing 'general.alignment' metadata: " +
                    std::string(e.what()) +
                    ". Using default alignment: " + std::to_string(alignment));
  }
  result.data_alignment = alignment; // Store the determined alignment
 
  uint64_t current_pos_metadata_stream = metadata_file.tellg();
  Logger::info("[GGUF_LOAD] Current file position (metadata stream) before padding seek: " +
               std::to_string(current_pos_metadata_stream));
  uint64_t padding = (alignment - (current_pos_metadata_stream % alignment)) % alignment;
  Logger::info("[GGUF_LOAD] Calculated padding: " + std::to_string(padding));
  
  uint64_t actual_data_start_offset_in_file = current_pos_metadata_stream + padding;
  Logger::info(
      "[GGUF_LOAD] Calculated actual_data_start_offset_in_file (for mmap): " +
      std::to_string(actual_data_start_offset_in_file));
  
  metadata_file.close();
  Logger::info("[GGUF_LOAD] Metadata ifstream closed.");
 
  if (!use_mmap) {
    Logger::info("[GGUF_LOAD] mmap is disabled by configuration. Loading tensor data into memory using OPTIMIZED bulk I/O.");
    
    uint64_t total_tensor_data_size = 0;
    for (const auto& tensor_info : result.tensor_infos) {
      total_tensor_data_size = std::max(total_tensor_data_size, tensor_info.offset + tensor_info.size_in_bytes);
    }
    
    if (total_tensor_data_size > 0) {
      result.tensor_data.resize(total_tensor_data_size);
      
      std::ifstream tensor_file(filename, std::ios::binary);
      if (!tensor_file.is_open()) {
        throw std::runtime_error("Failed to open file for tensor data reading: " + filename);
      }
      
      tensor_file.seekg(actual_data_start_offset_in_file);
      if (!tensor_file) {
        throw std::runtime_error("Failed to seek to tensor data start in file: " + filename);
      }
      
      // OPTIMIZATION: Use larger buffer for bulk reading to reduce I/O overhead
      constexpr size_t BULK_READ_BUFFER_SIZE = 64 * 1024 * 1024; // 64MB chunks
      size_t bytes_remaining = total_tensor_data_size;
      size_t bytes_read_total = 0;
      
      Logger::info("[GGUF_LOAD] Reading " + std::to_string(total_tensor_data_size) + " bytes in optimized " + std::to_string(BULK_READ_BUFFER_SIZE / (1024*1024)) + "MB chunks...");
      
      while (bytes_remaining > 0) {
        size_t chunk_size = std::min(bytes_remaining, BULK_READ_BUFFER_SIZE);
        
        tensor_file.read(reinterpret_cast<char*>(result.tensor_data.data() + bytes_read_total), chunk_size);
        if (!tensor_file) {
          throw std::runtime_error("Failed to read tensor data chunk at offset " + std::to_string(bytes_read_total) + " from file: " + filename);
        }
        
        bytes_read_total += chunk_size;
        bytes_remaining -= chunk_size;
        
        if (bytes_read_total % (256 * 1024 * 1024) == 0) { // Log every 256MB
          Logger::info("[GGUF_LOAD] Progress: " + std::to_string(bytes_read_total / (1024*1024)) + "MB / " + std::to_string(total_tensor_data_size / (1024*1024)) + "MB loaded");
        }
      }
      
      tensor_file.close();
      Logger::info("[GGUF_LOAD] Successfully loaded " + std::to_string(total_tensor_data_size) + " bytes of tensor data using optimized bulk I/O.");
      
      if (total_tensor_data_size >= 16) {
        std::stringstream ss_bytes;
        ss_bytes << "[GGUF_LOAD] First 16 bytes of tensor data: ";
        for (int i = 0; i < 16; ++i) {
          ss_bytes << "0x" << std::hex << static_cast<int>(result.tensor_data[i]) << " ";
        }
        Logger::info(ss_bytes.str());
      }
    } else {
      Logger::info("[GGUF_LOAD] No tensor data to load (total size is 0).");
    }
    
