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#define NOMINMAX
#endif
// C 系统头文件
#ifndef _WIN32
#include <fcntl.h>
#include <sys/mman.h>
#include <sys/types.h>
#include <unistd.h>
#else
#include <windows.h>
#endif
// C++ 标准库
#include <algorithm>
#include <chrono>
#include <cmath>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <filesystem>
#include <fstream>
#include <iostream>
#include <numeric>
#include <sstream>
#include <string>
#include <vector>
// 第三方库
#ifdef _OPENMP
#include <omp.h>
#endif
// 项目头文件
#include "neuroflow/backprop.hpp"
#include "neuroflow/generative.hpp"
#include "neuroflow/model.hpp"
#include "neuroflow/online_learning.hpp"
#include "weight_io.hpp"
using neuroflow::BPETokenizer;
using neuroflow::CausalLMConfig;
using neuroflow::CausalLMHead;
using neuroflow::FullBackpropEngine;
using neuroflow::InitStrategy;
using neuroflow::NeuroFlowModel;
using neuroflow::QuantType;
using neuroflow::Tensor;
using neuroflow::WeightInitializer;
namespace {
Tensor lm_linear_backward_input(const Tensor& output_grad, const Tensor& weight) {
size_t batch = output_grad.shape_[0];
size_t out_f = output_grad.shape_[1];
size_t in_f = weight.shape_[1];
Tensor input_grad({batch, in_f}, QuantType::FP32);
#ifdef USE_CUDA
if (CudaContext::instance().is_available() && output_grad.is_on_gpu()) {
input_grad.to_gpu();
CudaContext::instance().sgemm_rowmajor(false, false,
static_cast<int>(batch), static_cast<int>(in_f), static_cast<int>(out_f),
1.0f, output_grad.as_gpu_fp32(), static_cast<int>(out_f),
weight.as_gpu_fp32(), static_cast<int>(in_f),
0.0f, input_grad.as_gpu_fp32(), static_cast<int>(in_f));
input_grad.gpu_dirty_ = true;
return input_grad;
}
#endif
memset(input_grad.as_fp32(), 0, input_grad.data_size_);
if (out_f > 8192 && batch == 1) {
const float* og = output_grad.as_fp32();
const float* w = weight.as_fp32();
float* ig = input_grad.as_fp32();
float threshold = 1e-4f;
size_t n_active = 0;
for (size_t i = 0; i < out_f; ++i) {
if (std::abs(og[i]) >= threshold) n_active++;
}
if (n_active < out_f / 4) {
for (size_t i = 0; i < out_f; ++i) {
float g = og[i];
if (std::abs(g) < threshold) continue;
const float* w_row = w + i * in_f;
for (size_t j = 0; j < in_f; ++j) {
ig[j] += g * w_row[j];
}
}
return input_grad;
}
}
#ifdef USE_CBLAS
cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans,
batch, in_f, out_f,
1.0f, output_grad.as_fp32(), out_f, weight.as_fp32(), in_f,
0.0f, input_grad.as_fp32(), in_f);
#else
const float* og2 = output_grad.as_fp32();
const float* w2 = weight.as_fp32();
float* ig2 = input_grad.as_fp32();
for (size_t b = 0; b < batch; ++b) {
for (size_t j = 0; j < in_f; ++j) {
float sum = 0.0f;
for (size_t i = 0; i < out_f; ++i) {
sum += og2[b * out_f + i] * w2[i * in_f + j];
}
ig2[b * in_f + j] = sum;
}
}
#endif
return input_grad;
}
Tensor lm_linear_backward_weight(const Tensor& input, const Tensor& output_grad) {
size_t batch = input.shape_[0];
size_t in_f = input.shape_[1];
size_t out_f = output_grad.shape_[1];
Tensor weight_grad({out_f, in_f}, QuantType::FP32);
#ifdef USE_CUDA
if (CudaContext::instance().is_available() && output_grad.is_on_gpu()) {
weight_grad.to_gpu();
CudaContext::instance().sgemm_rowmajor(true, false,
static_cast<int>(out_f), static_cast<int>(in_f), static_cast<int>(batch),
1.0f / batch, output_grad.as_gpu_fp32(), static_cast<int>(out_f),
input.as_gpu_fp32(), static_cast<int>(in_f),
0.0f, weight_grad.as_gpu_fp32(), static_cast<int>(in_f));
weight_grad.gpu_dirty_ = true;
return weight_grad;
}
#endif
#ifdef USE_CBLAS
cblas_sgemm(CblasRowMajor, CblasTrans, CblasNoTrans,
out_f, in_f, batch,
1.0f / batch, output_grad.as_fp32(), out_f, input.as_fp32(), in_f,
0.0f, weight_grad.as_fp32(), in_f);
#else
const float* inp = input.as_fp32();
const float* og = output_grad.as_fp32();
float* wg = weight_grad.as_fp32();
for (size_t i = 0; i < out_f; ++i) {
for (size_t j = 0; j < in_f; ++j) {
float sum = 0.0f;
for (size_t b = 0; b < batch; ++b) {
sum += og[b * out_f + i] * inp[b * in_f + j];
}
wg[i * in_f + j] = sum / batch;
}
}
#endif
return weight_grad;
}
}
struct TrainConfig {
std::string config_path;
std::string tokenizer_path;
std::string data_path;
std::string output_dir = "./output";
int epochs = 10;
float learning_rate = 1e-4f;
uint32_t seed = 42;
std::string resume_path = "";
std::string init_strategy = "xavier";
int batch_size = 32;
int log_interval = 50;
int save_interval = 5000;
float grad_clip = 1.0f;
bool use_adam = false;
int grad_accum_steps = 4;
std::string vocab_mask_path = "";
int replay_buffer_size = 10000;
float replay_ratio = 0.25f;
bool use_cuda = false;
bool verbose = false;
};
TrainConfig parse_args(int argc, char* argv[]) {
TrainConfig cfg;
for (int i = 1; i < argc; ++i) {
std::string arg = argv[i];
if (arg == "--config" && i + 1 < argc) cfg.config_path = argv[++i];
else if (arg == "--tokenizer" && i + 1 < argc) cfg.tokenizer_path = argv[++i];
else if (arg == "--data" && i + 1 < argc) cfg.data_path = argv[++i];
else if (arg == "--output" && i + 1 < argc) cfg.output_dir = argv[++i];
else if (arg == "--epochs" && i + 1 < argc) cfg.epochs = std::atoi(argv[++i]);
else if (arg == "--lr" && i + 1 < argc) cfg.learning_rate = std::atof(argv[++i]);
else if (arg == "--seed" && i + 1 < argc) cfg.seed = static_cast<uint32_t>(std::atoi(argv[++i]));
else if (arg == "--resume" && i + 1 < argc) cfg.resume_path = argv[++i];
else if (arg == "--init-weights" && i + 1 < argc) cfg.init_strategy = argv[++i];
else if (arg == "--batch-size" && i + 1 < argc) cfg.batch_size = std::atoi(argv[++i]);
else if (arg == "--log-interval" && i + 1 < argc) cfg.log_interval = std::atoi(argv[++i]);
else if (arg == "--save-interval" && i + 1 < argc) cfg.save_interval = std::atoi(argv[++i]);
else if (arg == "--grad-clip" && i + 1 < argc) cfg.grad_clip = std::atof(argv[++i]);
else if (arg == "--adam") cfg.use_adam = true;
else if (arg == "--grad-accum" && i + 1 < argc) cfg.grad_accum_steps = std::atoi(argv[++i]);
else if (arg == "--vocab-mask" && i + 1 < argc) cfg.vocab_mask_path = argv[++i];
else if (arg == "--replay-buffer" && i + 1 < argc) cfg.replay_buffer_size = std::atoi(argv[++i]);
else if (arg == "--replay-ratio" && i + 1 < argc) cfg.replay_ratio = std::atof(argv[++i]);
else if (arg == "--use-cuda") cfg.use_cuda = true;
else if (arg == "--verbose") cfg.verbose = true;
}
return cfg;
}
namespace {
std::string read_file(const std::string& path) {
std::ifstream ifs(path);
if (!ifs) return "";
return std::string((std::istreambuf_iterator<char>(ifs)), std::istreambuf_iterator<char>());
}
std::string trim(const std::string& s) {
size_t start = s.find_first_not_of(" \t\r\n");
if (start == std::string::npos) return "";
size_t end = s.find_last_not_of(" \t\r\n");
return s.substr(start, end - start + 1);
}
std::string extract_json_string(const std::string& json, const std::string& key) {
std::string search = "\"" + key + "\"";
size_t pos = json.find(search);
if (pos == std::string::npos) return "";
pos = json.find(':', pos + search.size());
if (pos == std::string::npos) return "";
pos++;
while (pos < json.size() && (json[pos] == ' ' || json[pos] == '\t')) pos++;
if (pos >= json.size() || json[pos] != '"') return "";
size_t end = json.find('"', pos + 1);
if (end == std::string::npos) return "";
return json.substr(pos + 1, end - pos - 1);
}
size_t extract_json_number(const std::string& json, const std::string& key, size_t default_val = 0) {
std::string search = "\"" + key + "\"";
size_t pos = json.find(search);
if (pos == std::string::npos) return default_val;
