refactor(项目结构) 重构为 workspace 多成员项目
- 将项目拆分为三个 crate：minicpm-core（核心模型）、minicpm-convert（转换功能）、minicpm-inference（推理功能） - 添加两个示例：minimal-inference（最小推理）和 convert（模型转换） - 转换后自动拷贝 config.json 和 tokenizer.json 到 model 目录 - 更新 README 说明 workspace 结构和使用方式
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[package]
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name = "minicpm-convert"
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version = "0.1.0"
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edition = "2021"
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[dependencies]
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minicpm-core = { path = "../minicpm-core" }
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burn = { version = "0.21", features = ["std", "wgpu"] }
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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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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::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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use std::collections::HashMap;
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use std::path::Path;
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pub fn export_model<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...");
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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 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::<FullPrecisionSettings>::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!("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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device: &B::Device,
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) -> anyhow::Result<LlamaForCausalLM<B>> {
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let file = std::fs::File::open(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 mut tensors = HashMap::new();
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let mut shapes = HashMap::new();
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let mut dtypes = HashMap::new();
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let data_offset = 8 + header_len;
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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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if let Some(offsets) = info_obj.get("data_offsets") {
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if let Some(arr) = offsets.as_array() {
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let start = arr[0].as_u64().unwrap() as usize;
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let end = arr[1].as_u64().unwrap() as usize;
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let data = &mmap[data_offset + start..data_offset + end];
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tensors.insert(name.clone(), data.to_vec());
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}
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}
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if let Some(shape) = info_obj.get("shape") {
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if let Some(arr) = shape.as_array() {
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let shape_vec: Vec<usize> = arr
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.iter()
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.map(|v| v.as_u64().unwrap() as usize)
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.collect();
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shapes.insert(name.clone(), shape_vec);
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}
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}
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if let Some(dtype) = info_obj.get("dtype") {
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if let Some(s) = dtype.as_str() {
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dtypes.insert(name.clone(), s.to_string());
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}
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}
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}
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}
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}
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let mut model = model;
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if let Some(weight) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, "model.embed_tokens.weight", device) {
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let record = EmbeddingRecord {
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weight: Param::from_tensor(weight),
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};
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model.model.embed_tokens = model.model.embed_tokens.clone().load_record(record);
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}
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for i in 0..model.config.num_hidden_layers {
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let prefix = format!("model.layers.{i}");
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if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.self_attn.q_proj.weight"), device) {
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let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
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model.model.layers[i].self_attn.q_proj = model.model.layers[i].self_attn.q_proj.clone().load_record(record);
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}
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if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.self_attn.k_proj.weight"), device) {
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let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
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model.model.layers[i].self_attn.k_proj = model.model.layers[i].self_attn.k_proj.clone().load_record(record);
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}
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if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.self_attn.v_proj.weight"), device) {
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let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
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model.model.layers[i].self_attn.v_proj = model.model.layers[i].self_attn.v_proj.clone().load_record(record);
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}
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if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.self_attn.o_proj.weight"), device) {
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let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
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model.model.layers[i].self_attn.o_proj = model.model.layers[i].self_attn.o_proj.clone().load_record(record);
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}
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if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.mlp.gate_proj.weight"), device) {
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let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
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model.model.layers[i].mlp.gate_proj = model.model.layers[i].mlp.gate_proj.clone().load_record(record);
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}
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if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.mlp.up_proj.weight"), device) {
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let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
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model.model.layers[i].mlp.up_proj = model.model.layers[i].mlp.up_proj.clone().load_record(record);
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}
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if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.mlp.down_proj.weight"), device) {
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let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
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model.model.layers[i].mlp.down_proj = model.model.layers[i].mlp.down_proj.clone().load_record(record);
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}
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if let Some(w) = load_tensor_1d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.input_layernorm.weight"), device) {
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model.model.layers[i].input_layernorm.weight = Param::from_tensor(w);
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}