    return result; 
  }
 
#ifndef _WIN32
  result.file_descriptor = open(filename.c_str(), O_RDONLY);
  if (result.file_descriptor == -1) {
      throw std::runtime_error("GGUF Error: Failed to open file for mmap: " + filename + " - " + strerror(errno));
  }
  Logger::info("[GGUF_LOAD] File opened for mmap with fd: " + std::to_string(result.file_descriptor));
 
  struct stat file_stat;
  if (fstat(result.file_descriptor, &file_stat) == -1) {
      close(result.file_descriptor); 
      result.file_descriptor = -1;
      throw std::runtime_error("GGUF Error: Failed to fstat file for mmap: " + filename + " - " + strerror(errno));
  }
  uint64_t file_total_size = static_cast<uint64_t>(file_stat.st_size);
#else // _WIN32
  result.h_file = CreateFileA(
      filename.c_str(),
      GENERIC_READ,
      FILE_SHARE_READ,
      NULL,
      OPEN_EXISTING,
      FILE_ATTRIBUTE_NORMAL | FILE_FLAG_RANDOM_ACCESS, // Hint for mmap-like access
      NULL
  );
  if (result.h_file == INVALID_HANDLE_VALUE) {
      throw std::runtime_error("GGUF Error: Failed to open file for mmap (CreateFileA): " + filename + " - " + GetWindowsErrorString(GetLastError()));
  }
  Logger::info("[GGUF_LOAD] File opened for mmap with h_file: " + std::to_string(reinterpret_cast<uintptr_t>(result.h_file)));
  
  LARGE_INTEGER fileSizeWindows;
  if (!GetFileSizeEx(result.h_file, &fileSizeWindows)) {
      DWORD error_code = GetLastError();
      CloseHandle(result.h_file);
      result.h_file = INVALID_HANDLE_VALUE;
      throw std::runtime_error("GGUF Error: Failed to GetFileSizeEx for mmap: " + filename + " - " + GetWindowsErrorString(error_code));
  }
  uint64_t file_total_size = static_cast<uint64_t>(fileSizeWindows.QuadPart);
#endif
 
  if (file_total_size < actual_data_start_offset_in_file) {
#ifndef _WIN32
    close(result.file_descriptor); 
    result.file_descriptor = -1;
#else
    CloseHandle(result.h_file);
    result.h_file = INVALID_HANDLE_VALUE;
#endif
    throw std::runtime_error(
        "GGUF Error: File total size (" + std::to_string(file_total_size) + 
        ") is less than calculated actual_data_start_offset_in_file (" + std::to_string(actual_data_start_offset_in_file) + ").");
  }
 
  uint64_t tensor_data_block_size_on_disk = file_total_size - actual_data_start_offset_in_file;
  Logger::info("[GGUF_LOAD] Calculated tensor_data_block_size_on_disk (for mmap length calculation): " +
               std::to_string(tensor_data_block_size_on_disk) + " bytes.");
  
  long page_size;
#ifndef _WIN32
  page_size = sysconf(_SC_PAGE_SIZE);
  if (page_size == -1) {
      close(result.file_descriptor);
      result.file_descriptor = -1;
      throw std::runtime_error(std::string("GGUF Error: Failed to get page size using sysconf - ") + strerror(errno));
  }
#else // _WIN32
  SYSTEM_INFO sysInfo;
  GetSystemInfo(&sysInfo);
  // For MapViewOfFile, offsets must be aligned to dwAllocationGranularity.
  // Page size (dwPageSize) might be smaller, but dwAllocationGranularity is the key for mmap view offsets.
  page_size = static_cast<long>(sysInfo.dwAllocationGranularity); 
  if (page_size <= 0) { // Sanity check
      CloseHandle(result.h_file);
      result.h_file = INVALID_HANDLE_VALUE;
      throw std::runtime_error("GGUF Error: Failed to get valid system allocation granularity (page_size equivalent for mmap offset).");
  }
#endif
  Logger::info("[GGUF_LOAD] System page/allocation granularity for mmap offset: " + std::to_string(page_size));
 
  uint64_t mmap_offset = (actual_data_start_offset_in_file / page_size) * page_size; // Align offset down to page boundary
  result.offset_diff_for_mmap = static_cast<size_t>(actual_data_start_offset_in_file - mmap_offset);
  size_t mmap_length = static_cast<size_t>(tensor_data_block_size_on_disk + result.offset_diff_for_mmap);
 