pos = json.find(':', pos + search.size());
if (pos == std::string::npos) return default_val;
pos++;
while (pos < json.size() && (json[pos] == ' ' || json[pos] == '\t')) pos++;
size_t end = pos;
while (end < json.size() && (json[end] >= '0' && json[end] <= '9')) end++;
if (end == pos) return default_val;
return std::stoul(json.substr(pos, end - pos));
}
bool extract_json_bool(const std::string& json, const std::string& key, bool default_val = false) {
std::string search = "\"" + key + "\"";
size_t pos = json.find(search);
if (pos == std::string::npos) return default_val;
pos = json.find(':', pos + search.size());
if (pos == std::string::npos) return default_val;
pos++;
while (pos < json.size() && (json[pos] == ' ' || json[pos] == '\t')) pos++;
if (pos + 3 < json.size() && json.substr(pos, 4) == "true") return true;
if (pos + 4 < json.size() && json.substr(pos, 5) == "false") return false;
return default_val;
}
}
NeuroFlowModel::Config load_model_config(const std::string& json_path) {
NeuroFlowModel::Config cfg;
std::string json = read_file(json_path);
if (json.empty()) {
std::cerr << "配置加载: " << json_path << " (使用默认配置)" << std::endl;
return cfg;
}
cfg.input_dim = extract_json_number(json, "d_model", cfg.input_dim);
cfg.hidden_dim = extract_json_number(json, "hidden_dim", cfg.hidden_dim);
cfg.output_dim = extract_json_number(json, "output_dim", cfg.output_dim);
cfg.memory_dim = extract_json_number(json, "memory_dim", cfg.memory_dim);
cfg.memory_slots = extract_json_number(json, "memory_slots", cfg.memory_slots);
cfg.num_layers = extract_json_number(json, "num_layers", cfg.num_layers);
cfg.num_associations = extract_json_number(json, "num_associations", cfg.num_associations);
cfg.use_quantization = extract_json_bool(json, "use_quantization", cfg.use_quantization);
cfg.use_mla = extract_json_bool(json, "use_mla", cfg.use_mla);
cfg.use_causal_lm = extract_json_bool(json, "use_causal_lm", cfg.use_causal_lm);
cfg.mla_latent_dim = extract_json_number(json, "mla_latent_dim", cfg.mla_latent_dim);
cfg.vocab_size = extract_json_number(json, "vocab_size", cfg.vocab_size);
cfg.max_seq_len = extract_json_number(json, "max_seq_len", cfg.max_seq_len);
cfg.causal_window_size = extract_json_number(json, "causal_window_size", cfg.causal_window_size);
cfg.lm_num_attn_layers = extract_json_number(json, "lm_num_attn_layers", cfg.lm_num_attn_layers);
cfg.lm_num_attn_heads = extract_json_number(json, "lm_num_attn_heads", cfg.lm_num_attn_heads);
cfg.lm_n_kv_heads = extract_json_number(json, "lm_n_kv_heads", cfg.lm_n_kv_heads);
cfg.lm_use_rope = extract_json_bool(json, "lm_use_rope", cfg.lm_use_rope);
cfg.lm_use_qk_norm = extract_json_bool(json, "lm_use_qk_norm", cfg.lm_use_qk_norm);
cfg.lm_use_swiglu = extract_json_bool(json, "lm_use_swiglu", cfg.lm_use_swiglu);
cfg.lm_use_bridge = extract_json_bool(json, "lm_use_bridge", cfg.lm_use_bridge);
cfg.lm_pooling = extract_json_string(json, "lm_pooling");
if (cfg.lm_pooling.empty()) cfg.lm_pooling = "last";
std::cerr << "配置加载: " << json_path << std::endl;
std::cerr << " d_model=" << cfg.input_dim << " hidden_dim=" << cfg.hidden_dim
<< " output_dim=" << cfg.output_dim << " vocab_size=" << cfg.vocab_size << std::endl;
return cfg;
}
struct ResumeState {
bool found = false;
size_t step = 0;
int epoch = 0;
float loss = 0.0f;
float lr = 0.0f;
size_t config_d_model = 0;
size_t config_output_dim = 0;
};
ResumeState load_checkpoint(NeuroFlowModel& model, const std::string& ckpt_path) {
ResumeState state;
std::string model_path, state_path;
if (ckpt_path.size() >= 5 && ckpt_path.substr(ckpt_path.size() - 5) == ".nfv1") {
model_path = ckpt_path;
state_path = ckpt_path.substr(0, ckpt_path.size() - 5) + "/../training_state.json";
std::string dir = ckpt_path.substr(0, ckpt_path.find_last_of("/\\"));
state_path = dir + "/training_state.json";
} else {
model_path = ckpt_path + "/model.nfv1";
state_path = ckpt_path + "/training_state.json";
}
std::ifstream ifs(model_path, std::ios::binary);
if (!ifs) return state;
char magic[5] = {0};
ifs.read(magic, 4);
if (std::string(magic) != "NFv1") return state;
ifs.close();
std::ifstream state_ifs(state_path);
if (state_ifs) {
std::string content((std::istreambuf_iterator<char>(state_ifs)),
std::istreambuf_iterator<char>());
state.step = extract_json_number(content, "step", 0);
state.epoch = static_cast<int>(extract_json_number(content, "epoch", 0));
state.loss = std::atof(extract_json_string(content, "loss").c_str());
state.lr = std::atof(extract_json_string(content, "learning_rate").c_str());
state.config_d_model = extract_json_number(content, "config_d_model", 0);
state.config_output_dim = extract_json_number(content, "config_output_dim", 0);
}
if (state.config_d_model > 0 && state.config_d_model != model.config.input_dim) {
std::cerr << "警告: checkpoint的d_model=" << state.config_d_model
<< " 与当前config的d_model=" << model.config.input_dim
<< " 不匹配，可能导致维度错误！" << std::endl;
}
if (state.config_output_dim > 0 && state.config_output_dim != model.config.output_dim) {
std::cerr << "警告: checkpoint的output_dim=" << state.config_output_dim
<< " 与当前config的output_dim=" << model.config.output_dim
<< " 不匹配！" << std::endl;
}
model.load(model_path);
state.found = true;
return state;
}
bool load_lm_head(CausalLMHead& lm_head, const std::string& lm_path) {
std::ifstream ifs(lm_path, std::ios::binary);
if (!ifs) {
std::cerr << "LM Head checkpoint未找到: " << lm_path << std::endl;
return false;
}
char magic[5] = {0};
ifs.read(magic, 4);
if (std::string(magic) != "LMH2" && std::string(magic) != "LMH1") {
std::cerr << "LM Head格式不匹配: " << std::string(magic) << " (期望LMH2/LMH1)" << std::endl;
ifs.close();
return false;
}
auto read_named_tensor = [&]() -> std::pair<std::string, Tensor> {
uint32_t nl = 0; ifs.read((char*)&nl, 4);
if (nl == 0 || ifs.eof()) return {"", Tensor()};
std::string name(nl, '\0'); ifs.read(&name[0], nl);
uint32_t nd = 0; ifs.read((char*)&nd, 4);
std::vector<size_t> shape(nd);
for (size_t i = 0; i < nd; ++i) { uint32_t d = 0; ifs.read((char*)&d, 4); shape[i] = d; }
uint32_t ds = 0; ifs.read((char*)&ds, 4);
Tensor t(shape, QuantType::FP32);
if (t.data_size_ == ds) {
ifs.read((char*)t.data_.get(), ds);
} else {
ifs.seekg(ds, std::ios::cur);
t = Tensor();
}
return {name, std::move(t)};
};
std::unordered_map<std::string, Tensor*> tensor_map;
tensor_map["w_embed"] = &lm_head.w_embed_;
tensor_map["w_pos"] = &lm_head.w_pos_;
tensor_map["dw_kernel"] = &lm_head.dw_kernel_;
tensor_map["pw_conv.weight"] = &lm_head.pw_conv_->weight;
tensor_map["sae_encode.weight"] = &lm_head.sae_w_encode_->weight;
tensor_map["sae_decode.weight"] = &lm_head.sae_w_decode_->weight;
tensor_map["ntm_read.weight"] = &lm_head.ntm_w_read_->weight;
tensor_map["ntm_write.weight"] = &lm_head.ntm_w_write_->weight;
tensor_map["ntm_erase.weight"] = &lm_head.ntm_w_erase_->weight;
tensor_map["ntm_memory"] = &lm_head.ntm_memory_;
tensor_map["w_proj.weight"] = &lm_head.w_proj_->weight;
tensor_map["w_proj.bias"] = &lm_head.w_proj_->bias;
if (lm_head.bridge_) {
tensor_map["bridge.weight"] = &lm_head.bridge_->weight;
tensor_map["bridge.bias"] = &lm_head.bridge_->bias;
}
tensor_map["w_out.weight"] = &lm_head.w_out_->weight;
tensor_map["w_out.bias"] = &lm_head.w_out_->bias;
tensor_map["ln.weight"] = &lm_head.ln_->weight;
tensor_map["ln.bias"] = &lm_head.ln_->bias;
for (size_t i = 0; i < lm_head.attn_layers_.size(); ++i) {
std::string p = "attn" + std::to_string(i) + ".";
tensor_map[p + "w_q.weight"] = &lm_head.attn_layers_[i]->w_q->weight;
tensor_map[p + "w_q.bias"] = &lm_head.attn_layers_[i]->w_q->bias;
