chore(代码变更): 1. 清理无用代码 2. 更新依赖库 3. 优化项目结构 4. 修复小型bug 5. 增加注释以提高可读性
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@@ -9,4 +9,5 @@ minicpm-core = { path = "../minicpm-core", version = "0.1.2", registry = "gitea"
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burn = { version = "0.21", default-features = false, features = ["std"] }
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memmap2 = "0.9"
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anyhow = "1.0"
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serde_json = "1.0"
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serde_json = "1.0"
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serde = { version = "1.0", features = ["derive"] }
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@@ -2,7 +2,7 @@ use minicpm_core::config::LlamaConfig;
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use minicpm_core::model::LlamaForCausalLM;
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use burn::module::{Module, Param};
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use burn::nn::{EmbeddingRecord, LinearRecord};
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use burn::record::{FullPrecisionSettings, NamedMpkFileRecorder};
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use burn::record::{FullPrecisionSettings, HalfPrecisionSettings, NamedMpkFileRecorder};
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use burn::tensor::backend::Backend;
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use burn::tensor::{Float, Shape, Tensor, TensorData};
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use memmap2::Mmap;
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@@ -40,6 +40,39 @@ pub fn export_model<B: Backend>(
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println!("模型已成功导出到: {:?}", output_dir);
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Ok(())
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}
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/// 导出模型为半精度（f16），输出文件约为全精度的一半大小。
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pub fn export_model_half<B: Backend>(
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safetensors_path: &Path,
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config_path: &Path,
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tokenizer_path: &Path,
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output_dir: &Path,
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device: &B::Device,
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) -> anyhow::Result<()> {
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println!("开始转换 MiniCPM 模型为 Burn 格式（半精度 f16）...");
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println!("加载配置文件: {:?}", config_path);
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let config = LlamaConfig::from_json(config_path.to_str().unwrap())?;
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println!("创建模型结构...");
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let model = LlamaForCausalLM::<B>::new(config, device);
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println!("加载 safetensors 权重...");
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let model = load_safetensors(model, safetensors_path, device)?;
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println!("保存为半精度 MPK 格式...");
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std::fs::create_dir_all(output_dir)?;
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let output_path = output_dir.join("model");
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let recorder = NamedMpkFileRecorder::<HalfPrecisionSettings>::new();
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model.save_file(&output_path, &recorder)?;
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println!("拷贝配置文件和 tokenizer...");
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std::fs::copy(config_path, output_dir.join("config.json"))?;
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std::fs::copy(tokenizer_path, output_dir.join("tokenizer.json"))?;
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println!("半精度模型已成功导出到: {:?}", output_dir);
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Ok(())
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}
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pub fn load_safetensors<B: Backend>(
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model: LlamaForCausalLM<B>,
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path: &Path,
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@@ -256,3 +289,223 @@ fn f16_to_f32(f16: u16) -> f32 {
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let f32_bits = ((sign as u32) << 31) | (f32_exp << 23) | ((mant as u32) << 13);
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f32::from_bits(f32_bits)
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}
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// ==================== Q8 量化导出 ====================
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/// Q8 量化单个 f32 切片，返回 (scale, i8_data)
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fn quantize_q8(f32_data: &[f32]) -> (f32, Vec<i8>) {
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let max_abs = f32_data
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.iter()
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.map(|&x| x.abs())
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.fold(0.0f32, |a, b| a.max(b));
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let scale = if max_abs > 0.0 { max_abs / 127.0 } else { 1.0 };
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let q8: Vec<i8> = f32_data
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.iter()
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.map(|&x| (x / scale).round().clamp(-127.0, 127.0) as i8)
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.collect();
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(scale, q8)
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}
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/// Q8 导出元数据中的 tensor 条目
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#[derive(serde::Serialize, serde::Deserialize)]
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struct Q8TensorMeta {
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shape: Vec<usize>,
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scale: f32,
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offset: u64,
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numel: usize,
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}
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/// Q8 导出元数据
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#[derive(serde::Serialize, serde::Deserialize)]
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struct Q8Metadata {
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tensors: std::collections::HashMap<String, Q8TensorMeta>,
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}
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/// 导出模型为 Q8 量化格式（INT8），输出 model.q8.json + model.q8.bin。
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/// 2D 权重（Linear/Embedding）量化为 INT8；1D norm 权重存为 F16。
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pub fn export_model_q8(
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safetensors_path: &Path,
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config_path: &Path,
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tokenizer_path: &Path,
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output_dir: &Path,
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) -> anyhow::Result<()> {
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println!("开始转换 MiniCPM 模型为 Q8 量化格式...");
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println!("解析 safetensors 文件: {:?}", safetensors_path);
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let file = std::fs::File::open(safetensors_path)?;
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let mmap = unsafe { Mmap::map(&file)? };
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let header_len = u64::from_le_bytes(mmap[..8].try_into().unwrap()) as usize;
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let header_bytes = &mmap[8..8 + header_len];
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let header: serde_json::Value = serde_json::from_slice(header_bytes)?;
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let data_offset = 8 + header_len;
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// 收集 tensor 信息
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let mut tensor_infos: Vec<(String, Vec<usize>, String, usize, usize)> = Vec::new();
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if let Some(obj) = header.as_object() {
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for (name, info) in obj {
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if name == "__metadata__" {
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continue;
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}
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if let Some(info_obj) = info.as_object() {
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let mut shape = Vec::new();
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if let Some(s) = info_obj.get("shape") {
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if let Some(arr) = s.as_array() {
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shape = arr.iter().map(|v| v.as_u64().unwrap() as usize).collect();
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}
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}
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let mut dtype = String::new();
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if let Some(d) = info_obj.get("dtype") {
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dtype = d.as_str().unwrap_or("").to_string();
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}
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let mut start = 0;
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let mut end = 0;
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if let Some(offsets) = info_obj.get("data_offsets") {
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if let Some(arr) = offsets.as_array() {
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start = arr[0].as_u64().unwrap() as usize;