  Logger::info("[GGUF_LOAD] Aligning mmap: actual_data_start_offset_in_file=" + std::to_string(actual_data_start_offset_in_file) +
               ", mmap_offset=" + std::to_string(mmap_offset) + // This is the offset from file start for mmap view
               ", offset_diff_for_mmap=" + std::to_string(result.offset_diff_for_mmap) + // Bytes from mmap view start to actual tensor data start
               ", mmap_length=" + std::to_string(mmap_length)); // Total length of the mmap view
 
  if (mmap_length > 0) {
    result.mapped_tensor_data_size = mmap_length; 
#ifndef _WIN32
    result.mapped_tensor_data = mmap(nullptr, result.mapped_tensor_data_size, 
                                         PROT_READ, MAP_SHARED, 
                                         result.file_descriptor, static_cast<off_t>(mmap_offset));
#else // _WIN32
    result.h_map_file = CreateFileMapping(
        result.h_file,
        NULL,
        PAGE_READONLY,
        0, 
        0, 
        NULL 
    );
    if (result.h_map_file == NULL) {
        DWORD error_code = GetLastError();
        CloseHandle(result.h_file);
        result.h_file = INVALID_HANDLE_VALUE;
        throw std::runtime_error("GGUF Error: CreateFileMapping failed - " + GetWindowsErrorString(error_code));
    }
    
    // MapViewOfFile's dwFileOffsetHigh/Low parameters form the 64-bit offset.
    // This offset (mmap_offset) MUST be a multiple of dwAllocationGranularity (our page_size for Windows).
    DWORD mmap_offset_low = static_cast<DWORD>(mmap_offset & 0xFFFFFFFF);
    DWORD mmap_offset_high = static_cast<DWORD>((mmap_offset >> 32) & 0xFFFFFFFF);
 
    result.mapped_tensor_data = MapViewOfFile(
        result.h_map_file,
        FILE_MAP_READ,
        mmap_offset_high,
        mmap_offset_low,
        result.mapped_tensor_data_size // This is dwNumberOfBytesToMap
    );
#endif
    
    if (result.mapped_tensor_data == GGUFData::MMapFailure) { // Use platform-agnostic failure check
        int last_error = 0;
#ifndef _WIN32
        last_error = errno;
        // file_descriptor is closed by GGUFData destructor if it's still valid
#else
        last_error = GetLastError();
        // h_map_file and h_file are closed by GGUFData destructor if they are still valid
#endif
        result.mapped_tensor_data = nullptr; 
        result.mapped_tensor_data_size = 0;
        result.offset_diff_for_mmap = 0;   
        throw std::runtime_error("GGUF Error: mmap/MapViewOfFile failed. Aligned Offset: " + std::to_string(mmap_offset) +
                                 ", Mmap Length: " + std::to_string(mmap_length) + 
#ifndef _WIN32
                                 " - POSIX Error: " + strerror(last_error));
#else
                                 " - Windows Error: " + GetWindowsErrorString(last_error));
#endif
    }
    Logger::info("[GGUF_LOAD] Successfully mmapped tensor data block. Mapped Address: " + 
                 std::to_string(reinterpret_cast<uintptr_t>(result.mapped_tensor_data)) + 
                 ", Mapped Size: " + std::to_string(result.mapped_tensor_data_size) + 
                 " bytes from file offset " + std::to_string(mmap_offset));
 