tensor_map[p + "w_k.weight"] = &lm_head.attn_layers_[i]->w_k->weight;
tensor_map[p + "w_k.bias"] = &lm_head.attn_layers_[i]->w_k->bias;
tensor_map[p + "w_v.weight"] = &lm_head.attn_layers_[i]->w_v->weight;
tensor_map[p + "w_v.bias"] = &lm_head.attn_layers_[i]->w_v->bias;
tensor_map[p + "w_out.weight"] = &lm_head.attn_layers_[i]->w_out->weight;
tensor_map[p + "w_out.bias"] = &lm_head.attn_layers_[i]->w_out->bias;
tensor_map[p + "norm.weight"] = &lm_head.attn_layers_[i]->norm->weight;
tensor_map[p + "norm.bias"] = &lm_head.attn_layers_[i]->norm->bias;
}
size_t loaded = 0;
while (ifs) {
auto [name, tensor] = read_named_tensor();
if (name.empty()) break;
auto it = tensor_map.find(name);
if (it != tensor_map.end() && tensor.numel() > 0) {
if (it->second->shape_ == tensor.shape_) {
memcpy(it->second->data_.get(), tensor.data_.get(), tensor.data_size_);
loaded++;
} else {
std::cerr << " 跳过 '" << name << "': 形状不匹配" << std::endl;
}
} else if (name.find("w_qkv.") != std::string::npos) {
std::string base = name.substr(0, name.find("w_qkv."));
std::string suffix = name.substr(name.find("w_qkv.") + 6);
for (size_t i = 0; i < lm_head.attn_layers_.size(); ++i) {
std::string p = "attn" + std::to_string(i) + ".";
if (base != p) continue;
size_t d_model = lm_head.config_.d_model;
size_t head_dim = d_model / lm_head.config_.num_attn_heads;
size_t n_q = lm_head.attn_layers_[i]->n_q_heads_;
size_t n_kv = lm_head.attn_layers_[i]->n_kv_heads_;
if (suffix == "weight" && tensor.shape_.size() == 2 && tensor.shape_[0] == 3 * d_model) {
const float* src = tensor.as_fp32();
float* dq = lm_head.attn_layers_[i]->w_q->weight.as_fp32();
float* dk = lm_head.attn_layers_[i]->w_k->weight.as_fp32();
float* dv = lm_head.attn_layers_[i]->w_v->weight.as_fp32();
for (size_t r = 0; r < d_model; ++r) {
memcpy(dq + r * n_q * head_dim, src + r * 3 * d_model, n_q * head_dim * sizeof(float));
memcpy(dk + r * n_kv * head_dim, src + r * 3 * d_model + d_model, n_kv * head_dim * sizeof(float));
memcpy(dv + r * n_kv * head_dim, src + r * 3 * d_model + 2 * d_model, n_kv * head_dim * sizeof(float));
}
loaded++;
std::cerr << " 拆分旧格式 '" << name << "' -> w_q/w_k/w_v" << std::endl;
} else if (suffix == "bias" && tensor.shape_[0] == 3 * d_model) {
const float* src = tensor.as_fp32();
float* bq = lm_head.attn_layers_[i]->w_q->bias.as_fp32();
float* bk = lm_head.attn_layers_[i]->w_k->bias.as_fp32();
float* bv = lm_head.attn_layers_[i]->w_v->bias.as_fp32();
memcpy(bq, src, n_q * head_dim * sizeof(float));
memcpy(bk, src + d_model, n_kv * head_dim * sizeof(float));
memcpy(bv, src + 2 * d_model, n_kv * head_dim * sizeof(float));
loaded++;
std::cerr << " 拆分旧格式 '" << name << "' -> w_q/w_k/w_v bias" << std::endl;
}
}
}
}
ifs.close();
if (lm_head.config_.weight_tying) lm_head.tie_weights();
std::cerr << "LM Head已恢复: " << loaded << " 个张量从 " << lm_path << std::endl;
return loaded > 0;
}
NeuroFlowModel build_model(const NeuroFlowModel::Config& cfg, const TrainConfig& train_cfg,
ResumeState& resume_state) {
NeuroFlowModel model(cfg);
if (!train_cfg.resume_path.empty()) {
resume_state = load_checkpoint(model, train_cfg.resume_path);
if (resume_state.found) {
std::cerr << "从checkpoint恢复: " << train_cfg.resume_path
<< " (step=" << resume_state.step
<< ", epoch=" << resume_state.epoch
<< ", loss=" << resume_state.loss << ")" << std::endl;
} else {
throw std::runtime_error("无法加载checkpoint: " + train_cfg.resume_path);
}
} else {
InitStrategy strategy = InitStrategy::XAVIER_UNIFORM;
if (train_cfg.init_strategy == "kaiming") strategy = InitStrategy::KAIMING_NORMAL;
else if (train_cfg.init_strategy == "zeros") strategy = InitStrategy::ZEROS;
WeightInitializer::init_model_weights(model, strategy, train_cfg.seed);
std::cerr << "权重初始化: " << train_cfg.init_strategy << std::endl;
}
return model;
}
static void unescape_json(std::string& s) {
size_t pos = 0;
while ((pos = s.find('\\', pos)) != std::string::npos) {
if (pos + 1 < s.size()) {
char c = s[pos + 1];
if (c == 'n') { s.replace(pos, 2, "\n"); pos++; }
else if (c == 't') { s.replace(pos, 2, "\t"); pos++; }
else if (c == 'r') { s.replace(pos, 2, "\r"); pos++; }
else if (c == '"') { s.replace(pos, 2, "\""); pos++; }
else if (c == '\\') { s.replace(pos, 2, "\\"); pos++; }
else if (c == '/') { s.replace(pos, 2, "/"); pos++; }
else if (c == 'u' && pos + 5 < s.size()) { pos += 6; }
else { pos += 2; }
} else { pos++; }
}
}
struct LMSample {
std::vector<size_t> token_ids;
};
struct TextSpan {
size_t file_index;
size_t offset;
size_t length;
};
struct TrainingSample {
std::vector<float> input;
std::vector<float> target;
};
class StreamingDataLoader {
public:
StreamingDataLoader(const std::string& path, int batch_size,
BPETokenizer* tokenizer, size_t max_seq_len = 128,
size_t max_samples = 500000)
: batch_size_(batch_size), tokenizer_(tokenizer),
max_seq_len_(max_seq_len), max_samples_(max_samples),
current_idx_(0) {
index_files(path);
std::cerr << "StreamingDataLoader: " << spans_.size() << " 文本段已索引"
<< " (" << file_paths_.size() << " 文件)" << std::endl;
}
void index_files(const std::string& path) {
namespace fs = std::filesystem;
fs::path p(path);
if (fs::is_directory(p)) {
std::vector<fs::path> files;
for (auto& entry : fs::recursive_directory_iterator(p, fs::directory_options::follow_directory_symlink)) {
if (!entry.is_regular_file()) continue;
auto ext = entry.path().extension().string();
if (ext == ".txt" || ext == ".json" || ext == ".jsonl" || ext == ".csv" || ext == ".tsv") {
files.push_back(entry.path());
}
}
std::sort(files.begin(), files.end());
for (auto& f : files) index_single_file(f.string());
} else {
index_single_file(p.string());
}
}
void index_single_file(const std::string& file_path) {
namespace fs = std::filesystem;
std::ifstream ifs(file_path, std::ios::binary | std::ios::ate);
if (!ifs) return;
size_t file_size = static_cast<size_t>(ifs.tellg());
ifs.seekg(0, std::ios::beg);
std::string ext = fs::path(file_path).extension().string();
size_t file_idx = file_paths_.size();
file_paths_.push_back(file_path);
if (ext == ".jsonl" || ext == ".json") {
std::string line;
size_t line_offset = 0;
while (std::getline(ifs, line) && spans_.size() < max_samples_) {
if (line.size() < 12) { line_offset += line.size() + 1; continue; }
std::string text = extract_text_from_line(line);
if (text.size() >= 10) {
text_cache_.push_back(std::move(text));
spans_.push_back(TextSpan{SIZE_MAX, text_cache_.size() - 1, text_cache_.back().size()});
}
line_offset += line.size() + 1;
}
} else if (ext == ".txt") {
std::string line;
while (std::getline(ifs, line) && spans_.size() < max_samples_) {
if (line.size() < 10) continue;
text_cache_.push_back(std::move(line));
spans_.push_back(TextSpan{SIZE_MAX, text_cache_.size() - 1, text_cache_.back().size()});
}
} else if (ext == ".csv" || ext == ".tsv") {
char delim = (ext == ".tsv") ? '\t' : ',';
std::string line;
while (std::getline(ifs, line) && spans_.size() < max_samples_) {
size_t pos = line.find(delim);
if (pos == std::string::npos) continue;
std::string text = line.substr(pos + 1);
if (text.size() >= 10) {
text_cache_.push_back(std::move(text));
spans_.push_back(TextSpan{SIZE_MAX, text_cache_.size() - 1, text_cache_.back().size()});
}
}
}
}
std::string extract_text_from_line(const std::string& line) {
const char* fields[] = {"\"text\"", "\"content\"", "\"title\"", "\"question\"", "\"answer\""};
for (auto& field : fields) {
size_t pos = line.find(field);
if (pos == std::string::npos) continue;
size_t colon = line.find(':', pos + strlen(field));
if (colon == std::string::npos) continue;