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end = arr[1].as_u64().unwrap() as usize;
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}
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}
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tensor_infos.push((name.clone(), shape, dtype, start, end));
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}
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}
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}
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// 定义需要量化的 2D 权重名称（与 load_safetensors 中的列表对应）
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let mut q8_tensors: Vec<String> = Vec::new();
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for i in 0..32 {
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let p = format!("model.layers.{i}");
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q8_tensors.push(format!("{p}.self_attn.q_proj.weight"));
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q8_tensors.push(format!("{p}.self_attn.k_proj.weight"));
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q8_tensors.push(format!("{p}.self_attn.v_proj.weight"));
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q8_tensors.push(format!("{p}.self_attn.o_proj.weight"));
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q8_tensors.push(format!("{p}.mlp.gate_proj.weight"));
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q8_tensors.push(format!("{p}.mlp.up_proj.weight"));
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q8_tensors.push(format!("{p}.mlp.down_proj.weight"));
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}
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q8_tensors.push("model.embed_tokens.weight".to_string());
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q8_tensors.push("lm_head.weight".to_string());
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let q8_set: std::collections::HashSet<String> = q8_tensors.into_iter().collect();
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std::fs::create_dir_all(output_dir)?;
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let json_path = output_dir.join("model.q8.json");
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let bin_path = output_dir.join("model.q8.bin");
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let mut metadata: std::collections::HashMap<String, Q8TensorMeta> = std::collections::HashMap::new();
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let mut bin_file = std::fs::File::create(&bin_path)?;
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let mut current_offset: u64 = 0;
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// 先处理 2D 权重：量化为 INT8
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for (name, shape, dtype, start, end) in &tensor_infos {
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if shape.len() != 2 {
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continue;
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}
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if !q8_set.contains(name.as_str()) {
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continue;
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}
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println!("量化 2D 权重: {name} shape={shape:?} dtype={dtype}");
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let raw = &mmap[data_offset + *start..data_offset + *end];
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let f32_data = convert_to_f32(raw, dtype);
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let numel = f32_data.len();
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let (scale, q8_data) = quantize_q8(&f32_data);
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// 写入 INT8 数据
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let bytes: &[u8] = unsafe {
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std::slice::from_raw_parts(q8_data.as_ptr() as *const u8, q8_data.len())
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};
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use std::io::Write;
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bin_file.write_all(bytes)?;
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metadata.insert(
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name.clone(),
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Q8TensorMeta {
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shape: shape.clone(),
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scale,
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offset: current_offset,
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numel,
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},
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);
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current_offset += q8_data.len() as u64;
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}
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// 再处理 1D norm 权重：存为 F16
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for (name, shape, dtype, start, end) in &tensor_infos {
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if shape.len() != 1 {
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continue;
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}
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let raw = &mmap[data_offset + *start..data_offset + *end];
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let f32_data = convert_to_f32(raw, dtype);
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let numel = f32_data.len();
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// 转 F16 存储
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let mut f16_bytes = Vec::with_capacity(numel * 2);
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for &v in &f32_data {
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let f16_bits = f32_to_f16_bits(v);
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f16_bytes.extend_from_slice(&f16_bits.to_le_bytes());
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}
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use std::io::Write;
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bin_file.write_all(&f16_bytes)?;
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// 用负数 scale 表示 F16 类型（加载时 scale < 0 表示 F16）
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metadata.insert(
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name.clone(),
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Q8TensorMeta {
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shape: shape.clone(),
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scale: -1.0, // 标记为 F16
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offset: current_offset,
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numel,
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},
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);
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current_offset += f16_bytes.len() as u64;
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}
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// 写 JSON 元数据
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let meta = Q8Metadata { tensors: metadata };
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let json_str = serde_json::to_string_pretty(&meta)?;
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std::fs::write(&json_path, json_str)?;
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println!("拷贝配置文件和 tokenizer...");
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std::fs::copy(config_path, output_dir.join("config.json"))?;
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std::fs::copy(tokenizer_path, output_dir.join("tokenizer.json"))?;
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println!("Q8 量化模型已导出到: {:?}", output_dir);
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println!(" - model.q8.json (元数据)",);
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println!(" - model.q8.bin (INT8 + F16 权重)",);
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Ok(())
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}
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/// f32 → f16 位表示（返回 u16）
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fn f32_to_f16_bits(v: f32) -> u16 {
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let bits = v.to_bits();
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let sign = bits >> 31;
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let exp = (bits >> 23) & 0xFF;
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let mant = bits & 0x7FFFFF;
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let res: u32 = if exp == 0xFF {
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// NaN / Inf
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let f16_exp = 0x1F;
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let f16_mant = if mant == 0 { 0 } else { 0x3FF };
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(sign << 15) | (f16_exp << 10) | f16_mant
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} else if exp < 103 {
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// 下溢，返回 0
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0
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} else {
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let new_exp = (exp as i32) - 127 + 15;
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if new_exp >= 31 {
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// 上溢
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(sign << 15) | (0x1F << 10)
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} else if new_exp <= 0 {
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// 次正规数
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let mant_new = (mant | 0x800000) >> (113 - new_exp);
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(sign << 15) | (mant_new & 0x3FF)
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} else {
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let f16_mant = mant >> 13;
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(sign << 15) | ((new_exp as u32) << 10) | f16_mant
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}
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};
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res as u16
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}
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