    if (result.mapped_tensor_data_size >= (result.offset_diff_for_mmap + 16)) {
      std::stringstream ss_bytes;
      ss_bytes << "[GGUF_LOAD] First 16 bytes of *actual* tensor data (after offset_diff) in mmap: ";
      const uint8_t* actual_data_ptr_debug = static_cast<const uint8_t*>(result.mapped_tensor_data) + result.offset_diff_for_mmap;
      for (int i = 0; i < 16; ++i)
        ss_bytes << "0x" << std::hex << static_cast<int>(actual_data_ptr_debug[i]) << " ";
      Logger::info(ss_bytes.str());
    }
 
#ifndef _WIN32
    
    Logger::info("[GGUF_LOAD] Attempting to prefetch mmapped tensor data using posix_madvise(MADV_WILLNEED)...");
    uint8_t* actual_tensor_data_block_start_in_mmap = static_cast<uint8_t*>(result.mapped_tensor_data) + result.offset_diff_for_mmap;
    
    if (page_size <= 0) {
        Logger::error("[GGUF_LOAD] Invalid page_size for madvise alignment: " + std::to_string(page_size) + ". Skipping prefetch.");
    } else {
        for (const auto& tensor_info : result.tensor_infos) {
            if (tensor_info.size_in_bytes > 0) {
                uintptr_t exact_tensor_start_addr_val = reinterpret_cast<uintptr_t>(actual_tensor_data_block_start_in_mmap + tensor_info.offset);
                void* page_aligned_madvise_addr = reinterpret_cast<void*>(exact_tensor_start_addr_val - (exact_tensor_start_addr_val % static_cast<uintptr_t>(page_size)));
                size_t madvise_length = (exact_tensor_start_addr_val + tensor_info.size_in_bytes) - reinterpret_cast<uintptr_t>(page_aligned_madvise_addr);
 
                uintptr_t advised_region_start_val = reinterpret_cast<uintptr_t>(page_aligned_madvise_addr);
                uintptr_t advised_region_end_val = advised_region_start_val + madvise_length;
                uintptr_t overall_mmap_start_val = reinterpret_cast<uintptr_t>(result.mapped_tensor_data);
                uintptr_t overall_mmap_end_val = overall_mmap_start_val + result.mapped_tensor_data_size;
 
                if (advised_region_start_val >= overall_mmap_start_val && advised_region_end_val <= overall_mmap_end_val && advised_region_start_val < advised_region_end_val) { // Added check start < end
                    int ret = posix_madvise(page_aligned_madvise_addr, madvise_length, POSIX_MADV_WILLNEED);
                    if (ret != 0) {
                        Logger::warning("[GGUF_LOAD] posix_madvise failed for tensor '" + tensor_info.name + 
                                        "' (addr: " + std::to_string(reinterpret_cast<uintptr_t>(page_aligned_madvise_addr)) +
                                        ", len: " + std::to_string(madvise_length) +
                                        ") with error code " + std::to_string(errno) + 
                                        " (" + strerror(errno) + "). Skipping prefetch for this tensor.");
                    }
                } else {
                     Logger::warning("[GGUF_LOAD] Tensor '" + tensor_info.name + 
                                     "' calculated region for madvise is invalid or out of overall mmap bounds. Skipping prefetch. "
                                     /* ... detailed log as before ... */ );
                }
            }
        }
    }
    Logger::info("[GGUF_LOAD] Finished POSIX prefetching attempt with posix_madvise.");
    
#else // _WIN32
    Logger::info("[GGUF_LOAD] Tensor prefetching (posix_madvise) is currently implemented for POSIX systems. Skipping for Windows for now.");
#endif
 
  } else {
    Logger::info("[GGUF_LOAD] Tensor data block size (or mmap_length) is 0. Nothing to mmap.");
    result.mapped_tensor_data = nullptr; // Ensure it's null if not mapped
    result.mapped_tensor_data_size = 0;
    result.offset_diff_for_mmap = 0;
  }
  