size_t q1 = line.find('"', colon);
if (q1 == std::string::npos) continue;
size_t q2 = line.find('"', q1 + 1);
if (q2 == std::string::npos) continue;
std::string text = line.substr(q1 + 1, q2 - q1 - 1);
unescape_json(text);
return text;
}
return "";
}
LMSample get(size_t idx) const {
if (idx >= spans_.size()) return {};
const auto& span = spans_[idx];
const std::string& text = text_cache_[span.offset];
auto ids = tokenizer_->encode(text, max_seq_len_);
return LMSample{std::move(ids)};
}
bool has_next() const { return current_idx_ < spans_.size(); }
std::vector<LMSample> next_lm_batch_raw() {
std::vector<LMSample> batch;
for (int i = 0; i < batch_size_ && current_idx_ < spans_.size(); ++i) {
batch.push_back(get(current_idx_++));
}
return batch;
}
void reset() { current_idx_ = 0; }
void shuffle(std::mt19937& rng) {
std::shuffle(spans_.begin(), spans_.end(), rng);
}
size_t total_samples() const { return spans_.size(); }
size_t memory_usage_bytes() const {
size_t total = spans_.capacity() * sizeof(TextSpan);
for (auto& t : text_cache_) total += t.capacity();
return total;
}
private:
int batch_size_;
BPETokenizer* tokenizer_;
size_t max_seq_len_;
size_t max_samples_;
size_t current_idx_;
std::vector<std::string> file_paths_;
std::vector<TextSpan> spans_;
std::vector<std::string> text_cache_;
};
class DataLoader {
public:
enum class Mode { LM, CLASSIFICATION, REGRESSION };
DataLoader(const std::string& path, int batch_size, size_t input_dim, size_t output_dim,
Mode mode = Mode::LM, BPETokenizer* tokenizer = nullptr,
size_t max_seq_len = 128, size_t max_samples = 500000,
size_t max_memory_mb = 4096)
: path_(path), batch_size_(batch_size), input_dim_(input_dim), output_dim_(output_dim),
current_idx_(0), mode_(mode), tokenizer_(tokenizer), max_seq_len_(max_seq_len),
max_samples_(max_samples), max_memory_bytes_(max_memory_mb * 1024ULL * 1024ULL) {
load_data();
}
void load_data() {
if (path_.empty()) {
throw std::runtime_error("DataLoader: 数据路径为空，请指定--data参数");
}
namespace fs = std::filesystem;
fs::path p(path_);
if (!fs::exists(p)) {
throw std::runtime_error("DataLoader: 路径不存在: " + path_);
}
if (fs::is_directory(p)) {
load_directory(p.string());
} else {
load_file(p.string());
}
if (lm_samples_.empty() && samples_.empty()) {
throw std::runtime_error(
"DataLoader: 未能从 " + path_ + " 加载任何训练数据。"
"请检查文件格式（支持txt/json/jsonl/csv/tsv）和内容是否有效。");
}
if (!lm_samples_.empty()) {
std::cerr << "DataLoader: " << lm_samples_.size() << " LM样本已加载" << std::endl;
} else {
std::cerr << "DataLoader: " << samples_.size() << " 数值样本已加载" << std::endl;
}
}
void load_directory(const std::string& dir_path) {
namespace fs = std::filesystem;
size_t file_count = 0;
size_t skipped = 0;
std::cerr << "DataLoader: 扫描目录 " << dir_path << " ..." << std::endl;
std::vector<fs::path> files;
for (auto& entry : fs::recursive_directory_iterator(dir_path, fs::directory_options::follow_directory_symlink)) {
if (!entry.is_regular_file()) continue;
files.push_back(entry.path());
}
std::cerr << "DataLoader: 发现 " << files.size() << " 个文件" << std::endl;
std::sort(files.begin(), files.end());
for (size_t fi = 0; fi < files.size(); ++fi) {
auto& fpath = files[fi];
std::string ext = fpath.extension().string();
std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower);
if (ext == ".json" || ext == ".jsonl" || ext == ".txt" ||
ext == ".csv" || ext == ".tsv" || ext == ".tok1" || ext == ".bin") {
size_t file_size_mb = 0;
try { file_size_mb = fs::file_size(fpath) / 1024 / 1024; } catch (...) {}
std::cerr << "[" << (fi + 1) << "/" << files.size() << "] 加载: "
<< fpath.filename().string()
<< " (" << file_size_mb << " MB)" << std::endl;
auto load_start = std::chrono::steady_clock::now();
try {
load_file(fpath.string());
file_count++;
} catch (const std::exception& e) {
std::cerr << " 跳过: " << e.what() << std::endl;
skipped++;
}
auto load_end = std::chrono::steady_clock::now();
double load_sec = std::chrono::duration<double>(load_end - load_start).count();
size_t total = lm_samples_.size() + samples_.size();
std::cerr << " 完成: " << load_sec << "s, 累计 "
<< total << " 样本" << std::endl;
if (total >= max_samples_) {
std::cerr << "DataLoader: 已达采样上限 " << max_samples_
<< "，停止遍历" << std::endl;
break;
}
} else if (ext == ".parquet") {
skipped++;
}
}
std::cerr << "DataLoader: 遍历 " << file_count << " 个文件, 跳过 " << skipped << std::endl;
}
void load_file(const std::string& file_path) {
namespace fs = std::filesystem;
std::string ext = fs::path(file_path).extension().string();
std::transform(ext.begin(), ext.end(), ext.begin(), ::tolower);
{
std::ifstream ifs(file_path, std::ios::binary);
if (ifs) {
char magic[5] = {0};
ifs.read(magic, 4);
ifs.close();
if (std::string(magic) == "TOK1") {
load_tok1(file_path);
return;
}
}
}
if (mode_ == Mode::LM && tokenizer_) {
if (ext == ".json") {
size_t file_size = fs::file_size(file_path);
if (file_size > 512 * 1024 * 1024) {
load_json_mmap(file_path);
} else {
load_json(file_path);
}
}
else if (ext == ".jsonl") load_jsonl(file_path);
else if (ext == ".txt") load_txt(file_path);
else if (ext == ".csv" || ext == ".tsv") load_txt(file_path);
else if (ext == ".tok1" || ext == ".bin") load_tok1(file_path);
else if (ext == ".parquet") {
throw std::runtime_error("Parquet格式需Python预处理: python3 scripts/preprocess_corpus.py split --input <path>");
} else {
throw std::runtime_error("不支持的文件格式: " + ext);
}
} else {
if (ext == ".csv" || ext == ".tsv") load_csv(file_path);
else if (ext == ".tok1" || ext == ".bin") load_tok1(file_path);
else {
throw std::runtime_error("数值模式仅支持csv/tsv/tok1格式，收到: " + ext);
}
}
}
void load_tok1(const std::string& file_path) {
std::ifstream ifs(file_path, std::ios::binary);
if (!ifs) throw std::runtime_error("无法打开TOK1文件: " + file_path);
char magic[5] = {0};
ifs.read(magic, 4);
if (std::string(magic) != "TOK1") {
throw std::runtime_error("TOK1格式Magic不匹配: " + std::string(magic));
}
uint16_t version = 0;
ifs.read(reinterpret_cast<char*>(&version), 2);
uint32_t vocab_size = 0, max_seq = 0, total_samples = 0;
ifs.read(reinterpret_cast<char*>(&vocab_size), 4);
ifs.read(reinterpret_cast<char*>(&max_seq), 4);
ifs.read(reinterpret_cast<char*>(&total_samples), 4);
std::cerr << "TOK1: version=" << version << " vocab=" << vocab_size
<< " max_seq=" << max_seq << " samples=" << total_samples << std::endl;
size_t loaded = 0;
for (size_t i = 0; i < total_samples; ++i) {
uint16_t seq_len = 0;
ifs.read(reinterpret_cast<char*>(&seq_len), 2);
if (!ifs) break;
LMSample sample;
sample.token_ids.resize(seq_len);
ifs.read(reinterpret_cast<char*>(sample.token_ids.data()), seq_len * 4);
if (!ifs) break;
lm_samples_.push_back(std::move(sample));
loaded++;
if (lm_samples_.size() >= max_samples_) {
std::cerr << "TOK1: 达到采样上限 " << max_samples_ << std::endl;
break;
}
}
std::cerr << "TOK1: 加载 " << loaded << " 样本" << std::endl;
}
void load_json_mmap(const std::string& file_path) {
#ifdef _WIN32
load_json_chunked(file_path);
#else
int fd = open(file_path.c_str(), O_RDONLY);
if (fd < 0) throw std::runtime_error("无法mmap: " + file_path);
size_t file_size = lseek(fd, 0, SEEK_END);
lseek(fd, 0, SEEK_SET);
void* addr = mmap(nullptr, file_size, PROT_READ, MAP_PRIVATE, fd, 0);
if (addr == MAP_FAILED) {
close(fd);
throw std::runtime_error("mmap失败: " + file_path);
}
const char* data = static_cast<const char*>(addr);
std::cerr << "mmap: 映射 " << file_path << " (" << file_size / 1024 / 1024 << " MB)" << std::endl;
size_t pos = 0;
size_t brace_depth = 0;
std::string record;
const char* fields[] = {"\"text\"", "\"content\"", "\"title\"", "\"question\"", "\"answer\""};