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if let Some(w) = load_tensor_1d::<B>(&tensors, &shapes, &dtypes, &format!("{prefix}.post_attention_layernorm.weight"), device) {
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model.model.layers[i].post_attention_layernorm.weight = Param::from_tensor(w);
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}
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}
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if let Some(w) = load_tensor_1d::<B>(&tensors, &shapes, &dtypes, "model.norm.weight", device) {
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model.model.norm.weight = Param::from_tensor(w);
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}
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if let Some(w) = load_tensor_2d::<B>(&tensors, &shapes, &dtypes, "lm_head.weight", device) {
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let record = LinearRecord { weight: Param::from_tensor(w.transpose()), bias: None };
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model.lm_head = model.lm_head.clone().load_record(record);
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} else if model.config.tie_word_embeddings {
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let embed_weight = model.model.embed_tokens.weight.val();
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let record = LinearRecord { weight: Param::from_tensor(embed_weight.transpose()), bias: None };
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model.lm_head = model.lm_head.clone().load_record(record);
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}
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Ok(model)
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}
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fn load_tensor_2d<B: Backend>(
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tensors: &HashMap<String, Vec<u8>>,
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shapes: &HashMap<String, Vec<usize>>,
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dtypes: &HashMap<String, String>,
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name: &str,
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device: &B::Device,
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) -> Option<Tensor<B, 2, Float>> {
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let data = tensors.get(name)?;
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let shape = shapes.get(name)?;
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let dtype = dtypes.get(name)?;
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assert_eq!(shape.len(), 2, "Expected 2D tensor for {name}");
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let f32_data = convert_to_f32(data, dtype);
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let shape_arr: [usize; 2] = [shape[0], shape[1]];
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let tensor_data = TensorData::new(f32_data, Shape::new(shape_arr));
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Some(Tensor::from_data(tensor_data, device))
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}
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fn load_tensor_1d<B: Backend>(
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tensors: &HashMap<String, Vec<u8>>,
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shapes: &HashMap<String, Vec<usize>>,
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dtypes: &HashMap<String, String>,
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name: &str,
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device: &B::Device,
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) -> Option<Tensor<B, 1, Float>> {
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let data = tensors.get(name)?;
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let shape = shapes.get(name)?;
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let dtype = dtypes.get(name)?;
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assert_eq!(shape.len(), 1, "Expected 1D tensor for {name}");
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let f32_data = convert_to_f32(data, dtype);
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let shape_arr: [usize; 1] = [shape[0]];
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let tensor_data = TensorData::new(f32_data, Shape::new(shape_arr));
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Some(Tensor::from_data(tensor_data, device))
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}
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fn convert_to_f32(data: &[u8], dtype: &str) -> Vec<f32> {
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match dtype {
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"BF16" => data
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.chunks_exact(2)
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.map(|chunk| {
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let bytes: [u8; 2] = chunk.try_into().unwrap();
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bf16_to_f32(u16::from_le_bytes(bytes))
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})
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.collect(),
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"F16" => data
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.chunks_exact(2)
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.map(|chunk| {
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let bytes: [u8; 2] = chunk.try_into().unwrap();
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f16_to_f32(u16::from_le_bytes(bytes))
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})
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.collect(),
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"F32" => data
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.chunks_exact(4)
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.map(|chunk| {
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let bytes: [u8; 4] = chunk.try_into().unwrap();
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f32::from_le_bytes(bytes)
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})
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.collect(),
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_ => panic!("Unsupported dtype: {dtype}"),
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}
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}
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fn bf16_to_f32(bf16: u16) -> f32 {
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let f32_bits = (bf16 as u32) << 16;
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f32::from_bits(f32_bits)
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}
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fn f16_to_f32(f16: u16) -> f32 {
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let sign = (f16 >> 15) & 1;
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let exp = (f16 >> 10) & 0x1F;
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let mant = f16 & 0x3FF;
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if exp == 0 {
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if mant == 0 {
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return if sign == 0 { 0.0 } else { -0.0 };
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}
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let mut m = mant as u32;
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let mut e = 0u32;
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while (m & 0x400) == 0 {
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m <<= 1;
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e += 1;
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}
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m &= 0x3FF;
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let f32_exp = 127 - 14 - e;
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let f32_bits = ((sign as u32) << 31) | (f32_exp << 23) | ((m as u32) << 13);
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return f32::from_bits(f32_bits);
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}
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if exp == 0x1F {
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let f32_bits = ((sign as u32) << 31) | (0xFFu32 << 23) | ((mant as u32) << 13);
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return f32::from_bits(f32_bits);
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}
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let f32_exp = (exp as u32) + 127 - 15;
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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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