  Logger::info("GGUF metadata loaded and tensor data (if any) mmapped successfully.");
  return result;
}

References ARRAY, BOOL, Logger::error(), FLOAT32, FLOAT64, ggml_type_block_size(), ggml_type_name(), ggml_type_size(), GGUF_DEFAULT_ALIGNMENT, GGUF_MAGIC, GGUF_MAX_TENSOR_DIMS, gguf_value_type_size(), Logger::info(), INT16, INT32, INT64, INT8, j, k, GGUFData::MMapFailure, GGUFTensorInfo::name, GGUFTensorInfo::num_elements, GGUFTensorInfo::offset, read_gguf_string(), read_raw(), GGUFTensorInfo::shape, GGUFTensorInfo::size_in_bytes, STRING, GGUFArray::type, GGUFTensorInfo::type, UINT16, UINT32, UINT64, UINT8, and Logger::warning().

Referenced by TinyLlamaModel::TinyLlamaModel().

read_gguf_string()

std::string read_gguf_string

(

std::ifstream &

file

)

Reads a string from a GGUF format file.

Parameters

file	Input file stream positioned at the start of a string

Returns

The string read from the file

Exceptions

std::runtime_error

if string length exceeds GGUF_STRING_MAX_LENGTH

Definition at line 82 of file gguf_parser.cpp.

                                              {
  uint64_t len;
  read_raw(file, len);
  if (len > 0) {
    if (len > GGUF_STRING_MAX_LENGTH) {
      throw std::runtime_error(
          "GGUF Error: String length exceeds sanity limit: " +
          std::to_string(len));
    }
    std::vector<char> buf(static_cast<size_t>(len));
    file.read(buf.data(), static_cast<std::streamsize>(len));
    if (!file) {
      throw std::runtime_error("GGUF Error: Failed to read string data.");
    }
    return std::string(buf.data(), static_cast<size_t>(len));
  } else {
    return "";
  }
}

References GGUF_STRING_MAX_LENGTH, j, and read_raw().

Referenced by load_gguf_meta().

read_raw()

template<typename T >

void read_raw	(	std::ifstream &	file,
		T &	dest
	)

Reads raw binary data from a file stream.

Template Parameters

T	The type of data to read

Parameters

file	Input file stream
dest	Destination variable to store the read data

Definition at line 62 of file gguf_parser.cpp.

                                          {
  file.read(reinterpret_cast<char*>(&dest), sizeof(T));
  if (!file) {
    throw std::runtime_error(
        "GGUF Error: Failed to read data from file stream.");
  }
}

References j.

Referenced by load_gguf_meta(), and read_gguf_string().

read_raw< double >()

template void read_raw< double >	(	std::ifstream &	,
		double &
	)

read_raw< float >()

template void read_raw< float >	(	std::ifstream &	,
		float &
	)

read_raw< GGUFValueType >()

template void read_raw< GGUFValueType >	(	std::ifstream &	,
		GGUFValueType &
	)

read_raw< int16_t >()

template void read_raw< int16_t >	(	std::ifstream &	,
		int16_t &
	)

read_raw< int32_t >()

template void read_raw< int32_t >	(	std::ifstream &	,
		int32_t &
	)

read_raw< int64_t >()

template void read_raw< int64_t >	(	std::ifstream &	,
		int64_t &
	)

read_raw< int8_t >()

template void read_raw< int8_t >	(	std::ifstream &	,
		int8_t &
	)

read_raw< uint16_t >()

template void read_raw< uint16_t >	(	std::ifstream &	,
		uint16_t &
	)

read_raw< uint32_t >()

template void read_raw< uint32_t >	(	std::ifstream &	,
		uint32_t &
	)

read_raw< uint64_t >()

template void read_raw< uint64_t >	(	std::ifstream &	,
		uint64_t &
	)

read_raw< uint8_t >()

template void read_raw< uint8_t >	(	std::ifstream &	,
		uint8_t &
	)