while (pos < file_size && lm_samples_.size() < max_samples_) {
char ch = data[pos];
if (ch == '{') {
if (brace_depth == 0) record.clear();
brace_depth++;
} else if (ch == '}') {
brace_depth--;
if (brace_depth == 0 && !record.empty()) {
record += '}';
extract_text_from_json_record(record);
record.clear();
}
}
if (brace_depth > 0) record += ch;
pos++;
if (lm_samples_.size() % 100000 == 0 && lm_samples_.size() > 0 && pos % (100 * 1024 * 1024) == 0) {
std::cerr << " mmap进度: " << pos * 100 / file_size << "%, "
<< lm_samples_.size() << " 样本" << std::endl;
}
}
munmap(addr, file_size);
close(fd);
std::cerr << "mmap: 完成, " << lm_samples_.size() << " 样本" << std::endl;
#endif
}
void load_json_chunked(const std::string& file_path) {
const size_t chunk_size = 256 * 1024 * 1024;
std::ifstream ifs(file_path, std::ios::binary);
if (!ifs) throw std::runtime_error("无法打开: " + file_path);
size_t file_size = std::filesystem::file_size(file_path);
std::cerr << "分块读取: " << file_path << " (" << file_size / 1024 / 1024 << " MB)" << std::endl;
size_t offset = 0;
size_t brace_depth = 0;
std::string record;
std::string overlap;
while (offset < file_size && lm_samples_.size() < max_samples_) {
size_t read_size = std::min(chunk_size, file_size - offset);
std::string buffer;
buffer.resize(read_size);
ifs.read(&buffer[0], read_size);
if (!ifs) break;
if (!overlap.empty()) {
buffer = overlap + buffer;
overlap.clear();
}
size_t pos = 0;
while (pos < buffer.size() && lm_samples_.size() < max_samples_) {
char ch = buffer[pos];
if (ch == '{') {
if (brace_depth == 0) record.clear();
brace_depth++;
} else if (ch == '}') {
brace_depth--;
if (brace_depth == 0 && !record.empty()) {
record += '}';
extract_text_from_json_record(record);
record.clear();
}
}
if (brace_depth > 0 && record.size() < 1048576) record += ch;
pos++;
}
if (brace_depth > 0) {
overlap = record;
}
offset += read_size;
std::cerr << " 分块进度: " << offset * 100 / file_size << "%, "
<< lm_samples_.size() << " 样本" << std::endl;
}
if (!record.empty() && brace_depth == 0) {
extract_text_from_json_record(record);
}
}
void load_json(const std::string& file_path) {
std::ifstream ifs(file_path);
if (!ifs) throw std::runtime_error("无法打开: " + file_path);
std::string line;
size_t brace_depth = 0;
std::string record;
size_t records_found = 0;
while (std::getline(ifs, line)) {
for (size_t i = 0; i < line.size(); ++i) {
if (line[i] == '{') {
if (brace_depth == 0) record.clear();
brace_depth++;
} else if (line[i] == '}') {
brace_depth--;
if (brace_depth == 0 && !record.empty()) {
record += '}';
extract_text_from_json_record(record);
record.clear();
records_found++;
if (lm_samples_.size() >= max_samples_) return;
}
}
}
if (brace_depth > 0) {
if (record.size() < 1048576) {
record += line;
record += '\n';
}
}
}
if (records_found == 0) {
std::cerr << " 警告: 流式record解析未找到数据，尝试逐行文本提取" << std::endl;
ifs.clear();
ifs.seekg(0);
std::string line;
while (std::getline(ifs, line)) {
if (line.size() < 20) continue;
size_t q1 = line.find('"');
if (q1 == std::string::npos) continue;
size_t q2 = line.rfind('"');
if (q2 <= q1 + 1) continue;
std::string text = line.substr(q1 + 1, q2 - q1 - 1);
unescape_json(text);
if (text.size() >= 10) add_lm_sample(text);
if (lm_samples_.size() >= max_samples_) return;
}
}
}
void extract_text_from_json_record(const std::string& record) {
const char* fields[] = {"\"text\"", "\"content\"", "\"title\"", "\"question\"", "\"answer\""};
for (auto& field : fields) {
size_t pos = record.find(field);
if (pos == std::string::npos) continue;
size_t colon = record.find(':', pos + strlen(field));
if (colon == std::string::npos) continue;
colon++;
while (colon < record.size() && record[colon] != '"') colon++;
if (colon >= record.size()) continue;
colon++;
size_t end = colon;
while (end < record.size() && record[end] != '"') {
if (record[end] == '\\') end++;
end++;
}
std::string text = record.substr(colon, end - colon);
unescape_json(text);
if (text.size() >= 10) add_lm_sample(text);
}
}
void extract_all_text_fields(const std::string& content) {
size_t pos = 0;
const char* fields[] = {"\"text\"", "\"content\"", "\"title\"", "\"question\"", "\"answer\""};
while (pos < content.size()) {
size_t best_pos = std::string::npos;
for (auto& field : fields) {
size_t p = content.find(field, pos);
if (p != std::string::npos && (best_pos == std::string::npos || p < best_pos)) {
best_pos = p;
}
}
if (best_pos == std::string::npos) break;
size_t colon = content.find(':', best_pos);
if (colon == std::string::npos) break;
colon++;
while (colon < content.size() && content[colon] != '"') colon++;
if (colon >= content.size()) break;
colon++;
size_t end = colon;
while (end < content.size() && content[end] != '"') {
if (content[end] == '\\') end++;
end++;
}
if (end >= content.size()) break;
std::string text = content.substr(colon, end - colon);
unescape_json(text);
add_lm_sample(text);
pos = end + 1;
if (lm_samples_.size() >= max_samples_) return;
}
}
void load_jsonl(const std::string& file_path) {
std::ifstream ifs(file_path);
if (!ifs) throw std::runtime_error("无法打开: " + file_path);
std::string line;
size_t line_num = 0;
while (std::getline(ifs, line)) {
line_num++;
if (line.empty() || line[0] == '#') continue;
const char* fields[] = {"\"text\"", "\"content\"", "\"title\"", "\"question\"", "\"answer\""};
bool found = false;
for (auto& field : fields) {
size_t text_pos = line.find(field);
if (text_pos == std::string::npos) continue;
size_t colon = line.find(':', text_pos);
if (colon == std::string::npos) continue;
colon++;
while (colon < line.size() && line[colon] != '"') colon++;
if (colon >= line.size()) continue;
colon++;
size_t end = colon;
while (end < line.size() && line[end] != '"') {
if (line[end] == '\\') end++;
end++;
}
std::string text = line.substr(colon, end - colon);
unescape_json(text);
add_lm_sample(text);
found = true;
break;
}
if (!found && line_num <= 5) {
std::cerr << " jsonl行" << line_num << ": 未找到text/content字段" << std::endl;
}
if (lm_samples_.size() >= max_samples_) return;
}
}
void load_txt(const std::string& file_path) {
std::ifstream ifs(file_path);
if (!ifs) throw std::runtime_error("无法打开: " + file_path);
std::string line;
std::string paragraph;
size_t line_count = 0;
while (std::getline(ifs, line)) {
line_count++;
if (line.empty()) {
if (paragraph.size() >= 10) {
add_lm_sample(paragraph);
paragraph.clear();
if (lm_samples_.size() >= max_samples_) return;
}
} else {
if (!paragraph.empty()) paragraph += '\n';
paragraph += line;
if (paragraph.size() > 10000) {
add_lm_sample(paragraph);
paragraph.clear();
if (lm_samples_.size() >= max_samples_) return;
}
}
if (line_count % 500000 == 0) {
std::cerr << " load_txt: " << line_count << " 行, "
<< lm_samples_.size() << " 样本" << std::endl;
}
}
if (paragraph.size() >= 10) add_lm_sample(paragraph);
}
void load_csv(const std::string& file_path) {
std::ifstream ifs(file_path);
if (!ifs) throw std::runtime_error("无法打开: " + file_path);
std::string line;
while (std::getline(ifs, line)) {
if (line.empty() || line[0] == '#') continue;
TrainingSample s;
std::stringstream ss(line);
std::string val;
char delim = ',';
if (line.find('\t') != std::string::npos && line.find(',') == std::string::npos) {
delim = '\t';
}
bool in_target = false;
while (std::getline(ss, val, delim)) {
float v = std::atof(val.c_str());
if (!in_target) {
s.input.push_back(v);
if (s.input.size() == input_dim_) in_target = true;
} else {
s.target.push_back(v);
}
}
if (!s.input.empty() && !s.target.empty()) {
samples_.push_back(std::move(s));
}
if (samples_.size() >= max_samples_) return;
}
}
void add_lm_sample(const std::string& text) {
if (text.size() < 10) return;
if (!tokenizer_) return;
if (lm_samples_.size() >= max_samples_) return;
if (memory_usage_bytes() >= max_memory_bytes_) {
if (lm_samples_.size() % 100000 == 1) {
std::cerr << " 内存预算达到 " << max_memory_bytes_ / 1024 / 1024
<< " MB，停止加载" << std::endl;
}
return;
}
// BPE安全截断: 限制输入长度, 防止超长段落卡死encode
// max_seq_len_已由DataLoader构造时设定(默认128),
// 但text字符数可能远大于token数(中文3字节/token)
// 安全上限: max_seq_len_ * 4 字符 (留足UTF-8余量)
const size_t max_text_len = max_seq_len_ * 4;
if (text.size() > max_text_len) {
// 截断到安全长度, 保留前max_text_len字符
auto ids = tokenizer_->encode(text.substr(0, max_text_len), max_seq_len_);
if (ids.size() >= 4) {
lm_samples_.push_back(LMSample{std::move(ids)});
}
return;
}
auto ids = tokenizer_->encode(text, max_seq_len_);
if (ids.size() < 4) return;
lm_samples_.push_back(LMSample{std::move(ids)});
}
static void unescape_json_static(std::string& s) { unescape_json(s); }
bool has_next() const {
if (mode_ == Mode::LM) return current_idx_ < lm_samples_.size();
return current_idx_ < samples_.size();
}
std::vector<TrainingSample> next_batch() {
std::vector<TrainingSample> batch;
if (mode_ == Mode::LM) {
float vocab_scale = 1.0f;
if (tokenizer_) vocab_scale = 1.0f / static_cast<float>(tokenizer_->vocab_size());
for (int i = 0; i < batch_size_ && current_idx_ < lm_samples_.size(); ++i) {
auto& sample = lm_samples_[current_idx_++];
TrainingSample s;
s.input.resize(input_dim_, 0.0f);
s.target.resize(output_dim_, 0.0f);
size_t n = std::min(sample.token_ids.size(), input_dim_);
for (size_t j = 0; j < n; ++j) {
s.input[j] = static_cast<float>(sample.token_ids[j]) * vocab_scale;
}
for (size_t j = 0; j + 1 < n; ++j) {
size_t next_id = sample.token_ids[j + 1];
if (next_id < output_dim_) {
s.target[next_id] = 1.0f;
}
}
batch.push_back(std::move(s));
}
} else {
for (int i = 0; i < batch_size_ && current_idx_ < samples_.size(); ++i) {
batch.push_back(samples_[current_idx_++]);
}
}
return batch;
}
std::vector<LMSample> next_lm_batch_raw() {
std::vector<LMSample> batch;
for (int i = 0; i < batch_size_ && current_idx_ < lm_samples_.size(); ++i) {
batch.push_back(lm_samples_[current_idx_++]);
}
return batch;
}
void reset() { current_idx_ = 0; }
void shuffle(std::mt19937& rng) {
if (mode_ == Mode::LM) {
std::shuffle(lm_samples_.begin(), lm_samples_.end(), rng);
} else {
std::shuffle(samples_.begin(), samples_.end(), rng);
}
}
size_t total_samples() const {
if (mode_ == Mode::LM) return lm_samples_.size();
return samples_.size();
}
Mode mode() const { return mode_; }
size_t memory_usage_bytes() const {
size_t total = 0;
for (auto& s : lm_samples_) total += s.token_ids.size() * sizeof(size_t);
for (auto& s : samples_) total += (s.input.size() + s.target.size()) * sizeof(float);
return total;
}
private:
std::string path_;
int batch_size_;
size_t input_dim_;
size_t output_dim_;
size_t current_idx_;
Mode mode_;
BPETokenizer* tokenizer_;
size_t max_seq_len_;
size_t max_samples_;
size_t max_memory_bytes_;
std::vector<TrainingSample> samples_;
std::vector<LMSample> lm_samples_;
};
struct TrainMetrics {
float loss = 0.0f;
float lr = 0.0f;
size_t step = 0;
int epoch = 0;
float elapsed_seconds = 0.0f;
float grad_norm = 0.0f;
};
class MetricsLogger {
public:
MetricsLogger() : start_time_(std::chrono::steady_clock::now()) {}
void log_step(const TrainMetrics& m, int interval) {
if (m.step % interval != 0) return;
std::cerr << "[Epoch " << m.epoch << "][Step " << m.step
<< "] loss=" << m.loss << " lr=" << m.lr
<< " grad_norm=" << m.grad_norm
<< " elapsed=" << m.elapsed_seconds << "s" << std::endl;
}
void log_epoch(int epoch, float avg_loss, float elapsed) {
std::cerr << "=== Epoch " << epoch << " 完成, avg_loss=" << avg_loss
<< ", elapsed=" << elapsed << "s ===" << std::endl;
}
void save_training_log(const std::string& path, const std::vector<float>& losses) {
std::ofstream ofs(path);
if (!ofs) return;
ofs << "{\n \"losses\": [";
for (size_t i = 0; i < losses.size(); ++i) {
if (i > 0) ofs << ", ";
ofs << losses[i];
}
ofs << "],\n \"num_epochs\": " << losses.size() << "\n}";
ofs.close();
}
double total_elapsed() const {
auto now = std::chrono::steady_clock::now();
return std::chrono::duration<double>(now - start_time_).count();
}
private:
std::chrono::steady_clock::time_point start_time_;
};
void ensure_dir_exists(const std::string& dir) {
std::filesystem::create_directories(dir);
}
void save_checkpoint(const NeuroFlowModel& model, const TrainMetrics& metrics,
const std::string& output_dir, size_t step, int epoch,
const TrainConfig& cfg) {
// ═══════════════════════════════════════════════════════
// RAM Disk加速: 先写/dev/shm (1.3GB/s), 再后台拷贝到D盘 (125MB/s)
// 307MB checkpoint: shm写0.2s vs D盘写2.5s — 节省2.3s/次
// ═══════════════════════════════════════════════════════
std::string ckpt_name = "checkpoint_step" + std::to_string(step);
std::string ckpt_dir = output_dir + "/" + ckpt_name;
ensure_dir_exists(ckpt_dir);
// 1) 快速写入RAM Disk
std::string shm_dir = "/dev/shm/nf_ckpt/" + ckpt_name;
ensure_dir_exists(shm_dir);
model.save(shm_dir + "/model.nfv1");
std::ofstream shm_ofs(shm_dir + "/training_state.json");
if (shm_ofs) {
shm_ofs << "{\n"
<< " \"step\": " << step << ",\n"
<< " \"epoch\": " << epoch << ",\n"
<< " \"loss\": " << metrics.loss << ",\n"
<< " \"learning_rate\": " << metrics.lr << ",\n"
<< " \"grad_norm\": " << metrics.grad_norm << ",\n"
<< " \"config_d_model\": " << model.config.input_dim << ",\n"
<< " \"config_hidden_dim\": " << model.config.hidden_dim << ",\n"
<< " \"config_output_dim\": " << model.config.output_dim << ",\n"
<< " \"config_vocab_size\": " << model.config.vocab_size << ",\n"
<< " \"init_strategy\": \"" << cfg.init_strategy << "\",\n"
<< " \"seed\": " << cfg.seed << "\n"
<< "}\n";
shm_ofs.close();
}
// 2) 后台拷贝到持久目录 (不阻塞训练)
#ifndef _WIN32
pid_t pid = fork();
if (pid == 0) {
// 子进程: 拷贝后退出
execlp("cp", "cp", "-r", shm_dir.c_str(), ckpt_dir.c_str(), static_cast<char*>(nullptr));
_exit(1); // execlp失败
}
// 父进程不等待, 继续训练
std::cerr << " [ckpt] RAM→D盘后台拷贝: " << ckpt_name
<< " (pid=" << pid << ")" << std::endl;
#else
// Windows: 直接拷贝(无fork)
std::filesystem::copy(shm_dir, ckpt_dir,
std::filesystem::copy_options::recursive | std::filesystem::copy_options::overwrite_existing);
#endif
std::cerr << "Checkpoint已保存: step=" << step << " epoch=" << epoch
<< " loss=" << metrics.loss << std::endl;
}
std::unique_ptr<BPETokenizer> load_tokenizer(const std::string& tokenizer_path) {
auto tok = std::make_unique<BPETokenizer>(tokenizer_path);
std::cerr << "词表加载成功: " << tokenizer_path
<< " (vocab_size=" << tok->vocab_size() << ")" << std::endl;
return tok;
}
void train_loop(NeuroFlowModel& model, CausalLMHead& lm_head, DataLoader& loader, MetricsLogger& logger,
const TrainConfig& cfg, const ResumeState& resume_state,
const std::vector<uint8_t>& vocab_mask = {}) {
#ifdef _OPENMP
int max_threads = omp_get_max_threads();
std::cerr << "OpenMP最大线程数: " << max_threads << std::endl;
#else
std::cerr << "警告: 未启用OpenMP，非GEMM运算为单线程" << std::endl;
#endif
size_t vocab_sz = lm_head.config_.vocab_size;
size_t d_model = lm_head.config_.d_model;
float learning_rate = cfg.learning_rate;
std::cerr << "训练模式: CausalLMHead完整forward (embed→PE→Attn×"
<< lm_head.config_.num_attn_layers << "→Gate→SAE→NTM→LN→Pool→Proj→Out)" << std::endl;
std::cerr << " d_model=" << d_model << " vocab=" << vocab_sz
<< " pooling=" << lm_head.config_.pooling << std::endl;
std::vector<float> epoch_losses;
int start_epoch = resume_state.found ? resume_state.epoch : 0;
size_t start_step = resume_state.found ? resume_state.step : 0;
if (start_epoch > 0) {
std::cerr << "续训: 从epoch " << start_epoch + 1 << " 开始, 已完成 "
<< start_step << " 步" << std::endl;
}
auto save_lm_head = [&](const std::string& path) {
#ifdef USE_CUDA
auto sync_to_cpu = [](const Tensor& t) {
if (t.is_on_gpu()) { const_cast<Tensor&>(t).to_cpu(); }
};
sync_to_cpu(lm_head.w_embed_);
sync_to_cpu(lm_head.w_pos_);
sync_to_cpu(lm_head.dw_kernel_);
sync_to_cpu(lm_head.pw_conv_->weight);
sync_to_cpu(lm_head.sae_w_encode_->weight);
sync_to_cpu(lm_head.sae_w_decode_->weight);
sync_to_cpu(lm_head.ntm_w_read_->weight);
sync_to_cpu(lm_head.ntm_w_write_->weight);
sync_to_cpu(lm_head.ntm_w_erase_->weight);
sync_to_cpu(lm_head.ntm_memory_);
sync_to_cpu(lm_head.w_proj_->weight);
sync_to_cpu(lm_head.w_proj_->bias);
if (lm_head.bridge_) {
sync_to_cpu(lm_head.bridge_->weight);
sync_to_cpu(lm_head.bridge_->bias);
}
sync_to_cpu(lm_head.w_out_->weight);
sync_to_cpu(lm_head.w_out_->bias);
sync_to_cpu(lm_head.ln_->weight);
sync_to_cpu(lm_head.ln_->bias);
for (auto& attn : lm_head.attn_layers_) {
sync_to_cpu(attn->w_q->weight);
sync_to_cpu(attn->w_q->bias);
sync_to_cpu(attn->w_k->weight);
sync_to_cpu(attn->w_k->bias);
sync_to_cpu(attn->w_v->weight);
sync_to_cpu(attn->w_v->bias);
sync_to_cpu(attn->w_out->weight);
sync_to_cpu(attn->w_out->bias);
sync_to_cpu(attn->norm->weight);
sync_to_cpu(attn->norm->bias);
}
#endif
auto sl = [](std::ofstream& o, const std::string& n, const Tensor& t) {
uint32_t nl = n.size(); o.write((char*)&nl, 4); o.write(n.data(), nl);
uint32_t nd = t.shape_.size(); o.write((char*)&nd, 4);
for (auto d : t.shape_) { uint32_t dd = d; o.write((char*)&dd, 4); }
uint32_t ds = t.data_size_; o.write((char*)&ds, 4);
o.write((char*)t.data_.get(), ds);
};
std::ofstream o(path, std::ios::binary);
o.write("LMH2", 4);
sl(o, "w_embed", lm_head.w_embed_);
sl(o, "w_pos", lm_head.w_pos_);
sl(o, "dw_kernel", lm_head.dw_kernel_);
sl(o, "pw_conv.weight", lm_head.pw_conv_->weight);
sl(o, "sae_encode.weight", lm_head.sae_w_encode_->weight);
sl(o, "sae_decode.weight", lm_head.sae_w_decode_->weight);
sl(o, "ntm_read.weight", lm_head.ntm_w_read_->weight);
sl(o, "ntm_write.weight", lm_head.ntm_w_write_->weight);
sl(o, "ntm_erase.weight", lm_head.ntm_w_erase_->weight);
sl(o, "ntm_memory", lm_head.ntm_memory_);
sl(o, "w_proj.weight", lm_head.w_proj_->weight);
sl(o, "w_proj.bias", lm_head.w_proj_->bias);
if (lm_head.bridge_) {
sl(o, "bridge.weight", lm_head.bridge_->weight);
sl(o, "bridge.bias", lm_head.bridge_->bias);
}
sl(o, "w_out.weight", lm_head.w_out_->weight);
if (lm_head.w_out_->bias.data_) sl(o, "w_out.bias", lm_head.w_out_->bias);
sl(o, "ln.weight", lm_head.ln_->weight);
sl(o, "ln.bias", lm_head.ln_->bias);
for (size_t i = 0; i < lm_head.attn_layers_.size(); ++i) {
std::string p = "attn" + std::to_string(i) + ".";
sl(o, p + "w_q.weight", lm_head.attn_layers_[i]->w_q->weight);
sl(o, p + "w_q.bias", lm_head.attn_layers_[i]->w_q->bias);
sl(o, p + "w_k.weight", lm_head.attn_layers_[i]->w_k->weight);
sl(o, p + "w_k.bias", lm_head.attn_layers_[i]->w_k->bias);
sl(o, p + "w_v.weight", lm_head.attn_layers_[i]->w_v->weight);
sl(o, p + "w_v.bias", lm_head.attn_layers_[i]->w_v->bias);
sl(o, p + "w_out.weight", lm_head.attn_layers_[i]->w_out->weight);
sl(o, p + "w_out.bias", lm_head.attn_layers_[i]->w_out->bias);
sl(o, p + "norm.weight", lm_head.attn_layers_[i]->norm->weight);
sl(o, p + "norm.bias", lm_head.attn_layers_[i]->norm->bias);
}
uint32_t z = 0; o.write((char*)&z, 4); o.close();
};
lm_head.train();
for (int epoch = start_epoch; epoch < cfg.epochs; ++epoch) {
auto epoch_start = std::chrono::steady_clock::now();
loader.reset();
std::mt19937 shuffle_rng(cfg.seed + epoch);
loader.shuffle(shuffle_rng);
float epoch_loss = 0.0f;
size_t step_count = 0;
size_t global_step = start_step + epoch * ((loader.total_samples() + cfg.batch_size - 1) / cfg.batch_size);
while (loader.has_next()) {
auto batch = loader.next_lm_batch_raw();
if (batch.empty()) break;
float batch_loss = 0.0f;
float batch_grad_norm = 0.0f;
size_t batch_count = 0;
for (auto& sample : batch) {
if (sample.token_ids.size() < 2) continue;
std::vector<size_t> input_ids(sample.token_ids.begin(), sample.token_ids.end() - 1);
size_t target_id = sample.token_ids.back();
if (target_id >= vocab_sz) target_id = 1;
Tensor logits = lm_head.forward_for_training(input_ids);
float loss = 0.0f;
float grad_norm = 0.0f;
Tensor logits_grad({1, vocab_sz}, QuantType::FP32);
#ifdef USE_CUDA
if (cfg.use_cuda && logits.is_on_gpu()) {
logits_grad.to_gpu();
launch_cross_entropy_backward(logits_grad.as_gpu_fp32(), logits.as_gpu_fp32(),
static_cast<int>(target_id), static_cast<int>(vocab_sz), CudaContext::instance().stream());
logits_grad.gpu_dirty_ = true;
Tensor d_loss({1}, QuantType::FP32);
d_loss.to_gpu();
launch_cross_entropy(d_loss.as_gpu_fp32(), logits.as_gpu_fp32(),
static_cast<int>(target_id), static_cast<int>(vocab_sz), CudaContext::instance().stream());
CudaContext::instance().synchronize();
float loss_arr[1];
CudaContext::instance().copy_d2h(loss_arr, d_loss.as_gpu_fp32(), sizeof(float));
CudaContext::instance().synchronize();
loss = loss_arr[0];
if (!std::isfinite(loss)) {
std::cerr << "[WARN] NaN loss at step " << global_step << ", skipping" << std::endl;
continue;
}
grad_norm = 0.0f;
} else
#endif
{
const float* pred = logits.as_fp32();
float max_val = -1e30f;
for (size_t j = 0; j < vocab_sz; ++j) {
if (pred[j] > max_val) max_val = pred[j];
}
float sum_exp = 0.0f;
for (size_t j = 0; j < vocab_sz; ++j) {
sum_exp += std::exp(pred[j] - max_val);
}
float log_sum_exp = max_val + std::log(sum_exp);
loss = -(pred[target_id] - log_sum_exp);
if (!std::isfinite(loss)) {
std::cerr << "[WARN] NaN loss at step " << global_step << ", skipping" << std::endl;
continue;
}
float* lg = logits_grad.as_fp32();
for (size_t j = 0; j < vocab_sz; ++j) {
float softmax_val = std::exp(pred[j] - max_val) / sum_exp;
lg[j] = softmax_val;
if (j == target_id) lg[j] -= 1.0f;
grad_norm += lg[j] * lg[j];
}
}
auto lm_grads = lm_head.backward_from_logits(logits_grad);
float clip_val = cfg.grad_clip;
float gn = std::sqrt(grad_norm);
float clip_scale = 1.0f;
if (!std::isfinite(gn) || gn > clip_val && clip_val > 0.0f) {
clip_scale = clip_val / gn;
}
lm_head.apply_lm_gradients(lm_grads, learning_rate * clip_scale);
batch_loss += loss;
batch_grad_norm += grad_norm;
batch_count++;
}
if (batch_count > 0) {
epoch_loss += batch_loss / batch_count;
step_count++;
global_step++;
TrainMetrics metrics;
metrics.loss = batch_loss / batch_count;
metrics.lr = cfg.learning_rate;
metrics.step = global_step;
metrics.epoch = epoch + 1;
metrics.grad_norm = std::sqrt(batch_grad_norm / batch_count);
metrics.elapsed_seconds = static_cast<float>(logger.total_elapsed());
logger.log_step(metrics, cfg.log_interval);
if (cfg.save_interval > 0 && global_step % cfg.save_interval == 0) {
std::string cdir = cfg.output_dir + "/checkpoint_step" + std::to_string(global_step);
ensure_dir_exists(cdir);
model.save(cdir + "/model.nfv1");
save_lm_head(cdir + "/lm_head.nfv1");
std::cerr << "Checkpoint: step=" << global_step << " loss=" << metrics.loss << std::endl;
}
}
}
float avg_loss = (step_count > 0) ? epoch_loss / step_count : 0.0f;
epoch_losses.push_back(avg_loss);
auto epoch_end = std::chrono::steady_clock::now();
float epoch_elapsed = static_cast<float>(
std::chrono::duration<double>(epoch_end - epoch_start).count());
logger.log_epoch(epoch + 1, avg_loss, epoch_elapsed);
std::string cdir = cfg.output_dir + "/checkpoint_epoch" + std::to_string(epoch + 1);
ensure_dir_exists(cdir);
model.save(cdir + "/model.nfv1");
save_lm_head(cdir + "/lm_head.nfv1");
}
logger.save_training_log(cfg.output_dir + "/training_log.json", epoch_losses);
save_lm_head(cfg.output_dir + "/lm_head_final.nfv1");
std::cerr << "训练完成, LM Head已保存: " << cfg.output_dir << "/lm_head_final.nfv1" << std::endl;
}
int main(int argc, char* argv[]) {
setvbuf(stderr, nullptr, _IONBF, 0);
setvbuf(stdout, nullptr, _IONBF, 0);
std::ios::sync_with_stdio(false);
#ifdef USE_CBLAS
int ncpu = 1;
#ifdef _WIN32
SYSTEM_INFO si; GetSystemInfo(&si); ncpu = si.dwNumberOfProcessors;
#else
ncpu = sysconf(_SC_NPROCESSORS_ONLN);
#endif
if (ncpu < 1) ncpu = 1;
int blas_threads = std::max(1, ncpu / 2);
std::string nt = std::to_string(blas_threads);
#ifdef _WIN32
_putenv_s("OPENBLAS_NUM_THREADS", nt.c_str());
_putenv_s("GOTO_NUM_THREADS", nt.c_str());
#else
setenv("OPENBLAS_NUM_THREADS", nt.c_str(), 1);
setenv("GOTO_NUM_THREADS", nt.c_str(), 1);
#endif
std::cerr << "GEMM后端: OpenBLAS (多线程), BLAS线程: " << blas_threads << std::endl;
#else
std::cerr << "GEMM后端: 内置AVX2 (单线程!) — 安装libopenblas-dev可大幅提升性能" << std::endl;
#endif
TrainConfig cfg = parse_args(argc, argv);
if (cfg.config_path.empty() || cfg.tokenizer_path.empty()) {
std::cerr << "用法: neuroflow_train_v2 --config <config.json> --tokenizer <tokenizer.json>"
<< " [--data <data>] [--output <dir>] [--epochs <N>] [--lr <lr>]"
<< " [--seed <N>] [--resume <path>] [--init-weights <strategy>]"
<< " [--batch-size <N>] [--log-interval <N>] [--save-interval <N>]"
<< " [--grad-clip <val>] [--adam] [--grad-accum <N>]"
<< " [--use-cuda] [--verbose]" << std::endl;
return 1;
}
std::cerr << "======================================" << std::endl;
std::cerr << "NeuroFlow Train v2 (Full Backprop)" << std::endl;
std::cerr << "======================================" << std::endl;
std::cerr << "配置: " << cfg.config_path << std::endl;
std::cerr << "词表: " << cfg.tokenizer_path << std::endl;
std::cerr << "数据: " << (cfg.data_path.empty() ? "(随机)" : cfg.data_path) << std::endl;
std::cerr << "输出: " << cfg.output_dir << std::endl;
std::cerr << "Epochs: " << cfg.epochs << " LR: " << cfg.learning_rate
<< " Batch: " << cfg.batch_size
<< " GradAccum: " << cfg.grad_accum_steps
<< " (等效batch=" << cfg.batch_size * cfg.grad_accum_steps << ")" << std::endl;
#ifdef USE_CUDA
bool cuda_active = false;
if (cfg.use_cuda) {
if (CudaContext::instance().initialize(0)) {
cuda_active = true;
std::cerr << "GPU后端: 已启用 (RTX 3090)" << std::endl;
} else {
std::cerr << "[CUDA WARNING] GPU初始化失败，回退CPU后端" << std::endl;
cfg.use_cuda = false;
}
} else {
std::cerr << "GPU后端: 未启用 (使用--use-cuda启用)" << std::endl;
}
#else
if (cfg.use_cuda) {
std::cerr << "[CUDA WARNING] 编译时未启用CUDA支持 (NEUROFLOW_USE_CUDA=OFF)，回退CPU后端" << std::endl;
cfg.use_cuda = false;
}
#endif
auto model_cfg = load_model_config(cfg.config_path);
ensure_dir_exists(cfg.output_dir);
ResumeState resume_state;
auto model = build_model(model_cfg, cfg, resume_state);
auto tokenizer = load_tokenizer(cfg.tokenizer_path);
auto stats = model.get_stats();
std::cerr << "模型参数: " << stats.total_params << " 内存: " << stats.memory_bytes << " bytes" << std::endl;
CausalLMConfig lm_cfg;
lm_cfg.vocab_size = model_cfg.vocab_size;
lm_cfg.d_model = model_cfg.hidden_dim;
lm_cfg.max_seq_len = model_cfg.max_seq_len;
lm_cfg.causal_window_size = model_cfg.causal_window_size;
lm_cfg.sae_k = model_cfg.sae_k;
lm_cfg.ntm_memory_slots = model_cfg.ntm_memory_slots;
lm_cfg.use_mla = model_cfg.use_mla;
lm_cfg.mla_latent_dim = model_cfg.mla_latent_dim;
lm_cfg.mla_n_heads = model_cfg.mla_n_heads;
lm_cfg.mla_max_cache_len = 4096;
lm_cfg.weight_tying = true;
lm_cfg.num_attn_layers = model_cfg.lm_num_attn_layers;
lm_cfg.num_attn_heads = model_cfg.lm_num_attn_heads;
lm_cfg.n_kv_heads = model_cfg.lm_n_kv_heads;
lm_cfg.use_rope = model_cfg.lm_use_rope;
lm_cfg.use_qk_norm = model_cfg.lm_use_qk_norm;
lm_cfg.use_swiglu = model_cfg.lm_use_swiglu;
lm_cfg.use_bridge = model_cfg.lm_use_bridge;
lm_cfg.pooling = model_cfg.lm_pooling;
CausalLMHead lm_head(lm_cfg);
if (lm_cfg.weight_tying) lm_head.tie_weights();
if (!cfg.resume_path.empty()) {
std::string lm_ckpt_path;
if (cfg.resume_path.size() >= 5 && cfg.resume_path.substr(cfg.resume_path.size() - 5) == ".nfv1") {
std::string dir = cfg.resume_path.substr(0, cfg.resume_path.find_last_of("/\\"));
lm_ckpt_path = dir + "/lm_head.nfv1";
} else {
lm_ckpt_path = cfg.resume_path + "/lm_head.nfv1";
}
load_lm_head(lm_head, lm_ckpt_path);
}
std::cerr << "CausalLMHead: d_model=" << lm_cfg.d_model
<< " vocab=" << lm_cfg.vocab_size
<< " attn_layers=" << lm_cfg.num_attn_layers
<< " pooling=" << lm_cfg.pooling << std::endl;
#ifdef USE_CUDA
if (cfg.use_cuda && cuda_active) {
std::cerr << "传输模型参数到GPU..." << std::endl;
lm_head.w_embed_.to_gpu();
lm_head.w_pos_.to_gpu();
lm_head.dw_kernel_.to_gpu();
lm_head.pw_conv_->weight.to_gpu();
lm_head.sae_w_encode_->weight.to_gpu();
lm_head.sae_w_decode_->weight.to_gpu();
lm_head.ntm_w_read_->weight.to_gpu();
lm_head.ntm_w_write_->weight.to_gpu();
lm_head.ntm_w_erase_->weight.to_gpu();
lm_head.ntm_memory_.to_gpu();
lm_head.w_proj_->weight.to_gpu();
lm_head.w_proj_->bias.to_gpu();
lm_head.w_out_->weight.to_gpu();
if (lm_head.w_out_->bias.data_) lm_head.w_out_->bias.to_gpu();
lm_head.ln_->weight.to_gpu();
lm_head.ln_->bias.to_gpu();
for (auto& attn : lm_head.attn_layers_) {
attn->w_q->weight.to_gpu();
attn->w_q->bias.to_gpu();
attn->w_k->weight.to_gpu();
attn->w_k->bias.to_gpu();
attn->w_v->weight.to_gpu();
attn->w_v->bias.to_gpu();
attn->w_out->weight.to_gpu();
attn->w_out->bias.to_gpu();
attn->norm->weight.to_gpu();
attn->norm->bias.to_gpu();
}
CudaContext::instance().synchronize();
size_t free_mem = CudaContext::instance().free_memory();
size_t total_mem = CudaContext::instance().total_memory();
std::cerr << "GPU显存: " << (total_mem - free_mem) / (1024*1024)
<< " MB 已用 / " << total_mem / (1024*1024) << " MB 总计" << std::endl;
}
#endif
DataLoader loader(cfg.data_path, cfg.batch_size, model_cfg.input_dim, model_cfg.vocab_size,
DataLoader::Mode::LM, tokenizer.get(), model_cfg.max_seq_len,
500000, 4096);
std::cerr << "DataLoader内存预算: 4096 MB, 采样上限: 500000" << std::endl;
std::vector<uint8_t> vocab_mask;
if (!cfg.vocab_mask_path.empty()) {
std::ifstream vmf(cfg.vocab_mask_path);
if (vmf) {
std::string vm_content((std::istreambuf_iterator<char>(vmf)), std::istreambuf_iterator<char>());
size_t mask_start = vm_content.find("\"mask\"");
if (mask_start != std::string::npos) {
size_t arr_start = vm_content.find('[', mask_start);
size_t arr_end = vm_content.find(']', arr_start);
if (arr_start != std::string::npos && arr_end != std::string::npos) {
std::string arr_str = vm_content.substr(arr_start + 1, arr_end - arr_start - 1);
size_t pos = 0;
while (pos < arr_str.size()) {
while (pos < arr_str.size() && (arr_str[pos] == ' ' || arr_str[pos] == ',')) pos++;
if (pos >= arr_str.size()) break;
vocab_mask.push_back(static_cast<uint8_t>(arr_str[pos] - '0'));
pos++;
}
}
}
size_t active = 0;
for (auto m : vocab_mask) active += m;
std::cerr << "Vocab mask: " << cfg.vocab_mask_path
<< " (" << active << "/" << vocab_mask.size() << " active)" << std::endl;
} else {
std::cerr << "Warning: vocab mask not found: " << cfg.vocab_mask_path << std::endl;
}
}
MetricsLogger metrics_logger;
train_loop(model, lm_head, loader, metrics_logger, cfg, resume_state, vocab_mask);
model.save(cfg.output_dir + "/model_final.nfv1");
std::cerr << "NF模型已保存: " << cfg.output_dir << "/model_final.nfv1" << std::endl;
return 0;
}
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